2024-08-20T21:31:11.2710515Z Current runner version: '2.319.1' 2024-08-20T21:31:11.2717422Z Runner name: 'i-06cc3a2865b78f24e' 2024-08-20T21:31:11.2718076Z Runner group name: 'Default' 2024-08-20T21:31:11.2718978Z Machine name: 'ip-10-0-29-171' 2024-08-20T21:31:11.2736912Z Testing runner upgrade compatibility 2024-08-20T21:31:11.4635104Z ##[group]GITHUB_TOKEN Permissions 2024-08-20T21:31:11.4637161Z Actions: read 2024-08-20T21:31:11.4637561Z Attestations: read 2024-08-20T21:31:11.4637955Z Checks: read 2024-08-20T21:31:11.4638306Z Contents: read 2024-08-20T21:31:11.4638828Z Deployments: read 2024-08-20T21:31:11.4639229Z Discussions: read 2024-08-20T21:31:11.4639610Z Issues: read 2024-08-20T21:31:11.4639953Z Metadata: read 2024-08-20T21:31:11.4640319Z Packages: read 2024-08-20T21:31:11.4640726Z Pages: read 2024-08-20T21:31:11.4641071Z PullRequests: read 2024-08-20T21:31:11.4641496Z RepositoryProjects: read 2024-08-20T21:31:11.4641959Z SecurityEvents: read 2024-08-20T21:31:11.4642357Z Statuses: read 2024-08-20T21:31:11.4642897Z ##[endgroup] 2024-08-20T21:31:11.4646364Z Secret source: Actions 2024-08-20T21:31:11.4646953Z Prepare workflow directory 2024-08-20T21:31:11.5669774Z Prepare all required actions 2024-08-20T21:31:11.5854283Z Getting action download info 2024-08-20T21:31:11.7778681Z Download action repository 'pytorch/test-infra@main' (SHA:0c3a2634aaa2f638c8f640e743f03d696ce1191f) 2024-08-20T21:31:12.2810748Z Download action repository 'pytorch/pytorch@main' (SHA:15b5a0b67fc3f34fb0bf1afa6f91e0c4c2b7fd8d) 2024-08-20T21:31:15.4851730Z Download action repository 'aws-actions/configure-aws-credentials@v3' (SHA:50ac8dd1e1b10d09dac7b8727528b91bed831ac0) 2024-08-20T21:31:15.6422309Z Download action repository 'seemethere/upload-artifact-s3@v5' (SHA:baba72d0712b404f646cebe0730933554ebce96a) 2024-08-20T21:31:15.9267730Z Getting action download info 2024-08-20T21:31:16.0319882Z Download action repository 'malfet/checkout@silent-checkout' (SHA:e07af140b3ccefc05679e3755b9db68f4ee4589c) 2024-08-20T21:31:16.2079231Z Getting action download info 2024-08-20T21:31:16.3325710Z Download action repository 'nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482' (SHA:3e91a01664abd3c5cd539100d10d33b9c5b68482) 2024-08-20T21:31:16.4894757Z Uses: pytorch/pytorch/.github/workflows/_linux-test.yml@refs/pull/133712/merge (f2fb9405c2fa9f9502a76363091cce6fd8179736) 2024-08-20T21:31:16.4897785Z ##[group] Inputs 2024-08-20T21:31:16.4898247Z build-environment: linux-focal-py3.12-clang10 2024-08-20T21:31:16.4901339Z test-matrix: {"include": [{"config": "default", "shard": 1, "num_shards": 4, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 2, "num_shards": 4, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 3, "num_shards": 4, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 4, "num_shards": 4, "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"}]} 2024-08-20T21:31:16.4904957Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:31:16.4906028Z sync-tag: 2024-08-20T21:31:16.4906886Z timeout-minutes: 600 2024-08-20T21:31:16.4907228Z use-gha: 2024-08-20T21:31:16.4907520Z dashboard-tag: 2024-08-20T21:31:16.4907832Z s3-bucket: gha-artifacts 2024-08-20T21:31:16.4908196Z aws-role-to-assume: 2024-08-20T21:31:16.4908524Z ##[endgroup] 2024-08-20T21:31:16.4909334Z Complete job name: linux-focal-py3.12-clang10 / test (dynamo, 2, 3, amz2023.linux.2xlarge) 2024-08-20T21:31:16.5592666Z A job started hook has been configured by the self-hosted runner administrator 2024-08-20T21:31:16.5762894Z ##[group]Run '/home/ec2-user/runner-scripts/before_job.sh' 2024-08-20T21:31:16.5774083Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:31:16.5774633Z ##[endgroup] 2024-08-20T21:31:17.7816688Z Runner Type: amz2023.linux.2xlarge 2024-08-20T21:31:17.7817244Z Instance Type: c5.2xlarge 2024-08-20T21:31:17.7817965Z AMI Name: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64 2024-08-20T21:31:17.7818566Z AMI ID: ami-06c68f701d8090592 2024-08-20T21:31:24.3442192Z ##[group]Run pytorch/test-infra/.github/actions/setup-ssh@main 2024-08-20T21:31:24.3442822Z with: 2024-08-20T21:31:24.3443655Z github-secret: *** 2024-08-20T21:31:24.3444715Z instructions: All testing is done inside the container, to start an interactive session run: docker exec -it $(docker container ps --format '{{.ID}}') bash 2024-08-20T21:31:24.3445889Z activate-with-label: false 2024-08-20T21:31:24.3446246Z label: with-ssh 2024-08-20T21:31:24.3446572Z remove-existing-keys: true 2024-08-20T21:31:24.3446940Z fail-silently: true 2024-08-20T21:31:24.3447237Z env: 2024-08-20T21:31:24.3447505Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:31:24.3447851Z ##[endgroup] 2024-08-20T21:31:24.4373161Z Please see https://github.com/pytorch/pytorch/wiki/Debugging-using-with-ssh-for-Github-Actions for more info. 2024-08-20T21:31:24.6816117Z Grabbing public ssh keys from https://github.com/pytorchmergebot.keys 2024-08-20T21:31:24.7549103Z No SSH keys found for user pytorchmergebot 2024-08-20T21:31:24.7549951Z Grabbing public ssh keys from https://github.com/XuehaiPan.keys 2024-08-20T21:31:24.8422507Z ~/.ssh/authorized_keys file found on node, removing ~/.ssh and starting fresh 2024-08-20T21:31:24.8438705Z Public keys pulled and installed to /home/ec2-user/.ssh/authorized_keys 2024-08-20T21:31:24.8465186Z Login using: ssh ec2-user@ec2-3-236-174-249.compute-1.amazonaws.com 2024-08-20T21:31:24.8466505Z All testing is done inside the container, to start an interactive session run: 2024-08-20T21:31:24.8467378Z docker exec -it $(docker container ps --format '{{.ID}}') bash 2024-08-20T21:31:24.8587930Z ##[group]Run pytorch/pytorch/.github/actions/checkout-pytorch@main 2024-08-20T21:31:24.8588506Z with: 2024-08-20T21:31:24.8588785Z submodules: recursive 2024-08-20T21:31:24.8589141Z fetch-depth: 0 2024-08-20T21:31:24.8589412Z env: 2024-08-20T21:31:24.8589681Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:31:24.8590028Z ##[endgroup] 2024-08-20T21:31:24.8838294Z ##[group]Run retry () { 2024-08-20T21:31:24.8838796Z retry () { 2024-08-20T21:31:24.8839303Z  $* || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*) 2024-08-20T21:31:24.8839894Z } 2024-08-20T21:31:24.8840198Z echo "${GITHUB_WORKSPACE}" 2024-08-20T21:31:24.8840621Z if [ -z "${NO_SUDO}" ]; then 2024-08-20T21:31:24.8841115Z  retry sudo rm -rf "${GITHUB_WORKSPACE}" 2024-08-20T21:31:24.8841588Z else 2024-08-20T21:31:24.8841923Z  retry rm -rf "${GITHUB_WORKSPACE}" 2024-08-20T21:31:24.8842360Z fi 2024-08-20T21:31:24.8842704Z mkdir "${GITHUB_WORKSPACE}" 2024-08-20T21:31:24.8852958Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:31:24.8853512Z env: 2024-08-20T21:31:24.8853771Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:31:24.8854122Z NO_SUDO: 2024-08-20T21:31:24.8854394Z ##[endgroup] 2024-08-20T21:31:24.8883476Z /home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-08-20T21:31:25.0270736Z ##[group]Run malfet/checkout@silent-checkout 2024-08-20T21:31:25.0271194Z with: 2024-08-20T21:31:25.0271509Z ref: 40ec5f6ddd9787aca0449b24128343ff4c4a88b3 2024-08-20T21:31:25.0271971Z fetch-depth: 0 2024-08-20T21:31:25.0272280Z submodules: recursive 2024-08-20T21:31:25.0272607Z quiet-checkout: true 2024-08-20T21:31:25.0272957Z repository: pytorch/pytorch 2024-08-20T21:31:25.0273467Z token: *** 2024-08-20T21:31:25.0273752Z ssh-strict: true 2024-08-20T21:31:25.0274064Z persist-credentials: true 2024-08-20T21:31:25.0274425Z clean: true 2024-08-20T21:31:25.0274743Z sparse-checkout-cone-mode: true 2024-08-20T21:31:25.0275136Z lfs: false 2024-08-20T21:31:25.0275431Z set-safe-directory: true 2024-08-20T21:31:25.0275754Z env: 2024-08-20T21:31:25.0276266Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:31:25.0276609Z ##[endgroup] 2024-08-20T21:31:25.1293552Z Syncing repository: pytorch/pytorch 2024-08-20T21:31:25.1295778Z ##[group]Getting Git version info 2024-08-20T21:31:25.1297095Z Working directory is '/home/ec2-user/actions-runner/_work/pytorch/pytorch' 2024-08-20T21:31:25.1298023Z [command]/usr/bin/git version 2024-08-20T21:31:25.1298402Z git version 2.40.1 2024-08-20T21:31:25.1299860Z ##[endgroup] 2024-08-20T21:31:25.1312481Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/0a07f689-475f-4256-98dc-f676fe0f26fc' before making global git config changes 2024-08-20T21:31:25.1313830Z Adding repository directory to the temporary git global config as a safe directory 2024-08-20T21:31:25.1315080Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-08-20T21:31:25.1349139Z Deleting the contents of '/home/ec2-user/actions-runner/_work/pytorch/pytorch' 2024-08-20T21:31:25.1353425Z ##[group]Initializing the repository 2024-08-20T21:31:25.1356407Z [command]/usr/bin/git init /home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-08-20T21:31:25.1391779Z hint: Using 'master' as the name for the initial branch. This default branch name 2024-08-20T21:31:25.1392838Z hint: is subject to change. To configure the initial branch name to use in all 2024-08-20T21:31:25.1393678Z hint: of your new repositories, which will suppress this warning, call: 2024-08-20T21:31:25.1394290Z hint: 2024-08-20T21:31:25.1395092Z hint: git config --global init.defaultBranch 2024-08-20T21:31:25.1395579Z hint: 2024-08-20T21:31:25.1396392Z hint: Names commonly chosen instead of 'master' are 'main', 'trunk' and 2024-08-20T21:31:25.1397327Z hint: 'development'. The just-created branch can be renamed via this command: 2024-08-20T21:31:25.1398177Z hint: 2024-08-20T21:31:25.1398611Z hint: git branch -m 2024-08-20T21:31:25.1399571Z Initialized empty Git repository in /home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/ 2024-08-20T21:31:25.1403483Z [command]/usr/bin/git remote add origin https://github.com/pytorch/pytorch 2024-08-20T21:31:25.1436745Z ##[endgroup] 2024-08-20T21:31:25.1437318Z ##[group]Disabling automatic garbage collection 2024-08-20T21:31:25.1440774Z [command]/usr/bin/git config --local gc.auto 0 2024-08-20T21:31:25.1472421Z ##[endgroup] 2024-08-20T21:31:25.1472938Z ##[group]Setting up auth 2024-08-20T21:31:25.1479670Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2024-08-20T21:31:25.1513410Z [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' || :" 2024-08-20T21:31:25.1843247Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2024-08-20T21:31:25.1877356Z [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' || :" 2024-08-20T21:31:25.2163677Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic *** 2024-08-20T21:31:25.2213725Z ##[endgroup] 2024-08-20T21:31:25.2219954Z ##[group]Fetching the repository 2024-08-20T21:31:25.2221445Z [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/* 2024-08-20T21:31:28.3350267Z remote: Enumerating objects: 1008512 2024-08-20T21:31:28.3350906Z remote: Enumerating objects: 1010082, done. 2024-08-20T21:31:28.3351952Z remote: 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2024-08-20T21:31:28.3402685Z remote: Counting objects: 85% (1335/1570) 2024-08-20T21:31:28.3403212Z remote: Counting objects: 86% (1351/1570) 2024-08-20T21:31:28.3403743Z remote: Counting objects: 87% (1366/1570) 2024-08-20T21:31:28.3404259Z remote: Counting objects: 88% (1382/1570) 2024-08-20T21:31:28.3404793Z remote: Counting objects: 89% (1398/1570) 2024-08-20T21:31:28.3405321Z remote: Counting objects: 90% (1413/1570) 2024-08-20T21:31:28.3405851Z remote: Counting objects: 91% (1429/1570) 2024-08-20T21:31:28.3406376Z remote: Counting objects: 92% (1445/1570) 2024-08-20T21:31:28.3406908Z remote: Counting objects: 93% (1461/1570) 2024-08-20T21:31:28.3407435Z remote: Counting objects: 94% (1476/1570) 2024-08-20T21:31:28.3407956Z remote: Counting objects: 95% (1492/1570) 2024-08-20T21:31:28.3408482Z remote: Counting objects: 96% (1508/1570) 2024-08-20T21:31:28.3409008Z remote: Counting objects: 97% (1523/1570) 2024-08-20T21:31:28.3409523Z remote: Counting objects: 98% (1539/1570) 2024-08-20T21:31:28.3410051Z remote: Counting objects: 99% (1555/1570) 2024-08-20T21:31:28.3410578Z remote: Counting objects: 100% (1570/1570) 2024-08-20T21:31:28.3411134Z remote: Counting objects: 100% (1570/1570), done. 2024-08-20T21:31:28.3531455Z remote: Compressing objects: 0% (1/803) 2024-08-20T21:31:28.3751413Z remote: Compressing objects: 1% (9/803) 2024-08-20T21:31:28.3939178Z remote: Compressing objects: 2% (17/803) 2024-08-20T21:31:28.4271185Z remote: Compressing objects: 3% (25/803) 2024-08-20T21:31:28.5020938Z remote: Compressing objects: 4% (33/803) 2024-08-20T21:31:28.5738859Z remote: Compressing objects: 5% (41/803) 2024-08-20T21:31:28.6314577Z remote: Compressing objects: 6% (49/803) 2024-08-20T21:31:28.6680871Z remote: Compressing objects: 7% (57/803) 2024-08-20T21:31:28.7105494Z remote: Compressing objects: 8% (65/803) 2024-08-20T21:31:28.7423382Z remote: Compressing objects: 9% (73/803) 2024-08-20T21:31:28.7685999Z remote: Compressing objects: 10% (81/803) 2024-08-20T21:31:28.7941504Z remote: Compressing objects: 11% (89/803) 2024-08-20T21:31:28.8157892Z remote: Compressing objects: 12% (97/803) 2024-08-20T21:31:28.8305430Z remote: Compressing objects: 13% (105/803) 2024-08-20T21:31:28.8447342Z remote: Compressing objects: 14% (113/803) 2024-08-20T21:31:28.8546648Z remote: Compressing objects: 15% (121/803) 2024-08-20T21:31:28.8641416Z remote: Compressing objects: 16% (129/803) 2024-08-20T21:31:28.8727737Z remote: Compressing objects: 17% (137/803) 2024-08-20T21:31:28.8786725Z remote: Compressing objects: 18% (145/803) 2024-08-20T21:31:28.8829278Z remote: Compressing objects: 19% (153/803) 2024-08-20T21:31:28.8860054Z remote: Compressing objects: 20% (161/803) 2024-08-20T21:31:28.8887347Z remote: Compressing objects: 21% (169/803) 2024-08-20T21:31:28.8899977Z remote: Compressing objects: 22% (177/803) 2024-08-20T21:31:28.8902482Z remote: Compressing objects: 23% (185/803) 2024-08-20T21:31:28.8909167Z remote: Compressing objects: 24% (193/803) 2024-08-20T21:31:28.8919119Z remote: Compressing objects: 25% (201/803) 2024-08-20T21:31:28.8925829Z remote: Compressing objects: 26% (209/803) 2024-08-20T21:31:28.8931935Z remote: Compressing objects: 27% (217/803) 2024-08-20T21:31:28.8940983Z remote: Compressing objects: 28% (225/803) 2024-08-20T21:31:28.8950439Z remote: Compressing objects: 29% (233/803) 2024-08-20T21:31:28.8964650Z remote: Compressing objects: 30% (241/803) 2024-08-20T21:31:28.8980104Z remote: Compressing objects: 31% (249/803) 2024-08-20T21:31:28.8991153Z remote: Compressing objects: 32% (257/803) 2024-08-20T21:31:28.9006233Z remote: Compressing objects: 33% (265/803) 2024-08-20T21:31:28.9010774Z remote: Compressing objects: 34% (274/803) 2024-08-20T21:31:28.9027225Z remote: Compressing objects: 35% (282/803) 2024-08-20T21:31:28.9033936Z remote: Compressing objects: 36% (290/803) 2024-08-20T21:31:28.9039159Z remote: Compressing objects: 37% (298/803) 2024-08-20T21:31:28.9047236Z remote: Compressing objects: 38% (306/803) 2024-08-20T21:31:28.9057816Z remote: Compressing objects: 39% (314/803) 2024-08-20T21:31:28.9067102Z remote: Compressing objects: 40% (322/803) 2024-08-20T21:31:28.9073662Z remote: Compressing objects: 41% (330/803) 2024-08-20T21:31:28.9080454Z remote: Compressing objects: 42% (338/803) 2024-08-20T21:31:28.9090182Z remote: Compressing objects: 43% (346/803) 2024-08-20T21:31:28.9102262Z remote: Compressing objects: 44% (354/803) 2024-08-20T21:31:28.9105594Z remote: Compressing objects: 45% (362/803) 2024-08-20T21:31:28.9112896Z remote: Compressing objects: 46% (370/803) 2024-08-20T21:31:28.9118775Z remote: Compressing objects: 47% (378/803) 2024-08-20T21:31:28.9125496Z remote: Compressing objects: 48% (386/803) 2024-08-20T21:31:28.9130086Z remote: Compressing objects: 49% (394/803) 2024-08-20T21:31:28.9134586Z remote: Compressing objects: 50% (402/803) 2024-08-20T21:31:28.9138702Z remote: Compressing objects: 51% (410/803) 2024-08-20T21:31:28.9142496Z remote: Compressing objects: 52% (418/803) 2024-08-20T21:31:28.9146797Z remote: Compressing objects: 53% (426/803) 2024-08-20T21:31:28.9149742Z remote: Compressing objects: 54% (434/803) 2024-08-20T21:31:28.9151655Z remote: Compressing objects: 55% (442/803) 2024-08-20T21:31:28.9153639Z remote: Compressing objects: 56% (450/803) 2024-08-20T21:31:28.9156287Z remote: Compressing objects: 57% (458/803) 2024-08-20T21:31:28.9157538Z remote: Compressing objects: 58% (466/803) 2024-08-20T21:31:28.9159522Z remote: Compressing objects: 59% (474/803) 2024-08-20T21:31:28.9160726Z remote: Compressing objects: 60% (482/803) 2024-08-20T21:31:28.9161380Z remote: Compressing objects: 61% (490/803) 2024-08-20T21:31:28.9162127Z remote: Compressing objects: 62% (498/803) 2024-08-20T21:31:28.9163357Z remote: Compressing objects: 63% (506/803) 2024-08-20T21:31:28.9163904Z remote: Compressing objects: 64% (514/803) 2024-08-20T21:31:28.9169140Z remote: Compressing objects: 65% (522/803) 2024-08-20T21:31:28.9178058Z remote: Compressing objects: 66% (530/803) 2024-08-20T21:31:28.9184254Z remote: Compressing objects: 67% (539/803) 2024-08-20T21:31:28.9190207Z remote: Compressing objects: 68% (547/803) 2024-08-20T21:31:28.9196425Z remote: Compressing objects: 69% (555/803) 2024-08-20T21:31:28.9201559Z remote: Compressing objects: 70% (563/803) 2024-08-20T21:31:28.9206237Z remote: Compressing objects: 71% (571/803) 2024-08-20T21:31:28.9212932Z remote: Compressing objects: 72% (579/803) 2024-08-20T21:31:28.9216654Z remote: Compressing objects: 73% (587/803) 2024-08-20T21:31:28.9221328Z remote: Compressing objects: 74% (595/803) 2024-08-20T21:31:28.9225548Z remote: Compressing objects: 75% (603/803) 2024-08-20T21:31:28.9230011Z remote: Compressing objects: 76% (611/803) 2024-08-20T21:31:28.9233549Z remote: Compressing objects: 77% (619/803) 2024-08-20T21:31:28.9237235Z remote: Compressing objects: 78% (627/803) 2024-08-20T21:31:28.9240980Z remote: Compressing objects: 79% (635/803) 2024-08-20T21:31:28.9244160Z remote: Compressing objects: 80% (643/803) 2024-08-20T21:31:28.9247326Z remote: Compressing objects: 81% (651/803) 2024-08-20T21:31:28.9250117Z remote: Compressing objects: 82% (659/803) 2024-08-20T21:31:28.9253650Z remote: Compressing objects: 83% (667/803) 2024-08-20T21:31:28.9261758Z remote: Compressing objects: 84% (675/803) 2024-08-20T21:31:28.9262904Z remote: Compressing objects: 85% (683/803) 2024-08-20T21:31:28.9265033Z remote: Compressing objects: 86% (691/803) 2024-08-20T21:31:28.9267321Z remote: Compressing objects: 87% (699/803) 2024-08-20T21:31:28.9269692Z remote: Compressing objects: 88% (707/803) 2024-08-20T21:31:28.9272644Z remote: Compressing objects: 89% (715/803) 2024-08-20T21:31:28.9275521Z remote: Compressing objects: 90% (723/803) 2024-08-20T21:31:28.9278007Z remote: Compressing objects: 91% (731/803) 2024-08-20T21:31:28.9279194Z remote: Compressing objects: 92% (739/803) 2024-08-20T21:31:28.9280255Z remote: Compressing objects: 93% (747/803) 2024-08-20T21:31:28.9281697Z remote: Compressing objects: 94% (755/803) 2024-08-20T21:31:28.9283703Z remote: Compressing objects: 95% (763/803) 2024-08-20T21:31:28.9285579Z remote: Compressing objects: 96% (771/803) 2024-08-20T21:31:28.9286622Z remote: Compressing objects: 97% (779/803) 2024-08-20T21:31:28.9288835Z remote: Compressing objects: 98% (787/803) 2024-08-20T21:31:28.9290413Z remote: Compressing objects: 99% (795/803) 2024-08-20T21:31:28.9291748Z remote: Compressing objects: 100% (803/803) 2024-08-20T21:31:28.9292336Z remote: Compressing objects: 100% (803/803), done. 2024-08-20T21:31:51.1126451Z remote: Total 1010082 (delta 1011), reused 1204 (delta 764), pack-reused 1008512 (from 1) 2024-08-20T21:32:20.3673637Z [command]/usr/bin/git rev-parse --verify --quiet 40ec5f6ddd9787aca0449b24128343ff4c4a88b3^{object} 2024-08-20T21:32:20.3703753Z 40ec5f6ddd9787aca0449b24128343ff4c4a88b3 2024-08-20T21:32:20.3708448Z ##[endgroup] 2024-08-20T21:32:20.3709306Z ##[group]Determining the checkout info 2024-08-20T21:32:20.3710083Z ##[endgroup] 2024-08-20T21:32:20.3710895Z ##[group]Checking out the ref 2024-08-20T21:32:20.3714266Z [command]/usr/bin/git checkout --quiet --force 40ec5f6ddd9787aca0449b24128343ff4c4a88b3 2024-08-20T21:32:21.8530926Z ##[endgroup] 2024-08-20T21:32:21.8531831Z ##[group]Setting up auth for fetching submodules 2024-08-20T21:32:21.8534571Z [command]/usr/bin/git config --global http.https://github.com/.extraheader AUTHORIZATION: basic *** 2024-08-20T21:32:21.8586344Z [command]/usr/bin/git config --global --unset-all url.https://github.com/.insteadOf 2024-08-20T21:32:21.8619725Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf git@github.com: 2024-08-20T21:32:21.8652277Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf org-21003710@github.com: 2024-08-20T21:32:21.8683472Z ##[endgroup] 2024-08-20T21:32:21.8684125Z ##[group]Fetching submodules 2024-08-20T21:32:21.8687260Z [command]/usr/bin/git submodule sync --recursive 2024-08-20T21:32:21.9003567Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --recursive 2024-08-20T21:32:21.9319937Z Submodule 'android/libs/fbjni' (https://github.com/facebookincubator/fbjni.git) registered for path 'android/libs/fbjni' 2024-08-20T21:32:21.9321670Z Submodule 'third_party/NNPACK_deps/FP16' (https://github.com/Maratyszcza/FP16.git) registered for path 'third_party/FP16' 2024-08-20T21:32:21.9324011Z Submodule 'third_party/NNPACK_deps/FXdiv' (https://github.com/Maratyszcza/FXdiv.git) registered for path 'third_party/FXdiv' 2024-08-20T21:32:21.9327082Z Submodule 'third_party/NNPACK' (https://github.com/Maratyszcza/NNPACK.git) registered for path 'third_party/NNPACK' 2024-08-20T21:32:21.9330959Z Submodule 'third_party/VulkanMemoryAllocator' (https://github.com/GPUOpen-LibrariesAndSDKs/VulkanMemoryAllocator.git) registered for path 'third_party/VulkanMemoryAllocator' 2024-08-20T21:32:21.9333149Z Submodule 'third_party/XNNPACK' (https://github.com/google/XNNPACK.git) registered for path 'third_party/XNNPACK' 2024-08-20T21:32:21.9336682Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/benchmark' 2024-08-20T21:32:21.9340523Z Submodule 'third_party/cpp-httplib' (https://github.com/yhirose/cpp-httplib.git) registered for path 'third_party/cpp-httplib' 2024-08-20T21:32:21.9343553Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo.git) registered for path 'third_party/cpuinfo' 2024-08-20T21:32:21.9347420Z Submodule 'third_party/cudnn_frontend' (https://github.com/NVIDIA/cudnn-frontend.git) registered for path 'third_party/cudnn_frontend' 2024-08-20T21:32:21.9369444Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/cutlass' 2024-08-20T21:32:21.9371662Z Submodule 'third_party/eigen' (https://gitlab.com/libeigen/eigen.git) registered for path 'third_party/eigen' 2024-08-20T21:32:21.9373806Z Submodule 'third_party/fbgemm' (https://github.com/pytorch/fbgemm) registered for path 'third_party/fbgemm' 2024-08-20T21:32:21.9376143Z Submodule 'third_party/flatbuffers' (https://github.com/google/flatbuffers.git) registered for path 'third_party/flatbuffers' 2024-08-20T21:32:21.9378607Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/fmt' 2024-08-20T21:32:21.9381267Z Submodule 'third_party/gemmlowp/gemmlowp' (https://github.com/google/gemmlowp.git) registered for path 'third_party/gemmlowp/gemmlowp' 2024-08-20T21:32:21.9383996Z Submodule 'third_party/gloo' (https://github.com/facebookincubator/gloo) registered for path 'third_party/gloo' 2024-08-20T21:32:21.9386694Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/googletest' 2024-08-20T21:32:21.9389156Z Submodule 'third_party/ideep' (https://github.com/intel/ideep) registered for path 'third_party/ideep' 2024-08-20T21:32:21.9393297Z Submodule 'third_party/ittapi' (https://github.com/intel/ittapi.git) registered for path 'third_party/ittapi' 2024-08-20T21:32:21.9398334Z Submodule 'third_party/kineto' (https://github.com/pytorch/kineto) registered for path 'third_party/kineto' 2024-08-20T21:32:21.9403430Z Submodule 'third_party/mimalloc' (https://github.com/microsoft/mimalloc.git) registered for path 'third_party/mimalloc' 2024-08-20T21:32:21.9408185Z Submodule 'third_party/nccl/nccl' (https://github.com/NVIDIA/nccl) registered for path 'third_party/nccl/nccl' 2024-08-20T21:32:21.9413328Z Submodule 'third_party/nlohmann' (https://github.com/nlohmann/json.git) registered for path 'third_party/nlohmann' 2024-08-20T21:32:21.9417545Z Submodule 'third_party/onnx' (https://github.com/onnx/onnx.git) registered for path 'third_party/onnx' 2024-08-20T21:32:21.9423000Z Submodule 'third_party/opentelemetry-cpp' (https://github.com/open-telemetry/opentelemetry-cpp.git) registered for path 'third_party/opentelemetry-cpp' 2024-08-20T21:32:21.9428109Z Submodule 'third_party/pocketfft' (https://github.com/mreineck/pocketfft) registered for path 'third_party/pocketfft' 2024-08-20T21:32:21.9433569Z Submodule 'third_party/protobuf' (https://github.com/protocolbuffers/protobuf.git) registered for path 'third_party/protobuf' 2024-08-20T21:32:21.9439198Z Submodule 'third_party/NNPACK_deps/psimd' (https://github.com/Maratyszcza/psimd.git) registered for path 'third_party/psimd' 2024-08-20T21:32:21.9444924Z Submodule 'third_party/NNPACK_deps/pthreadpool' (https://github.com/Maratyszcza/pthreadpool.git) registered for path 'third_party/pthreadpool' 2024-08-20T21:32:21.9450538Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/pybind11' 2024-08-20T21:32:21.9456644Z Submodule 'third_party/python-peachpy' (https://github.com/malfet/PeachPy.git) registered for path 'third_party/python-peachpy' 2024-08-20T21:32:21.9462206Z Submodule 'third_party/sleef' (https://github.com/shibatch/sleef) registered for path 'third_party/sleef' 2024-08-20T21:32:21.9469735Z Submodule 'third_party/tensorpipe' (https://github.com/pytorch/tensorpipe.git) registered for path 'third_party/tensorpipe' 2024-08-20T21:32:21.9503053Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/android/libs/fbjni'... 2024-08-20T21:32:22.2205796Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FP16'... 2024-08-20T21:32:22.4429230Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FXdiv'... 2024-08-20T21:32:22.6256276Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/NNPACK'... 2024-08-20T21:32:22.8933620Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/VulkanMemoryAllocator'... 2024-08-20T21:32:25.1824611Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/XNNPACK'... 2024-08-20T21:32:40.1375780Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/benchmark'... 2024-08-20T21:32:40.5630504Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpp-httplib'... 2024-08-20T21:32:41.0557667Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpuinfo'... 2024-08-20T21:32:41.7240059Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cudnn_frontend'... 2024-08-20T21:32:42.9441127Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cutlass'... 2024-08-20T21:32:45.3387216Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/eigen'... 2024-08-20T21:32:51.8910320Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm'... 2024-08-20T21:32:53.7673413Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flatbuffers'... 2024-08-20T21:32:55.7227695Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fmt'... 2024-08-20T21:32:57.3601497Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gemmlowp/gemmlowp'... 2024-08-20T21:32:57.7783122Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gloo'... 2024-08-20T21:32:58.1088776Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/googletest'... 2024-08-20T21:32:59.4920142Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep'... 2024-08-20T21:32:59.8911856Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ittapi'... 2024-08-20T21:33:00.1730100Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto'... 2024-08-20T21:33:01.7402293Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/mimalloc'... 2024-08-20T21:33:02.5232764Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/nccl/nccl'... 2024-08-20T21:33:03.2301083Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/nlohmann'... 2024-08-20T21:33:09.9381830Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx'... 2024-08-20T21:33:12.3067959Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp'... 2024-08-20T21:33:18.9773282Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pocketfft'... 2024-08-20T21:33:19.2589766Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/protobuf'... 2024-08-20T21:33:30.0020393Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/psimd'... 2024-08-20T21:33:30.1737846Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pthreadpool'... 2024-08-20T21:33:30.3618155Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pybind11'... 2024-08-20T21:33:31.3846945Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/python-peachpy'... 2024-08-20T21:33:31.6943447Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/sleef'... 2024-08-20T21:33:32.3824734Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe'... 2024-08-20T21:33:32.8232149Z Submodule path 'android/libs/fbjni': checked out '7e1e1fe3858c63c251c637ae41a20de425dde96f' 2024-08-20T21:33:32.8349601Z Submodule path 'third_party/FP16': checked out '4dfe081cf6bcd15db339cf2680b9281b8451eeb3' 2024-08-20T21:33:32.8439537Z Submodule path 'third_party/FXdiv': checked out 'b408327ac2a15ec3e43352421954f5b1967701d1' 2024-08-20T21:33:32.8683889Z Submodule path 'third_party/NNPACK': checked out 'c07e3a0400713d546e0dea2d5466dd22ea389c73' 2024-08-20T21:33:32.9067364Z Submodule path 'third_party/VulkanMemoryAllocator': checked out 'a6bfc237255a6bac1513f7c1ebde6d8aed6b5191' 2024-08-20T21:33:33.8861775Z Submodule path 'third_party/XNNPACK': checked out 'fcbf55af6cf28a4627bcd1f703ab7ad843f0f3a2' 2024-08-20T21:33:33.9100163Z Submodule path 'third_party/benchmark': checked out '0d98dba29d66e93259db7daa53a9327df767a415' 2024-08-20T21:33:33.9568346Z Submodule path 'third_party/cpp-httplib': checked out '3b6597bba913d51161383657829b7e644e59c006' 2024-08-20T21:33:34.0566769Z Submodule path 'third_party/cpuinfo': checked out '3c8b1533ac03dd6531ab6e7b9245d488f13a82a5' 2024-08-20T21:33:34.0915883Z Submodule path 'third_party/cudnn_frontend': checked out '23511ba176243f27b3b275da1fb3814ea805a171' 2024-08-20T21:33:34.6079826Z Submodule path 'third_party/cutlass': checked out 'bbe579a9e3beb6ea6626d9227ec32d0dae119a49' 2024-08-20T21:33:34.8688722Z Submodule path 'third_party/eigen': checked out '3147391d946bb4b6c68edd901f2add6ac1f31f8c' 2024-08-20T21:33:34.9531822Z Submodule path 'third_party/fbgemm': checked out 'dbc3157bf256f1339b3fa1fef2be89ac4078be0e' 2024-08-20T21:33:34.9550778Z Submodule 'third_party/asmjit' (https://github.com/asmjit/asmjit.git) registered for path 'third_party/fbgemm/third_party/asmjit' 2024-08-20T21:33:34.9553643Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo) registered for path 'third_party/fbgemm/third_party/cpuinfo' 2024-08-20T21:33:34.9555965Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/fbgemm/third_party/cutlass' 2024-08-20T21:33:34.9558927Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/fbgemm/third_party/googletest' 2024-08-20T21:33:34.9561872Z Submodule 'third_party/hipify_torch' (https://github.com/ROCmSoftwarePlatform/hipify_torch.git) registered for path 'third_party/fbgemm/third_party/hipify_torch' 2024-08-20T21:33:34.9589199Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/asmjit'... 2024-08-20T21:33:36.0082378Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/cpuinfo'... 2024-08-20T21:33:36.6381585Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/cutlass'... 2024-08-20T21:33:38.8499675Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/googletest'... 2024-08-20T21:33:40.1305302Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/hipify_torch'... 2024-08-20T21:33:40.4991192Z Submodule path 'third_party/fbgemm/third_party/asmjit': checked out 'd3fbf7c9bc7c1d1365a94a45614b91c5a3706b81' 2024-08-20T21:33:40.5981487Z Submodule path 'third_party/fbgemm/third_party/cpuinfo': checked out 'ed8b86a253800bafdb7b25c5c399f91bff9cb1f3' 2024-08-20T21:33:41.0078085Z Submodule path 'third_party/fbgemm/third_party/cutlass': checked out 'fc9ebc645b63f3a6bc80aaefde5c063fb72110d6' 2024-08-20T21:33:41.0735070Z Submodule path 'third_party/fbgemm/third_party/googletest': checked out 'cbf019de22c8dd37b2108da35b2748fd702d1796' 2024-08-20T21:33:41.0869229Z Submodule path 'third_party/fbgemm/third_party/hipify_torch': checked out '23f53b025b466d8ec3c45d52290d3442f7fbe6b1' 2024-08-20T21:33:41.2073314Z Submodule path 'third_party/flatbuffers': checked out '01834de25e4bf3975a9a00e816292b1ad0fe184b' 2024-08-20T21:33:41.2509460Z Submodule path 'third_party/fmt': checked out '0c9fce2ffefecfdce794e1859584e25877b7b592' 2024-08-20T21:33:41.2925993Z Submodule path 'third_party/gemmlowp/gemmlowp': checked out '3fb5c176c17c765a3492cd2f0321b0dab712f350' 2024-08-20T21:33:41.3188087Z Submodule path 'third_party/gloo': checked out '5354032ea08eadd7fc4456477f7f7c6308818509' 2024-08-20T21:33:41.3659392Z Submodule path 'third_party/googletest': checked out 'e2239ee6043f73722e7aa812a459f54a28552929' 2024-08-20T21:33:41.3790632Z Submodule path 'third_party/ideep': checked out '55ca0191687aaf19aca5cdb7881c791e3bea442b' 2024-08-20T21:33:41.3807361Z Submodule 'mkl-dnn' (https://github.com/intel/mkl-dnn.git) registered for path 'third_party/ideep/mkl-dnn' 2024-08-20T21:33:41.3832359Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep/mkl-dnn'... 2024-08-20T21:33:56.3915973Z Submodule path 'third_party/ideep/mkl-dnn': checked out '1137e04ec0b5251ca2b4400a4fd3c667ce843d67' 2024-08-20T21:33:56.4113102Z Submodule path 'third_party/ittapi': checked out '5b8a7d7422611c3a0d799fb5fc5dd4abfae35b42' 2024-08-20T21:33:56.5036843Z Submodule path 'third_party/kineto': checked out 'd9753139d181b9ff42872465aac0e5d3018be415' 2024-08-20T21:33:56.5057619Z Submodule 'libkineto/third_party/dynolog' (https://github.com/facebookincubator/dynolog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog' 2024-08-20T21:33:56.5059503Z Submodule 'libkineto/third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/fmt' 2024-08-20T21:33:56.5061699Z Submodule 'libkineto/third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/googletest' 2024-08-20T21:33:56.5092792Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog'... 2024-08-20T21:33:57.1234645Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/fmt'... 2024-08-20T21:33:58.5786749Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/googletest'... 2024-08-20T21:33:59.9607009Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog': checked out '7d04a0053a845370ae06ce317a22a48e9edcc74e' 2024-08-20T21:33:59.9624300Z Submodule 'third_party/DCGM' (https://github.com/NVIDIA/DCGM.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2024-08-20T21:33:59.9626416Z Submodule 'third_party/cpr' (https://github.com/libcpr/cpr.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2024-08-20T21:33:59.9629575Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2024-08-20T21:33:59.9632358Z Submodule 'third_party/gflags' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-08-20T21:33:59.9635064Z Submodule 'third_party/glog' (https://github.com/google/glog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2024-08-20T21:33:59.9638199Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2024-08-20T21:33:59.9641455Z Submodule 'third_party/json' (https://github.com/nlohmann/json.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2024-08-20T21:33:59.9644721Z Submodule 'third_party/pfs' (https://github.com/dtrugman/pfs.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2024-08-20T21:33:59.9677000Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'... 2024-08-20T21:34:00.8710095Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'... 2024-08-20T21:34:01.2691163Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'... 2024-08-20T21:34:02.6965687Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'... 2024-08-20T21:34:03.0096017Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/glog'... 2024-08-20T21:34:03.6964560Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'... 2024-08-20T21:34:05.2346432Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/json'... 2024-08-20T21:34:12.2118847Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'... 2024-08-20T21:34:12.6054752Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM': checked out 'ffde4e54bc7249a6039a5e6b45b395141e1217f9' 2024-08-20T21:34:12.6249451Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr': checked out '871ed52d350214a034f6ef8a3b8f51c5ce1bd400' 2024-08-20T21:34:12.6635772Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt': checked out 'cd4af11efc9c622896a3e4cb599fa28668ca3d05' 2024-08-20T21:34:12.6778477Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags': checked out 'e171aa2d15ed9eb17054558e0b3a6a413bb01067' 2024-08-20T21:34:12.6795210Z Submodule 'doc' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2024-08-20T21:34:12.6823941Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'... 2024-08-20T21:34:13.0135460Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc': checked out '8411df715cf522606e3b1aca386ddfc0b63d34b4' 2024-08-20T21:34:13.0336200Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog': checked out 'b33e3bad4c46c8a6345525fd822af355e5ef9446' 2024-08-20T21:34:13.0774346Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest': checked out '58d77fa8070e8cec2dc1ed015d66b454c8d78850' 2024-08-20T21:34:13.1803910Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json': checked out '4f8fba14066156b73f1189a2b8bd568bde5284c5' 2024-08-20T21:34:13.1982874Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs': checked out 'f68a2fa8ea36c783bdd760371411fcb495aa3150' 2024-08-20T21:34:13.2417680Z Submodule path 'third_party/kineto/libkineto/third_party/fmt': checked out '0041a40c1350ba702d475b9c4ad62da77caea164' 2024-08-20T21:34:13.3035328Z Submodule path 'third_party/kineto/libkineto/third_party/googletest': checked out '7aca84427f224eeed3144123d5230d5871e93347' 2024-08-20T21:34:13.3434737Z Submodule path 'third_party/mimalloc': checked out 'b66e3214d8a104669c2ec05ae91ebc26a8f5ab78' 2024-08-20T21:34:13.3687331Z Submodule path 'third_party/nccl/nccl': checked out 'ab2b89c4c339bd7f816fbc114a4b05d386b66290' 2024-08-20T21:34:13.4769035Z Submodule path 'third_party/nlohmann': checked out '87cda1d6646592ac5866dc703c8e1839046a6806' 2024-08-20T21:34:13.8411370Z Submodule path 'third_party/onnx': checked out '3bf92c03a9f27eba3bda1e5b9e63ea20ec213557' 2024-08-20T21:34:13.8449553Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/onnx/third_party/benchmark' 2024-08-20T21:34:13.8451670Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/onnx/third_party/pybind11' 2024-08-20T21:34:13.8480725Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx/third_party/benchmark'... 2024-08-20T21:34:14.3293571Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx/third_party/pybind11'... 2024-08-20T21:34:15.3495019Z Submodule path 'third_party/onnx/third_party/benchmark': checked out '2dd015dfef425c866d9a43f2c67d8b52d709acb6' 2024-08-20T21:34:15.3843724Z Submodule path 'third_party/onnx/third_party/pybind11': checked out '5b0a6fc2017fcc176545afe3e09c9f9885283242' 2024-08-20T21:34:15.4547162Z Submodule path 'third_party/opentelemetry-cpp': checked out 'a799f4aed9c94b765dcdaabaeab7d5e7e2310878' 2024-08-20T21:34:15.4567745Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark) registered for path 'third_party/opentelemetry-cpp/third_party/benchmark' 2024-08-20T21:34:15.4569878Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/opentelemetry-cpp/third_party/googletest' 2024-08-20T21:34:15.4572257Z Submodule 'third_party/ms-gsl' (https://github.com/microsoft/GSL) registered for path 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2024-08-20T21:34:15.4574997Z Submodule 'third_party/nlohmann-json' (https://github.com/nlohmann/json) registered for path 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2024-08-20T21:34:15.4577989Z Submodule 'third_party/opentelemetry-proto' (https://github.com/open-telemetry/opentelemetry-proto) registered for path 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2024-08-20T21:34:15.4580496Z Submodule 'third_party/opentracing-cpp' (https://github.com/opentracing/opentracing-cpp.git) registered for path 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-08-20T21:34:15.4583481Z 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" 2024-08-20T21:34:44.4086054Z Entering 'android/libs/fbjni' 2024-08-20T21:34:44.4141167Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/android/libs/fbjni/config remote.origin.url 2024-08-20T21:34:44.4158142Z Entering 'third_party/FP16' 2024-08-20T21:34:44.4213538Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FP16/config remote.origin.url 2024-08-20T21:34:44.4230044Z Entering 'third_party/FXdiv' 2024-08-20T21:34:44.4285899Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FXdiv/config remote.origin.url 2024-08-20T21:34:44.4302473Z Entering 'third_party/NNPACK' 2024-08-20T21:34:44.4357385Z 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file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/gloo/config remote.origin.url 2024-08-20T21:34:44.5721046Z Entering 'third_party/googletest' 2024-08-20T21:34:44.5775331Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/googletest/config remote.origin.url 2024-08-20T21:34:44.5792245Z Entering 'third_party/ideep' 2024-08-20T21:34:44.5847061Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/config remote.origin.url 2024-08-20T21:34:44.5862756Z Entering 'third_party/ideep/mkl-dnn' 2024-08-20T21:34:44.5917008Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/modules/mkl-dnn/config remote.origin.url 2024-08-20T21:34:44.5942268Z Entering 'third_party/ittapi' 2024-08-20T21:34:44.5998061Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ittapi/config remote.origin.url 2024-08-20T21:34:44.6015212Z 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 2024-08-20T21:34:44.6303055Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2024-08-20T21:34:44.6356544Z 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 2024-08-20T21:34:44.6373050Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-08-20T21:34:44.6427380Z 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 2024-08-20T21:34:44.6442016Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2024-08-20T21:34:44.6496988Z 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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 2024-08-20T21:34:44.6938434Z Entering 'third_party/mimalloc' 2024-08-20T21:34:44.6994286Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/mimalloc/config remote.origin.url 2024-08-20T21:34:44.7012439Z Entering 'third_party/nccl/nccl' 2024-08-20T21:34:44.7066392Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/nccl/nccl/config remote.origin.url 2024-08-20T21:34:44.7083796Z Entering 'third_party/nlohmann' 2024-08-20T21:34:44.7139269Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/nlohmann/config remote.origin.url 2024-08-20T21:34:44.7157036Z Entering 'third_party/onnx' 2024-08-20T21:34:44.7212302Z 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 2024-08-20T21:34:44.7795280Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-08-20T21:34:44.7885991Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/opentracing-cpp/config remote.origin.url 2024-08-20T21:34:44.7888384Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-08-20T21:34:44.7916747Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/config remote.origin.url 2024-08-20T21:34:44.7930518Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2024-08-20T21:34:44.7982910Z 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 2024-08-20T21:34:44.8000999Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2024-08-20T21:34:44.8053036Z 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 2024-08-20T21:34:44.8070545Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-08-20T21:34:44.8122200Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/tools/vcpkg/config remote.origin.url 2024-08-20T21:34:44.8159245Z Entering 'third_party/pocketfft' 2024-08-20T21:34:44.8212842Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/pocketfft/config remote.origin.url 2024-08-20T21:34:44.8228627Z Entering 'third_party/protobuf' 2024-08-20T21:34:44.8281386Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/config remote.origin.url 2024-08-20T21:34:44.8304592Z Entering 'third_party/protobuf/third_party/benchmark' 2024-08-20T21:34:44.8356220Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/modules/third_party/benchmark/config remote.origin.url 2024-08-20T21:34:44.8372040Z Entering 'third_party/protobuf/third_party/googletest' 2024-08-20T21:34:44.8423653Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/modules/third_party/googletest/config remote.origin.url 2024-08-20T21:34:44.8441230Z Entering 'third_party/psimd' 2024-08-20T21:34:44.8495404Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/psimd/config remote.origin.url 2024-08-20T21:34:44.8510666Z Entering 'third_party/pthreadpool' 2024-08-20T21:34:44.8562525Z 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Entering 'third_party/tensorpipe/third_party/googletest' 2024-08-20T21:34:44.8903658Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/googletest/config remote.origin.url 2024-08-20T21:34:44.8919089Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-08-20T21:34:44.8970382Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libnop/config remote.origin.url 2024-08-20T21:34:44.8984713Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-08-20T21:34:44.9035211Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libuv/config remote.origin.url 2024-08-20T21:34:44.9050843Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-08-20T21:34:44.9102279Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/pybind11/config 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2024-08-20T21:34:45.5978064Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2024-08-20T21:34:45.6020480Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2024-08-20T21:34:45.6061065Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-08-20T21:34:45.6103058Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-08-20T21:34:45.6143043Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2024-08-20T21:34:45.6187223Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2024-08-20T21:34:45.6230632Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-08-20T21:34:45.6293616Z Entering 'third_party/pocketfft' 2024-08-20T21:34:45.6337858Z Entering 'third_party/protobuf' 2024-08-20T21:34:45.6384694Z Entering 'third_party/protobuf/third_party/benchmark' 2024-08-20T21:34:45.6427911Z Entering 'third_party/protobuf/third_party/googletest' 2024-08-20T21:34:45.6471110Z Entering 'third_party/psimd' 2024-08-20T21:34:45.6515142Z Entering 'third_party/pthreadpool' 2024-08-20T21:34:45.6557198Z Entering 'third_party/pybind11' 2024-08-20T21:34:45.6599362Z Entering 'third_party/python-peachpy' 2024-08-20T21:34:45.6641510Z Entering 'third_party/sleef' 2024-08-20T21:34:45.6683781Z Entering 'third_party/tensorpipe' 2024-08-20T21:34:45.6726294Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-08-20T21:34:45.6768271Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-08-20T21:34:45.6809540Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-08-20T21:34:45.6850605Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-08-20T21:34:45.6891155Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-08-20T21:34:45.6950720Z ##[endgroup] 2024-08-20T21:34:45.6993199Z [command]/usr/bin/git log -1 --format='%H' 2024-08-20T21:34:45.7023608Z '40ec5f6ddd9787aca0449b24128343ff4c4a88b3' 2024-08-20T21:34:45.7248740Z Prepare all required actions 2024-08-20T21:34:45.7249233Z Getting action download info 2024-08-20T21:34:45.8873773Z ##[group]Run ./.github/actions/setup-linux 2024-08-20T21:34:45.8874206Z env: 2024-08-20T21:34:45.8874487Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:34:45.8874823Z ##[endgroup] 2024-08-20T21:34:45.8944040Z ##[group]Run set -euo pipefail 2024-08-20T21:34:45.8944496Z set -euo pipefail 2024-08-20T21:34:45.8944876Z function get_ec2_metadata() { 2024-08-20T21:34:45.8945563Z  # Pulled from instance metadata endpoint for EC2 2024-08-20T21:34:45.8946522Z  # see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html 2024-08-20T21:34:45.8947330Z  category=$1 2024-08-20T21:34:45.8947868Z  # If it is GCP runner (runner name contains gcp), do not run this 2024-08-20T21:34:45.8948532Z  runner_name_str=i-06cc3a2865b78f24e 2024-08-20T21:34:45.8949048Z  if [[ -f /.inarc ]]; then 2024-08-20T21:34:45.8949548Z  echo "ARC Runner, no info on ec2 metadata" 2024-08-20T21:34:45.8950118Z  elif [[ $runner_name_str == *"gcp"* ]]; then 2024-08-20T21:34:45.8950839Z  echo "Runner is from Google Cloud Platform, No info on ec2 metadata" 2024-08-20T21:34:45.8951477Z  else 2024-08-20T21:34:45.8951961Z  curl -fsSL "http://169.254.169.254/latest/meta-data/${category}" 2024-08-20T21:34:45.8952557Z  fi 2024-08-20T21:34:45.8952833Z } 2024-08-20T21:34:45.8953173Z echo "ami-id: $(get_ec2_metadata ami-id)" 2024-08-20T21:34:45.8953777Z echo "instance-id: $(get_ec2_metadata instance-id)" 2024-08-20T21:34:45.8954458Z echo "instance-type: $(get_ec2_metadata instance-type)" 2024-08-20T21:34:45.8955034Z echo "system info $(uname -a)" 2024-08-20T21:34:45.8962546Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:34:45.8963061Z env: 2024-08-20T21:34:45.8963329Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:34:45.8963674Z ##[endgroup] 2024-08-20T21:34:45.9065308Z ami-id: ami-06c68f701d8090592 2024-08-20T21:34:45.9120659Z instance-id: i-06cc3a2865b78f24e 2024-08-20T21:34:45.9177872Z instance-type: c5.2xlarge 2024-08-20T21:34:45.9189689Z system info Linux ip-10-0-29-171.ec2.internal 6.1.94-99.176.amzn2023.x86_64 #1 SMP PREEMPT_DYNAMIC Tue Jun 18 14:57:56 UTC 2024 x86_64 x86_64 x86_64 GNU/Linux 2024-08-20T21:34:45.9216821Z ##[group]Run echo "IN_ARC_RUNNER=$([ -f /.inarc ] && echo true || echo false)" >> $GITHUB_OUTPUT 2024-08-20T21:34:45.9217825Z echo "IN_ARC_RUNNER=$([ -f /.inarc ] && echo true || echo false)" >> $GITHUB_OUTPUT 2024-08-20T21:34:45.9224521Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:34:45.9225050Z env: 2024-08-20T21:34:45.9225316Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:34:45.9225662Z ##[endgroup] 2024-08-20T21:34:45.9318331Z ##[group]Run if systemctl is-active --quiet docker; then 2024-08-20T21:34:45.9319256Z if systemctl is-active --quiet docker; then 2024-08-20T21:34:45.9319820Z  echo "Docker daemon is running..."; 2024-08-20T21:34:45.9320278Z else 2024-08-20T21:34:45.9320772Z  echo "Starting docker deamon..." && sudo systemctl start docker; 2024-08-20T21:34:45.9321383Z fi 2024-08-20T21:34:45.9327618Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:34:45.9328147Z env: 2024-08-20T21:34:45.9328430Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:34:45.9328763Z ##[endgroup] 2024-08-20T21:34:45.9410877Z Docker daemon is running... 2024-08-20T21:34:45.9463476Z ##[group]Run nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482 2024-08-20T21:34:45.9464065Z with: 2024-08-20T21:34:45.9464327Z shell: bash 2024-08-20T21:34:45.9464605Z timeout_minutes: 5 2024-08-20T21:34:45.9464929Z max_attempts: 3 2024-08-20T21:34:45.9465241Z retry_wait_seconds: 30 2024-08-20T21:34:45.9468733Z command: AWS_ACCOUNT_ID=$(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 2024-08-20T21:34:45.9472238Z polling_interval_seconds: 1 2024-08-20T21:34:45.9472598Z warning_on_retry: true 2024-08-20T21:34:45.9472943Z continue_on_error: false 2024-08-20T21:34:45.9473280Z env: 2024-08-20T21:34:45.9473533Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:34:45.9473888Z AWS_RETRY_MODE: standard 2024-08-20T21:34:45.9474235Z AWS_MAX_ATTEMPTS: 5 2024-08-20T21:34:45.9474564Z AWS_DEFAULT_REGION: us-east-1 2024-08-20T21:34:45.9474942Z ##[endgroup] 2024-08-20T21:34:47.2525973Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2024-08-20T21:34:47.2527223Z Configure a credential helper to remove this warning. See 2024-08-20T21:34:47.2528685Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2024-08-20T21:34:47.2529489Z 2024-08-20T21:34:47.2529599Z Login Succeeded 2024-08-20T21:34:48.0059199Z Command completed after 1 attempt(s). 2024-08-20T21:34:48.0122490Z ##[group]Run env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2024-08-20T21:34:48.0123252Z env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2024-08-20T21:34:48.0123935Z env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2024-08-20T21:34:48.0130331Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:34:48.0130834Z env: 2024-08-20T21:34:48.0131106Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:34:48.0131454Z ##[endgroup] 2024-08-20T21:34:48.0227675Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2024-08-20T21:34:48.0228495Z # ignore expansion of "docker ps -q" since it could be empty 2024-08-20T21:34:48.0229112Z # shellcheck disable=SC2046 2024-08-20T21:34:48.0229574Z docker stop $(docker ps -q) || true 2024-08-20T21:34:48.0230050Z # Prune all of the docker images 2024-08-20T21:34:48.0230504Z docker system prune -af 2024-08-20T21:34:48.0237294Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:34:48.0237812Z env: 2024-08-20T21:34:48.0238084Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:34:48.0238654Z ##[endgroup] 2024-08-20T21:34:48.0530098Z "docker stop" requires at least 1 argument. 2024-08-20T21:34:48.0531092Z See 'docker stop --help'. 2024-08-20T21:34:48.0531579Z 2024-08-20T21:34:48.0531814Z Usage: docker stop [OPTIONS] CONTAINER [CONTAINER...] 2024-08-20T21:34:48.0532227Z 2024-08-20T21:34:48.0532373Z Stop one or more running containers 2024-08-20T21:34:48.0846563Z Total reclaimed space: 0B 2024-08-20T21:34:48.0887564Z ##[group]Run set +e 2024-08-20T21:34:48.0887942Z set +e 2024-08-20T21:34:48.0888226Z set -x 2024-08-20T21:34:48.0888523Z  2024-08-20T21:34:48.0888844Z PT_DOMAIN=download.pytorch.org 2024-08-20T21:34:48.0889673Z # TODO: Flaky access to download.pytorch.org https://github.com/pytorch/pytorch/issues/100400, 2024-08-20T21:34:48.0890836Z # cleaning this up once the issue is fixed. There are more than one resolved IP here, the last 2024-08-20T21:34:48.0891647Z # one is returned at random 2024-08-20T21:34:48.0892185Z RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" | tail -n1) 2024-08-20T21:34:48.0892713Z  2024-08-20T21:34:48.0893019Z if [ -z "${RESOLVED_IP}" ]; then 2024-08-20T21:34:48.0893652Z  echo "Couldn't resolve ${PT_DOMAIN}, retrying with Google DNS..." 2024-08-20T21:34:48.0894617Z  RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" @8.8.8.8 | tail -n1) 2024-08-20T21:34:48.0895199Z  2024-08-20T21:34:48.0895524Z  if [ -z "${RESOLVED_IP}" ]; then 2024-08-20T21:34:48.0896376Z  echo "Couldn't resolve ${PT_DOMAIN}, exiting..." 2024-08-20T21:34:48.0896907Z  exit 1 2024-08-20T21:34:48.0897222Z  fi 2024-08-20T21:34:48.0897488Z fi 2024-08-20T21:34:48.0897760Z  2024-08-20T21:34:48.0898116Z if grep -r "${PT_DOMAIN}" /etc/hosts; then 2024-08-20T21:34:48.0898771Z  # Clean up any old records first 2024-08-20T21:34:48.0899291Z  sudo sed -i "/${PT_DOMAIN}/d" /etc/hosts 2024-08-20T21:34:48.0899752Z fi 2024-08-20T21:34:48.0900008Z  2024-08-20T21:34:48.0900444Z echo "${RESOLVED_IP} ${PT_DOMAIN}" | sudo tee -a /etc/hosts 2024-08-20T21:34:48.0901009Z cat /etc/hosts 2024-08-20T21:34:48.0907363Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:34:48.0907891Z env: 2024-08-20T21:34:48.0908179Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:34:48.0908514Z ##[endgroup] 2024-08-20T21:34:48.0934510Z + PT_DOMAIN=download.pytorch.org 2024-08-20T21:34:48.0940781Z ++ dig -4 +short download.pytorch.org 2024-08-20T21:34:48.0941291Z ++ tail -n1 2024-08-20T21:34:48.1431872Z + RESOLVED_IP=18.165.98.58 2024-08-20T21:34:48.1432868Z + '[' -z 18.165.98.58 ']' 2024-08-20T21:34:48.1433410Z + grep -r download.pytorch.org /etc/hosts 2024-08-20T21:34:48.1448600Z + echo '18.165.98.58 download.pytorch.org' 2024-08-20T21:34:48.1450093Z + sudo tee -a /etc/hosts 2024-08-20T21:34:48.3723131Z 18.165.98.58 download.pytorch.org 2024-08-20T21:34:48.3740783Z + cat /etc/hosts 2024-08-20T21:34:48.3750994Z 127.0.0.1 localhost localhost.localdomain localhost4 localhost4.localdomain4 2024-08-20T21:34:48.3764981Z ::1 localhost6 localhost6.localdomain6 2024-08-20T21:34:48.3765532Z 18.165.98.58 download.pytorch.org 2024-08-20T21:34:48.3970172Z ##[group]Run pytorch/test-infra/.github/actions/calculate-docker-image@main 2024-08-20T21:34:48.3970832Z with: 2024-08-20T21:34:48.3971789Z docker-image-name: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:34:48.3972910Z docker-build-dir: .ci/docker 2024-08-20T21:34:48.3973298Z working-directory: . 2024-08-20T21:34:48.3973781Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-20T21:34:48.3974320Z force-push: false 2024-08-20T21:34:48.3974632Z env: 2024-08-20T21:34:48.3974898Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:34:48.3975228Z ##[endgroup] 2024-08-20T21:34:48.4000611Z ##[group]Run set -ex 2024-08-20T21:34:48.4000981Z set -ex 2024-08-20T21:34:48.4001281Z  2024-08-20T21:34:48.4001852Z # If the docker build directory or the build script doesn't exist, the action will 2024-08-20T21:34:48.4002928Z # gracefully return the docker image name as it is. Pulling docker image in Linux 2024-08-20T21:34:48.4003763Z # job could then download the pre-built image as usual 2024-08-20T21:34:48.4004542Z if [[ ! -d "${DOCKER_BUILD_DIR}" ]] || [[ ! -f "${DOCKER_BUILD_DIR}/build.sh" ]]; then 2024-08-20T21:34:48.4005251Z  echo "skip=true" >> "${GITHUB_OUTPUT}" 2024-08-20T21:34:48.4005915Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2024-08-20T21:34:48.4006484Z  2024-08-20T21:34:48.4007005Z  echo "There is no Docker build script in ${REPO_NAME} repo, skipping..." 2024-08-20T21:34:48.4007667Z  exit 0 2024-08-20T21:34:48.4007950Z else 2024-08-20T21:34:48.4008309Z  echo "skip=false" >> "${GITHUB_OUTPUT}" 2024-08-20T21:34:48.4008766Z fi 2024-08-20T21:34:48.4009019Z  2024-08-20T21:34:48.4009494Z if [[ "${DOCKER_IMAGE_NAME}" == *"${DOCKER_REGISTRY}/${REPO_NAME}"* ]]; then 2024-08-20T21:34:48.4010411Z  # The docker image name already includes the ECR prefix and tag, so we can just 2024-08-20T21:34:48.4011216Z  # use it as it is, but first let's extract the tag 2024-08-20T21:34:48.4011950Z  DOCKER_TAG=$(echo "${DOCKER_IMAGE_NAME}" | awk -F '[:,]' '{print $2}') 2024-08-20T21:34:48.4012712Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2024-08-20T21:34:48.4013443Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2024-08-20T21:34:48.4014012Z else 2024-08-20T21:34:48.4014610Z  DOCKER_TAG=$(git rev-parse HEAD:"${DOCKER_BUILD_DIR}") 2024-08-20T21:34:48.4015310Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2024-08-20T21:34:48.4016247Z  echo "docker-image=${DOCKER_REGISTRY}/${REPO_NAME}/${DOCKER_IMAGE_NAME}:${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2024-08-20T21:34:48.4017050Z fi 2024-08-20T21:34:48.4026287Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:34:48.4026789Z env: 2024-08-20T21:34:48.4027067Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:34:48.4027421Z REPO_NAME: pytorch 2024-08-20T21:34:48.4028403Z DOCKER_IMAGE_NAME: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:34:48.4029498Z DOCKER_BUILD_DIR: .ci/docker 2024-08-20T21:34:48.4030009Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-20T21:34:48.4030540Z ##[endgroup] 2024-08-20T21:34:48.4058929Z + [[ ! -d .ci/docker ]] 2024-08-20T21:34:48.4059511Z + [[ ! -f .ci/docker/build.sh ]] 2024-08-20T21:34:48.4060025Z + echo skip=false 2024-08-20T21:34:48.4062020Z + [[ 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* ]] 2024-08-20T21:34:48.4067771Z ++ echo 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:34:48.4068935Z ++ awk -F '[:,]' '{print $2}' 2024-08-20T21:34:48.4092368Z + DOCKER_TAG=f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:34:48.4093680Z + echo docker-tag=f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:34:48.4096256Z + echo docker-image=308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:34:48.4127158Z ##[group]Run set +e 2024-08-20T21:34:48.4127546Z set +e 2024-08-20T21:34:48.4127850Z set -x 2024-08-20T21:34:48.4128120Z  2024-08-20T21:34:48.4128388Z login() { 2024-08-20T21:34:48.4129080Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2024-08-20T21:34:48.4129849Z } 2024-08-20T21:34:48.4130104Z  2024-08-20T21:34:48.4130376Z retry () { 2024-08-20T21:34:48.4130767Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2024-08-20T21:34:48.4131222Z } 2024-08-20T21:34:48.4131489Z  2024-08-20T21:34:48.4131789Z retry login "${DOCKER_REGISTRY}" 2024-08-20T21:34:48.4132213Z  2024-08-20T21:34:48.4132705Z # Check if image already exists, if it does then skip building it 2024-08-20T21:34:48.4133453Z if docker manifest inspect "${DOCKER_IMAGE}"; then 2024-08-20T21:34:48.4133963Z  exit 0 2024-08-20T21:34:48.4134256Z fi 2024-08-20T21:34:48.4134535Z  2024-08-20T21:34:48.4135031Z # NB: This part requires a full checkout. Otherwise, the merge base will 2024-08-20T21:34:48.4135908Z # be empty. The default action would be to continue rebuild the image 2024-08-20T21:34:48.4136685Z if [[ "$BASE_REVISION" = "$(git rev-parse HEAD)" ]]; then 2024-08-20T21:34:48.4137369Z  # if we're on the base branch then use the parent commit 2024-08-20T21:34:48.4137982Z  MERGE_BASE=$(git rev-parse HEAD~) 2024-08-20T21:34:48.4138421Z else 2024-08-20T21:34:48.4138880Z  # otherwise we're on a PR, so use the most recent base commit 2024-08-20T21:34:48.4139596Z  MERGE_BASE=$(git merge-base HEAD "$BASE_REVISION") 2024-08-20T21:34:48.4140115Z fi 2024-08-20T21:34:48.4140371Z  2024-08-20T21:34:48.4140681Z if [[ -z "${MERGE_BASE}" ]]; then 2024-08-20T21:34:48.4141199Z  echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2024-08-20T21:34:48.4141843Z  2024-08-20T21:34:48.4142513Z  echo "Finding merge base only works with full checkout, please set fetch-depth to 0, continuing ..." 2024-08-20T21:34:48.4143327Z  exit 0 2024-08-20T21:34:48.4143629Z fi 2024-08-20T21:34:48.4143886Z  2024-08-20T21:34:48.4144332Z if ! git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}"; then 2024-08-20T21:34:48.4145386Z  echo "Directory '${DOCKER_BUILD_DIR}' not found in commit $MERGE_BASE, you should rebase onto a more recent commit" 2024-08-20T21:34:48.4146234Z  exit 1 2024-08-20T21:34:48.4146528Z fi 2024-08-20T21:34:48.4146796Z  2024-08-20T21:34:48.4147283Z PREVIOUS_DOCKER_TAG=$(git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}") 2024-08-20T21:34:48.4148296Z # If no image exists but the hash is the same as the previous hash then we should error out here 2024-08-20T21:34:48.4149221Z if [[ "${PREVIOUS_DOCKER_TAG}" == "${DOCKER_TAG}" ]]; then 2024-08-20T21:34:48.4150254Z  echo "WARNING: Something has gone wrong and the previous image isn't available for the merge-base of your branch" 2024-08-20T21:34:48.4151413Z  echo " Will re-build docker image to store in local cache, TTS may be longer" 2024-08-20T21:34:48.4152313Z fi 2024-08-20T21:34:48.4152588Z  2024-08-20T21:34:48.4153078Z echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2024-08-20T21:34:48.4159716Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:34:48.4160235Z env: 2024-08-20T21:34:48.4160496Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:34:48.4160869Z DOCKER_BUILD_DIR: .ci/docker 2024-08-20T21:34:48.4161351Z BASE_REVISION: 91f3d614142df02f619e44a68e3d9e0dfeba49ec 2024-08-20T21:34:48.4162507Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:34:48.4163661Z DOCKER_TAG: f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:34:48.4164307Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-20T21:34:48.4164854Z ##[endgroup] 2024-08-20T21:34:48.4191805Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-20T21:34:48.4192487Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-20T21:34:48.4194571Z + aws ecr get-login-password --region us-east-1 2024-08-20T21:34:48.4195780Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-20T21:34:49.0565500Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2024-08-20T21:34:49.0566430Z Configure a credential helper to remove this warning. See 2024-08-20T21:34:49.0567445Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2024-08-20T21:34:49.0568028Z 2024-08-20T21:34:49.0568143Z Login Succeeded 2024-08-20T21:34:49.0578770Z + docker manifest inspect 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:34:49.2647634Z { 2024-08-20T21:34:49.2648100Z "schemaVersion": 2, 2024-08-20T21:34:49.2648996Z "mediaType": "application/vnd.docker.distribution.manifest.v2+json", 2024-08-20T21:34:49.2649764Z "config": { 2024-08-20T21:34:49.2650637Z "mediaType": "application/vnd.docker.container.image.v1+json", 2024-08-20T21:34:49.2651447Z "size": 43416, 2024-08-20T21:34:49.2652320Z "digest": "sha256:2aaa7058a7e1d063370f2bda46a0fffde19193dd83023b51f0f1d1c55b4a88e4" 2024-08-20T21:34:49.2653416Z }, 2024-08-20T21:34:49.2653798Z "layers": [ 2024-08-20T21:34:49.2654222Z { 2024-08-20T21:34:49.2654930Z "mediaType": 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"sha256:9841596ece9eebfc9229b66dac9085e17c614f6e2697b9b6787e50cef1fffebe" 2024-08-20T21:34:49.2856881Z }, 2024-08-20T21:34:49.2857117Z { 2024-08-20T21:34:49.2857548Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:49.2858127Z "size": 139, 2024-08-20T21:34:49.2858691Z "digest": "sha256:689d9bf507d0f1a37ad9857c17601e3ba4ce677cc7bd6a5ec6778e35b7092eb4" 2024-08-20T21:34:49.2859331Z }, 2024-08-20T21:34:49.2859568Z { 2024-08-20T21:34:49.2860074Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:49.2860645Z "size": 32, 2024-08-20T21:34:49.2861210Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-08-20T21:34:49.2861870Z }, 2024-08-20T21:34:49.2862099Z { 2024-08-20T21:34:49.2862548Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:49.2863126Z "size": 160, 2024-08-20T21:34:49.2863691Z "digest": "sha256:ca5838b6767dabfb99553effdca2c070fb09432fc2f2aa55fd32b97c6a3bb69f" 2024-08-20T21:34:49.2864355Z }, 2024-08-20T21:34:49.2864594Z { 2024-08-20T21:34:49.2865030Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:49.2865609Z "size": 908, 2024-08-20T21:34:49.2866172Z "digest": "sha256:2ab493b780e6673f1c4155bc7c7ec7ba0a9aea98bc440070691ebffab08d0499" 2024-08-20T21:34:49.2866813Z }, 2024-08-20T21:34:49.2867052Z { 2024-08-20T21:34:49.2867497Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:49.2868066Z "size": 701, 2024-08-20T21:34:49.2868626Z "digest": "sha256:9841596ece9eebfc9229b66dac9085e17c614f6e2697b9b6787e50cef1fffebe" 2024-08-20T21:34:49.2869280Z }, 2024-08-20T21:34:49.2869504Z { 2024-08-20T21:34:49.2869946Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:49.2870521Z "size": 134, 2024-08-20T21:34:49.2871064Z "digest": "sha256:aa49767ed126d96b9027bf94b97dfc987878b8042215cc45e015f63b12fd8bf6" 2024-08-20T21:34:49.2871710Z }, 2024-08-20T21:34:49.2871946Z { 2024-08-20T21:34:49.2872383Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:49.2872968Z "size": 32, 2024-08-20T21:34:49.2873533Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-08-20T21:34:49.2874177Z }, 2024-08-20T21:34:49.2874416Z { 2024-08-20T21:34:49.2874860Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:49.2875435Z "size": 159, 2024-08-20T21:34:49.2876016Z "digest": "sha256:9c112f6fb43acecc3e1aa531f9c026a74f4bdcf7fa2b1216b5b479a8625a20ae" 2024-08-20T21:34:49.2876683Z }, 2024-08-20T21:34:49.2876957Z { 2024-08-20T21:34:49.2877403Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:49.2877983Z "size": 1579, 2024-08-20T21:34:49.2878660Z "digest": 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"sha256:af18f4fae28b398c90fab467ce76ba5e34629be182ed51b3df983cbf0ccad439" 2024-08-20T21:34:49.2902132Z }, 2024-08-20T21:34:49.2902370Z { 2024-08-20T21:34:49.2902800Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:49.2903379Z "size": 7943, 2024-08-20T21:34:49.2903962Z "digest": "sha256:e415411fbadd9d6e13dca54a8decfc6b0b31c219fe6362707f6d7aeba1f932c7" 2024-08-20T21:34:49.2904621Z }, 2024-08-20T21:34:49.2904859Z { 2024-08-20T21:34:49.2905301Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:49.2905867Z "size": 8066, 2024-08-20T21:34:49.2906432Z "digest": "sha256:8801c8ab2ed327d5ae39c98fb179f59118a94c6de66189eb9809bb0147464cc0" 2024-08-20T21:34:49.2907081Z }, 2024-08-20T21:34:49.2907305Z { 2024-08-20T21:34:49.2907757Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:49.2908335Z "size": 301, 2024-08-20T21:34:49.2908877Z "digest": "sha256:33d589b886753bb2d002c6d637579ac3e17d9c4de498c0d1d8a17bd2cb738313" 2024-08-20T21:34:49.2909520Z }, 2024-08-20T21:34:49.2909758Z { 2024-08-20T21:34:49.2910186Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:49.2910764Z "size": 32, 2024-08-20T21:34:49.2911331Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-08-20T21:34:49.2912147Z }, 2024-08-20T21:34:49.2912390Z { 2024-08-20T21:34:49.2912835Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:49.2913405Z "size": 108, 2024-08-20T21:34:49.2913954Z "digest": "sha256:005437c751415d116f09202fbaaf1962a998da11c25613e1a0a2420b02fd419d" 2024-08-20T21:34:49.2914606Z }, 2024-08-20T21:34:49.2914835Z { 2024-08-20T21:34:49.2915285Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:49.2915865Z "size": 54145661, 2024-08-20T21:34:49.2916453Z "digest": "sha256:3ec2dad3f44186ff7db7ad2623c04eda6ef73bb99fc66d1ed44a42ec0b7a9402" 2024-08-20T21:34:49.2917128Z }, 2024-08-20T21:34:49.2917366Z { 2024-08-20T21:34:49.2917799Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:49.2918517Z "size": 32, 2024-08-20T21:34:49.2919087Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-08-20T21:34:49.2919750Z } 2024-08-20T21:34:49.2919981Z ] 2024-08-20T21:34:49.2920220Z } 2024-08-20T21:34:49.3034476Z ##[group]Run tag=${ECR_DOCKER_IMAGE##*/} 2024-08-20T21:34:49.3034990Z tag=${ECR_DOCKER_IMAGE##*/} 2024-08-20T21:34:49.3035550Z echo "docker pull ghcr.io/pytorch/ci-image:${tag/:/-}" 2024-08-20T21:34:49.3043226Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:34:49.3043746Z env: 2024-08-20T21:34:49.3044019Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:34:49.3045049Z ECR_DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:34:49.3046103Z ##[endgroup] 2024-08-20T21:34:49.3074595Z docker pull ghcr.io/pytorch/ci-image:pytorch-linux-focal-py3.12-clang10-f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:34:49.3131643Z ##[group]Run pytorch/test-infra/.github/actions/pull-docker-image@main 2024-08-20T21:34:49.3132249Z with: 2024-08-20T21:34:49.3133189Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:34:49.3134416Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-20T21:34:49.3134938Z env: 2024-08-20T21:34:49.3135204Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:34:49.3135549Z ##[endgroup] 2024-08-20T21:34:49.3155239Z ##[group]Run set -x 2024-08-20T21:34:49.3155582Z set -x 2024-08-20T21:34:49.3155879Z set +e 2024-08-20T21:34:49.3156165Z  2024-08-20T21:34:49.3156419Z login() { 2024-08-20T21:34:49.3157108Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2024-08-20T21:34:49.3157867Z } 2024-08-20T21:34:49.3158120Z  2024-08-20T21:34:49.3158566Z retry () { 2024-08-20T21:34:49.3158950Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2024-08-20T21:34:49.3159415Z } 2024-08-20T21:34:49.3159684Z  2024-08-20T21:34:49.3159982Z retry login "${DOCKER_REGISTRY}" 2024-08-20T21:34:49.3160418Z  2024-08-20T21:34:49.3160680Z set -e 2024-08-20T21:34:49.3161147Z # ignore output since only exit code is used for conditional 2024-08-20T21:34:49.3161886Z # only pull docker image if it's not available locally 2024-08-20T21:34:49.3162690Z if ! docker inspect --type=image "${DOCKER_IMAGE}" >/dev/null 2>/dev/null; then 2024-08-20T21:34:49.3163422Z  retry docker pull "${DOCKER_IMAGE}" 2024-08-20T21:34:49.3163858Z fi 2024-08-20T21:34:49.3169770Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:34:49.3170284Z env: 2024-08-20T21:34:49.3170546Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:34:49.3171569Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:34:49.3172765Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-20T21:34:49.3173443Z ##[endgroup] 2024-08-20T21:34:49.3198765Z + set +e 2024-08-20T21:34:49.3199508Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-20T21:34:49.3200181Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-20T21:34:49.3202990Z + aws ecr get-login-password --region us-east-1 2024-08-20T21:34:49.3204266Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-20T21:34:49.9243805Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2024-08-20T21:34:49.9244778Z Configure a credential helper to remove this warning. See 2024-08-20T21:34:49.9246145Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2024-08-20T21:34:49.9247027Z 2024-08-20T21:34:49.9247197Z Login Succeeded 2024-08-20T21:34:49.9257697Z + set -e 2024-08-20T21:34:49.9258939Z + docker inspect --type=image 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:34:49.9395027Z + retry docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:34:49.9397239Z + docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:34:50.1671000Z f6d216893d65c7b8ae43df4daaf247db808378e9: Pulling from pytorch/pytorch-linux-focal-py3.12-clang10 2024-08-20T21:34:50.1672520Z 560c024910be: Pulling fs layer 2024-08-20T21:34:50.1673168Z 9973d24424a5: Pulling fs layer 2024-08-20T21:34:50.1673860Z 31cf2448eae3: Pulling fs layer 2024-08-20T21:34:50.1674531Z 2cbc8585686f: Pulling fs layer 2024-08-20T21:34:50.1675207Z 8c4852dbedba: Pulling fs layer 2024-08-20T21:34:50.1676228Z dc477a9bbcc6: Pulling fs layer 2024-08-20T21:34:50.1676921Z 8c153b899f1d: Pulling fs layer 2024-08-20T21:34:50.1677562Z 0b1c4336fa8b: Pulling fs layer 2024-08-20T21:34:50.1678244Z b012c0f517e6: Pulling fs layer 2024-08-20T21:34:50.1679067Z 4fe699dadca1: Pulling fs layer 2024-08-20T21:34:50.1679715Z d67ce3a9cb4b: Pulling fs layer 2024-08-20T21:34:50.1680382Z 9841596ece9e: Pulling fs layer 2024-08-20T21:34:50.1681029Z e72835db4035: Pulling fs layer 2024-08-20T21:34:50.1681635Z 4f4fb700ef54: Pulling fs layer 2024-08-20T21:34:50.1682296Z 8c4852dbedba: Waiting 2024-08-20T21:34:50.1682849Z 8c153b899f1d: Waiting 2024-08-20T21:34:50.1683423Z 8ef04e16f963: Pulling fs layer 2024-08-20T21:34:50.1684062Z 5e6c0035b872: Pulling fs layer 2024-08-20T21:34:50.1684800Z 59b7b891e32d: Pulling fs layer 2024-08-20T21:34:50.1685445Z dc477a9bbcc6: Waiting 2024-08-20T21:34:50.1686106Z 5f4e7925ebfb: Pulling fs layer 2024-08-20T21:34:50.1686725Z 0b1c4336fa8b: Waiting 2024-08-20T21:34:50.1687277Z 12a450969960: Pulling fs layer 2024-08-20T21:34:50.1687957Z b8c108e0e581: Pulling fs layer 2024-08-20T21:34:50.1688647Z d3e5b0194b60: Pulling fs layer 2024-08-20T21:34:50.1689295Z 9841596ece9e: Waiting 2024-08-20T21:34:50.1689872Z 4fe699dadca1: Waiting 2024-08-20T21:34:50.1690455Z d67ce3a9cb4b: Waiting 2024-08-20T21:34:50.1690997Z e72835db4035: Waiting 2024-08-20T21:34:50.1691544Z 3e4dba8f4feb: Pulling fs layer 2024-08-20T21:34:50.1691953Z 4f4fb700ef54: Waiting 2024-08-20T21:34:50.1692277Z 4dd8f5b900be: Pulling fs layer 2024-08-20T21:34:50.1692662Z f0966db14aac: Pulling fs layer 2024-08-20T21:34:50.1693041Z 3ca232406eff: Pulling fs layer 2024-08-20T21:34:50.1693405Z 93684c5d1426: Pulling fs layer 2024-08-20T21:34:50.1693832Z 5f4e7925ebfb: Waiting 2024-08-20T21:34:50.1694167Z 891f1a671536: Pulling fs layer 2024-08-20T21:34:50.1694563Z 12a450969960: Waiting 2024-08-20T21:34:50.1694880Z 8ef04e16f963: Waiting 2024-08-20T21:34:50.1695265Z 2d1cde488756: Pulling fs layer 2024-08-20T21:34:50.1695618Z 5e6c0035b872: Waiting 2024-08-20T21:34:50.1696309Z 1b670936c6e7: Pulling fs layer 2024-08-20T21:34:50.1696764Z 59b7b891e32d: Waiting 2024-08-20T21:34:50.1697085Z c89135369bac: Pulling fs layer 2024-08-20T21:34:50.1697451Z d3e5b0194b60: Waiting 2024-08-20T21:34:50.1698000Z e32451be40c1: Pulling fs layer 2024-08-20T21:34:50.1698426Z b8c108e0e581: Waiting 2024-08-20T21:34:50.1698729Z 2cbc8585686f: Waiting 2024-08-20T21:34:50.1699059Z 7647e82c5c3f: Pulling fs layer 2024-08-20T21:34:50.1699418Z 3e4dba8f4feb: Waiting 2024-08-20T21:34:50.1699756Z 2d334a3c11fd: Pulling fs layer 2024-08-20T21:34:50.1700141Z e9ea16c3c5d4: Pulling fs layer 2024-08-20T21:34:50.1700507Z 66a5808f4ad5: Pulling fs layer 2024-08-20T21:34:50.1700871Z 2d1cde488756: Waiting 2024-08-20T21:34:50.1701205Z b92de615fef3: Pulling fs layer 2024-08-20T21:34:50.1701557Z 891f1a671536: Waiting 2024-08-20T21:34:50.1701893Z 56dd63029e04: Pulling fs layer 2024-08-20T21:34:50.1702255Z 3ca232406eff: Waiting 2024-08-20T21:34:50.1702574Z 0e19dd6ac611: Pulling fs layer 2024-08-20T21:34:50.1702949Z 93684c5d1426: Waiting 2024-08-20T21:34:50.1703266Z 4dd8f5b900be: Waiting 2024-08-20T21:34:50.1703569Z f0966db14aac: Waiting 2024-08-20T21:34:50.1703907Z deffd0debe5e: Pulling fs layer 2024-08-20T21:34:50.1704278Z e32451be40c1: Waiting 2024-08-20T21:34:50.1704582Z 2d334a3c11fd: Waiting 2024-08-20T21:34:50.1704897Z 66a5808f4ad5: Waiting 2024-08-20T21:34:50.1705208Z 1b670936c6e7: Waiting 2024-08-20T21:34:50.1705506Z 56dd63029e04: Waiting 2024-08-20T21:34:50.1705819Z b92de615fef3: Waiting 2024-08-20T21:34:50.1706137Z 0e19dd6ac611: Waiting 2024-08-20T21:34:50.1706453Z 823a4c61790a: Pulling fs layer 2024-08-20T21:34:50.1706831Z 52c09d8bb114: Pulling fs layer 2024-08-20T21:34:50.1707209Z fc113046ff5b: Pulling fs layer 2024-08-20T21:34:50.1707577Z 58fcfcf8962b: Pulling fs layer 2024-08-20T21:34:50.1707953Z deffd0debe5e: Waiting 2024-08-20T21:34:50.1708296Z a6bbbb7aff78: Pulling fs layer 2024-08-20T21:34:50.1708659Z 58c762741f83: Pulling fs layer 2024-08-20T21:34:50.1709020Z 823a4c61790a: Waiting 2024-08-20T21:34:50.1709493Z fc113046ff5b: Waiting 2024-08-20T21:34:50.1709818Z 36d6dbd6c3c6: Pulling fs layer 2024-08-20T21:34:50.1710202Z f6e31afd70a7: Pulling fs layer 2024-08-20T21:34:50.1710570Z 52c09d8bb114: Waiting 2024-08-20T21:34:50.1710891Z 4076a363f04c: Pulling fs layer 2024-08-20T21:34:50.1711257Z a6bbbb7aff78: Waiting 2024-08-20T21:34:50.1711596Z da1b55b13c80: Pulling fs layer 2024-08-20T21:34:50.1711954Z 58fcfcf8962b: Waiting 2024-08-20T21:34:50.1712290Z 852b7bd929d0: Pulling fs layer 2024-08-20T21:34:50.1712653Z 58c762741f83: Waiting 2024-08-20T21:34:50.1712954Z 36d6dbd6c3c6: Waiting 2024-08-20T21:34:50.1713286Z 43c374b7d87c: Pulling fs layer 2024-08-20T21:34:50.1713661Z 2ae4d1692345: Pulling fs layer 2024-08-20T21:34:50.1714013Z da1b55b13c80: Waiting 2024-08-20T21:34:50.1714326Z 4076a363f04c: Waiting 2024-08-20T21:34:50.1714655Z 02fd8160fb61: Pulling fs layer 2024-08-20T21:34:50.1715006Z 852b7bd929d0: Waiting 2024-08-20T21:34:50.1715335Z 1860f83d1894: Pulling fs layer 2024-08-20T21:34:50.1715702Z f6e31afd70a7: Waiting 2024-08-20T21:34:50.1716009Z 2ae4d1692345: Waiting 2024-08-20T21:34:50.1716343Z 1beebf68aabb: Pulling fs layer 2024-08-20T21:34:50.1716733Z e0cbebc1b4e2: Pulling fs layer 2024-08-20T21:34:50.1717102Z 3e8f996f54cd: Pulling fs layer 2024-08-20T21:34:50.1717473Z 43c374b7d87c: Waiting 2024-08-20T21:34:50.1717804Z 689d9bf507d0: Pulling fs layer 2024-08-20T21:34:50.1718157Z 1beebf68aabb: Waiting 2024-08-20T21:34:50.1718611Z 1860f83d1894: Waiting 2024-08-20T21:34:50.1718932Z e0cbebc1b4e2: Waiting 2024-08-20T21:34:50.1719254Z ca5838b6767d: Pulling fs layer 2024-08-20T21:34:50.1719640Z 2ab493b780e6: Pulling fs layer 2024-08-20T21:34:50.1720007Z 3e8f996f54cd: Waiting 2024-08-20T21:34:50.1720327Z aa49767ed126: Pulling fs layer 2024-08-20T21:34:50.1720693Z 689d9bf507d0: Waiting 2024-08-20T21:34:50.1721008Z 2ab493b780e6: Waiting 2024-08-20T21:34:50.1721323Z 9c112f6fb43a: Pulling fs layer 2024-08-20T21:34:50.1721690Z ca5838b6767d: Waiting 2024-08-20T21:34:50.1722003Z 056972111ac5: Pulling fs layer 2024-08-20T21:34:50.1722386Z 37490d77553a: Pulling fs layer 2024-08-20T21:34:50.1722750Z 9c112f6fb43a: Waiting 2024-08-20T21:34:50.1723148Z 056972111ac5: Waiting 2024-08-20T21:34:50.1723481Z 55ffcd4cda01: Pulling fs layer 2024-08-20T21:34:50.1723984Z 37490d77553a: Waiting 2024-08-20T21:34:50.1724340Z 55ffcd4cda01: Waiting 2024-08-20T21:34:50.1724675Z 4085287a7508: Pulling fs layer 2024-08-20T21:34:50.1725140Z 45f64d78b248: Pulling fs layer 2024-08-20T21:34:50.1725550Z c6ce826bc67b: Pulling fs layer 2024-08-20T21:34:50.1725975Z 0c6508f0dedc: Pulling fs layer 2024-08-20T21:34:50.1726413Z 4085287a7508: Waiting 2024-08-20T21:34:50.1726744Z af18f4fae28b: Pulling fs layer 2024-08-20T21:34:50.1727196Z e415411fbadd: Pulling fs layer 2024-08-20T21:34:50.1727575Z c6ce826bc67b: Waiting 2024-08-20T21:34:50.1727956Z 8801c8ab2ed3: Pulling fs layer 2024-08-20T21:34:50.1728349Z 33d589b88675: Pulling fs layer 2024-08-20T21:34:50.1728793Z af18f4fae28b: Waiting 2024-08-20T21:34:50.1729104Z e415411fbadd: Waiting 2024-08-20T21:34:50.1729485Z 005437c75141: Pulling fs layer 2024-08-20T21:34:50.1729904Z 3ec2dad3f441: Pulling fs layer 2024-08-20T21:34:50.1730336Z 3ec2dad3f441: Waiting 2024-08-20T21:34:50.1730658Z 005437c75141: Waiting 2024-08-20T21:34:50.1730968Z 33d589b88675: Waiting 2024-08-20T21:34:50.2682134Z 9973d24424a5: Verifying Checksum 2024-08-20T21:34:50.2682732Z 9973d24424a5: Download complete 2024-08-20T21:34:50.3652633Z 2cbc8585686f: Verifying Checksum 2024-08-20T21:34:50.3653400Z 2cbc8585686f: Download complete 2024-08-20T21:34:50.5006273Z 560c024910be: Verifying Checksum 2024-08-20T21:34:50.5007086Z 560c024910be: Download complete 2024-08-20T21:34:50.5789101Z dc477a9bbcc6: Verifying Checksum 2024-08-20T21:34:50.5789857Z dc477a9bbcc6: Download complete 2024-08-20T21:34:50.6510740Z 8c153b899f1d: Verifying Checksum 2024-08-20T21:34:50.6511718Z 8c153b899f1d: Download complete 2024-08-20T21:34:50.7208395Z 0b1c4336fa8b: Download complete 2024-08-20T21:34:50.7985568Z b012c0f517e6: Verifying Checksum 2024-08-20T21:34:50.7986356Z b012c0f517e6: Download complete 2024-08-20T21:34:50.8799189Z 4fe699dadca1: Download complete 2024-08-20T21:34:50.9729777Z d67ce3a9cb4b: Download complete 2024-08-20T21:34:51.0479427Z 9841596ece9e: Verifying Checksum 2024-08-20T21:34:51.0480334Z 9841596ece9e: Download complete 2024-08-20T21:34:51.2070889Z 8c4852dbedba: Download complete 2024-08-20T21:34:51.2193766Z 4f4fb700ef54: Verifying Checksum 2024-08-20T21:34:51.2194730Z 4f4fb700ef54: Download complete 2024-08-20T21:34:51.2962836Z 8ef04e16f963: Verifying Checksum 2024-08-20T21:34:51.2963501Z 8ef04e16f963: Download complete 2024-08-20T21:34:51.3899444Z 5e6c0035b872: Verifying Checksum 2024-08-20T21:34:51.3900087Z 5e6c0035b872: Download complete 2024-08-20T21:34:51.4609552Z 59b7b891e32d: Verifying Checksum 2024-08-20T21:34:51.4610264Z 59b7b891e32d: Download complete 2024-08-20T21:34:51.5074078Z 560c024910be: Pull complete 2024-08-20T21:34:51.5238322Z 9973d24424a5: Pull complete 2024-08-20T21:34:51.5546116Z 5f4e7925ebfb: Verifying Checksum 2024-08-20T21:34:51.5546693Z 5f4e7925ebfb: Download complete 2024-08-20T21:34:51.6283246Z 12a450969960: Download complete 2024-08-20T21:34:51.7041295Z b8c108e0e581: Verifying Checksum 2024-08-20T21:34:51.7041791Z b8c108e0e581: Download complete 2024-08-20T21:34:51.7789877Z d3e5b0194b60: Verifying Checksum 2024-08-20T21:34:51.7790395Z d3e5b0194b60: Download complete 2024-08-20T21:34:51.8477296Z 3e4dba8f4feb: Download complete 2024-08-20T21:34:51.9094930Z 4dd8f5b900be: Verifying Checksum 2024-08-20T21:34:51.9095707Z 4dd8f5b900be: Download complete 2024-08-20T21:34:53.1721855Z f0966db14aac: Verifying Checksum 2024-08-20T21:34:53.1722702Z f0966db14aac: Download complete 2024-08-20T21:34:53.2435591Z 3ca232406eff: Verifying Checksum 2024-08-20T21:34:53.2436440Z 3ca232406eff: Download complete 2024-08-20T21:34:53.3133502Z 93684c5d1426: Verifying Checksum 2024-08-20T21:34:53.3134249Z 93684c5d1426: Download complete 2024-08-20T21:34:53.3526127Z 31cf2448eae3: Verifying Checksum 2024-08-20T21:34:53.3527021Z 31cf2448eae3: Download complete 2024-08-20T21:34:53.3813791Z 891f1a671536: Verifying Checksum 2024-08-20T21:34:53.3814369Z 891f1a671536: Download complete 2024-08-20T21:34:53.4397731Z 2d1cde488756: Download complete 2024-08-20T21:34:53.4589606Z 1b670936c6e7: Download complete 2024-08-20T21:34:53.5424622Z e32451be40c1: Download complete 2024-08-20T21:34:53.6051767Z 7647e82c5c3f: Verifying Checksum 2024-08-20T21:34:53.6052737Z 7647e82c5c3f: Download complete 2024-08-20T21:34:53.6831615Z 2d334a3c11fd: Download complete 2024-08-20T21:34:53.7516297Z e9ea16c3c5d4: Verifying Checksum 2024-08-20T21:34:53.7517145Z e9ea16c3c5d4: Download complete 2024-08-20T21:34:53.8201332Z 66a5808f4ad5: Verifying Checksum 2024-08-20T21:34:53.8202113Z 66a5808f4ad5: Download complete 2024-08-20T21:34:53.8896629Z b92de615fef3: Download complete 2024-08-20T21:34:53.9872657Z 56dd63029e04: Verifying Checksum 2024-08-20T21:34:53.9873480Z 56dd63029e04: Download complete 2024-08-20T21:34:54.0577947Z 0e19dd6ac611: Verifying Checksum 2024-08-20T21:34:54.0578701Z 0e19dd6ac611: Download complete 2024-08-20T21:34:54.1309375Z deffd0debe5e: Verifying Checksum 2024-08-20T21:34:54.1310090Z deffd0debe5e: Download complete 2024-08-20T21:34:54.2184741Z 823a4c61790a: Download complete 2024-08-20T21:34:54.3278787Z 52c09d8bb114: Download complete 2024-08-20T21:34:54.4031898Z fc113046ff5b: Download complete 2024-08-20T21:34:54.8887229Z 58fcfcf8962b: Download complete 2024-08-20T21:34:54.9686658Z a6bbbb7aff78: Verifying Checksum 2024-08-20T21:34:54.9687563Z a6bbbb7aff78: Download complete 2024-08-20T21:34:55.0438966Z 58c762741f83: Download complete 2024-08-20T21:34:55.1074928Z 36d6dbd6c3c6: Verifying Checksum 2024-08-20T21:34:55.1076034Z 36d6dbd6c3c6: Download complete 2024-08-20T21:34:55.1791583Z f6e31afd70a7: Verifying Checksum 2024-08-20T21:34:55.1792455Z f6e31afd70a7: Download complete 2024-08-20T21:34:55.4365080Z 4076a363f04c: Verifying Checksum 2024-08-20T21:34:55.4365795Z 4076a363f04c: Download complete 2024-08-20T21:34:55.5248949Z da1b55b13c80: Verifying Checksum 2024-08-20T21:34:55.5250046Z da1b55b13c80: Download complete 2024-08-20T21:34:55.6040392Z 852b7bd929d0: Verifying Checksum 2024-08-20T21:34:55.6041272Z 852b7bd929d0: Download complete 2024-08-20T21:34:55.6857006Z 43c374b7d87c: Verifying Checksum 2024-08-20T21:34:55.6857884Z 43c374b7d87c: Download complete 2024-08-20T21:34:55.7526549Z 2ae4d1692345: Verifying Checksum 2024-08-20T21:34:55.7527348Z 2ae4d1692345: Download complete 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2024-08-20T21:36:30.6722140Z Digest: sha256:53bea9665c81f7bdf502e7eb907e80d01ec46372a842842380b5284cbc52773e 2024-08-20T21:36:30.6756305Z Status: Downloaded newer image for 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:36:30.6786226Z 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:36:30.6835739Z ##[group]Run echo "IN_ARC_RUNNER=$([ -f /.inarc ] && echo true || echo false)" >> "$GITHUB_OUTPUT" 2024-08-20T21:36:30.6836724Z echo "IN_ARC_RUNNER=$([ -f /.inarc ] && echo true || echo false)" >> "$GITHUB_OUTPUT" 2024-08-20T21:36:30.6844415Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:36:30.6844939Z env: 2024-08-20T21:36:30.6845215Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:30.6845548Z ##[endgroup] 2024-08-20T21:36:30.6941698Z ##[group]Run python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 2024-08-20T21:36:30.6942520Z python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 2024-08-20T21:36:30.6943251Z python3 -m tools.stats.monitor > usage_log.txt 2>&1 & 2024-08-20T21:36:30.6943943Z echo "monitor-script-pid=${!}" >> "${GITHUB_OUTPUT}" 2024-08-20T21:36:30.6950638Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:36:30.6951159Z env: 2024-08-20T21:36:30.6951435Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:30.6951771Z ##[endgroup] 2024-08-20T21:36:31.3519322Z Defaulting to user installation because normal site-packages is not writeable 2024-08-20T21:36:31.8363387Z Collecting psutil==5.9.1 2024-08-20T21:36:31.8748567Z Downloading psutil-5.9.1-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (281 kB) 2024-08-20T21:36:31.9264487Z Collecting nvidia-ml-py==11.525.84 2024-08-20T21:36:31.9313083Z Downloading nvidia_ml_py-11.525.84-py3-none-any.whl (34 kB) 2024-08-20T21:36:32.0247898Z Installing collected packages: psutil, nvidia-ml-py 2024-08-20T21:36:32.1980324Z Successfully installed nvidia-ml-py-11.525.84 psutil-5.9.1 2024-08-20T21:36:32.4176573Z Prepare all required actions 2024-08-20T21:36:32.4177121Z Getting action download info 2024-08-20T21:36:32.5205693Z Download action repository 'seemethere/download-artifact-s3@v4' (SHA:1da556a7aa0a088e3153970611f6c432d58e80e6) 2024-08-20T21:36:32.7399646Z Download action repository 'actions/download-artifact@v3' (SHA:9bc31d5ccc31df68ecc42ccf4149144866c47d8a) 2024-08-20T21:36:32.8665882Z ##[group]Run ./.github/actions/download-build-artifacts 2024-08-20T21:36:32.8666389Z with: 2024-08-20T21:36:32.8666672Z name: linux-focal-py3.12-clang10 2024-08-20T21:36:32.8667090Z s3-bucket: gha-artifacts 2024-08-20T21:36:32.8667428Z env: 2024-08-20T21:36:32.8667688Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:32.8668034Z ##[endgroup] 2024-08-20T21:36:32.8725016Z ##[group]Run seemethere/download-artifact-s3@v4 2024-08-20T21:36:32.8725484Z with: 2024-08-20T21:36:32.8725766Z name: linux-focal-py3.12-clang10 2024-08-20T21:36:32.8726373Z s3-bucket: gha-artifacts 2024-08-20T21:36:32.8726773Z region: us-east-1 2024-08-20T21:36:32.8727071Z env: 2024-08-20T21:36:32.8727323Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:32.8727667Z ##[endgroup] 2024-08-20T21:36:33.3921199Z (node:39736) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2024-08-20T21:36:33.3921967Z 2024-08-20T21:36:33.3922237Z Please migrate your code to use AWS SDK for JavaScript (v3). 2024-08-20T21:36:33.3923005Z For more information, check the migration guide at https://a.co/7PzMCcy 2024-08-20T21:36:33.3924049Z (Use `node --trace-warnings ...` to show where the warning was created) 2024-08-20T21:36:33.4707012Z Found 1 objects with prefix pytorch/pytorch/10479309237/linux-focal-py3.12-clang10/ 2024-08-20T21:36:33.4708130Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip 2024-08-20T21:36:37.3194416Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip 2024-08-20T21:36:37.3202183Z Artifact download has finished successfully 2024-08-20T21:36:37.3389287Z ##[group]Run unzip -o artifacts.zip 2024-08-20T21:36:37.3389732Z unzip -o artifacts.zip 2024-08-20T21:36:37.3396602Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:36:37.3397121Z env: 2024-08-20T21:36:37.3397380Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:37.3397729Z ##[endgroup] 2024-08-20T21:36:37.3477887Z Archive: artifacts.zip 2024-08-20T21:36:37.3478933Z creating: dist/ 2024-08-20T21:36:38.3380958Z inflating: dist/torch-2.5.0a0+git40ec5f6-cp312-cp312-linux_x86_64.whl 2024-08-20T21:36:38.3381693Z creating: build/custom_test_artifacts/ 2024-08-20T21:36:38.3382326Z creating: build/custom_test_artifacts/custom-op-build/ 2024-08-20T21:36:38.3383085Z creating: 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build/bin/vec_test_all_types_AVX512 2024-08-20T21:36:43.1071475Z inflating: build/bin/vec_test_all_types_AVX2 2024-08-20T21:36:43.1408939Z inflating: build/bin/vec_test_all_types_DEFAULT 2024-08-20T21:36:43.1458488Z inflating: build/bin/FileStoreTest 2024-08-20T21:36:43.1510407Z inflating: build/bin/TCPStoreTest 2024-08-20T21:36:43.1560182Z inflating: build/bin/HashStoreTest 2024-08-20T21:36:43.1609116Z inflating: build/bin/BackoffTest 2024-08-20T21:36:43.1672217Z inflating: build/bin/ProcessGroupGlooTest 2024-08-20T21:36:43.1675019Z inflating: build/bin/example_allreduce 2024-08-20T21:36:43.1726963Z inflating: build/bin/test_dist_autograd 2024-08-20T21:36:43.1729314Z inflating: build/bin/parallel_benchmark 2024-08-20T21:36:43.1738986Z inflating: build/bin/aot_model_compiler_test 2024-08-20T21:36:43.1805962Z inflating: build/bin/test_cpp_rpc 2024-08-20T21:36:43.1870801Z inflating: build/bin/test_mobile_nnc 2024-08-20T21:36:43.1917798Z inflating: build/bin/op_allowlist_test 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2024-08-20T21:36:43.2921602Z inflating: build/bin/apply_utils_test 2024-08-20T21:36:43.3248052Z inflating: build/bin/test_lazy 2024-08-20T21:36:43.3296085Z inflating: build/bin/weakref_test 2024-08-20T21:36:43.3349605Z inflating: build/bin/NamedTensor_test 2024-08-20T21:36:43.3404751Z inflating: build/bin/scalar_test 2024-08-20T21:36:43.3472695Z inflating: build/bin/Dict_test 2024-08-20T21:36:43.3524348Z inflating: build/bin/broadcast_test 2024-08-20T21:36:43.3584842Z inflating: build/bin/basic 2024-08-20T21:36:43.3639354Z inflating: build/bin/cpu_generator_test 2024-08-20T21:36:43.3701880Z inflating: build/bin/MaybeOwned_test 2024-08-20T21:36:43.3752177Z inflating: build/bin/test_parallel 2024-08-20T21:36:43.3853941Z inflating: build/bin/kernel_lambda_test 2024-08-20T21:36:43.3904545Z inflating: build/bin/cpu_profiling_allocator_test 2024-08-20T21:36:43.3954385Z inflating: build/bin/half_test 2024-08-20T21:36:43.4083825Z inflating: build/bin/kernel_lambda_legacy_test 2024-08-20T21:36:43.4131878Z inflating: build/bin/cpu_allocator_test 2024-08-20T21:36:43.4184841Z inflating: build/bin/static_runtime_bench 2024-08-20T21:36:43.4233466Z inflating: build/bin/Dimname_test 2024-08-20T21:36:43.4234950Z inflating: build/bin/verify_api_visibility 2024-08-20T21:36:43.4532533Z inflating: build/bin/static_runtime_test 2024-08-20T21:36:43.4588709Z inflating: build/bin/atest 2024-08-20T21:36:43.4639173Z inflating: build/bin/memory_overlapping_test 2024-08-20T21:36:43.4686540Z inflating: build/bin/dispatch_key_set_test 2024-08-20T21:36:43.4776918Z inflating: build/bin/cpu_rng_test 2024-08-20T21:36:43.4835826Z inflating: build/bin/inline_container_test 2024-08-20T21:36:43.4838085Z inflating: build/bin/thread_init_test 2024-08-20T21:36:43.4885910Z inflating: build/bin/operators_test 2024-08-20T21:36:43.4984156Z inflating: build/bin/List_test 2024-08-20T21:36:43.5032944Z inflating: build/bin/wrapdim_test 2024-08-20T21:36:43.5080835Z inflating: build/bin/operator_name_test 2024-08-20T21:36:43.6405718Z inflating: build/bin/test_api 2024-08-20T21:36:43.6452434Z inflating: build/bin/dlconvertor_test 2024-08-20T21:36:43.6509188Z inflating: build/bin/extension_backend_test 2024-08-20T21:36:43.6556239Z inflating: build/bin/lazy_tensor_test 2024-08-20T21:36:43.6606166Z inflating: build/bin/undefined_tensor_test 2024-08-20T21:36:43.6700403Z inflating: build/bin/ivalue_test 2024-08-20T21:36:43.6748460Z inflating: build/bin/CppSignature_test 2024-08-20T21:36:43.6803026Z inflating: build/bin/scalar_tensor_test 2024-08-20T21:36:43.6854117Z inflating: build/bin/mobile_memory_cleanup 2024-08-20T21:36:43.7177638Z inflating: build/bin/op_registration_test 2024-08-20T21:36:43.7228237Z inflating: build/bin/math_kernel_test 2024-08-20T21:36:43.7279242Z inflating: build/bin/memory_format_test 2024-08-20T21:36:43.7333198Z inflating: build/bin/native_test 2024-08-20T21:36:43.7381944Z inflating: build/bin/packedtensoraccessor_test 2024-08-20T21:36:43.7429464Z inflating: build/bin/reduce_ops_test 2024-08-20T21:36:43.7498318Z inflating: build/bin/pow_test 2024-08-20T21:36:43.7552369Z inflating: build/bin/quantized_test 2024-08-20T21:36:43.7600558Z inflating: build/bin/reportMemoryUsage_test 2024-08-20T21:36:43.7650831Z inflating: build/bin/test_edge_op_registration 2024-08-20T21:36:43.7654829Z inflating: build/bin/torch_shm_manager 2024-08-20T21:36:43.7671599Z inflating: build/bin/tutorial_tensorexpr 2024-08-20T21:36:43.8720703Z inflating: build/bin/test_tensorexpr 2024-08-20T21:36:43.9311675Z inflating: build/bin/test_jit 2024-08-20T21:36:43.9312350Z creating: .additional_ci_files/ 2024-08-20T21:36:43.9372495Z inflating: .additional_ci_files/test-times.json 2024-08-20T21:36:43.9614451Z inflating: .additional_ci_files/test-class-times.json 2024-08-20T21:36:43.9651981Z ##[group]Run rm artifacts.zip 2024-08-20T21:36:43.9652376Z rm artifacts.zip 2024-08-20T21:36:43.9660567Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:36:43.9661075Z env: 2024-08-20T21:36:43.9661354Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:43.9661706Z ##[endgroup] 2024-08-20T21:36:44.0267136Z ##[group]Run df -H 2024-08-20T21:36:44.0267442Z df -H 2024-08-20T21:36:44.0273702Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:36:44.0274402Z env: 2024-08-20T21:36:44.0274675Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:44.0275024Z ##[endgroup] 2024-08-20T21:36:44.0489423Z Filesystem Size Used Avail Use% Mounted on 2024-08-20T21:36:44.0490281Z devtmpfs 4.2M 0 4.2M 0% /dev 2024-08-20T21:36:44.0490792Z tmpfs 8.2G 0 8.2G 0% /dev/shm 2024-08-20T21:36:44.0491274Z tmpfs 3.3G 488k 3.3G 1% /run 2024-08-20T21:36:44.0491936Z /dev/nvme0n1p1 161G 21G 141G 13% / 2024-08-20T21:36:44.0492429Z tmpfs 8.2G 4.1k 8.2G 1% /tmp 2024-08-20T21:36:44.0492913Z /dev/nvme0n1p128 11M 1.4M 9.2M 13% /boot/efi 2024-08-20T21:36:44.0578350Z Prepare all required actions 2024-08-20T21:36:44.0578825Z Getting action download info 2024-08-20T21:36:44.1928056Z ##[group]Run ./.github/actions/download-td-artifacts 2024-08-20T21:36:44.1928532Z with: 2024-08-20T21:36:44.1928783Z env: 2024-08-20T21:36:44.1929056Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:44.1929388Z ##[endgroup] 2024-08-20T21:36:44.3027046Z ##[group]Run seemethere/download-artifact-s3@v4 2024-08-20T21:36:44.3027516Z with: 2024-08-20T21:36:44.3027785Z name: td_results 2024-08-20T21:36:44.3028096Z s3-bucket: gha-artifacts 2024-08-20T21:36:44.3028459Z region: us-east-1 2024-08-20T21:36:44.3028761Z env: 2024-08-20T21:36:44.3029014Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:44.3029366Z ##[endgroup] 2024-08-20T21:36:44.8133205Z (node:39756) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2024-08-20T21:36:44.8134004Z 2024-08-20T21:36:44.8134349Z Please migrate your code to use AWS SDK for JavaScript (v3). 2024-08-20T21:36:44.8135098Z For more information, check the migration guide at https://a.co/7PzMCcy 2024-08-20T21:36:44.8136206Z (Use `node --trace-warnings ...` to show where the warning was created) 2024-08-20T21:36:44.8954475Z Found 1 objects with prefix pytorch/pytorch/10479309237/td_results/ 2024-08-20T21:36:44.8955693Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/td_results.json 2024-08-20T21:36:44.9540989Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/td_results.json 2024-08-20T21:36:44.9547531Z Artifact download has finished successfully 2024-08-20T21:36:44.9761909Z ##[group]Run mkdir -p .additional_ci_files 2024-08-20T21:36:44.9762711Z mkdir -p .additional_ci_files 2024-08-20T21:36:44.9763687Z mv td_results.json .additional_ci_files/td_results.json 2024-08-20T21:36:44.9774737Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:36:44.9775583Z env: 2024-08-20T21:36:44.9776051Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:44.9776643Z ##[endgroup] 2024-08-20T21:36:45.0301138Z ##[group]Run .github/scripts/parse_ref.py 2024-08-20T21:36:45.0301642Z .github/scripts/parse_ref.py 2024-08-20T21:36:45.0307890Z shell: /usr/bin/bash -e {0} 2024-08-20T21:36:45.0308246Z env: 2024-08-20T21:36:45.0308519Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:45.0308870Z ##[endgroup] 2024-08-20T21:36:45.0759155Z Prepare all required actions 2024-08-20T21:36:45.0862134Z ##[group]Run ./.github/actions/get-workflow-job-id 2024-08-20T21:36:45.0862615Z with: 2024-08-20T21:36:45.0863288Z github-token: *** 2024-08-20T21:36:45.0863579Z env: 2024-08-20T21:36:45.0863851Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:45.0864199Z ##[endgroup] 2024-08-20T21:36:45.0986729Z ##[group]Run set -eux 2024-08-20T21:36:45.0987062Z set -eux 2024-08-20T21:36:45.0987687Z python3 .github/scripts/get_workflow_job_id.py "${GITHUB_RUN_ID}" "${RUNNER_NAME}" 2024-08-20T21:36:45.0994421Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:36:45.0994924Z env: 2024-08-20T21:36:45.0995200Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:45.0995760Z GITHUB_TOKEN: *** 2024-08-20T21:36:45.0996812Z ##[endgroup] 2024-08-20T21:36:45.1023478Z + python3 .github/scripts/get_workflow_job_id.py 10479309237 i-06cc3a2865b78f24e 2024-08-20T21:36:46.8637556Z setting job-id=29025268893 2024-08-20T21:36:46.8639678Z setting job-name=linux-focal-py3.12-clang10 / test (dynamo, 2, 3, amz2023.linux.2xlarge) 2024-08-20T21:36:46.8981241Z Prepare all required actions 2024-08-20T21:36:46.8981736Z Getting action download info 2024-08-20T21:36:47.0052505Z ##[group]Run ./.github/actions/filter-test-configs 2024-08-20T21:36:47.0052970Z with: 2024-08-20T21:36:47.0053451Z github-token: *** 2024-08-20T21:36:47.0056311Z test-matrix: {"include": [{"config": "default", "shard": 1, "num_shards": 4, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 2, "num_shards": 4, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 3, "num_shards": 4, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 4, "num_shards": 4, "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"}]} 2024-08-20T21:36:47.0059605Z job-name: linux-focal-py3.12-clang10 / test (dynamo, 2, 3, amz2023.linux.2xlarge) 2024-08-20T21:36:47.0060252Z env: 2024-08-20T21:36:47.0060519Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:47.0060866Z ##[endgroup] 2024-08-20T21:36:47.0126823Z ##[group]Run nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482 2024-08-20T21:36:47.0127416Z with: 2024-08-20T21:36:47.0127680Z shell: bash 2024-08-20T21:36:47.0127964Z timeout_minutes: 10 2024-08-20T21:36:47.0128291Z max_attempts: 5 2024-08-20T21:36:47.0128606Z retry_wait_seconds: 30 2024-08-20T21:36:47.0129794Z 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 2024-08-20T21:36:47.0131149Z polling_interval_seconds: 1 2024-08-20T21:36:47.0131525Z warning_on_retry: true 2024-08-20T21:36:47.0131875Z continue_on_error: false 2024-08-20T21:36:47.0132214Z env: 2024-08-20T21:36:47.0132480Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:47.0133032Z GITHUB_TOKEN: *** 2024-08-20T21:36:47.0133325Z ##[endgroup] 2024-08-20T21:36:47.1114999Z + python3 -m pip install requests==2.27.1 pyyaml==6.0.1 2024-08-20T21:36:47.4239773Z Defaulting to user installation because normal site-packages is not writeable 2024-08-20T21:36:47.5991572Z Collecting requests==2.27.1 2024-08-20T21:36:47.6361104Z Downloading requests-2.27.1-py2.py3-none-any.whl (63 kB) 2024-08-20T21:36:47.8770335Z Collecting pyyaml==6.0.1 2024-08-20T21:36:47.8845258Z Downloading PyYAML-6.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (738 kB) 2024-08-20T21:36:47.9513447Z Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (2.10) 2024-08-20T21:36:48.0334438Z Collecting certifi>=2017.4.17 2024-08-20T21:36:48.0383436Z Downloading certifi-2024.7.4-py3-none-any.whl (162 kB) 2024-08-20T21:36:48.0670643Z Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (1.25.10) 2024-08-20T21:36:48.3719250Z Collecting charset-normalizer~=2.0.0 2024-08-20T21:36:48.3766637Z Downloading charset_normalizer-2.0.12-py3-none-any.whl (39 kB) 2024-08-20T21:36:48.4751136Z Installing collected packages: charset-normalizer, certifi, requests, pyyaml 2024-08-20T21:36:48.7865388Z Successfully installed certifi-2024.7.4 charset-normalizer-2.0.12 pyyaml-6.0.1 requests-2.27.1 2024-08-20T21:36:49.0764838Z Command completed after 1 attempt(s). 2024-08-20T21:36:49.0822725Z ##[group]Run set -x 2024-08-20T21:36:49.0823065Z set -x 2024-08-20T21:36:49.0823361Z  2024-08-20T21:36:49.0823924Z # Use relative path here as this could be checked out anywhere, not necessarily 2024-08-20T21:36:49.0824630Z # in runner workspace 2024-08-20T21:36:49.0825179Z python3 "${GITHUB_ACTION_PATH}/../../scripts/parse_ref.py" 2024-08-20T21:36:49.0832300Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:36:49.0832995Z env: 2024-08-20T21:36:49.0833414Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:49.0833775Z ##[endgroup] 2024-08-20T21:36:49.0860643Z + python3 /home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/filter-test-configs/../../scripts/parse_ref.py 2024-08-20T21:36:49.1169307Z ##[group]Run echo "Workflow: ${GITHUB_WORKFLOW}" 2024-08-20T21:36:49.1169868Z echo "Workflow: ${GITHUB_WORKFLOW}" 2024-08-20T21:36:49.1170341Z echo "Job name: ${JOB_NAME}" 2024-08-20T21:36:49.1170730Z  2024-08-20T21:36:49.1171290Z # Use relative path here as this could be checked out anywhere, not necessarily 2024-08-20T21:36:49.1172003Z # in runner workspace 2024-08-20T21:36:49.1172588Z python3 "${GITHUB_ACTION_PATH}/../../scripts/filter_test_configs.py" \ 2024-08-20T21:36:49.1173260Z  --workflow "${GITHUB_WORKFLOW}" \ 2024-08-20T21:36:49.1173724Z  --job-name "${JOB_NAME}" \ 2024-08-20T21:36:49.1176754Z  --test-matrix "{"include": [{"config": "default", "shard": 1, "num_shards": 4, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 2, "num_shards": 4, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 3, "num_shards": 4, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 4, "num_shards": 4, "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"}]}" \ 2024-08-20T21:36:49.1180229Z  --selected-test-configs "" \ 2024-08-20T21:36:49.1180700Z  --pr-number "${PR_NUMBER}" \ 2024-08-20T21:36:49.1181113Z  --tag "${TAG}" \ 2024-08-20T21:36:49.1181510Z  --event-name "${EVENT_NAME}" \ 2024-08-20T21:36:49.1181964Z  --schedule "${SCHEDULE}" \ 2024-08-20T21:36:49.1182391Z  --branch "${HEAD_BRANCH}" 2024-08-20T21:36:49.1188826Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:36:49.1189343Z env: 2024-08-20T21:36:49.1189600Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:49.1190167Z GITHUB_TOKEN: *** 2024-08-20T21:36:49.1190751Z JOB_NAME: linux-focal-py3.12-clang10 / test (dynamo, 2, 3, amz2023.linux.2xlarge) 2024-08-20T21:36:49.1191410Z PR_NUMBER: 133712 2024-08-20T21:36:49.1191712Z TAG: 2024-08-20T21:36:49.1191991Z EVENT_NAME: pull_request 2024-08-20T21:36:49.1192323Z SCHEDULE: 2024-08-20T21:36:49.1192606Z HEAD_BRANCH: 2024-08-20T21:36:49.1192896Z ##[endgroup] 2024-08-20T21:36:49.1218962Z Workflow: pull 2024-08-20T21:36:49.1219761Z Job name: linux-focal-py3.12-clang10 / test (dynamo, 2, 3, amz2023.linux.2xlarge) 2024-08-20T21:36:49.3809547Z INFO:root:Found no test-config label on the PR, so all test configs are included 2024-08-20T21:36:49.5629562Z ##[group]Run echo "Filtered matrix:" 2024-08-20T21:36:49.5630041Z echo "Filtered matrix:" 2024-08-20T21:36:49.5632994Z echo "{"include": [{"config": "default", "shard": 1, "num_shards": 4, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 2, "num_shards": 4, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 3, "num_shards": 4, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 4, "num_shards": 4, "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"}]}" 2024-08-20T21:36:49.5635913Z  2024-08-20T21:36:49.5636183Z echo 2024-08-20T21:36:49.5636564Z echo "Is the current job unstable? False" 2024-08-20T21:36:49.5637021Z  2024-08-20T21:36:49.5637285Z echo 2024-08-20T21:36:49.5637642Z echo "Is keep-going label set? False" 2024-08-20T21:36:49.5638250Z  2024-08-20T21:36:49.5638663Z echo 2024-08-20T21:36:49.5639114Z echo "Renabled issues? " 2024-08-20T21:36:49.5645510Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:36:49.5646028Z env: 2024-08-20T21:36:49.5646304Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:49.5646639Z ##[endgroup] 2024-08-20T21:36:49.5672813Z Filtered matrix: 2024-08-20T21:36:49.5677297Z {include: [{config: default, shard: 1, num_shards: 4, runner: amz2023.linux.2xlarge}, {config: default, shard: 2, num_shards: 4, runner: amz2023.linux.2xlarge}, {config: default, shard: 3, num_shards: 4, runner: amz2023.linux.2xlarge}, {config: default, shard: 4, num_shards: 4, 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}]} 2024-08-20T21:36:49.5680197Z 2024-08-20T21:36:49.5680346Z Is the current job unstable? False 2024-08-20T21:36:49.5680649Z 2024-08-20T21:36:49.5681053Z Is keep-going label set? False 2024-08-20T21:36:49.5681314Z 2024-08-20T21:36:49.5681443Z Renabled issues? 2024-08-20T21:36:49.5789174Z ##[group]Run echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2024-08-20T21:36:49.5789951Z echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2024-08-20T21:36:49.5796812Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:36:49.5797324Z env: 2024-08-20T21:36:49.5797603Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:49.5798016Z JOB_TIMEOUT: 600 2024-08-20T21:36:49.5798310Z ##[endgroup] 2024-08-20T21:36:49.6069398Z ##[group]Run set -x 2024-08-20T21:36:49.6069822Z set -x 2024-08-20T21:36:49.6070114Z  2024-08-20T21:36:49.6070468Z if [[ $TEST_CONFIG == 'multigpu' ]]; then 2024-08-20T21:36:49.6071021Z  TEST_COMMAND=.ci/pytorch/multigpu-test.sh 2024-08-20T21:36:49.6071627Z elif [[ $BUILD_ENVIRONMENT == *onnx* ]]; then 2024-08-20T21:36:49.6072151Z  TEST_COMMAND=.ci/onnx/test.sh 2024-08-20T21:36:49.6072552Z else 2024-08-20T21:36:49.6072889Z  TEST_COMMAND=.ci/pytorch/test.sh 2024-08-20T21:36:49.6073313Z fi 2024-08-20T21:36:49.6073568Z  2024-08-20T21:36:49.6074041Z # detached container should get cleaned up by teardown_ec2_linux 2024-08-20T21:36:49.6074835Z # TODO: Stop building test binaries as part of the build phase 2024-08-20T21:36:49.6075529Z # Used for GPU_FLAG since that doesn't play nice 2024-08-20T21:36:49.6076151Z # shellcheck disable=SC2086,SC2090 2024-08-20T21:36:49.6076610Z container_name=$(docker run \ 2024-08-20T21:36:49.6077035Z  ${GPU_FLAG:-} \ 2024-08-20T21:36:49.6077405Z  -e BUILD_ENVIRONMENT \ 2024-08-20T21:36:49.6077792Z  -e PR_NUMBER \ 2024-08-20T21:36:49.6078157Z  -e GITHUB_ACTIONS \ 2024-08-20T21:36:49.6078694Z  -e GITHUB_REPOSITORY \ 2024-08-20T21:36:49.6079091Z  -e GITHUB_WORKFLOW \ 2024-08-20T21:36:49.6079910Z  -e GITHUB_JOB \ 2024-08-20T21:36:49.6080271Z  -e GITHUB_RUN_ID \ 2024-08-20T21:36:49.6080658Z  -e GITHUB_RUN_NUMBER \ 2024-08-20T21:36:49.6081054Z  -e GITHUB_RUN_ATTEMPT \ 2024-08-20T21:36:49.6081454Z  -e JOB_ID \ 2024-08-20T21:36:49.6081797Z  -e JOB_NAME \ 2024-08-20T21:36:49.6082127Z  -e BASE_SHA \ 2024-08-20T21:36:49.6082474Z  -e BRANCH \ 2024-08-20T21:36:49.6082805Z  -e SHA1 \ 2024-08-20T21:36:49.6083134Z  -e AWS_DEFAULT_REGION \ 2024-08-20T21:36:49.6083544Z  -e IN_WHEEL_TEST \ 2024-08-20T21:36:49.6083928Z  -e SHARD_NUMBER \ 2024-08-20T21:36:49.6084283Z  -e TEST_CONFIG \ 2024-08-20T21:36:49.6084654Z  -e NUM_TEST_SHARDS \ 2024-08-20T21:36:49.6085051Z  -e REENABLED_ISSUES \ 2024-08-20T21:36:49.6085668Z  -e CONTINUE_THROUGH_ERROR \ 2024-08-20T21:36:49.6086101Z  -e VERBOSE_TEST_LOGS \ 2024-08-20T21:36:49.6086511Z  -e TEST_SHOWLOCALS \ 2024-08-20T21:36:49.6086889Z  -e NO_TEST_TIMEOUT \ 2024-08-20T21:36:49.6087268Z  -e NO_TD \ 2024-08-20T21:36:49.6087611Z  -e TD_DISTRIBUTED \ 2024-08-20T21:36:49.6087977Z  -e PR_LABELS \ 2024-08-20T21:36:49.6088387Z  -e MAX_JOBS="$(nproc --ignore=2)" \ 2024-08-20T21:36:49.6088855Z  -e SCCACHE_BUCKET \ 2024-08-20T21:36:49.6089239Z  -e SCCACHE_S3_KEY_PREFIX \ 2024-08-20T21:36:49.6089653Z  -e XLA_CUDA \ 2024-08-20T21:36:49.6090056Z  -e XLA_CLANG_CACHE_S3_BUCKET_NAME \ 2024-08-20T21:36:49.6090562Z  -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK \ 2024-08-20T21:36:49.6091100Z  -e PYTORCH_TEST_RERUN_DISABLED_TESTS \ 2024-08-20T21:36:49.6091626Z  -e SKIP_SCCACHE_INITIALIZATION=1 \ 2024-08-20T21:36:49.6092097Z  -e HUGGING_FACE_HUB_TOKEN \ 2024-08-20T21:36:49.6092553Z  -e SCRIBE_GRAPHQL_ACCESS_TOKEN \ 2024-08-20T21:36:49.6093007Z  -e DASHBOARD_TAG \ 2024-08-20T21:36:49.6093478Z  --env-file="/tmp/github_env_${GITHUB_RUN_ID}" \ 2024-08-20T21:36:49.6094054Z  --security-opt seccomp=unconfined \ 2024-08-20T21:36:49.6094528Z  --cap-add=SYS_PTRACE \ 2024-08-20T21:36:49.6094920Z  --ipc=host \ 2024-08-20T21:36:49.6095259Z  --shm-size="${SHM_SIZE}" \ 2024-08-20T21:36:49.6095658Z  --tty \ 2024-08-20T21:36:49.6096295Z  --detach \ 2024-08-20T21:36:49.6096810Z  --name="${container_name}" \ 2024-08-20T21:36:49.6097240Z  --user jenkins \ 2024-08-20T21:36:49.6097741Z  -v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \ 2024-08-20T21:36:49.6098309Z  -w /var/lib/jenkins/workspace \ 2024-08-20T21:36:49.6098758Z  "${DOCKER_IMAGE}" 2024-08-20T21:36:49.6099110Z ) 2024-08-20T21:36:49.6099505Z # Propagate download.pytorch.org IP to container 2024-08-20T21:36:49.6100485Z grep download.pytorch.org /etc/hosts | docker exec -i "${container_name}" sudo bash -c "/bin/cat >> /etc/hosts" 2024-08-20T21:36:49.6101527Z echo "DOCKER_CONTAINER_ID=${container_name}" >> "${GITHUB_ENV}" 2024-08-20T21:36:49.6102484Z docker exec -t "${container_name}" sh -c "pip install $(echo dist/*.whl)[opt-einsum] && ${TEST_COMMAND}" 2024-08-20T21:36:49.6109377Z shell: /usr/bin/bash -e {0} 2024-08-20T21:36:49.6109732Z env: 2024-08-20T21:36:49.6109989Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:49.6110420Z BUILD_ENVIRONMENT: linux-focal-py3.12-clang10 2024-08-20T21:36:49.6110888Z PR_NUMBER: 133712 2024-08-20T21:36:49.6111210Z GITHUB_REPOSITORY: pytorch/pytorch 2024-08-20T21:36:49.6111631Z GITHUB_WORKFLOW: pull 2024-08-20T21:36:49.6111963Z GITHUB_JOB: test 2024-08-20T21:36:49.6112260Z GITHUB_RUN_ID: 10479309237 2024-08-20T21:36:49.6112625Z GITHUB_RUN_NUMBER: 238988 2024-08-20T21:36:49.6112984Z GITHUB_RUN_ATTEMPT: 1 2024-08-20T21:36:49.6113295Z JOB_ID: 29025268893 2024-08-20T21:36:49.6113869Z JOB_NAME: linux-focal-py3.12-clang10 / test (dynamo, 2, 3, amz2023.linux.2xlarge) 2024-08-20T21:36:49.6114544Z BRANCH: pull/133712 2024-08-20T21:36:49.6114926Z SHA1: 40ec5f6ddd9787aca0449b24128343ff4c4a88b3 2024-08-20T21:36:49.6115451Z BASE_SHA: 91f3d614142df02f619e44a68e3d9e0dfeba49ec 2024-08-20T21:36:49.6115926Z TEST_CONFIG: dynamo 2024-08-20T21:36:49.6116241Z SHARD_NUMBER: 2 2024-08-20T21:36:49.6116527Z NUM_TEST_SHARDS: 3 2024-08-20T21:36:49.6116843Z REENABLED_ISSUES: 2024-08-20T21:36:49.6117183Z CONTINUE_THROUGH_ERROR: False 2024-08-20T21:36:49.6117557Z VERBOSE_TEST_LOGS: False 2024-08-20T21:36:49.6117911Z TEST_SHOWLOCALS: False 2024-08-20T21:36:49.6118254Z NO_TEST_TIMEOUT: False 2024-08-20T21:36:49.6118699Z NO_TD: False 2024-08-20T21:36:49.6118998Z TD_DISTRIBUTED: False 2024-08-20T21:36:49.6119574Z SCCACHE_BUCKET: ossci-compiler-cache-circleci-v2 2024-08-20T21:36:49.6120065Z SCCACHE_S3_KEY_PREFIX: pull 2024-08-20T21:36:49.6120435Z SHM_SIZE: 1g 2024-08-20T21:36:49.6121386Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:36:49.6122420Z XLA_CUDA: 2024-08-20T21:36:49.6122917Z XLA_CLANG_CACHE_S3_BUCKET_NAME: ossci-compiler-clang-cache-circleci-xla 2024-08-20T21:36:49.6123560Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK: 0 2024-08-20T21:36:49.6123985Z PYTORCH_TEST_RERUN_DISABLED_TESTS: 0 2024-08-20T21:36:49.6124395Z DASHBOARD_TAG: 2024-08-20T21:36:49.6124710Z HUGGING_FACE_HUB_TOKEN: 2024-08-20T21:36:49.6125057Z SCRIBE_GRAPHQL_ACCESS_TOKEN: 2024-08-20T21:36:49.6125435Z ##[endgroup] 2024-08-20T21:36:49.6152338Z + [[ dynamo == \m\u\l\t\i\g\p\u ]] 2024-08-20T21:36:49.6153031Z + [[ linux-focal-py3.12-clang10 == *onnx* ]] 2024-08-20T21:36:49.6153516Z + TEST_COMMAND=.ci/pytorch/test.sh 2024-08-20T21:36:49.6161102Z +++ nproc --ignore=2 2024-08-20T21:36:49.6310359Z ++ 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 2024-08-20T21:36:54.6692621Z + container_name=c78658a3dfdfbcf27d398914d87deab2e05563bfce93bca4b69e2ba0b625e59d 2024-08-20T21:36:54.6694322Z + grep download.pytorch.org /etc/hosts 2024-08-20T21:36:54.6697345Z + docker exec -i c78658a3dfdfbcf27d398914d87deab2e05563bfce93bca4b69e2ba0b625e59d sudo bash -c '/bin/cat >> /etc/hosts' 2024-08-20T21:36:54.8297089Z + echo DOCKER_CONTAINER_ID=c78658a3dfdfbcf27d398914d87deab2e05563bfce93bca4b69e2ba0b625e59d 2024-08-20T21:36:54.8302149Z ++ echo dist/torch-2.5.0a0+git40ec5f6-cp312-cp312-linux_x86_64.whl 2024-08-20T21:36:54.8305484Z + docker exec -t c78658a3dfdfbcf27d398914d87deab2e05563bfce93bca4b69e2ba0b625e59d sh -c 'pip install dist/torch-2.5.0a0+git40ec5f6-cp312-cp312-linux_x86_64.whl[opt-einsum] && .ci/pytorch/test.sh' 2024-08-20T21:36:55.4272539Z Processing ./dist/torch-2.5.0a0+git40ec5f6-cp312-cp312-linux_x86_64.whl (from torch==2.5.0a0+git40ec5f6) 2024-08-20T21:36:55.8844207Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+git40ec5f6->torch==2.5.0a0+git40ec5f6) (3.13.1) 2024-08-20T21:36:55.8847215Z 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->torch==2.5.0a0+git40ec5f6) (4.12.2) 2024-08-20T21:36:55.8850568Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+git40ec5f6->torch==2.5.0a0+git40ec5f6) (2.8.8) 2024-08-20T21:36:55.8854015Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+git40ec5f6->torch==2.5.0a0+git40ec5f6) (3.1.4) 2024-08-20T21:36:55.8858150Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+git40ec5f6->torch==2.5.0a0+git40ec5f6) (2024.6.1) 2024-08-20T21:36:55.8870499Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+git40ec5f6->torch==2.5.0a0+git40ec5f6) (72.1.0) 2024-08-20T21:36:55.8876162Z 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->torch==2.5.0a0+git40ec5f6) (1.13.1) 2024-08-20T21:36:55.8893684Z 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->torch==2.5.0a0+git40ec5f6) (1.3.0) 2024-08-20T21:36:55.8913731Z Requirement already satisfied: opt-einsum>=3.3 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+git40ec5f6->torch==2.5.0a0+git40ec5f6) (3.3.0) 2024-08-20T21:36:55.8928561Z Requirement already satisfied: numpy>=1.7 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from opt-einsum>=3.3->torch==2.5.0a0+git40ec5f6->torch==2.5.0a0+git40ec5f6) (1.26.0) 2024-08-20T21:36:55.9034910Z 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->torch==2.5.0a0+git40ec5f6) (2.1.5) 2024-08-20T21:36:56.0848210Z Installing collected packages: torch 2024-08-20T21:37:06.3328135Z Successfully installed torch-2.5.0a0+git40ec5f6 2024-08-20T21:37:06.4387797Z + export TERM=vt100 2024-08-20T21:37:06.4388304Z + TERM=vt100 2024-08-20T21:37:06.4389143Z ++ dirname .ci/pytorch/test.sh 2024-08-20T21:37:06.4412893Z + source .ci/pytorch/common.sh 2024-08-20T21:37:06.4424715Z +++ dirname .ci/pytorch/common.sh 2024-08-20T21:37:06.4431739Z ++ source .ci/pytorch/common_utils.sh 2024-08-20T21:37:06.4440350Z +++ declare -f -t trap_add 2024-08-20T21:37:06.4446517Z ++ set -ex 2024-08-20T21:37:06.4447257Z ++ [[ linux-focal-py3.12-clang10 == *rocm* ]] 2024-08-20T21:37:06.4448078Z ++ BUILD_TEST_LIBTORCH=0 2024-08-20T21:37:06.4448544Z + [[ linux-focal-py3.12-clang10 != *rocm* ]] 2024-08-20T21:37:06.4451212Z ++ stat -c %u /var/lib/jenkins/workspace 2024-08-20T21:37:06.4496521Z + WORKSPACE_ORIGINAL_OWNER_ID=1000 2024-08-20T21:37:06.4497286Z + trap_add cleanup_workspace EXIT 2024-08-20T21:37:06.4497917Z + trap_add_cmd=cleanup_workspace 2024-08-20T21:37:06.4498303Z + shift 2024-08-20T21:37:06.4498566Z + for trap_add_name in "$@" 2024-08-20T21:37:06.4504770Z +++ trap -p EXIT 2024-08-20T21:37:06.4507286Z ++ eval 'extract_trap_cmd ' 2024-08-20T21:37:06.4507964Z +++ extract_trap_cmd 2024-08-20T21:37:06.4508519Z +++ printf '%s\n' '' 2024-08-20T21:37:06.4509115Z ++ printf '%s\n' cleanup_workspace 2024-08-20T21:37:06.4510972Z + trap -- ' 2024-08-20T21:37:06.4511530Z cleanup_workspace' EXIT 2024-08-20T21:37:06.4512350Z + sudo chown -R jenkins /var/lib/jenkins/workspace 2024-08-20T21:37:06.9591179Z + git config --global --add safe.directory /var/lib/jenkins/workspace 2024-08-20T21:37:06.9885430Z + echo 'Environment variables:' 2024-08-20T21:37:06.9899188Z Environment variables: 2024-08-20T21:37:06.9899576Z + env 2024-08-20T21:37:06.9909183Z INSTALLED_DB=yes 2024-08-20T21:37:06.9910248Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-08-20T21:37:06.9911346Z CONTINUE_THROUGH_ERROR=False 2024-08-20T21:37:06.9912196Z BUILD_ENVIRONMENT=linux-focal-py3.12-clang10 2024-08-20T21:37:06.9912982Z HOSTNAME=c78658a3dfdf 2024-08-20T21:37:06.9914552Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_517c88d9-d569-481f-992c-97ed3bd32000 2024-08-20T21:37:06.9916098Z GITHUB_ACTION=__self 2024-08-20T21:37:06.9916687Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2024-08-20T21:37:06.9917377Z GITHUB_RUN_NUMBER=238988 2024-08-20T21:37:06.9917999Z TEST_CONFIG=dynamo 2024-08-20T21:37:06.9918646Z GITHUB_REPOSITORY_OWNER_ID=21003710 2024-08-20T21:37:06.9919526Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all 2024-08-20T21:37:06.9920294Z SCRIBE_GRAPHQL_ACCESS_TOKEN= 2024-08-20T21:37:06.9920790Z GITHUB_TRIGGERING_ACTOR=pytorchmergebot 2024-08-20T21:37:06.9922062Z GITHUB_REF_TYPE=branch 2024-08-20T21:37:06.9922670Z TORCH_CUDA_ARCH_LIST=Maxwell 2024-08-20T21:37:06.9923344Z BASE_SHA=91f3d614142df02f619e44a68e3d9e0dfeba49ec 2024-08-20T21:37:06.9924074Z XLA_CUDA= 2024-08-20T21:37:06.9924476Z HUGGING_FACE_HUB_TOKEN= 2024-08-20T21:37:06.9928873Z *** 2024-08-20T21:37:06.9929202Z GITHUB_REPOSITORY_ID=65600975 2024-08-20T21:37:06.9929605Z GITHUB_ACTIONS=true 2024-08-20T21:37:06.9929973Z SHA1=40ec5f6ddd9787aca0449b24128343ff4c4a88b3 2024-08-20T21:37:06.9930509Z GITHUB_SHA=f2fb9405c2fa9f9502a76363091cce6fd8179736 2024-08-20T21:37:06.9931267Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/pull/133712/merge 2024-08-20T21:37:06.9931941Z UCC_HOME=/usr 2024-08-20T21:37:06.9932234Z VERBOSE_TEST_LOGS=False 2024-08-20T21:37:06.9932590Z GITHUB_REF=refs/pull/133712/merge 2024-08-20T21:37:06.9932972Z SHARD_NUMBER=2 2024-08-20T21:37:06.9933266Z GITHUB_REF_PROTECTED=false 2024-08-20T21:37:06.9933618Z HOME=/var/lib/jenkins 2024-08-20T21:37:06.9933972Z GITHUB_API_URL=https://api.github.com 2024-08-20T21:37:06.9934436Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2024-08-20T21:37:06.9934839Z UCX_COMMIT= 2024-08-20T21:37:06.9935114Z SCCACHE_S3_KEY_PREFIX=pull 2024-08-20T21:37:06.9935463Z NUM_TEST_SHARDS=3 2024-08-20T21:37:06.9935763Z UCX_HOME=/usr 2024-08-20T21:37:06.9936727Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_517c88d9-d569-481f-992c-97ed3bd32000 2024-08-20T21:37:06.9937964Z JOB_NAME=linux-focal-py3.12-clang10 / test (dynamo, 2, 3, amz2023.linux.2xlarge) 2024-08-20T21:37:06.9939252Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_517c88d9-d569-481f-992c-97ed3bd32000 2024-08-20T21:37:06.9940726Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json 2024-08-20T21:37:06.9941424Z GITHUB_EVENT_NAME=pull_request 2024-08-20T21:37:06.9941801Z DASHBOARD_TAG= 2024-08-20T21:37:06.9942105Z GITHUB_RUN_ID=10479309237 2024-08-20T21:37:06.9943105Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_517c88d9-d569-481f-992c-97ed3bd32000 2024-08-20T21:37:06.9944060Z GITHUB_ACTOR=pytorchmergebot 2024-08-20T21:37:06.9944429Z PR_NUMBER=133712 2024-08-20T21:37:06.9944726Z DESIRED_CUDA= 2024-08-20T21:37:06.9945005Z GITHUB_RUN_ATTEMPT=1 2024-08-20T21:37:06.9945342Z ANACONDA_PYTHON_VERSION=3.12 2024-08-20T21:37:06.9945786Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2024-08-20T21:37:06.9946261Z TERM=vt100 2024-08-20T21:37:06.9946542Z INSTALLED_VISION=yes 2024-08-20T21:37:06.9946850Z BRANCH=pull/133712 2024-08-20T21:37:06.9947176Z OPENSSL_ROOT_DIR=/opt/openssl 2024-08-20T21:37:06.9947552Z CUDA_PATH=/usr/local/cuda 2024-08-20T21:37:06.9948355Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux 2024-08-20T21:37:06.9949157Z GITHUB_SERVER_URL=https://github.com 2024-08-20T21:37:06.9949571Z UCC_COMMIT= 2024-08-20T21:37:06.9949835Z REENABLED_ISSUES= 2024-08-20T21:37:06.9950126Z DOCS= 2024-08-20T21:37:06.9950395Z INSTALLED_ANDROID= 2024-08-20T21:37:06.9950680Z SHLVL=1 2024-08-20T21:37:06.9950936Z MAX_JOBS=6 2024-08-20T21:37:06.9951217Z GITHUB_ACTOR_ID=97764156 2024-08-20T21:37:06.9951677Z GITHUB_WORKFLOW_SHA=f2fb9405c2fa9f9502a76363091cce6fd8179736 2024-08-20T21:37:06.9952219Z GITHUB_REF_NAME=133712/merge 2024-08-20T21:37:06.9952870Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla 2024-08-20T21:37:06.9953462Z GITHUB_JOB=test 2024-08-20T21:37:06.9953762Z NO_TEST_TIMEOUT=False 2024-08-20T21:37:06.9954091Z TD_DISTRIBUTED=False 2024-08-20T21:37:06.9954430Z GITHUB_REPOSITORY=pytorch/pytorch 2024-08-20T21:37:06.9954834Z GITHUB_RETENTION_DAYS=90 2024-08-20T21:37:06.9955193Z OPENSSL_DIR=/opt/openssl 2024-08-20T21:37:06.9955529Z GITHUB_ACTION_REPOSITORY= 2024-08-20T21:37:06.9956646Z 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 2024-08-20T21:37:06.9957838Z GITHUB_BASE_REF=gh/XuehaiPan/146/base 2024-08-20T21:37:06.9958508Z INSTALLED_ACL= 2024-08-20T21:37:06.9958794Z CI=true 2024-08-20T21:37:06.9959083Z GITHUB_REPOSITORY_OWNER=pytorch 2024-08-20T21:37:06.9959447Z JOB_ID=29025268893 2024-08-20T21:37:06.9959760Z INSTALLED_PROTOBUF=yes 2024-08-20T21:37:06.9960121Z GITHUB_HEAD_REF=gh/XuehaiPan/146/head 2024-08-20T21:37:06.9960514Z GITHUB_ACTION_REF= 2024-08-20T21:37:06.9960987Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2 2024-08-20T21:37:06.9961475Z TEST_SHOWLOCALS=False 2024-08-20T21:37:06.9961797Z GITHUB_WORKFLOW=pull 2024-08-20T21:37:06.9962138Z DEBIAN_FRONTEND=noninteractive 2024-08-20T21:37:06.9963134Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_517c88d9-d569-481f-992c-97ed3bd32000 2024-08-20T21:37:06.9964000Z NO_TD=False 2024-08-20T21:37:06.9964305Z SKIP_SCCACHE_INITIALIZATION=1 2024-08-20T21:37:06.9964673Z _=/usr/bin/env 2024-08-20T21:37:06.9965160Z ++ python -c 'import site; print(site.getsitepackages()[0])' 2024-08-20T21:37:07.0264770Z + TORCH_INSTALL_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch 2024-08-20T21:37:07.0266248Z + TORCH_BIN_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/bin 2024-08-20T21:37:07.0267549Z + TORCH_LIB_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib 2024-08-20T21:37:07.0268518Z + TORCH_TEST_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/test 2024-08-20T21:37:07.0269169Z + BUILD_DIR=build 2024-08-20T21:37:07.0269496Z + BUILD_RENAMED_DIR=build_renamed 2024-08-20T21:37:07.0269880Z + BUILD_BIN_DIR=build/bin 2024-08-20T21:37:07.0270333Z + SHARD_NUMBER=2 2024-08-20T21:37:07.0270635Z + NUM_TEST_SHARDS=3 2024-08-20T21:37:07.0271327Z + export VALGRIND=ON 2024-08-20T21:37:07.0271653Z + VALGRIND=ON 2024-08-20T21:37:07.0272085Z + [[ linux-focal-py3.12-clang10 == *clang9* ]] 2024-08-20T21:37:07.0272520Z + [[ 0 == \1 ]] 2024-08-20T21:37:07.0272815Z + [[ False == \1 ]] 2024-08-20T21:37:07.0273240Z + [[ linux-focal-py3.12-clang10 != *bazel* ]] 2024-08-20T21:37:07.0273738Z ++ realpath build/custom_test_artifacts 2024-08-20T21:37:07.0297945Z + CUSTOM_TEST_ARTIFACT_BUILD_DIR=/var/lib/jenkins/workspace/build/custom_test_artifacts 2024-08-20T21:37:07.0298777Z + [[ -n '' ]] 2024-08-20T21:37:07.0299132Z + echo 'Environment variables' 2024-08-20T21:37:07.0299523Z Environment variables 2024-08-20T21:37:07.0299835Z + env 2024-08-20T21:37:07.0304889Z INSTALLED_DB=yes 2024-08-20T21:37:07.0305837Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-08-20T21:37:07.0306807Z CONTINUE_THROUGH_ERROR=False 2024-08-20T21:37:07.0307690Z BUILD_ENVIRONMENT=linux-focal-py3.12-clang10 2024-08-20T21:37:07.0308488Z HOSTNAME=c78658a3dfdf 2024-08-20T21:37:07.0310060Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_517c88d9-d569-481f-992c-97ed3bd32000 2024-08-20T21:37:07.0311696Z GITHUB_ACTION=__self 2024-08-20T21:37:07.0312314Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2024-08-20T21:37:07.0313055Z GITHUB_RUN_NUMBER=238988 2024-08-20T21:37:07.0313462Z TEST_CONFIG=dynamo 2024-08-20T21:37:07.0313786Z GITHUB_REPOSITORY_OWNER_ID=21003710 2024-08-20T21:37:07.0314425Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all 2024-08-20T21:37:07.0315018Z SCRIBE_GRAPHQL_ACCESS_TOKEN= 2024-08-20T21:37:07.0315779Z GITHUB_TRIGGERING_ACTOR=pytorchmergebot 2024-08-20T21:37:07.0316379Z GITHUB_REF_TYPE=branch 2024-08-20T21:37:07.0316719Z TORCH_CUDA_ARCH_LIST=Maxwell 2024-08-20T21:37:07.0317136Z BASE_SHA=91f3d614142df02f619e44a68e3d9e0dfeba49ec 2024-08-20T21:37:07.0317594Z XLA_CUDA= 2024-08-20T21:37:07.0317871Z HUGGING_FACE_HUB_TOKEN= 2024-08-20T21:37:07.0318431Z *** 2024-08-20T21:37:07.0318707Z GITHUB_REPOSITORY_ID=65600975 2024-08-20T21:37:07.0319081Z GITHUB_ACTIONS=true 2024-08-20T21:37:07.0319503Z SHA1=40ec5f6ddd9787aca0449b24128343ff4c4a88b3 2024-08-20T21:37:07.0320019Z GITHUB_SHA=f2fb9405c2fa9f9502a76363091cce6fd8179736 2024-08-20T21:37:07.0320771Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/pull/133712/merge 2024-08-20T21:37:07.0321441Z UCC_HOME=/usr 2024-08-20T21:37:07.0321973Z VERBOSE_TEST_LOGS=False 2024-08-20T21:37:07.0322328Z GITHUB_REF=refs/pull/133712/merge 2024-08-20T21:37:07.0322714Z SHARD_NUMBER=2 2024-08-20T21:37:07.0323006Z GITHUB_REF_PROTECTED=false 2024-08-20T21:37:07.0323364Z HOME=/var/lib/jenkins 2024-08-20T21:37:07.0323736Z GITHUB_API_URL=https://api.github.com 2024-08-20T21:37:07.0324171Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2024-08-20T21:37:07.0324565Z UCX_COMMIT= 2024-08-20T21:37:07.0324856Z SCCACHE_S3_KEY_PREFIX=pull 2024-08-20T21:37:07.0325194Z NUM_TEST_SHARDS=3 2024-08-20T21:37:07.0325489Z UCX_HOME=/usr 2024-08-20T21:37:07.0326437Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_517c88d9-d569-481f-992c-97ed3bd32000 2024-08-20T21:37:07.0327650Z JOB_NAME=linux-focal-py3.12-clang10 / test (dynamo, 2, 3, amz2023.linux.2xlarge) 2024-08-20T21:37:07.0328882Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_517c88d9-d569-481f-992c-97ed3bd32000 2024-08-20T21:37:07.0330105Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json 2024-08-20T21:37:07.0330809Z GITHUB_EVENT_NAME=pull_request 2024-08-20T21:37:07.0331165Z DASHBOARD_TAG= 2024-08-20T21:37:07.0331467Z GITHUB_RUN_ID=10479309237 2024-08-20T21:37:07.0332471Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_517c88d9-d569-481f-992c-97ed3bd32000 2024-08-20T21:37:07.0333407Z GITHUB_ACTOR=pytorchmergebot 2024-08-20T21:37:07.0333774Z PR_NUMBER=133712 2024-08-20T21:37:07.0334071Z DESIRED_CUDA= 2024-08-20T21:37:07.0334347Z GITHUB_RUN_ATTEMPT=1 2024-08-20T21:37:07.0334661Z VALGRIND=ON 2024-08-20T21:37:07.0334957Z ANACONDA_PYTHON_VERSION=3.12 2024-08-20T21:37:07.0335529Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2024-08-20T21:37:07.0336010Z TERM=vt100 2024-08-20T21:37:07.0336288Z INSTALLED_VISION=yes 2024-08-20T21:37:07.0336595Z BRANCH=pull/133712 2024-08-20T21:37:07.0336918Z OPENSSL_ROOT_DIR=/opt/openssl 2024-08-20T21:37:07.0337282Z CUDA_PATH=/usr/local/cuda 2024-08-20T21:37:07.0338106Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux 2024-08-20T21:37:07.0338912Z GITHUB_SERVER_URL=https://github.com 2024-08-20T21:37:07.0339319Z UCC_COMMIT= 2024-08-20T21:37:07.0339584Z REENABLED_ISSUES= 2024-08-20T21:37:07.0339877Z DOCS= 2024-08-20T21:37:07.0340126Z INSTALLED_ANDROID= 2024-08-20T21:37:07.0340419Z SHLVL=1 2024-08-20T21:37:07.0340673Z MAX_JOBS=6 2024-08-20T21:37:07.0340935Z GITHUB_ACTOR_ID=97764156 2024-08-20T21:37:07.0341407Z GITHUB_WORKFLOW_SHA=f2fb9405c2fa9f9502a76363091cce6fd8179736 2024-08-20T21:37:07.0341944Z GITHUB_REF_NAME=133712/merge 2024-08-20T21:37:07.0342578Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla 2024-08-20T21:37:07.0343179Z GITHUB_JOB=test 2024-08-20T21:37:07.0343477Z NO_TEST_TIMEOUT=False 2024-08-20T21:37:07.0343788Z TD_DISTRIBUTED=False 2024-08-20T21:37:07.0344133Z GITHUB_REPOSITORY=pytorch/pytorch 2024-08-20T21:37:07.0344538Z GITHUB_RETENTION_DAYS=90 2024-08-20T21:37:07.0344878Z OPENSSL_DIR=/opt/openssl 2024-08-20T21:37:07.0345230Z GITHUB_ACTION_REPOSITORY= 2024-08-20T21:37:07.0346356Z 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 2024-08-20T21:37:07.0347553Z GITHUB_BASE_REF=gh/XuehaiPan/146/base 2024-08-20T21:37:07.0347956Z INSTALLED_ACL= 2024-08-20T21:37:07.0348238Z CI=true 2024-08-20T21:37:07.0348510Z GITHUB_REPOSITORY_OWNER=pytorch 2024-08-20T21:37:07.0348891Z JOB_ID=29025268893 2024-08-20T21:37:07.0349203Z INSTALLED_PROTOBUF=yes 2024-08-20T21:37:07.0349550Z GITHUB_HEAD_REF=gh/XuehaiPan/146/head 2024-08-20T21:37:07.0349966Z GITHUB_ACTION_REF= 2024-08-20T21:37:07.0350422Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2 2024-08-20T21:37:07.0350893Z TEST_SHOWLOCALS=False 2024-08-20T21:37:07.0351220Z GITHUB_WORKFLOW=pull 2024-08-20T21:37:07.0351559Z DEBIAN_FRONTEND=noninteractive 2024-08-20T21:37:07.0352533Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_517c88d9-d569-481f-992c-97ed3bd32000 2024-08-20T21:37:07.0353501Z NO_TD=False 2024-08-20T21:37:07.0353800Z SKIP_SCCACHE_INITIALIZATION=1 2024-08-20T21:37:07.0354154Z _=/usr/bin/env 2024-08-20T21:37:07.0354499Z + echo 'Testing pytorch' 2024-08-20T21:37:07.0354843Z Testing pytorch 2024-08-20T21:37:07.0355151Z + export LANG=C.UTF-8 2024-08-20T21:37:07.0355491Z + LANG=C.UTF-8 2024-08-20T21:37:07.0432867Z + PR_NUMBER=133712 2024-08-20T21:37:07.0433468Z + [[ dynamo == \d\e\f\a\u\l\t ]] 2024-08-20T21:37:07.0434232Z + [[ dynamo == \d\i\s\t\r\i\b\u\t\e\d ]] 2024-08-20T21:37:07.0434938Z + [[ dynamo == \s\l\o\w ]] 2024-08-20T21:37:07.0435917Z + [[ linux-focal-py3.12-clang10 == *slow-gradcheck* ]] 2024-08-20T21:37:07.0436970Z + [[ linux-focal-py3.12-clang10 == *cuda* ]] 2024-08-20T21:37:07.0437594Z + [[ linux-focal-py3.12-clang10 == *rocm* ]] 2024-08-20T21:37:07.0438152Z + [[ linux-focal-py3.12-clang10 == *xpu* ]] 2024-08-20T21:37:07.0438720Z + [[ dynamo == *crossref* ]] 2024-08-20T21:37:07.0439217Z + [[ linux-focal-py3.12-clang10 == *rocm* ]] 2024-08-20T21:37:07.0439779Z + [[ linux-focal-py3.12-clang10 == *xpu* ]] 2024-08-20T21:37:07.0440342Z + [[ linux-focal-py3.12-clang10 != *-bazel-* ]] 2024-08-20T21:37:07.0440898Z + pip_install --user ninja==1.10.2 2024-08-20T21:37:07.0441459Z + pip install --progress-bar off --user ninja==1.10.2 2024-08-20T21:37:07.5469570Z Collecting ninja==1.10.2 2024-08-20T21:37:07.5765501Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl.metadata (5.0 kB) 2024-08-20T21:37:07.5889489Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (108 kB) 2024-08-20T21:37:07.7587426Z Installing collected packages: ninja 2024-08-20T21:37:07.7676476Z  WARNING: The script ninja is installed in '/var/lib/jenkins/.local/bin' which is not on PATH. 2024-08-20T21:37:07.7678051Z Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. 2024-08-20T21:37:07.7737958Z Successfully installed ninja-1.10.2 2024-08-20T21:37:07.8687532Z + 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 2024-08-20T21:37:07.8689896Z + 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 2024-08-20T21:37:07.8691493Z + [[ linux-focal-py3.12-clang10 == *aarch64* ]] 2024-08-20T21:37:07.8691966Z + install_tlparse 2024-08-20T21:37:07.8692472Z + pip_install --user tlparse==0.3.25 2024-08-20T21:37:07.8693048Z + pip install --progress-bar off --user tlparse==0.3.25 2024-08-20T21:37:08.2836179Z Collecting tlparse==0.3.25 2024-08-20T21:37:08.3010685Z Downloading tlparse-0.3.25-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.7 kB) 2024-08-20T21:37:08.3139599Z Downloading tlparse-0.3.25-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.2 MB) 2024-08-20T21:37:08.5182715Z Installing collected packages: tlparse 2024-08-20T21:37:08.5567963Z Successfully installed tlparse-0.3.25 2024-08-20T21:37:08.6524277Z ++ python -m site --user-base 2024-08-20T21:37:08.6721208Z + 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 2024-08-20T21:37:08.6723386Z + [[ linux-focal-py3.12-clang10 == *asan* ]] 2024-08-20T21:37:08.6724127Z + [[ linux-focal-py3.12-clang10 == *-debug* ]] 2024-08-20T21:37:08.6725095Z + [[ linux-focal-py3.12-clang10 != *-bazel-* ]] 2024-08-20T21:37:08.6726100Z + echo 'We are not in debug mode: linux-focal-py3.12-clang10. Expect the assertion to pass' 2024-08-20T21:37:08.6727208Z We are not in debug mode: linux-focal-py3.12-clang10. Expect the assertion to pass 2024-08-20T21:37:08.6728186Z + cd test 2024-08-20T21:37:08.6728727Z + python -c 'import torch; torch._C._crash_if_debug_asserts_fail(424242)' 2024-08-20T21:37:10.3373626Z + [[ dynamo == \n\o\g\p\u\_\N\O\_\A\V\X\2 ]] 2024-08-20T21:37:10.3374188Z + [[ dynamo == \n\o\g\p\u\_\A\V\X\5\1\2 ]] 2024-08-20T21:37:10.3377328Z + DYNAMO_BENCHMARK_FLAGS=() 2024-08-20T21:37:10.3378358Z + [[ dynamo == *pr_time_benchmarks* ]] 2024-08-20T21:37:10.3379149Z + [[ dynamo == *dynamo_eager* ]] 2024-08-20T21:37:10.3379733Z + [[ dynamo == *aot_eager* ]] 2024-08-20T21:37:10.3380103Z + [[ dynamo == *aot_inductor* ]] 2024-08-20T21:37:10.3380498Z + [[ dynamo == *inductor* ]] 2024-08-20T21:37:10.3380863Z + [[ dynamo == *dynamic* ]] 2024-08-20T21:37:10.3381236Z + [[ dynamo == *cpu* ]] 2024-08-20T21:37:10.3381835Z + DYNAMO_BENCHMARK_FLAGS+=(--device cuda) 2024-08-20T21:37:10.3413701Z + [[ linux-focal-py3.12-clang10 == *libtorch* ]] 2024-08-20T21:37:10.3414351Z + [[ linux-focal-py3.12-clang10 == *-bazel-* ]] 2024-08-20T21:37:10.3416810Z + cd test 2024-08-20T21:37:10.3417800Z + python -c 'import torch; print(torch.__config__.show())' 2024-08-20T21:37:11.6728591Z PyTorch built with: 2024-08-20T21:37:11.6729631Z - GCC 4.2 2024-08-20T21:37:11.6729975Z - C++ Version: 201703 2024-08-20T21:37:11.6730342Z - clang 10.0.0 2024-08-20T21:37:11.6731238Z - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications 2024-08-20T21:37:11.6732407Z - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) 2024-08-20T21:37:11.6733104Z - OpenMP 201511 (a.k.a. OpenMP 4.5) 2024-08-20T21:37:11.6733647Z - LAPACK is enabled (usually provided by MKL) 2024-08-20T21:37:11.6734699Z - NNPACK is enabled 2024-08-20T21:37:11.6735109Z - CPU capability usage: AVX512 2024-08-20T21:37:11.6743650Z - 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 -Werror -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, 2024-08-20T21:37:11.6751478Z 2024-08-20T21:37:11.9915513Z + cd test 2024-08-20T21:37:11.9916401Z + python -c 'import torch; print(torch.__config__.parallel_info())' 2024-08-20T21:37:13.3110742Z ATen/Parallel: 2024-08-20T21:37:13.3111371Z at::get_num_threads() : 4 2024-08-20T21:37:13.3112109Z at::get_num_interop_threads() : 4 2024-08-20T21:37:13.3112554Z OpenMP 201511 (a.k.a. OpenMP 4.5) 2024-08-20T21:37:13.3112960Z omp_get_max_threads() : 4 2024-08-20T21:37:13.3114064Z Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications 2024-08-20T21:37:13.3114932Z mkl_get_max_threads() : 4 2024-08-20T21:37:13.3115614Z Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) 2024-08-20T21:37:13.3116258Z std::thread::hardware_concurrency() : 8 2024-08-20T21:37:13.3116692Z Environment variables: 2024-08-20T21:37:13.3117037Z OMP_NUM_THREADS : [not set] 2024-08-20T21:37:13.3117397Z MKL_NUM_THREADS : [not set] 2024-08-20T21:37:13.3118080Z ATen parallel backend: OpenMP 2024-08-20T21:37:13.3118453Z 2024-08-20T21:37:13.6288970Z + [[ linux-focal-py3.12-clang10 == *aarch64* ]] 2024-08-20T21:37:13.6289694Z + [[ dynamo == *backward* ]] 2024-08-20T21:37:13.6290080Z + [[ dynamo == *xla* ]] 2024-08-20T21:37:13.6290452Z + [[ dynamo == *executorch* ]] 2024-08-20T21:37:13.6290838Z + [[ dynamo == \j\i\t\_\l\e\g\a\c\y ]] 2024-08-20T21:37:13.6291393Z + [[ linux-focal-py3.12-clang10 == *libtorch* ]] 2024-08-20T21:37:13.6291879Z + [[ dynamo == distributed ]] 2024-08-20T21:37:13.6292336Z + [[ dynamo == *inductor_distributed* ]] 2024-08-20T21:37:13.6293059Z + [[ dynamo == *inductor-halide* ]] 2024-08-20T21:37:13.6294008Z + [[ dynamo == *inductor-micro-benchmark* ]] 2024-08-20T21:37:13.6294469Z + [[ dynamo == *huggingface* ]] 2024-08-20T21:37:13.6294852Z + [[ dynamo == *timm* ]] 2024-08-20T21:37:13.6295210Z + [[ dynamo == *torchbench* ]] 2024-08-20T21:37:13.6295666Z + [[ dynamo == *inductor_cpp_wrapper_abi_compatible* ]] 2024-08-20T21:37:13.6296424Z + [[ dynamo == *inductor* ]] 2024-08-20T21:37:13.6296798Z + [[ dynamo == *dynamo* ]] 2024-08-20T21:37:13.6297135Z + install_torchvision 2024-08-20T21:37:13.6297463Z + local orig_preload 2024-08-20T21:37:13.6297780Z + local commit 2024-08-20T21:37:13.6298079Z ++ get_pinned_commit vision 2024-08-20T21:37:13.6298481Z ++ cat .github/ci_commit_pins/vision.txt 2024-08-20T21:37:13.6322291Z + commit=d23a6e1664d20707c11781299611436e1f0c104f 2024-08-20T21:37:13.6323088Z + orig_preload= 2024-08-20T21:37:13.6323477Z + '[' -n '' ']' 2024-08-20T21:37:13.6324697Z + pip_install --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2024-08-20T21:37:13.6326309Z + pip install --progress-bar off --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2024-08-20T21:37:14.0010910Z Collecting git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2024-08-20T21:37:14.0016647Z Cloning https://github.com/pytorch/vision.git (to revision d23a6e1664d20707c11781299611436e1f0c104f) to /tmp/pip-req-build-tio9j6c3 2024-08-20T21:37:14.0048711Z Running command git clone --filter=blob:none --quiet https://github.com/pytorch/vision.git /tmp/pip-req-build-tio9j6c3 2024-08-20T21:37:16.1389306Z Running command git rev-parse -q --verify 'sha^d23a6e1664d20707c11781299611436e1f0c104f' 2024-08-20T21:37:16.1411632Z Running command git fetch -q https://github.com/pytorch/vision.git d23a6e1664d20707c11781299611436e1f0c104f 2024-08-20T21:37:17.5562054Z Running command git checkout -q d23a6e1664d20707c11781299611436e1f0c104f 2024-08-20T21:37:17.8561886Z Resolved https://github.com/pytorch/vision.git to commit d23a6e1664d20707c11781299611436e1f0c104f 2024-08-20T21:37:20.1453157Z Preparing metadata (setup.py) ... [?25l- \ done 2024-08-20T21:37:20.1487585Z [?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) 2024-08-20T21:37:20.1491072Z 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) 2024-08-20T21:37:20.1496302Z 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) 2024-08-20T21:37:20.1547564Z 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) 2024-08-20T21:37:20.1551679Z 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) 2024-08-20T21:37:20.1556026Z 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) 2024-08-20T21:37:20.1559737Z 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) 2024-08-20T21:37:20.1563313Z 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) 2024-08-20T21:37:20.1575354Z 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) 2024-08-20T21:37:20.1581015Z 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) 2024-08-20T21:37:20.1595481Z 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) 2024-08-20T21:37:20.1711798Z 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) 2024-08-20T21:37:20.1811421Z Building wheels for collected packages: torchvision 2024-08-20T21:38:35.8459372Z Building wheel for torchvision (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - done 2024-08-20T21:38:35.8496639Z [?25h Created wheel for torchvision: filename=torchvision-0.19.0a0+d23a6e1-cp312-cp312-linux_x86_64.whl size=1116928 sha256=cdf60ae7063b83f284c989bd11814cc2b47e7bdc68eaaa7923a2d138fcf1ae40 2024-08-20T21:38:35.8498522Z Stored in directory: /var/lib/jenkins/.cache/pip/wheels/b9/aa/81/39d3509ec629531316195ffac7a7b05ff7603f393064d63ec9 2024-08-20T21:38:35.8536929Z Successfully built torchvision 2024-08-20T21:38:35.9930256Z Installing collected packages: torchvision 2024-08-20T21:38:36.4857673Z Successfully installed torchvision-0.19.0a0+d23a6e1 2024-08-20T21:38:36.6233386Z + '[' -n '' ']' 2024-08-20T21:38:36.6233857Z + test_dynamo_shard 2 2024-08-20T21:38:36.6236028Z + [[ -z 3 ]] 2024-08-20T21:38:36.6236470Z + python tools/dynamo/verify_dynamo.py 2024-08-20T21:38:38.0910722Z Python version: 3.12.4 2024-08-20T21:38:38.0911464Z `torch` version: 2.5.0a0+git40ec5f6 2024-08-20T21:38:38.0912228Z CUDA version: None 2024-08-20T21:38:38.0912735Z ROCM version: None 2024-08-20T21:38:38.0913019Z 2024-08-20T21:38:39.0634400Z CUDA not available -- skipping CUDA check on eager backend 2024-08-20T21:38:39.0635222Z 2024-08-20T21:38:40.0345022Z CUDA not available -- skipping CUDA check on aot_eager backend 2024-08-20T21:38:40.0345881Z 2024-08-20T21:38:50.4265826Z CUDA not available -- skipping CUDA check on inductor backend 2024-08-20T21:38:50.4266302Z 2024-08-20T21:38:50.4266457Z All required checks passed 2024-08-20T21:38:51.1986711Z + python test/run_test.py --dynamo --exclude-inductor-tests --exclude-jit-executor --exclude-distributed-tests --exclude-torch-export-tests --shard 2 3 --verbose 2024-08-20T21:38:51.3124420Z /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 2024-08-20T21:38:51.3125665Z import pkg_resources 2024-08-20T21:38:53.4257154Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:38:54.4960452Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2024-08-20T21:38:54.5418654Z Ignoring disabled issues: [''] 2024-08-20T21:38:54.5541639Z Found test times from artifacts 2024-08-20T21:38:54.6008656Z Found test times from artifacts 2024-08-20T21:38:54.6026944Z Running all tests 2024-08-20T21:38:54.6145367Z Running parallel tests on 3 processes 2024-08-20T21:38:54.6152719Z Name: tests to run (est. time: 52.19min) 2024-08-20T21:38:54.6153592Z Serial tests (4): 2024-08-20T21:38:54.6154176Z doctests 1/1 2024-08-20T21:38:54.6154716Z test_nn 1/2 2024-08-20T21:38:54.6155311Z test_cpp_extensions_jit 1/1 2024-08-20T21:38:54.6156431Z test_torch 1/1 2024-08-20T21:38:54.6157014Z Parallel tests (106): 2024-08-20T21:38:54.6157675Z dynamo/test_trace_rules 1/1 2024-08-20T21:38:54.6158517Z dynamo/test_repros 1/1 2024-08-20T21:38:54.6159223Z dynamo/test_higher_order_ops 1/1 2024-08-20T21:38:54.6160030Z dynamo/test_export 1/1 2024-08-20T21:38:54.6160700Z dynamo/test_exc 1/1 2024-08-20T21:38:54.6161344Z dynamo/test_ctx_manager 1/1 2024-08-20T21:38:54.6162173Z dynamo/test_activation_checkpointing 1/1 2024-08-20T21:38:54.6163078Z dynamo/test_optimizers 1/1 2024-08-20T21:38:54.6163798Z dynamo/test_backends 1/1 2024-08-20T21:38:54.6164559Z dynamo/test_skip_non_tensor 1/1 2024-08-20T21:38:54.6165372Z dynamo/test_python_autograd 1/1 2024-08-20T21:38:54.6166198Z dynamo/test_verify_correctness 1/1 2024-08-20T21:38:54.6167005Z dynamo/test_exceptions 1/1 2024-08-20T21:38:54.6167741Z dynamo/test_base_output 1/1 2024-08-20T21:38:54.6168482Z functorch/test_vmap 2/3 2024-08-20T21:38:54.6169204Z dynamo/test_structured_trace 1/1 2024-08-20T21:38:54.6169991Z dynamo/test_hooks 1/1 2024-08-20T21:38:54.6170665Z dynamo/test_profiler 1/1 2024-08-20T21:38:54.6171374Z dynamo/test_recompile_ux 1/1 2024-08-20T21:38:54.6172142Z dynamo/test_deviceguard 1/1 2024-08-20T21:38:54.6172865Z test_linalg 2/4 2024-08-20T21:38:54.6173435Z test_linalg 3/4 2024-08-20T21:38:54.6174015Z test_linalg 4/4 2024-08-20T21:38:54.6174619Z dynamo/test_debug_utils 1/1 2024-08-20T21:38:54.6175339Z test_cuda_multigpu 1/1 2024-08-20T21:38:54.6176028Z test_comparison_utils 1/1 2024-08-20T21:38:54.6176740Z test_mkl_verbose 1/1 2024-08-20T21:38:54.6177591Z test_mkldnn_verbose 1/1 2024-08-20T21:38:54.6178122Z test_custom_ops 1/1 2024-08-20T21:38:54.6178610Z test_ao_sparsity 1/1 2024-08-20T21:38:54.6179185Z functorch/test_eager_transforms 1/1 2024-08-20T21:38:54.6179822Z test_optim 1/1 2024-08-20T21:38:54.6180305Z test_masked 1/1 2024-08-20T21:38:54.6180780Z test_view_ops 1/1 2024-08-20T21:38:54.6181161Z test_indexing 1/1 2024-08-20T21:38:54.6181469Z test_monitor 1/1 2024-08-20T21:38:54.6181840Z benchmark_utils/test_benchmark_utils 1/1 2024-08-20T21:38:54.6182343Z test_binary_ufuncs 1/2 2024-08-20T21:38:54.6182691Z test_futures 1/1 2024-08-20T21:38:54.6183002Z nn/test_embedding 1/1 2024-08-20T21:38:54.6183362Z test_functional_optim 1/1 2024-08-20T21:38:54.6183727Z test_mkldnn 1/1 2024-08-20T21:38:54.6184013Z test_weak 1/1 2024-08-20T21:38:54.6184322Z lazy/test_debug_util 1/1 2024-08-20T21:38:54.6184703Z test_sparse_semi_structured 1/1 2024-08-20T21:38:54.6185101Z nn/test_module_hooks 1/1 2024-08-20T21:38:54.6185507Z test_functional_autograd_benchmark 1/1 2024-08-20T21:38:54.6185946Z test_mkldnn_fusion 1/1 2024-08-20T21:38:54.6186280Z test_model_dump 1/1 2024-08-20T21:38:54.6186619Z test_function_schema 1/1 2024-08-20T21:38:54.6186993Z test_scatter_gather_ops 1/1 2024-08-20T21:38:54.6187370Z test_schema_check 1/1 2024-08-20T21:38:54.6187718Z lazy/test_reuse_ir 1/1 2024-08-20T21:38:54.6188071Z test_unary_ufuncs 1/1 2024-08-20T21:38:54.6188406Z test_quantization 2/5 2024-08-20T21:38:54.6188754Z test_quantization 5/5 2024-08-20T21:38:54.6189093Z test_package 1/1 2024-08-20T21:38:54.6189407Z test_type_promotion 1/1 2024-08-20T21:38:54.6189765Z test_sparse_csr 1/1 2024-08-20T21:38:54.6190097Z test_jiterator 1/1 2024-08-20T21:38:54.6190414Z test_namedtensor 1/1 2024-08-20T21:38:54.6190764Z test_legacy_vmap 1/1 2024-08-20T21:38:54.6191109Z test_flop_counter 1/1 2024-08-20T21:38:54.6191438Z functorch/test_ac 1/1 2024-08-20T21:38:54.6191802Z profiler/test_profiler 1/1 2024-08-20T21:38:54.6192224Z torch_np/numpy_tests/core/test_numeric 1/1 2024-08-20T21:38:54.6192659Z test_fx_passes 1/1 2024-08-20T21:38:54.6193013Z lazy/test_functionalization 1/1 2024-08-20T21:38:54.6193489Z torch_np/numpy_tests/lib/test_function_base 1/1 2024-08-20T21:38:54.6194157Z torch_np/numpy_tests/lib/test_histograms 1/1 2024-08-20T21:38:54.6194633Z nn/test_load_state_dict 1/1 2024-08-20T21:38:54.6195015Z test_matmul_cuda 1/1 2024-08-20T21:38:54.6195397Z torch_np/numpy_tests/core/test_indexing 1/1 2024-08-20T21:38:54.6196254Z torch_np/numpy_tests/lib/test_type_check 1/1 2024-08-20T21:38:54.6196780Z torch_np/numpy_tests/core/test_scalarmath 1/1 2024-08-20T21:38:54.6197238Z optim/test_swa_utils 1/1 2024-08-20T21:38:54.6197603Z test_jit_llga_fuser 1/1 2024-08-20T21:38:54.6198023Z test_vulkan 1/1 2024-08-20T21:38:54.6198604Z torch_np/numpy_tests/core/test_shape_base 1/1 2024-08-20T21:38:54.6199118Z torch_np/numpy_tests/core/test_dtype 1/1 2024-08-20T21:38:54.6199623Z torch_np/numpy_tests/core/test_getlimits 1/1 2024-08-20T21:38:54.6200086Z torch_np/test_function_base 1/1 2024-08-20T21:38:54.6200485Z nn/test_init 1/1 2024-08-20T21:38:54.6200827Z torch_np/test_ndarray_methods 1/1 2024-08-20T21:38:54.6201280Z torch_np/numpy_tests/linalg/test_linalg 1/1 2024-08-20T21:38:54.6201797Z torch_np/numpy_tests/lib/test_index_tricks 1/1 2024-08-20T21:38:54.6202324Z torch_np/numpy_tests/lib/test_twodim_base 1/1 2024-08-20T21:38:54.6202855Z torch_np/numpy_tests/core/test_scalar_methods 1/1 2024-08-20T21:38:54.6203348Z lazy/test_generator 1/1 2024-08-20T21:38:54.6203770Z torch_np/numpy_tests/fft/test_pocketfft 1/1 2024-08-20T21:38:54.6204265Z torch_np/numpy_tests/core/test_dlpack 1/1 2024-08-20T21:38:54.6204777Z torch_np/numpy_tests/lib/test_shape_base_ 1/1 2024-08-20T21:38:54.6205296Z torch_np/numpy_tests/fft/test_helper 1/1 2024-08-20T21:38:54.6205974Z torch_np/numpy_tests/core/test_numerictypes 1/1 2024-08-20T21:38:54.6206525Z torch_np/numpy_tests/lib/test_arraysetops 1/1 2024-08-20T21:38:54.6207020Z torch_np/test_scalars_0D_arrays 1/1 2024-08-20T21:38:54.6207459Z profiler/test_memory_profiler 1/1 2024-08-20T21:38:54.6207927Z torch_np/numpy_tests/lib/test_arraypad 1/1 2024-08-20T21:38:54.6208390Z test_jit_autocast 1/1 2024-08-20T21:38:54.6208745Z profiler/test_profiler_tree 1/1 2024-08-20T21:38:54.6209221Z torch_np/numpy_tests/core/test_scalar_ctors 1/1 2024-08-20T21:38:54.6209713Z torch_np/test_reductions 1/1 2024-08-20T21:38:54.6210102Z profiler/test_torch_tidy 1/1 2024-08-20T21:38:54.6210517Z profiler/test_execution_trace 1/1 2024-08-20T21:38:54.6210954Z profiler/test_record_function 1/1 2024-08-20T21:38:54.6211369Z lazy/test_bindings 1/1 2024-08-20T21:38:54.6211721Z Name: excluded (est. time: 0.0min) 2024-08-20T21:38:54.6212114Z Serial tests (0): 2024-08-20T21:38:54.6212420Z Parallel tests (0): 2024-08-20T21:38:54.6329069Z Running doctests 1/1 ... [2024-08-20 21:38:54.632397] 2024-08-20T21:38:54.7037486Z Start doctest_module('/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch') 2024-08-20T21:38:54.7038925Z Listing tests 2024-08-20T21:38:54.9763189Z msg = Cannot scrape callname=meshgrid in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=426. 2024-08-20T21:38:54.9765746Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:54.9767413Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors. 2024-08-20T21:38:54.9768395Z 2024-08-20T21:38:54.9768830Z This is helpful when you want to visualize data over some 2024-08-20T21:38:54.9770052Z range of inputs. See below for a plotting example. 2024-08-20T21:38:54.9770787Z 2024-08-20T21:38:54.9771356Z Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as 2024-08-20T21:38:54.9772747Z inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`, 2024-08-20T21:38:54.9774233Z this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots 2024-08-20T21:38:54.9775630Z G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where 2024-08-20T21:38:54.9776906Z the output :math:`G_i` is constructed by expanding :math:`T_i` 2024-08-20T21:38:54.9778390Z to the result shape. 2024-08-20T21:38:54.9778857Z 2024-08-20T21:38:54.9779093Z .. note:: 2024-08-20T21:38:54.9779854Z 0D inputs are treated equivalently to 1D inputs of a 2024-08-20T21:38:54.9780833Z single element. 2024-08-20T21:38:54.9781272Z 2024-08-20T21:38:54.9781487Z .. warning:: 2024-08-20T21:38:54.9782330Z `torch.meshgrid(*tensors)` currently has the same behavior 2024-08-20T21:38:54.9783741Z as calling `numpy.meshgrid(*arrays, indexing='ij')`. 2024-08-20T21:38:54.9784505Z 2024-08-20T21:38:54.9784923Z In the future `torch.meshgrid` will transition to 2024-08-20T21:38:54.9810127Z `indexing='xy'` as the default. 2024-08-20T21:38:54.9810900Z 2024-08-20T21:38:54.9811369Z https://github.com/pytorch/pytorch/issues/50276 tracks 2024-08-20T21:38:54.9812977Z this issue with the goal of migrating to NumPy's behavior. 2024-08-20T21:38:54.9813799Z 2024-08-20T21:38:54.9814053Z .. seealso:: 2024-08-20T21:38:54.9814436Z 2024-08-20T21:38:54.9814872Z :func:`torch.cartesian_prod` has the same effect but it 2024-08-20T21:38:54.9815993Z collects the data in a tensor of vectors. 2024-08-20T21:38:54.9816671Z 2024-08-20T21:38:54.9816870Z Args: 2024-08-20T21:38:54.9817898Z tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be 2024-08-20T21:38:54.9819424Z treated as tensors of size :math:`(1,)` automatically 2024-08-20T21:38:54.9820196Z 2024-08-20T21:38:54.9820672Z indexing: (str, optional): the indexing mode, either "xy" 2024-08-20T21:38:54.9822471Z or "ij", defaults to "ij". See warning for future changes. 2024-08-20T21:38:54.9823319Z 2024-08-20T21:38:54.9823756Z If "xy" is selected, the first dimension corresponds 2024-08-20T21:38:54.9824968Z to the cardinality of the second input and the second 2024-08-20T21:38:54.9826197Z dimension corresponds to the cardinality of the first 2024-08-20T21:38:54.9827233Z input. 2024-08-20T21:38:54.9827636Z 2024-08-20T21:38:54.9828041Z If "ij" is selected, the dimensions are in the same 2024-08-20T21:38:54.9829142Z order as the cardinality of the inputs. 2024-08-20T21:38:54.9829805Z 2024-08-20T21:38:54.9830007Z Returns: 2024-08-20T21:38:54.9830774Z seq (sequence of Tensors): If the input has :math:`N` 2024-08-20T21:38:54.9832152Z tensors of size :math:`S_0 \ldots S_{N-1}``, then the 2024-08-20T21:38:54.9833381Z output will also have :math:`N` tensors, where each tensor 2024-08-20T21:38:54.9834658Z is of shape :math:`(S_0, ..., S_{N-1})`. 2024-08-20T21:38:54.9835278Z 2024-08-20T21:38:54.9835511Z Example:: 2024-08-20T21:38:54.9835852Z 2024-08-20T21:38:54.9836117Z >>> x = torch.tensor([1, 2, 3]) 2024-08-20T21:38:54.9836974Z >>> y = torch.tensor([4, 5, 6]) 2024-08-20T21:38:54.9837576Z 2024-08-20T21:38:54.9838230Z Observe the element-wise pairings across the grid, (1, 4), 2024-08-20T21:38:54.9839566Z (1, 5), ..., (3, 6). This is the same thing as the 2024-08-20T21:38:54.9840499Z cartesian product. 2024-08-20T21:38:54.9841558Z >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij') 2024-08-20T21:38:54.9842546Z >>> grid_x 2024-08-20T21:38:54.9843141Z tensor([[1, 1, 1], 2024-08-20T21:38:54.9843807Z [2, 2, 2], 2024-08-20T21:38:54.9844458Z [3, 3, 3]]) 2024-08-20T21:38:54.9845114Z >>> grid_y 2024-08-20T21:38:54.9845736Z tensor([[4, 5, 6], 2024-08-20T21:38:54.9846406Z [4, 5, 6], 2024-08-20T21:38:54.9847040Z [4, 5, 6]]) 2024-08-20T21:38:54.9847524Z 2024-08-20T21:38:54.9847963Z This correspondence can be seen when these grids are 2024-08-20T21:38:54.9848974Z stacked properly. 2024-08-20T21:38:54.9850261Z >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))), 2024-08-20T21:38:54.9851486Z ... torch.cartesian_prod(x, y)) 2024-08-20T21:38:54.9852351Z True 2024-08-20T21:38:54.9852682Z 2024-08-20T21:38:54.9853146Z `torch.meshgrid` is commonly used to produce a grid for 2024-08-20T21:38:54.9854152Z plotting. 2024-08-20T21:38:54.9854883Z >>> # xdoctest: +REQUIRES(module:matplotlib) 2024-08-20T21:38:54.9856087Z >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW) 2024-08-20T21:38:54.9857099Z >>> import matplotlib.pyplot as plt 2024-08-20T21:38:54.9858260Z >>> xs = torch.linspace(-5, 5, steps=100) 2024-08-20T21:38:54.9859365Z >>> ys = torch.linspace(-5, 5, steps=100) 2024-08-20T21:38:54.9860527Z >>> x, y = torch.meshgrid(xs, ys, indexing='xy') 2024-08-20T21:38:54.9861568Z >>> z = torch.sin(torch.sqrt(x * x + y * y)) 2024-08-20T21:38:54.9862638Z >>> ax = plt.axes(projection='3d') 2024-08-20T21:38:54.9863667Z >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy()) 2024-08-20T21:38:54.9864617Z >>> plt.show() 2024-08-20T21:38:54.9865050Z 2024-08-20T21:38:54.9865382Z .. image:: ../_static/img/meshgrid.png 2024-08-20T21:38:54.9866214Z :width: 512 2024-08-20T21:38:54.9866619Z 2024-08-20T21:38:54.9866810Z 2024-08-20T21:38:54.9867910Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:54.9868903Z 2024-08-20T21:38:54.9870819Z msg = Cannot scrape callname=_unique_impl in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=815. 2024-08-20T21:38:54.9873338Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:54.9875574Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> Tuple[Tensor, Tensor, Tensor] 2024-08-20T21:38:54.9876968Z 2024-08-20T21:38:54.9877347Z Returns the unique elements of the input tensor. 2024-08-20T21:38:54.9878071Z 2024-08-20T21:38:54.9879038Z .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that 2024-08-20T21:38:54.9880899Z this function also eliminates non-consecutive duplicate values. 2024-08-20T21:38:54.9881802Z 2024-08-20T21:38:54.9882432Z .. note:: Currently in the CUDA implementation and the CPU implementation, 2024-08-20T21:38:54.9884217Z `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument. 2024-08-20T21:38:54.9886210Z Sorting could be slow, so if your input tensor is already sorted, it is recommended to use 2024-08-20T21:38:54.9887850Z :func:`torch.unique_consecutive` which avoids the sorting. 2024-08-20T21:38:54.9888660Z 2024-08-20T21:38:54.9888851Z Args: 2024-08-20T21:38:54.9889418Z input (Tensor): the input tensor 2024-08-20T21:38:54.9890585Z sorted (bool): Whether to sort the unique elements in ascending order 2024-08-20T21:38:54.9891804Z before returning as output. 2024-08-20T21:38:54.9892938Z return_inverse (bool): Whether to also return the indices for where 2024-08-20T21:38:54.9894464Z elements in the original input ended up in the returned unique list. 2024-08-20T21:38:54.9896285Z return_counts (bool): Whether to also return the counts for each unique 2024-08-20T21:38:54.9897439Z element. 2024-08-20T21:38:54.9898376Z dim (int, optional): the dimension to operate upon. If ``None``, the 2024-08-20T21:38:54.9899887Z unique of the flattened input is returned. Otherwise, each of the 2024-08-20T21:38:54.9901352Z tensors indexed by the given dimension is treated as one of the 2024-08-20T21:38:54.9902812Z elements to apply the unique operation upon. See examples for more 2024-08-20T21:38:54.9903987Z details. Default: ``None`` 2024-08-20T21:38:54.9904561Z 2024-08-20T21:38:54.9905005Z Returns: 2024-08-20T21:38:54.9906110Z (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing 2024-08-20T21:38:54.9907208Z 2024-08-20T21:38:54.9907985Z - **output** (*Tensor*): the output list of unique scalar elements. 2024-08-20T21:38:54.9909332Z - **inverse_indices** (*Tensor*): (optional) if 2024-08-20T21:38:54.9910523Z :attr:`return_inverse` is True, there will be an additional 2024-08-20T21:38:54.9911887Z returned tensor (same shape as input) representing the indices 2024-08-20T21:38:54.9913307Z for where elements in the original input map to in the output; 2024-08-20T21:38:54.9914689Z otherwise, this function will only return a single tensor. 2024-08-20T21:38:54.9915953Z - **counts** (*Tensor*): (optional) if 2024-08-20T21:38:54.9917061Z :attr:`return_counts` is True, there will be an additional 2024-08-20T21:38:54.9918488Z returned tensor (same shape as output or output.size(dim), 2024-08-20T21:38:54.9919866Z if dim was specified) representing the number of occurrences 2024-08-20T21:38:54.9921013Z for each unique value or tensor. 2024-08-20T21:38:54.9921646Z 2024-08-20T21:38:54.9921868Z Example:: 2024-08-20T21:38:54.9922212Z 2024-08-20T21:38:54.9922772Z >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long)) 2024-08-20T21:38:54.9923884Z >>> output 2024-08-20T21:38:54.9924431Z tensor([1, 2, 3]) 2024-08-20T21:38:54.9924873Z 2024-08-20T21:38:54.9925188Z >>> output, inverse_indices = torch.unique( 2024-08-20T21:38:54.9926728Z ... torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True) 2024-08-20T21:38:54.9927937Z >>> output 2024-08-20T21:38:54.9928507Z tensor([1, 2, 3]) 2024-08-20T21:38:54.9929158Z >>> inverse_indices 2024-08-20T21:38:54.9929814Z tensor([0, 2, 1, 2]) 2024-08-20T21:38:54.9930277Z 2024-08-20T21:38:54.9930601Z >>> output, inverse_indices = torch.unique( 2024-08-20T21:38:54.9931945Z ... torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True) 2024-08-20T21:38:54.9933151Z >>> output 2024-08-20T21:38:54.9933712Z tensor([1, 2, 3]) 2024-08-20T21:38:54.9934356Z >>> inverse_indices 2024-08-20T21:38:54.9934992Z tensor([[0, 2], 2024-08-20T21:38:54.9935595Z [1, 2]]) 2024-08-20T21:38:54.9936002Z 2024-08-20T21:38:54.9936247Z >>> a = torch.tensor([ 2024-08-20T21:38:54.9936917Z ... [ 2024-08-20T21:38:54.9937476Z ... [1, 1, 0, 0], 2024-08-20T21:38:54.9938194Z ... [1, 1, 0, 0], 2024-08-20T21:38:54.9938862Z ... [0, 0, 1, 1], 2024-08-20T21:38:54.9939554Z ... ], 2024-08-20T21:38:54.9940094Z ... [ 2024-08-20T21:38:54.9940631Z ... [0, 0, 1, 1], 2024-08-20T21:38:54.9941336Z ... [0, 0, 1, 1], 2024-08-20T21:38:54.9942058Z ... [1, 1, 1, 1], 2024-08-20T21:38:54.9942723Z ... ], 2024-08-20T21:38:54.9943265Z ... [ 2024-08-20T21:38:54.9943826Z ... [1, 1, 0, 0], 2024-08-20T21:38:54.9944493Z ... [1, 1, 0, 0], 2024-08-20T21:38:54.9945197Z ... [0, 0, 1, 1], 2024-08-20T21:38:54.9945895Z ... ], 2024-08-20T21:38:54.9946424Z ... ]) 2024-08-20T21:38:54.9946763Z 2024-08-20T21:38:54.9947385Z >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]` 2024-08-20T21:38:54.9948923Z >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match 2024-08-20T21:38:54.9950150Z >>> # each other, so one of them will be removed. 2024-08-20T21:38:54.9951030Z >>> (a[0, :, :] == a[2, :, :]).all() 2024-08-20T21:38:54.9951770Z tensor(True) 2024-08-20T21:38:54.9952386Z >>> a_unique_dim0 = torch.unique(a, dim=0) 2024-08-20T21:38:54.9953458Z >>> a_unique_dim0 2024-08-20T21:38:54.9954071Z tensor([[[0, 0, 1, 1], 2024-08-20T21:38:54.9954720Z [0, 0, 1, 1], 2024-08-20T21:38:54.9955338Z [1, 1, 1, 1]], 2024-08-20T21:38:54.9955968Z [[1, 1, 0, 0], 2024-08-20T21:38:54.9956571Z [1, 1, 0, 0], 2024-08-20T21:38:54.9957191Z [0, 0, 1, 1]]]) 2024-08-20T21:38:54.9957625Z 2024-08-20T21:38:54.9958572Z >>> # Notice which sub-tensors from `a` match with the sub-tensors from 2024-08-20T21:38:54.9959636Z >>> # `a_unique_dim0`: 2024-08-20T21:38:54.9960392Z >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all() 2024-08-20T21:38:54.9961207Z tensor(True) 2024-08-20T21:38:54.9961899Z >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all() 2024-08-20T21:38:54.9962668Z tensor(True) 2024-08-20T21:38:54.9963032Z 2024-08-20T21:38:54.9963557Z >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are 2024-08-20T21:38:54.9964874Z >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of 2024-08-20T21:38:54.9965871Z >>> # them will be removed. 2024-08-20T21:38:54.9966602Z >>> (a[:, 0, :] == a[:, 1, :]).all() 2024-08-20T21:38:54.9967326Z tensor(True) 2024-08-20T21:38:54.9967878Z >>> torch.unique(a, dim=1) 2024-08-20T21:38:54.9968573Z tensor([[[0, 0, 1, 1], 2024-08-20T21:38:54.9969201Z [1, 1, 0, 0]], 2024-08-20T21:38:54.9969846Z [[1, 1, 1, 1], 2024-08-20T21:38:54.9970477Z [0, 0, 1, 1]], 2024-08-20T21:38:54.9971106Z [[0, 0, 1, 1], 2024-08-20T21:38:54.9971908Z [1, 1, 0, 0]]]) 2024-08-20T21:38:54.9972368Z 2024-08-20T21:38:54.9972921Z >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared. 2024-08-20T21:38:54.9974282Z >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and 2024-08-20T21:38:54.9975507Z >>> # `a[:, :, 3]` match each other as well. So in this case, two of the 2024-08-20T21:38:54.9976724Z >>> # sub-tensors will be removed. 2024-08-20T21:38:54.9977535Z >>> (a[:, :, 0] == a[:, :, 1]).all() 2024-08-20T21:38:54.9978263Z tensor(True) 2024-08-20T21:38:54.9978860Z >>> (a[:, :, 2] == a[:, :, 3]).all() 2024-08-20T21:38:54.9979586Z tensor(True) 2024-08-20T21:38:54.9980143Z >>> torch.unique(a, dim=2) 2024-08-20T21:38:54.9980824Z tensor([[[0, 1], 2024-08-20T21:38:54.9981431Z [0, 1], 2024-08-20T21:38:54.9982001Z [1, 0]], 2024-08-20T21:38:54.9982565Z [[1, 0], 2024-08-20T21:38:54.9983128Z [1, 0], 2024-08-20T21:38:54.9983668Z [1, 1]], 2024-08-20T21:38:54.9984212Z [[0, 1], 2024-08-20T21:38:54.9984770Z [0, 1], 2024-08-20T21:38:54.9985337Z [1, 0]]]) 2024-08-20T21:38:54.9985906Z 2024-08-20T21:38:54.9987002Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:54.9987916Z 2024-08-20T21:38:54.9989735Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=560. 2024-08-20T21:38:54.9991847Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:54.9992788Z 2024-08-20T21:38:54.9993227Z Load a model from a github repo or a local directory. 2024-08-20T21:38:54.9993914Z 2024-08-20T21:38:54.9994491Z Note: Loading a model is the typical use case, but this can also be used to 2024-08-20T21:38:54.9996147Z for loading other objects such as tokenizers, loss functions, etc. 2024-08-20T21:38:54.9996974Z 2024-08-20T21:38:54.9997524Z If ``source`` is 'github', ``repo_or_dir`` is expected to be 2024-08-20T21:38:54.9998741Z of the form ``repo_owner/repo_name[:ref]`` with an optional 2024-08-20T21:38:54.9999669Z ref (a tag or a branch). 2024-08-20T21:38:55.0000064Z 2024-08-20T21:38:55.0000601Z If ``source`` is 'local', ``repo_or_dir`` is expected to be a 2024-08-20T21:38:55.0001898Z path to a local directory. 2024-08-20T21:38:55.0002323Z 2024-08-20T21:38:55.0002498Z Args: 2024-08-20T21:38:55.0003214Z repo_or_dir (str): If ``source`` is 'github', 2024-08-20T21:38:55.0004517Z this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with 2024-08-20T21:38:55.0006524Z an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified, 2024-08-20T21:38:55.0008289Z the default branch is assumed to be ``main`` if it exists, and otherwise ``master``. 2024-08-20T21:38:55.0009968Z If ``source`` is 'local' then it should be a path to a local directory. 2024-08-20T21:38:55.0011297Z model (str): the name of a callable (entrypoint) defined in the 2024-08-20T21:38:55.0012390Z repo/dir's ``hubconf.py``. 2024-08-20T21:38:55.0013328Z *args (optional): the corresponding args for callable ``model``. 2024-08-20T21:38:55.0014720Z source (str, optional): 'github' or 'local'. Specifies how 2024-08-20T21:38:55.0016100Z ``repo_or_dir`` is to be interpreted. Default is 'github'. 2024-08-20T21:38:55.0017387Z trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``. 2024-08-20T21:38:55.0018799Z This parameter was introduced in v1.12 and helps ensuring that users 2024-08-20T21:38:55.0019965Z only run code from repos that they trust. 2024-08-20T21:38:55.0020564Z 2024-08-20T21:38:55.0021206Z - If ``False``, a prompt will ask the user whether the repo should 2024-08-20T21:38:55.0022192Z be trusted. 2024-08-20T21:38:55.0023487Z - If ``True``, the repo will be added to the trusted list and loaded 2024-08-20T21:38:55.0024610Z without requiring explicit confirmation. 2024-08-20T21:38:55.0025792Z - If ``"check"``, the repo will be checked against the list of 2024-08-20T21:38:55.0027054Z trusted repos in the cache. If it is not present in that list, the 2024-08-20T21:38:55.0028417Z behaviour will fall back onto the ``trust_repo=False`` option. 2024-08-20T21:38:55.0029801Z - If ``None``: this will raise a warning, inviting the user to set 2024-08-20T21:38:55.0031052Z ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This 2024-08-20T21:38:55.0032355Z is only present for backward compatibility and will be removed in 2024-08-20T21:38:55.0033359Z v2.0. 2024-08-20T21:38:55.0033691Z 2024-08-20T21:38:55.0034218Z Default is ``None`` and will eventually change to ``"check"`` in v2.0. 2024-08-20T21:38:55.0035561Z force_reload (bool, optional): whether to force a fresh download of 2024-08-20T21:38:55.0036863Z the github repo unconditionally. Does not have any effect if 2024-08-20T21:38:55.0038114Z ``source = 'local'``. Default is ``False``. 2024-08-20T21:38:55.0039418Z verbose (bool, optional): If ``False``, mute messages about hitting 2024-08-20T21:38:55.0040751Z local caches. Note that the message about first download cannot be 2024-08-20T21:38:55.0042137Z muted. Does not have any effect if ``source = 'local'``. 2024-08-20T21:38:55.0043069Z Default is ``True``. 2024-08-20T21:38:55.0044211Z skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit 2024-08-20T21:38:55.0045936Z specified by the ``github`` argument properly belongs to the repo owner. This will make 2024-08-20T21:38:55.0047933Z requests to the GitHub API; you can specify a non-default GitHub token by setting the 2024-08-20T21:38:55.0049415Z ``GITHUB_TOKEN`` environment variable. Default is ``False``. 2024-08-20T21:38:55.0050683Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2024-08-20T21:38:55.0051503Z 2024-08-20T21:38:55.0051683Z Returns: 2024-08-20T21:38:55.0052438Z The output of the ``model`` callable when called with the given 2024-08-20T21:38:55.0053479Z ``*args`` and ``**kwargs``. 2024-08-20T21:38:55.0054140Z 2024-08-20T21:38:55.0054318Z Example: 2024-08-20T21:38:55.0054905Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-08-20T21:38:55.0055737Z >>> # from a github repo 2024-08-20T21:38:55.0056357Z >>> repo = "pytorch/vision" 2024-08-20T21:38:55.0057039Z >>> model = torch.hub.load( 2024-08-20T21:38:55.0057927Z ... repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1" 2024-08-20T21:38:55.0058829Z ... ) 2024-08-20T21:38:55.0059335Z >>> # from a local directory 2024-08-20T21:38:55.0060064Z >>> path = "/some/local/path/pytorch/vision" 2024-08-20T21:38:55.0060841Z >>> # xdoctest: +SKIP 2024-08-20T21:38:55.0061829Z >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT") 2024-08-20T21:38:55.0062739Z 2024-08-20T21:38:55.0063560Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:55.0064441Z 2024-08-20T21:38:55.0065980Z 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. 2024-08-20T21:38:55.0068204Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:55.0069463Z Download object at the given URL to a local path. 2024-08-20T21:38:55.0070080Z 2024-08-20T21:38:55.0070276Z Args: 2024-08-20T21:38:55.0070821Z url (str): URL of the object to download 2024-08-20T21:38:55.0072003Z dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file`` 2024-08-20T21:38:55.0073761Z hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``. 2024-08-20T21:38:55.0075291Z Default: None 2024-08-20T21:38:55.0076290Z progress (bool, optional): whether or not to display a progress bar to stderr 2024-08-20T21:38:55.0077411Z Default: True 2024-08-20T21:38:55.0077786Z 2024-08-20T21:38:55.0077983Z Example: 2024-08-20T21:38:55.0078715Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-08-20T21:38:55.0079613Z >>> # xdoctest: +REQUIRES(POSIX) 2024-08-20T21:38:55.0080395Z >>> torch.hub.download_url_to_file( 2024-08-20T21:38:55.0081694Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth", 2024-08-20T21:38:55.0082774Z ... "/tmp/temporary_file", 2024-08-20T21:38:55.0083442Z ... ) 2024-08-20T21:38:55.0083726Z 2024-08-20T21:38:55.0083894Z 2024-08-20T21:38:55.0084885Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:55.0085767Z 2024-08-20T21:38:55.0087234Z 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. 2024-08-20T21:38:55.0089521Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:55.0090758Z Loads the Torch serialized object at the given URL. 2024-08-20T21:38:55.0091427Z 2024-08-20T21:38:55.0091869Z If downloaded file is a zip file, it will be automatically 2024-08-20T21:38:55.0092829Z decompressed. 2024-08-20T21:38:55.0093154Z 2024-08-20T21:38:55.0093844Z If the object is already present in `model_dir`, it's deserialized and 2024-08-20T21:38:55.0094895Z returned. 2024-08-20T21:38:55.0095720Z The default value of ``model_dir`` is ``/checkpoints`` where 2024-08-20T21:38:55.0097328Z ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`. 2024-08-20T21:38:55.0098151Z 2024-08-20T21:38:55.0098336Z Args: 2024-08-20T21:38:55.0098898Z url (str): URL of the object to download 2024-08-20T21:38:55.0099973Z model_dir (str, optional): directory in which to save the object 2024-08-20T21:38:55.0101644Z map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load) 2024-08-20T21:38:55.0103459Z progress (bool, optional): whether or not to display a progress bar to stderr. 2024-08-20T21:38:55.0104968Z Default: True 2024-08-20T21:38:55.0106191Z check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention 2024-08-20T21:38:55.0108059Z ``filename-.ext`` where ```` is the first eight or more 2024-08-20T21:38:55.0109562Z digits of the SHA256 hash of the contents of the file. The hash is used to 2024-08-20T21:38:55.0111051Z ensure unique names and to verify the contents of the file. 2024-08-20T21:38:55.0112108Z Default: False 2024-08-20T21:38:55.0113488Z file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set. 2024-08-20T21:38:55.0115541Z weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects. 2024-08-20T21:38:55.0117334Z Recommended for untrusted sources. See :func:`~torch.load` for more details. 2024-08-20T21:38:55.0118505Z 2024-08-20T21:38:55.0118701Z Example: 2024-08-20T21:38:55.0119396Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-08-20T21:38:55.0120443Z >>> state_dict = torch.hub.load_state_dict_from_url( 2024-08-20T21:38:55.0121858Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth" 2024-08-20T21:38:55.0122847Z ... ) 2024-08-20T21:38:55.0123132Z 2024-08-20T21:38:55.0123298Z 2024-08-20T21:38:55.0124346Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:55.0125288Z 2024-08-20T21:38:55.0126918Z msg = Cannot scrape callname=Library.fallback in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=334. 2024-08-20T21:38:55.0129575Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-08-20T21:38:55.0131153Z Registers the function implementation as the fallback for the given key. 2024-08-20T21:38:55.0132041Z 2024-08-20T21:38:55.0132630Z This function only works for a library with global namespace ("_"). 2024-08-20T21:38:55.0133551Z 2024-08-20T21:38:55.0133747Z Args: 2024-08-20T21:38:55.0134816Z fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel` 2024-08-20T21:38:55.0136123Z to register a fallthrough. 2024-08-20T21:38:55.0137646Z dispatch_key: dispatch key that the input function should be registered for. By default, it uses 2024-08-20T21:38:55.0139171Z the dispatch key that the library was created with. 2024-08-20T21:38:55.0140968Z with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument 2024-08-20T21:38:55.0143296Z to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls. 2024-08-20T21:38:55.0144598Z 2024-08-20T21:38:55.0144832Z Example:: 2024-08-20T21:38:55.0145395Z >>> my_lib = Library("_", "IMPL") 2024-08-20T21:38:55.0146257Z >>> def fallback_kernel(op, *args, **kwargs): 2024-08-20T21:38:55.0147187Z >>> # Handle all autocast ops generically 2024-08-20T21:38:55.0147980Z >>> # ... 2024-08-20T21:38:55.0148696Z >>> my_lib.fallback(fallback_kernel, "Autocast") 2024-08-20T21:38:55.0149463Z 2024-08-20T21:38:55.0151648Z Original Error: IndentationError('expected an indented block after function definition on line 2', ('', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7)) 2024-08-20T21:38:55.0153588Z 2024-08-20T21:38:55.0153898Z my_lib.fallback(fallback_kernel, "Autocast") 2024-08-20T21:38:55.0154687Z ^ 2024-08-20T21:38:55.0156557Z msg = Cannot scrape callname=register_fake in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=692. 2024-08-20T21:38:55.0159141Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-08-20T21:38:55.0160679Z Register a FakeTensor implementation ("fake impl") for this operator. 2024-08-20T21:38:55.0161811Z 2024-08-20T21:38:55.0162275Z Also sometimes known as a "meta kernel", "abstract impl". 2024-08-20T21:38:55.0163035Z 2024-08-20T21:38:55.0163676Z An "FakeTensor implementation" specifies the behavior of this operator on 2024-08-20T21:38:55.0165212Z Tensors that carry no data ("FakeTensor"). Given some input Tensors with 2024-08-20T21:38:55.0166724Z certain properties (sizes/strides/storage_offset/device), it specifies 2024-08-20T21:38:55.0167977Z what the properties of the output Tensors are. 2024-08-20T21:38:55.0168628Z 2024-08-20T21:38:55.0169211Z The FakeTensor implementation has the same signature as the operator. 2024-08-20T21:38:55.0170646Z It is run for both FakeTensors and meta tensors. To write a FakeTensor 2024-08-20T21:38:55.0172053Z implementation, assume that all Tensor inputs to the operator are 2024-08-20T21:38:55.0173392Z regular CPU/CUDA/Meta tensors, but they do not have storage, and 2024-08-20T21:38:55.0174758Z you are trying to return regular CPU/CUDA/Meta tensor(s) as output. 2024-08-20T21:38:55.0176218Z The FakeTensor implementation must consist of only PyTorch operations 2024-08-20T21:38:55.0177625Z (and may not directly access the storage or data of any input or 2024-08-20T21:38:55.0178657Z intermediate Tensors). 2024-08-20T21:38:55.0179097Z 2024-08-20T21:38:55.0179480Z This API may be used as a decorator (see examples). 2024-08-20T21:38:55.0180171Z 2024-08-20T21:38:55.0180524Z For a detailed guide on custom ops, please see 2024-08-20T21:38:55.0181750Z https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html 2024-08-20T21:38:55.0182675Z 2024-08-20T21:38:55.0183062Z Examples: 2024-08-20T21:38:55.0183605Z >>> import torch 2024-08-20T21:38:55.0184219Z >>> import numpy as np 2024-08-20T21:38:55.0184897Z >>> from torch import Tensor 2024-08-20T21:38:55.0185602Z >>> 2024-08-20T21:38:55.0186589Z >>> # Example 1: an operator without data-dependent output shape 2024-08-20T21:38:55.0187935Z >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=()) 2024-08-20T21:38:55.0189495Z >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2024-08-20T21:38:55.0190822Z >>> raise NotImplementedError("Implementation goes here") 2024-08-20T21:38:55.0191769Z >>> 2024-08-20T21:38:55.0192481Z >>> @torch.library.register_fake("mylib::custom_linear") 2024-08-20T21:38:55.0193444Z >>> def _(x, weight, bias): 2024-08-20T21:38:55.0194157Z >>> assert x.dim() == 2 2024-08-20T21:38:55.0194908Z >>> assert weight.dim() == 2 2024-08-20T21:38:55.0195723Z >>> assert bias.dim() == 1 2024-08-20T21:38:55.0196844Z >>> assert x.shape[1] == weight.shape[1] 2024-08-20T21:38:55.0197787Z >>> assert weight.shape[0] == bias.shape[0] 2024-08-20T21:38:55.0198798Z >>> assert x.device == weight.device 2024-08-20T21:38:55.0199563Z >>> 2024-08-20T21:38:55.0200132Z >>> return (x @ weight.t()) + bias 2024-08-20T21:38:55.0200891Z >>> 2024-08-20T21:38:55.0201584Z >>> with torch._subclasses.fake_tensor.FakeTensorMode(): 2024-08-20T21:38:55.0202550Z >>> x = torch.randn(2, 3) 2024-08-20T21:38:55.0203312Z >>> w = torch.randn(3, 3) 2024-08-20T21:38:55.0204042Z >>> b = torch.randn(3) 2024-08-20T21:38:55.0204878Z >>> y = torch.ops.mylib.custom_linear(x, w, b) 2024-08-20T21:38:55.0205700Z >>> 2024-08-20T21:38:55.0206213Z >>> assert y.shape == (2, 3) 2024-08-20T21:38:55.0206921Z >>> 2024-08-20T21:38:55.0207857Z >>> # Example 2: an operator with data-dependent output shape 2024-08-20T21:38:55.0209181Z >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=()) 2024-08-20T21:38:55.0210500Z >>> def custom_nonzero(x: Tensor) -> Tensor: 2024-08-20T21:38:55.0211392Z >>> x_np = x.numpy(force=True) 2024-08-20T21:38:55.0212558Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2024-08-20T21:38:55.0213528Z >>> return torch.tensor(res, device=x.device) 2024-08-20T21:38:55.0214365Z >>> 2024-08-20T21:38:55.0215103Z >>> @torch.library.register_fake("mylib::custom_nonzero") 2024-08-20T21:38:55.0216055Z >>> def _(x): 2024-08-20T21:38:55.0216947Z >>> # Number of nonzero-elements is data-dependent. 2024-08-20T21:38:55.0218054Z >>> # Since we cannot peek at the data in an fake impl, 2024-08-20T21:38:55.0219167Z >>> # we use the ctx object to construct a new symint that 2024-08-20T21:38:55.0220334Z >>> # represents the data-dependent size. 2024-08-20T21:38:55.0221264Z >>> ctx = torch.library.get_ctx() 2024-08-20T21:38:55.0222120Z >>> nnz = ctx.new_dynamic_size() 2024-08-20T21:38:55.0222925Z >>> shape = [nnz, x.dim()] 2024-08-20T21:38:55.0223852Z >>> result = x.new_empty(shape, dtype=torch.int64) 2024-08-20T21:38:55.0224719Z >>> return result 2024-08-20T21:38:55.0225348Z >>> 2024-08-20T21:38:55.0226069Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2024-08-20T21:38:55.0227067Z >>> 2024-08-20T21:38:55.0227656Z >>> x = torch.tensor([0, 1, 2, 3, 4, 0]) 2024-08-20T21:38:55.0228898Z >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x) 2024-08-20T21:38:55.0230123Z >>> trace.print_readable() 2024-08-20T21:38:55.0230826Z >>> 2024-08-20T21:38:55.0231715Z >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x)) 2024-08-20T21:38:55.0232653Z 2024-08-20T21:38:55.0232833Z 2024-08-20T21:38:55.0234996Z Original Error: IndentationError('expected an indented block after function definition on line 37', ('', 38, 1, '_._ = None\n', 38, 2)) 2024-08-20T21:38:55.0236701Z 2024-08-20T21:38:55.0236889Z _._ = None 2024-08-20T21:38:55.0237348Z ^ 2024-08-20T21:38:55.0239418Z msg = Cannot scrape callname=register_autograd in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=813. 2024-08-20T21:38:55.0241855Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:55.0243178Z Register a backward formula for this custom op. 2024-08-20T21:38:55.0243844Z 2024-08-20T21:38:55.0244408Z In order for an operator to work with autograd, you need to register 2024-08-20T21:38:55.0245499Z a backward formula: 2024-08-20T21:38:55.0246421Z 1. You must tell us how to compute gradients during the backward pass 2024-08-20T21:38:55.0247594Z by providing us a "backward" function. 2024-08-20T21:38:55.0248774Z 2. If you need any values from the forward to compute gradients, you can 2024-08-20T21:38:55.0250016Z use `setup_context` to save values for backward. 2024-08-20T21:38:55.0250676Z 2024-08-20T21:38:55.0251254Z ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``: 2024-08-20T21:38:55.0252835Z - ``grads`` is one or more gradients. The number of gradients matches 2024-08-20T21:38:55.0253985Z the number of outputs of the operator. 2024-08-20T21:38:55.0255184Z The ``ctx`` object is `the same ctx object `_ used by 2024-08-20T21:38:55.0256723Z :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the 2024-08-20T21:38:55.0258040Z same as :meth:`torch.autograd.Function.backward`. 2024-08-20T21:38:55.0258772Z 2024-08-20T21:38:55.0259300Z ``setup_context(ctx, inputs, output)`` runs during the forward pass. 2024-08-20T21:38:55.0260749Z Please save quantities needed for backward onto the ``ctx`` object via 2024-08-20T21:38:55.0262231Z either :meth:`torch.autograd.function.FunctionCtx.save_for_backward` 2024-08-20T21:38:55.0263657Z or assigning them as attributes of ``ctx``. If your custom op has 2024-08-20T21:38:55.0265198Z kwarg-only arguments, we expect the signature of ``setup_context`` 2024-08-20T21:38:55.0266588Z to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``. 2024-08-20T21:38:55.0267602Z 2024-08-20T21:38:55.0268153Z Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is, 2024-08-20T21:38:55.0269627Z they may not directly access :meth:`torch.Tensor.data_ptr` and they must 2024-08-20T21:38:55.0271352Z not depend on or mutate global state. If you need a non-traceable backward, 2024-08-20T21:38:55.0272893Z you can make it a separate custom_op that you call inside ``backward_fn``. 2024-08-20T21:38:55.0273813Z 2024-08-20T21:38:55.0274001Z Examples: 2024-08-20T21:38:55.0274528Z >>> import torch 2024-08-20T21:38:55.0275168Z >>> import numpy as np 2024-08-20T21:38:55.0275880Z >>> from torch import Tensor 2024-08-20T21:38:55.0276593Z >>> 2024-08-20T21:38:55.0277410Z >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=()) 2024-08-20T21:38:55.0278794Z >>> def numpy_sin(x: Tensor) -> Tensor: 2024-08-20T21:38:55.0279630Z >>> x_np = x.cpu().numpy() 2024-08-20T21:38:55.0280397Z >>> y_np = np.sin(x_np) 2024-08-20T21:38:55.0281253Z >>> return torch.from_numpy(y_np).to(device=x.device) 2024-08-20T21:38:55.0282149Z >>> 2024-08-20T21:38:55.0282972Z >>> def setup_context(ctx, inputs, output) -> Tensor: 2024-08-20T21:38:55.0283838Z >>> x, = inputs 2024-08-20T21:38:55.0284513Z >>> ctx.save_for_backward(x) 2024-08-20T21:38:55.0285236Z >>> 2024-08-20T21:38:55.0285744Z >>> def backward(ctx, grad): 2024-08-20T21:38:55.0286494Z >>> x, = ctx.saved_tensors 2024-08-20T21:38:55.0287236Z >>> return grad * x.cos() 2024-08-20T21:38:55.0288080Z >>> 2024-08-20T21:38:55.0288676Z >>> torch.library.register_autograd( 2024-08-20T21:38:55.0289729Z ... "mylib::numpy_sin", backward, setup_context=setup_context 2024-08-20T21:38:55.0290671Z ... ) 2024-08-20T21:38:55.0291151Z >>> 2024-08-20T21:38:55.0291745Z >>> x = torch.randn(3, requires_grad=True) 2024-08-20T21:38:55.0292569Z >>> y = numpy_sin(x) 2024-08-20T21:38:55.0293441Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2024-08-20T21:38:55.0294551Z >>> assert torch.allclose(grad_x, x.cos()) 2024-08-20T21:38:55.0295380Z >>> 2024-08-20T21:38:55.0296472Z >>> # Example with a keyword-only arg 2024-08-20T21:38:55.0297620Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2024-08-20T21:38:55.0299005Z >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor: 2024-08-20T21:38:55.0300001Z >>> x_np = x.cpu().numpy() 2024-08-20T21:38:55.0300798Z >>> y_np = x_np * val 2024-08-20T21:38:55.0301679Z >>> return torch.from_numpy(y_np).to(device=x.device) 2024-08-20T21:38:55.0302576Z >>> 2024-08-20T21:38:55.0303604Z >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor: 2024-08-20T21:38:55.0304811Z >>> ctx.val = keyword_only_inputs["val"] 2024-08-20T21:38:55.0305640Z >>> 2024-08-20T21:38:55.0306156Z >>> def backward(ctx, grad): 2024-08-20T21:38:55.0306875Z >>> return grad * ctx.val 2024-08-20T21:38:55.0307565Z >>> 2024-08-20T21:38:55.0308134Z >>> torch.library.register_autograd( 2024-08-20T21:38:55.0309158Z ... "mylib::numpy_mul", backward, setup_context=setup_context 2024-08-20T21:38:55.0310106Z ... ) 2024-08-20T21:38:55.0310602Z >>> 2024-08-20T21:38:55.0311185Z >>> x = torch.randn(3, requires_grad=True) 2024-08-20T21:38:55.0312064Z >>> y = numpy_mul(x, val=3.14) 2024-08-20T21:38:55.0313047Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2024-08-20T21:38:55.0314211Z >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14)) 2024-08-20T21:38:55.0314984Z 2024-08-20T21:38:55.0315157Z 2024-08-20T21:38:55.0316226Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:55.0317457Z 2024-08-20T21:38:55.0319048Z msg = Cannot scrape callname=opcheck in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=1221. 2024-08-20T21:38:55.0321345Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:55.0322892Z Given an operator and some sample arguments, tests if the operator is 2024-08-20T21:38:55.0324053Z registered correctly. 2024-08-20T21:38:55.0324480Z 2024-08-20T21:38:55.0325056Z That is, when you use the torch.library/TORCH_LIBRARY APIs to create a 2024-08-20T21:38:55.0326602Z custom op, you specified metadata (e.g. mutability info) about the custom op 2024-08-20T21:38:55.0328176Z and these APIs require that the functions you pass them satisfy certain 2024-08-20T21:38:55.0329690Z properties (e.g. no data pointer access in the fake/meta/abstract kernel) 2024-08-20T21:38:55.0330973Z ``opcheck`` tests these metadata and properties. 2024-08-20T21:38:55.0331668Z 2024-08-20T21:38:55.0331943Z Concretely, we test the following: 2024-08-20T21:38:55.0333023Z - test_schema: if the operator's schema is correct. 2024-08-20T21:38:55.0334389Z - test_autograd_registration: if autograd was registered correctly. 2024-08-20T21:38:55.0335830Z - test_faketensor: If the operator has a FakeTensor kernel 2024-08-20T21:38:55.0337107Z (and if it is correct). The FakeTensor kernel is necessary ( 2024-08-20T21:38:55.0338495Z but not sufficient) for the operator to work with PyTorch compilation 2024-08-20T21:38:55.0339696Z APIs (torch.compile/export/FX). 2024-08-20T21:38:55.0340919Z - test_aot_dispatch_dynamic: If the operator has correct behavior 2024-08-20T21:38:55.0342497Z with PyTorch compilation APIs (torch.compile/export/FX). 2024-08-20T21:38:55.0343797Z This checks that the outputs (and gradients, if applicable) are the 2024-08-20T21:38:55.0345204Z same under eager-mode PyTorch and torch.compile. 2024-08-20T21:38:55.0346206Z This test is a superset of ``test_faketensor``. 2024-08-20T21:38:55.0346880Z 2024-08-20T21:38:55.0347371Z For best results, please call ``opcheck`` multiple times with a 2024-08-20T21:38:55.0348608Z representative set of inputs. If your operator supports 2024-08-20T21:38:55.0349970Z autograd, please use ``opcheck`` with inputs with ``requires_grad = True``; 2024-08-20T21:38:55.0351452Z if your operator supports multiple devices (e.g. CPU and CUDA), please 2024-08-20T21:38:55.0352778Z use ``opcheck`` with inputs on all supported devices. 2024-08-20T21:38:55.0353486Z 2024-08-20T21:38:55.0353691Z Args: 2024-08-20T21:38:55.0354447Z op: The operator. Must either be a function decorated with 2024-08-20T21:38:55.0355847Z :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket 2024-08-20T21:38:55.0357439Z found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo) 2024-08-20T21:38:55.0358771Z args: The args to the operator 2024-08-20T21:38:55.0359602Z kwargs: The kwargs to the operator 2024-08-20T21:38:55.0360628Z test_utils: Tests that we should run. Default: all of them. 2024-08-20T21:38:55.0361705Z Example: ("test_schema", "test_faketensor") 2024-08-20T21:38:55.0362823Z raise_exception: If we should raise an exception on the first 2024-08-20T21:38:55.0364033Z error. If False, we will return a dict with information 2024-08-20T21:38:55.0365045Z on if each test passed or not. 2024-08-20T21:38:55.0365614Z 2024-08-20T21:38:55.0365830Z .. warning:: 2024-08-20T21:38:55.0366165Z 2024-08-20T21:38:55.0366717Z opcheck and :func:`torch.autograd.gradcheck` test different things; 2024-08-20T21:38:55.0368147Z opcheck tests if your usage of torch.library APIs is correct while 2024-08-20T21:38:55.0369535Z :func:`torch.autograd.gradcheck` tests if your autograd formula is 2024-08-20T21:38:55.0370941Z mathematically correct. Use both to test custom ops that support 2024-08-20T21:38:55.0372254Z gradient computation. 2024-08-20T21:38:55.0372719Z 2024-08-20T21:38:55.0372905Z Example: 2024-08-20T21:38:55.0373232Z 2024-08-20T21:38:55.0373586Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-20T21:38:55.0374765Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2024-08-20T21:38:55.0376137Z >>> def numpy_add(x: Tensor, y: float) -> Tensor: 2024-08-20T21:38:55.0377047Z >>> x_np = x.numpy(force=True) 2024-08-20T21:38:55.0377837Z >>> z_np = x_np + y 2024-08-20T21:38:55.0378631Z >>> return torch.from_numpy(z_np).to(x.device) 2024-08-20T21:38:55.0379427Z >>> 2024-08-20T21:38:55.0379956Z >>> @numpy_sin.register_fake 2024-08-20T21:38:55.0380667Z >>> def _(x, y): 2024-08-20T21:38:55.0381297Z >>> return torch.empty_like(x) 2024-08-20T21:38:55.0382028Z >>> 2024-08-20T21:38:55.0382610Z >>> def setup_context(ctx, inputs, output): 2024-08-20T21:38:55.0383441Z >>> y, = inputs 2024-08-20T21:38:55.0384049Z >>> ctx.y = y 2024-08-20T21:38:55.0384608Z >>> 2024-08-20T21:38:55.0385100Z >>> def backward(ctx, grad): 2024-08-20T21:38:55.0385865Z >>> return grad * ctx.y, None 2024-08-20T21:38:55.0386585Z >>> 2024-08-20T21:38:55.0387375Z >>> numpy_sin.register_autograd(backward, setup_context=setup_context) 2024-08-20T21:38:55.0388420Z >>> 2024-08-20T21:38:55.0388924Z >>> sample_inputs = [ 2024-08-20T21:38:55.0389585Z >>> (torch.randn(3), 3.14), 2024-08-20T21:38:55.0390609Z >>> (torch.randn(2, 3, device='cuda'), 2.718), 2024-08-20T21:38:55.0391814Z >>> (torch.randn(1, 10, requires_grad=True), 1.234), 2024-08-20T21:38:55.0393125Z >>> (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18), 2024-08-20T21:38:55.0394115Z >>> ] 2024-08-20T21:38:55.0394592Z >>> 2024-08-20T21:38:55.0395099Z >>> for args in sample_inputs: 2024-08-20T21:38:55.0396218Z >>> torch.library.opcheck(foo, args) 2024-08-20T21:38:55.0396831Z 2024-08-20T21:38:55.0397025Z 2024-08-20T21:38:55.0398050Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:55.0399093Z 2024-08-20T21:38:55.0707085Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py line=1042. 2024-08-20T21:38:55.0710126Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:55.0712438Z load(f, map_location=None, pickle_module=pickle, *, weights_only=False, mmap=None, **pickle_load_args) 2024-08-20T21:38:55.0714049Z 2024-08-20T21:38:55.0714515Z Loads an object saved with :func:`torch.save` from a file. 2024-08-20T21:38:55.0715602Z 2024-08-20T21:38:55.0716578Z :func:`torch.load` uses Python's unpickling facilities but treats storages, 2024-08-20T21:38:55.0718516Z which underlie tensors, specially. They are first deserialized on the 2024-08-20T21:38:55.0720482Z CPU and are then moved to the device they were saved from. If this fails 2024-08-20T21:38:55.0722680Z (e.g. because the run time system doesn't have certain devices), an exception 2024-08-20T21:38:55.0724515Z is raised. However, storages can be dynamically remapped to an alternative 2024-08-20T21:38:55.0726132Z set of devices using the :attr:`map_location` argument. 2024-08-20T21:38:55.0726984Z 2024-08-20T21:38:55.0727796Z If :attr:`map_location` is a callable, it will be called once for each serialized 2024-08-20T21:38:55.0729765Z storage with two arguments: storage and location. The storage argument 2024-08-20T21:38:55.0731542Z will be the initial deserialization of the storage, residing on the CPU. 2024-08-20T21:38:55.0733318Z Each serialized storage has a location tag associated with it which 2024-08-20T21:38:55.0735150Z identifies the device it was saved from, and this tag is the second 2024-08-20T21:38:55.0737791Z argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'`` 2024-08-20T21:38:55.0740008Z for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors. 2024-08-20T21:38:55.0741711Z :attr:`map_location` should return either ``None`` or a storage. If 2024-08-20T21:38:55.0743435Z :attr:`map_location` returns a storage, it will be used as the final deserialized 2024-08-20T21:38:55.0745147Z object, already moved to the right device. Otherwise, :func:`torch.load` will 2024-08-20T21:38:55.0747063Z fall back to the default behavior, as if :attr:`map_location` wasn't specified. 2024-08-20T21:38:55.0748010Z 2024-08-20T21:38:55.0748620Z If :attr:`map_location` is a :class:`torch.device` object or a string containing 2024-08-20T21:38:55.0750142Z a device tag, it indicates the location where all tensors should be loaded. 2024-08-20T21:38:55.0751263Z 2024-08-20T21:38:55.0751955Z Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags 2024-08-20T21:38:55.0753454Z appearing in the file (keys), to ones that specify where to put the 2024-08-20T21:38:55.0754490Z storages (values). 2024-08-20T21:38:55.0754853Z 2024-08-20T21:38:55.0755407Z User extensions can register their own location tags and tagging and 2024-08-20T21:38:55.0756852Z deserialization methods using :func:`torch.serialization.register_package`. 2024-08-20T21:38:55.0758048Z 2024-08-20T21:38:55.0758230Z Args: 2024-08-20T21:38:55.0759693Z f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`), 2024-08-20T21:38:55.0761602Z or a string or os.PathLike object containing a file name 2024-08-20T21:38:55.0763110Z map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage 2024-08-20T21:38:55.0764366Z locations 2024-08-20T21:38:55.0765266Z pickle_module: module used for unpickling metadata and objects (has to 2024-08-20T21:38:55.0766521Z match the :attr:`pickle_module` used to serialize file) 2024-08-20T21:38:55.0767744Z weights_only: Indicates whether unpickler should be restricted to 2024-08-20T21:38:55.0768946Z loading only tensors, primitive types, dictionaries 2024-08-20T21:38:55.0770159Z and any types added via :func:`torch.serialization.add_safe_globals`. 2024-08-20T21:38:55.0772281Z mmap: Indicates whether the file should be mmaped rather than loading all the storages into memory. 2024-08-20T21:38:55.0801170Z Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they 2024-08-20T21:38:55.0803290Z are moved to the location that they were tagged with when saving, or specified by ``map_location``. This 2024-08-20T21:38:55.0805705Z second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the 2024-08-20T21:38:55.0807514Z tensor storages from disk to CPU memory in the first step, ``f`` is mmaped. 2024-08-20T21:38:55.0808979Z pickle_load_args: (Python 3 only) optional keyword arguments passed over to 2024-08-20T21:38:55.0810359Z :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g., 2024-08-20T21:38:55.0811370Z :attr:`errors=...`. 2024-08-20T21:38:55.0811805Z 2024-08-20T21:38:55.0812018Z .. warning:: 2024-08-20T21:38:55.0812834Z :func:`torch.load()` unless `weights_only` parameter is set to `True`, 2024-08-20T21:38:55.0814111Z uses ``pickle`` module implicitly, which is known to be insecure. 2024-08-20T21:38:55.0815601Z It is possible to construct malicious pickle data which will execute arbitrary code 2024-08-20T21:38:55.0817186Z during unpickling. Never load data that could have come from an untrusted 2024-08-20T21:38:55.0818849Z source in an unsafe mode, or that could have been tampered with. **Only load data you trust**. 2024-08-20T21:38:55.0819925Z 2024-08-20T21:38:55.0820526Z .. note:: 2024-08-20T21:38:55.0821465Z When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors 2024-08-20T21:38:55.0823366Z will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')`` 2024-08-20T21:38:55.0825049Z and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint. 2024-08-20T21:38:55.0826052Z 2024-08-20T21:38:55.0826246Z .. note:: 2024-08-20T21:38:55.0827359Z By default, we decode byte strings as ``utf-8``. This is to avoid a common error 2024-08-20T21:38:55.0828944Z case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...`` 2024-08-20T21:38:55.0830298Z when loading files saved by Python 2 in Python 3. If this default 2024-08-20T21:38:55.0831751Z is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how 2024-08-20T21:38:55.0833441Z these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them 2024-08-20T21:38:55.0835099Z to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them 2024-08-20T21:38:55.0836564Z as byte arrays which can be decoded later with ``byte_array.decode(...)``. 2024-08-20T21:38:55.0837454Z 2024-08-20T21:38:55.0837643Z Example: 2024-08-20T21:38:55.0838242Z >>> # xdoctest: +SKIP("undefined filepaths") 2024-08-20T21:38:55.0839299Z >>> torch.load("tensors.pt", weights_only=True) 2024-08-20T21:38:55.0840177Z # Load all tensors onto the CPU 2024-08-20T21:38:55.0841303Z >>> torch.load("tensors.pt", map_location=torch.device("cpu"), weights_only=True) 2024-08-20T21:38:55.0842849Z # Load all tensors onto the CPU, using a function 2024-08-20T21:38:55.0843718Z >>> torch.load( 2024-08-20T21:38:55.0844674Z ... "tensors.pt", map_location=lambda storage, loc: storage, weights_only=True 2024-08-20T21:38:55.0845738Z ... ) 2024-08-20T21:38:55.0846259Z # Load all tensors onto GPU 1 2024-08-20T21:38:55.0846986Z >>> torch.load( 2024-08-20T21:38:55.0847553Z ... "tensors.pt", 2024-08-20T21:38:55.0848354Z ... map_location=lambda storage, loc: storage.cuda(1), 2024-08-20T21:38:55.0849692Z ... weights_only=True, 2024-08-20T21:38:55.0850795Z ... ) # type: ignore[attr-defined] 2024-08-20T21:38:55.0851584Z # Map tensors from GPU 1 to GPU 0 2024-08-20T21:38:55.0852736Z >>> torch.load("tensors.pt", map_location={"cuda:1": "cuda:0"}, weights_only=True) 2024-08-20T21:38:55.0853937Z # Load tensor from io.BytesIO object 2024-08-20T21:38:55.0855123Z # Loading from a buffer setting weights_only=False, warning this can be unsafe 2024-08-20T21:38:55.0856320Z >>> with open("tensor.pt", "rb") as f: 2024-08-20T21:38:55.0857149Z ... buffer = io.BytesIO(f.read()) 2024-08-20T21:38:55.0857989Z >>> torch.load(buffer, weights_only=False) 2024-08-20T21:38:55.0859134Z # Load a module with 'ascii' encoding for unpickling 2024-08-20T21:38:55.0860409Z # Loading from a module setting weights_only=False, warning this can be unsafe 2024-08-20T21:38:55.0861769Z >>> torch.load("module.pt", encoding="ascii", weights_only=False) 2024-08-20T21:38:55.0862715Z 2024-08-20T21:38:55.0863706Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:55.0864591Z 2024-08-20T21:38:55.4338705Z msg = Cannot scrape callname=cudart in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/__init__.py line=341. 2024-08-20T21:38:55.4341148Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-08-20T21:38:55.4342442Z Retrieves the CUDA runtime API module. 2024-08-20T21:38:55.4343067Z 2024-08-20T21:38:55.4343077Z 2024-08-20T21:38:55.4343724Z This function initializes the CUDA runtime environment if it is not already 2024-08-20T21:38:55.4345172Z initialized and returns the CUDA runtime API module (_cudart). The CUDA 2024-08-20T21:38:55.4347030Z runtime API module provides access to various CUDA runtime functions. 2024-08-20T21:38:55.4347910Z 2024-08-20T21:38:55.4348102Z Args: 2024-08-20T21:38:55.4348549Z ``None`` 2024-08-20T21:38:55.4348902Z 2024-08-20T21:38:55.4349093Z Returns: 2024-08-20T21:38:55.4349730Z module: The CUDA runtime API module (_cudart). 2024-08-20T21:38:55.4350382Z 2024-08-20T21:38:55.4350582Z Raises: 2024-08-20T21:38:55.4351687Z RuntimeError: If CUDA cannot be re-initialized in a forked subprocess. 2024-08-20T21:38:55.4353609Z AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable. 2024-08-20T21:38:55.4354909Z 2024-08-20T21:38:55.4355255Z Example of CUDA operations with profiling: 2024-08-20T21:38:55.4356079Z >>> import torch 2024-08-20T21:38:55.4356807Z >>> from torch.cuda import cudart, check_error 2024-08-20T21:38:55.4357633Z >>> import os 2024-08-20T21:38:55.4358159Z >>> 2024-08-20T21:38:55.4359015Z >>> os.environ['CUDA_PROFILE'] = '1' 2024-08-20T21:38:55.4359789Z >>> 2024-08-20T21:38:55.4360374Z >>> def perform_cuda_operations_with_streams(): 2024-08-20T21:38:55.4361314Z >>> stream = torch.cuda.Stream() 2024-08-20T21:38:55.4362192Z >>> with torch.cuda.stream(stream): 2024-08-20T21:38:55.4363277Z >>> x = torch.randn(100, 100, device='cuda') 2024-08-20T21:38:55.4364400Z >>> y = torch.randn(100, 100, device='cuda') 2024-08-20T21:38:55.4365286Z >>> z = torch.mul(x, y) 2024-08-20T21:38:55.4366020Z >>> return z 2024-08-20T21:38:55.4366878Z >>> 2024-08-20T21:38:55.4367445Z >>> torch.cuda.synchronize() 2024-08-20T21:38:55.4368268Z >>> print("====== Start nsys profiling ======") 2024-08-20T21:38:55.4369228Z >>> check_error(cudart().cudaProfilerStart()) 2024-08-20T21:38:55.4370229Z >>> with torch.autograd.profiler.emit_nvtx(): 2024-08-20T21:38:55.4371289Z >>> result = perform_cuda_operations_with_streams() 2024-08-20T21:38:55.4372306Z >>> print("CUDA operations completed.") 2024-08-20T21:38:55.4373349Z >>> check_error(torch.cuda.cudart().cudaProfilerStop()) 2024-08-20T21:38:55.4374399Z >>> print("====== End nsys profiling ======") 2024-08-20T21:38:55.4375056Z 2024-08-20T21:38:55.4375573Z To run this example and save the profiling information, execute: 2024-08-20T21:38:55.4377563Z >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2024-08-20T21:38:55.4378746Z 2024-08-20T21:38:55.4379408Z This command profiles the CUDA operations in the provided script and saves 2024-08-20T21:38:55.4380794Z the profiling information to a file named `trace_name.prof`. 2024-08-20T21:38:55.4382349Z The `--profile-from-start off` option ensures that profiling starts only 2024-08-20T21:38:55.4383671Z after the `cudaProfilerStart` call in the script. 2024-08-20T21:38:55.4385120Z The `--csv` and `--print-summary` options format the profiling output as a 2024-08-20T21:38:55.4386363Z CSV file and print a summary, respectively. 2024-08-20T21:38:55.4387823Z The `-o` option specifies the output file name, and the `-f` option forces the 2024-08-20T21:38:55.4389119Z overwrite of the output file if it already exists. 2024-08-20T21:38:55.4389964Z 2024-08-20T21:38:55.4392238Z 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)) 2024-08-20T21:38:55.4394208Z 2024-08-20T21:38:55.4395335Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2024-08-20T21:38:55.4397055Z ^ 2024-08-20T21:38:55.4464385Z 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. 2024-08-20T21:38:55.4467290Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:55.4468289Z 2024-08-20T21:38:55.4468966Z Append the given callback function to this ``Future``, which will be run 2024-08-20T21:38:55.4470435Z when the ``Future`` is completed. Multiple callbacks can be added to 2024-08-20T21:38:55.4471843Z the same ``Future``, but the order in which they will be executed cannot 2024-08-20T21:38:55.4473145Z be guaranteed (to enforce a certain order consider chaining: 2024-08-20T21:38:55.4474456Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which 2024-08-20T21:38:55.4475909Z is the reference to this ``Future``. The callback function can use the 2024-08-20T21:38:55.4477340Z :meth:`value` method to get the value. Note that if this ``Future`` is 2024-08-20T21:38:55.4478910Z already completed, the given callback will be run immediately inline. 2024-08-20T21:38:55.4479797Z 2024-08-20T21:38:55.4480522Z If the ``Future``'s value contains tensors that reside on GPUs, the 2024-08-20T21:38:55.4481948Z callback might be invoked while the async kernels that are populating 2024-08-20T21:38:55.4483437Z those tensors haven't yet finished executing on the device. However, the 2024-08-20T21:38:55.4484727Z callback will be invoked with some dedicated streams set as current 2024-08-20T21:38:55.4486032Z (fetched from a global pool) which will be synchronized with those 2024-08-20T21:38:55.4487443Z kernels. Hence any operation performed by the callback on these tensors 2024-08-20T21:38:55.4488806Z will be scheduled on the device after the kernels complete. In other 2024-08-20T21:38:55.4490591Z words, as long as the callback doesn't switch streams, it can safely 2024-08-20T21:38:55.4492014Z manipulate the result without any additional synchronization. This is 2024-08-20T21:38:55.4493447Z similar to the non-blocking behavior of :meth:`wait`. 2024-08-20T21:38:55.4494159Z 2024-08-20T21:38:55.4494699Z Similarly, if the callback returns a value that contains tensors that 2024-08-20T21:38:55.4496632Z reside on a GPU, it can do so even if the kernels that are producing 2024-08-20T21:38:55.4498053Z these tensors are still running on the device, as long as the callback 2024-08-20T21:38:55.4499627Z didn't change streams during its execution. If one wants to change 2024-08-20T21:38:55.4501174Z streams, one must be careful to re-synchronize them with the original 2024-08-20T21:38:55.4502529Z streams, that is, those that were current when the callback was invoked. 2024-08-20T21:38:55.4503355Z 2024-08-20T21:38:55.4503526Z Args: 2024-08-20T21:38:55.4504274Z callback(``Callable``): a ``Callable`` that takes this ``Future`` as 2024-08-20T21:38:55.4505371Z the only argument. 2024-08-20T21:38:55.4505932Z 2024-08-20T21:38:55.4506113Z Returns: 2024-08-20T21:38:55.4506815Z A new ``Future`` object that holds the return value of the 2024-08-20T21:38:55.4507990Z ``callback`` and will be marked as completed when the given 2024-08-20T21:38:55.4508999Z ``callback`` finishes. 2024-08-20T21:38:55.4509378Z 2024-08-20T21:38:55.4509832Z .. note:: Note that if the callback function throws, either 2024-08-20T21:38:55.4511084Z through the original future being completed with an exception and 2024-08-20T21:38:55.4512477Z calling ``fut.wait()``, or through other code in the callback, the 2024-08-20T21:38:55.4513819Z future returned by ``then`` will be marked appropriately with the 2024-08-20T21:38:55.4515120Z encountered error. However, if this callback later completes 2024-08-20T21:38:55.4516483Z additional futures, those futures are not marked as completed with 2024-08-20T21:38:55.4517811Z an error and the user is responsible for handling completion/waiting 2024-08-20T21:38:55.4518996Z on those futures independently. 2024-08-20T21:38:55.4519479Z 2024-08-20T21:38:55.4519686Z Example:: 2024-08-20T21:38:55.4520250Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2024-08-20T21:38:55.4521057Z >>> def callback(fut): 2024-08-20T21:38:55.4522059Z ... print(f"RPC return value is {fut.wait()}.") 2024-08-20T21:38:55.4522929Z >>> fut = torch.futures.Future() 2024-08-20T21:38:55.4523827Z >>> # The inserted callback will print the return value when 2024-08-20T21:38:55.4524755Z >>> # receiving the response from "worker1" 2024-08-20T21:38:55.4525510Z >>> cb_fut = fut.then(callback) 2024-08-20T21:38:55.4526186Z >>> chain_cb_fut = cb_fut.then( 2024-08-20T21:38:55.4527005Z ... lambda x : print(f"Chained cb done. {x.wait()}") 2024-08-20T21:38:55.4527791Z ... ) 2024-08-20T21:38:55.4528223Z >>> fut.set_result(5) 2024-08-20T21:38:55.4528788Z RPC return value is 5. 2024-08-20T21:38:55.4529388Z Chained cb done. None 2024-08-20T21:38:55.4529771Z 2024-08-20T21:38:55.4530590Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:55.4531469Z 2024-08-20T21:38:55.4533048Z 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. 2024-08-20T21:38:55.4535507Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:55.4536395Z 2024-08-20T21:38:55.4536934Z Set the result for this ``Future``, which will mark this ``Future`` as 2024-08-20T21:38:55.4538240Z completed and trigger all attached callbacks. Note that a ``Future`` 2024-08-20T21:38:55.4539251Z cannot be marked completed twice. 2024-08-20T21:38:55.4539721Z 2024-08-20T21:38:55.4540262Z If the result contains tensors that reside on GPUs, this method can be 2024-08-20T21:38:55.4541531Z called even if the asynchronous kernels that are populating those 2024-08-20T21:38:55.4543331Z tensors haven't yet completed running on the device, provided that the 2024-08-20T21:38:55.4544676Z streams on which those kernels were enqueued are set as the current ones 2024-08-20T21:38:55.4546090Z when this method is called. Put simply, it's safe to call this method 2024-08-20T21:38:55.4547364Z immediately after launching those kernels, without any additional 2024-08-20T21:38:55.4548846Z synchronization, as long as one doesn't change streams in between. This 2024-08-20T21:38:55.4550241Z method will record events on all the relevant current streams and will 2024-08-20T21:38:55.4551553Z use them to ensure proper scheduling for all the consumers of this 2024-08-20T21:38:55.4552533Z ``Future``. 2024-08-20T21:38:55.4552814Z 2024-08-20T21:38:55.4552999Z Args: 2024-08-20T21:38:55.4553639Z result (object): the result object of this ``Future``. 2024-08-20T21:38:55.4554337Z 2024-08-20T21:38:55.4554540Z Example:: 2024-08-20T21:38:55.4555116Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2024-08-20T21:38:55.4555959Z >>> import threading 2024-08-20T21:38:55.4556527Z >>> import time 2024-08-20T21:38:55.4557083Z >>> def slow_set_future(fut, value): 2024-08-20T21:38:55.4557794Z ... time.sleep(0.5) 2024-08-20T21:38:55.4558581Z ... fut.set_result(value) 2024-08-20T21:38:55.4559289Z >>> fut = torch.futures.Future() 2024-08-20T21:38:55.4559994Z >>> t = threading.Thread( 2024-08-20T21:38:55.4560618Z ... target=slow_set_future, 2024-08-20T21:38:55.4561354Z ... args=(fut, torch.ones(2) * 3) 2024-08-20T21:38:55.4562021Z ... ) 2024-08-20T21:38:55.4562453Z >>> t.start() 2024-08-20T21:38:55.4562954Z >>> print(fut.wait()) 2024-08-20T21:38:55.4563495Z tensor([3., 3.]) 2024-08-20T21:38:55.4563994Z >>> t.join() 2024-08-20T21:38:55.4564300Z 2024-08-20T21:38:55.4565133Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:55.4566036Z 2024-08-20T21:38:55.4739771Z msg = Cannot scrape callname=sum in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py line=201. 2024-08-20T21:38:55.4742043Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:55.4742939Z Return the sum of each row of the given sparse tensor. 2024-08-20T21:38:55.4743720Z 2024-08-20T21:38:55.4744145Z Returns the sum of each row of the sparse tensor :attr:`input` in the given 2024-08-20T21:38:55.4745018Z dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions, 2024-08-20T21:38:55.4745880Z reduce over all of them. When sum over all ``sparse_dim``, this method 2024-08-20T21:38:55.4746668Z returns a dense tensor instead of a sparse tensor. 2024-08-20T21:38:55.4747042Z 2024-08-20T21:38:55.4747524Z All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output 2024-08-20T21:38:55.4748417Z tensor having :attr:`dim` fewer dimensions than :attr:`input`. 2024-08-20T21:38:55.4748944Z 2024-08-20T21:38:55.4749264Z During backward, only gradients at ``nnz`` locations of :attr:`input` 2024-08-20T21:38:55.4750183Z will propagate back. Note that the gradients of :attr:`input` is coalesced. 2024-08-20T21:38:55.4750786Z 2024-08-20T21:38:55.4750893Z Args: 2024-08-20T21:38:55.4751302Z input (Tensor): the input sparse tensor 2024-08-20T21:38:55.4752154Z dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce 2024-08-20T21:38:55.4752932Z over all dims. 2024-08-20T21:38:55.4753619Z dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. 2024-08-20T21:38:55.4754405Z Default: dtype of :attr:`input`. 2024-08-20T21:38:55.4754733Z 2024-08-20T21:38:55.4754889Z Example:: 2024-08-20T21:38:55.4755144Z 2024-08-20T21:38:55.4755251Z >>> nnz = 3 2024-08-20T21:38:55.4755573Z >>> dims = [5, 5, 2, 3] 2024-08-20T21:38:55.4756148Z >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), 2024-08-20T21:38:55.4757005Z torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) 2024-08-20T21:38:55.4757710Z >>> V = torch.randn(nnz, dims[2], dims[3]) 2024-08-20T21:38:55.4758264Z >>> size = torch.Size(dims) 2024-08-20T21:38:55.4759370Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-08-20T21:38:55.4760009Z >>> S = torch.sparse_coo_tensor(I, V, size) 2024-08-20T21:38:55.4760474Z >>> S 2024-08-20T21:38:55.4760863Z tensor(indices=tensor([[2, 0, 3], 2024-08-20T21:38:55.4761348Z [2, 4, 1]]), 2024-08-20T21:38:55.4761965Z values=tensor([[[-0.6438, -1.6467, 1.4004], 2024-08-20T21:38:55.4762642Z [ 0.3411, 0.0918, -0.2312]], 2024-08-20T21:38:55.4763074Z 2024-08-20T21:38:55.4763313Z [[ 0.5348, 0.0634, -2.0494], 2024-08-20T21:38:55.4763972Z [-0.7125, -1.0646, 2.1844]], 2024-08-20T21:38:55.4764306Z 2024-08-20T21:38:55.4764637Z [[ 0.1276, 0.1874, -0.6334], 2024-08-20T21:38:55.4765203Z [-1.9682, -0.5340, 0.7483]]]), 2024-08-20T21:38:55.4765837Z size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo) 2024-08-20T21:38:55.4766301Z 2024-08-20T21:38:55.4766611Z # when sum over only part of sparse_dims, return a sparse tensor 2024-08-20T21:38:55.4767295Z >>> torch.sparse.sum(S, [1, 3]) 2024-08-20T21:38:55.4767831Z tensor(indices=tensor([[0, 2, 3]]), 2024-08-20T21:38:55.4768413Z values=tensor([[-1.4512, 0.4073], 2024-08-20T21:38:55.4768977Z [-0.8901, 0.2017], 2024-08-20T21:38:55.4769590Z [-0.3183, -1.7539]]), 2024-08-20T21:38:55.4770187Z size=(5, 2), nnz=3, layout=torch.sparse_coo) 2024-08-20T21:38:55.4770545Z 2024-08-20T21:38:55.4770831Z # when sum over all sparse dim, return a dense tensor 2024-08-20T21:38:55.4771378Z # with summed dims squeezed 2024-08-20T21:38:55.4771911Z >>> torch.sparse.sum(S, [0, 1, 3]) 2024-08-20T21:38:55.4772468Z tensor([-2.6596, -1.1450]) 2024-08-20T21:38:55.4772843Z 2024-08-20T21:38:55.4773535Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:55.4774277Z 2024-08-20T21:38:56.0511819Z msg = Cannot scrape callname=vmap in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/apis.py line=40. 2024-08-20T21:38:56.0514165Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:56.0515150Z 2024-08-20T21:38:56.0515794Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that 2024-08-20T21:38:56.0517161Z maps ``func`` over some dimension of the inputs. Semantically, vmap 2024-08-20T21:38:56.0518692Z pushes the map into PyTorch operations called by ``func``, effectively 2024-08-20T21:38:56.0519750Z vectorizing those operations. 2024-08-20T21:38:56.0520183Z 2024-08-20T21:38:56.0520735Z vmap is useful for handling batch dimensions: one can write a function 2024-08-20T21:38:56.0522125Z ``func`` that runs on examples and then lift it to a function that can 2024-08-20T21:38:56.0523460Z take batches of examples with ``vmap(func)``. vmap can also be used to 2024-08-20T21:38:56.0524678Z compute batched gradients when composed with autograd. 2024-08-20T21:38:56.0525374Z 2024-08-20T21:38:56.0525576Z .. note:: 2024-08-20T21:38:56.0526481Z :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for 2024-08-20T21:38:56.0528042Z convenience. Use whichever one you'd like. 2024-08-20T21:38:56.0528831Z 2024-08-20T21:38:56.0529035Z Args: 2024-08-20T21:38:56.0529824Z func (function): A Python function that takes one or more arguments. 2024-08-20T21:38:56.0530745Z Must return one or more Tensors. 2024-08-20T21:38:56.0531632Z in_dims (int or nested structure): Specifies which dimension of the 2024-08-20T21:38:56.0533099Z inputs should be mapped over. ``in_dims`` should have a 2024-08-20T21:38:56.0534255Z structure like the inputs. If the ``in_dim`` for a particular 2024-08-20T21:38:56.0535437Z input is None, then that indicates there is no map dimension. 2024-08-20T21:38:56.0536401Z Default: 0. 2024-08-20T21:38:56.0537270Z out_dims (int or Tuple[int]): Specifies where the mapped dimension 2024-08-20T21:38:56.0538616Z should appear in the outputs. If ``out_dims`` is a Tuple, then 2024-08-20T21:38:56.0539789Z it should have one element per output. Default: 0. 2024-08-20T21:38:56.0540903Z randomness (str): Specifies whether the randomness in this 2024-08-20T21:38:56.0542447Z vmap should be the same or different across batches. If 'different', 2024-08-20T21:38:56.0543910Z the randomness for each batch will be different. If 'same', the 2024-08-20T21:38:56.0545442Z randomness will be the same across batches. If 'error', any calls to 2024-08-20T21:38:56.0546873Z random functions will error. Default: 'error'. WARNING: this flag 2024-08-20T21:38:56.0548137Z only applies to random PyTorch operations and does not apply to 2024-08-20T21:38:56.0549456Z Python's random module or numpy randomness. 2024-08-20T21:38:56.0550637Z chunk_size (None or int): If None (default), apply a single vmap over inputs. 2024-08-20T21:38:56.0552034Z If not None, then compute the vmap :attr:`chunk_size` samples at a time. 2024-08-20T21:38:56.0553809Z Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop. 2024-08-20T21:38:56.0555639Z If you run into memory issues computing the vmap, please try a non-None chunk_size. 2024-08-20T21:38:56.0556904Z 2024-08-20T21:38:56.0557085Z Returns: 2024-08-20T21:38:56.0557785Z Returns a new "batched" function. It takes the same inputs as 2024-08-20T21:38:56.0559120Z ``func``, except each input has an extra dimension at the index 2024-08-20T21:38:56.0560329Z specified by ``in_dims``. It takes returns the same outputs as 2024-08-20T21:38:56.0561516Z ``func``, except each output has an extra dimension at the index 2024-08-20T21:38:56.0562497Z specified by ``out_dims``. 2024-08-20T21:38:56.0562992Z 2024-08-20T21:38:56.0563180Z .. warning: 2024-08-20T21:38:56.0564165Z :func:`vmap` works best with functional-style code. Please do not 2024-08-20T21:38:56.0565953Z perform any side-effects in ``func``, with the exception of 2024-08-20T21:38:56.0567366Z in-place PyTorch operations. Examples of side-effects include mutating 2024-08-20T21:38:56.0568819Z Python data structures and assigning values to variables not captured 2024-08-20T21:38:56.0569889Z in ``func``. 2024-08-20T21:38:56.0570194Z 2024-08-20T21:38:56.0570816Z One example of using :func:`vmap` is to compute batched dot products. PyTorch 2024-08-20T21:38:56.0572422Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully 2024-08-20T21:38:56.0573878Z rummaging through docs, use :func:`vmap` to construct a new function. 2024-08-20T21:38:56.0574680Z 2024-08-20T21:38:56.0575297Z >>> torch.dot # [D], [D] -> [] 2024-08-20T21:38:56.0576558Z >>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N] 2024-08-20T21:38:56.0577664Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2024-08-20T21:38:56.0578583Z >>> batched_dot(x, y) 2024-08-20T21:38:56.0578964Z 2024-08-20T21:38:56.0579576Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler 2024-08-20T21:38:56.0580675Z model authoring experience. 2024-08-20T21:38:56.0581119Z 2024-08-20T21:38:56.0581368Z >>> batch_size, feature_size = 3, 5 2024-08-20T21:38:56.0582343Z >>> weights = torch.randn(feature_size, requires_grad=True) 2024-08-20T21:38:56.0583205Z >>> 2024-08-20T21:38:56.0583687Z >>> def model(feature_vec): 2024-08-20T21:38:56.0584450Z >>> # Very simple linear model with activation 2024-08-20T21:38:56.0585341Z >>> return feature_vec.dot(weights).relu() 2024-08-20T21:38:56.0586401Z >>> 2024-08-20T21:38:56.0587028Z >>> examples = torch.randn(batch_size, feature_size) 2024-08-20T21:38:56.0587905Z >>> result = torch.vmap(model)(examples) 2024-08-20T21:38:56.0588535Z 2024-08-20T21:38:56.0589137Z :func:`vmap` can also help vectorize computations that were previously difficult 2024-08-20T21:38:56.0590897Z or impossible to batch. One example is higher-order gradient computation. 2024-08-20T21:38:56.0592472Z The PyTorch autograd engine computes vjps (vector-Jacobian products). 2024-08-20T21:38:56.0594074Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually 2024-08-20T21:38:56.0595577Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`, 2024-08-20T21:38:56.0597557Z we can vectorize the whole computation, computing the Jacobian in a single 2024-08-20T21:38:56.0598780Z call to ``autograd.grad``. 2024-08-20T21:38:56.0599219Z 2024-08-20T21:38:56.0599389Z >>> # Setup 2024-08-20T21:38:56.0599859Z >>> N = 5 2024-08-20T21:38:56.0600351Z >>> f = lambda x: x ** 2 2024-08-20T21:38:56.0601041Z >>> x = torch.randn(N, requires_grad=True) 2024-08-20T21:38:56.0601741Z >>> y = f(x) 2024-08-20T21:38:56.0602216Z >>> I_N = torch.eye(N) 2024-08-20T21:38:56.0602783Z >>> 2024-08-20T21:38:56.0603271Z >>> # Sequential approach 2024-08-20T21:38:56.0604224Z >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0] 2024-08-20T21:38:56.0605317Z >>> for v in I_N.unbind()] 2024-08-20T21:38:56.0606124Z >>> jacobian = torch.stack(jacobian_rows) 2024-08-20T21:38:56.0606802Z >>> 2024-08-20T21:38:56.0607280Z >>> # vectorized gradient computation 2024-08-20T21:38:56.0607999Z >>> def get_vjp(v): 2024-08-20T21:38:56.0608618Z >>> return torch.autograd.grad(y, x, v) 2024-08-20T21:38:56.0609410Z >>> jacobian = torch.vmap(get_vjp)(I_N) 2024-08-20T21:38:56.0609936Z 2024-08-20T21:38:56.0610643Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions 2024-08-20T21:38:56.0611714Z 2024-08-20T21:38:56.0612269Z >>> torch.dot # [D], [D] -> [] 2024-08-20T21:38:56.0613873Z >>> batched_dot = torch.vmap(torch.vmap(torch.dot)) # [N1, N0, D], [N1, N0, D] -> [N1, N0] 2024-08-20T21:38:56.0615175Z >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5) 2024-08-20T21:38:56.0616460Z >>> batched_dot(x, y) # tensor of size [2, 3] 2024-08-20T21:38:56.0617014Z 2024-08-20T21:38:56.0617662Z If the inputs are not batched along the first dimension, ``in_dims`` specifies 2024-08-20T21:38:56.0618897Z the dimension that each inputs are batched along as 2024-08-20T21:38:56.0619539Z 2024-08-20T21:38:56.0620066Z >>> torch.dot # [N], [N] -> [] 2024-08-20T21:38:56.0621381Z >>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D] 2024-08-20T21:38:56.0622495Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2024-08-20T21:38:56.0623754Z >>> batched_dot(x, y) # output is [5] instead of [2] if batched along the 0th dimension 2024-08-20T21:38:56.0624696Z 2024-08-20T21:38:56.0625353Z If there are multiple inputs each of which is batched along different dimensions, 2024-08-20T21:38:56.0626773Z ``in_dims`` must be a tuple with the batch dimension for each input as 2024-08-20T21:38:56.0627577Z 2024-08-20T21:38:56.0628096Z >>> torch.dot # [D], [D] -> [] 2024-08-20T21:38:56.0629508Z >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N] 2024-08-20T21:38:56.0630689Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2024-08-20T21:38:56.0632147Z >>> batched_dot(x, y) # second arg doesn't have a batch dim because in_dim[1] was None 2024-08-20T21:38:56.0633132Z 2024-08-20T21:38:56.0633802Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct 2024-08-20T21:38:56.0634924Z matching the shape of the input: 2024-08-20T21:38:56.0635400Z 2024-08-20T21:38:56.0636249Z >>> f = lambda dict: torch.dot(dict['x'], dict['y']) 2024-08-20T21:38:56.0637217Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2024-08-20T21:38:56.0638083Z >>> input = {'x': x, 'y': y} 2024-08-20T21:38:56.0639207Z >>> batched_dot = torch.vmap(f, in_dims=({'x': 0, 'y': None},)) 2024-08-20T21:38:56.0640088Z >>> batched_dot(input) 2024-08-20T21:38:56.0640491Z 2024-08-20T21:38:56.0641195Z By default, the output is batched along the first dimension. However, it can be batched 2024-08-20T21:38:56.0642499Z along any dimension by using ``out_dims`` 2024-08-20T21:38:56.0643039Z 2024-08-20T21:38:56.0643241Z >>> f = lambda x: x ** 2 2024-08-20T21:38:56.0643869Z >>> x = torch.randn(2, 5) 2024-08-20T21:38:56.0644539Z >>> batched_pow = torch.vmap(f, out_dims=1) 2024-08-20T21:38:56.0645336Z >>> batched_pow(x) # [5, 2] 2024-08-20T21:38:56.0645764Z 2024-08-20T21:38:56.0646498Z For any function that uses kwargs, the returned function will not batch the kwargs but will 2024-08-20T21:38:56.0647749Z accept kwargs 2024-08-20T21:38:56.0648075Z 2024-08-20T21:38:56.0648307Z >>> x = torch.randn([2, 5]) 2024-08-20T21:38:56.0648989Z >>> def fn(x, scale=4.): 2024-08-20T21:38:56.0649607Z >>> return x * scale 2024-08-20T21:38:56.0650219Z >>> 2024-08-20T21:38:56.0650737Z >>> batched_pow = torch.vmap(fn) 2024-08-20T21:38:56.0651538Z >>> assert torch.allclose(batched_pow(x), x * 4) 2024-08-20T21:38:56.0652728Z >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5] 2024-08-20T21:38:56.0653584Z 2024-08-20T21:38:56.0653788Z .. note:: 2024-08-20T21:38:56.0654851Z vmap does not provide general autobatching or handle variable-length 2024-08-20T21:38:56.0655936Z sequences out of the box. 2024-08-20T21:38:56.0656412Z 2024-08-20T21:38:56.0657203Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:56.0658179Z 2024-08-20T21:38:56.9832649Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:38:57.1473540Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:38:57.1563979Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:38:57.3776814Z 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. 2024-08-20T21:38:57.3778728Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:57.3780177Z Create a custom operator whose implementation is backed by 1+ triton kernels. 2024-08-20T21:38:57.3780854Z 2024-08-20T21:38:57.3781399Z Use this instead of :func:`torch.library.custom_op` when the implementation 2024-08-20T21:38:57.3782740Z consists of 1+ triton kernels. :func:`torch.library.custom_op` treats 2024-08-20T21:38:57.3783544Z custom operators as opaque (:func:`torch.compile` and 2024-08-20T21:38:57.3784291Z :func:`torch.export.export` will never trace into them), but ``triton_op`` 2024-08-20T21:38:57.3785289Z makes the implementation visible to these subsystems, allowing them 2024-08-20T21:38:57.3785958Z to optimize the triton kernel(s). 2024-08-20T21:38:57.3786268Z 2024-08-20T21:38:57.3786732Z Note that ``fn`` must only consist of calls to PyTorch-understood 2024-08-20T21:38:57.3788132Z operators and triton kernels. Any triton kernels called inside ``fn`` 2024-08-20T21:38:57.3789360Z must be wrapped in a call to :func:`torch._library.capture_triton``. 2024-08-20T21:38:57.3790189Z 2024-08-20T21:38:57.3790372Z Args: 2024-08-20T21:38:57.3791260Z name (str): A name for the custom op that looks like "{namespace}::{name}", 2024-08-20T21:38:57.3792943Z e.g. "mylib::my_linear". The name is used as the op's stable identifier 2024-08-20T21:38:57.3793914Z in PyTorch subsystems (e.g. torch.export, FX graphs). 2024-08-20T21:38:57.3795058Z To avoid name collisions, please use your project name as the namespace; 2024-08-20T21:38:57.3796232Z e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace. 2024-08-20T21:38:57.3797212Z mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates. 2024-08-20T21:38:57.3798318Z This MUST be accurate, otherwise, the behavior is undefined. If "unknown", 2024-08-20T21:38:57.3799541Z it pessimistically assumes that all inputs to the operator are being mutated. 2024-08-20T21:38:57.3800493Z schema (None | str): A schema string for the operator. If None 2024-08-20T21:38:57.3801440Z (recommended) we'll infer a schema for the operator from its type 2024-08-20T21:38:57.3802287Z annotations. We recommend letting us infer a schema unless you 2024-08-20T21:38:57.3802965Z have a specific reason not to. 2024-08-20T21:38:57.3803616Z Example: "(Tensor x, int y) -> (Tensor, Tensor)". 2024-08-20T21:38:57.3804031Z 2024-08-20T21:38:57.3804146Z Example:: 2024-08-20T21:38:57.3804338Z 2024-08-20T21:38:57.3804593Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-20T21:38:57.3805061Z >>> import torch 2024-08-20T21:38:57.3805595Z >>> from torch._library import triton_op, capture_triton 2024-08-20T21:38:57.3806177Z >>> 2024-08-20T21:38:57.3806441Z >>> import triton 2024-08-20T21:38:57.3806904Z >>> from triton import language as tl 2024-08-20T21:38:57.3807334Z >>> 2024-08-20T21:38:57.3807666Z >>> @triton.jit 2024-08-20T21:38:57.3807998Z >>> def add_kernel( 2024-08-20T21:38:57.3808423Z >>> in_ptr0, 2024-08-20T21:38:57.3808741Z >>> in_ptr1, 2024-08-20T21:38:57.3809127Z >>> out_ptr, 2024-08-20T21:38:57.3809474Z >>> n_elements, 2024-08-20T21:38:57.3809894Z >>> BLOCK_SIZE: "tl.constexpr", 2024-08-20T21:38:57.3810333Z >>> ): 2024-08-20T21:38:57.3810733Z >>> pid = tl.program_id(axis=0) 2024-08-20T21:38:57.3811198Z >>> block_start = pid * BLOCK_SIZE 2024-08-20T21:38:57.3811819Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2024-08-20T21:38:57.3812415Z >>> mask = offsets < n_elements 2024-08-20T21:38:57.3812900Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2024-08-20T21:38:57.3813689Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2024-08-20T21:38:57.3814247Z >>> output = x + y 2024-08-20T21:38:57.3814784Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2024-08-20T21:38:57.3815253Z >>> 2024-08-20T21:38:57.3815695Z >>> @triton_op("mylib::add", mutates_args={}) 2024-08-20T21:38:57.3816470Z >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: 2024-08-20T21:38:57.3817069Z >>> output = torch.empty_like(x) 2024-08-20T21:38:57.3817543Z >>> n_elements = output.numel() 2024-08-20T21:38:57.3818040Z >>> 2024-08-20T21:38:57.3818333Z >>> def grid(meta): 2024-08-20T21:38:57.3818843Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2024-08-20T21:38:57.3819352Z >>> 2024-08-20T21:38:57.3819835Z >>> # NB: we need to wrap the triton kernel in a call to capture_triton 2024-08-20T21:38:57.3820656Z >>> capture_triton(add_kernel)[grid](x, y, output, n_elements, 16) 2024-08-20T21:38:57.3821220Z >>> return output 2024-08-20T21:38:57.3821571Z >>> 2024-08-20T21:38:57.3821856Z >>> @torch.compile 2024-08-20T21:38:57.3822190Z >>> def f(x, y): 2024-08-20T21:38:57.3822535Z >>> return add(x, y) 2024-08-20T21:38:57.3822897Z >>> 2024-08-20T21:38:57.3823197Z >>> x = torch.randn(3, device="cuda") 2024-08-20T21:38:57.3823753Z >>> y = torch.randn(3, device="cuda") 2024-08-20T21:38:57.3824241Z >>> 2024-08-20T21:38:57.3824507Z >>> z = f(x, y) 2024-08-20T21:38:57.3824974Z >>> assert torch.allclose(z, x + y) 2024-08-20T21:38:57.3825315Z 2024-08-20T21:38:57.3825411Z 2024-08-20T21:38:57.3826009Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:57.3826517Z 2024-08-20T21:38:57.3827384Z 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. 2024-08-20T21:38:57.3828719Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:57.3829509Z Allows capture of a triton kernel into a graph via make_fx or 2024-08-20T21:38:57.3830192Z non-strict export (coming soon). 2024-08-20T21:38:57.3830501Z 2024-08-20T21:38:57.3830913Z These technologies perform Dispatcher-based tracing (via 2024-08-20T21:38:57.3831600Z ``__torch_dispatch__``) and cannot see calls to raw triton kernels. 2024-08-20T21:38:57.3832328Z The ``capture_triton`` API returns a new callable that can actually 2024-08-20T21:38:57.3832900Z be traced into a graph. 2024-08-20T21:38:57.3833151Z 2024-08-20T21:38:57.3833256Z Examples: 2024-08-20T21:38:57.3833424Z 2024-08-20T21:38:57.3833562Z >>> # xdoctest: +SKIP 2024-08-20T21:38:57.3833911Z >>> import torch 2024-08-20T21:38:57.3834253Z >>> import triton 2024-08-20T21:38:57.3834645Z >>> from triton import language as tl 2024-08-20T21:38:57.3835220Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2024-08-20T21:38:57.3835964Z >>> from torch._higher_order_ops.triton_kernel_wrap import capture_triton 2024-08-20T21:38:57.3836555Z >>> 2024-08-20T21:38:57.3836813Z >>> @triton.jit 2024-08-20T21:38:57.3837143Z >>> def add_kernel( 2024-08-20T21:38:57.3837487Z >>> in_ptr0, 2024-08-20T21:38:57.3837799Z >>> in_ptr1, 2024-08-20T21:38:57.3838123Z >>> out_ptr, 2024-08-20T21:38:57.3838639Z >>> n_elements, 2024-08-20T21:38:57.3839009Z >>> BLOCK_SIZE: "tl.constexpr", 2024-08-20T21:38:57.3839439Z >>> ): 2024-08-20T21:38:57.3839764Z >>> pid = tl.program_id(axis=0) 2024-08-20T21:38:57.3840223Z >>> block_start = pid * BLOCK_SIZE 2024-08-20T21:38:57.3840760Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2024-08-20T21:38:57.3841287Z >>> mask = offsets < n_elements 2024-08-20T21:38:57.3841886Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2024-08-20T21:38:57.3842429Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2024-08-20T21:38:57.3842905Z >>> output = x + y 2024-08-20T21:38:57.3843354Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2024-08-20T21:38:57.3843836Z >>> 2024-08-20T21:38:57.3844174Z >>> def add(x, y): 2024-08-20T21:38:57.3844589Z >>> output = torch.empty_like(x) 2024-08-20T21:38:57.3845060Z >>> n_elements = output.numel() 2024-08-20T21:38:57.3845476Z >>> 2024-08-20T21:38:57.3845746Z >>> def grid_fn(meta): 2024-08-20T21:38:57.3846262Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2024-08-20T21:38:57.3846778Z >>> 2024-08-20T21:38:57.3847214Z >>> capture_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16) 2024-08-20T21:38:57.3847798Z >>> return output 2024-08-20T21:38:57.3848148Z >>> 2024-08-20T21:38:57.3848448Z >>> x = torch.randn(3, device="cuda") 2024-08-20T21:38:57.3848929Z >>> y = torch.randn(3, device="cuda") 2024-08-20T21:38:57.3849375Z >>> gm = make_fx(add)(x, y) 2024-08-20T21:38:57.3849756Z >>> print(gm.code) 2024-08-20T21:38:57.3850131Z >>> # def forward(self, x_1, y_1): 2024-08-20T21:38:57.3850777Z >>> # empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False) 2024-08-20T21:38:57.3851601Z >>> # triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation( 2024-08-20T21:38:57.3852286Z >>> # kernel_idx = 0, constant_args_idx = 0, 2024-08-20T21:38:57.3852888Z >>> # grid = [(1, 1, 1)], kwargs = { 2024-08-20T21:38:57.3853587Z >>> # 'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like, 2024-08-20T21:38:57.3854231Z >>> # 'n_elements': 3, 'BLOCK_SIZE': 16 2024-08-20T21:38:57.3854695Z >>> # }) 2024-08-20T21:38:57.3855046Z >>> # return empty_like 2024-08-20T21:38:57.3855320Z 2024-08-20T21:38:57.3855414Z 2024-08-20T21:38:57.3855981Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:57.3856486Z 2024-08-20T21:38:57.5628023Z 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. 2024-08-20T21:38:57.5630300Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:57.5631148Z 2024-08-20T21:38:57.5631724Z Raises an AssertionError if two items are not equal up to desired 2024-08-20T21:38:57.5632734Z precision. 2024-08-20T21:38:57.5633055Z 2024-08-20T21:38:57.5633533Z .. note:: It is recommended to use one of `assert_allclose`, 2024-08-20T21:38:57.5634740Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-08-20T21:38:57.5635895Z instead of this function for more consistent floating point 2024-08-20T21:38:57.5636761Z comparisons. 2024-08-20T21:38:57.5637096Z 2024-08-20T21:38:57.5637671Z The test verifies that the elements of `actual` and `desired` satisfy. 2024-08-20T21:38:57.5638661Z 2024-08-20T21:38:57.5639205Z ``abs(desired-actual) < float64(1.5 * 10**(-decimal))`` 2024-08-20T21:38:57.5639971Z 2024-08-20T21:38:57.5640590Z That is a looser test than originally documented, but agrees with what the 2024-08-20T21:38:57.5641895Z actual implementation in `assert_array_almost_equal` did up to rounding 2024-08-20T21:38:57.5643089Z vagaries. An exception is raised at conflicting values. For ndarrays this 2024-08-20T21:38:57.5643917Z delegates to assert_array_almost_equal 2024-08-20T21:38:57.5644277Z 2024-08-20T21:38:57.5644451Z Parameters 2024-08-20T21:38:57.5644944Z ---------- 2024-08-20T21:38:57.5645340Z actual : array_like 2024-08-20T21:38:57.5645856Z The object to check. 2024-08-20T21:38:57.5646464Z desired : array_like 2024-08-20T21:38:57.5647457Z The expected object. 2024-08-20T21:38:57.5648105Z decimal : int, optional 2024-08-20T21:38:57.5648681Z Desired precision, default is 7. 2024-08-20T21:38:57.5649135Z err_msg : str, optional 2024-08-20T21:38:57.5649573Z The error message to be printed in case of failure. 2024-08-20T21:38:57.5650077Z verbose : bool, optional 2024-08-20T21:38:57.5650588Z If True, the conflicting values are appended to the error message. 2024-08-20T21:38:57.5651066Z 2024-08-20T21:38:57.5651162Z Raises 2024-08-20T21:38:57.5651445Z ------ 2024-08-20T21:38:57.5651692Z AssertionError 2024-08-20T21:38:57.5652153Z If actual and desired are not equal up to specified precision. 2024-08-20T21:38:57.5652594Z 2024-08-20T21:38:57.5652713Z See Also 2024-08-20T21:38:57.5652978Z -------- 2024-08-20T21:38:57.5653450Z assert_allclose: Compare two array_like objects for equality with desired 2024-08-20T21:38:57.5654122Z relative and/or absolute precision. 2024-08-20T21:38:57.5654728Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-08-20T21:38:57.5655189Z 2024-08-20T21:38:57.5655287Z Examples 2024-08-20T21:38:57.5655568Z -------- 2024-08-20T21:38:57.5655924Z >>> from torch._numpy.testing import assert_almost_equal 2024-08-20T21:38:57.5656486Z >>> assert_almost_equal(2.3333333333333, 2.33333334) 2024-08-20T21:38:57.5657061Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) 2024-08-20T21:38:57.5657589Z Traceback (most recent call last): 2024-08-20T21:38:57.5657970Z ... 2024-08-20T21:38:57.5658232Z AssertionError: 2024-08-20T21:38:57.5658565Z Arrays are not almost equal to 10 decimals 2024-08-20T21:38:57.5669167Z ACTUAL: 2.3333333333333 2024-08-20T21:38:57.5669923Z DESIRED: 2.33333334 2024-08-20T21:38:57.5670155Z 2024-08-20T21:38:57.5670357Z >>> assert_almost_equal(np.array([1.0,2.3333333333333]), 2024-08-20T21:38:57.5670914Z ... np.array([1.0,2.33333334]), decimal=9) 2024-08-20T21:38:57.5671400Z Traceback (most recent call last): 2024-08-20T21:38:57.5671794Z ... 2024-08-20T21:38:57.5672058Z AssertionError: 2024-08-20T21:38:57.5672396Z Arrays are not almost equal to 9 decimals 2024-08-20T21:38:57.5672827Z 2024-08-20T21:38:57.5673126Z Mismatched elements: 1 / 2 (50%) 2024-08-20T21:38:57.5673707Z Max absolute difference: 6.666699636781459e-09 2024-08-20T21:38:57.5674284Z Max relative difference: 2.8571569790287484e-09 2024-08-20T21:38:57.5674813Z x: torch.ndarray([1.0000, 2.3333], dtype=float64) 2024-08-20T21:38:57.5675340Z y: torch.ndarray([1.0000, 2.3333], dtype=float64) 2024-08-20T21:38:57.5675702Z 2024-08-20T21:38:57.5675708Z 2024-08-20T21:38:57.5676139Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:57.5676650Z 2024-08-20T21:38:57.5677588Z 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. 2024-08-20T21:38:57.5679092Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:57.5679641Z 2024-08-20T21:38:57.5679949Z Raises an AssertionError if two items are not equal up to significant 2024-08-20T21:38:57.5680540Z digits. 2024-08-20T21:38:57.5680685Z 2024-08-20T21:38:57.5680959Z .. note:: It is recommended to use one of `assert_allclose`, 2024-08-20T21:38:57.5681602Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-08-20T21:38:57.5682287Z instead of this function for more consistent floating point 2024-08-20T21:38:57.5682839Z comparisons. 2024-08-20T21:38:57.5683048Z 2024-08-20T21:38:57.5683296Z Given two numbers, check that they are approximately equal. 2024-08-20T21:38:57.5684018Z Approximately equal is defined as the number of significant digits 2024-08-20T21:38:57.5684589Z that agree. 2024-08-20T21:38:57.5684750Z 2024-08-20T21:38:57.5684855Z Parameters 2024-08-20T21:38:57.5685151Z ---------- 2024-08-20T21:38:57.5685420Z actual : scalar 2024-08-20T21:38:57.5685713Z The object to check. 2024-08-20T21:38:57.5686164Z desired : scalar 2024-08-20T21:38:57.5686477Z The expected object. 2024-08-20T21:38:57.5686818Z significant : int, optional 2024-08-20T21:38:57.5687213Z Desired precision, default is 7. 2024-08-20T21:38:57.5687636Z err_msg : str, optional 2024-08-20T21:38:57.5688054Z The error message to be printed in case of failure. 2024-08-20T21:38:57.5688560Z verbose : bool, optional 2024-08-20T21:38:57.5689084Z If True, the conflicting values are appended to the error message. 2024-08-20T21:38:57.5689546Z 2024-08-20T21:38:57.5689646Z Raises 2024-08-20T21:38:57.5689923Z ------ 2024-08-20T21:38:57.5690180Z AssertionError 2024-08-20T21:38:57.5690733Z If actual and desired are not equal up to specified precision. 2024-08-20T21:38:57.5691174Z 2024-08-20T21:38:57.5691286Z See Also 2024-08-20T21:38:57.5691556Z -------- 2024-08-20T21:38:57.5692034Z assert_allclose: Compare two array_like objects for equality with desired 2024-08-20T21:38:57.5692712Z relative and/or absolute precision. 2024-08-20T21:38:57.5693326Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-08-20T21:38:57.5693783Z 2024-08-20T21:38:57.5693885Z Examples 2024-08-20T21:38:57.5694166Z -------- 2024-08-20T21:38:57.5694784Z >>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20) # doctest: +SKIP 2024-08-20T21:38:57.5695783Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20, # doctest: +SKIP 2024-08-20T21:38:57.5696786Z ... significant=8) 2024-08-20T21:38:57.5697550Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20, # doctest: +SKIP 2024-08-20T21:38:57.5698405Z ... significant=8) 2024-08-20T21:38:57.5699094Z Traceback (most recent call last): 2024-08-20T21:38:57.5699470Z ... 2024-08-20T21:38:57.5699741Z AssertionError: 2024-08-20T21:38:57.5700111Z Items are not equal to 8 significant digits: 2024-08-20T21:38:57.5700600Z ACTUAL: 1.234567e-21 2024-08-20T21:38:57.5701031Z DESIRED: 1.2345672e-21 2024-08-20T21:38:57.5701257Z 2024-08-20T21:38:57.5701536Z the evaluated condition that raises the exception is 2024-08-20T21:38:57.5702074Z 2024-08-20T21:38:57.5702418Z >>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1) 2024-08-20T21:38:57.5702917Z True 2024-08-20T21:38:57.5703120Z 2024-08-20T21:38:57.5703126Z 2024-08-20T21:38:57.5703884Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:57.5704529Z 2024-08-20T21:38:57.5705475Z 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. 2024-08-20T21:38:57.5706848Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:57.5707392Z 2024-08-20T21:38:57.5707678Z Raises an AssertionError if two array_like objects are not equal. 2024-08-20T21:38:57.5708146Z 2024-08-20T21:38:57.5708436Z Given two array_like objects, check that the shape is equal and all 2024-08-20T21:38:57.5709217Z elements of these objects are equal (but see the Notes for the special 2024-08-20T21:38:57.5709975Z handling of a scalar). An exception is raised at shape mismatch or 2024-08-20T21:38:57.5710747Z conflicting values. In contrast to the standard usage in numpy, NaNs 2024-08-20T21:38:57.5711538Z are compared like numbers, no assertion is raised if both objects have 2024-08-20T21:38:57.5712140Z NaNs in the same positions. 2024-08-20T21:38:57.5712399Z 2024-08-20T21:38:57.5712717Z The usual caution for verifying equality with floating point numbers is 2024-08-20T21:38:57.5713305Z advised. 2024-08-20T21:38:57.5713454Z 2024-08-20T21:38:57.5713560Z Parameters 2024-08-20T21:38:57.5713857Z ---------- 2024-08-20T21:38:57.5714124Z x : array_like 2024-08-20T21:38:57.5714423Z The actual object to check. 2024-08-20T21:38:57.5714806Z y : array_like 2024-08-20T21:38:57.5715124Z The desired, expected object. 2024-08-20T21:38:57.5715504Z err_msg : str, optional 2024-08-20T21:38:57.5716108Z The error message to be printed in case of failure. 2024-08-20T21:38:57.5716619Z verbose : bool, optional 2024-08-20T21:38:57.5717133Z If True, the conflicting values are appended to the error message. 2024-08-20T21:38:57.5717726Z strict : bool, optional 2024-08-20T21:38:57.5718251Z If True, raise an AssertionError when either the shape or the data 2024-08-20T21:38:57.5719076Z type of the array_like objects does not match. The special 2024-08-20T21:38:57.5719786Z handling for scalars mentioned in the Notes section is disabled. 2024-08-20T21:38:57.5720259Z 2024-08-20T21:38:57.5720356Z Raises 2024-08-20T21:38:57.5720638Z ------ 2024-08-20T21:38:57.5720886Z AssertionError 2024-08-20T21:38:57.5721255Z If actual and desired objects are not equal. 2024-08-20T21:38:57.5721602Z 2024-08-20T21:38:57.5721717Z See Also 2024-08-20T21:38:57.5721984Z -------- 2024-08-20T21:38:57.5722462Z assert_allclose: Compare two array_like objects for equality with desired 2024-08-20T21:38:57.5723142Z relative and/or absolute precision. 2024-08-20T21:38:57.5723760Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-08-20T21:38:57.5724222Z 2024-08-20T21:38:57.5724319Z Notes 2024-08-20T21:38:57.5724588Z ----- 2024-08-20T21:38:57.5725011Z When one of `x` and `y` is a scalar and the other is array_like, the 2024-08-20T21:38:57.5725779Z function checks that each element of the array_like object is equal to 2024-08-20T21:38:57.5726722Z the scalar. This behaviour can be disabled with the `strict` parameter. 2024-08-20T21:38:57.5727207Z 2024-08-20T21:38:57.5727308Z Examples 2024-08-20T21:38:57.5727600Z -------- 2024-08-20T21:38:57.5728073Z The first assert does not raise an exception: 2024-08-20T21:38:57.5728418Z 2024-08-20T21:38:57.5728626Z >>> np.testing.assert_array_equal([1.0,2.33333,np.nan], 2024-08-20T21:38:57.5729188Z ... [np.exp(0),2.33333, np.nan]) 2024-08-20T21:38:57.5729534Z 2024-08-20T21:38:57.5729871Z Use `assert_allclose` or one of the nulp (number of floating point values) 2024-08-20T21:38:57.5730500Z functions for these cases instead: 2024-08-20T21:38:57.5730798Z 2024-08-20T21:38:57.5730990Z >>> np.testing.assert_allclose([1.0,np.pi,np.nan], 2024-08-20T21:38:57.5731538Z ... [1, np.sqrt(np.pi)**2, np.nan], 2024-08-20T21:38:57.5732117Z ... rtol=1e-10, atol=0) 2024-08-20T21:38:57.5732440Z 2024-08-20T21:38:57.5732728Z As mentioned in the Notes section, `assert_array_equal` has special 2024-08-20T21:38:57.5733510Z handling for scalars. Here the test checks that each value in `x` is 3: 2024-08-20T21:38:57.5733994Z 2024-08-20T21:38:57.5734146Z >>> x = np.full((2, 5), fill_value=3) 2024-08-20T21:38:57.5734578Z >>> np.testing.assert_array_equal(x, 3) 2024-08-20T21:38:57.5734898Z 2024-08-20T21:38:57.5735206Z Use `strict` to raise an AssertionError when comparing a scalar with an 2024-08-20T21:38:57.5735785Z array: 2024-08-20T21:38:57.5735929Z 2024-08-20T21:38:57.5736129Z >>> np.testing.assert_array_equal(x, 3, strict=True) 2024-08-20T21:38:57.5736649Z Traceback (most recent call last): 2024-08-20T21:38:57.5737087Z ... 2024-08-20T21:38:57.5737339Z AssertionError: 2024-08-20T21:38:57.5737646Z Arrays are not equal 2024-08-20T21:38:57.5737963Z 2024-08-20T21:38:57.5738242Z (shapes (2, 5), () mismatch) 2024-08-20T21:38:57.5738623Z x: torch.ndarray([[3, 3, 3, 3, 3], 2024-08-20T21:38:57.5739018Z [3, 3, 3, 3, 3]]) 2024-08-20T21:38:57.5739342Z y: torch.ndarray(3) 2024-08-20T21:38:57.5739559Z 2024-08-20T21:38:57.5739851Z The `strict` parameter also ensures that the array data types match: 2024-08-20T21:38:57.5740329Z 2024-08-20T21:38:57.5740456Z >>> x = np.array([2, 2, 2]) 2024-08-20T21:38:57.5740874Z >>> y = np.array([2., 2., 2.], dtype=np.float32) 2024-08-20T21:38:57.5741409Z >>> np.testing.assert_array_equal(x, y, strict=True) 2024-08-20T21:38:57.5741922Z Traceback (most recent call last): 2024-08-20T21:38:57.5742309Z ... 2024-08-20T21:38:57.5742557Z AssertionError: 2024-08-20T21:38:57.5742950Z Arrays are not equal 2024-08-20T21:38:57.5743264Z 2024-08-20T21:38:57.5743605Z (dtypes dtype("int64"), dtype("float32") mismatch) 2024-08-20T21:38:57.5744100Z x: torch.ndarray([2, 2, 2]) 2024-08-20T21:38:57.5744480Z y: torch.ndarray([2., 2., 2.]) 2024-08-20T21:38:57.5744742Z 2024-08-20T21:38:57.5745178Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:57.5745705Z 2024-08-20T21:38:57.5746667Z 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. 2024-08-20T21:38:57.5748084Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:57.5748611Z 2024-08-20T21:38:57.5748903Z Raises an AssertionError if two objects are not equal up to desired 2024-08-20T21:38:57.5749480Z precision. 2024-08-20T21:38:57.5749638Z 2024-08-20T21:38:57.5749915Z .. note:: It is recommended to use one of `assert_allclose`, 2024-08-20T21:38:57.5750571Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-08-20T21:38:57.5751244Z instead of this function for more consistent floating point 2024-08-20T21:38:57.5751799Z comparisons. 2024-08-20T21:38:57.5752004Z 2024-08-20T21:38:57.5752348Z The test verifies identical shapes and that the elements of ``actual`` and 2024-08-20T21:38:57.5752955Z ``desired`` satisfy. 2024-08-20T21:38:57.5753169Z 2024-08-20T21:38:57.5753405Z ``abs(desired-actual) < 1.5 * 10**(-decimal)`` 2024-08-20T21:38:57.5753746Z 2024-08-20T21:38:57.5754090Z That is a looser test than originally documented, but agrees with what the 2024-08-20T21:38:57.5754992Z actual implementation did up to rounding vagaries. An exception is raised 2024-08-20T21:38:57.5755843Z at shape mismatch or conflicting values. In contrast to the standard usage 2024-08-20T21:38:57.5756668Z in numpy, NaNs are compared like numbers, no assertion is raised if both 2024-08-20T21:38:57.5757327Z objects have NaNs in the same positions. 2024-08-20T21:38:57.5757640Z 2024-08-20T21:38:57.5757744Z Parameters 2024-08-20T21:38:57.5758038Z ---------- 2024-08-20T21:38:57.5758311Z x : array_like 2024-08-20T21:38:57.5758739Z The actual object to check. 2024-08-20T21:38:57.5759120Z y : array_like 2024-08-20T21:38:57.5759423Z The desired, expected object. 2024-08-20T21:38:57.5759828Z decimal : int, optional 2024-08-20T21:38:57.5760201Z Desired precision, default is 6. 2024-08-20T21:38:57.5760607Z err_msg : str, optional 2024-08-20T21:38:57.5761044Z The error message to be printed in case of failure. 2024-08-20T21:38:57.5761546Z verbose : bool, optional 2024-08-20T21:38:57.5762081Z If True, the conflicting values are appended to the error message. 2024-08-20T21:38:57.5762543Z 2024-08-20T21:38:57.5762640Z Raises 2024-08-20T21:38:57.5762918Z ------ 2024-08-20T21:38:57.5763178Z AssertionError 2024-08-20T21:38:57.5763628Z If actual and desired are not equal up to specified precision. 2024-08-20T21:38:57.5764083Z 2024-08-20T21:38:57.5764185Z See Also 2024-08-20T21:38:57.5764466Z -------- 2024-08-20T21:38:57.5764927Z assert_allclose: Compare two array_like objects for equality with desired 2024-08-20T21:38:57.5765602Z relative and/or absolute precision. 2024-08-20T21:38:57.5766226Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-08-20T21:38:57.5766666Z 2024-08-20T21:38:57.5766769Z Examples 2024-08-20T21:38:57.5767051Z -------- 2024-08-20T21:38:57.5767374Z the first assert does not raise an exception 2024-08-20T21:38:57.5767711Z 2024-08-20T21:38:57.5767944Z >>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan], 2024-08-20T21:38:57.5768523Z ... [1.0,2.333,np.nan]) 2024-08-20T21:38:57.5768860Z 2024-08-20T21:38:57.5769115Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2024-08-20T21:38:57.5769699Z ... [1.0,2.33339,np.nan], decimal=5) 2024-08-20T21:38:57.5770294Z Traceback (most recent call last): 2024-08-20T21:38:57.5770683Z ... 2024-08-20T21:38:57.5770934Z AssertionError: 2024-08-20T21:38:57.5771287Z Arrays are not almost equal to 5 decimals 2024-08-20T21:38:57.5771718Z 2024-08-20T21:38:57.5772007Z Mismatched elements: 1 / 3 (33.3%) 2024-08-20T21:38:57.5772525Z Max absolute difference: 5.999999999994898e-05 2024-08-20T21:38:57.5773100Z Max relative difference: 2.5713661239633743e-05 2024-08-20T21:38:57.5773652Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2024-08-20T21:38:57.5774269Z y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64) 2024-08-20T21:38:57.5774662Z 2024-08-20T21:38:57.5774908Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2024-08-20T21:38:57.5775505Z ... [1.0,2.33333, 5], decimal=5) 2024-08-20T21:38:57.5775990Z Traceback (most recent call last): 2024-08-20T21:38:57.5776374Z ... 2024-08-20T21:38:57.5776639Z AssertionError: 2024-08-20T21:38:57.5776970Z Arrays are not almost equal to 5 decimals 2024-08-20T21:38:57.5777404Z 2024-08-20T21:38:57.5777704Z x and y nan location mismatch: 2024-08-20T21:38:57.5778173Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2024-08-20T21:38:57.5778777Z y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64) 2024-08-20T21:38:57.5779153Z 2024-08-20T21:38:57.5779159Z 2024-08-20T21:38:57.5779590Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:57.5780098Z 2024-08-20T21:38:57.5781135Z 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. 2024-08-20T21:38:57.5783002Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:57.5783829Z Context manager that resets warning registry for catching warnings 2024-08-20T21:38:57.5784283Z 2024-08-20T21:38:57.5784643Z Warnings can be slippery, because, whenever a warning is triggered, Python 2024-08-20T21:38:57.5785470Z adds a ``__warningregistry__`` member to the *calling* module. This makes 2024-08-20T21:38:57.5786292Z it impossible to retrigger the warning in this module, whatever you put in 2024-08-20T21:38:57.5787142Z the warnings filters. This context manager accepts a sequence of `modules` 2024-08-20T21:38:57.5787844Z as a keyword argument to its constructor and: 2024-08-20T21:38:57.5788194Z 2024-08-20T21:38:57.5788519Z * stores and removes any ``__warningregistry__`` entries in given `modules` 2024-08-20T21:38:57.5789130Z on entry; 2024-08-20T21:38:57.5789571Z * resets ``__warningregistry__`` to its previous state on exit. 2024-08-20T21:38:57.5790002Z 2024-08-20T21:38:57.5790324Z This makes it possible to trigger any warning afresh inside the context 2024-08-20T21:38:57.5791066Z manager without disturbing the state of warnings outside. 2024-08-20T21:38:57.5791474Z 2024-08-20T21:38:57.5791810Z For compatibility with Python 3.0, please consider all arguments to be 2024-08-20T21:38:57.5792446Z keyword-only. 2024-08-20T21:38:57.5792651Z 2024-08-20T21:38:57.5792757Z Parameters 2024-08-20T21:38:57.5793072Z ---------- 2024-08-20T21:38:57.5793360Z record : bool, optional 2024-08-20T21:38:57.5793859Z Specifies whether warnings should be captured by a custom 2024-08-20T21:38:57.5794612Z implementation of ``warnings.showwarning()`` and be appended to a list 2024-08-20T21:38:57.5795411Z returned by the context manager. Otherwise None is returned by the 2024-08-20T21:38:57.5796509Z context manager. The objects appended to the list are arguments whose 2024-08-20T21:38:57.5797234Z attributes mirror the arguments to ``showwarning()``. 2024-08-20T21:38:57.5797772Z modules : sequence, optional 2024-08-20T21:38:57.5798455Z Sequence of modules for which to reset warnings registry on entry and 2024-08-20T21:38:57.5799561Z restore on exit. To work correctly, all 'ignore' filters should 2024-08-20T21:38:57.5800376Z filter by one of these modules. 2024-08-20T21:38:57.5800677Z 2024-08-20T21:38:57.5800781Z Examples 2024-08-20T21:38:57.5801088Z -------- 2024-08-20T21:38:57.5801375Z >>> import warnings 2024-08-20T21:38:57.5801846Z >>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP 2024-08-20T21:38:57.5802455Z ... modules=[np.core.fromnumeric]): 2024-08-20T21:38:57.5803006Z ... warnings.simplefilter('always') 2024-08-20T21:38:57.5803697Z ... warnings.filterwarnings('ignore', module='np.core.fromnumeric') 2024-08-20T21:38:57.5804430Z ... # do something that raises a warning but ignore those in 2024-08-20T21:38:57.5804990Z ... # np.core.fromnumeric 2024-08-20T21:38:57.5805348Z 2024-08-20T21:38:57.5805916Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:57.5806421Z 2024-08-20T21:38:57.7440001Z 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. 2024-08-20T21:38:57.7442023Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:57.7442892Z Applies a 1D convolution over a quantized input signal composed of 2024-08-20T21:38:57.7443507Z several quantized input planes. 2024-08-20T21:38:57.7443798Z 2024-08-20T21:38:57.7444104Z For details on input arguments, parameters, and implementation see 2024-08-20T21:38:57.7444711Z :class:`~torch.nn.Conv1d`. 2024-08-20T21:38:57.7444968Z 2024-08-20T21:38:57.7445109Z .. note:: 2024-08-20T21:38:57.7445821Z Only `zeros` is supported for the :attr:`padding_mode` argument. 2024-08-20T21:38:57.7446291Z 2024-08-20T21:38:57.7446401Z .. note:: 2024-08-20T21:38:57.7446832Z Only `torch.quint8` is supported for the input data type. 2024-08-20T21:38:57.7447250Z 2024-08-20T21:38:57.7447255Z 2024-08-20T21:38:57.7447362Z Attributes: 2024-08-20T21:38:57.7447857Z weight (Tensor): packed tensor derived from the learnable weight 2024-08-20T21:38:57.7448470Z parameter. 2024-08-20T21:38:57.7448966Z scale (Tensor): scalar for the output scale 2024-08-20T21:38:57.7449555Z zero_point (Tensor): scalar for the output zero point 2024-08-20T21:38:57.7449963Z 2024-08-20T21:38:57.7450171Z See :class:`~torch.nn.Conv1d` for other attributes. 2024-08-20T21:38:57.7450544Z 2024-08-20T21:38:57.7450671Z Examples:: 2024-08-20T21:38:57.7450846Z 2024-08-20T21:38:57.7451056Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) 2024-08-20T21:38:57.7451632Z >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2) 2024-08-20T21:38:57.7452159Z >>> input = torch.randn(20, 16, 100) 2024-08-20T21:38:57.7452610Z >>> # quantize input to quint8 2024-08-20T21:38:57.7453036Z >>> # xdoctest: +SKIP 2024-08-20T21:38:57.7453586Z >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, 2024-08-20T21:38:57.7454239Z ... dtype=torch.quint8) 2024-08-20T21:38:57.7454735Z >>> output = m(q_input) 2024-08-20T21:38:57.7454996Z 2024-08-20T21:38:57.7455108Z 2024-08-20T21:38:57.7455683Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:57.7456205Z 2024-08-20T21:38:57.7650559Z 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. 2024-08-20T21:38:57.7651941Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:57.7652696Z A quantized long short-term memory (LSTM). 2024-08-20T21:38:57.7653061Z 2024-08-20T21:38:57.7653479Z For the description and the argument types, please, refer to :class:`~torch.nn.LSTM` 2024-08-20T21:38:57.7654054Z 2024-08-20T21:38:57.7654164Z Attributes: 2024-08-20T21:38:57.7654526Z layers : instances of the `_LSTMLayer` 2024-08-20T21:38:57.7655104Z 2024-08-20T21:38:57.7655242Z .. note:: 2024-08-20T21:38:57.7655717Z To access the weights and biases, you need to access them per layer. 2024-08-20T21:38:57.7656447Z See examples in :class:`~torch.ao.nn.quantizable.LSTM` 2024-08-20T21:38:57.7656854Z 2024-08-20T21:38:57.7656981Z Examples:: 2024-08-20T21:38:57.7657275Z >>> # xdoctest: +SKIP 2024-08-20T21:38:57.7657666Z >>> custom_module_config = { 2024-08-20T21:38:57.7658222Z ... 'float_to_observed_custom_module_class': { 2024-08-20T21:38:57.7658745Z ... nn.LSTM: nn.quantizable.LSTM, 2024-08-20T21:38:57.7659191Z ... }, 2024-08-20T21:38:57.7659667Z ... 'observed_to_quantized_custom_module_class': { 2024-08-20T21:38:57.7660241Z ... nn.quantizable.LSTM: nn.quantized.LSTM, 2024-08-20T21:38:57.7660723Z ... } 2024-08-20T21:38:57.7661018Z ... } 2024-08-20T21:38:57.7661480Z >>> tq.prepare(model, prepare_custom_module_class=custom_module_config) 2024-08-20T21:38:57.7662268Z >>> tq.convert(model, convert_custom_module_class=custom_module_config) 2024-08-20T21:38:57.7662849Z 2024-08-20T21:38:57.7663406Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:57.7663932Z 2024-08-20T21:38:57.8601181Z 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. 2024-08-20T21:38:57.8602788Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:57.8603552Z Squashes the sparse masks into the appropriate tensors. 2024-08-20T21:38:57.8604263Z 2024-08-20T21:38:57.8604584Z If either the `params_to_keep` or `params_to_keep_per_layer` is set, 2024-08-20T21:38:57.8605317Z the module will have a `sparse_params` dict attached to it. 2024-08-20T21:38:57.8605767Z 2024-08-20T21:38:57.8605875Z Args: 2024-08-20T21:38:57.8606333Z params_to_keep: List of keys to save in the module or a dict 2024-08-20T21:38:57.8606998Z representing the modules and keys that will have 2024-08-20T21:38:57.8607589Z sparsity parameters saved 2024-08-20T21:38:57.8608239Z params_to_keep_per_layer: Dict to specify the params that should be 2024-08-20T21:38:57.8608932Z saved for specific layers. The keys in the dict 2024-08-20T21:38:57.8609579Z should be the module fqn, while the values should 2024-08-20T21:38:57.8610229Z be a list of strings with the names of the variables 2024-08-20T21:38:57.8610833Z to save in the `sparse_params` 2024-08-20T21:38:57.8611182Z 2024-08-20T21:38:57.8611293Z Examples: 2024-08-20T21:38:57.8611673Z >>> # xdoctest: +SKIP("locals are undefined") 2024-08-20T21:38:57.8612261Z >>> # Don't save any sparse params 2024-08-20T21:38:57.8612734Z >>> sparsifier.squash_mask() 2024-08-20T21:38:57.8613316Z >>> hasattr(model.submodule1, 'sparse_params') 2024-08-20T21:38:57.8613791Z False 2024-08-20T21:38:57.8613974Z 2024-08-20T21:38:57.8614136Z >>> # Keep sparse params per layer 2024-08-20T21:38:57.8614614Z >>> sparsifier.squash_mask( 2024-08-20T21:38:57.8615079Z ... params_to_keep_per_layer={ 2024-08-20T21:38:57.8615647Z ... 'submodule1.linear1': ('foo', 'bar'), 2024-08-20T21:38:57.8616255Z ... 'submodule2.linear42': ('baz',) 2024-08-20T21:38:57.8616715Z ... }) 2024-08-20T21:38:57.8617138Z >>> print(model.submodule1.linear1.sparse_params) 2024-08-20T21:38:57.8617709Z {'foo': 42, 'bar': 24} 2024-08-20T21:38:57.8618201Z >>> print(model.submodule2.linear42.sparse_params) 2024-08-20T21:38:57.8618734Z {'baz': 0.1} 2024-08-20T21:38:57.8619104Z 2024-08-20T21:38:57.8619280Z >>> # Keep sparse params for all layers 2024-08-20T21:38:57.8619949Z >>> sparsifier.squash_mask(params_to_keep=('foo', 'bar')) 2024-08-20T21:38:57.8620593Z >>> print(model.submodule1.linear1.sparse_params) 2024-08-20T21:38:57.8621146Z {'foo': 42, 'bar': 24} 2024-08-20T21:38:57.8621638Z >>> print(model.submodule2.linear42.sparse_params) 2024-08-20T21:38:57.8622203Z {'foo': 42, 'bar': 24} 2024-08-20T21:38:57.8622463Z 2024-08-20T21:38:57.8622752Z >>> # Keep some sparse params for all layers, and specific ones for 2024-08-20T21:38:57.8623349Z >>> # some other layers 2024-08-20T21:38:57.8623777Z >>> sparsifier.squash_mask( 2024-08-20T21:38:57.8624289Z ... params_to_keep=('foo', 'bar'), 2024-08-20T21:38:57.8624781Z ... params_to_keep_per_layer={ 2024-08-20T21:38:57.8625346Z ... 'submodule2.linear42': ('baz',) 2024-08-20T21:38:57.8625799Z ... }) 2024-08-20T21:38:57.8626227Z >>> print(model.submodule1.linear1.sparse_params) 2024-08-20T21:38:57.8626791Z {'foo': 42, 'bar': 24} 2024-08-20T21:38:57.8627272Z >>> print(model.submodule2.linear42.sparse_params) 2024-08-20T21:38:57.8627885Z {'foo': 42, 'bar': 24, 'baz': 0.1} 2024-08-20T21:38:57.8628311Z 2024-08-20T21:38:57.8628877Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:57.8629396Z 2024-08-20T21:38:57.9448738Z 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. 2024-08-20T21:38:57.9451641Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:57.9452235Z 2024-08-20T21:38:57.9452648Z Config object that specifies the supported data types passed as arguments to 2024-08-20T21:38:57.9453526Z quantize ops in the reference model spec, for input and output activations, 2024-08-20T21:38:57.9454169Z weights, and biases. 2024-08-20T21:38:57.9454394Z 2024-08-20T21:38:57.9454604Z For example, consider the following reference model: 2024-08-20T21:38:57.9454981Z 2024-08-20T21:38:57.9455283Z quant1 - [dequant1 - fp32_linear - quant2] - dequant2 2024-08-20T21:38:57.9455658Z 2024-08-20T21:38:57.9455983Z The pattern in the square brackets refers to the reference pattern of 2024-08-20T21:38:57.9456775Z statically quantized linear. Setting the input dtype as `torch.quint8` 2024-08-20T21:38:57.9457591Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument 2024-08-20T21:38:57.9458425Z to the first quantize op (quant1). Similarly, setting the output dtype as 2024-08-20T21:38:57.9459220Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to 2024-08-20T21:38:57.9459843Z the second quantize op (quant2). 2024-08-20T21:38:57.9460114Z 2024-08-20T21:38:57.9460443Z Note that the dtype here does not refer to the interface dtypes of the 2024-08-20T21:38:57.9461219Z op. For example, the "input dtype" here is not the dtype of the input 2024-08-20T21:38:57.9461995Z tensor passed to the quantized linear op. Though it can still be the 2024-08-20T21:38:57.9462760Z same as the interface dtype, this is not always the case, e.g. the 2024-08-20T21:38:57.9463514Z interface dtype is fp32 in dynamic quantization but the "input dtype" 2024-08-20T21:38:57.9464303Z specified in the DTypeConfig would still be quint8. The semantics of 2024-08-20T21:38:57.9465090Z dtypes here are the same as the semantics of the dtypes specified in 2024-08-20T21:38:57.9465685Z the observers. 2024-08-20T21:38:57.9465866Z 2024-08-20T21:38:57.9466190Z These dtypes are matched against the ones specified in the user's 2024-08-20T21:38:57.9466968Z QConfig. If there is a match, and the QConfig satisfies the constraints 2024-08-20T21:38:57.9467768Z specified in the DTypeConfig (if any), then we will quantize the given 2024-08-20T21:38:57.9468552Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and 2024-08-20T21:38:57.9469347Z the pattern will not be quantized. 2024-08-20T21:38:57.9469630Z 2024-08-20T21:38:57.9469781Z Example usage:: 2024-08-20T21:38:57.9469964Z 2024-08-20T21:38:57.9470098Z >>> # xdoctest: +SKIP(failing) 2024-08-20T21:38:57.9470522Z >>> dtype_config1 = DTypeConfig( 2024-08-20T21:38:57.9470963Z ... input_dtype=torch.quint8, 2024-08-20T21:38:57.9471379Z ... output_dtype=torch.quint8, 2024-08-20T21:38:57.9471815Z ... weight_dtype=torch.qint8, 2024-08-20T21:38:57.9472243Z ... bias_dtype=torch.float) 2024-08-20T21:38:57.9472522Z 2024-08-20T21:38:57.9472665Z >>> dtype_config2 = DTypeConfig( 2024-08-20T21:38:57.9473132Z ... input_dtype=DTypeWithConstraints( 2024-08-20T21:38:57.9473606Z ... dtype=torch.quint8, 2024-08-20T21:38:57.9474011Z ... quant_min_lower_bound=0, 2024-08-20T21:38:57.9474454Z ... quant_max_upper_bound=255, 2024-08-20T21:38:57.9474871Z ... ), 2024-08-20T21:38:57.9475210Z ... output_dtype=DTypeWithConstraints( 2024-08-20T21:38:57.9475682Z ... dtype=torch.quint8, 2024-08-20T21:38:57.9476098Z ... quant_min_lower_bound=0, 2024-08-20T21:38:57.9476539Z ... quant_max_upper_bound=255, 2024-08-20T21:38:57.9476943Z ... ), 2024-08-20T21:38:57.9477299Z ... weight_dtype=DTypeWithConstraints( 2024-08-20T21:38:57.9477768Z ... dtype=torch.qint8, 2024-08-20T21:38:57.9478247Z ... quant_min_lower_bound=-128, 2024-08-20T21:38:57.9478847Z ... quant_max_upper_bound=127, 2024-08-20T21:38:57.9479258Z ... ), 2024-08-20T21:38:57.9479643Z ... bias_dtype=torch.float) 2024-08-20T21:38:57.9479939Z 2024-08-20T21:38:57.9480072Z >>> dtype_config1.input_dtype 2024-08-20T21:38:57.9480459Z torch.quint8 2024-08-20T21:38:57.9480645Z 2024-08-20T21:38:57.9480776Z >>> dtype_config2.input_dtype 2024-08-20T21:38:57.9481162Z torch.quint8 2024-08-20T21:38:57.9481354Z 2024-08-20T21:38:57.9481547Z >>> dtype_config2.input_dtype_with_constraints 2024-08-20T21:38:57.9482622Z DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None) 2024-08-20T21:38:57.9483524Z 2024-08-20T21:38:57.9483954Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:57.9484462Z 2024-08-20T21:38:58.0635133Z 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. 2024-08-20T21:38:58.0639428Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:58.0640459Z 2024-08-20T21:38:58.0641271Z Takes in optional filter values and generates two tables with desired information. 2024-08-20T21:38:58.0642226Z 2024-08-20T21:38:58.0642885Z The generated tables are presented in both a list-of-lists format 2024-08-20T21:38:58.0643824Z 2024-08-20T21:38:58.0644386Z The reason for the two tables are that they handle different things: 2024-08-20T21:38:58.0645718Z 1.) the first table handles all tensor level information 2024-08-20T21:38:58.0647042Z 2.) the second table handles and displays all channel based information 2024-08-20T21:38:58.0647881Z 2024-08-20T21:38:58.0648700Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2024-08-20T21:38:58.0650928Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2024-08-20T21:38:58.0653044Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2024-08-20T21:38:58.0654270Z 2024-08-20T21:38:58.0654494Z Tensor table columns: 2024-08-20T21:38:58.0655382Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2024-08-20T21:38:58.0656694Z ---- --------- --------- --------- --------- --------- 2024-08-20T21:38:58.0657824Z 2024-08-20T21:38:58.0658160Z Per-Channel table columns: 2024-08-20T21:38:58.0659181Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2024-08-20T21:38:58.0660567Z ---- --------- ------- --------- --------- --------- --------- 2024-08-20T21:38:58.0661298Z 2024-08-20T21:38:58.0661492Z Args: 2024-08-20T21:38:58.0662375Z feature_filter (str, optional): Filters the features presented to only those that 2024-08-20T21:38:58.0663559Z contain this filter substring 2024-08-20T21:38:58.0664474Z Default = "", results in all the features being printed 2024-08-20T21:38:58.0665872Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-08-20T21:38:58.0667560Z Default = "", results in all the modules in the reports to be visible in the table 2024-08-20T21:38:58.0668567Z 2024-08-20T21:38:58.0668843Z Returns a dictionary with two keys: 2024-08-20T21:38:58.0669828Z (Dict[str, Tuple[List, List]]) A dict containing two keys: 2024-08-20T21:38:58.0670772Z "tensor_level_info", "channel_level_info" 2024-08-20T21:38:58.0671651Z Each key maps to a tuple with: 2024-08-20T21:38:58.0672473Z A list of the headers of each table 2024-08-20T21:38:58.0673417Z A list of lists containing the table information row by row 2024-08-20T21:38:58.0674685Z The 0th index row will contain the headers of the columns 2024-08-20T21:38:58.0675770Z The rest of the rows will contain data 2024-08-20T21:38:58.0676384Z 2024-08-20T21:38:58.0676577Z Example Use: 2024-08-20T21:38:58.0677475Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:38:58.0678587Z >>> mod_report_visualizer.generate_filtered_tables( 2024-08-20T21:38:58.0679575Z ... feature_filter = "per_channel_min", 2024-08-20T21:38:58.0680468Z ... module_fqn_filter = "block1" 2024-08-20T21:38:58.0681773Z ... ) # generates table with per_channel_min info for all modules in block 1 of the model 2024-08-20T21:38:58.0682851Z 2024-08-20T21:38:58.0683762Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:58.0684759Z 2024-08-20T21:38:58.0687417Z 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. 2024-08-20T21:38:58.0690617Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:58.0691589Z 2024-08-20T21:38:58.0692344Z Takes in optional filter values and prints out formatted tables of the information. 2024-08-20T21:38:58.0693414Z 2024-08-20T21:38:58.0694336Z The reason for the two tables printed out instead of one large one are that they handle different things: 2024-08-20T21:38:58.0696417Z 1.) the first table handles all tensor level information 2024-08-20T21:38:58.0697705Z 2.) the second table handles and displays all channel based information 2024-08-20T21:38:58.0698629Z 2024-08-20T21:38:58.0699557Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2024-08-20T21:38:58.0701753Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2024-08-20T21:38:58.0703875Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2024-08-20T21:38:58.0705163Z 2024-08-20T21:38:58.0705404Z Tensor table columns: 2024-08-20T21:38:58.0706282Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2024-08-20T21:38:58.0707640Z ---- --------- --------- --------- --------- --------- 2024-08-20T21:38:58.0708354Z 2024-08-20T21:38:58.0708682Z Per-Channel table columns: 2024-08-20T21:38:58.0709143Z 2024-08-20T21:38:58.0709753Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2024-08-20T21:38:58.0711511Z ---- --------- ------- --------- --------- --------- --------- 2024-08-20T21:38:58.0712268Z 2024-08-20T21:38:58.0712449Z Args: 2024-08-20T21:38:58.0713410Z feature_filter (str, optional): Filters the features presented to only those that 2024-08-20T21:38:58.0714642Z contain this filter substring 2024-08-20T21:38:58.0715574Z Default = "", results in all the features being printed 2024-08-20T21:38:58.0716904Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-08-20T21:38:58.0718728Z Default = "", results in all the modules in the reports to be visible in the table 2024-08-20T21:38:58.0719687Z 2024-08-20T21:38:58.0719890Z Example Use: 2024-08-20T21:38:58.0720506Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:38:58.0721551Z >>> mod_report_visualizer.generate_table_visualization( 2024-08-20T21:38:58.0722591Z ... feature_filter = "per_channel_min", 2024-08-20T21:38:58.0723482Z ... module_fqn_filter = "block1" 2024-08-20T21:38:58.0724236Z ... ) 2024-08-20T21:38:58.0725007Z >>> # prints out neatly formatted table with per_channel_min info 2024-08-20T21:38:58.0726161Z >>> # for all modules in block 1 of the model 2024-08-20T21:38:58.0726814Z 2024-08-20T21:38:58.0727698Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:58.0728613Z 2024-08-20T21:38:58.0731265Z 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. 2024-08-20T21:38:58.0735000Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:58.0736023Z 2024-08-20T21:38:58.0736659Z Takes in a feature and optional module_filter and plots of the desired data. 2024-08-20T21:38:58.0737640Z 2024-08-20T21:38:58.0738379Z For per channel features, it averages the value across the channels and plots a point 2024-08-20T21:38:58.0740151Z per module. The reason for this is that for models with hundreds of channels, it can 2024-08-20T21:38:58.0741961Z be hard to differentiate one channel line from another, and so the point of generating 2024-08-20T21:38:58.0743740Z a single average point per module is to give a sense of general trends that encourage 2024-08-20T21:38:58.0745009Z further deep dives. 2024-08-20T21:38:58.0745359Z 2024-08-20T21:38:58.0745520Z Note: 2024-08-20T21:38:58.0746440Z Only features in the report that have tensor value data are plottable by this class 2024-08-20T21:38:58.0747845Z When the tensor information is plotted, it will plot: 2024-08-20T21:38:58.0748833Z idx as the x val, feature value as the y_val 2024-08-20T21:38:58.0749899Z When the channel information is plotted, it will plot: 2024-08-20T21:38:58.0751389Z the first idx of each module as the x val, feature value as the y_val [for each channel] 2024-08-20T21:38:58.0753105Z The reason for this is that we want to be able to compare values across the 2024-08-20T21:38:58.0754713Z channels for same layer, and it will be hard if values are staggered by idx 2024-08-20T21:38:58.0756149Z This means each module is represented by only 1 x value 2024-08-20T21:38:58.0757098Z Args: 2024-08-20T21:38:58.0757783Z feature_filter (str): Filters the features presented to only those that 2024-08-20T21:38:58.0759098Z contain this filter substring 2024-08-20T21:38:58.0760312Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-08-20T21:38:58.0762004Z Default = "", results in all the modules in the reports to be visible in the table 2024-08-20T21:38:58.0763017Z 2024-08-20T21:38:58.0763217Z Example Use: 2024-08-20T21:38:58.0763825Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:38:58.0764861Z >>> mod_report_visualizer.generate_plot_visualization( 2024-08-20T21:38:58.0765870Z ... feature_filter = "per_channel_min", 2024-08-20T21:38:58.0766995Z ... module_fqn_filter = "block1" 2024-08-20T21:38:58.0767742Z ... ) 2024-08-20T21:38:58.0768405Z >>> # outputs line plot of per_channel_min information for all 2024-08-20T21:38:58.0769884Z >>> # modules in block1 of model each channel gets it's own line, 2024-08-20T21:38:58.0771342Z >>> # and it's plotted across the in-order modules on the x-axis 2024-08-20T21:38:58.0772162Z 2024-08-20T21:38:58.0772916Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:58.0773873Z 2024-08-20T21:38:58.0776645Z 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. 2024-08-20T21:38:58.0779951Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:58.0780915Z 2024-08-20T21:38:58.0781672Z Takes in a feature and optional module_filter and plots the histogram of desired data. 2024-08-20T21:38:58.0782778Z 2024-08-20T21:38:58.0782962Z Note: 2024-08-20T21:38:58.0783972Z Only features in the report that have tensor value data can be viewed as a histogram 2024-08-20T21:38:58.0785756Z If you want to plot a histogram from all the channel values of a specific feature for 2024-08-20T21:38:58.0787533Z a specific model, make sure to specify both the model and the feature properly 2024-08-20T21:38:58.0789244Z in the filters and you should be able to see a distribution of the channel data 2024-08-20T21:38:58.0790188Z 2024-08-20T21:38:58.0790390Z Args: 2024-08-20T21:38:58.0791558Z feature_filter (str, optional): Filters the features presented to only those that 2024-08-20T21:38:58.0792862Z contain this filter substring 2024-08-20T21:38:58.0793808Z Default = "", results in all the features being printed 2024-08-20T21:38:58.0795213Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-08-20T21:38:58.0797181Z Default = "", results in all the modules in the reports to be visible in the table 2024-08-20T21:38:58.0798944Z num_bins (int, optional): The number of bins to create the histogram with 2024-08-20T21:38:58.0800369Z Default = 10, the values will be split into 10 equal sized bins 2024-08-20T21:38:58.0800966Z 2024-08-20T21:38:58.0801111Z Example Use: 2024-08-20T21:38:58.0801611Z >>> # xdoctest: +SKIP 2024-08-20T21:38:58.0802734Z >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization( 2024-08-20T21:38:58.0804099Z ... feature_filter = "per_channel_min", 2024-08-20T21:38:58.0804952Z ... module_fqn_filter = "block1" 2024-08-20T21:38:58.0805657Z ... ) 2024-08-20T21:38:58.0806606Z # outputs histogram of per_channel_min information for all modules in block1 of model 2024-08-20T21:38:58.0808285Z information is gathered across all channels for all modules in block 1 for the 2024-08-20T21:38:58.0809878Z per_channel_min and is displayed in a histogram of equally sized bins 2024-08-20T21:38:58.0810755Z 2024-08-20T21:38:58.0811611Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:58.0812531Z 2024-08-20T21:38:58.3330342Z 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. 2024-08-20T21:38:58.3333020Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-08-20T21:38:58.3334004Z 2024-08-20T21:38:58.3334764Z Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh. 2024-08-20T21:38:58.3336454Z The submesh created consists of the dimensions and the communicators indicated by 2024-08-20T21:38:58.3337686Z ``mesh_dim_names`` 2024-08-20T21:38:58.3338023Z 2024-08-20T21:38:58.3338212Z Args: 2024-08-20T21:38:58.3339091Z mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the 2024-08-20T21:38:58.3341008Z mesh dimension of the DeviceMesh to create the submesh for. 2024-08-20T21:38:58.3342012Z Returns: 2024-08-20T21:38:58.3342481Z A :class:`DeviceMesh` object 2024-08-20T21:38:58.3342975Z 2024-08-20T21:38:58.3343727Z The following program runs on each process/rank in an SPMD manner in a world size of 8. 2024-08-20T21:38:58.3345086Z In the first example: 2024-08-20T21:38:58.3346179Z Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]). 2024-08-20T21:38:58.3347917Z Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of DeviceMesh:([4, 5, 6, 7]). 2024-08-20T21:38:58.3349525Z Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of DeviceMesh:([0, 4]). 2024-08-20T21:38:58.3351070Z Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of DeviceMesh:([1, 5]). 2024-08-20T21:38:58.3352593Z Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of DeviceMesh:([2, 6]). 2024-08-20T21:38:58.3354174Z Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of DeviceMesh:([3, 7]). 2024-08-20T21:38:58.3355166Z 2024-08-20T21:38:58.3355391Z In the second example: 2024-08-20T21:38:58.3356594Z Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]). 2024-08-20T21:38:58.3358597Z Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]). 2024-08-20T21:38:58.3360439Z Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]). 2024-08-20T21:38:58.3362570Z Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]). 2024-08-20T21:38:58.3363691Z 2024-08-20T21:38:58.3363908Z Example:: 2024-08-20T21:38:58.3364436Z >>> # xdoctest: +SKIP("no rank") 2024-08-20T21:38:58.3365328Z >>> from torch.distributed.device_mesh import DeviceMesh 2024-08-20T21:38:58.3366237Z >>> 2024-08-20T21:38:58.3367005Z >>> # Initialize a 2D device mesh as (2, 4) to represent the topology 2024-08-20T21:38:58.3368516Z >>> # of cross-host(dim 0), and within-host (dim 1). 2024-08-20T21:38:58.3369828Z >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2024-08-20T21:38:58.3370948Z >>> tp_mesh = mesh_2d["tp"] 2024-08-20T21:38:58.3371661Z >>> dp_mesh = mesh_2d["dp"] 2024-08-20T21:38:58.3372316Z >>> 2024-08-20T21:38:58.3372814Z >>> # Initialize a 3D mesh. 2024-08-20T21:38:58.3374034Z >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp")) 2024-08-20T21:38:58.3376006Z >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh. 2024-08-20T21:38:58.3377430Z >>> dp_cp_mesh = mesh_3d["dp", "cp"] 2024-08-20T21:38:58.3378227Z >>> cp_dp_mesh = mesh_3d["cp", "dp"] 2024-08-20T21:38:58.3378762Z 2024-08-20T21:38:58.3380872Z 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)) 2024-08-20T21:38:58.3382802Z 2024-08-20T21:38:58.3398624Z mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2024-08-20T21:38:58.3400119Z ^ 2024-08-20T21:38:58.3735052Z 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. 2024-08-20T21:38:58.3737894Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:58.3738937Z 2024-08-20T21:38:58.3739586Z Gathers picklable objects from the whole group in a single process. 2024-08-20T21:38:58.3740411Z 2024-08-20T21:38:58.3741030Z Similar to :func:`gather`, but Python objects can be passed in. Note that the 2024-08-20T21:38:58.3742324Z object must be picklable in order to be gathered. 2024-08-20T21:38:58.3743383Z 2024-08-20T21:38:58.3743561Z Args: 2024-08-20T21:38:58.3744171Z obj (Any): Input object. Must be picklable. 2024-08-20T21:38:58.3745350Z object_gather_list (list[Any]): Output list. On the ``dst`` rank, it 2024-08-20T21:38:58.3746729Z should be correctly sized as the size of the group for this 2024-08-20T21:38:58.3748378Z collective and will contain the output. Must be ``None`` on non-dst 2024-08-20T21:38:58.3749554Z ranks. (default is ``None``) 2024-08-20T21:38:58.3751047Z dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). (default is 0) 2024-08-20T21:38:58.3753026Z group: (ProcessGroup, optional): The process group to work on. If None, 2024-08-20T21:38:58.3754478Z the default process group will be used. Default is ``None``. 2024-08-20T21:38:58.3755322Z 2024-08-20T21:38:58.3755516Z Returns: 2024-08-20T21:38:58.3756288Z None. On the ``dst`` rank, ``object_gather_list`` will contain the 2024-08-20T21:38:58.3757354Z output of the collective. 2024-08-20T21:38:58.3757810Z 2024-08-20T21:38:58.3758556Z .. note:: Note that this API differs slightly from the gather collective 2024-08-20T21:38:58.3760062Z since it does not provide an async_op handle and thus will be a blocking 2024-08-20T21:38:58.3761223Z call. 2024-08-20T21:38:58.3761510Z 2024-08-20T21:38:58.3762324Z .. note:: For NCCL-based processed groups, internal tensor representations 2024-08-20T21:38:58.3763906Z of objects must be moved to the GPU device before communication takes 2024-08-20T21:38:58.3765219Z place. In this case, the device used is given by 2024-08-20T21:38:58.3766976Z ``torch.cuda.current_device()`` and it is the user's responsiblity to 2024-08-20T21:38:58.3768430Z ensure that this is set so that each rank has an individual GPU, via 2024-08-20T21:38:58.3769614Z ``torch.cuda.set_device()``. 2024-08-20T21:38:58.3770129Z 2024-08-20T21:38:58.3770348Z .. warning:: 2024-08-20T21:38:58.3771175Z :func:`gather_object` uses ``pickle`` module implicitly, which is 2024-08-20T21:38:58.3772628Z known to be insecure. It is possible to construct malicious pickle data 2024-08-20T21:38:58.3774097Z which will execute arbitrary code during unpickling. Only call this 2024-08-20T21:38:58.3775261Z function with data you trust. 2024-08-20T21:38:58.3775756Z 2024-08-20T21:38:58.3775963Z .. warning:: 2024-08-20T21:38:58.3776856Z Calling :func:`gather_object` with GPU tensors is not well supported 2024-08-20T21:38:58.3778557Z and inefficient as it incurs GPU -> CPU transfer since tensors would be 2024-08-20T21:38:58.3779942Z pickled. Please consider using :func:`gather` instead. 2024-08-20T21:38:58.3780728Z 2024-08-20T21:38:58.3780930Z Example:: 2024-08-20T21:38:58.3781570Z >>> # xdoctest: +SKIP("need process group init") 2024-08-20T21:38:58.3782671Z >>> # Note: Process group initialization omitted on each rank. 2024-08-20T21:38:58.3783720Z >>> import torch.distributed as dist 2024-08-20T21:38:58.3784581Z >>> # Assumes world_size of 3. 2024-08-20T21:38:58.3785556Z >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object 2024-08-20T21:38:58.3786664Z >>> output = [None for _ in gather_objects] 2024-08-20T21:38:58.3787532Z >>> dist.gather_object( 2024-08-20T21:38:58.3788210Z ... gather_objects[dist.get_rank()], 2024-08-20T21:38:58.3789115Z ... output if dist.get_rank() == 0 else None, 2024-08-20T21:38:58.3789978Z ... dst=0 2024-08-20T21:38:58.3790489Z ... ) 2024-08-20T21:38:58.3790938Z >>> # On rank 0 2024-08-20T21:38:58.3791501Z >>> output 2024-08-20T21:38:58.3792165Z ['foo', 12, {1: 2}] 2024-08-20T21:38:58.3792582Z 2024-08-20T21:38:58.3793387Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:58.3794396Z 2024-08-20T21:38:58.3946981Z msg = Cannot scrape callname=__doc__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/launch.py line=2. 2024-08-20T21:38:58.3951107Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:58.3951674Z 2024-08-20T21:38:58.3951826Z Module ``torch.distributed.launch``. 2024-08-20T21:38:58.3952148Z 2024-08-20T21:38:58.3952564Z ``torch.distributed.launch`` is a module that spawns up multiple distributed 2024-08-20T21:38:58.3953415Z training processes on each of the training nodes. 2024-08-20T21:38:58.3953797Z 2024-08-20T21:38:58.3953935Z .. warning:: 2024-08-20T21:38:58.3954112Z 2024-08-20T21:38:58.3954605Z This module is going to be deprecated in favor of :ref:`torchrun `. 2024-08-20T21:38:58.3955160Z 2024-08-20T21:38:58.3955602Z The utility can be used for single-node distributed training, in which one or 2024-08-20T21:38:58.3956476Z more processes per node will be spawned. The utility can be used for either 2024-08-20T21:38:58.3957319Z CPU training or GPU training. If the utility is used for GPU training, 2024-08-20T21:38:58.3958145Z each distributed process will be operating on a single GPU. This can achieve 2024-08-20T21:38:58.3959199Z well-improved single-node training performance. It can also be used in 2024-08-20T21:38:58.3960143Z multi-node distributed training, by spawning up multiple processes on each node 2024-08-20T21:38:58.3961073Z for well-improved multi-node distributed training performance as well. 2024-08-20T21:38:58.3961902Z This will especially be beneficial for systems with multiple Infiniband 2024-08-20T21:38:58.3962834Z interfaces that have direct-GPU support, since all of them can be utilized for 2024-08-20T21:38:58.3963519Z aggregated communication bandwidth. 2024-08-20T21:38:58.3963808Z 2024-08-20T21:38:58.3964785Z In both cases of single-node distributed training or multi-node distributed 2024-08-20T21:38:58.3965646Z training, this utility will launch the given number of processes per node 2024-08-20T21:38:58.3966575Z (``--nproc-per-node``). If used for GPU training, this number needs to be less 2024-08-20T21:38:58.3967404Z or equal to the number of GPUs on the current system (``nproc_per_node``), 2024-08-20T21:38:58.3968210Z and each process will be operating on a single GPU from *GPU 0 to 2024-08-20T21:38:58.3968842Z GPU (nproc_per_node - 1)*. 2024-08-20T21:38:58.3969083Z 2024-08-20T21:38:58.3969207Z **How to use this module:** 2024-08-20T21:38:58.3969467Z 2024-08-20T21:38:58.3969727Z 1. Single-Node multi-process distributed training 2024-08-20T21:38:58.3970107Z 2024-08-20T21:38:58.3970214Z :: 2024-08-20T21:38:58.3970350Z 2024-08-20T21:38:58.3970764Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-08-20T21:38:58.3971590Z YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other 2024-08-20T21:38:58.3972205Z arguments of your training script) 2024-08-20T21:38:58.3972540Z 2024-08-20T21:38:58.3972919Z 2. Multi-Node multi-process distributed training: (e.g. two nodes) 2024-08-20T21:38:58.3973381Z 2024-08-20T21:38:58.3973385Z 2024-08-20T21:38:58.3973585Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)* 2024-08-20T21:38:58.3973968Z 2024-08-20T21:38:58.3974065Z :: 2024-08-20T21:38:58.3974214Z 2024-08-20T21:38:58.3974610Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-08-20T21:38:58.3975400Z --nnodes=2 --node-rank=0 --master-addr="192.168.1.1" 2024-08-20T21:38:58.3976147Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2024-08-20T21:38:58.3976823Z and all other arguments of your training script) 2024-08-20T21:38:58.3977205Z 2024-08-20T21:38:58.3977319Z Node 2: 2024-08-20T21:38:58.3977468Z 2024-08-20T21:38:58.3977566Z :: 2024-08-20T21:38:58.3977716Z 2024-08-20T21:38:58.3978116Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-08-20T21:38:58.3978901Z --nnodes=2 --node-rank=1 --master-addr="192.168.1.1" 2024-08-20T21:38:58.3979642Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2024-08-20T21:38:58.3980317Z and all other arguments of your training script) 2024-08-20T21:38:58.3980805Z 2024-08-20T21:38:58.3981040Z 3. To look up what optional arguments this module offers: 2024-08-20T21:38:58.3981439Z 2024-08-20T21:38:58.3981551Z :: 2024-08-20T21:38:58.3981686Z 2024-08-20T21:38:58.3981932Z python -m torch.distributed.launch --help 2024-08-20T21:38:58.3982293Z 2024-08-20T21:38:58.3982298Z 2024-08-20T21:38:58.3982419Z **Important Notices:** 2024-08-20T21:38:58.3982632Z 2024-08-20T21:38:58.3982975Z 1. This utility and multi-process distributed (single-node or 2024-08-20T21:38:58.3983806Z multi-node) GPU training currently only achieves the best performance using 2024-08-20T21:38:58.3984683Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to 2024-08-20T21:38:58.3985333Z use for GPU training. 2024-08-20T21:38:58.3985543Z 2024-08-20T21:38:58.3985942Z 2. In your training program, you must parse the command-line argument: 2024-08-20T21:38:58.3986804Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module. 2024-08-20T21:38:58.3987634Z If your training program uses GPUs, you should ensure that your code only 2024-08-20T21:38:58.3988421Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by: 2024-08-20T21:38:58.3988874Z 2024-08-20T21:38:58.3989007Z Parsing the local_rank argument 2024-08-20T21:38:58.3989292Z 2024-08-20T21:38:58.3989386Z :: 2024-08-20T21:38:58.3989524Z 2024-08-20T21:38:58.3989659Z >>> # xdoctest: +SKIP 2024-08-20T21:38:58.3989997Z >>> import argparse 2024-08-20T21:38:58.3990393Z >>> parser = argparse.ArgumentParser() 2024-08-20T21:38:58.3991069Z >>> parser.add_argument("--local-rank", "--local_rank", type=int) 2024-08-20T21:38:58.3991700Z >>> args = parser.parse_args() 2024-08-20T21:38:58.3992000Z 2024-08-20T21:38:58.3992159Z Set your device to local rank using either 2024-08-20T21:38:58.3992495Z 2024-08-20T21:38:58.3992591Z :: 2024-08-20T21:38:58.3992725Z 2024-08-20T21:38:58.3993028Z >>> torch.cuda.set_device(args.local_rank) # before your code runs 2024-08-20T21:38:58.3993522Z 2024-08-20T21:38:58.3993665Z or 2024-08-20T21:38:58.3993855Z 2024-08-20T21:38:58.3993952Z :: 2024-08-20T21:38:58.3994089Z 2024-08-20T21:38:58.3994285Z >>> with torch.cuda.device(args.local_rank): 2024-08-20T21:38:58.3994743Z >>> # your code to run 2024-08-20T21:38:58.3995102Z >>> ... 2024-08-20T21:38:58.3995272Z 2024-08-20T21:38:58.3995413Z .. versionchanged:: 2.0.0 2024-08-20T21:38:58.3995643Z 2024-08-20T21:38:58.3996365Z The launcher will passes the ``--local-rank=`` argument to your script. 2024-08-20T21:38:58.3997338Z From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the 2024-08-20T21:38:58.3998120Z previously used underscored ``--local_rank``. 2024-08-20T21:38:58.3998550Z 2024-08-20T21:38:58.3998903Z For backward compatibility, it may be necessary for users to handle both 2024-08-20T21:38:58.3999862Z cases in their argument parsing code. This means including both ``"--local-rank"`` 2024-08-20T21:38:58.4000824Z and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is 2024-08-20T21:38:58.4001673Z provided, the launcher will trigger an error: "error: unrecognized arguments: 2024-08-20T21:38:58.4002598Z --local-rank=". For training code that only supports PyTorch 2.0.0+, 2024-08-20T21:38:58.4003369Z including ``"--local-rank"`` should be sufficient. 2024-08-20T21:38:58.4003735Z 2024-08-20T21:38:58.4004087Z 3. In your training program, you are supposed to call the following function 2024-08-20T21:38:58.4004936Z at the beginning to start the distributed backend. It is strongly recommended 2024-08-20T21:38:58.4005797Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work, 2024-08-20T21:38:58.4006567Z but ``env://`` is the one that is officially supported by this module. 2024-08-20T21:38:58.4007024Z 2024-08-20T21:38:58.4007141Z :: 2024-08-20T21:38:58.4007277Z 2024-08-20T21:38:58.4007631Z >>> torch.distributed.init_process_group(backend='YOUR BACKEND', 2024-08-20T21:38:58.4008559Z >>> init_method='env://') 2024-08-20T21:38:58.4008916Z 2024-08-20T21:38:58.4009276Z 4. In your training program, you can either use regular distributed functions 2024-08-20T21:38:58.4010128Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your 2024-08-20T21:38:58.4010944Z training program uses GPUs for training and you would like to use 2024-08-20T21:38:58.4011670Z :func:`torch.nn.parallel.DistributedDataParallel` module, 2024-08-20T21:38:58.4012224Z here is how to configure it. 2024-08-20T21:38:58.4012487Z 2024-08-20T21:38:58.4012583Z :: 2024-08-20T21:38:58.4012739Z 2024-08-20T21:38:58.4013021Z >>> model = torch.nn.parallel.DistributedDataParallel(model, 2024-08-20T21:38:58.4013684Z >>> device_ids=[args.local_rank], 2024-08-20T21:38:58.4014273Z >>> output_device=args.local_rank) 2024-08-20T21:38:58.4014681Z 2024-08-20T21:38:58.4015026Z Please ensure that ``device_ids`` argument is set to be the only GPU device id 2024-08-20T21:38:58.4015887Z that your code will be operating on. This is generally the local rank of the 2024-08-20T21:38:58.4016730Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``, 2024-08-20T21:38:58.4017556Z and ``output_device`` needs to be ``args.local_rank`` in order to use this 2024-08-20T21:38:58.4018146Z utility 2024-08-20T21:38:58.4018294Z 2024-08-20T21:38:58.4018669Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable 2024-08-20T21:38:58.4019603Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with 2024-08-20T21:38:58.4020483Z ``--use-env=True``. You must adjust the subprocess example above to replace 2024-08-20T21:38:58.4021326Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher 2024-08-20T21:38:58.4022211Z will not pass ``--local-rank`` when you specify this flag. 2024-08-20T21:38:58.4022634Z 2024-08-20T21:38:58.4022744Z .. warning:: 2024-08-20T21:38:58.4022915Z 2024-08-20T21:38:58.4023223Z ``local_rank`` is NOT globally unique: it is only unique per process 2024-08-20T21:38:58.4024028Z on a machine. Thus, don't use it to decide if you should, e.g., 2024-08-20T21:38:58.4024643Z write to a networked filesystem. See 2024-08-20T21:38:58.4025267Z https://github.com/pytorch/pytorch/issues/12042 for an example of 2024-08-20T21:38:58.4026024Z how things can go wrong if you don't do this correctly. 2024-08-20T21:38:58.4026439Z 2024-08-20T21:38:58.4026444Z 2024-08-20T21:38:58.4026449Z 2024-08-20T21:38:58.4026454Z 2024-08-20T21:38:58.4026878Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:58.4027399Z 2024-08-20T21:38:58.4654239Z 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. 2024-08-20T21:38:58.4656004Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:58.4656662Z 2024-08-20T21:38:58.4657029Z Creates an :class:`ShardedTensor` from local shards and the global metadata. 2024-08-20T21:38:58.4657847Z Needs to be called on all ranks in an SPMD fashion. 2024-08-20T21:38:58.4658265Z 2024-08-20T21:38:58.4658364Z Args: 2024-08-20T21:38:58.4658980Z local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list 2024-08-20T21:38:58.4659868Z of shards that represent the local shards on this rank. 2024-08-20T21:38:58.4660689Z global_size (int...): a list, tuple, or `torch.Size` of integers defining the 2024-08-20T21:38:58.4661450Z shape of the overall sharded tensor. 2024-08-20T21:38:58.4661852Z 2024-08-20T21:38:58.4661978Z Keyword args: 2024-08-20T21:38:58.4662534Z process_group (ProcessGroup, optional): The process group to work on. If None, 2024-08-20T21:38:58.4663335Z the default process group will be used. 2024-08-20T21:38:58.4664304Z init_rrefs (bool, optional): Whether or not to initialize 2024-08-20T21:38:58.4665082Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2024-08-20T21:38:58.4665928Z Need to initialize the RPC Framework if specified as ``True``. 2024-08-20T21:38:58.4666584Z Default: ``False``. 2024-08-20T21:38:58.4666828Z 2024-08-20T21:38:58.4666931Z Returns: 2024-08-20T21:38:58.4667381Z A :class:`ShardedTensor` object handle on this rank 2024-08-20T21:38:58.4667779Z 2024-08-20T21:38:58.4667784Z 2024-08-20T21:38:58.4667891Z Examples: 2024-08-20T21:38:58.4668512Z Suppose we want construct a sharded tensor on two ranks, global size = (10, 5), 2024-08-20T21:38:58.4669415Z each shard have a (5, 5) local tensor, we can do it like below: 2024-08-20T21:38:58.4669949Z 2024-08-20T21:38:58.4670056Z on rank 0: 2024-08-20T21:38:58.4670481Z >>> # xdoctest: +SKIP("not distributed") 2024-08-20T21:38:58.4670978Z >>> local_shard_metadata = ShardMetadata( 2024-08-20T21:38:58.4671526Z >>> shard_offsets=[0, 0], 2024-08-20T21:38:58.4672012Z >>> shard_lengths=[5, 5], 2024-08-20T21:38:58.4672407Z >>> placement="rank:0/cuda:0" 2024-08-20T21:38:58.4672879Z >>> ) 2024-08-20T21:38:58.4673308Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2024-08-20T21:38:58.4674096Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2024-08-20T21:38:58.4674624Z 2024-08-20T21:38:58.4674731Z on rank 1: 2024-08-20T21:38:58.4675154Z >>> # xdoctest: +SKIP("not distributed") 2024-08-20T21:38:58.4675644Z >>> local_shard_metadata = ShardMetadata( 2024-08-20T21:38:58.4676334Z >>> shard_offsets=[5, 0], 2024-08-20T21:38:58.4676823Z >>> shard_lengths=[5, 5], 2024-08-20T21:38:58.4677211Z >>> placement="rank:1/cuda:1" 2024-08-20T21:38:58.4677690Z >>> ) 2024-08-20T21:38:58.4678153Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2024-08-20T21:38:58.4679076Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2024-08-20T21:38:58.4679525Z 2024-08-20T21:38:58.4680084Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:58.4680690Z 2024-08-20T21:38:58.4772129Z 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. 2024-08-20T21:38:58.4773748Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:58.4774282Z 2024-08-20T21:38:58.4774696Z Initialize a ShardedTensor given only one local tensor, global sharded tensor 2024-08-20T21:38:58.4775365Z size and sharding spec on each rank. 2024-08-20T21:38:58.4775679Z 2024-08-20T21:38:58.4775778Z Args: 2024-08-20T21:38:58.4776254Z local_tensor (Tensor): Single tensor of local shard stored in each rank. 2024-08-20T21:38:58.4777090Z sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): 2024-08-20T21:38:58.4777872Z The specification describing how to shard the Tensor. 2024-08-20T21:38:58.4778600Z global_size (Sequence[int]): Size of the sharded tensor. 2024-08-20T21:38:58.4779582Z process_group (ProcessGroup, optional): The process group to aggregate on. 2024-08-20T21:38:58.4780542Z Default: None 2024-08-20T21:38:58.4781346Z init_rrefs (bool, optional): Whether or not to initialize 2024-08-20T21:38:58.4782145Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2024-08-20T21:38:58.4782880Z Need to initialize the RPC Framework if specified as ``True``. 2024-08-20T21:38:58.4783463Z Default: ``False``. 2024-08-20T21:38:58.4783698Z 2024-08-20T21:38:58.4783813Z Returns: 2024-08-20T21:38:58.4784307Z A :class:`ShardedTensor` sharded based on the given sharding_spec with local 2024-08-20T21:38:58.4784987Z tensor stored in the current rank. 2024-08-20T21:38:58.4785571Z 2024-08-20T21:38:58.4785692Z Examples: 2024-08-20T21:38:58.4785968Z >>> # xdoctest: +SKIP 2024-08-20T21:38:58.4786396Z >>> # All tensors below are of torch.int64 type. 2024-08-20T21:38:58.4786926Z >>> # We have 2 process groups, 2 ranks. 2024-08-20T21:38:58.4787498Z >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank 2024-08-20T21:38:58.4788206Z >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2])) 2024-08-20T21:38:58.4788773Z >>> local_tensor 2024-08-20T21:38:58.4789092Z tensor([[1, 2, 3, 4]]) # Rank 0 2024-08-20T21:38:58.4789500Z tensor([[3, 4, 5, 6]]) # Rank 1 2024-08-20T21:38:58.4789898Z >>> sharding_dim = 0 2024-08-20T21:38:58.4790279Z >>> sharding_spec = ChunkShardingSpec( 2024-08-20T21:38:58.4790732Z dim=sharding_dim, 2024-08-20T21:38:58.4791098Z placements=[ 2024-08-20T21:38:58.4791430Z "rank:0/cuda:0", 2024-08-20T21:38:58.4791810Z "rank:1/cuda:1", 2024-08-20T21:38:58.4792175Z ], 2024-08-20T21:38:58.4792438Z ) 2024-08-20T21:38:58.4792954Z >>> st = ShardedTensor._init_from_local_tensor(local_tensor, sharding_spec, [2, 4]) 2024-08-20T21:38:58.4793589Z >>> st 2024-08-20T21:38:58.4793850Z ShardedTensor( 2024-08-20T21:38:58.4794182Z ShardedTensorMetadata( 2024-08-20T21:38:58.4794572Z shards_metadata=[ 2024-08-20T21:38:58.4795182Z ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0), 2024-08-20T21:38:58.4796524Z ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1), 2024-08-20T21:38:58.4797190Z ], 2024-08-20T21:38:58.4797614Z size=torch.Size([2, 4]) 2024-08-20T21:38:58.4798003Z ) 2024-08-20T21:38:58.4798278Z >>> st.local_tensor() 2024-08-20T21:38:58.4798710Z tensor([1, 2, 3, 4]) # Rank 0 2024-08-20T21:38:58.4799106Z tensor([3, 4, 5, 6]) # Rank 1 2024-08-20T21:38:58.4799368Z 2024-08-20T21:38:58.4799777Z Warning: This API is experimental and subject to change. It lacks of a fully across 2024-08-20T21:38:58.4800683Z rank validations, and we only validate the local shard on the current rank. 2024-08-20T21:38:58.4801557Z We fully rely on the user to ensure local tensor is sharded based on the 2024-08-20T21:38:58.4802188Z sharding spec. 2024-08-20T21:38:58.4802404Z 2024-08-20T21:38:58.4802922Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:58.4803437Z 2024-08-20T21:38:58.4804616Z 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. 2024-08-20T21:38:58.4806132Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:58.4806676Z 2024-08-20T21:38:58.4807035Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support 2024-08-20T21:38:58.4807695Z single local shard. 2024-08-20T21:38:58.4807904Z 2024-08-20T21:38:58.4808310Z If ``resharding_spec`` is same as the original one, this becomes a no-op. 2024-08-20T21:38:58.4809127Z If only ``resharding_spec`` shares the same sharding dim with the original one, 2024-08-20T21:38:58.4809787Z we swap local shards directly. 2024-08-20T21:38:58.4810423Z For more generic cases, we merge different shards across different ranks and split 2024-08-20T21:38:58.4811322Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API. 2024-08-20T21:38:58.4811873Z 2024-08-20T21:38:58.4811970Z Args: 2024-08-20T21:38:58.4812525Z resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The 2024-08-20T21:38:58.4813310Z specification describing how the tensor is sharded. 2024-08-20T21:38:58.4813710Z 2024-08-20T21:38:58.4813810Z Returns: 2024-08-20T21:38:58.4814255Z A :class:`ShardedTensor` object whose local shards are resharded. 2024-08-20T21:38:58.4814704Z 2024-08-20T21:38:58.4814931Z Examples: 2024-08-20T21:38:58.4815223Z >>> # xdoctest: +SKIP 2024-08-20T21:38:58.4815626Z >>> # We have 2 process groups, 2 ranks. 2024-08-20T21:38:58.4816206Z >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank 2024-08-20T21:38:58.4816795Z >>> tensor = torch.stack([tensor, tensor]) 2024-08-20T21:38:58.4817239Z >>> tensor 2024-08-20T21:38:58.4817577Z tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0 2024-08-20T21:38:58.4818058Z tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1 2024-08-20T21:38:58.4818549Z tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2 2024-08-20T21:38:58.4819045Z tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3 2024-08-20T21:38:58.4819497Z >>> sharding_dim = 0 2024-08-20T21:38:58.4819869Z >>> spec = ChunkShardingSpec( 2024-08-20T21:38:58.4820277Z dim=sharding_dim, 2024-08-20T21:38:58.4820631Z placements=[ 2024-08-20T21:38:58.4820975Z "rank:0/cuda:0", 2024-08-20T21:38:58.4821355Z "rank:1/cuda:1", 2024-08-20T21:38:58.4821721Z "rank:2/cuda:2", 2024-08-20T21:38:58.4822094Z "rank:3/cuda:3", 2024-08-20T21:38:58.4822448Z ], 2024-08-20T21:38:58.4822709Z ) 2024-08-20T21:38:58.4822998Z >>> current_offsets = [0] * 2 2024-08-20T21:38:58.4823407Z >>> current_offsets[0] = rank * 2 2024-08-20T21:38:58.4823833Z >>> shard_metadata = ShardMetadata( 2024-08-20T21:38:58.4824346Z shard_offsets=copy.deepcopy(current_offsets), 2024-08-20T21:38:58.4824857Z shard_sizes=tensor.size(), 2024-08-20T21:38:58.4825308Z placement=spec.placements[rank], 2024-08-20T21:38:58.4825740Z ) 2024-08-20T21:38:58.4826087Z >>> local_shards = [ 2024-08-20T21:38:58.4826406Z Shard( 2024-08-20T21:38:58.4826713Z tensor=tensor, 2024-08-20T21:38:58.4827108Z metadata=shard_metadata, 2024-08-20T21:38:58.4827501Z ) 2024-08-20T21:38:58.4827777Z ] 2024-08-20T21:38:58.4828243Z >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size()) 2024-08-20T21:38:58.4828852Z >>> sharding_dim = 1 2024-08-20T21:38:58.4829251Z >>> resharding_spec = ChunkShardingSpec( 2024-08-20T21:38:58.4829708Z dim=sharding_dim, 2024-08-20T21:38:58.4830061Z placements=[ 2024-08-20T21:38:58.4830403Z "rank:0/cuda:0", 2024-08-20T21:38:58.4830765Z "rank:1/cuda:1", 2024-08-20T21:38:58.4831139Z "rank:2/cuda:2", 2024-08-20T21:38:58.4831508Z "rank:3/cuda:3", 2024-08-20T21:38:58.4831850Z ], 2024-08-20T21:38:58.4832128Z ) 2024-08-20T21:38:58.4832429Z >>> st.reshard(resharding_spec) 2024-08-20T21:38:58.4832859Z >>> tensor = st.local_shards()[0].tensor 2024-08-20T21:38:58.4833285Z >>> tensor 2024-08-20T21:38:58.4833666Z tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0 2024-08-20T21:38:58.4834219Z tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1 2024-08-20T21:38:58.4834799Z tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2 2024-08-20T21:38:58.4835374Z tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3 2024-08-20T21:38:58.4835747Z 2024-08-20T21:38:58.4836202Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:58.4836711Z 2024-08-20T21:38:58.4929644Z 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. 2024-08-20T21:38:58.4931803Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:58.4932349Z 2024-08-20T21:38:58.4932690Z Representation of a sharding plan, describes how to shard a module 2024-08-20T21:38:58.4933558Z across hosts. `plan` is used to shard module parameters according to the spec provided, 2024-08-20T21:38:58.4934518Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output 2024-08-20T21:38:58.4935720Z layout of a module with a spec, and when to convert back to data parallel fashion. 2024-08-20T21:38:58.4936280Z 2024-08-20T21:38:58.4936378Z Args: 2024-08-20T21:38:58.4936920Z plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`, 2024-08-20T21:38:58.4937704Z :class:`torch.distributed._shard.sharder.Sharder`]): 2024-08-20T21:38:58.4938619Z a dict describes how to shard a module, there're currently two ways to shard a module: 2024-08-20T21:38:58.4939572Z 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of 2024-08-20T21:38:58.4940275Z a parameter to a `ShardingSpec`. 2024-08-20T21:38:58.4941000Z 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module 2024-08-20T21:38:58.4941693Z to a `Sharder` object. 2024-08-20T21:38:58.4942430Z output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional): 2024-08-20T21:38:58.4943544Z a dict specifies the layout of a module's output which produces a ShardedTensor, 2024-08-20T21:38:58.4944456Z keyed by the name of module to ShardingSpec("" in key means the root module). 2024-08-20T21:38:58.4945114Z Default: `None` 2024-08-20T21:38:58.4945681Z return_local_tensor (List[str], optional): a list of string, each element enables 2024-08-20T21:38:58.4946663Z a module's sharded output to be returned as a Tensor from its local shards to 2024-08-20T21:38:58.4947566Z ensure further processing in a data parallel fashion. ("" in list means the 2024-08-20T21:38:58.4948190Z root module). 2024-08-20T21:38:58.4948639Z Default: None 2024-08-20T21:38:58.4948957Z Example: 2024-08-20T21:38:58.4949528Z Suppose we want to shard a module with two linear layers and then run it with DDP, we also 2024-08-20T21:38:58.4950563Z want to convert the output of the second linear layer back to DDP, we can do it as follows: 2024-08-20T21:38:58.4951161Z 2024-08-20T21:38:58.4951412Z >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d) 2024-08-20T21:38:58.4951944Z >>> class MyModule(nn.Module): 2024-08-20T21:38:58.4952423Z >>> def __init__(self) -> None: 2024-08-20T21:38:58.4952847Z >>> super().__init__() 2024-08-20T21:38:58.4953230Z >>> self.fc1 = nn.Linear() 2024-08-20T21:38:58.4953649Z >>> self.gelu = nn.GELU() 2024-08-20T21:38:58.4954068Z >>> self.fc2 = nn.Linear() 2024-08-20T21:38:58.4954470Z >>> self.relu = nn.Linear() 2024-08-20T21:38:58.4954863Z >>> 2024-08-20T21:38:58.4955151Z >>> def forward(self, input): 2024-08-20T21:38:58.4955676Z >>> return self.relu(self.fc2(self.gelu(self.fc1(input)))) 2024-08-20T21:38:58.4956099Z 2024-08-20T21:38:58.4956105Z 2024-08-20T21:38:58.4956282Z >>> # xdoctest: +SKIP("Undefined spec1, spec2) 2024-08-20T21:38:58.4956784Z >>> sharding_plan = ShardingPlan( 2024-08-20T21:38:58.4957194Z >>> plan={ 2024-08-20T21:38:58.4957499Z >>> "fc1.weight": spec1, 2024-08-20T21:38:58.4957909Z >>> "fc2.weight": spec2 2024-08-20T21:38:58.4958288Z >>> }, 2024-08-20T21:38:58.4958705Z >>> output_plan={ 2024-08-20T21:38:58.4959059Z >>> "fc2": output_spec 2024-08-20T21:38:58.4959435Z >>> }, 2024-08-20T21:38:58.4959732Z >>> return_local_tensor=["fc2"] 2024-08-20T21:38:58.4960135Z >>> ) 2024-08-20T21:38:58.4960286Z 2024-08-20T21:38:58.4960726Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:58.4961235Z 2024-08-20T21:38:58.5763021Z 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. 2024-08-20T21:38:58.5765234Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:58.5765764Z 2024-08-20T21:38:58.5766202Z ``local_map`` is an experimental API that allows users to apply on :class:`DTensors` 2024-08-20T21:38:58.5767334Z a function that is written to be applied on :class:`~torch.Tensors`. 2024-08-20T21:38:58.5767804Z 2024-08-20T21:38:58.5767917Z Args: 2024-08-20T21:38:58.5768356Z func (Callable): the function to be applied on each local shard of 2024-08-20T21:38:58.5768949Z :class:`DTensor`s. 2024-08-20T21:38:58.5769496Z out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]): 2024-08-20T21:38:58.5770424Z the desired placements of the :class:`DTensor`s in ``func``'s flattened output. 2024-08-20T21:38:58.5771317Z If the flattened ``output`` is a single value, the ``out_placements`` should be 2024-08-20T21:38:58.5772196Z of type `PlacementType`. Otherwise if the flattened ``output`` has multiple 2024-08-20T21:38:58.5773049Z values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1 2024-08-20T21:38:58.5773743Z mapping to the flattened ``output``. 2024-08-20T21:38:58.5774366Z Besides, for :class:`Tensor` output, we use `PlacementType` as its 2024-08-20T21:38:58.5775283Z placements (a `Tuple[Placement]` value). For non-:class:`Tensor` output, 2024-08-20T21:38:58.5776240Z the `PlacementType` should be `None`. 2024-08-20T21:38:58.5777369Z Note that the only exception is when no :class:`DTensor` argument is passed 2024-08-20T21:38:58.5778543Z in. In this case, even if `out_placements` is not `None`, the result function 2024-08-20T21:38:58.5779375Z should ignore the desired placements because the application is not on 2024-08-20T21:38:58.5779997Z :class:`DTensors`. 2024-08-20T21:38:58.5780598Z in_placements (Tuple[`PlacementType`, ...], optional): 2024-08-20T21:38:58.5781462Z the required placements of the :class:`DTensor`s in ``func``'s flattened input. 2024-08-20T21:38:58.5782326Z If ``in_placements`` is specified, ``local_map`` would examine whether the 2024-08-20T21:38:58.5783153Z placements of each :class:`DTensor` argument is the same as the required 2024-08-20T21:38:58.5783911Z placements or not. If the placements are not the same and 2024-08-20T21:38:58.5784688Z ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if 2024-08-20T21:38:58.5785567Z ``redistribute_inputs`` is `True`, the argument will be first redistributed to 2024-08-20T21:38:58.5786441Z the required sharding placements before passing its local tensor to ``func``. 2024-08-20T21:38:58.5787303Z The only exception is when required placements are not ``None`` and the 2024-08-20T21:38:58.5788157Z argument is a :class:`torch.Tensor`. In this case, the placements examination 2024-08-20T21:38:58.5789008Z will be skipped and the argument will be directly passed to ``func``. 2024-08-20T21:38:58.5789820Z If ``in_placements`` is ``None``, no placements examination will be performed. 2024-08-20T21:38:58.5790454Z Default: None 2024-08-20T21:38:58.5790845Z device_mesh (:class:`DeviceMesh`, optional): 2024-08-20T21:38:58.5791494Z the device mesh that all the :class:`DTensor`s are placed on. If not 2024-08-20T21:38:58.5792397Z specified, this will be inferred from the input :class:`DTensor`s' device 2024-08-20T21:38:58.5793239Z mesh. `local_map` requires every :class:`DTensor`s to be placed on the same 2024-08-20T21:38:58.5793872Z device mesh. Default: None. 2024-08-20T21:38:58.5794316Z redistribute_inputs (bool, optional): 2024-08-20T21:38:58.5795003Z the bool value indicating whether to reshard the input :class:`DTensor`s when 2024-08-20T21:38:58.5796178Z their placements are different from the required input placements. If this 2024-08-20T21:38:58.5797049Z value is ``False`` and some :class:`DTensor` input has a different placement, 2024-08-20T21:38:58.5797757Z an exception will be raised. Default: False. 2024-08-20T21:38:58.5798116Z 2024-08-20T21:38:58.5798233Z Returns: 2024-08-20T21:38:58.5798862Z A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor` 2024-08-20T21:38:58.5799896Z and returns a :class:`DTensor` constructed from the return value of ``func``. 2024-08-20T21:38:58.5800409Z 2024-08-20T21:38:58.5800523Z Raises: 2024-08-20T21:38:58.5801042Z AssertionError: If the input :class:`DTensor`s are not placed on the same device 2024-08-20T21:38:58.5801949Z mesh, or if they are placed on a different device mesh than the ``device_mesh`` 2024-08-20T21:38:58.5802611Z argument passed in. 2024-08-20T21:38:58.5802832Z 2024-08-20T21:38:58.5803305Z AssertionError: For any non-:class:`DTensor` output, we require its corresponding 2024-08-20T21:38:58.5804231Z output placement in ``out_placements`` be None. An AssertionError will be raised 2024-08-20T21:38:58.5804902Z if this is not the case. 2024-08-20T21:38:58.5805146Z 2024-08-20T21:38:58.5805521Z ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs 2024-08-20T21:38:58.5806257Z a redistribution according to ``in_placements``. 2024-08-20T21:38:58.5806641Z 2024-08-20T21:38:58.5806746Z Example: 2024-08-20T21:38:58.5807053Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:38:58.5807540Z >>> def mm_allreduce_forward(device_mesh, W, X): 2024-08-20T21:38:58.5808069Z >>> partial_sum_tensor = torch.mm(W, X) 2024-08-20T21:38:58.5808741Z >>> reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh) 2024-08-20T21:38:58.5809368Z >>> return reduced_tensor 2024-08-20T21:38:58.5809740Z >>> 2024-08-20T21:38:58.5810073Z >>> W = torch.randn(12, 8, requires_grad=False) 2024-08-20T21:38:58.5810730Z >>> X = torch.randn(8, 16, requires_grad=False) 2024-08-20T21:38:58.5811205Z >>> Y = torch.mm(W, X) 2024-08-20T21:38:58.5811813Z >>> row_wise = [Shard(0)] # row-wise sharding placements on 1-d mesh 2024-08-20T21:38:58.5812603Z >>> col_wise = [Shard(1)] # col-wise sharding placements on 1-d mesh 2024-08-20T21:38:58.5813156Z >>> 2024-08-20T21:38:58.5813689Z >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor convertion 2024-08-20T21:38:58.5814404Z >>> local_mm_allreduce_forward = local_map( 2024-08-20T21:38:58.5814873Z >>> mm_allreduce_forward, 2024-08-20T21:38:58.5815301Z >>> out_placements=[Replicate()], 2024-08-20T21:38:58.5815763Z >>> in_placements=[col_wise, row_wise], 2024-08-20T21:38:58.5816231Z >>> device_mesh=device_mesh, 2024-08-20T21:38:58.5816624Z >>> ) 2024-08-20T21:38:58.5816870Z >>> 2024-08-20T21:38:58.5817493Z >>> W_dt = distribute_tensor(W, device_mesh, (col_wise)) # col-wisely sharded W tensor 2024-08-20T21:38:58.5818507Z >>> X_dt = distribute_tensor(X, device_mesh, (row_wise)) # row-wisely sharded X tensor 2024-08-20T21:38:58.5819549Z >>> Y_dt = local_mm_allreduce_forward(device_mesh, W_dt, X_dt) # apply local_mm_allreduce_forward to DTensors 2024-08-20T21:38:58.5820201Z 2024-08-20T21:38:58.5820469Z NOTE: This API is currently experimental and subject to change 2024-08-20T21:38:58.5820920Z 2024-08-20T21:38:58.5821335Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:58.5821843Z 2024-08-20T21:38:58.5823031Z 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. 2024-08-20T21:38:58.5824589Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:58.5825115Z 2024-08-20T21:38:58.5825479Z ``register_sharding`` is an experimental API that allows users to register sharding 2024-08-20T21:38:58.5826413Z strategies for an operator when the tensor inputs and outputs are :class:`DTensor`s. 2024-08-20T21:38:58.5827453Z It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``, 2024-08-20T21:38:58.5828376Z e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2) 2024-08-20T21:38:58.5829387Z when users would like to overwrite default sharding strategies of existing operators. 2024-08-20T21:38:58.5829964Z 2024-08-20T21:38:58.5830061Z Args: 2024-08-20T21:38:58.5830386Z op (Union[OpOverload, List[OpOverload]]): 2024-08-20T21:38:58.5831014Z An op or a list of ops to register the customized sharding function. 2024-08-20T21:38:58.5831505Z 2024-08-20T21:38:58.5831606Z Returns: 2024-08-20T21:38:58.5832151Z A function decorator which can be used to wrap a function that defines the sharding 2024-08-20T21:38:58.5833086Z strategy for the operator specified in ``op``. The defined sharding strategy will be 2024-08-20T21:38:58.5834047Z registered to DTensor and will override the default sharding strategy if DTensor has 2024-08-20T21:38:58.5835032Z already implemented the operator. The customized sharding function takes the same inputs 2024-08-20T21:38:58.5835988Z as the original op (except that if an arg is a :class:`torch.Tensor`, it will be 2024-08-20T21:38:58.5836992Z replaced by a tensor-like object that DTensor uses internally). The function should 2024-08-20T21:38:58.5838032Z return a sequence of 2-tuples, each specifying acceptable output placements and its 2024-08-20T21:38:58.5838916Z corresponding intput placements. 2024-08-20T21:38:58.5839210Z 2024-08-20T21:38:58.5839312Z Example: 2024-08-20T21:38:58.5839623Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:38:58.5840117Z >>> @register_sharding(aten._softmax.default) 2024-08-20T21:38:58.5840666Z >>> def custom_softmax_sharding(x, dim, half_to_float): 2024-08-20T21:38:58.5841255Z >>> softmax_dim = dim if dim >= 0 else dim + x.ndim 2024-08-20T21:38:58.5841860Z >>> acceptable_shardings = [] 2024-08-20T21:38:58.5842244Z >>> 2024-08-20T21:38:58.5842644Z >>> all_replicate = ([Replicate()], [Replicate(), None, None]) 2024-08-20T21:38:58.5843267Z >>> acceptable_shardings.append(all_replicate) 2024-08-20T21:38:58.5843721Z >>> 2024-08-20T21:38:58.5844044Z >>> for sharding_dim in range(x.ndim): 2024-08-20T21:38:58.5844539Z >>> if sharding_dim != softmax_dim: 2024-08-20T21:38:58.5844976Z >>> all_sharded = ( 2024-08-20T21:38:58.5845401Z >>> [Shard(sharding_dim)], 2024-08-20T21:38:58.5845898Z >>> [Shard(sharding_dim), None, None], 2024-08-20T21:38:58.5846342Z >>> ) 2024-08-20T21:38:58.5846747Z >>> acceptable_shardings.append(all_sharded) 2024-08-20T21:38:58.5847213Z >>> 2024-08-20T21:38:58.5847486Z >>> return acceptable_shardings 2024-08-20T21:38:58.5847798Z 2024-08-20T21:38:58.5848235Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:58.5848759Z 2024-08-20T21:38:58.6628679Z 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. 2024-08-20T21:38:58.6631164Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:58.6631716Z 2024-08-20T21:38:58.6631919Z Run post-localSGD algorithm. 2024-08-20T21:38:58.6632172Z 2024-08-20T21:38:58.6632571Z This DDP communication hook is used for running post-localSGD algorithm, 2024-08-20T21:38:58.6633285Z by combining with a model averaging component (e.g., 2024-08-20T21:38:58.6634127Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`) 2024-08-20T21:38:58.6634892Z that runs after the optimizer step. 2024-08-20T21:38:58.6635197Z 2024-08-20T21:38:58.6635291Z Args: 2024-08-20T21:38:58.6635823Z state (PostLocalSGDState): State information to run post-localSGD. 2024-08-20T21:38:58.6636675Z Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD. 2024-08-20T21:38:58.6637970Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2024-08-20T21:38:58.6639554Z Note that since DDP comm hook only supports single process single device mode, 2024-08-20T21:38:58.6640311Z only exactly one tensor is stored in this bucket. 2024-08-20T21:38:58.6640682Z 2024-08-20T21:38:58.6640782Z Returns: 2024-08-20T21:38:58.6641292Z Future handler of the communication, which updates the gradients in place. 2024-08-20T21:38:58.6641808Z 2024-08-20T21:38:58.6641944Z Example:: 2024-08-20T21:38:58.6642212Z >>> # xdoctest: +SKIP 2024-08-20T21:38:58.6642780Z >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup, 2024-08-20T21:38:58.6643463Z start_localSGD_iter=10) 2024-08-20T21:38:58.6644031Z >>> ddp_model.register_comm_hook(state, post_localSGD_hook) 2024-08-20T21:38:58.6644935Z >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``. 2024-08-20T21:38:58.6646106Z >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module. 2024-08-20T21:38:58.6646782Z 2024-08-20T21:38:58.6647223Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:58.6647732Z 2024-08-20T21:38:58.6682296Z 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. 2024-08-20T21:38:58.6685116Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:58.6686107Z 2024-08-20T21:38:58.6686367Z Implement PowerSGD algorithm. 2024-08-20T21:38:58.6686628Z 2024-08-20T21:38:58.6687138Z This DDP communication hook implements PowerSGD gradient compression 2024-08-20T21:38:58.6687950Z algorithm described in the `paper `_. 2024-08-20T21:38:58.6688789Z Once gradient tensors are aggregated across all workers, this hook applies 2024-08-20T21:38:58.6689429Z compression as follows: 2024-08-20T21:38:58.6689654Z 2024-08-20T21:38:58.6690448Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups: 2024-08-20T21:38:58.6691267Z 2024-08-20T21:38:58.6691871Z 1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth. 2024-08-20T21:38:58.6692643Z 2024-08-20T21:38:58.6693234Z 1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases). 2024-08-20T21:38:58.6693991Z 2024-08-20T21:38:58.6694140Z 2. Handles uncompressed tensors: 2024-08-20T21:38:58.6694415Z 2024-08-20T21:38:58.6695153Z 2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression; 2024-08-20T21:38:58.6696265Z 2024-08-20T21:38:58.6696745Z 2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor. 2024-08-20T21:38:58.6697412Z 2024-08-20T21:38:58.6697744Z 3. Handles the tensors that should be compressed by PowerSGD compression: 2024-08-20T21:38:58.6698238Z 2024-08-20T21:38:58.6698711Z 3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M, 2024-08-20T21:38:58.6699728Z such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized; 2024-08-20T21:38:58.6700358Z 2024-08-20T21:38:58.6700559Z 3.2. Computes each P in Ps, which is equal to MQ; 2024-08-20T21:38:58.6700941Z 2024-08-20T21:38:58.6701076Z 3.3. Allreduces Ps as a batch; 2024-08-20T21:38:58.6701353Z 2024-08-20T21:38:58.6701510Z 3.4. Orthogonalizes each P in Ps; 2024-08-20T21:38:58.6701802Z 2024-08-20T21:38:58.6702090Z 3.5. Computes each Q in Qs, which is approximately equal to M^TP; 2024-08-20T21:38:58.6702555Z 2024-08-20T21:38:58.6702685Z 3.6. Allreduces Qs as a batch; 2024-08-20T21:38:58.6702956Z 2024-08-20T21:38:58.6703406Z 3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T. 2024-08-20T21:38:58.6704167Z 2024-08-20T21:38:58.6704757Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations. 2024-08-20T21:38:58.6705906Z This not only gives the user more control over the tradeoff between speedup and accuracy, 2024-08-20T21:38:58.6707118Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers. 2024-08-20T21:38:58.6707898Z 2024-08-20T21:38:58.6708013Z Args: 2024-08-20T21:38:58.6708756Z state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc. 2024-08-20T21:38:58.6710045Z To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter`` 2024-08-20T21:38:58.6710873Z and ``min_compression_rate``. 2024-08-20T21:38:58.6711892Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2024-08-20T21:38:58.6713011Z Note that since DDP comm hook only supports single process single device mode, 2024-08-20T21:38:58.6713771Z only exactly one tensor is stored in this bucket. 2024-08-20T21:38:58.6714141Z 2024-08-20T21:38:58.6714261Z Returns: 2024-08-20T21:38:58.6714760Z Future handler of the communication, which updates the gradients in place. 2024-08-20T21:38:58.6715286Z 2024-08-20T21:38:58.6715411Z Example:: 2024-08-20T21:38:58.6715702Z >>> # xdoctest: +SKIP 2024-08-20T21:38:58.6716296Z >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, 2024-08-20T21:38:58.6717167Z start_powerSGD_iter=10, min_compression_rate=0.5) 2024-08-20T21:38:58.6717800Z >>> ddp_model.register_comm_hook(state, powerSGD_hook) 2024-08-20T21:38:58.6718179Z 2024-08-20T21:38:58.6718768Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:58.6719276Z 2024-08-20T21:38:58.6726971Z 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. 2024-08-20T21:38:58.6729569Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:58.6730113Z 2024-08-20T21:38:58.6730456Z Averages parameters periodically after the warm-up stage. 2024-08-20T21:38:58.6730867Z 2024-08-20T21:38:58.6731344Z This can be used for running `post-local SGD `_, 2024-08-20T21:38:58.6732143Z by running :class:`~torch.nn.DistributedDataParallel` (DDP) 2024-08-20T21:38:58.6732913Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`. 2024-08-20T21:38:58.6733417Z 2024-08-20T21:38:58.6733513Z Args: 2024-08-20T21:38:58.6733891Z period (int): The number of steps per model averaging. 2024-08-20T21:38:58.6734666Z Usually the period should be greater than ``1`` to reduce the communication cost. 2024-08-20T21:38:58.6735426Z Otherwise, only DDP needs to be used. 2024-08-20T21:38:58.6736150Z warmup_steps (int): The number of warm-up steps. During this stage, 2024-08-20T21:38:58.6736763Z model averaging is skipped. 2024-08-20T21:38:58.6737434Z process_group: The process group to be used for all-reduce. 2024-08-20T21:38:58.6738066Z If ``None``, the default process group, which 2024-08-20T21:38:58.6738707Z is created by :func:`torch.distributed.init_process_group`, 2024-08-20T21:38:58.6739323Z will be used. (default: ``None``) 2024-08-20T21:38:58.6739659Z 2024-08-20T21:38:58.6739802Z Example:: 2024-08-20T21:38:58.6739956Z 2024-08-20T21:38:58.6740128Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:38:58.6740589Z >>> import torch 2024-08-20T21:38:58.6740943Z >>> import torch.distributed as dist 2024-08-20T21:38:58.6741692Z >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD 2024-08-20T21:38:58.6742797Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2024-08-20T21:38:58.6743633Z >>> import torch.nn as nn 2024-08-20T21:38:58.6744211Z >>> 2024-08-20T21:38:58.6757026Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2024-08-20T21:38:58.6757936Z >>> torch.cuda.set_device(rank) 2024-08-20T21:38:58.6758785Z >>> module = nn.Linear(1, 1, bias=False).cuda() 2024-08-20T21:38:58.6759819Z >>> model = nn.parallel.DistributedDataParallel( 2024-08-20T21:38:58.6760880Z >>> module, device_ids=[rank], output_device=rank 2024-08-20T21:38:58.6761692Z >>> ) 2024-08-20T21:38:58.6762560Z >>> # Register a post-localSGD communication hook. 2024-08-20T21:38:58.6763771Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2024-08-20T21:38:58.6764573Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-08-20T21:38:58.6765058Z >>> 2024-08-20T21:38:58.6765606Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2024-08-20T21:38:58.6766394Z >>> # After 100 steps, run model averaging every 4 steps. 2024-08-20T21:38:58.6767236Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-08-20T21:38:58.6768216Z >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2024-08-20T21:38:58.6768867Z >>> for step in range(0, 200): 2024-08-20T21:38:58.6769268Z >>> optimizer.zero_grad() 2024-08-20T21:38:58.6769682Z >>> loss = loss_fn(output, labels) 2024-08-20T21:38:58.6770281Z >>> loss.backward() 2024-08-20T21:38:58.6770629Z >>> optimizer.step() 2024-08-20T21:38:58.6771147Z >>> # Will average model parameters globally every 4 steps. Thus, 2024-08-20T21:38:58.6771976Z >>> # inter-node communication only occurs every 4 iterations after 2024-08-20T21:38:58.6772596Z >>> # the initial ``warmup_steps`` period. 2024-08-20T21:38:58.6773159Z >>> averager.average_parameters(model.parameters()) 2024-08-20T21:38:58.6773558Z 2024-08-20T21:38:58.6773978Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:58.6774486Z 2024-08-20T21:38:58.6775883Z 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. 2024-08-20T21:38:58.6777620Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:58.6778143Z 2024-08-20T21:38:58.6778605Z Runs hierarchical model averaging (`hierarchical SGD `_). 2024-08-20T21:38:58.6779237Z 2024-08-20T21:38:58.6779668Z Process groups of different sizes are organized in a hierarchy, and they average parameters 2024-08-20T21:38:58.6780627Z by using different periods concurrently after the warm-up stage. 2024-08-20T21:38:58.6781681Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager` 2024-08-20T21:38:58.6782995Z that supports `post-local SGD `_, which essentially only supports 2024-08-20T21:38:58.6784168Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine 2024-08-20T21:38:58.6785287Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`. 2024-08-20T21:38:58.6786452Z Similarly, the process groups within this class do not have such an intra-machine process 2024-08-20T21:38:58.6787527Z subgroup, which should be embedded by the post-local SGD communication hook instead. 2024-08-20T21:38:58.6788105Z 2024-08-20T21:38:58.6788201Z Args: 2024-08-20T21:38:58.6788721Z period_group_size_dict: An ordered dict mapping keys of model averaging period to 2024-08-20T21:38:58.6789536Z process group size, used for initializing process groups of 2024-08-20T21:38:58.6790423Z different sizes in a hierarchy to average parameters concurrently. 2024-08-20T21:38:58.6791232Z Particularly, at each iteration, there will be at most a single 2024-08-20T21:38:58.6792129Z process group that runs averaging -- the period of such group should 2024-08-20T21:38:58.6792956Z have the largest period which the current step can be divided by. 2024-08-20T21:38:58.6793689Z For example, if the dict has three keys: 2, 4, and 8, 2024-08-20T21:38:58.6794421Z then this means totally three process groups will be created to 2024-08-20T21:38:58.6795185Z average parameters every 2, 4, and 8 iterations, respectively. 2024-08-20T21:38:58.6796274Z At the 4th iteration, only the second process group will run 2024-08-20T21:38:58.6797005Z averaging, because the first process group should be a 2024-08-20T21:38:58.6797757Z subset of the second process group, and no need to execute the first 2024-08-20T21:38:58.6798528Z process group redundantly. 2024-08-20T21:38:58.6799183Z On the other hand, the third process group can only be triggered 2024-08-20T21:38:58.6799982Z every 8 iterations, so it will not be triggered at the 4th iteration. 2024-08-20T21:38:58.6801051Z warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped. 2024-08-20T21:38:58.6802443Z process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging. 2024-08-20T21:38:58.6803501Z If ``None``, the default process group, which is created 2024-08-20T21:38:58.6804247Z by :func:`torch.distributed.init_process_group`, will be used. 2024-08-20T21:38:58.6804921Z (default: ``None``) 2024-08-20T21:38:58.6805272Z 2024-08-20T21:38:58.6805417Z Example:: 2024-08-20T21:38:58.6805783Z >>> # xdoctest: +SKIP('undefined rank') 2024-08-20T21:38:58.6806269Z >>> from collections import OrderedDict 2024-08-20T21:38:58.6806711Z >>> import torch 2024-08-20T21:38:58.6807054Z >>> import torch.distributed as dist 2024-08-20T21:38:58.6807754Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2024-08-20T21:38:58.6808441Z >>> PostLocalSGDState, 2024-08-20T21:38:58.6808815Z >>> post_localSGD_hook, 2024-08-20T21:38:58.6809179Z >>> ) 2024-08-20T21:38:58.6809849Z >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD 2024-08-20T21:38:58.6810646Z >>> import torch.nn as nn 2024-08-20T21:38:58.6810998Z >>> 2024-08-20T21:38:58.6811395Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2024-08-20T21:38:58.6811946Z >>> torch.cuda.set_device(rank) 2024-08-20T21:38:58.6812404Z >>> module = nn.Linear(1, 1, bias=False).to(rank) 2024-08-20T21:38:58.6812973Z >>> model = nn.parallel.DistributedDataParallel( 2024-08-20T21:38:58.6813555Z >>> module, device_ids=[rank], output_device=rank 2024-08-20T21:38:58.6814009Z >>> ) 2024-08-20T21:38:58.6814440Z >>> # Register a post-localSGD communication hook. 2024-08-20T21:38:58.6815319Z >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4. 2024-08-20T21:38:58.6816052Z >>> subgroup, _ = dist.new_subgroups() 2024-08-20T21:38:58.6816802Z >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100) 2024-08-20T21:38:58.6817612Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-08-20T21:38:58.6818084Z >>> 2024-08-20T21:38:58.6818639Z >>> # Average parameters among each group of 8 processes every 4 iterations, and among all 2024-08-20T21:38:58.6819516Z >>> # the 16 processes every 16 iterations. 2024-08-20T21:38:58.6820105Z >>> averager = hierarchicalSGD.HierarchicalModelAverager( 2024-08-20T21:38:58.6820871Z >>> period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100) 2024-08-20T21:38:58.6821841Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-08-20T21:38:58.6822860Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2024-08-20T21:38:58.6823655Z >>> # After 100 steps, run model averaging at two levels. 2024-08-20T21:38:58.6824174Z >>> for step in range(0, 200): 2024-08-20T21:38:58.6824584Z >>> optimizer.zero_grad() 2024-08-20T21:38:58.6824986Z >>> loss = loss_fn(output, labels) 2024-08-20T21:38:58.6825413Z >>> loss.backward() 2024-08-20T21:38:58.6825769Z >>> optimizer.step() 2024-08-20T21:38:58.6826214Z >>> # Average parameters after ``optimizer.step()``. 2024-08-20T21:38:58.6827106Z >>> # Thus, the inter-node communication only occurs periodically after ``warmup_steps``. 2024-08-20T21:38:58.6827909Z >>> averager.average_parameters(model.parameters()) 2024-08-20T21:38:58.6828291Z 2024-08-20T21:38:58.6828403Z .. warning :: 2024-08-20T21:38:58.6828967Z The last group size in the dict must be the size of the provided ``process_group``, 2024-08-20T21:38:58.6829850Z which indicates model averaging at the highest level of the hierarchy. 2024-08-20T21:38:58.6830920Z If ``process_group`` is not provided, then the last group size should be equal to the world size. 2024-08-20T21:38:58.6831561Z 2024-08-20T21:38:58.6831669Z .. warning :: 2024-08-20T21:38:58.6832160Z `HierarchicalModelAverager` is experimental and subject to change. 2024-08-20T21:38:58.6832636Z 2024-08-20T21:38:58.6833079Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:58.6833593Z 2024-08-20T21:38:58.6965837Z 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. 2024-08-20T21:38:58.6967500Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:58.6968250Z 2024-08-20T21:38:58.6968839Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint 2024-08-20T21:38:58.6970227Z on the coordinator rank, and then broadcast and shard each tensor to all ranks. 2024-08-20T21:38:58.6970786Z 2024-08-20T21:38:58.6971119Z . N.B. Intended to be used with DynamicMetaLoadPlanner 2024-08-20T21:38:58.6971709Z 2024-08-20T21:38:58.6971938Z .. warning:: 2024-08-20T21:38:58.6972487Z Current implementation only supports loading Tensors. 2024-08-20T21:38:58.6972878Z 2024-08-20T21:38:58.6973023Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:38:58.6973455Z >>> sd = {"mode": model} 2024-08-20T21:38:58.6973795Z >>> dcp.load( 2024-08-20T21:38:58.6974059Z >>> sd, 2024-08-20T21:38:58.6974427Z >>> storage_reader=BroadcastingTorchSaveReader(), 2024-08-20T21:38:58.6974964Z >>> planner=DynamicMetaLoadPlanner(), 2024-08-20T21:38:58.6975415Z >>> checkpoint_id="path_to_model.pt" 2024-08-20T21:38:58.6975823Z >>> ) 2024-08-20T21:38:58.6975964Z 2024-08-20T21:38:58.6976429Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:58.6976939Z 2024-08-20T21:38:58.6978071Z 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. 2024-08-20T21:38:58.6979588Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:58.6980126Z 2024-08-20T21:38:58.6980634Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, 2024-08-20T21:38:58.6982115Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a 2024-08-20T21:38:58.6982918Z metadata file, like Torch Save files. 2024-08-20T21:38:58.6983217Z 2024-08-20T21:38:58.6983473Z . N.B. Intended to be used with BroadcastingTorchSaveReader 2024-08-20T21:38:58.6983909Z 2024-08-20T21:38:58.6984019Z .. warning:: 2024-08-20T21:38:58.6984425Z Current implementation only supports loading Tensors. 2024-08-20T21:38:58.6984819Z 2024-08-20T21:38:58.6984963Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:38:58.6985389Z >>> sd = {"mode": model} 2024-08-20T21:38:58.6985724Z >>> dcp.load( 2024-08-20T21:38:58.6985989Z >>> sd, 2024-08-20T21:38:58.6986362Z >>> storage_reader=BroadcastingTorchSaveReader(), 2024-08-20T21:38:58.6986899Z >>> planner=DynamicMetaLoadPlanner(), 2024-08-20T21:38:58.6987350Z >>> checkpoint_id="path_to_model.pt" 2024-08-20T21:38:58.6987754Z >>> ) 2024-08-20T21:38:58.6987897Z 2024-08-20T21:38:58.6988333Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:58.6988845Z 2024-08-20T21:38:58.7028789Z 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. 2024-08-20T21:38:58.7031171Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:58.7031718Z 2024-08-20T21:38:58.7032041Z Load a state_dict in conjunction with FSDP sharded optimizer state. 2024-08-20T21:38:58.7032520Z 2024-08-20T21:38:58.7032746Z This is the current recommended way to checkpoint FSDP. 2024-08-20T21:38:58.7033264Z >>> # xdoctest: +SKIP 2024-08-20T21:38:58.7033857Z >>> import torch.distributed.checkpoint as dist_cp 2024-08-20T21:38:58.7034346Z >>> # Save 2024-08-20T21:38:58.7034635Z >>> model: torch.nn.Model 2024-08-20T21:38:58.7035012Z >>> optim_params = model.parameters() 2024-08-20T21:38:58.7035508Z >>> optim = torch.optim.SGD(optim_params, lr=0.01) 2024-08-20T21:38:58.7035982Z >>> # Save 2024-08-20T21:38:58.7036436Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT): 2024-08-20T21:38:58.7037029Z >>> state_dict = { 2024-08-20T21:38:58.7037456Z >>> "optimizer": FSDP.optim_state_dict(model, optim), 2024-08-20T21:38:58.7037963Z >>> "model": model.state_dict() 2024-08-20T21:38:58.7038522Z >>> } 2024-08-20T21:38:58.7038820Z >>> dist_cp.save_state_dict( 2024-08-20T21:38:58.7039205Z >>> state_dict=optim_state, 2024-08-20T21:38:58.7039732Z >>> storage_writer=dist_cp.FileSystemWriter("checkpoint"), 2024-08-20T21:38:58.7040338Z >>> planner=dist_cp.DefaultSavePlanner(), 2024-08-20T21:38:58.7040782Z >>> ) 2024-08-20T21:38:58.7041035Z >>> 2024-08-20T21:38:58.7041279Z >>> # Load 2024-08-20T21:38:58.7041744Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT): 2024-08-20T21:38:58.7042409Z >>> model_state_dict = model_tp.state_dict() 2024-08-20T21:38:58.7042865Z >>> checkpoint = { 2024-08-20T21:38:58.7043206Z >>> "model": model_state_dict 2024-08-20T21:38:58.7043596Z >>> } 2024-08-20T21:38:58.7043878Z >>> dist_cp.load_state_dict( 2024-08-20T21:38:58.7044260Z >>> state_dict=checkpoint, 2024-08-20T21:38:58.7044792Z >>> storage_reader=dist_cp.FileSystemReader(checkpoint_file), 2024-08-20T21:38:58.7045404Z >>> planner=dist_cp.DefaultLoadPlanner(), 2024-08-20T21:38:58.7045840Z >>> ) 2024-08-20T21:38:58.7046204Z >>> model.load_state_dict(checkpoint["model_state"]) 2024-08-20T21:38:58.7046676Z >>> 2024-08-20T21:38:58.7047044Z >>> optim_state = dist_cp.load_sharded_optimizer_state_dict( 2024-08-20T21:38:58.7047578Z >>> model_state_dict, 2024-08-20T21:38:58.7047963Z >>> optimizer_key="optimizer", 2024-08-20T21:38:58.7048485Z >>> storage_reader=dist_cp.FileSystemReader("checkpoint"), 2024-08-20T21:38:58.7049000Z >>> ) 2024-08-20T21:38:58.7049255Z >>> 2024-08-20T21:38:58.7049579Z >>> flattened_osd = FSDP.optim_state_dict_to_load( 2024-08-20T21:38:58.7050268Z >>> model, optim, optim_state["optimizer"] 2024-08-20T21:38:58.7050712Z >>> ) 2024-08-20T21:38:58.7050949Z >>> 2024-08-20T21:38:58.7051257Z >>> optim.load_state_dict(flattened_osd) 2024-08-20T21:38:58.7051584Z 2024-08-20T21:38:58.7052058Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:58.7052567Z 2024-08-20T21:38:58.7056127Z 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. 2024-08-20T21:38:58.7058510Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:58.7059065Z 2024-08-20T21:38:58.7059517Z Abstract class defining the protocol used by save_state_dict to plan the save process. 2024-08-20T21:38:58.7060175Z 2024-08-20T21:38:58.7060580Z SavePlanners are stateful objects that can be used to customize the whole save process. 2024-08-20T21:38:58.7061168Z 2024-08-20T21:38:58.7061595Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it 2024-08-20T21:38:58.7062328Z will be visible to the whole process. 2024-08-20T21:38:58.7062626Z 2024-08-20T21:38:58.7063069Z A planner subclass can expect the following sequence of calls during save_state_dict: 2024-08-20T21:38:58.7063834Z 2024-08-20T21:38:58.7064055Z 1) set_up_planner - called on all ranks. 2024-08-20T21:38:58.7064616Z Signals the start of a checkpoint save. 2024-08-20T21:38:58.7065119Z 2024-08-20T21:38:58.7065470Z 2) create_local_plan - called on all ranks. 2024-08-20T21:38:58.7066879Z Process the state_dict and produces a `SavePlan` that will be sent for global planning. 2024-08-20T21:38:58.7067848Z 2024-08-20T21:38:58.7068422Z 3) create_global_plan - called on the coordinator rank only. 2024-08-20T21:38:58.7069713Z Takes the SavePlan from all ranks and make any global decision. 2024-08-20T21:38:58.7070547Z 2024-08-20T21:38:58.7070821Z 4) finish_plan - called on all ranks. 2024-08-20T21:38:58.7071439Z This gives each rank a chance to adjust to global planning decisions. 2024-08-20T21:38:58.7071915Z 2024-08-20T21:38:58.7072198Z 5) resolve_data - called multiple times on each rank 2024-08-20T21:38:58.7072853Z Lookups a value on the `state_dict` for the storage layer to write. 2024-08-20T21:38:58.7073330Z 2024-08-20T21:38:58.7073751Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as 2024-08-20T21:38:58.7074607Z most changes can be expressed by changes in a single method. 2024-08-20T21:38:58.7075026Z 2024-08-20T21:38:58.7075182Z There are 3 usual patterns of extension: 2024-08-20T21:38:58.7075509Z 2024-08-20T21:38:58.7075870Z Rewriting state_dict. This is the simplest way to extend the save process as it 2024-08-20T21:38:58.7076785Z doesn't requite understanding the intrincacies of how SavePlan works: 2024-08-20T21:38:58.7077263Z 2024-08-20T21:38:58.7077423Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:38:58.7077903Z >>> class RenamePlanner(DefaultSavePlanner): 2024-08-20T21:38:58.7078493Z >>> def set_up_planner( 2024-08-20T21:38:58.7078847Z >>> self, 2024-08-20T21:38:58.7079164Z >>> state_dict: STATE_DICT_TYPE, 2024-08-20T21:38:58.7079637Z >>> storage_meta: Optional[StorageMeta], 2024-08-20T21:38:58.7080108Z >>> is_coordinator: bool, 2024-08-20T21:38:58.7080518Z >>> ) -> None: 2024-08-20T21:38:58.7080861Z >>> # prefix all keys with `foo_`` 2024-08-20T21:38:58.7081621Z >>> super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator) 2024-08-20T21:38:58.7082228Z 2024-08-20T21:38:58.7082711Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted 2024-08-20T21:38:58.7083373Z 2024-08-20T21:38:58.7083521Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:38:58.7083994Z >>> class FP16Planner(DefaultSavePlanner): 2024-08-20T21:38:58.7084448Z >>> def create_local_plan(self): 2024-08-20T21:38:58.7085028Z >>> plan = super().create_local_plan() 2024-08-20T21:38:58.7085482Z >>> for p in plan: 2024-08-20T21:38:58.7085879Z >>> if p.tensor_data is not None: 2024-08-20T21:38:58.7086419Z >>> p.tensor_data.properties.dtype = torch.float16 2024-08-20T21:38:58.7086934Z >>> return plan 2024-08-20T21:38:58.7087252Z >>> 2024-08-20T21:38:58.7087533Z >>> def resolve_data(self, write_item): 2024-08-20T21:38:58.7088023Z >>> item = super().resolve_data(write_item) 2024-08-20T21:38:58.7088760Z >>> return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16) 2024-08-20T21:38:58.7089327Z 2024-08-20T21:38:58.7089922Z Using the global planning step to make central decisions that can't be made individually by each rank 2024-08-20T21:38:58.7090592Z 2024-08-20T21:38:58.7090736Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:38:58.7091182Z >>> from itertools import islice 2024-08-20T21:38:58.7091581Z >>> from dataclasses import replace 2024-08-20T21:38:58.7092108Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner): 2024-08-20T21:38:58.7093012Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0 2024-08-20T21:38:58.7093825Z >>> # This sample doesn't handle ShardedTensors 2024-08-20T21:38:58.7094350Z >>> def create_global_plan(self, all_plans): 2024-08-20T21:38:58.7094814Z >>> def chunk(it, size): 2024-08-20T21:38:58.7095182Z >>> it = iter(it) 2024-08-20T21:38:58.7095656Z >>> return list(iter(lambda: tuple(islice(it, size)), ())) 2024-08-20T21:38:58.7096492Z >>> all_plans = [ 2024-08-20T21:38:58.7097070Z >>> replace(plan, items=items) for plan, items in 2024-08-20T21:38:58.7097712Z >>> zip(all_plans, chunk(all_plans[0].items, len(all_plans))) 2024-08-20T21:38:58.7098247Z >>> ] 2024-08-20T21:38:58.7098616Z >>> return super().create_global_plan(all_plans) 2024-08-20T21:38:58.7098980Z 2024-08-20T21:38:58.7099356Z Finally, some planners need to save additional metadata in the checkpoint, this is 2024-08-20T21:38:58.7100293Z accomplished by having each rank contribute their data items in the local plan and 2024-08-20T21:38:58.7100997Z the global planner aggregate them: 2024-08-20T21:38:58.7101282Z 2024-08-20T21:38:58.7101426Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:38:58.7101943Z >>> class SaveExtraDataPlanner(DefaultSavePlanner): 2024-08-20T21:38:58.7102569Z >>> def create_local_plan(self) -> SavePlan: 2024-08-20T21:38:58.7103060Z >>> plan = super().create_local_plan() 2024-08-20T21:38:58.7103683Z >>> return replace(plan, planner_data="per-rank-data") 2024-08-20T21:38:58.7104175Z >>> 2024-08-20T21:38:58.7104822Z >>> def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]: 2024-08-20T21:38:58.7105687Z >>> global_plan, metadata = super().create_global_plan(all_plans) 2024-08-20T21:38:58.7106355Z >>> merged_data = [p.planner_data for p in global_plan] 2024-08-20T21:38:58.7106991Z >>> metadata = replace(metadata, planner_data=merged_data) 2024-08-20T21:38:58.7107532Z >>> return global_plan, metadata 2024-08-20T21:38:58.7107846Z 2024-08-20T21:38:58.7108269Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:58.7108782Z 2024-08-20T21:38:58.7109831Z 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. 2024-08-20T21:38:58.7111264Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:58.7111789Z 2024-08-20T21:38:58.7112186Z Abstract class defining the protocol used by load_state_dict to plan the load process. 2024-08-20T21:38:58.7112774Z 2024-08-20T21:38:58.7113173Z LoadPlanner are stateful objects that can be used to customize the whole load process. 2024-08-20T21:38:58.7113760Z 2024-08-20T21:38:58.7114286Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it 2024-08-20T21:38:58.7115026Z will be visible to the whole process. 2024-08-20T21:38:58.7115327Z 2024-08-20T21:38:58.7115721Z A planner subclass can expect the following sequence of calls during load_state_dict: 2024-08-20T21:38:58.7116303Z 2024-08-20T21:38:58.7116509Z 1) set_up_planner - called on all ranks. 2024-08-20T21:38:58.7117006Z Signals the start of loading a checkpoint. 2024-08-20T21:38:58.7117343Z 2024-08-20T21:38:58.7117556Z 2) create_local_plan - called on all ranks. 2024-08-20T21:38:58.7118299Z Process the state_dict and produces a `LoadPlan` that will be sent for global planning. 2024-08-20T21:38:58.7118991Z 2024-08-20T21:38:58.7119310Z 3) create_global_plan - called on the coordinator rank only. 2024-08-20T21:38:58.7120010Z Takes the LoadPlan from all ranks and make any global decision. 2024-08-20T21:38:58.7120457Z 2024-08-20T21:38:58.7120706Z 4) load_bytes - called multiple times on each rank 2024-08-20T21:38:58.7121380Z This is called once per non-tensor value in state_dict. 2024-08-20T21:38:58.7121775Z 2024-08-20T21:38:58.7122169Z 5) resolve_tensor and commit_tensor - called multiple times on each rank 2024-08-20T21:38:58.7122900Z They are called in pair for each Tensor value in state_dict. 2024-08-20T21:38:58.7123338Z 2024-08-20T21:38:58.7123758Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as 2024-08-20T21:38:58.7124618Z most changes can be expressed by changes in a single method. 2024-08-20T21:38:58.7125036Z 2024-08-20T21:38:58.7125214Z There are two usual patterns of extension: 2024-08-20T21:38:58.7125539Z 2024-08-20T21:38:58.7125986Z Rewriting state_dict. This is the simplest way to extend the load process as it 2024-08-20T21:38:58.7126955Z doesn't requite understanding the intrincacies of how LoadPlan works. We need 2024-08-20T21:38:58.7127812Z to keep a reference to the original state_dict as load happens in place so 2024-08-20T21:38:58.7128474Z we need to be able to perform it in place 2024-08-20T21:38:58.7128808Z 2024-08-20T21:38:58.7128951Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:38:58.7129439Z >>> class RenamePlanner(DefaultLoadPlanner): 2024-08-20T21:38:58.7129893Z >>> def set_up_planner( 2024-08-20T21:38:58.7130239Z >>> self, 2024-08-20T21:38:58.7130566Z >>> state_dict: STATE_DICT_TYPE, 2024-08-20T21:38:58.7130983Z >>> metadata: Metadata, 2024-08-20T21:38:58.7131375Z >>> is_coordinator: bool, 2024-08-20T21:38:58.7131786Z >>> ) -> None: 2024-08-20T21:38:58.7132127Z >>> self.original_state_dict = state_dict 2024-08-20T21:38:58.7132717Z >>> state_dict = {"foo_" + k: v for k, v in state_dict.items()} 2024-08-20T21:38:58.7133230Z >>> 2024-08-20T21:38:58.7133515Z >>> if self.flatten_sharded_tensors: 2024-08-20T21:38:58.7134058Z >>> state_dict = _flatten_sharded_tensors(state_dict) 2024-08-20T21:38:58.7134545Z >>> 2024-08-20T21:38:58.7134816Z >>> if self.flatten_state_dict: 2024-08-20T21:38:58.7135375Z >>> state_dict, self.mappings = flatten_state_dict(state_dict) 2024-08-20T21:38:58.7135899Z >>> 2024-08-20T21:38:58.7136171Z >>> self.state_dict = state_dict 2024-08-20T21:38:58.7136606Z >>> self.metadata = metadata 2024-08-20T21:38:58.7137058Z >>> self.is_coordinator = is_coordinator 2024-08-20T21:38:58.7137475Z >>> 2024-08-20T21:38:58.7137779Z >>> def load_bytes(self, read_item, value): 2024-08-20T21:38:58.7138247Z >>> # Remove the "foo_" prefix 2024-08-20T21:38:58.7138992Z >>> self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False) 2024-08-20T21:38:58.7139605Z 2024-08-20T21:38:58.7139611Z 2024-08-20T21:38:58.7139959Z Modifying resolve_tensor and commit_tensor to handle load time transformation. 2024-08-20T21:38:58.7140490Z 2024-08-20T21:38:58.7140635Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:38:58.7141152Z >>> class MetaModelMaterialize(DefaultSavePlanner): 2024-08-20T21:38:58.7141759Z >>> def resolve_tensor(self, read_item): 2024-08-20T21:38:58.7142272Z >>> tensor = super().resolve_tensor(read_item) 2024-08-20T21:38:58.7142827Z >>> return torch.empty_like(tensor, device="cpu") 2024-08-20T21:38:58.7143280Z >>> 2024-08-20T21:38:58.7143603Z >>> def commit_tensor(self, read_item, tensor): 2024-08-20T21:38:58.7144168Z >>> self.state_dict[read_item.dest_index.fqn] = tensor 2024-08-20T21:38:58.7144550Z 2024-08-20T21:38:58.7144972Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:58.7145495Z 2024-08-20T21:38:58.7279108Z 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. 2024-08-20T21:38:58.7280666Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:58.7281216Z 2024-08-20T21:38:58.7281409Z Load a distributed ``state_dict`` in SPMD style. 2024-08-20T21:38:58.7281833Z 2024-08-20T21:38:58.7282092Z Each rank will try to read the least amount of data necessary 2024-08-20T21:38:58.7282859Z to fullfill the requested `state_dict`. When loading :class:`ShardedTensor` 2024-08-20T21:38:58.7283706Z or :class:`DTensor` instances, each rank only reads data for their local shards. 2024-08-20T21:38:58.7284249Z 2024-08-20T21:38:58.7284620Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2024-08-20T21:38:58.7285545Z load will first call ``state_dict`` before attempting deserialization, followed by 2024-08-20T21:38:58.7286318Z ``load_state_dict`` once the deserialization is complete. 2024-08-20T21:38:58.7286950Z 2024-08-20T21:38:58.7287084Z .. warning:: 2024-08-20T21:38:58.7287499Z All tensors in ``state_dict`` must be allocated on their 2024-08-20T21:38:58.7288132Z destination device *prior to* calling this function. 2024-08-20T21:38:58.7288513Z 2024-08-20T21:38:58.7288936Z All non-tensor data is loaded using `torch.load()` and modified in place 2024-08-20T21:38:58.7289559Z on state_dict. 2024-08-20T21:38:58.7289746Z 2024-08-20T21:38:58.7289867Z .. warning:: 2024-08-20T21:38:58.7290320Z Users must call `load_state_dict` on the root module to ensure load 2024-08-20T21:38:58.7291100Z pos-processing and non-tensor data properly propagates. 2024-08-20T21:38:58.7291524Z 2024-08-20T21:38:58.7291624Z .. note: 2024-08-20T21:38:58.7292104Z If no process group is initialized, this function will assume the intent 2024-08-20T21:38:58.7292925Z is to load a checkpoint into the local process. This can be useful in the 2024-08-20T21:38:58.7293790Z case of local inference, and when using regular Tensors (as opposed to DTensor 2024-08-20T21:38:58.7294442Z or ShardedTensor) 2024-08-20T21:38:58.7294655Z 2024-08-20T21:38:58.7294754Z .. note: 2024-08-20T21:38:58.7295101Z Rank 0 is assumed to be the coordinator rank. 2024-08-20T21:38:58.7295447Z 2024-08-20T21:38:58.7295563Z Args: 2024-08-20T21:38:58.7296230Z state_dict (Dict[str, Any]): The state_dict to save. 2024-08-20T21:38:58.7296807Z checkpoint_id (Union[str, os.PathLike, None]): 2024-08-20T21:38:58.7297487Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-08-20T21:38:58.7298262Z depends on the storage. It can be a path to a folder or to a file. 2024-08-20T21:38:58.7299093Z It can also be a key if the storage is a key-value store. 2024-08-20T21:38:58.7299632Z (Default: ``None``) 2024-08-20T21:38:58.7300036Z storage_reader (Optional[StorageReader]): 2024-08-20T21:38:58.7300671Z Instance of StorageWriter used to perform reads. If this is not 2024-08-20T21:38:58.7301418Z specified, DCP will automatically infer the reader based on the 2024-08-20T21:38:58.7302153Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-08-20T21:38:58.7302728Z be raised. (Default: ``None``) 2024-08-20T21:38:58.7303170Z planner (Optional[LoadPlanner]): 2024-08-20T21:38:58.7304059Z Instance of LoadPlanner. If this is not specificed, the default 2024-08-20T21:38:58.7304802Z planner will be used. (Default: ``None``) 2024-08-20T21:38:58.7305637Z process_group (Optional[ProcessGroup]): 2024-08-20T21:38:58.7306744Z ProcessGroup to be used for cross-rank synchronization. 2024-08-20T21:38:58.7307701Z (Default: ``None``) 2024-08-20T21:38:58.7308112Z 2024-08-20T21:38:58.7308238Z Returns: 2024-08-20T21:38:58.7308626Z None. 2024-08-20T21:38:58.7308819Z 2024-08-20T21:38:58.7308932Z Examples 2024-08-20T21:38:58.7309271Z >>> # xdoctest: +SKIP 2024-08-20T21:38:58.7309639Z >>> my_model = MyModule() 2024-08-20T21:38:58.7310164Z >>> optimizer = Adagrad(my_model.parameters()) 2024-08-20T21:38:58.7310745Z >>> model_state_dict = my_model.state_dict() 2024-08-20T21:38:58.7311545Z >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader("/checkpoint/1") 2024-08-20T21:38:58.7312220Z 2024-08-20T21:38:58.7312435Z >>> torch.distributed.checkpoint.load_state_dict( 2024-08-20T21:38:58.7313007Z >>> state_dict=model_state_dict, 2024-08-20T21:38:58.7313673Z >>> storage_reader=fs_storage_reader, 2024-08-20T21:38:58.7314305Z >>> ) 2024-08-20T21:38:58.7314566Z 2024-08-20T21:38:58.7314912Z >>> # module.load_state_dict() function might have customized steps 2024-08-20T21:38:58.7315544Z >>> # to flush the state_dict, must call it to 2024-08-20T21:38:58.7316038Z >>> # ensure correct behavior. 2024-08-20T21:38:58.7316490Z >>> my_model.load_state_dict(model_state_dict) 2024-08-20T21:38:58.7316846Z 2024-08-20T21:38:58.7316958Z .. note:: 2024-08-20T21:38:58.7317593Z load_state_dict uses collectives to coordinate reads across ranks. 2024-08-20T21:38:58.7318579Z For NCCL-based process groups, internal tensor representations of 2024-08-20T21:38:58.7319407Z objects must be moved to the GPU device before communication takes place. 2024-08-20T21:38:58.7320255Z In this case, the device used is given by ``torch.cuda.current_device()`` 2024-08-20T21:38:58.7321178Z and it is the user's responsibility to ensure that this is set so that each 2024-08-20T21:38:58.7321965Z rank has an individual GPU, via ``torch.cuda.set_device()``. 2024-08-20T21:38:58.7322393Z 2024-08-20T21:38:58.7322825Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:58.7323333Z 2024-08-20T21:38:58.7324383Z 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. 2024-08-20T21:38:58.7325831Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:58.7326359Z 2024-08-20T21:38:58.7326512Z Save a distributed model in SPMD style. 2024-08-20T21:38:58.7326841Z 2024-08-20T21:38:58.7327100Z This function is different from ``torch.save()`` as it handles 2024-08-20T21:38:58.7327899Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards. 2024-08-20T21:38:58.7328437Z 2024-08-20T21:38:58.7328805Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2024-08-20T21:38:58.7329555Z save will call ``state_dict`` before serialization. 2024-08-20T21:38:58.7329940Z 2024-08-20T21:38:58.7330051Z .. warning:: 2024-08-20T21:38:58.7330570Z There is no guarantees of Backwards Compatibility across PyTorch versions 2024-08-20T21:38:58.7331194Z for saved state_dicts. 2024-08-20T21:38:58.7331440Z 2024-08-20T21:38:58.7331544Z .. warning:: 2024-08-20T21:38:58.7332016Z If using the `process_group` argument, make sure that only its ranks 2024-08-20T21:38:58.7332775Z call `save_state_dict` and that all data in state_dict belong to it. 2024-08-20T21:38:58.7333250Z 2024-08-20T21:38:58.7333351Z .. note:: 2024-08-20T21:38:58.7333969Z When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of 2024-08-20T21:38:58.7334877Z the shard_group should be calling `save_state_dict` and the corresponding process 2024-08-20T21:38:58.7335660Z group needs to be passed in. 2024-08-20T21:38:58.7335933Z 2024-08-20T21:38:58.7336051Z .. note:: 2024-08-20T21:38:58.7336594Z If no process group is available, this function assumes the intention is to save the 2024-08-20T21:38:58.7337311Z state_dict in the local process. 2024-08-20T21:38:58.7337621Z 2024-08-20T21:38:58.7337721Z .. note: 2024-08-20T21:38:58.7338068Z Rank 0 is assumed to be the coordinator rank. 2024-08-20T21:38:58.7338418Z 2024-08-20T21:38:58.7338423Z 2024-08-20T21:38:58.7338518Z Args: 2024-08-20T21:38:58.7338883Z state_dict (Dict[str, Any]): The state_dict to save. 2024-08-20T21:38:58.7339450Z checkpoint_id (Union[str, os.PathLike, None]): 2024-08-20T21:38:58.7340106Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-08-20T21:38:58.7340894Z depends on the storage. It can be a path to a folder or to a file. 2024-08-20T21:38:58.7341699Z It can also be a key if the storage is a key-value store. 2024-08-20T21:38:58.7342226Z (Default: ``None``) 2024-08-20T21:38:58.7342635Z storage_writer (Optional[StorageWriter]): 2024-08-20T21:38:58.7343266Z Instance of StorageWriter used to perform writes. If this is not 2024-08-20T21:38:58.7344005Z specified, DCP will automatically infer the writer based on the 2024-08-20T21:38:58.7344744Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-08-20T21:38:58.7345339Z be raised. (Default: ``None``) 2024-08-20T21:38:58.7345766Z planner (Optional[SavePlanner]): 2024-08-20T21:38:58.7346426Z Instance of SavePlanner. If this is not specificed, the default 2024-08-20T21:38:58.7347063Z planner will be used. (Default: ``None``) 2024-08-20T21:38:58.7347576Z process_group (Optional[ProcessGroup]): 2024-08-20T21:38:58.7348216Z ProcessGroup to be used for cross-rank synchronization. 2024-08-20T21:38:58.7348761Z (Default: ``None``) 2024-08-20T21:38:58.7349001Z 2024-08-20T21:38:58.7349115Z Returns: 2024-08-20T21:38:58.7349472Z Metadata: Metadata object for the saved checkpoint. 2024-08-20T21:38:58.7349865Z 2024-08-20T21:38:58.7349966Z Example: 2024-08-20T21:38:58.7350245Z >>> # xdoctest: +SKIP 2024-08-20T21:38:58.7350586Z >>> my_model = MyModule() 2024-08-20T21:38:58.7350852Z 2024-08-20T21:38:58.7350995Z >>> state_dict = {"model": my_model} 2024-08-20T21:38:58.7351291Z 2024-08-20T21:38:58.7351724Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2024-08-20T21:38:58.7352481Z >>> torch.distributed.checkpoint.save( 2024-08-20T21:38:58.7352957Z >>> state_dict=state_dict, 2024-08-20T21:38:58.7353396Z >>> storage_writer=fs_storage_writer, 2024-08-20T21:38:58.7353815Z >>> ) 2024-08-20T21:38:58.7353985Z 2024-08-20T21:38:58.7354089Z .. note:: 2024-08-20T21:38:58.7354554Z save_state_dict uses collectives to coordinate writes across ranks. 2024-08-20T21:38:58.7355398Z For NCCL-based process groups, internal tensor representations of 2024-08-20T21:38:58.7356221Z objects must be moved to the GPU device before communication takes place. 2024-08-20T21:38:58.7357068Z In this case, the device used is given by ``torch.cuda.current_device()`` 2024-08-20T21:38:58.7357953Z and it is the user's responsibility to ensure that this is set so that 2024-08-20T21:38:58.7358863Z each rank has an individual GPU, via ``torch.cuda.set_device()``. 2024-08-20T21:38:58.7359322Z 2024-08-20T21:38:58.7359767Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:58.7360274Z 2024-08-20T21:38:58.7361426Z 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. 2024-08-20T21:38:58.7362881Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:58.7363868Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the 2024-08-20T21:38:58.7364920Z staging storage (defaults to CPU memory), and then calls the `save` in a separate thread. 2024-08-20T21:38:58.7365502Z 2024-08-20T21:38:58.7365614Z .. warning:: 2024-08-20T21:38:58.7366027Z This feature is experimental and subject to change. 2024-08-20T21:38:58.7366410Z 2024-08-20T21:38:58.7366524Z Args: 2024-08-20T21:38:58.7366886Z state_dict (Dict[str, Any]): The state_dict to save. 2024-08-20T21:38:58.7367468Z checkpoint_id (Union[str, os.PathLike, None]): 2024-08-20T21:38:58.7368150Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-08-20T21:38:58.7368941Z depends on the storage. It can be a path to a folder or to a file. 2024-08-20T21:38:58.7369736Z It can also be a key if the storage is a key-value store. 2024-08-20T21:38:58.7370280Z (Default: ``None``) 2024-08-20T21:38:58.7370716Z storage_writer (Optional[StorageWriter]): 2024-08-20T21:38:58.7371436Z Instance of StorageWriter used to perform 'stage' and 'save'. If 2024-08-20T21:38:58.7372261Z this is not specified, DCP will automatically infer the writer based on the 2024-08-20T21:38:58.7373075Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-08-20T21:38:58.7373665Z be raised. (Default: ``None``) 2024-08-20T21:38:58.7374129Z planner (Optional[SavePlanner]): 2024-08-20T21:38:58.7374735Z Instance of SavePlanner. If this is not specificed, the default 2024-08-20T21:38:58.7375375Z planner will be used. (Default: ``None``) 2024-08-20T21:38:58.7375978Z process_group (Optional[ProcessGroup]): 2024-08-20T21:38:58.7376657Z ProcessGroup to be used for cross-rank synchronization. 2024-08-20T21:38:58.7377190Z (Default: ``None``) 2024-08-20T21:38:58.7377459Z 2024-08-20T21:38:58.7377564Z Returns: 2024-08-20T21:38:58.7378046Z Future: A future holding the resultant Metadata object from `save`. 2024-08-20T21:38:58.7378516Z 2024-08-20T21:38:58.7378636Z Example: 2024-08-20T21:38:58.7378921Z >>> # xdoctest: +SKIP 2024-08-20T21:38:58.7379296Z >>> my_model = MyModule() 2024-08-20T21:38:58.7379559Z 2024-08-20T21:38:58.7379724Z >>> state_dict = {"model": my_model} 2024-08-20T21:38:58.7380037Z 2024-08-20T21:38:58.7380468Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2024-08-20T21:38:58.7381358Z >>> checkpoint_future = torch.distributed.checkpoint.async_save( 2024-08-20T21:38:58.7381956Z >>> state_dict=state_dict, 2024-08-20T21:38:58.7382398Z >>> storage_writer=fs_storage_writer, 2024-08-20T21:38:58.7382832Z >>> ) 2024-08-20T21:38:58.7383103Z >>> 2024-08-20T21:38:58.7383377Z >>> # ... do some work ... 2024-08-20T21:38:58.7383748Z >>> 2024-08-20T21:38:58.7384050Z >>> checkpoint_future.result() 2024-08-20T21:38:58.7384350Z 2024-08-20T21:38:58.7384446Z 2024-08-20T21:38:58.7385020Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:58.7385530Z 2024-08-20T21:38:58.7417627Z 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. 2024-08-20T21:38:58.7419189Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:58.7419712Z 2024-08-20T21:38:58.7419995Z Initialize rendezvous event object and record its operations. 2024-08-20T21:38:58.7420432Z 2024-08-20T21:38:58.7420539Z Args: 2024-08-20T21:38:58.7420860Z run_id (str): The run id of the rendezvous. 2024-08-20T21:38:58.7421404Z message (str): The message describing the event. 2024-08-20T21:38:58.7422114Z node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED). 2024-08-20T21:38:58.7423177Z name (str): Event name. (E.g. Current action being performed). 2024-08-20T21:38:58.7423769Z hostname (str): Hostname of the node. 2024-08-20T21:38:58.7424270Z pid (Optional[int]): The process id of the node. 2024-08-20T21:38:58.7424999Z master_endpoint (str): The master endpoint for the rendezvous store, if known. 2024-08-20T21:38:58.7425930Z local_id (Optional[int]): The local_id of the node, if defined in dynamic_rendezvous.py 2024-08-20T21:38:58.7426703Z rank (Optional[int]): The rank of the node, if known. 2024-08-20T21:38:58.7427190Z Returns: 2024-08-20T21:38:58.7427448Z None 2024-08-20T21:38:58.7427689Z Example: 2024-08-20T21:38:58.7428026Z >>> # See DynamicRendezvousHandler class 2024-08-20T21:38:58.7428482Z >>> def _record( 2024-08-20T21:38:58.7428782Z ... self, 2024-08-20T21:38:58.7429084Z ... message: str, 2024-08-20T21:38:58.7429502Z ... node_state: NodeState = NodeState.RUNNING, 2024-08-20T21:38:58.7429988Z ... rank: Optional[int] = None, 2024-08-20T21:38:58.7430455Z ... ) -> None: 2024-08-20T21:38:58.7430802Z ... construct_and_record_rdzv_event( 2024-08-20T21:38:58.7431344Z ... name=f"{self.__class__.__name__}.{get_method_name()}", 2024-08-20T21:38:58.7431905Z ... run_id=self._settings.run_id, 2024-08-20T21:38:58.7432352Z ... message=message, 2024-08-20T21:38:58.7432728Z ... node_state=node_state, 2024-08-20T21:38:58.7433172Z ... hostname=self._this_node.addr, 2024-08-20T21:38:58.7433630Z ... pid=self._this_node.pid, 2024-08-20T21:38:58.7434082Z ... local_id=self._this_node.local_id, 2024-08-20T21:38:58.7434633Z ... rank=rank, 2024-08-20T21:38:58.7434970Z ... ) 2024-08-20T21:38:58.7435138Z 2024-08-20T21:38:58.7435565Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:58.7436088Z 2024-08-20T21:38:59.0166342Z 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. 2024-08-20T21:38:59.0168225Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:59.0168756Z 2024-08-20T21:38:59.0169042Z This configures FSDP-native mixed precision training. 2024-08-20T21:38:59.0169441Z 2024-08-20T21:38:59.0169549Z Attributes: 2024-08-20T21:38:59.0170095Z param_dtype (Optional[torch.dtype]): This specifies the dtype for model 2024-08-20T21:38:59.0170872Z parameters during forward and backward and thus the dtype for 2024-08-20T21:38:59.0171625Z forward and backward computation. Outside forward and backward, the 2024-08-20T21:38:59.0172401Z *sharded* parameters are kept in full precision (e.g. for the 2024-08-20T21:38:59.0173143Z optimizer step), and for model checkpointing, the parameters are 2024-08-20T21:38:59.0173833Z always saved in full precision. (Default: ``None``) 2024-08-20T21:38:59.0174504Z reduce_dtype (Optional[torch.dtype]): This specifies the dtype for 2024-08-20T21:38:59.0175368Z gradient reduction (i.e. reduce-scatter or all-reduce). If this is 2024-08-20T21:38:59.0176118Z ``None`` but ``param_dtype`` is not ``None``, then this takes on 2024-08-20T21:38:59.0176827Z the ``param_dtype`` value, still running gradient reduction in low 2024-08-20T21:38:59.0177592Z precision. This is permitted to differ from ``param_dtype``, e.g. 2024-08-20T21:38:59.0178356Z to force gradient reduction to run in full precision. (Default: 2024-08-20T21:38:59.0178904Z ``None``) 2024-08-20T21:38:59.0179391Z buffer_dtype (Optional[torch.dtype]): This specifies the dtype for 2024-08-20T21:38:59.0180148Z buffers. FSDP does not shard buffers. Rather, FSDP casts them to 2024-08-20T21:38:59.0180869Z ``buffer_dtype`` in the first forward pass and keeps them in that 2024-08-20T21:38:59.0181616Z dtype thereafter. For model checkpointing, the buffers are saved 2024-08-20T21:38:59.0182596Z in full precision except for ``LOCAL_STATE_DICT``. (Default: 2024-08-20T21:38:59.0183120Z ``None``) 2024-08-20T21:38:59.0183585Z keep_low_precision_grads (bool): If ``False``, then FSDP upcasts 2024-08-20T21:38:59.0184330Z gradients to full precision after the backward pass in preparation 2024-08-20T21:38:59.0185106Z for the optimizer step. If ``True``, then FSDP keeps the gradients 2024-08-20T21:38:59.0185862Z in the dtype used for gradient reduction, which can save memory if 2024-08-20T21:38:59.0186620Z using a custom optimizer that supports running in low precision. 2024-08-20T21:38:59.0187207Z (Default: ``False``) 2024-08-20T21:38:59.0187731Z cast_forward_inputs (bool): If ``True``, then this FSDP module casts 2024-08-20T21:38:59.0188488Z its forward args and kwargs to ``param_dtype``. This is to ensure 2024-08-20T21:38:59.0189243Z that parameter and input dtypes match for forward computation, as 2024-08-20T21:38:59.0190005Z required by many ops. This may need to be set to ``True`` when only 2024-08-20T21:38:59.0190785Z applying mixed precision to some but not all FSDP modules, in which 2024-08-20T21:38:59.0191657Z case a mixed-precision FSDP submodule needs to recast its inputs. 2024-08-20T21:38:59.0192238Z (Default: ``False``) 2024-08-20T21:38:59.0192779Z cast_root_forward_inputs (bool): If ``True``, then the root FSDP module 2024-08-20T21:38:59.0193537Z casts its forward args and kwargs to ``param_dtype``, overriding 2024-08-20T21:38:59.0194343Z the value of ``cast_forward_inputs``. For non-root FSDP modules, 2024-08-20T21:38:59.0195078Z this does not do anything. (Default: ``True``) 2024-08-20T21:38:59.0195750Z _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies 2024-08-20T21:38:59.0196788Z module classes to ignore for mixed precision when using an 2024-08-20T21:38:59.0197461Z ``auto_wrap_policy``: Modules of these classes will have FSDP 2024-08-20T21:38:59.0198198Z applied to them separately with mixed precision disabled (meaning 2024-08-20T21:38:59.0199284Z that the final FSDP construction would deviate from the specified 2024-08-20T21:38:59.0200013Z policy). If ``auto_wrap_policy`` is not specified, then this does 2024-08-20T21:38:59.0200756Z not do anything. This API is experimental and subject to change. 2024-08-20T21:38:59.0201358Z (Default: ``(_BatchNorm,)``) 2024-08-20T21:38:59.0201643Z 2024-08-20T21:38:59.0201892Z .. note:: This API is experimental and subject to change. 2024-08-20T21:38:59.0202292Z 2024-08-20T21:38:59.0202606Z .. note:: Only floating point tensors are cast to their specified dtypes. 2024-08-20T21:38:59.0203095Z 2024-08-20T21:38:59.0203354Z .. note:: In ``summon_full_params``, parameters are forced to full 2024-08-20T21:38:59.0203927Z precision, but buffers are not. 2024-08-20T21:38:59.0204214Z 2024-08-20T21:38:59.0216980Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when 2024-08-20T21:38:59.0218042Z their inputs are in a low precision like ``float16`` or ``bfloat16``. 2024-08-20T21:38:59.0219054Z Disabling FSDP's mixed precision for those norm modules only means that 2024-08-20T21:38:59.0219862Z the affine parameters are kept in ``float32``. However, this incurs 2024-08-20T21:38:59.0220727Z separate all-gathers and reduce-scatters for those norm modules, which 2024-08-20T21:38:59.0221547Z may be inefficient, so if the workload permits, the user should prefer 2024-08-20T21:38:59.0222234Z to still apply mixed precision to those modules. 2024-08-20T21:38:59.0222599Z 2024-08-20T21:38:59.0222928Z .. note:: By default, if the user passes a model with any ``_BatchNorm`` 2024-08-20T21:38:59.0223692Z modules and specifies an ``auto_wrap_policy``, then the batch norm 2024-08-20T21:38:59.0224468Z modules will have FSDP applied to them separately with mixed precision 2024-08-20T21:38:59.0225457Z disabled. See the ``_module_classes_to_ignore`` argument. 2024-08-20T21:38:59.0225862Z 2024-08-20T21:38:59.0226147Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and 2024-08-20T21:38:59.0226897Z ``cast_forward_inputs=False`` by default. For the root FSDP instance, 2024-08-20T21:38:59.0227598Z its ``cast_root_forward_inputs`` takes precedence over its 2024-08-20T21:38:59.0228304Z ``cast_forward_inputs``. For non-root FSDP instances, their 2024-08-20T21:38:59.0229023Z ``cast_root_forward_inputs`` values are ignored. The default setting is 2024-08-20T21:38:59.0229808Z sufficient for the typical case where each FSDP instance has the same 2024-08-20T21:38:59.0230591Z ``MixedPrecision`` configuration and only needs to cast inputs to the 2024-08-20T21:38:59.0231390Z ``param_dtype`` at the beginning of the model's forward pass. 2024-08-20T21:38:59.0231808Z 2024-08-20T21:38:59.0232115Z .. note:: For nested FSDP instances with different ``MixedPrecision`` 2024-08-20T21:38:59.0232904Z configurations, we recommend setting individual ``cast_forward_inputs`` 2024-08-20T21:38:59.0233712Z values to configure casting inputs or not before each instance's 2024-08-20T21:38:59.0234449Z forward. In such a case, since the casts happen before each FSDP 2024-08-20T21:38:59.0235273Z instance's forward, a parent FSDP instance should have its non-FSDP 2024-08-20T21:38:59.0236049Z submodules run before its FSDP submodules to avoid the activation dtype 2024-08-20T21:38:59.0236847Z being changed due to a different ``MixedPrecision`` configuration. 2024-08-20T21:38:59.0237316Z 2024-08-20T21:38:59.0237436Z Example:: 2024-08-20T21:38:59.0237607Z 2024-08-20T21:38:59.0237894Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:38:59.0238699Z >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3)) 2024-08-20T21:38:59.0239240Z >>> model[1] = FSDP( 2024-08-20T21:38:59.0239575Z >>> model[1], 2024-08-20T21:38:59.0240202Z >>> mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True), 2024-08-20T21:38:59.0240890Z >>> ) 2024-08-20T21:38:59.0241164Z >>> model = FSDP( 2024-08-20T21:38:59.0241495Z >>> model, 2024-08-20T21:38:59.0242107Z >>> mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), 2024-08-20T21:38:59.0242789Z >>> ) 2024-08-20T21:38:59.0242955Z 2024-08-20T21:38:59.0243266Z The above shows a working example. On the other hand, if ``model[1]`` 2024-08-20T21:38:59.0244028Z were replaced with ``model[0]``, meaning that the submodule using 2024-08-20T21:38:59.0244790Z different ``MixedPrecision`` ran its forward first, then ``model[1]`` 2024-08-20T21:38:59.0245551Z would incorrectly see ``float16`` activations instead of ``bfloat16`` 2024-08-20T21:38:59.0246127Z ones. 2024-08-20T21:38:59.0246277Z 2024-08-20T21:38:59.0246282Z 2024-08-20T21:38:59.0246745Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:59.0247256Z 2024-08-20T21:38:59.0326133Z 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. 2024-08-20T21:38:59.0328159Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:59.0329231Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2024-08-20T21:38:59.0330032Z 2024-08-20T21:38:59.0330818Z Also takes (optional) configuration for the model's and optimizer's state dict. 2024-08-20T21:38:59.0332258Z The target module does not have to be a FSDP module. If the target 2024-08-20T21:38:59.0333177Z module is a FSDP module, its ``state_dict_type`` will also be changed. 2024-08-20T21:38:59.0333674Z 2024-08-20T21:38:59.0334139Z .. note:: This API should be called for only the top-level (root) 2024-08-20T21:38:59.0335065Z module. 2024-08-20T21:38:59.0335252Z 2024-08-20T21:38:59.0335668Z .. note:: This API enables users to transparently use the conventional 2024-08-20T21:38:59.0336491Z ``state_dict`` API to take model checkpoints in cases where the 2024-08-20T21:38:59.0337315Z root FSDP module is wrapped by another ``nn.Module``. For example, 2024-08-20T21:38:59.0338249Z the following will ensure ``state_dict`` is called on all non-FSDP 2024-08-20T21:38:59.0339119Z instances, while dispatching into `sharded_state_dict` implementation 2024-08-20T21:38:59.0339809Z for FSDP: 2024-08-20T21:38:59.0340008Z 2024-08-20T21:38:59.0340222Z Example:: 2024-08-20T21:38:59.0340407Z 2024-08-20T21:38:59.0340592Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:38:59.0341156Z >>> model = DDP(FSDP(...)) 2024-08-20T21:38:59.0341590Z >>> FSDP.set_state_dict_type( 2024-08-20T21:38:59.0342084Z >>> model, 2024-08-20T21:38:59.0342561Z >>> StateDictType.SHARDED_STATE_DICT, 2024-08-20T21:38:59.0343256Z >>> state_dict_config = ShardedStateDictConfig(offload_to_cpu=True), 2024-08-20T21:38:59.0344140Z >>> optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True), 2024-08-20T21:38:59.0344744Z >>> ) 2024-08-20T21:38:59.0345173Z >>> param_state_dict = model.state_dict() 2024-08-20T21:38:59.0345835Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2024-08-20T21:38:59.0346244Z 2024-08-20T21:38:59.0346395Z Args: 2024-08-20T21:38:59.0346888Z module (torch.nn.Module): Root module. 2024-08-20T21:38:59.0347652Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2024-08-20T21:38:59.0348554Z state_dict_config (Optional[StateDictConfig]): the configuration for the 2024-08-20T21:38:59.0349291Z target ``state_dict_type``. 2024-08-20T21:38:59.0350039Z optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration 2024-08-20T21:38:59.0350795Z for the optimizer state dict. 2024-08-20T21:38:59.0351208Z 2024-08-20T21:38:59.0351312Z Returns: 2024-08-20T21:38:59.0351841Z A StateDictSettings that include the previous state_dict type and 2024-08-20T21:38:59.0352474Z configuration for the module. 2024-08-20T21:38:59.0352968Z 2024-08-20T21:38:59.0353645Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:59.0354216Z 2024-08-20T21:38:59.0355610Z 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. 2024-08-20T21:38:59.0357555Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:59.0358631Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2024-08-20T21:38:59.0359266Z 2024-08-20T21:38:59.0359830Z This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of 2024-08-20T21:38:59.0360744Z :meth:`set_state_dict_type` for the detail. 2024-08-20T21:38:59.0361090Z 2024-08-20T21:38:59.0361201Z Example:: 2024-08-20T21:38:59.0361470Z 2024-08-20T21:38:59.0361662Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:38:59.0362159Z >>> model = DDP(FSDP(...)) 2024-08-20T21:38:59.0362583Z >>> with FSDP.state_dict_type( 2024-08-20T21:38:59.0363085Z >>> model, 2024-08-20T21:38:59.0363500Z >>> StateDictType.SHARDED_STATE_DICT, 2024-08-20T21:38:59.0363943Z >>> ): 2024-08-20T21:38:59.0364294Z >>> checkpoint = model.state_dict() 2024-08-20T21:38:59.0364626Z 2024-08-20T21:38:59.0364739Z Args: 2024-08-20T21:38:59.0365147Z module (torch.nn.Module): Root module. 2024-08-20T21:38:59.0365921Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2024-08-20T21:38:59.0366746Z state_dict_config (Optional[StateDictConfig]): the model ``state_dict`` 2024-08-20T21:38:59.0367448Z configuration for the target ``state_dict_type``. 2024-08-20T21:38:59.0368172Z optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer 2024-08-20T21:38:59.0369042Z ``state_dict`` configuration for the target ``state_dict_type``. 2024-08-20T21:38:59.0369602Z 2024-08-20T21:38:59.0370177Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:59.0370701Z 2024-08-20T21:38:59.0390448Z 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. 2024-08-20T21:38:59.0393599Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:59.0394445Z 2024-08-20T21:38:59.0394976Z Transform the state-dict of an optimizer corresponding to a sharded model. 2024-08-20T21:38:59.0395608Z 2024-08-20T21:38:59.0396408Z The given state-dict can be transformed to one of three types: 2024-08-20T21:38:59.0397858Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict. 2024-08-20T21:38:59.0398996Z 2024-08-20T21:38:59.0399331Z For full optimizer state_dict, all states are unflattened and not sharded. 2024-08-20T21:38:59.0400163Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to 2024-08-20T21:38:59.0400745Z avoid OOM. 2024-08-20T21:38:59.0401102Z 2024-08-20T21:38:59.0401431Z For sharded optimizer state_dict, all states are unflattened but sharded. 2024-08-20T21:38:59.0402235Z CPU only can be specified via :meth:`state_dict_type` to further save 2024-08-20T21:38:59.0402799Z memory. 2024-08-20T21:38:59.0402963Z 2024-08-20T21:38:59.0403276Z For local state_dict, no transformation will be performed. But a state 2024-08-20T21:38:59.0404088Z will be converted from nn.Tensor to ShardedTensor to represent its sharding 2024-08-20T21:38:59.0404751Z nature (this is not supported yet). 2024-08-20T21:38:59.0405039Z 2024-08-20T21:38:59.0405156Z Example:: 2024-08-20T21:38:59.0405328Z 2024-08-20T21:38:59.0405500Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:38:59.0406176Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2024-08-20T21:38:59.0406889Z >>> from torch.distributed.fsdp import StateDictType 2024-08-20T21:38:59.0407525Z >>> from torch.distributed.fsdp import FullStateDictConfig 2024-08-20T21:38:59.0408228Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2024-08-20T21:38:59.0408791Z >>> # Save a checkpoint 2024-08-20T21:38:59.0409151Z >>> model, optim = ... 2024-08-20T21:38:59.0409519Z >>> FSDP.set_state_dict_type( 2024-08-20T21:38:59.0409885Z >>> model, 2024-08-20T21:38:59.0410229Z >>> StateDictType.FULL_STATE_DICT, 2024-08-20T21:38:59.0410730Z >>> FullStateDictConfig(rank0_only=False), 2024-08-20T21:38:59.0411266Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-08-20T21:38:59.0411738Z >>> ) 2024-08-20T21:38:59.0412030Z >>> state_dict = model.state_dict() 2024-08-20T21:38:59.0412548Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2024-08-20T21:38:59.0413141Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2024-08-20T21:38:59.0413627Z >>> # Load a checkpoint 2024-08-20T21:38:59.0413973Z >>> model, optim = ... 2024-08-20T21:38:59.0414422Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2024-08-20T21:38:59.0414927Z >>> FSDP.set_state_dict_type( 2024-08-20T21:38:59.0415299Z >>> model, 2024-08-20T21:38:59.0415755Z >>> StateDictType.FULL_STATE_DICT, 2024-08-20T21:38:59.0416251Z >>> FullStateDictConfig(rank0_only=False), 2024-08-20T21:38:59.0416936Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-08-20T21:38:59.0417413Z >>> ) 2024-08-20T21:38:59.0417721Z >>> model.load_state_dict(state_dict) 2024-08-20T21:38:59.0418228Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2024-08-20T21:38:59.0418758Z >>> model, optim, optim_state_dict 2024-08-20T21:38:59.0419179Z >>> ) 2024-08-20T21:38:59.0419487Z >>> optim.load_state_dict(optim_state_dict) 2024-08-20T21:38:59.0419829Z 2024-08-20T21:38:59.0419925Z Args: 2024-08-20T21:38:59.0420352Z model (torch.nn.Module): Root module (which may or may not be a 2024-08-20T21:38:59.0421076Z :class:`FullyShardedDataParallel` instance) whose parameters 2024-08-20T21:38:59.0421689Z were passed into the optimizer ``optim``. 2024-08-20T21:38:59.0422395Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2024-08-20T21:38:59.0422929Z parameters. 2024-08-20T21:38:59.0423420Z optim_state_dict (Dict[str, Any]): the target optimizer state_dict to 2024-08-20T21:38:59.0424208Z transform. If the value is None, optim.state_dict() will be used. ( 2024-08-20T21:38:59.0424798Z Default: ``None``) 2024-08-20T21:38:59.0425440Z group (dist.ProcessGroup): Model's process group across which parameters 2024-08-20T21:38:59.0426213Z are sharded or ``None`` if using the default process group. ( 2024-08-20T21:38:59.0426765Z Default: ``None``) 2024-08-20T21:38:59.0426991Z 2024-08-20T21:38:59.0427088Z Returns: 2024-08-20T21:38:59.0427531Z Dict[str, Any]: A :class:`dict` containing the optimizer state for 2024-08-20T21:38:59.0428225Z ``model``. The sharding of the optimizer state is based on 2024-08-20T21:38:59.0428812Z ``state_dict_type``. 2024-08-20T21:38:59.0429045Z 2024-08-20T21:38:59.0429470Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:59.0429997Z 2024-08-20T21:38:59.0431382Z 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. 2024-08-20T21:38:59.0433126Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:59.0433646Z 2024-08-20T21:38:59.0434288Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model. 2024-08-20T21:38:59.0434987Z 2024-08-20T21:38:59.0435208Z Given a ``optim_state_dict`` that is transformed through 2024-08-20T21:38:59.0435898Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer 2024-08-20T21:38:59.0436665Z state_dict that can be loaded to ``optim`` which is the optimizer for 2024-08-20T21:38:59.0437407Z ``model``. ``model`` must be sharded by FullyShardedDataParallel. 2024-08-20T21:38:59.0437838Z 2024-08-20T21:38:59.0438011Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:38:59.0438820Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2024-08-20T21:38:59.0439563Z >>> from torch.distributed.fsdp import StateDictType 2024-08-20T21:38:59.0440189Z >>> from torch.distributed.fsdp import FullStateDictConfig 2024-08-20T21:38:59.0440888Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2024-08-20T21:38:59.0441465Z >>> # Save a checkpoint 2024-08-20T21:38:59.0441811Z >>> model, optim = ... 2024-08-20T21:38:59.0442176Z >>> FSDP.set_state_dict_type( 2024-08-20T21:38:59.0442550Z >>> model, 2024-08-20T21:38:59.0442874Z >>> StateDictType.FULL_STATE_DICT, 2024-08-20T21:38:59.0443368Z >>> FullStateDictConfig(rank0_only=False), 2024-08-20T21:38:59.0443928Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-08-20T21:38:59.0444386Z >>> ) 2024-08-20T21:38:59.0444681Z >>> state_dict = model.state_dict() 2024-08-20T21:38:59.0445130Z >>> original_osd = optim.state_dict() 2024-08-20T21:38:59.0445599Z >>> optim_state_dict = FSDP.optim_state_dict( 2024-08-20T21:38:59.0446048Z >>> model, 2024-08-20T21:38:59.0446424Z >>> optim, 2024-08-20T21:38:59.0446743Z >>> optim_state_dict=original_osd 2024-08-20T21:38:59.0447153Z >>> ) 2024-08-20T21:38:59.0447504Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2024-08-20T21:38:59.0447979Z >>> # Load a checkpoint 2024-08-20T21:38:59.0448345Z >>> model, optim = ... 2024-08-20T21:38:59.0448784Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2024-08-20T21:38:59.0449277Z >>> FSDP.set_state_dict_type( 2024-08-20T21:38:59.0449649Z >>> model, 2024-08-20T21:38:59.0449988Z >>> StateDictType.FULL_STATE_DICT, 2024-08-20T21:38:59.0450468Z >>> FullStateDictConfig(rank0_only=False), 2024-08-20T21:38:59.0451026Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-08-20T21:38:59.0451500Z >>> ) 2024-08-20T21:38:59.0451785Z >>> model.load_state_dict(state_dict) 2024-08-20T21:38:59.0452299Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2024-08-20T21:38:59.0452820Z >>> model, optim, optim_state_dict 2024-08-20T21:38:59.0453221Z >>> ) 2024-08-20T21:38:59.0453545Z >>> optim.load_state_dict(optim_state_dict) 2024-08-20T21:38:59.0453873Z 2024-08-20T21:38:59.0453978Z Args: 2024-08-20T21:38:59.0454391Z model (torch.nn.Module): Root module (which may or may not be a 2024-08-20T21:38:59.0455122Z :class:`FullyShardedDataParallel` instance) whose parameters 2024-08-20T21:38:59.0455748Z were passed into the optimizer ``optim``. 2024-08-20T21:38:59.0456397Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2024-08-20T21:38:59.0456929Z parameters. 2024-08-20T21:38:59.0457495Z optim_state_dict (Dict[str, Any]): The optimizer states to be loaded. 2024-08-20T21:38:59.0458251Z is_named_optimizer (bool): Is this optimizer a NamedOptimizer or 2024-08-20T21:38:59.0458997Z KeyedOptimizer. Only set to True if ``optim`` is TorchRec's 2024-08-20T21:38:59.0459748Z KeyedOptimizer or torch.distributed's NamedOptimizer. 2024-08-20T21:38:59.0460440Z load_directly (bool): If this is set to True, this API will also 2024-08-20T21:38:59.0461151Z call optim.load_state_dict(result) before returning the result. 2024-08-20T21:38:59.0461909Z Otherwise, users are responsible to call ``optim.load_state_dict()`` 2024-08-20T21:38:59.0462511Z (Default: ``False``) 2024-08-20T21:38:59.0463155Z group (dist.ProcessGroup): Model's process group across which parameters 2024-08-20T21:38:59.0463925Z are sharded or ``None`` if using the default process group. ( 2024-08-20T21:38:59.0464471Z Default: ``None``) 2024-08-20T21:38:59.0464701Z 2024-08-20T21:38:59.0465120Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:59.0465644Z 2024-08-20T21:38:59.0611631Z 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. 2024-08-20T21:38:59.0614315Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:59.0615325Z 2024-08-20T21:38:59.0615830Z RemoteModule instance can only be created after RPC initialization. 2024-08-20T21:38:59.0616664Z 2024-08-20T21:38:59.0617270Z It creates a user-specified module on a specified remote node. 2024-08-20T21:38:59.0618497Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2024-08-20T21:38:59.0619243Z executed on the remote node. 2024-08-20T21:38:59.0619926Z It takes care of autograd recording to ensure the backward pass propagates 2024-08-20T21:38:59.0620713Z gradients back to the corresponding remote module. 2024-08-20T21:38:59.0621734Z It can be shared across processors using `RPC framework `__, 2024-08-20T21:38:59.0622770Z without incurring any overheads of copying the actual module, 2024-08-20T21:38:59.0623548Z which is equivalent to an :class:`~torch.distributed.rpc.RRef` 2024-08-20T21:38:59.0624453Z pointing to the remote module. 2024-08-20T21:38:59.0625250Z 2024-08-20T21:38:59.0625782Z The arguments of ``forward_async`` and ``forward`` are the same as 2024-08-20T21:38:59.0626970Z the ``forward`` method of the module returned by the ``module_cls``. 2024-08-20T21:38:59.0627702Z 2024-08-20T21:38:59.0628371Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now. 2024-08-20T21:38:59.0629021Z 2024-08-20T21:38:59.0629424Z Particularly, to create a hybrid model, typically the local modules should be 2024-08-20T21:38:59.0630737Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``). 2024-08-20T21:38:59.0631620Z Hybrid Example: 2024-08-20T21:38:59.0631964Z >>> class HybridModel(nn.Module): 2024-08-20T21:38:59.0632616Z >>> def __init__(self) -> None: 2024-08-20T21:38:59.0633148Z >>> nn.Module.__init__(self) 2024-08-20T21:38:59.0633702Z >>> self.remote_embedding = RemoteModule(...) 2024-08-20T21:38:59.0634292Z >>> self.local_linear = nn.Linear(...) 2024-08-20T21:38:59.0634717Z 2024-08-20T21:38:59.0635116Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2024-08-20T21:38:59.0636597Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``, 2024-08-20T21:38:59.0637994Z the generated ``RemoteModule`` will have 2 methods in signature of 2024-08-20T21:38:59.0639331Z ``def forward(input: Tensor) -> Tensor:`` and 2024-08-20T21:38:59.0640233Z ``def forward_async(input: Tensor) -> Future[Tensor]:``. 2024-08-20T21:38:59.0640641Z 2024-08-20T21:38:59.0640764Z .. note:: 2024-08-20T21:38:59.0641438Z If the remote module is placed on a cuda device, 2024-08-20T21:38:59.0642236Z any input CPU tensors will be automatically moved to the same cuda device, 2024-08-20T21:38:59.0643520Z and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend. 2024-08-20T21:38:59.0644373Z 2024-08-20T21:38:59.0644483Z Args: 2024-08-20T21:38:59.0645245Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-08-20T21:38:59.0646370Z The device can be a local device or a remote device specified by one of the following remote 2024-08-20T21:38:59.0647174Z formats: 2024-08-20T21:38:59.0647421Z 2024-08-20T21:38:59.0647632Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2024-08-20T21:38:59.0648257Z 2. "/" (ex: "trainer0/cuda:0"). 2024-08-20T21:38:59.0648662Z 2024-08-20T21:38:59.0649018Z In addition, the device field can be optional and the default value is "cpu". 2024-08-20T21:38:59.0649776Z module_cls (nn.Module): For example, 2024-08-20T21:38:59.0650214Z >>> class MyModule(nn.Module): 2024-08-20T21:38:59.0650635Z >>> def forward(input): 2024-08-20T21:38:59.0651038Z >>> return input + 1 2024-08-20T21:38:59.0651404Z >>> 2024-08-20T21:38:59.0651697Z >>> module_cls = MyModule 2024-08-20T21:38:59.0652320Z args (Sequence, optional): args to be passed to ``module_cls``. 2024-08-20T21:38:59.0653020Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2024-08-20T21:38:59.0653836Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2024-08-20T21:38:59.0654747Z to be created. The type object should be decorated by @torch.jit.interface. 2024-08-20T21:38:59.0655773Z If not provided, the generated RemoteModule is not torchscript-able. 2024-08-20T21:38:59.0656589Z Warning, this is an experimental API and susceptible to frequent changes. 2024-08-20T21:38:59.0657111Z 2024-08-20T21:38:59.0657212Z Returns: 2024-08-20T21:38:59.0657710Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-08-20T21:38:59.0658711Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2024-08-20T21:38:59.0659576Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-08-20T21:38:59.0660462Z on the user-provided module on the remote side. 2024-08-20T21:38:59.0660836Z 2024-08-20T21:38:59.0660948Z Example:: 2024-08-20T21:38:59.0661321Z Run the following code in two different processes: 2024-08-20T21:38:59.0661693Z 2024-08-20T21:38:59.0661840Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:38:59.0662265Z >>> # On worker 0: 2024-08-20T21:38:59.0662669Z >>> import torch 2024-08-20T21:38:59.0663031Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:38:59.0663501Z >>> from torch import nn, Tensor 2024-08-20T21:38:59.0664106Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-08-20T21:38:59.0664670Z >>> 2024-08-20T21:38:59.0665002Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-08-20T21:38:59.0665525Z >>> remote_linear_module = RemoteModule( 2024-08-20T21:38:59.0666012Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-08-20T21:38:59.0666461Z >>> ) 2024-08-20T21:38:59.0666748Z >>> input = torch.randn(128, 20) 2024-08-20T21:38:59.0667242Z >>> ret_fut = remote_linear_module.forward_async(input) 2024-08-20T21:38:59.0667746Z >>> ret = ret_fut.wait() 2024-08-20T21:38:59.0668103Z >>> rpc.shutdown() 2024-08-20T21:38:59.0668310Z 2024-08-20T21:38:59.0668420Z >>> # On worker 1: 2024-08-20T21:38:59.0668734Z >>> import torch 2024-08-20T21:38:59.0669098Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:38:59.0669512Z >>> 2024-08-20T21:38:59.0669844Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-08-20T21:38:59.0670314Z >>> rpc.shutdown() 2024-08-20T21:38:59.0670519Z 2024-08-20T21:38:59.0671026Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:59.0671561Z 2024-08-20T21:38:59.0672687Z 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. 2024-08-20T21:38:59.0674243Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:59.0674768Z 2024-08-20T21:38:59.0675224Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef. 2024-08-20T21:38:59.0675835Z 2024-08-20T21:38:59.0676372Z This alternate initialization method can be particularly useful if we want to create multiple 2024-08-20T21:38:59.0677425Z RemoteModule instances that share the same underlying module and reduce memory consumption. 2024-08-20T21:38:59.0678039Z 2024-08-20T21:38:59.0678539Z Moreover, this also provides a workaround for passing script RemoteModule over RPC, 2024-08-20T21:38:59.0679354Z which is not supported. The recommended way is as follows: 2024-08-20T21:38:59.0679765Z 2024-08-20T21:38:59.0679916Z 1. the sender creates a RemoteModule; 2024-08-20T21:38:59.0680434Z 2. the sender sends its ``module_rref`` over RPC; 2024-08-20T21:38:59.0681279Z 3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``. 2024-08-20T21:38:59.0681931Z 2024-08-20T21:38:59.0682043Z Example:: 2024-08-20T21:38:59.0682419Z Run the following code in two different processes: 2024-08-20T21:38:59.0682792Z 2024-08-20T21:38:59.0682952Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:38:59.0683369Z >>> # On worker 0: 2024-08-20T21:38:59.0683678Z >>> import torch 2024-08-20T21:38:59.0684044Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:38:59.0684497Z >>> from torch import nn, Tensor 2024-08-20T21:38:59.0685097Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-08-20T21:38:59.0685675Z >>> 2024-08-20T21:38:59.0686011Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-08-20T21:38:59.0686495Z >>> remote_module = RemoteModule( 2024-08-20T21:38:59.0686967Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-08-20T21:38:59.0687410Z >>> ) 2024-08-20T21:38:59.0688159Z >>> 2024-08-20T21:38:59.0688455Z >>> remote_module1 = rpc.rpc_sync( 2024-08-20T21:38:59.0688872Z >>> "worker1/cpu", 2024-08-20T21:38:59.0689259Z >>> RemoteModule.init_from_module_rref, 2024-08-20T21:38:59.0689807Z >>> ("worker1/cpu", remote_module1.get_module_rref()), 2024-08-20T21:38:59.0690289Z >>> ) 2024-08-20T21:38:59.0690544Z >>> rpc.shutdown() 2024-08-20T21:38:59.0690765Z 2024-08-20T21:38:59.0690877Z >>> # On worker 1: 2024-08-20T21:38:59.0691198Z >>> import torch 2024-08-20T21:38:59.0691551Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:38:59.0691977Z >>> 2024-08-20T21:38:59.0692320Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-08-20T21:38:59.0692434Z >>> rpc.shutdown() 2024-08-20T21:38:59.0692440Z 2024-08-20T21:38:59.0692533Z Args: 2024-08-20T21:38:59.0693099Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-08-20T21:38:59.0693545Z The device can be a local device or a remote device specified by one of the following remote 2024-08-20T21:38:59.0693652Z formats: 2024-08-20T21:38:59.0693658Z 2024-08-20T21:38:59.0693867Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2024-08-20T21:38:59.0694074Z 2. "/" (ex: "trainer0/cuda:0"). 2024-08-20T21:38:59.0694080Z 2024-08-20T21:38:59.0694454Z In addition, the device field can be optional and the default value is "cpu". 2024-08-20T21:38:59.0694811Z module_rref (RRef[nn.Module]): The module reference shared by both the caller and 2024-08-20T21:38:59.0694942Z the created remote module. 2024-08-20T21:38:59.0695434Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2024-08-20T21:38:59.0695787Z to be created. The type object should be decorated by @torch.jit.interface. 2024-08-20T21:38:59.0696729Z If not provided, the generated RemoteModule is not torchscript-able. 2024-08-20T21:38:59.0697084Z Warning, this is an experimental API and susceptible to frequent changes. 2024-08-20T21:38:59.0697097Z 2024-08-20T21:38:59.0697196Z Returns: 2024-08-20T21:38:59.0697532Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-08-20T21:38:59.0697954Z user-provided ``module_rref``, it has a blocking ``forward`` method and an 2024-08-20T21:38:59.0698321Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-08-20T21:38:59.0698627Z on the user-provided module on the remote side. 2024-08-20T21:38:59.0698636Z 2024-08-20T21:38:59.0699233Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:59.0699244Z 2024-08-20T21:38:59.0700237Z 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. 2024-08-20T21:38:59.0700686Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:59.0700697Z 2024-08-20T21:38:59.0701011Z A RemoteModule instance can only be created after RPC initialization. 2024-08-20T21:38:59.0701017Z 2024-08-20T21:38:59.0701378Z It creates a user-specified module on a specified remote node. 2024-08-20T21:38:59.0701718Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2024-08-20T21:38:59.0701850Z executed on the remote node. 2024-08-20T21:38:59.0702262Z It takes care of autograd recording to ensure the backward pass propagates 2024-08-20T21:38:59.0702503Z gradients back to the corresponding remote module. 2024-08-20T21:38:59.0702510Z 2024-08-20T21:38:59.0702819Z It generates two methods ``forward_async`` and ``forward`` based on the 2024-08-20T21:38:59.0703142Z signature of the ``forward`` method of ``module_cls``. ``forward_async`` 2024-08-20T21:38:59.0703486Z runs asynchronously and returns a Future. The arguments of ``forward_async`` 2024-08-20T21:38:59.0703783Z and ``forward`` are the same as the ``forward`` method of the module 2024-08-20T21:38:59.0704072Z returned by the ``module_cls``. 2024-08-20T21:38:59.0704078Z 2024-08-20T21:38:59.0704369Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2024-08-20T21:38:59.0704837Z that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``, 2024-08-20T21:38:59.0705150Z the generated ``RemoteModule`` will have 2 methods with the signatures: 2024-08-20T21:38:59.0705157Z 2024-08-20T21:38:59.0705379Z | ``def forward(input: Tensor) -> Tensor:`` 2024-08-20T21:38:59.0705684Z | ``def forward_async(input: Tensor) -> Future[Tensor]:`` 2024-08-20T21:38:59.0705690Z 2024-08-20T21:38:59.0705788Z Args: 2024-08-20T21:38:59.0706317Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-08-20T21:38:59.0706801Z The format should be "/", where the device field can be parsed as torch.device type. 2024-08-20T21:38:59.0707001Z E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0". 2024-08-20T21:38:59.0707365Z In addition, the device field can be optional and the default value is "cpu". 2024-08-20T21:38:59.0707728Z module_cls (nn.Module): Class for the module to be created remotely. For example, 2024-08-20T21:38:59.0707735Z 2024-08-20T21:38:59.0707877Z >>> class MyModule(nn.Module): 2024-08-20T21:38:59.0708020Z >>> def forward(input): 2024-08-20T21:38:59.0708145Z >>> return input + 1 2024-08-20T21:38:59.0708257Z >>> 2024-08-20T21:38:59.0708380Z >>> module_cls = MyModule 2024-08-20T21:38:59.0708386Z 2024-08-20T21:38:59.0708753Z args (Sequence, optional): args to be passed to ``module_cls``. 2024-08-20T21:38:59.0709035Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2024-08-20T21:38:59.0709041Z 2024-08-20T21:38:59.0709140Z Returns: 2024-08-20T21:38:59.0709475Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-08-20T21:38:59.0709893Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2024-08-20T21:38:59.0710258Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-08-20T21:38:59.0710505Z on the user-provided module on the remote side. 2024-08-20T21:38:59.0710527Z 2024-08-20T21:38:59.0710638Z Example:: 2024-08-20T21:38:59.0710848Z Run the following code in two different processes: 2024-08-20T21:38:59.0710854Z 2024-08-20T21:38:59.0711019Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:38:59.0711131Z >>> # On worker 0: 2024-08-20T21:38:59.0711239Z >>> import torch 2024-08-20T21:38:59.0711419Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:38:59.0711553Z >>> from torch import nn, Tensor 2024-08-20T21:38:59.0711857Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-08-20T21:38:59.0711965Z >>> 2024-08-20T21:38:59.0712149Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-08-20T21:38:59.0712310Z >>> remote_linear_module = RemoteModule( 2024-08-20T21:38:59.0712497Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-08-20T21:38:59.0712595Z >>> ) 2024-08-20T21:38:59.0712726Z >>> input = torch.randn(128, 20) 2024-08-20T21:38:59.0712957Z >>> ret_fut = remote_linear_module.forward_async(input) 2024-08-20T21:38:59.0713075Z >>> ret = ret_fut.wait() 2024-08-20T21:38:59.0713205Z >>> rpc.shutdown() 2024-08-20T21:38:59.0713211Z 2024-08-20T21:38:59.0713321Z >>> # On worker 1: 2024-08-20T21:38:59.0713428Z >>> import torch 2024-08-20T21:38:59.0713601Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:38:59.0713697Z >>> 2024-08-20T21:38:59.0713881Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-08-20T21:38:59.0714005Z >>> rpc.shutdown() 2024-08-20T21:38:59.0714011Z 2024-08-20T21:38:59.0714265Z Furthermore, a more practical example that is combined with 2024-08-20T21:38:59.0714922Z `DistributedDataParallel `__ (DDP) 2024-08-20T21:38:59.0715472Z can be found in this `tutorial `__. 2024-08-20T21:38:59.0715478Z 2024-08-20T21:38:59.0715895Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:59.0715901Z 2024-08-20T21:38:59.0801582Z 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. 2024-08-20T21:38:59.0802343Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:59.0802354Z 2024-08-20T21:38:59.0802969Z DistributedOptimizer takes remote references to parameters scattered 2024-08-20T21:38:59.0803585Z across workers and applies the given optimizer locally for each parameter. 2024-08-20T21:38:59.0803595Z 2024-08-20T21:38:59.0804198Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order 2024-08-20T21:38:59.0804572Z to retrieve the gradients for specific parameters. 2024-08-20T21:38:59.0804582Z 2024-08-20T21:38:59.0804783Z Concurrent calls to 2024-08-20T21:38:59.0805100Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`, 2024-08-20T21:38:59.0805286Z either from the same or different clients, will 2024-08-20T21:38:59.0805688Z be serialized on each worker -- as each worker's optimizer can only work 2024-08-20T21:38:59.0806009Z on one set of gradients at a time. However, there is no guarantee that 2024-08-20T21:38:59.0806417Z the full forward-backward-optimizer sequence will execute for one client 2024-08-20T21:38:59.0806897Z at a time. This means that the gradients being applied may not correspond 2024-08-20T21:38:59.0807234Z to the latest forward pass executed on a given worker. Also, there is no 2024-08-20T21:38:59.0807374Z guaranteed ordering across workers. 2024-08-20T21:38:59.0807380Z 2024-08-20T21:38:59.0807722Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled 2024-08-20T21:38:59.0808074Z by default, so that optimizer updates are not blocked by the Python Global 2024-08-20T21:38:59.0808418Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed 2024-08-20T21:38:59.0808768Z Model Parallel). This feature is currently enabled for most optimizers. You 2024-08-20T21:38:59.0809132Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support 2024-08-20T21:38:59.0809264Z for your own custom optimizers. 2024-08-20T21:38:59.0809270Z 2024-08-20T21:38:59.0809381Z Args: 2024-08-20T21:38:59.0809647Z optimizer_class (optim.Optimizer): the class of optimizer to 2024-08-20T21:38:59.0809785Z instantiate on each worker. 2024-08-20T21:38:59.0810094Z params_rref (list[RRef]): list of RRefs to local or remote parameters 2024-08-20T21:38:59.0810205Z to optimize. 2024-08-20T21:38:59.0810509Z args: arguments to pass to the optimizer constructor on each worker. 2024-08-20T21:38:59.0810839Z kwargs: arguments to pass to the optimizer constructor on each worker. 2024-08-20T21:38:59.0810845Z 2024-08-20T21:38:59.0810961Z Example:: 2024-08-20T21:38:59.0811121Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:38:59.0811350Z >>> import torch.distributed.autograd as dist_autograd 2024-08-20T21:38:59.0811511Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:38:59.0811654Z >>> from torch import optim 2024-08-20T21:38:59.0811917Z >>> from torch.distributed.optim import DistributedOptimizer 2024-08-20T21:38:59.0812013Z >>> 2024-08-20T21:38:59.0812205Z >>> with dist_autograd.context() as context_id: 2024-08-20T21:38:59.0812321Z >>> # Forward pass. 2024-08-20T21:38:59.0812605Z >>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3)) 2024-08-20T21:38:59.0812913Z >>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1)) 2024-08-20T21:38:59.0813127Z >>> loss = rref1.to_here() + rref2.to_here() 2024-08-20T21:38:59.0813392Z >>> 2024-08-20T21:38:59.0813526Z >>> # Backward pass. 2024-08-20T21:38:59.0813735Z >>> dist_autograd.backward(context_id, [loss.sum()]) 2024-08-20T21:38:59.0813890Z >>> 2024-08-20T21:38:59.0814073Z >>> # Optimizer. 2024-08-20T21:38:59.0814356Z >>> dist_optim = DistributedOptimizer( 2024-08-20T21:38:59.0814559Z >>> optim.SGD, 2024-08-20T21:38:59.0814768Z >>> [rref1, rref2], 2024-08-20T21:38:59.0814949Z >>> lr=0.05, 2024-08-20T21:38:59.0815129Z >>> ) 2024-08-20T21:38:59.0815320Z >>> dist_optim.step(context_id) 2024-08-20T21:38:59.0815328Z 2024-08-20T21:38:59.0815692Z __ https://github.com/pytorch/tutorials/pull/1465 2024-08-20T21:38:59.0815710Z 2024-08-20T21:38:59.0816485Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:59.0816497Z 2024-08-20T21:38:59.0818608Z 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. 2024-08-20T21:38:59.0819423Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:59.0819430Z 2024-08-20T21:38:59.0820092Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD `_, 2024-08-20T21:38:59.0820290Z This optimizer runs local optimizer at every step. 2024-08-20T21:38:59.0820863Z After the warm-up stage, it averages parameters periodically afer the local optimizer is applied. 2024-08-20T21:38:59.0820869Z 2024-08-20T21:38:59.0820963Z Args: 2024-08-20T21:38:59.0821107Z optim: The local optimizer. 2024-08-20T21:38:59.0821571Z averager: A model averager instance to run post-localSGD algorithm. 2024-08-20T21:38:59.0821578Z 2024-08-20T21:38:59.0821689Z Example:: 2024-08-20T21:38:59.0821694Z 2024-08-20T21:38:59.0821882Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:38:59.0821993Z >>> import torch 2024-08-20T21:38:59.0822142Z >>> import torch.distributed as dist 2024-08-20T21:38:59.0822540Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2024-08-20T21:38:59.0822664Z >>> import torch.nn as nn 2024-08-20T21:38:59.0822934Z >>> from torch.distributed.optim import PostLocalSGDOptimizer 2024-08-20T21:38:59.0823327Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2024-08-20T21:38:59.0823453Z >>> PostLocalSGDState, 2024-08-20T21:38:59.0823588Z >>> post_localSGD_hook, 2024-08-20T21:38:59.0823703Z >>> ) 2024-08-20T21:38:59.0823799Z >>> 2024-08-20T21:38:59.0824024Z >>> model = nn.parallel.DistributedDataParallel( 2024-08-20T21:38:59.0824222Z >>> module, device_ids=[rank], output_device=rank 2024-08-20T21:38:59.0824317Z >>> ) 2024-08-20T21:38:59.0824425Z >>> 2024-08-20T21:38:59.0824687Z >>> # Register a post-localSGD communication hook. 2024-08-20T21:38:59.0825085Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2024-08-20T21:38:59.0825319Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-08-20T21:38:59.0825417Z >>> 2024-08-20T21:38:59.0825776Z >>> # Create a post-localSGD optimizer that wraps a local optimizer. 2024-08-20T21:38:59.0826152Z >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as 2024-08-20T21:38:59.0826379Z >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-08-20T21:38:59.0826685Z >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01) 2024-08-20T21:38:59.0826831Z >>> opt = PostLocalSGDOptimizer( 2024-08-20T21:38:59.0826952Z >>> optim=local_optim, 2024-08-20T21:38:59.0827313Z >>> averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2024-08-20T21:38:59.0827412Z >>> ) 2024-08-20T21:38:59.0827508Z >>> 2024-08-20T21:38:59.0827856Z >>> # In the first 100 steps, DDP runs global gradient averaging at every step. 2024-08-20T21:38:59.0828458Z >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default), 2024-08-20T21:38:59.0829091Z >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer. 2024-08-20T21:38:59.0829236Z >>> for step in range(0, 200): 2024-08-20T21:38:59.0829352Z >>> opt.zero_grad() 2024-08-20T21:38:59.0829510Z >>> loss = loss_fn(output, labels) 2024-08-20T21:38:59.0829629Z >>> loss.backward() 2024-08-20T21:38:59.0829740Z >>> opt.step() 2024-08-20T21:38:59.0829746Z 2024-08-20T21:38:59.0830178Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:59.0830184Z 2024-08-20T21:38:59.0936100Z 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. 2024-08-20T21:38:59.0936820Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:59.0936859Z 2024-08-20T21:38:59.0937934Z Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group. 2024-08-20T21:38:59.0937944Z 2024-08-20T21:38:59.0938262Z The sharing is done as described by ZeRO_. 2024-08-20T21:38:59.0938270Z 2024-08-20T21:38:59.0938622Z The local optimizer instance in each rank is only 2024-08-20T21:38:59.0939231Z responsible for updating approximately ``1 / world_size`` parameters and 2024-08-20T21:38:59.0939784Z hence only needs to keep ``1 / world_size`` optimizer states. After 2024-08-20T21:38:59.0940403Z parameters are updated locally, each rank will broadcast its parameters to 2024-08-20T21:38:59.0941106Z all other peers to keep all model replicas in the same state. 2024-08-20T21:38:59.0941387Z ``ZeroRedundancyOptimizer`` can be used in conjunction with 2024-08-20T21:38:59.0941849Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak 2024-08-20T21:38:59.0941967Z memory consumption. 2024-08-20T21:38:59.0941978Z 2024-08-20T21:38:59.0942418Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number 2024-08-20T21:38:59.0942753Z of parameters at each rank. Each parameter belongs to a single rank and is 2024-08-20T21:38:59.0943099Z not divided among ranks. The partition is arbitrary and might not match the 2024-08-20T21:38:59.0943283Z the parameter registration or usage order. 2024-08-20T21:38:59.0943289Z 2024-08-20T21:38:59.0943391Z Arguments: 2024-08-20T21:38:59.0943664Z params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s 2024-08-20T21:38:59.0943956Z or :class:`dict` s giving all parameters, which will be sharded 2024-08-20T21:38:59.0944075Z across ranks. 2024-08-20T21:38:59.0944082Z 2024-08-20T21:38:59.0944189Z Keyword Args: 2024-08-20T21:38:59.0944509Z optimizer_class (:class:`torch.nn.Optimizer`): the class of the local 2024-08-20T21:38:59.0944616Z optimizer. 2024-08-20T21:38:59.0944917Z process_group (``ProcessGroup``, optional): ``torch.distributed`` 2024-08-20T21:38:59.0945189Z ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by 2024-08-20T21:38:59.0945385Z :meth:`torch.distributed.init_process_group`). 2024-08-20T21:38:59.0945706Z parameters_as_bucket_view (bool, optional): if ``True``, parameters are 2024-08-20T21:38:59.0946007Z packed into buckets to speed up communication, and ``param.data`` 2024-08-20T21:38:59.0946294Z fields point to bucket views at different offsets; if ``False``, 2024-08-20T21:38:59.0946595Z each individual parameter is communicated separately, and each 2024-08-20T21:38:59.0946807Z ``params.data`` stays intact (default: ``False``). 2024-08-20T21:38:59.0947072Z overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is 2024-08-20T21:38:59.0947436Z overlapped with :class:`DistributedDataParallel` 's gradient 2024-08-20T21:38:59.0947722Z synchronization; this requires (1) either a functional optimizer 2024-08-20T21:38:59.0948108Z for the ``optimizer_class`` argument or one with a functional 2024-08-20T21:38:59.0948347Z equivalent and (2) registering a DDP communication hook 2024-08-20T21:38:59.0948619Z constructed from one of the functions in ``ddp_zero_hook.py``; 2024-08-20T21:38:59.0948862Z parameters are packed into buckets matching those in 2024-08-20T21:38:59.0949087Z :class:`DistributedDataParallel`, meaning that the 2024-08-20T21:38:59.0949292Z ``parameters_as_bucket_view`` argument is ignored. 2024-08-20T21:38:59.0949577Z If ``False``, :meth:`step` runs disjointly after the backward pass 2024-08-20T21:38:59.0949694Z (per normal). 2024-08-20T21:38:59.0949817Z (default: ``False``) 2024-08-20T21:38:59.0950126Z **defaults: any trailing arguments, which are forwarded to the local 2024-08-20T21:38:59.0950231Z optimizer. 2024-08-20T21:38:59.0950237Z 2024-08-20T21:38:59.0950371Z Example:: 2024-08-20T21:38:59.0950377Z 2024-08-20T21:38:59.0950499Z >>> # xdoctest: +SKIP 2024-08-20T21:38:59.0950621Z >>> import torch.nn as nn 2024-08-20T21:38:59.0950917Z >>> from torch.distributed.optim import ZeroRedundancyOptimizer 2024-08-20T21:38:59.0951203Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2024-08-20T21:38:59.0951524Z >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)]) 2024-08-20T21:38:59.0951687Z >>> ddp = DDP(model, device_ids=[rank]) 2024-08-20T21:38:59.0951839Z >>> opt = ZeroRedundancyOptimizer( 2024-08-20T21:38:59.0951962Z >>> ddp.parameters(), 2024-08-20T21:38:59.0952137Z >>> optimizer_class=torch.optim.Adam, 2024-08-20T21:38:59.0952292Z >>> lr=0.01 2024-08-20T21:38:59.0952390Z >>> ) 2024-08-20T21:38:59.0952535Z >>> ddp(inputs).sum().backward() 2024-08-20T21:38:59.0952640Z >>> opt.step() 2024-08-20T21:38:59.0952647Z 2024-08-20T21:38:59.0952765Z .. warning:: 2024-08-20T21:38:59.0953049Z Currently, ``ZeroRedundancyOptimizer`` requires that all of the 2024-08-20T21:38:59.0953304Z passed-in parameters are the same dense type. 2024-08-20T21:38:59.0953309Z 2024-08-20T21:38:59.0953425Z .. warning:: 2024-08-20T21:38:59.0953727Z If you pass ``overlap_with_ddp=True``, be wary of the following: Given 2024-08-20T21:38:59.0954008Z the way that overlapping :class:`DistributedDataParallel` with 2024-08-20T21:38:59.0954337Z :class:`ZeroRedundancyOptimizer` is currently implemented, the first 2024-08-20T21:38:59.0954653Z two or three training iterations do not perform parameter updates in 2024-08-20T21:38:59.0954929Z the optimizer step, depending on if ``static_graph=False`` or 2024-08-20T21:38:59.0955181Z ``static_graph=True``, respectively. This is because it needs 2024-08-20T21:38:59.0955423Z information about the gradient bucketing strategy used by 2024-08-20T21:38:59.0955734Z :class:`DistributedDataParallel`, which is not finalized until the 2024-08-20T21:38:59.0956010Z second forward pass if ``static_graph=False`` or until the third 2024-08-20T21:38:59.0956309Z forward pass if ``static_graph=True``. To adjust for this, one option 2024-08-20T21:38:59.0956456Z is to prepend dummy inputs. 2024-08-20T21:38:59.0956461Z 2024-08-20T21:38:59.0956799Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change. 2024-08-20T21:38:59.0956805Z 2024-08-20T21:38:59.0956975Z .. _ZeRO: https://arxiv.org/abs/1910.02054 2024-08-20T21:38:59.0956999Z 2024-08-20T21:38:59.0957004Z 2024-08-20T21:38:59.0957426Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:59.0957432Z 2024-08-20T21:38:59.1121744Z 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. 2024-08-20T21:38:59.1122599Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:59.1122611Z 2024-08-20T21:38:59.1123241Z Custom reducer class that can be used to specify a custom operation that 2024-08-20T21:38:59.1123974Z reduces losses of multiple microbatches into one value. 2024-08-20T21:38:59.1123985Z 2024-08-20T21:38:59.1124175Z Example: 2024-08-20T21:38:59.1124403Z >>> # xdoctest: +SKIP 2024-08-20T21:38:59.1124665Z >>> sum_reducer = _CustomReducer( 2024-08-20T21:38:59.1124790Z >>> torch.tensor(0.0), 2024-08-20T21:38:59.1124906Z >>> lambda a, b: a + b 2024-08-20T21:38:59.1125011Z >>> ) 2024-08-20T21:38:59.1125016Z 2024-08-20T21:38:59.1125438Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:59.1125444Z 2024-08-20T21:38:59.1580098Z 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. 2024-08-20T21:38:59.1580906Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:59.1580918Z 2024-08-20T21:38:59.1581551Z A decorator for a function indicating that the return value of the function 2024-08-20T21:38:59.1582143Z is guaranteed to be a :class:`~torch.futures.Future` object and this 2024-08-20T21:38:59.1582717Z function can run asynchronously on the RPC callee. More specifically, the 2024-08-20T21:38:59.1583289Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped 2024-08-20T21:38:59.1583861Z function and installs subsequent processing steps as a callback to that 2024-08-20T21:38:59.1584481Z :class:`~torch.futures.Future`. The installed callback will read the value 2024-08-20T21:38:59.1585043Z from the :class:`~torch.futures.Future` when completed and send the 2024-08-20T21:38:59.1585838Z value back as the RPC response. That also means the returned 2024-08-20T21:38:59.1586427Z :class:`~torch.futures.Future` only exists on the callee side and is never 2024-08-20T21:38:59.1587058Z sent through RPC. This decorator is useful when the wrapped function's 2024-08-20T21:38:59.1587342Z (``fn``) execution needs to pause and resume due to, e.g., containing 2024-08-20T21:38:59.1587680Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals. 2024-08-20T21:38:59.1587687Z 2024-08-20T21:38:59.1588012Z .. note:: To enable asynchronous execution, applications must pass the 2024-08-20T21:38:59.1588339Z function object returned by this decorator to RPC APIs. If RPC detected 2024-08-20T21:38:59.1588660Z attributes installed by this decorator, it knows that this function 2024-08-20T21:38:59.1588917Z returns a ``Future`` object and will handle that accordingly. 2024-08-20T21:38:59.1589221Z However, this does not mean this decorator has to be outmost one when 2024-08-20T21:38:59.1589550Z defining a function. For example, when combined with ``@staticmethod`` 2024-08-20T21:38:59.1589845Z or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the 2024-08-20T21:38:59.1590172Z inner decorator to allow the target function be recognized as a static 2024-08-20T21:38:59.1590499Z or class function. This target function can still execute asynchronously 2024-08-20T21:38:59.1590826Z because, when accessed, the static or class method preserves attributes 2024-08-20T21:38:59.1591040Z installed by ``@rpc.functions.async_execution``. 2024-08-20T21:38:59.1591047Z 2024-08-20T21:38:59.1591108Z 2024-08-20T21:38:59.1591224Z Example:: 2024-08-20T21:38:59.1591508Z The returned :class:`~torch.futures.Future` object can come from 2024-08-20T21:38:59.1591684Z :meth:`~torch.distributed.rpc.rpc_async`, 2024-08-20T21:38:59.1592009Z :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future` 2024-08-20T21:38:59.1592248Z constructor. The example below shows directly using the 2024-08-20T21:38:59.1592424Z :class:`~torch.futures.Future` returned by 2024-08-20T21:38:59.1592589Z :meth:`~torch.futures.Future.then`. 2024-08-20T21:38:59.1592595Z 2024-08-20T21:38:59.1592750Z >>> from torch.distributed import rpc 2024-08-20T21:38:59.1592847Z >>> 2024-08-20T21:38:59.1593009Z >>> # omitting setup and shutdown RPC 2024-08-20T21:38:59.1593225Z >>> 2024-08-20T21:38:59.1593339Z >>> # On all workers 2024-08-20T21:38:59.1593498Z >>> @rpc.functions.async_execution 2024-08-20T21:38:59.1593647Z >>> def async_add_chained(to, x, y, z): 2024-08-20T21:38:59.1593941Z >>> # This function runs on "worker1" and returns immediately when 2024-08-20T21:38:59.1594211Z >>> # the callback is installed through the `then(cb)` API. In the 2024-08-20T21:38:59.1594483Z >>> # mean time, the `rpc_async` to "worker2" can run concurrently. 2024-08-20T21:38:59.1594731Z >>> # When the return value of that `rpc_async` arrives at 2024-08-20T21:38:59.1594998Z >>> # "worker1", "worker1" will run the lambda function accordingly 2024-08-20T21:38:59.1595271Z >>> # and set the value for the previously returned `Future`, which 2024-08-20T21:38:59.1595547Z >>> # will then trigger RPC to send the result back to "worker0". 2024-08-20T21:38:59.1595778Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-08-20T21:38:59.1596187Z >>> lambda fut: fut.wait() + z 2024-08-20T21:38:59.1596304Z >>> ) 2024-08-20T21:38:59.1596403Z >>> 2024-08-20T21:38:59.1596511Z >>> # On worker0 2024-08-20T21:38:59.1596649Z >>> # xdoctest: +SKIP 2024-08-20T21:38:59.1596772Z >>> ret = rpc.rpc_sync( 2024-08-20T21:38:59.1596896Z >>> "worker1", 2024-08-20T21:38:59.1597012Z >>> async_add_chained, 2024-08-20T21:38:59.1597174Z >>> args=("worker2", torch.ones(2), 1, 1) 2024-08-20T21:38:59.1597290Z >>> ) 2024-08-20T21:38:59.1597449Z >>> print(ret) # prints tensor([3., 3.]) 2024-08-20T21:38:59.1597455Z 2024-08-20T21:38:59.1597855Z When combined with TorchScript decorators, this decorator must be the 2024-08-20T21:38:59.1597979Z outmost one. 2024-08-20T21:38:59.1597984Z 2024-08-20T21:38:59.1598114Z >>> from torch import Tensor 2024-08-20T21:38:59.1598264Z >>> from torch.futures import Future 2024-08-20T21:38:59.1598547Z >>> from torch.distributed import rpc 2024-08-20T21:38:59.1598693Z >>> 2024-08-20T21:38:59.1598926Z >>> # omitting setup and shutdown RPC 2024-08-20T21:38:59.1599038Z >>> 2024-08-20T21:38:59.1599150Z >>> # On all workers 2024-08-20T21:38:59.1599268Z >>> @torch.jit.script 2024-08-20T21:38:59.1599585Z >>> def script_add(x: Tensor, y: Tensor) -> Tensor: 2024-08-20T21:38:59.1599695Z >>> return x + y 2024-08-20T21:38:59.1599807Z >>> 2024-08-20T21:38:59.1599953Z >>> @rpc.functions.async_execution 2024-08-20T21:38:59.1600072Z >>> @torch.jit.script 2024-08-20T21:38:59.1600434Z >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]: 2024-08-20T21:38:59.1600632Z >>> return rpc.rpc_async(to, script_add, (x, y)) 2024-08-20T21:38:59.1600729Z >>> 2024-08-20T21:38:59.1600848Z >>> # On worker0 2024-08-20T21:38:59.1600965Z >>> ret = rpc.rpc_sync( 2024-08-20T21:38:59.1601069Z >>> "worker1", 2024-08-20T21:38:59.1601189Z >>> async_add, 2024-08-20T21:38:59.1601351Z >>> args=("worker2", torch.ones(2), 1) 2024-08-20T21:38:59.1601448Z >>> ) 2024-08-20T21:38:59.1601618Z >>> print(ret) # prints tensor([2., 2.]) 2024-08-20T21:38:59.1601625Z 2024-08-20T21:38:59.1601934Z When combined with static or class method, this decorator must be the 2024-08-20T21:38:59.1602037Z inner one. 2024-08-20T21:38:59.1602043Z 2024-08-20T21:38:59.1602208Z >>> from torch.distributed import rpc 2024-08-20T21:38:59.1602305Z >>> 2024-08-20T21:38:59.1602466Z >>> # omitting setup and shutdown RPC 2024-08-20T21:38:59.1602560Z >>> 2024-08-20T21:38:59.1602671Z >>> # On all workers 2024-08-20T21:38:59.1602826Z >>> class AsyncExecutionClass: 2024-08-20T21:38:59.1602922Z >>> 2024-08-20T21:38:59.1603034Z >>> @staticmethod 2024-08-20T21:38:59.1603197Z >>> @rpc.functions.async_execution 2024-08-20T21:38:59.1603352Z >>> def static_async_add(to, x, y, z): 2024-08-20T21:38:59.1603592Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-08-20T21:38:59.1603864Z >>> lambda fut: fut.wait() + z 2024-08-20T21:38:59.1603966Z >>> ) 2024-08-20T21:38:59.1604062Z >>> 2024-08-20T21:38:59.1604187Z >>> @classmethod 2024-08-20T21:38:59.1604339Z >>> @rpc.functions.async_execution 2024-08-20T21:38:59.1604505Z >>> def class_async_add(cls, to, x, y, z): 2024-08-20T21:38:59.1604690Z >>> ret_fut = torch.futures.Future() 2024-08-20T21:38:59.1604898Z >>> rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-08-20T21:38:59.1605107Z >>> lambda fut: ret_fut.set_result(fut.wait() + z) 2024-08-20T21:38:59.1605232Z >>> ) 2024-08-20T21:38:59.1605348Z >>> return ret_fut 2024-08-20T21:38:59.1605447Z >>> 2024-08-20T21:38:59.1605615Z >>> @rpc.functions.async_execution 2024-08-20T21:38:59.1605784Z >>> def bound_async_add(self, to, x, y, z): 2024-08-20T21:38:59.1606039Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-08-20T21:38:59.1606197Z >>> lambda fut: fut.wait() + z 2024-08-20T21:38:59.1606301Z >>> ) 2024-08-20T21:38:59.1606415Z >>> 2024-08-20T21:38:59.1606523Z >>> # On worker0 2024-08-20T21:38:59.1606642Z >>> ret = rpc.rpc_sync( 2024-08-20T21:38:59.1606768Z >>> "worker1", 2024-08-20T21:38:59.1606953Z >>> AsyncExecutionClass.static_async_add, 2024-08-20T21:38:59.1607110Z >>> args=("worker2", torch.ones(2), 1, 2) 2024-08-20T21:38:59.1607227Z >>> ) 2024-08-20T21:38:59.1607387Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-20T21:38:59.1607484Z >>> 2024-08-20T21:38:59.1607679Z >>> ret = rpc.rpc_sync( 2024-08-20T21:38:59.1607791Z >>> "worker1", 2024-08-20T21:38:59.1607967Z >>> AsyncExecutionClass.class_async_add, 2024-08-20T21:38:59.1608140Z >>> args=("worker2", torch.ones(2), 1, 2) 2024-08-20T21:38:59.1608241Z >>> ) 2024-08-20T21:38:59.1608394Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-20T21:38:59.1608404Z 2024-08-20T21:38:59.1608638Z This decorator also works with RRef helpers, i.e., . 2024-08-20T21:38:59.1608831Z :meth:`torch.distributed.rpc.RRef.rpc_sync`, 2024-08-20T21:38:59.1609062Z :meth:`torch.distributed.rpc.RRef.rpc_async`, and 2024-08-20T21:38:59.1609251Z :meth:`torch.distributed.rpc.RRef.remote`. 2024-08-20T21:38:59.1609257Z 2024-08-20T21:38:59.1609410Z >>> from torch.distributed import rpc 2024-08-20T21:38:59.1609525Z >>> 2024-08-20T21:38:59.1609709Z >>> # reuse the AsyncExecutionClass class above 2024-08-20T21:38:59.1609926Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-08-20T21:38:59.1610245Z >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2) 2024-08-20T21:38:59.1610403Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-20T21:38:59.1610499Z >>> 2024-08-20T21:38:59.1610722Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-08-20T21:38:59.1611062Z >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait() 2024-08-20T21:38:59.1611231Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-20T21:38:59.1611327Z >>> 2024-08-20T21:38:59.1611536Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-08-20T21:38:59.1611885Z >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here() 2024-08-20T21:38:59.1612042Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-20T21:38:59.1612047Z 2024-08-20T21:38:59.1612479Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:59.1612485Z 2024-08-20T21:38:59.1626401Z 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. 2024-08-20T21:38:59.1627146Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:59.1627192Z 2024-08-20T21:38:59.1627868Z Set device mapping between each RPC caller and callee pair. This 2024-08-20T21:38:59.1628315Z function can be called multiple times to incrementally add 2024-08-20T21:38:59.1628577Z device placement configurations. 2024-08-20T21:38:59.1628607Z 2024-08-20T21:38:59.1628783Z Args: 2024-08-20T21:38:59.1629004Z to (str): Callee name. 2024-08-20T21:38:59.1629525Z device_map (Dict of int, str, or torch.device): Device placement 2024-08-20T21:38:59.1629969Z mappings from this worker to the callee. This map must be 2024-08-20T21:38:59.1630094Z invertible. 2024-08-20T21:38:59.1630100Z 2024-08-20T21:38:59.1630212Z Example: 2024-08-20T21:38:59.1630365Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:38:59.1630479Z >>> # both workers 2024-08-20T21:38:59.1630600Z >>> def add(x, y): 2024-08-20T21:38:59.1630866Z >>> print(x) # tensor([1., 1.], device='cuda:1') 2024-08-20T21:38:59.1631004Z >>> return x + y, (x + y).to(2) 2024-08-20T21:38:59.1631112Z >>> 2024-08-20T21:38:59.1631226Z >>> # on worker 0 2024-08-20T21:38:59.1631411Z >>> options = TensorPipeRpcBackendOptions( 2024-08-20T21:38:59.1631547Z >>> num_worker_threads=8, 2024-08-20T21:38:59.1631692Z >>> device_maps={"worker1": {0: 1}} 2024-08-20T21:38:59.1631939Z >>> # maps worker0's cuda:0 to worker1's cuda:1 2024-08-20T21:38:59.1632050Z >>> ) 2024-08-20T21:38:59.1632220Z >>> options.set_device_map("worker1", {1: 2}) 2024-08-20T21:38:59.1632467Z >>> # maps worker0's cuda:1 to worker1's cuda:2 2024-08-20T21:38:59.1632563Z >>> 2024-08-20T21:38:59.1632673Z >>> rpc.init_rpc( 2024-08-20T21:38:59.1632792Z >>> "worker0", 2024-08-20T21:38:59.1632971Z >>> rank=0, 2024-08-20T21:38:59.1633084Z >>> world_size=2, 2024-08-20T21:38:59.1633275Z >>> backend=rpc.BackendType.TENSORPIPE, 2024-08-20T21:38:59.1633409Z >>> rpc_backend_options=options 2024-08-20T21:38:59.1633505Z >>> ) 2024-08-20T21:38:59.1633617Z >>> 2024-08-20T21:38:59.1633737Z >>> x = torch.ones(2) 2024-08-20T21:38:59.1633960Z >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1)) 2024-08-20T21:38:59.1634240Z >>> # The first argument will be moved to cuda:1 on worker1. When 2024-08-20T21:38:59.1634500Z >>> # sending the return value back, it will follow the invert of 2024-08-20T21:38:59.1634755Z >>> # the device map, and hence will be moved back to cuda:0 and 2024-08-20T21:38:59.1634891Z >>> # cuda:1 on worker0 2024-08-20T21:38:59.1635166Z >>> print(rets[0]) # tensor([2., 2.], device='cuda:0') 2024-08-20T21:38:59.1635445Z >>> print(rets[1]) # tensor([2., 2.], device='cuda:1') 2024-08-20T21:38:59.1635451Z 2024-08-20T21:38:59.1635870Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:59.1635877Z 2024-08-20T21:38:59.1852344Z 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. 2024-08-20T21:38:59.1853200Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:59.1853213Z 2024-08-20T21:38:59.1854387Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to 2024-08-20T21:38:59.1854840Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``. 2024-08-20T21:38:59.1854847Z 2024-08-20T21:38:59.1854952Z Keyword Args: 2024-08-20T21:38:59.1855233Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2024-08-20T21:38:59.1855726Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2024-08-20T21:38:59.1856441Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2024-08-20T21:38:59.1856636Z as a placeholder. default: None. 2024-08-20T21:38:59.1856940Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2024-08-20T21:38:59.1858005Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2024-08-20T21:38:59.1858990Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2024-08-20T21:38:59.1859284Z input_kwarg_layouts (Dict[str, Placement]): 2024-08-20T21:38:59.1860355Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2024-08-20T21:38:59.1871204Z default: None 2024-08-20T21:38:59.1871718Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2024-08-20T21:38:59.1872621Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2024-08-20T21:38:59.1872832Z have the desired DTensor layouts. default: None. 2024-08-20T21:38:59.1872991Z use_local_output (bool, optional): 2024-08-20T21:38:59.1873523Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2024-08-20T21:38:59.1873633Z Returns: 2024-08-20T21:38:59.1874257Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs. 2024-08-20T21:38:59.1874267Z 2024-08-20T21:38:59.1874398Z Example:: 2024-08-20T21:38:59.1874545Z >>> # xdoctest: +SKIP(failing) 2024-08-20T21:38:59.1874981Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput 2024-08-20T21:38:59.1875248Z >>> from torch.distributed.device_mesh import init_device_mesh 2024-08-20T21:38:59.1875364Z >>> ... 2024-08-20T21:38:59.1875995Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2024-08-20T21:38:59.1876169Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2024-08-20T21:38:59.1876284Z >>> 2024-08-20T21:38:59.1876773Z >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor 2024-08-20T21:38:59.1876977Z >>> # and then redistributed to Replicated DTensor. 2024-08-20T21:38:59.1877114Z >>> parallelize_module( 2024-08-20T21:38:59.1877304Z >>> block, # this can be a submodule or module 2024-08-20T21:38:59.1877427Z >>> tp_mesh, 2024-08-20T21:38:59.1877550Z >>> parallelize_plan={ 2024-08-20T21:38:59.1877708Z >>> "attn": PrepareModuleInput( 2024-08-20T21:38:59.1877916Z >>> input_layouts=(Shard(0), None, None, ...), 2024-08-20T21:38:59.1878153Z >>> desired_input_layouts=(Replicate(), None, None, ...) 2024-08-20T21:38:59.1878258Z >>> ), 2024-08-20T21:38:59.1878498Z >>> } 2024-08-20T21:38:59.1878598Z >>> ) 2024-08-20T21:38:59.1878605Z 2024-08-20T21:38:59.1879048Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:59.1879055Z 2024-08-20T21:38:59.1880159Z 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. 2024-08-20T21:38:59.1880597Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:59.1880603Z 2024-08-20T21:38:59.1881301Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to 2024-08-20T21:38:59.1881750Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``. 2024-08-20T21:38:59.1881757Z 2024-08-20T21:38:59.1881878Z Keyword Args: 2024-08-20T21:38:59.1882098Z output_layouts (Union[Placement, Tuple[Placement]]): 2024-08-20T21:38:59.1882600Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2024-08-20T21:38:59.1883182Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2024-08-20T21:38:59.1883377Z ``None`` need to be specified as a placeholder. 2024-08-20T21:38:59.1883719Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2024-08-20T21:38:59.1884309Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2024-08-20T21:38:59.1884458Z have the desired DTensor layouts. 2024-08-20T21:38:59.1884602Z use_local_output (bool, optional): 2024-08-20T21:38:59.1885149Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2024-08-20T21:38:59.1885250Z Returns: 2024-08-20T21:38:59.1885762Z A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs. 2024-08-20T21:38:59.1885769Z 2024-08-20T21:38:59.1885878Z Example:: 2024-08-20T21:38:59.1886009Z >>> # xdoctest: +SKIP(failing) 2024-08-20T21:38:59.1886463Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput 2024-08-20T21:38:59.1886725Z >>> from torch.distributed.device_mesh import init_device_mesh 2024-08-20T21:38:59.1886830Z >>> ... 2024-08-20T21:38:59.1887286Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2024-08-20T21:38:59.1887454Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2024-08-20T21:38:59.1887552Z >>> 2024-08-20T21:38:59.1888142Z >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor 2024-08-20T21:38:59.1888334Z >>> # and then redistributed to Sharded DTensor. 2024-08-20T21:38:59.1888471Z >>> parallelize_module( 2024-08-20T21:38:59.1888720Z >>> block, # this can be a submodule or module 2024-08-20T21:38:59.1888831Z >>> tp_mesh, 2024-08-20T21:38:59.1889032Z >>> parallelize_plan = PrepareModuleOutput( 2024-08-20T21:38:59.1889179Z >>> output_layouts=Replicate(), 2024-08-20T21:38:59.1889338Z >>> desired_output_layouts=Shard(0) 2024-08-20T21:38:59.1889456Z >>> ) 2024-08-20T21:38:59.1889554Z >>> ) 2024-08-20T21:38:59.1889560Z 2024-08-20T21:38:59.1889984Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:59.1889990Z 2024-08-20T21:38:59.2476016Z 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. 2024-08-20T21:38:59.2477690Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:59.2478277Z 2024-08-20T21:38:59.2478691Z The `MixtureSameFamily` distribution implements a (batch of) mixture 2024-08-20T21:38:59.2479527Z distribution where all component are from different parameterizations of 2024-08-20T21:38:59.2480348Z the same distribution type. It is parameterized by a `Categorical` 2024-08-20T21:38:59.2481072Z "selecting distribution" (over `k` component) and a component 2024-08-20T21:38:59.2481791Z distribution, i.e., a `Distribution` with a rightmost batch shape 2024-08-20T21:38:59.2482495Z (equal to `[k]`) which indexes each (batch of) component. 2024-08-20T21:38:59.2482883Z 2024-08-20T21:38:59.2483014Z Examples:: 2024-08-20T21:38:59.2483189Z 2024-08-20T21:38:59.2483350Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:38:59.2483966Z >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally 2024-08-20T21:38:59.2484574Z >>> # weighted normal distributions 2024-08-20T21:38:59.2485024Z >>> mix = D.Categorical(torch.ones(5,)) 2024-08-20T21:38:59.2485559Z >>> comp = D.Normal(torch.randn(5,), torch.rand(5,)) 2024-08-20T21:38:59.2486093Z >>> gmm = MixtureSameFamily(mix, comp) 2024-08-20T21:38:59.2486411Z 2024-08-20T21:38:59.2486701Z >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally 2024-08-20T21:38:59.2487339Z >>> # weighted bivariate normal distributions 2024-08-20T21:38:59.2487849Z >>> mix = D.Categorical(torch.ones(5,)) 2024-08-20T21:38:59.2488299Z >>> comp = D.Independent(D.Normal( 2024-08-20T21:38:59.2489051Z ... torch.randn(5,2), torch.rand(5,2)), 1) 2024-08-20T21:38:59.2489564Z >>> gmm = MixtureSameFamily(mix, comp) 2024-08-20T21:38:59.2489882Z 2024-08-20T21:38:59.2490136Z >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each 2024-08-20T21:38:59.2490855Z >>> # consisting of 5 random weighted bivariate normal distributions 2024-08-20T21:38:59.2491481Z >>> mix = D.Categorical(torch.rand(3,5)) 2024-08-20T21:38:59.2491941Z >>> comp = D.Independent(D.Normal( 2024-08-20T21:38:59.2492439Z ... torch.randn(3,5,2), torch.rand(3,5,2)), 1) 2024-08-20T21:38:59.2492958Z >>> gmm = MixtureSameFamily(mix, comp) 2024-08-20T21:38:59.2493284Z 2024-08-20T21:38:59.2493380Z Args: 2024-08-20T21:38:59.2493899Z mixture_distribution: `torch.distributions.Categorical`-like 2024-08-20T21:38:59.2494605Z instance. Manages the probability of selecting component. 2024-08-20T21:38:59.2495278Z The number of categories must match the rightmost batch 2024-08-20T21:38:59.2496246Z dimension of the `component_distribution`. Must have either 2024-08-20T21:38:59.2496884Z scalar `batch_shape` or `batch_shape` matching 2024-08-20T21:38:59.2497511Z `component_distribution.batch_shape[:-1]` 2024-08-20T21:38:59.2498223Z component_distribution: `torch.distributions.Distribution`-like 2024-08-20T21:38:59.2499161Z instance. Right-most batch dimension indexes component. 2024-08-20T21:38:59.2499573Z 2024-08-20T21:38:59.2500010Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:59.2500517Z 2024-08-20T21:38:59.2597299Z 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. 2024-08-20T21:38:59.2599736Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:59.2600507Z 2024-08-20T21:38:59.2600782Z Creates a RelaxedBernoulli distribution, parametrized by 2024-08-20T21:38:59.2601477Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits` 2024-08-20T21:38:59.2602225Z (but not both). This is a relaxed version of the `Bernoulli` distribution, 2024-08-20T21:38:59.2602988Z so the values are in (0, 1), and has reparametrizable samples. 2024-08-20T21:38:59.2603433Z 2024-08-20T21:38:59.2603559Z Example:: 2024-08-20T21:38:59.2603716Z 2024-08-20T21:38:59.2604005Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-08-20T21:38:59.2604539Z >>> m = RelaxedBernoulli(torch.tensor([2.2]), 2024-08-20T21:38:59.2605077Z ... torch.tensor([0.1, 0.2, 0.3, 0.99])) 2024-08-20T21:38:59.2605550Z >>> m.sample() 2024-08-20T21:38:59.2605896Z tensor([ 0.2951, 0.3442, 0.8918, 0.9021]) 2024-08-20T21:38:59.2606229Z 2024-08-20T21:38:59.2606324Z Args: 2024-08-20T21:38:59.2606656Z temperature (Tensor): relaxation temperature 2024-08-20T21:38:59.2607229Z probs (Number, Tensor): the probability of sampling `1` 2024-08-20T21:38:59.2607931Z logits (Number, Tensor): the log-odds of sampling `1` 2024-08-20T21:38:59.2608311Z 2024-08-20T21:38:59.2608741Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:59.2609255Z 2024-08-20T21:38:59.2613198Z 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. 2024-08-20T21:38:59.2615441Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:59.2616079Z 2024-08-20T21:38:59.2616391Z Creates a RelaxedOneHotCategorical distribution parametrized by 2024-08-20T21:38:59.2617232Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`. 2024-08-20T21:38:59.2618146Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so 2024-08-20T21:38:59.2618970Z its samples are on simplex, and are reparametrizable. 2024-08-20T21:38:59.2619373Z 2024-08-20T21:38:59.2619488Z Example:: 2024-08-20T21:38:59.2619887Z 2024-08-20T21:38:59.2620171Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-08-20T21:38:59.2620838Z >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]), 2024-08-20T21:38:59.2621507Z ... torch.tensor([0.1, 0.2, 0.3, 0.4])) 2024-08-20T21:38:59.2622072Z >>> m.sample() 2024-08-20T21:38:59.2622411Z tensor([ 0.1294, 0.2324, 0.3859, 0.2523]) 2024-08-20T21:38:59.2622814Z 2024-08-20T21:38:59.2622916Z Args: 2024-08-20T21:38:59.2623250Z temperature (Tensor): relaxation temperature 2024-08-20T21:38:59.2623829Z probs (Tensor): event probabilities 2024-08-20T21:38:59.2624491Z logits (Tensor): unnormalized log probability for each event 2024-08-20T21:38:59.2624925Z 2024-08-20T21:38:59.2625443Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:59.2626033Z 2024-08-20T21:38:59.5888076Z 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. 2024-08-20T21:38:59.5890690Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:59.5892050Z Return a new dict with new, potentially nested, key value pair 2024-08-20T21:38:59.5892572Z 2024-08-20T21:38:59.5892932Z >>> purchase = {'name': 'Alice', 2024-08-20T21:38:59.5893813Z ... 'order': {'items': ['Apple', 'Orange'], 2024-08-20T21:38:59.5894850Z ... 'costs': [0.50, 1.25]}, 2024-08-20T21:38:59.5896251Z ... 'credit card': '5555-1234-1234-1234'} 2024-08-20T21:38:59.5897700Z >>> assoc_in(purchase, ['order', 'costs'], [0.25, 1.00]) # doctest: +SKIP 2024-08-20T21:38:59.5898867Z {'credit card': '5555-1234-1234-1234', 2024-08-20T21:38:59.5899667Z 'name': 'Alice', 2024-08-20T21:38:59.5900290Z 'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}} 2024-08-20T21:38:59.5900811Z 2024-08-20T21:38:59.5901362Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:59.5901901Z 2024-08-20T21:38:59.5902979Z 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. 2024-08-20T21:38:59.5904501Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:59.5905234Z Update value in a (potentially) nested dictionary 2024-08-20T21:38:59.5905597Z 2024-08-20T21:38:59.5905701Z inputs: 2024-08-20T21:38:59.5906059Z d - dictionary on which to operate 2024-08-20T21:38:59.5906767Z keys - list or tuple giving the location of the value to be changed in d 2024-08-20T21:38:59.5907473Z func - function to operate on that value 2024-08-20T21:38:59.5907812Z 2024-08-20T21:38:59.5908115Z If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the 2024-08-20T21:38:59.5908920Z original dictionary with v replaced by func(v), but does not mutate the 2024-08-20T21:38:59.5909528Z original dictionary. 2024-08-20T21:38:59.5909765Z 2024-08-20T21:38:59.5910100Z If k0 is not a key in d, update_in creates nested dictionaries to the depth 2024-08-20T21:38:59.5910925Z specified by the keys, with the innermost value set to func(default). 2024-08-20T21:38:59.5911403Z 2024-08-20T21:38:59.5911529Z >>> inc = lambda x: x + 1 2024-08-20T21:38:59.5911972Z >>> update_in({'a': 0}, ['a'], inc) 2024-08-20T21:38:59.5912403Z {'a': 1} 2024-08-20T21:38:59.5912563Z 2024-08-20T21:38:59.5912763Z >>> transaction = {'name': 'Alice', 2024-08-20T21:38:59.5913320Z ... 'purchase': {'items': ['Apple', 'Orange'], 2024-08-20T21:38:59.5913941Z ... 'costs': [0.50, 1.25]}, 2024-08-20T21:38:59.5914530Z ... 'credit card': '5555-1234-1234-1234'} 2024-08-20T21:38:59.5915241Z >>> update_in(transaction, ['purchase', 'costs'], sum) # doctest: +SKIP 2024-08-20T21:38:59.5915912Z {'credit card': '5555-1234-1234-1234', 2024-08-20T21:38:59.5916553Z 'name': 'Alice', 2024-08-20T21:38:59.5917048Z 'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}} 2024-08-20T21:38:59.5917460Z 2024-08-20T21:38:59.5917631Z >>> # updating a value when k0 is not in d 2024-08-20T21:38:59.5918149Z >>> update_in({}, [1, 2, 3], str, default="bar") 2024-08-20T21:38:59.5918778Z {1: {2: {3: 'bar'}}} 2024-08-20T21:38:59.5919214Z >>> update_in({1: 'foo'}, [2, 3, 4], inc, 0) 2024-08-20T21:38:59.5919712Z {1: 'foo', 2: {3: {4: 1}}} 2024-08-20T21:38:59.5920042Z 2024-08-20T21:38:59.5920608Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:59.5921119Z 2024-08-20T21:38:59.5922196Z 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. 2024-08-20T21:38:59.5923747Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:59.5924494Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys. 2024-08-20T21:38:59.5924902Z 2024-08-20T21:38:59.5925179Z If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless 2024-08-20T21:38:59.5925913Z ``no_default`` is specified, then it raises KeyError or IndexError. 2024-08-20T21:38:59.5926358Z 2024-08-20T21:38:59.5926678Z ``get_in`` is a generalization of ``operator.getitem`` for nested data 2024-08-20T21:38:59.5927325Z structures such as dictionaries and lists. 2024-08-20T21:38:59.5927683Z 2024-08-20T21:38:59.5927873Z >>> transaction = {'name': 'Alice', 2024-08-20T21:38:59.5928447Z ... 'purchase': {'items': ['Apple', 'Orange'], 2024-08-20T21:38:59.5929197Z ... 'costs': [0.50, 1.25]}, 2024-08-20T21:38:59.5929795Z ... 'credit card': '5555-1234-1234-1234'} 2024-08-20T21:38:59.5930397Z >>> get_in(['purchase', 'items', 0], transaction) 2024-08-20T21:38:59.5930872Z 'Apple' 2024-08-20T21:38:59.5931227Z >>> get_in(['name'], transaction) 2024-08-20T21:38:59.5931650Z 'Alice' 2024-08-20T21:38:59.5932030Z >>> get_in(['purchase', 'total'], transaction) 2024-08-20T21:38:59.5932647Z >>> get_in(['purchase', 'items', 'apple'], transaction) 2024-08-20T21:38:59.5933272Z >>> get_in(['purchase', 'items', 10], transaction) 2024-08-20T21:38:59.5933864Z >>> get_in(['purchase', 'total'], transaction, 0) 2024-08-20T21:38:59.5934320Z 0 2024-08-20T21:38:59.5934657Z >>> get_in(['y'], {}, no_default=True) 2024-08-20T21:38:59.5935092Z Traceback (most recent call last): 2024-08-20T21:38:59.5935497Z ... 2024-08-20T21:38:59.5935803Z KeyError: 'y' 2024-08-20T21:38:59.5935988Z 2024-08-20T21:38:59.5936094Z See Also: 2024-08-20T21:38:59.5936384Z itertoolz.get 2024-08-20T21:38:59.5936714Z operator.getitem 2024-08-20T21:38:59.5937022Z 2024-08-20T21:38:59.5937589Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:59.5938098Z 2024-08-20T21:38:59.5939185Z 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. 2024-08-20T21:38:59.5940698Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:59.5941358Z Group a collection by a key function 2024-08-20T21:38:59.5941668Z 2024-08-20T21:38:59.5941984Z >>> names = ['Alice', 'Bob', 'Charlie', 'Dan', 'Edith', 'Frank'] 2024-08-20T21:38:59.5942567Z >>> groupby(len, names) # doctest: +SKIP 2024-08-20T21:38:59.5943207Z {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} 2024-08-20T21:38:59.5943639Z 2024-08-20T21:38:59.5943782Z >>> iseven = lambda x: x % 2 == 0 2024-08-20T21:38:59.5944321Z >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP 2024-08-20T21:38:59.5944879Z {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} 2024-08-20T21:38:59.5945210Z 2024-08-20T21:38:59.5945453Z Non-callable keys imply grouping on a member. 2024-08-20T21:38:59.5945897Z 2024-08-20T21:38:59.5946197Z >>> groupby('gender', [{'name': 'Alice', 'gender': 'F'}, 2024-08-20T21:38:59.5946812Z ... {'name': 'Bob', 'gender': 'M'}, 2024-08-20T21:38:59.5947476Z ... {'name': 'Charlie', 'gender': 'M'}]) # doctest:+SKIP 2024-08-20T21:38:59.5948100Z {'F': [{'gender': 'F', 'name': 'Alice'}], 2024-08-20T21:38:59.5948621Z 'M': [{'gender': 'M', 'name': 'Bob'}, 2024-08-20T21:38:59.5949129Z {'gender': 'M', 'name': 'Charlie'}]} 2024-08-20T21:38:59.5949461Z 2024-08-20T21:38:59.5949648Z Not to be confused with ``itertools.groupby`` 2024-08-20T21:38:59.5950001Z 2024-08-20T21:38:59.5950129Z See Also: 2024-08-20T21:38:59.5950393Z countby 2024-08-20T21:38:59.5950666Z 2024-08-20T21:38:59.5951227Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:59.5951739Z 2024-08-20T21:38:59.9312220Z 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. 2024-08-20T21:38:59.9313662Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:59.9314533Z Applies Batch Normalization over a N-Dimensional input. 2024-08-20T21:38:59.9315210Z 2024-08-20T21:38:59.9316182Z The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper 2024-08-20T21:38:59.9317419Z `Batch Normalization: Accelerating Deep Network Training by Reducing 2024-08-20T21:38:59.9318192Z Internal Covariate Shift `__ . 2024-08-20T21:38:59.9318768Z 2024-08-20T21:38:59.9319223Z .. math:: 2024-08-20T21:38:59.9319417Z 2024-08-20T21:38:59.9319886Z y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta 2024-08-20T21:38:59.9320417Z 2024-08-20T21:38:59.9320838Z The mean and standard-deviation are calculated per-dimension over all 2024-08-20T21:38:59.9321759Z mini-batches of the same process groups. :math:`\gamma` and :math:`\beta` 2024-08-20T21:38:59.9322603Z are learnable parameter vectors of size `C` (where `C` is the input size). 2024-08-20T21:38:59.9323373Z By default, the elements of :math:`\gamma` are sampled from 2024-08-20T21:38:59.9324096Z :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. 2024-08-20T21:38:59.9325022Z The standard-deviation is calculated via the biased estimator, equivalent to 2024-08-20T21:38:59.9325704Z `torch.var(input, unbiased=False)`. 2024-08-20T21:38:59.9326006Z 2024-08-20T21:38:59.9326361Z Also by default, during training this layer keeps running estimates of its 2024-08-20T21:38:59.9327186Z computed mean and variance, which are then used for normalization during 2024-08-20T21:38:59.9328027Z evaluation. The running estimates are kept with a default :attr:`momentum` 2024-08-20T21:38:59.9328640Z of 0.1. 2024-08-20T21:38:59.9328798Z 2024-08-20T21:38:59.9329132Z If :attr:`track_running_stats` is set to ``False``, this layer then does not 2024-08-20T21:38:59.9329939Z keep running estimates, and batch statistics are instead used during 2024-08-20T21:38:59.9330536Z evaluation time as well. 2024-08-20T21:38:59.9330778Z 2024-08-20T21:38:59.9330886Z .. note:: 2024-08-20T21:38:59.9331370Z This :attr:`momentum` argument is different from one used in optimizer 2024-08-20T21:38:59.9332177Z classes and the conventional notion of momentum. Mathematically, the 2024-08-20T21:38:59.9332850Z update rule for running statistics here is 2024-08-20T21:38:59.9333731Z :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, 2024-08-20T21:38:59.9334630Z where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the 2024-08-20T21:38:59.9335232Z new observed value. 2024-08-20T21:38:59.9335467Z 2024-08-20T21:38:59.9335896Z Because the Batch Normalization is done for each channel in the ``C`` dimension, computing 2024-08-20T21:38:59.9337091Z statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch 2024-08-20T21:38:59.9337937Z Normalization or Spatio-temporal Batch Normalization. 2024-08-20T21:38:59.9338338Z 2024-08-20T21:38:59.9338529Z Currently :class:`SyncBatchNorm` only supports 2024-08-20T21:38:59.9339296Z :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use 2024-08-20T21:38:59.9340174Z :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert 2024-08-20T21:38:59.9340934Z :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping 2024-08-20T21:38:59.9341501Z Network with DDP. 2024-08-20T21:38:59.9341727Z 2024-08-20T21:38:59.9341827Z Args: 2024-08-20T21:38:59.9342214Z num_features: :math:`C` from an expected input of size 2024-08-20T21:38:59.9342721Z :math:`(N, C, +)` 2024-08-20T21:38:59.9343245Z eps: a value added to the denominator for numerical stability. 2024-08-20T21:38:59.9343885Z Default: ``1e-5`` 2024-08-20T21:38:59.9344383Z momentum: the value used for the running_mean and running_var 2024-08-20T21:38:59.9345122Z computation. Can be set to ``None`` for cumulative moving average 2024-08-20T21:38:59.9345761Z (i.e. simple average). Default: 0.1 2024-08-20T21:38:59.9346374Z affine: a boolean value that when set to ``True``, this module has 2024-08-20T21:38:59.9347047Z learnable affine parameters. Default: ``True`` 2024-08-20T21:38:59.9347730Z track_running_stats: a boolean value that when set to ``True``, this 2024-08-20T21:38:59.9348592Z module tracks the running mean and variance, and when set to ``False``, 2024-08-20T21:38:59.9349427Z this module does not track such statistics, and initializes statistics 2024-08-20T21:38:59.9350224Z buffers :attr:`running_mean` and :attr:`running_var` as ``None``. 2024-08-20T21:38:59.9351035Z When these buffers are ``None``, this module always uses batch statistics. 2024-08-20T21:38:59.9351758Z in both training and eval modes. Default: ``True`` 2024-08-20T21:38:59.9352481Z process_group: synchronization of stats happen within each process group 2024-08-20T21:38:59.9353308Z individually. Default behavior is synchronization across the whole 2024-08-20T21:38:59.9353881Z world 2024-08-20T21:38:59.9354074Z 2024-08-20T21:38:59.9354176Z Shape: 2024-08-20T21:38:59.9354539Z - Input: :math:`(N, C, +)` 2024-08-20T21:38:59.9355063Z - Output: :math:`(N, C, +)` (same shape as input) 2024-08-20T21:38:59.9355437Z 2024-08-20T21:38:59.9355549Z .. note:: 2024-08-20T21:38:59.9356063Z Synchronization of batchnorm statistics occurs only while training, i.e. 2024-08-20T21:38:59.9356843Z synchronization is disabled when ``model.eval()`` is set or if 2024-08-20T21:38:59.9357464Z ``self.training`` is otherwise ``False``. 2024-08-20T21:38:59.9357815Z 2024-08-20T21:38:59.9357924Z Examples:: 2024-08-20T21:38:59.9358102Z 2024-08-20T21:38:59.9358236Z >>> # xdoctest: +SKIP 2024-08-20T21:38:59.9358746Z >>> # With Learnable Parameters 2024-08-20T21:38:59.9359200Z >>> m = nn.SyncBatchNorm(100) 2024-08-20T21:38:59.9359672Z >>> # creating process group (optional) 2024-08-20T21:38:59.9360199Z >>> # ranks is a list of int identifying rank ids. 2024-08-20T21:38:59.9360708Z >>> ranks = list(range(8)) 2024-08-20T21:38:59.9361122Z >>> r1, r2 = ranks[:4], ranks[4:] 2024-08-20T21:38:59.9361620Z >>> # Note: every rank calls into new_group for every 2024-08-20T21:38:59.9362221Z >>> # process group created, even if that rank is not 2024-08-20T21:38:59.9362733Z >>> # part of the group. 2024-08-20T21:38:59.9363331Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2024-08-20T21:38:59.9364131Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2024-08-20T21:38:59.9364838Z >>> # Without Learnable Parameters 2024-08-20T21:38:59.9365446Z >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group) 2024-08-20T21:38:59.9366095Z >>> input = torch.randn(20, 100, 35, 45, 10) 2024-08-20T21:38:59.9366569Z >>> output = m(input) 2024-08-20T21:38:59.9366815Z 2024-08-20T21:38:59.9366967Z >>> # network is nn.BatchNorm layer 2024-08-20T21:38:59.9367675Z >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group) 2024-08-20T21:38:59.9368464Z >>> # only single gpu per process is currently supported 2024-08-20T21:38:59.9369187Z >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel( 2024-08-20T21:38:59.9369825Z >>> sync_bn_network, 2024-08-20T21:38:59.9370341Z >>> device_ids=[args.local_rank], 2024-08-20T21:38:59.9370890Z >>> output_device=args.local_rank) 2024-08-20T21:38:59.9371336Z 2024-08-20T21:38:59.9371923Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:59.9372431Z 2024-08-20T21:38:59.9373546Z 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. 2024-08-20T21:38:59.9375040Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:59.9376003Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers. 2024-08-20T21:38:59.9376630Z 2024-08-20T21:38:59.9376807Z Args: 2024-08-20T21:38:59.9377333Z module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers 2024-08-20T21:38:59.9378147Z process_group (optional): process group to scope synchronization, 2024-08-20T21:38:59.9378764Z default is the whole world 2024-08-20T21:38:59.9379089Z 2024-08-20T21:38:59.9379194Z Returns: 2024-08-20T21:38:59.9379748Z The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm` 2024-08-20T21:38:59.9380595Z layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer, 2024-08-20T21:38:59.9381400Z a new :class:`torch.nn.SyncBatchNorm` layer object will be returned 2024-08-20T21:38:59.9381990Z instead. 2024-08-20T21:38:59.9382185Z 2024-08-20T21:38:59.9382297Z Example:: 2024-08-20T21:38:59.9382493Z 2024-08-20T21:38:59.9382654Z >>> # Network with nn.BatchNorm layer 2024-08-20T21:38:59.9383195Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-20T21:38:59.9383710Z >>> module = torch.nn.Sequential( 2024-08-20T21:38:59.9384201Z >>> torch.nn.Linear(20, 100), 2024-08-20T21:38:59.9384713Z >>> torch.nn.BatchNorm1d(100), 2024-08-20T21:38:59.9385156Z >>> ).cuda() 2024-08-20T21:38:59.9385596Z >>> # creating process group (optional) 2024-08-20T21:38:59.9386152Z >>> # ranks is a list of int identifying rank ids. 2024-08-20T21:38:59.9386647Z >>> ranks = list(range(8)) 2024-08-20T21:38:59.9387088Z >>> r1, r2 = ranks[:4], ranks[4:] 2024-08-20T21:38:59.9387615Z >>> # Note: every rank calls into new_group for every 2024-08-20T21:38:59.9388200Z >>> # process group created, even if that rank is not 2024-08-20T21:38:59.9388718Z >>> # part of the group. 2024-08-20T21:38:59.9389171Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:38:59.9389850Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2024-08-20T21:38:59.9390643Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2024-08-20T21:38:59.9391524Z >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group) 2024-08-20T21:38:59.9392191Z 2024-08-20T21:38:59.9392304Z 2024-08-20T21:38:59.9392881Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:59.9393405Z 2024-08-20T21:38:59.9584774Z 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. 2024-08-20T21:38:59.9587019Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:59.9587543Z 2024-08-20T21:38:59.9588043Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`. 2024-08-20T21:38:59.9588663Z 2024-08-20T21:38:59.9589076Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can 2024-08-20T21:38:59.9589966Z be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively. 2024-08-20T21:38:59.9590492Z 2024-08-20T21:38:59.9590946Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be 2024-08-20T21:38:59.9591979Z a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input; a `NamedShape` 2024-08-20T21:38:59.9592766Z (tuple of `(name, size)` tuples) for `NamedTensor` input. 2024-08-20T21:38:59.9593171Z 2024-08-20T21:38:59.9593268Z Shape: 2024-08-20T21:38:59.9593849Z - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at 2024-08-20T21:38:59.9594708Z dimension :attr:`dim` and :math:`*` means any number of dimensions including none. 2024-08-20T21:38:59.9595663Z - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and 2024-08-20T21:38:59.9596881Z :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`. 2024-08-20T21:38:59.9597227Z 2024-08-20T21:38:59.9597339Z Args: 2024-08-20T21:38:59.9597683Z dim (Union[int, str]): Dimension to be unflattened 2024-08-20T21:38:59.9598659Z unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension 2024-08-20T21:38:59.9599409Z 2024-08-20T21:38:59.9599527Z Examples: 2024-08-20T21:38:59.9599810Z >>> input = torch.randn(2, 50) 2024-08-20T21:38:59.9600215Z >>> # With tuple of ints 2024-08-20T21:38:59.9600584Z >>> m = nn.Sequential( 2024-08-20T21:38:59.9600922Z >>> nn.Linear(50, 50), 2024-08-20T21:38:59.9601302Z >>> nn.Unflatten(1, (2, 5, 5)) 2024-08-20T21:38:59.9601689Z >>> ) 2024-08-20T21:38:59.9601950Z >>> output = m(input) 2024-08-20T21:38:59.9602296Z >>> output.size() 2024-08-20T21:38:59.9602625Z torch.Size([2, 2, 5, 5]) 2024-08-20T21:38:59.9602970Z >>> # With torch.Size 2024-08-20T21:38:59.9603316Z >>> m = nn.Sequential( 2024-08-20T21:38:59.9603672Z >>> nn.Linear(50, 50), 2024-08-20T21:38:59.9604074Z >>> nn.Unflatten(1, torch.Size([2, 5, 5])) 2024-08-20T21:38:59.9604516Z >>> ) 2024-08-20T21:38:59.9604789Z >>> output = m(input) 2024-08-20T21:38:59.9605116Z >>> output.size() 2024-08-20T21:38:59.9605442Z torch.Size([2, 2, 5, 5]) 2024-08-20T21:38:59.9605848Z >>> # With namedshape (tuple of tuples) 2024-08-20T21:38:59.9606489Z >>> input = torch.randn(2, 50, names=('N', 'features')) 2024-08-20T21:38:59.9607247Z >>> unflatten = nn.Unflatten('features', (('C', 2), ('H', 5), ('W', 5))) 2024-08-20T21:38:59.9607859Z >>> output = unflatten(input) 2024-08-20T21:38:59.9608239Z >>> output.size() 2024-08-20T21:38:59.9608583Z torch.Size([2, 2, 5, 5]) 2024-08-20T21:38:59.9608821Z 2024-08-20T21:38:59.9609249Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:59.9609752Z 2024-08-20T21:38:59.9952695Z 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. 2024-08-20T21:38:59.9954162Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:59.9954961Z Creates a criterion that measures the triplet loss given input 2024-08-20T21:38:59.9956065Z tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor, 2024-08-20T21:38:59.9956813Z positive, and negative examples, respectively), and a nonnegative, 2024-08-20T21:38:59.9957697Z real-valued function ("distance function") used to compute the relationship 2024-08-20T21:38:59.9958640Z between the anchor and positive example ("positive distance") and the 2024-08-20T21:38:59.9959342Z anchor and negative example ("negative distance"). 2024-08-20T21:38:59.9959718Z 2024-08-20T21:38:59.9960096Z The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``) 2024-08-20T21:38:59.9960690Z can be described as: 2024-08-20T21:38:59.9960917Z 2024-08-20T21:38:59.9961066Z .. math:: 2024-08-20T21:38:59.9961429Z \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad 2024-08-20T21:38:59.9962118Z l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} 2024-08-20T21:38:59.9962537Z 2024-08-20T21:38:59.9962994Z where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function 2024-08-20T21:38:59.9963958Z quantifying the closeness of two tensors, referred to as the :attr:`distance_function`; 2024-08-20T21:38:59.9964874Z and :math:`margin` is a nonnegative margin representing the minimum difference 2024-08-20T21:38:59.9965746Z between the positive and negative distances that is required for the loss to 2024-08-20T21:38:59.9966607Z be 0. The input tensors have :math:`N` elements each and can be of any shape 2024-08-20T21:38:59.9967259Z that the distance function can handle. 2024-08-20T21:38:59.9967584Z 2024-08-20T21:38:59.9967799Z If :attr:`reduction` is not ``'none'`` 2024-08-20T21:38:59.9968395Z (default ``'mean'``), then: 2024-08-20T21:38:59.9968658Z 2024-08-20T21:38:59.9968782Z .. math:: 2024-08-20T21:38:59.9969053Z \ell(x, y) = 2024-08-20T21:38:59.9969368Z \begin{cases} 2024-08-20T21:38:59.9969939Z \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ 2024-08-20T21:38:59.9970763Z \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} 2024-08-20T21:38:59.9971312Z \end{cases} 2024-08-20T21:38:59.9971498Z 2024-08-20T21:38:59.9971849Z See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet 2024-08-20T21:38:59.9972698Z loss for input tensors using the :math:`l_p` distance as the distance function. 2024-08-20T21:38:59.9973239Z 2024-08-20T21:38:59.9973339Z Args: 2024-08-20T21:38:59.9973955Z distance_function (Callable, optional): A nonnegative, real-valued function that 2024-08-20T21:38:59.9974754Z quantifies the closeness of two tensors. If not specified, 2024-08-20T21:38:59.9975434Z `nn.PairwiseDistance` will be used. Default: ``None`` 2024-08-20T21:38:59.9976223Z margin (float, optional): A nonnegative margin representing the minimum difference 2024-08-20T21:38:59.9977144Z between the positive and negative distances required for the loss to be 0. Larger 2024-08-20T21:38:59.9978106Z margins penalize cases where the negative examples are not distant enough from the 2024-08-20T21:38:59.9978914Z anchors, relative to the positives. Default: :math:`1`. 2024-08-20T21:38:59.9979687Z swap (bool, optional): Whether to use the distance swap described in the paper 2024-08-20T21:38:59.9980561Z `Learning shallow convolutional feature descriptors with triplet losses` by 2024-08-20T21:38:59.9981461Z V. Balntas, E. Riba et al. If True, and if the positive example is closer to the 2024-08-20T21:38:59.9982392Z negative example than the anchor is, swaps the positive example and the anchor in 2024-08-20T21:38:59.9983130Z the loss computation. Default: ``False``. 2024-08-20T21:38:59.9983875Z reduction (str, optional): Specifies the (optional) reduction to apply to the output: 2024-08-20T21:38:59.9984818Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2024-08-20T21:38:59.9985718Z ``'mean'``: the sum of the output will be divided by the number of 2024-08-20T21:38:59.9986596Z elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'`` 2024-08-20T21:38:59.9987135Z 2024-08-20T21:38:59.9987140Z 2024-08-20T21:38:59.9987241Z Shape: 2024-08-20T21:38:59.9987860Z - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions 2024-08-20T21:38:59.9988548Z as supported by the distance function. 2024-08-20T21:38:59.9989364Z - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar 2024-08-20T21:38:59.9990030Z otherwise. 2024-08-20T21:38:59.9990232Z 2024-08-20T21:38:59.9990341Z Examples:: 2024-08-20T21:38:59.9990539Z 2024-08-20T21:38:59.9990669Z >>> # Initialize embeddings 2024-08-20T21:38:59.9991094Z >>> embedding = nn.Embedding(1000, 128) 2024-08-20T21:38:59.9991574Z >>> anchor_ids = torch.randint(0, 1000, (1,)) 2024-08-20T21:38:59.9992104Z >>> positive_ids = torch.randint(0, 1000, (1,)) 2024-08-20T21:38:59.9992631Z >>> negative_ids = torch.randint(0, 1000, (1,)) 2024-08-20T21:38:59.9993113Z >>> anchor = embedding(anchor_ids) 2024-08-20T21:38:59.9993573Z >>> positive = embedding(positive_ids) 2024-08-20T21:38:59.9994051Z >>> negative = embedding(negative_ids) 2024-08-20T21:38:59.9994460Z >>> 2024-08-20T21:38:59.9994803Z >>> # Built-in Distance Function 2024-08-20T21:38:59.9995215Z >>> triplet_loss = \ 2024-08-20T21:38:59.9996081Z >>> nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance()) 2024-08-20T21:38:59.9996965Z >>> output = triplet_loss(anchor, positive, negative) 2024-08-20T21:38:59.9997491Z >>> output.backward() 2024-08-20T21:38:59.9997907Z >>> 2024-08-20T21:38:59.9998191Z >>> # Custom Distance Function 2024-08-20T21:38:59.9998744Z >>> def l_infinity(x1, x2): 2024-08-20T21:38:59.9999458Z >>> return torch.max(torch.abs(x1 - x2), dim=1).values 2024-08-20T21:38:59.9999955Z >>> 2024-08-20T21:39:00.0000380Z >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time") 2024-08-20T21:39:00.0000928Z >>> triplet_loss = ( 2024-08-20T21:39:00.0001528Z >>> nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5)) 2024-08-20T21:39:00.0002284Z >>> output = triplet_loss(anchor, positive, negative) 2024-08-20T21:39:00.0002788Z >>> output.backward() 2024-08-20T21:39:00.0003105Z >>> 2024-08-20T21:39:00.0003407Z >>> # Custom Distance Function (Lambda) 2024-08-20T21:39:00.0003832Z >>> triplet_loss = ( 2024-08-20T21:39:00.0004240Z >>> nn.TripletMarginWithDistanceLoss( 2024-08-20T21:39:00.0004968Z >>> distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y))) 2024-08-20T21:39:00.0005638Z >>> output = triplet_loss(anchor, positive, negative) 2024-08-20T21:39:00.0006125Z >>> output.backward() 2024-08-20T21:39:00.0006366Z 2024-08-20T21:39:00.0006469Z Reference: 2024-08-20T21:39:00.0007083Z V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses: 2024-08-20T21:39:00.0007933Z http://www.bmva.org/bmvc/2016/papers/paper119/index.html 2024-08-20T21:39:00.0008447Z 2024-08-20T21:39:00.0009019Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17)) 2024-08-20T21:39:00.0009528Z 2024-08-20T21:39:00.0579381Z 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. 2024-08-20T21:39:00.0582000Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:00.0583412Z Computes a partial inverse of :class:`MaxPool2d`. 2024-08-20T21:39:00.0584147Z 2024-08-20T21:39:00.0585019Z :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost. 2024-08-20T21:39:00.0586073Z 2024-08-20T21:39:00.0586694Z :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d` 2024-08-20T21:39:00.0588685Z including the indices of the maximal values and computes a partial inverse 2024-08-20T21:39:00.0590236Z in which all non-maximal values are set to zero. 2024-08-20T21:39:00.0590924Z 2024-08-20T21:39:00.0591126Z Note: 2024-08-20T21:39:00.0592201Z This operation may behave nondeterministically when the input indices has repeat values. 2024-08-20T21:39:00.0594322Z See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. 2024-08-20T21:39:00.0595643Z 2024-08-20T21:39:00.0596551Z .. note:: :class:`MaxPool2d` can map several input sizes to the same output 2024-08-20T21:39:00.0597967Z sizes. Hence, the inversion process can get ambiguous. 2024-08-20T21:39:00.0599409Z To accommodate this, you can provide the needed output size 2024-08-20T21:39:00.0600840Z as an additional argument :attr:`output_size` in the forward call. 2024-08-20T21:39:00.0602072Z See the Inputs and Example below. 2024-08-20T21:39:00.0602702Z 2024-08-20T21:39:00.0602893Z Args: 2024-08-20T21:39:00.0603657Z kernel_size (int or tuple): Size of the max pooling window. 2024-08-20T21:39:00.0604889Z stride (int or tuple): Stride of the max pooling window. 2024-08-20T21:39:00.0605959Z It is set to :attr:`kernel_size` by default. 2024-08-20T21:39:00.0607137Z padding (int or tuple): Padding that was added to the input 2024-08-20T21:39:00.0607999Z 2024-08-20T21:39:00.0608174Z Inputs: 2024-08-20T21:39:00.0608946Z - `input`: the input Tensor to invert 2024-08-20T21:39:00.0610510Z - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d` 2024-08-20T21:39:00.0611930Z - `output_size` (optional): the targeted output size 2024-08-20T21:39:00.0612672Z 2024-08-20T21:39:00.0612866Z Shape: 2024-08-20T21:39:00.0613832Z - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. 2024-08-20T21:39:00.0615488Z - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where 2024-08-20T21:39:00.0616431Z 2024-08-20T21:39:00.0616662Z .. math:: 2024-08-20T21:39:00.0618003Z H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} 2024-08-20T21:39:00.0619120Z 2024-08-20T21:39:00.0619332Z .. math:: 2024-08-20T21:39:00.0620727Z W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} 2024-08-20T21:39:00.0621861Z 2024-08-20T21:39:00.0622311Z or as given by :attr:`output_size` in the call operator 2024-08-20T21:39:00.0623065Z 2024-08-20T21:39:00.0623272Z Example:: 2024-08-20T21:39:00.0623610Z 2024-08-20T21:39:00.0624037Z >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True) 2024-08-20T21:39:00.0625108Z >>> unpool = nn.MaxUnpool2d(2, stride=2) 2024-08-20T21:39:00.0626095Z >>> input = torch.tensor([[[[ 1., 2., 3., 4.], 2024-08-20T21:39:00.0627089Z [ 5., 6., 7., 8.], 2024-08-20T21:39:00.0628008Z [ 9., 10., 11., 12.], 2024-08-20T21:39:00.0628917Z [13., 14., 15., 16.]]]]) 2024-08-20T21:39:00.0629796Z >>> output, indices = pool(input) 2024-08-20T21:39:00.0630588Z >>> unpool(output, indices) 2024-08-20T21:39:00.0631339Z tensor([[[[ 0., 0., 0., 0.], 2024-08-20T21:39:00.0632149Z [ 0., 6., 0., 8.], 2024-08-20T21:39:00.0632940Z [ 0., 0., 0., 0.], 2024-08-20T21:39:00.0633710Z [ 0., 14., 0., 16.]]]]) 2024-08-20T21:39:00.0634830Z >>> # Now using output_size to resolve an ambiguous size for the inverse 2024-08-20T21:39:00.0636121Z >>> input = torch.tensor([[[[ 1., 2., 3., 4., 5.], 2024-08-20T21:39:00.0637124Z [ 6., 7., 8., 9., 10.], 2024-08-20T21:39:00.0638483Z [11., 12., 13., 14., 15.], 2024-08-20T21:39:00.0639428Z [16., 17., 18., 19., 20.]]]]) 2024-08-20T21:39:00.0640344Z >>> output, indices = pool(input) 2024-08-20T21:39:00.0641321Z >>> # This call will not work without specifying output_size 2024-08-20T21:39:00.0642460Z >>> unpool(output, indices, output_size=input.size()) 2024-08-20T21:39:00.0643465Z tensor([[[[ 0., 0., 0., 0., 0.], 2024-08-20T21:39:00.0644284Z [ 0., 7., 0., 9., 0.], 2024-08-20T21:39:00.0645095Z [ 0., 0., 0., 0., 0.], 2024-08-20T21:39:00.0645905Z [ 0., 17., 0., 19., 0.]]]]) 2024-08-20T21:39:00.0646470Z 2024-08-20T21:39:00.0646478Z 2024-08-20T21:39:00.0646643Z 2024-08-20T21:39:00.0647810Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:00.0648770Z 2024-08-20T21:39:00.0923388Z 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. 2024-08-20T21:39:00.0925913Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:00.0927098Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings. 2024-08-20T21:39:00.0927790Z 2024-08-20T21:39:00.0928260Z For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`, 2024-08-20T21:39:00.0929039Z and with 2D inputs, this class 2024-08-20T21:39:00.0929318Z 2024-08-20T21:39:00.0930086Z * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``, 2024-08-20T21:39:00.0931179Z * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``, 2024-08-20T21:39:00.0932251Z * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``. 2024-08-20T21:39:00.0932889Z 2024-08-20T21:39:00.0933406Z However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these 2024-08-20T21:39:00.0934207Z operations. 2024-08-20T21:39:00.0934386Z 2024-08-20T21:39:00.0934819Z EmbeddingBag also supports per-sample weights as an argument to the forward 2024-08-20T21:39:00.0935687Z pass. This scales the output of the Embedding before performing a weighted 2024-08-20T21:39:00.0936558Z reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the 2024-08-20T21:39:00.0937436Z only supported ``mode`` is ``"sum"``, which computes a weighted sum according to 2024-08-20T21:39:00.0938079Z :attr:`per_sample_weights`. 2024-08-20T21:39:00.0938353Z 2024-08-20T21:39:00.0938452Z Args: 2024-08-20T21:39:00.0938864Z num_embeddings (int): size of the dictionary of embeddings 2024-08-20T21:39:00.0939502Z embedding_dim (int): the size of each embedding vector 2024-08-20T21:39:00.0940356Z max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm` 2024-08-20T21:39:00.0941214Z is renormalized to have norm :attr:`max_norm`. 2024-08-20T21:39:00.0942231Z norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``. 2024-08-20T21:39:00.0943384Z scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of 2024-08-20T21:39:00.0944369Z the words in the mini-batch. Default ``False``. 2024-08-20T21:39:00.0945074Z Note: this option is not supported when ``mode="max"``. 2024-08-20T21:39:00.0945880Z mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag. 2024-08-20T21:39:00.0946754Z ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights` 2024-08-20T21:39:00.0947700Z into consideration. ``"mean"`` computes the average of the values 2024-08-20T21:39:00.0948452Z in the bag, ``"max"`` computes the max value over each bag. 2024-08-20T21:39:00.0949053Z Default: ``"mean"`` 2024-08-20T21:39:00.0949870Z sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See 2024-08-20T21:39:00.0950896Z Notes for more details regarding sparse gradients. Note: this option is not 2024-08-20T21:39:00.0951605Z supported when ``mode="max"``. 2024-08-20T21:39:00.0952505Z include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element 2024-08-20T21:39:00.0953546Z is equivalent to the size of `indices`. This matches the CSR format. 2024-08-20T21:39:00.0954524Z padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the 2024-08-20T21:39:00.0955556Z gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated 2024-08-20T21:39:00.0956478Z during training, i.e. it remains as a fixed "pad". For a newly constructed 2024-08-20T21:39:00.0957396Z EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all 2024-08-20T21:39:00.0958307Z zeros, but can be updated to another value to be used as the padding vector. 2024-08-20T21:39:00.0959919Z Note that the embedding vector at :attr:`padding_idx` is excluded from the 2024-08-20T21:39:00.0960606Z reduction. 2024-08-20T21:39:00.0960917Z 2024-08-20T21:39:00.0961043Z Attributes: 2024-08-20T21:39:00.0961787Z weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)` 2024-08-20T21:39:00.0962623Z initialized from :math:`\mathcal{N}(0, 1)`. 2024-08-20T21:39:00.0963000Z 2024-08-20T21:39:00.0963151Z Examples:: 2024-08-20T21:39:00.0963326Z 2024-08-20T21:39:00.0963569Z >>> # an EmbeddingBag module containing 10 tensors of size 3 2024-08-20T21:39:00.0964334Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum') 2024-08-20T21:39:00.0964918Z >>> # a batch of 2 samples of 4 indices each 2024-08-20T21:39:00.0965532Z >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long) 2024-08-20T21:39:00.0966187Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2024-08-20T21:39:00.0966830Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-08-20T21:39:00.0979455Z >>> embedding_sum(input, offsets) 2024-08-20T21:39:00.0980251Z tensor([[-0.8861, -5.4350, -0.0523], 2024-08-20T21:39:00.0980761Z [ 1.1306, -2.5798, -1.0044]]) 2024-08-20T21:39:00.0981100Z 2024-08-20T21:39:00.0981249Z >>> # Example with padding_idx 2024-08-20T21:39:00.0981933Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2) 2024-08-20T21:39:00.0982669Z >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long) 2024-08-20T21:39:00.0983332Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2024-08-20T21:39:00.0983853Z >>> embedding_sum(input, offsets) 2024-08-20T21:39:00.0984305Z tensor([[ 0.0000, 0.0000, 0.0000], 2024-08-20T21:39:00.0984801Z [-0.7082, 3.2145, -2.6251]]) 2024-08-20T21:39:00.0985117Z 2024-08-20T21:39:00.0985374Z >>> # An EmbeddingBag can be loaded from an Embedding like so 2024-08-20T21:39:00.0986003Z >>> embedding = nn.Embedding(10, 3, padding_idx=2) 2024-08-20T21:39:00.0986584Z >>> embedding_sum = nn.EmbeddingBag.from_pretrained( 2024-08-20T21:39:00.0987099Z embedding.weight, 2024-08-20T21:39:00.0987738Z padding_idx=embedding.padding_idx, 2024-08-20T21:39:00.0988232Z mode='sum') 2024-08-20T21:39:00.0988563Z 2024-08-20T21:39:00.0989132Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:00.0989642Z 2024-08-20T21:39:00.1315678Z 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. 2024-08-20T21:39:00.1318219Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:00.1319319Z 2024-08-20T21:39:00.1320038Z Context manager for training with uneven inputs across processes in DDP. 2024-08-20T21:39:00.1320985Z 2024-08-20T21:39:00.1321556Z This context manager will keep track of already-joined DDP processes, 2024-08-20T21:39:00.1322342Z and "shadow" the forward and backward passes by inserting collective 2024-08-20T21:39:00.1323182Z communication operations to match with the ones created by non-joined 2024-08-20T21:39:00.1324000Z DDP processes. This will ensure each collective call has a corresponding 2024-08-20T21:39:00.1324880Z call by already-joined DDP processes, preventing hangs or errors that 2024-08-20T21:39:00.1325615Z would otherwise happen when training with uneven inputs across 2024-08-20T21:39:00.1326368Z processes. Alternatively, if the flag ``throw_on_early_termination`` is 2024-08-20T21:39:00.1327164Z specified to be ``True``, all trainers will throw an error once one rank 2024-08-20T21:39:00.1327924Z runs out of inputs, allowing these errors to be caught and handled 2024-08-20T21:39:00.1328501Z according to application logic. 2024-08-20T21:39:00.1329067Z 2024-08-20T21:39:00.1329381Z Once all DDP processes have joined, the context manager will broadcast 2024-08-20T21:39:00.1330187Z the model corresponding to the last joined process to all processes to 2024-08-20T21:39:00.1330853Z ensure the model is the same across all processes 2024-08-20T21:39:00.1331357Z (which is guaranteed by DDP). 2024-08-20T21:39:00.1331608Z 2024-08-20T21:39:00.1331915Z To use this to enable training with uneven inputs across processes, 2024-08-20T21:39:00.1332682Z simply wrap this context manager around your training loop. No further 2024-08-20T21:39:00.1333394Z modifications to the model or data loading is required. 2024-08-20T21:39:00.1333789Z 2024-08-20T21:39:00.1333930Z .. warning:: 2024-08-20T21:39:00.1334401Z If the model or training loop this context manager is wrapped around 2024-08-20T21:39:00.1335137Z has additional distributed collective operations, such as 2024-08-20T21:39:00.1335903Z ``SyncBatchNorm`` in the model's forward pass, then the flag 2024-08-20T21:39:00.1336612Z ``throw_on_early_termination`` must be enabled. This is because this 2024-08-20T21:39:00.1337415Z context manager is not aware of non-DDP collective communication. 2024-08-20T21:39:00.1338125Z This flag will cause all ranks to throw when any one rank 2024-08-20T21:39:00.1338825Z exhausts inputs, allowing these errors to be caught and recovered 2024-08-20T21:39:00.1339394Z from across all ranks. 2024-08-20T21:39:00.1339639Z 2024-08-20T21:39:00.1339733Z Args: 2024-08-20T21:39:00.1340133Z divide_by_initial_world_size (bool): If ``True``, will divide 2024-08-20T21:39:00.1340828Z gradients by the initial ``world_size`` DDP training was launched 2024-08-20T21:39:00.1341533Z with. If ``False``, will compute the effective world size 2024-08-20T21:39:00.1342208Z (number of ranks that have not depleted their inputs yet) and 2024-08-20T21:39:00.1342828Z divide gradients by that during allreduce. Set 2024-08-20T21:39:00.1343449Z ``divide_by_initial_world_size=True`` to ensure every input 2024-08-20T21:39:00.1344162Z sample including the uneven inputs have equal weight in terms of 2024-08-20T21:39:00.1344856Z how much they contribute to the global gradient. This is 2024-08-20T21:39:00.1345516Z achieved by always dividing the gradient by the initial 2024-08-20T21:39:00.1346325Z ``world_size`` even when we encounter uneven inputs. If you set 2024-08-20T21:39:00.1347010Z this to ``False``, we divide the gradient by the remaining 2024-08-20T21:39:00.1347693Z number of nodes. This ensures parity with training on a smaller 2024-08-20T21:39:00.1348400Z ``world_size`` although it also means the uneven inputs would 2024-08-20T21:39:00.1349209Z contribute more towards the global gradient. Typically, you 2024-08-20T21:39:00.1350080Z would want to set this to ``True`` for cases where the last few 2024-08-20T21:39:00.1351334Z inputs of your training job are uneven. In extreme cases, where 2024-08-20T21:39:00.1352492Z there is a large discrepancy in the number of inputs, setting 2024-08-20T21:39:00.1353484Z this to ``False`` might provide better results. 2024-08-20T21:39:00.1354545Z enable (bool): Whether to enable uneven input detection or not. Pass 2024-08-20T21:39:00.1355276Z in ``enable=False`` to disable in cases where you know that 2024-08-20T21:39:00.1355948Z inputs are even across participating processes. Default is 2024-08-20T21:39:00.1356573Z ``True``. 2024-08-20T21:39:00.1357018Z throw_on_early_termination (bool): Whether to throw an error 2024-08-20T21:39:00.1357676Z or continue training when at least one rank has exhausted 2024-08-20T21:39:00.1358481Z inputs. If ``True``, will throw upon the first rank reaching end 2024-08-20T21:39:00.1359182Z of data. If ``False``, will continue training with a smaller 2024-08-20T21:39:00.1360024Z effective world size until all ranks are joined. Note that if 2024-08-20T21:39:00.1360660Z this flag is specified, then the flag 2024-08-20T21:39:00.1361236Z ``divide_by_initial_world_size`` would be ignored. Default 2024-08-20T21:39:00.1361758Z is ``False``. 2024-08-20T21:39:00.1361960Z 2024-08-20T21:39:00.1361965Z 2024-08-20T21:39:00.1362093Z Example:: 2024-08-20T21:39:00.1362264Z 2024-08-20T21:39:00.1362412Z >>> # xdoctest: +SKIP("Distributed") 2024-08-20T21:39:00.1362839Z >>> import torch 2024-08-20T21:39:00.1363182Z >>> import torch.distributed as dist 2024-08-20T21:39:00.1363605Z >>> import os 2024-08-20T21:39:00.1363955Z >>> import torch.multiprocessing as mp 2024-08-20T21:39:00.1364393Z >>> import torch.nn as nn 2024-08-20T21:39:00.1364775Z >>> # On each spawned worker 2024-08-20T21:39:00.1365162Z >>> def worker(rank): 2024-08-20T21:39:00.1365616Z >>> dist.init_process_group("nccl", rank=rank, world_size=2) 2024-08-20T21:39:00.1366172Z >>> torch.cuda.set_device(rank) 2024-08-20T21:39:00.1366668Z >>> model = nn.Linear(1, 1, bias=False).to(rank) 2024-08-20T21:39:00.1367349Z >>> model = torch.nn.parallel.DistributedDataParallel( 2024-08-20T21:39:00.1367967Z >>> model, device_ids=[rank], output_device=rank 2024-08-20T21:39:00.1368432Z >>> ) 2024-08-20T21:39:00.1368782Z >>> # Rank 1 gets one more input than rank 0. 2024-08-20T21:39:00.1369397Z >>> inputs = [torch.tensor([1]).float() for _ in range(10 + rank)] 2024-08-20T21:39:00.1369962Z >>> with model.join(): 2024-08-20T21:39:00.1370326Z >>> for _ in range(5): 2024-08-20T21:39:00.1370727Z >>> for inp in inputs: 2024-08-20T21:39:00.1371169Z >>> loss = model(inp).sum() 2024-08-20T21:39:00.1371621Z >>> loss.backward() 2024-08-20T21:39:00.1372176Z >>> # Without the join() API, the below synchronization will hang 2024-08-20T21:39:00.1372978Z >>> # blocking for rank 1's allreduce to complete. 2024-08-20T21:39:00.1373519Z >>> torch.cuda.synchronize(device=rank) 2024-08-20T21:39:00.1373853Z 2024-08-20T21:39:00.1374272Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:00.1374795Z 2024-08-20T21:39:00.1375990Z 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. 2024-08-20T21:39:00.1377661Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:00.1378184Z 2024-08-20T21:39:00.1378640Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction. 2024-08-20T21:39:00.1379247Z 2024-08-20T21:39:00.1379532Z Registers an optimizer with DDP such that the optimization for a 2024-08-20T21:39:00.1380349Z parameter will run immediately when that parameter's gradient is 2024-08-20T21:39:00.1381165Z finished with reduction, instead of waiting for all parameters' 2024-08-20T21:39:00.1381913Z gradients to finish reduction. This can result in a training speedup 2024-08-20T21:39:00.1382687Z depending on your workload since the optimizer can run while gradient 2024-08-20T21:39:00.1383492Z reduction for other parameters are still ongoing. In addition, this has 2024-08-20T21:39:00.1384307Z the potential to reduce peak memory consumption during training, as it 2024-08-20T21:39:00.1385145Z only needs to load the per-parameter optimizer states of a single 2024-08-20T21:39:00.1385981Z parameter at a time, instead of loading all per-parameter optimizer 2024-08-20T21:39:00.1386564Z states at once. 2024-08-20T21:39:00.1386744Z 2024-08-20T21:39:00.1386838Z Args: 2024-08-20T21:39:00.1387268Z optim (Type): a ``torch.optim.Optimizer`` class to be registered 2024-08-20T21:39:00.1387841Z as a fused optimizer. 2024-08-20T21:39:00.1388287Z *args (Sequence[Any]): Arguments to forward to `optim`. 2024-08-20T21:39:00.1389052Z optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters 2024-08-20T21:39:00.1389828Z to optimize, similar to `params` argument of traditional `torch.optim` 2024-08-20T21:39:00.1390587Z Optimizers. If this is omitted, all DDP model parameters will be 2024-08-20T21:39:00.1391152Z optimized. 2024-08-20T21:39:00.1391619Z **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`. 2024-08-20T21:39:00.1392076Z 2024-08-20T21:39:00.1392206Z .. warning :: 2024-08-20T21:39:00.1392748Z _register_fused_optim should only be called once on a DDP instance, 2024-08-20T21:39:00.1393523Z and registering multiple fused optimizers for the same DDP model 2024-08-20T21:39:00.1394147Z is not currently supported. Please ping 2024-08-20T21:39:00.1394801Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-08-20T21:39:00.1395403Z for your use case. 2024-08-20T21:39:00.1395610Z 2024-08-20T21:39:00.1395733Z .. warning :: 2024-08-20T21:39:00.1396444Z _register_fused_optim and register_comm_hook currently do not 2024-08-20T21:39:00.1397168Z compose together, meaning that custom DDP communication hooks are 2024-08-20T21:39:00.1397864Z not supported with overlapped optimizers. Please ping 2024-08-20T21:39:00.1398698Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-08-20T21:39:00.1399512Z for your use case. 2024-08-20T21:39:00.1399725Z 2024-08-20T21:39:00.1399850Z .. warning :: 2024-08-20T21:39:00.1400324Z Gradient accumulation and DDP `no_sync` are currently not supported 2024-08-20T21:39:00.1400970Z with overlapped optimizer. Please ping 2024-08-20T21:39:00.1401614Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-08-20T21:39:00.1402203Z for your use case. 2024-08-20T21:39:00.1402422Z 2024-08-20T21:39:00.1402525Z Example:: 2024-08-20T21:39:00.1402692Z 2024-08-20T21:39:00.1402869Z >>> # xdoctest: +SKIP("No rendezvous handler") 2024-08-20T21:39:00.1403765Z >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...') 2024-08-20T21:39:00.1404615Z >>> net = torch.nn.parallel.DistributedDataParallel(model, pg) 2024-08-20T21:39:00.1405200Z >>> lr = 1e-2 2024-08-20T21:39:00.1405509Z >>> betas = (0.9, 0.99) 2024-08-20T21:39:00.1405857Z >>> eps = 1e-6 2024-08-20T21:39:00.1406362Z >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps) 2024-08-20T21:39:00.1407322Z >>> # Example with subset of parameters 2024-08-20T21:39:00.1407818Z >>> params_to_opt = [list(net.parameters())[0]] 2024-08-20T21:39:00.1408308Z >>> net._register_fused_optim( 2024-08-20T21:39:00.1408921Z ... torch.optim.Adam, lr, optim_params=params_to_opt, betas=betas, eps=eps 2024-08-20T21:39:00.1409519Z ... ) 2024-08-20T21:39:00.1409687Z 2024-08-20T21:39:00.1410111Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:00.1410622Z 2024-08-20T21:39:00.1622538Z 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. 2024-08-20T21:39:00.1624529Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:00.1625489Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``. 2024-08-20T21:39:00.1626081Z 2024-08-20T21:39:00.1626474Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2024-08-20T21:39:00.1627598Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2024-08-20T21:39:00.1628688Z This function is used to facilitate the computation to adopt NHWC kernels, which 2024-08-20T21:39:00.1630204Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2024-08-20T21:39:00.1631205Z 2024-08-20T21:39:00.1631386Z .. note:: 2024-08-20T21:39:00.1631966Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2024-08-20T21:39:00.1633111Z than the utility function ``convert_conv2d_weight_memory_format``. Any 2024-08-20T21:39:00.1634103Z layer with 4d weight will be affected by ``model.to``, which does not 2024-08-20T21:39:00.1634975Z necessarily benefit from conversion to specified ``memory_format``. 2024-08-20T21:39:00.1635874Z One place we are confident in is that NHWC(channels_last) conversion for 2024-08-20T21:39:00.1636771Z convolution in cuDNN, as it is beneficial to run convolution in NHWC, 2024-08-20T21:39:00.1637649Z even in cases where we have to apply permutation to input tensors. 2024-08-20T21:39:00.1638197Z 2024-08-20T21:39:00.1638713Z Hence our strategy here is to convert only the weight of convolution to 2024-08-20T21:39:00.1639431Z channels_last. This ensures that; 2024-08-20T21:39:00.1640105Z 1. Fast convolution kernels will be used, the benefit of which could 2024-08-20T21:39:00.1641022Z outweigh overhead of permutation (if input is not in the same format). 2024-08-20T21:39:00.1641925Z 2. No unnecessary permutations are applied on layers that do not benefit 2024-08-20T21:39:00.1642651Z from memory_format conversion. 2024-08-20T21:39:00.1642949Z 2024-08-20T21:39:00.1643375Z The optimal case is that, layers between convolution layers are channels 2024-08-20T21:39:00.1644300Z last compatible. Input tensor would be permuted to channels last when it 2024-08-20T21:39:00.1645190Z encounters the first convolution layer and stay in that memory format. 2024-08-20T21:39:00.1646099Z Hence following convolutions will not need to permute its input tensor. 2024-08-20T21:39:00.1646684Z 2024-08-20T21:39:00.1647035Z In case where a channels last incompatible layer is between convolution 2024-08-20T21:39:00.1647961Z layers, we need to permute the input tensor back to contiguous format 2024-08-20T21:39:00.1648862Z for that layer. The input tensor will go through the remaining layers in 2024-08-20T21:39:00.1649765Z contiguous format and be permuted to channels last when it encounters 2024-08-20T21:39:00.1650739Z another convolution layer. There's no point in propagating that 2024-08-20T21:39:00.1651612Z permutation to an earlier layer, as most layers are quite agnostic to 2024-08-20T21:39:00.1652442Z ``memory_format``. 2024-08-20T21:39:00.1652748Z 2024-08-20T21:39:00.1653095Z This claim might change when PyTorch supports fusion of permutation, as 2024-08-20T21:39:00.1653996Z there might have been a better spot to fuse the permutation other than 2024-08-20T21:39:00.1654733Z immediately before a convolution. 2024-08-20T21:39:00.1655128Z 2024-08-20T21:39:00.1655245Z Args: 2024-08-20T21:39:00.1655780Z module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container 2024-08-20T21:39:00.1656424Z ``nn.Module`` 2024-08-20T21:39:00.1656985Z memory_format: user specified ``memory_format``, 2024-08-20T21:39:00.1657692Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2024-08-20T21:39:00.1658216Z 2024-08-20T21:39:00.1658323Z Returns: 2024-08-20T21:39:00.1658692Z The original module with updated ``nn.Conv2d`` 2024-08-20T21:39:00.1659136Z 2024-08-20T21:39:00.1659252Z Example: 2024-08-20T21:39:00.1659616Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-20T21:39:00.1660284Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2024-08-20T21:39:00.1661013Z >>> input = torch.randint(1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda") 2024-08-20T21:39:00.1661730Z >>> model = nn.Sequential( 2024-08-20T21:39:00.1662162Z >>> nn.Conv2d(8, 4, 3)).cuda().half() 2024-08-20T21:39:00.1662623Z >>> # This is identical to: 2024-08-20T21:39:00.1663233Z >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2024-08-20T21:39:00.1664280Z >>> model = nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2024-08-20T21:39:00.1664954Z >>> out = model(input) 2024-08-20T21:39:00.1665288Z 2024-08-20T21:39:00.1665881Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:00.1666387Z 2024-08-20T21:39:00.1667577Z 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. 2024-08-20T21:39:00.1669047Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:00.1669850Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format`` 2024-08-20T21:39:00.1670688Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2024-08-20T21:39:00.1671642Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2024-08-20T21:39:00.1672569Z This function is used to facilitate the computation to adopt NHWC kernels, which 2024-08-20T21:39:00.1673563Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2024-08-20T21:39:00.1674267Z 2024-08-20T21:39:00.1674388Z .. note:: 2024-08-20T21:39:00.1674911Z Calling ``model.to(memory_format=torch.channels_last_3d)`` is more aggressive 2024-08-20T21:39:00.1675738Z than the utility function ``convert_conv3d_weight_memory_format``. Any 2024-08-20T21:39:00.1676540Z layer with 4d weight will be affected by ``model.to``, which does not 2024-08-20T21:39:00.1677330Z necessarily benefit from conversion to specified ``memory_format``. 2024-08-20T21:39:00.1678149Z One place we are confident in is that NDHWC(channels_last_3d) conversion for 2024-08-20T21:39:00.1679099Z convolution in cuDNN, as it is beneficial to run convolution in NDHWC, 2024-08-20T21:39:00.1679893Z even in cases where we have to apply permutation to input tensors. 2024-08-20T21:39:00.1680357Z 2024-08-20T21:39:00.1680703Z Hence our strategy here is to convert only the weight of convolution to 2024-08-20T21:39:00.1681351Z channels_last_3d. This ensures that; 2024-08-20T21:39:00.1681986Z 1. Fast convolution kernels will be used, the benefit of which could 2024-08-20T21:39:00.1682789Z outweigh overhead of permutation (if input is not in the same format). 2024-08-20T21:39:00.1683703Z 2. No unnecessary permutations are applied on layers that do not benefit 2024-08-20T21:39:00.1684351Z from memory_format conversion. 2024-08-20T21:39:00.1684646Z 2024-08-20T21:39:00.1684991Z The optimal case is that, layers between convolution layers are channels 2024-08-20T21:39:00.1685816Z last compatible. Input tensor would be permuted to channels last when it 2024-08-20T21:39:00.1686641Z encounters the first convolution layer and stay in that memory format. 2024-08-20T21:39:00.1687491Z Hence following convolutions will not need to permute its input tensor. 2024-08-20T21:39:00.1688053Z 2024-08-20T21:39:00.1688392Z In case where a channels last incompatible layer is between convolution 2024-08-20T21:39:00.1689194Z layers, we need to permute the input tensor back to contiguous format 2024-08-20T21:39:00.1690004Z for that layer. The input tensor will go through the remaining layers in 2024-08-20T21:39:00.1690827Z contiguous format and be permuted to channels last when it encounters 2024-08-20T21:39:00.1691692Z another convolution layer. There's no point in propagating that 2024-08-20T21:39:00.1692472Z permutation to an earlier layer, as most layers are quite agnostic to 2024-08-20T21:39:00.1693083Z ``memory_format``. 2024-08-20T21:39:00.1693310Z 2024-08-20T21:39:00.1693638Z This claim might change when PyTorch supports fusion of permutation, as 2024-08-20T21:39:00.1694468Z there might have been a better spot to fuse the permutation other than 2024-08-20T21:39:00.1695203Z immediately before a convolution. 2024-08-20T21:39:00.1695515Z 2024-08-20T21:39:00.1695629Z Args: 2024-08-20T21:39:00.1696381Z module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container 2024-08-20T21:39:00.1696997Z ``nn.Module`` 2024-08-20T21:39:00.1697502Z memory_format: user specified ``memory_format``, 2024-08-20T21:39:00.1698124Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2024-08-20T21:39:00.1698560Z 2024-08-20T21:39:00.1698699Z Returns: 2024-08-20T21:39:00.1699230Z The original module with updated ``nn.Conv3d`` 2024-08-20T21:39:00.1699591Z 2024-08-20T21:39:00.1699698Z Example: 2024-08-20T21:39:00.1700066Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-20T21:39:00.1700643Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2024-08-20T21:39:00.1701367Z >>> input = torch.randint(1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda") 2024-08-20T21:39:00.1702033Z >>> model = nn.Sequential( 2024-08-20T21:39:00.1702467Z >>> nn.Conv3d(8, 4, 3)).cuda().half() 2024-08-20T21:39:00.1702914Z >>> # This is identical to: 2024-08-20T21:39:00.1703551Z >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2024-08-20T21:39:00.1704463Z >>> model = nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2024-08-20T21:39:00.1705150Z >>> out = model(input) 2024-08-20T21:39:00.1705485Z 2024-08-20T21:39:00.1706082Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:00.1706589Z 2024-08-20T21:39:00.1866968Z 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. 2024-08-20T21:39:00.1868963Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:00.1869814Z Prune tensor by removing random channels along the specified dimension. 2024-08-20T21:39:00.1870366Z 2024-08-20T21:39:00.1870810Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2024-08-20T21:39:00.1871762Z by removing the specified ``amount`` of (currently unpruned) channels 2024-08-20T21:39:00.1872891Z along the specified ``dim`` selected at random. 2024-08-20T21:39:00.1874259Z Modifies module in place (and also return the modified module) 2024-08-20T21:39:00.1874855Z by: 2024-08-20T21:39:00.1875032Z 2024-08-20T21:39:00.1875445Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-08-20T21:39:00.1876514Z binary mask applied to the parameter ``name`` by the pruning method. 2024-08-20T21:39:00.1877388Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-08-20T21:39:00.1878239Z original (unpruned) parameter is stored in a new parameter named 2024-08-20T21:39:00.1879083Z ``name+'_orig'``. 2024-08-20T21:39:00.1879403Z 2024-08-20T21:39:00.1879577Z Args: 2024-08-20T21:39:00.1880247Z module (nn.Module): module containing the tensor to prune 2024-08-20T21:39:00.1881335Z name (str): parameter name within ``module`` on which pruning 2024-08-20T21:39:00.1882229Z will act. 2024-08-20T21:39:00.1883060Z amount (int or float): quantity of parameters to prune. 2024-08-20T21:39:00.1884037Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-08-20T21:39:00.1884823Z fraction of parameters to prune. If ``int``, it represents the 2024-08-20T21:39:00.1885557Z absolute number of parameters to prune. 2024-08-20T21:39:00.1886421Z dim (int): index of the dim along which we define channels to prune. 2024-08-20T21:39:00.1886992Z 2024-08-20T21:39:00.1887164Z Returns: 2024-08-20T21:39:00.1888048Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-08-20T21:39:00.1888788Z 2024-08-20T21:39:00.1888988Z Examples: 2024-08-20T21:39:00.1889535Z >>> # xdoctest: +SKIP 2024-08-20T21:39:00.1890268Z >>> m = prune.random_structured( 2024-08-20T21:39:00.1890905Z ... nn.Linear(5, 3), 'weight', amount=3, dim=1 2024-08-20T21:39:00.1891358Z ... ) 2024-08-20T21:39:00.1891783Z >>> columns_pruned = int(sum(torch.sum(m.weight, dim=0) == 0)) 2024-08-20T21:39:00.1892344Z >>> print(columns_pruned) 2024-08-20T21:39:00.1892702Z 3 2024-08-20T21:39:00.1892952Z 2024-08-20T21:39:00.1893524Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:00.1894033Z 2024-08-20T21:39:00.1894956Z 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. 2024-08-20T21:39:00.1896557Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:00.1897615Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension. 2024-08-20T21:39:00.1898247Z 2024-08-20T21:39:00.1898572Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2024-08-20T21:39:00.1899631Z by removing the specified ``amount`` of (currently unpruned) channels 2024-08-20T21:39:00.1900418Z along the specified ``dim`` with the lowest L\ ``n``-norm. 2024-08-20T21:39:00.1901108Z Modifies module in place (and also return the modified module) 2024-08-20T21:39:00.1901660Z by: 2024-08-20T21:39:00.1901806Z 2024-08-20T21:39:00.1902191Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-08-20T21:39:00.1902982Z binary mask applied to the parameter ``name`` by the pruning method. 2024-08-20T21:39:00.1903768Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-08-20T21:39:00.1904530Z original (unpruned) parameter is stored in a new parameter named 2024-08-20T21:39:00.1905135Z ``name+'_orig'``. 2024-08-20T21:39:00.1905367Z 2024-08-20T21:39:00.1905469Z Args: 2024-08-20T21:39:00.1905884Z module (nn.Module): module containing the tensor to prune 2024-08-20T21:39:00.1906561Z name (str): parameter name within ``module`` on which pruning 2024-08-20T21:39:00.1907109Z will act. 2024-08-20T21:39:00.1907574Z amount (int or float): quantity of parameters to prune. 2024-08-20T21:39:00.1908387Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-08-20T21:39:00.1909101Z fraction of parameters to prune. If ``int``, it represents the 2024-08-20T21:39:00.1909734Z absolute number of parameters to prune. 2024-08-20T21:39:00.1910446Z n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid 2024-08-20T21:39:00.1911126Z entries for argument ``p`` in :func:`torch.norm`. 2024-08-20T21:39:00.1911817Z dim (int): index of the dim along which we define channels to prune. 2024-08-20T21:39:00.1912601Z importance_scores (torch.Tensor): tensor of importance scores (of same 2024-08-20T21:39:00.1913383Z shape as module parameter) used to compute mask for pruning. 2024-08-20T21:39:00.1914159Z The values in this tensor indicate the importance of the corresponding 2024-08-20T21:39:00.1914846Z elements in the parameter being pruned. 2024-08-20T21:39:00.1915520Z If unspecified or None, the module parameter will be used in its place. 2024-08-20T21:39:00.1916042Z 2024-08-20T21:39:00.1916149Z Returns: 2024-08-20T21:39:00.1916641Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-08-20T21:39:00.1917125Z 2024-08-20T21:39:00.1917229Z Examples: 2024-08-20T21:39:00.1917568Z >>> from torch.nn.utils import prune 2024-08-20T21:39:00.1918033Z >>> m = prune.ln_structured( 2024-08-20T21:39:00.1918796Z ... nn.Conv2d(5, 3, 2), 'weight', amount=0.3, dim=1, n=float('-inf') 2024-08-20T21:39:00.1919350Z ... ) 2024-08-20T21:39:00.1919619Z 2024-08-20T21:39:00.1920260Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:00.1920787Z 2024-08-20T21:39:00.1921693Z 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. 2024-08-20T21:39:00.1923045Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:00.1923576Z 2024-08-20T21:39:00.1924175Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``. 2024-08-20T21:39:00.1924919Z 2024-08-20T21:39:00.1925052Z Modifies modules in place by: 2024-08-20T21:39:00.1925322Z 2024-08-20T21:39:00.1925693Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-08-20T21:39:00.1926476Z binary mask applied to the parameter ``name`` by the pruning method. 2024-08-20T21:39:00.1927236Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-08-20T21:39:00.1927998Z original (unpruned) parameter is stored in a new parameter named 2024-08-20T21:39:00.1928604Z ``name+'_orig'``. 2024-08-20T21:39:00.1928801Z 2024-08-20T21:39:00.1928913Z Args: 2024-08-20T21:39:00.1929316Z parameters (Iterable of (module, name) tuples): parameters of 2024-08-20T21:39:00.1930039Z the model to prune in a global fashion, i.e. by aggregating all 2024-08-20T21:39:00.1930792Z weights prior to deciding which ones to prune. module must be of 2024-08-20T21:39:00.1931457Z type :class:`nn.Module`, and name must be a string. 2024-08-20T21:39:00.1932141Z pruning_method (function): a valid pruning function from this module, 2024-08-20T21:39:00.1932877Z or a custom one implemented by the user that satisfies the 2024-08-20T21:39:00.1933676Z implementation guidelines and has ``PRUNING_TYPE='unstructured'``. 2024-08-20T21:39:00.1934482Z importance_scores (dict): a dictionary mapping (module, name) tuples to 2024-08-20T21:39:00.1935362Z the corresponding parameter's importance scores tensor. The tensor 2024-08-20T21:39:00.1936134Z should be the same shape as the parameter, and is used for computing 2024-08-20T21:39:00.1936743Z mask for pruning. 2024-08-20T21:39:00.1937276Z If unspecified or None, the parameter will be used in place of its 2024-08-20T21:39:00.1937874Z importance scores. 2024-08-20T21:39:00.1938364Z kwargs: other keyword arguments such as: 2024-08-20T21:39:00.1938987Z amount (int or float): quantity of parameters to prune across the 2024-08-20T21:39:00.1939582Z specified parameters. 2024-08-20T21:39:00.1940073Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-08-20T21:39:00.1940770Z fraction of parameters to prune. If ``int``, it represents the 2024-08-20T21:39:00.1941392Z absolute number of parameters to prune. 2024-08-20T21:39:00.1941730Z 2024-08-20T21:39:00.1941828Z Raises: 2024-08-20T21:39:00.1942256Z TypeError: if ``PRUNING_TYPE != 'unstructured'`` 2024-08-20T21:39:00.1942622Z 2024-08-20T21:39:00.1942731Z Note: 2024-08-20T21:39:00.1943235Z Since global structured pruning doesn't make much sense unless the 2024-08-20T21:39:00.1943994Z norm is normalized by the size of the parameter, we now limit the 2024-08-20T21:39:00.1944658Z scope of global pruning to unstructured methods. 2024-08-20T21:39:00.1945029Z 2024-08-20T21:39:00.1945131Z Examples: 2024-08-20T21:39:00.1945447Z >>> from torch.nn.utils import prune 2024-08-20T21:39:00.1945918Z >>> from collections import OrderedDict 2024-08-20T21:39:00.1946381Z >>> net = nn.Sequential(OrderedDict([ 2024-08-20T21:39:00.1946885Z ... ('first', nn.Linear(10, 4)), 2024-08-20T21:39:00.1947372Z ... ('second', nn.Linear(4, 1)), 2024-08-20T21:39:00.1947754Z ... ])) 2024-08-20T21:39:00.1948047Z >>> parameters_to_prune = ( 2024-08-20T21:39:00.1948492Z ... (net.first, 'weight'), 2024-08-20T21:39:00.1948927Z ... (net.second, 'weight'), 2024-08-20T21:39:00.1949308Z ... ) 2024-08-20T21:39:00.1949666Z >>> prune.global_unstructured( 2024-08-20T21:39:00.1950067Z ... parameters_to_prune, 2024-08-20T21:39:00.1950509Z ... pruning_method=prune.L1Unstructured, 2024-08-20T21:39:00.1950969Z ... amount=10, 2024-08-20T21:39:00.1951263Z ... ) 2024-08-20T21:39:00.1951737Z >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0)) 2024-08-20T21:39:00.1952331Z tensor(10) 2024-08-20T21:39:00.1952502Z 2024-08-20T21:39:00.1952507Z 2024-08-20T21:39:00.1952942Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:00.1953451Z 2024-08-20T21:39:00.1954316Z 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. 2024-08-20T21:39:00.1955648Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:00.1956845Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``. 2024-08-20T21:39:00.1957569Z 2024-08-20T21:39:00.1957862Z Modifies module in place (and also return the modified module) by: 2024-08-20T21:39:00.1958439Z 2024-08-20T21:39:00.1958833Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-08-20T21:39:00.1959627Z binary mask applied to the parameter ``name`` by the pruning method. 2024-08-20T21:39:00.1960413Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-08-20T21:39:00.1961160Z original (unpruned) parameter is stored in a new parameter named 2024-08-20T21:39:00.1961779Z ``name+'_orig'``. 2024-08-20T21:39:00.1961996Z 2024-08-20T21:39:00.1962110Z Args: 2024-08-20T21:39:00.1962502Z module (nn.Module): module containing the tensor to prune 2024-08-20T21:39:00.1963187Z name (str): parameter name within ``module`` on which pruning 2024-08-20T21:39:00.1963731Z will act. 2024-08-20T21:39:00.1964177Z mask (Tensor): binary mask to be applied to the parameter. 2024-08-20T21:39:00.1964608Z 2024-08-20T21:39:00.1964711Z Returns: 2024-08-20T21:39:00.1965203Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-08-20T21:39:00.1965692Z 2024-08-20T21:39:00.1965798Z Examples: 2024-08-20T21:39:00.1966221Z >>> from torch.nn.utils import prune 2024-08-20T21:39:00.1966695Z >>> m = prune.custom_from_mask( 2024-08-20T21:39:00.1967339Z ... nn.Linear(5, 3), name='bias', mask=torch.tensor([0, 1, 0]) 2024-08-20T21:39:00.1967862Z ... ) 2024-08-20T21:39:00.1968157Z >>> print(m.bias_mask) 2024-08-20T21:39:00.1968528Z tensor([0., 1., 0.]) 2024-08-20T21:39:00.1968763Z 2024-08-20T21:39:00.1968856Z 2024-08-20T21:39:00.1969415Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:00.1969922Z 2024-08-20T21:39:00.4125163Z 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. 2024-08-20T21:39:00.4126561Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:00.4127603Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA). 2024-08-20T21:39:00.4128335Z 2024-08-20T21:39:00.4128686Z Stochastic Weight Averaging was proposed in `Averaging Weights Leads to 2024-08-20T21:39:00.4129516Z Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii 2024-08-20T21:39:00.4130288Z Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson 2024-08-20T21:39:00.4130873Z (UAI 2018). 2024-08-20T21:39:00.4131053Z 2024-08-20T21:39:00.4131362Z Exponential Moving Average is a variation of `Polyak averaging`_, 2024-08-20T21:39:00.4132152Z but using exponential weights instead of equal weights across iterations. 2024-08-20T21:39:00.4132670Z 2024-08-20T21:39:00.4133362Z AveragedModel class creates a copy of the provided module :attr:`model` 2024-08-20T21:39:00.4134399Z on the device :attr:`device` and allows to compute running averages of the 2024-08-20T21:39:00.4135216Z parameters of the :attr:`model`. 2024-08-20T21:39:00.4135731Z 2024-08-20T21:39:00.4135922Z Args: 2024-08-20T21:39:00.4136457Z model (torch.nn.Module): model to use with SWA/EMA 2024-08-20T21:39:00.4137170Z device (torch.device, optional): if provided, the averaged model will be 2024-08-20T21:39:00.4137820Z stored on the :attr:`device` 2024-08-20T21:39:00.4138408Z avg_fn (function, optional): the averaging function used to update 2024-08-20T21:39:00.4139150Z parameters; the function must take in the current value of the 2024-08-20T21:39:00.4139895Z :class:`AveragedModel` parameter, the current value of :attr:`model` 2024-08-20T21:39:00.4140653Z parameter, and the number of models already averaged; if None, 2024-08-20T21:39:00.4141334Z an equally weighted average is used (default: None) 2024-08-20T21:39:00.4142033Z multi_avg_fn (function, optional): the averaging function used to update 2024-08-20T21:39:00.4142855Z parameters inplace; the function must take in the current values of the 2024-08-20T21:39:00.4143729Z :class:`AveragedModel` parameters as a list, the current values of :attr:`model` 2024-08-20T21:39:00.4144603Z parameters as a list, and the number of models already averaged; if None, 2024-08-20T21:39:00.4145343Z an equally weighted average is used (default: None) 2024-08-20T21:39:00.4146037Z use_buffers (bool): if ``True``, it will compute running averages for 2024-08-20T21:39:00.4146829Z both the parameters and the buffers of the model. (default: ``False``) 2024-08-20T21:39:00.4147323Z 2024-08-20T21:39:00.4147426Z Example: 2024-08-20T21:39:00.4147782Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:39:00.4148317Z >>> loader, optimizer, model, loss_fn = ... 2024-08-20T21:39:00.4148897Z >>> swa_model = torch.optim.swa_utils.AveragedModel(model) 2024-08-20T21:39:00.4149624Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 2024-08-20T21:39:00.4150275Z >>> T_max=300) 2024-08-20T21:39:00.4150893Z >>> swa_start = 160 2024-08-20T21:39:00.4151332Z >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05) 2024-08-20T21:39:00.4151831Z >>> for i in range(300): 2024-08-20T21:39:00.4152239Z >>> for input, target in loader: 2024-08-20T21:39:00.4152713Z >>> optimizer.zero_grad() 2024-08-20T21:39:00.4153215Z >>> loss_fn(model(input), target).backward() 2024-08-20T21:39:00.4153692Z >>> optimizer.step() 2024-08-20T21:39:00.4154103Z >>> if i > swa_start: 2024-08-20T21:39:00.4154552Z >>> swa_model.update_parameters(model) 2024-08-20T21:39:00.4155025Z >>> swa_scheduler.step() 2024-08-20T21:39:00.4155440Z >>> else: 2024-08-20T21:39:00.4155774Z >>> scheduler.step() 2024-08-20T21:39:00.4156147Z >>> 2024-08-20T21:39:00.4156525Z >>> # Update bn statistics for the swa_model at the end 2024-08-20T21:39:00.4157139Z >>> torch.optim.swa_utils.update_bn(loader, swa_model) 2024-08-20T21:39:00.4157531Z 2024-08-20T21:39:00.4157981Z You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters. 2024-08-20T21:39:00.4158977Z If no averaging function is provided, the default is to compute 2024-08-20T21:39:00.4159755Z equally-weighted average of the weights (SWA). 2024-08-20T21:39:00.4160117Z 2024-08-20T21:39:00.4160235Z Example: 2024-08-20T21:39:00.4160578Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:39:00.4161220Z >>> # Compute exponential moving averages of the weights and buffers 2024-08-20T21:39:00.4162002Z >>> ema_model = torch.optim.swa_utils.AveragedModel(model, 2024-08-20T21:39:00.4162722Z >>> torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True) 2024-08-20T21:39:00.4163217Z 2024-08-20T21:39:00.4163344Z .. note:: 2024-08-20T21:39:00.4163841Z When using SWA/EMA with models containing Batch Normalization you may 2024-08-20T21:39:00.4164624Z need to update the activation statistics for Batch Normalization. 2024-08-20T21:39:00.4165444Z This can be done either by using the :meth:`torch.optim.swa_utils.update_bn` 2024-08-20T21:39:00.4166303Z or by setting :attr:`use_buffers` to `True`. The first approach updates the 2024-08-20T21:39:00.4167243Z statistics in a post-training step by passing data through the model. The 2024-08-20T21:39:00.4168092Z second does it during the parameter update phase by averaging all buffers. 2024-08-20T21:39:00.4168957Z Empirical evidence has shown that updating the statistics in normalization 2024-08-20T21:39:00.4169801Z layers increases accuracy, but you may wish to empirically test which 2024-08-20T21:39:00.4170486Z approach yields the best results in your problem. 2024-08-20T21:39:00.4170872Z 2024-08-20T21:39:00.4170978Z .. note:: 2024-08-20T21:39:00.4171527Z :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model. 2024-08-20T21:39:00.4172081Z 2024-08-20T21:39:00.4172194Z .. note:: 2024-08-20T21:39:00.4172632Z When :meth:`update_parameters` is called for the first time (i.e. 2024-08-20T21:39:00.4173351Z :attr:`n_averaged` is `0`) the parameters of `model` are copied 2024-08-20T21:39:00.4174078Z to the parameters of :class:`AveragedModel`. For every subsequent 2024-08-20T21:39:00.4174798Z call of :meth:`update_parameters` the function `avg_fn` is used 2024-08-20T21:39:00.4175372Z to update the parameters. 2024-08-20T21:39:00.4175641Z 2024-08-20T21:39:00.4175960Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2024-08-20T21:39:00.4176591Z https://arxiv.org/abs/1803.05407 2024-08-20T21:39:00.4177238Z .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should 2024-08-20T21:39:00.4177851Z Average: 2024-08-20T21:39:00.4178172Z https://arxiv.org/abs/1806.05594 2024-08-20T21:39:00.4178926Z .. _SWALP: Stochastic Weight Averaging in Low-Precision Training: 2024-08-20T21:39:00.4179529Z https://arxiv.org/abs/1904.11943 2024-08-20T21:39:00.4180214Z .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That 2024-08-20T21:39:00.4180819Z Generalizes Well: 2024-08-20T21:39:00.4181195Z https://arxiv.org/abs/2001.02312 2024-08-20T21:39:00.4181611Z .. _Polyak averaging: 2024-08-20T21:39:00.4182143Z https://paperswithcode.com/method/polyak-averaging 2024-08-20T21:39:00.4182638Z 2024-08-20T21:39:00.4183191Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:00.4183712Z 2024-08-20T21:39:00.4184590Z 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. 2024-08-20T21:39:00.4185879Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:00.4186709Z Anneals the learning rate in each parameter group to a fixed value. 2024-08-20T21:39:00.4187175Z 2024-08-20T21:39:00.4187515Z This learning rate scheduler is meant to be used with Stochastic Weight 2024-08-20T21:39:00.4188302Z Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`). 2024-08-20T21:39:00.4188790Z 2024-08-20T21:39:00.4188893Z Args: 2024-08-20T21:39:00.4189291Z optimizer (torch.optim.Optimizer): wrapped optimizer 2024-08-20T21:39:00.4189991Z swa_lrs (float or list): the learning rate value for all param groups 2024-08-20T21:39:00.4190648Z together or separately for each group. 2024-08-20T21:39:00.4191378Z annealing_epochs (int): number of epochs in the annealing phase 2024-08-20T21:39:00.4191934Z (default: 10) 2024-08-20T21:39:00.4192465Z annealing_strategy (str): "cos" or "linear"; specifies the annealing 2024-08-20T21:39:00.4193238Z strategy: "cos" for cosine annealing, "linear" for linear annealing 2024-08-20T21:39:00.4193821Z (default: "cos") 2024-08-20T21:39:00.4194410Z last_epoch (int): the index of the last epoch (default: -1) 2024-08-20T21:39:00.4194833Z 2024-08-20T21:39:00.4195104Z The :class:`SWALR` scheduler can be used together with other 2024-08-20T21:39:00.4196159Z schedulers to switch to a constant learning rate late in the training 2024-08-20T21:39:00.4196768Z as in the example below. 2024-08-20T21:39:00.4197027Z 2024-08-20T21:39:00.4197130Z Example: 2024-08-20T21:39:00.4197489Z >>> # xdoctest: +SKIP("Undefined variables") 2024-08-20T21:39:00.4197984Z >>> loader, optimizer, model = ... 2024-08-20T21:39:00.4198539Z >>> lr_lambda = lambda epoch: 0.9 2024-08-20T21:39:00.4199394Z >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, 2024-08-20T21:39:00.4199993Z >>> lr_lambda=lr_lambda) 2024-08-20T21:39:00.4200527Z >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, 2024-08-20T21:39:00.4201182Z >>> anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05) 2024-08-20T21:39:00.4201713Z >>> swa_start = 160 2024-08-20T21:39:00.4202075Z >>> for i in range(300): 2024-08-20T21:39:00.4202494Z >>> for input, target in loader: 2024-08-20T21:39:00.4202952Z >>> optimizer.zero_grad() 2024-08-20T21:39:00.4203455Z >>> loss_fn(model(input), target).backward() 2024-08-20T21:39:00.4203952Z >>> optimizer.step() 2024-08-20T21:39:00.4204354Z >>> if i > swa_start: 2024-08-20T21:39:00.4204760Z >>> swa_scheduler.step() 2024-08-20T21:39:00.4205167Z >>> else: 2024-08-20T21:39:00.4205498Z >>> scheduler.step() 2024-08-20T21:39:00.4205788Z 2024-08-20T21:39:00.4206092Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2024-08-20T21:39:00.4206723Z https://arxiv.org/abs/1803.05407 2024-08-20T21:39:00.4207111Z 2024-08-20T21:39:00.4207704Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:00.4208404Z 2024-08-20T21:39:00.8400169Z 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. 2024-08-20T21:39:00.8402890Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:00.8403638Z Asserts that ``actual`` and ``expected`` are close. 2024-08-20T21:39:00.8404024Z 2024-08-20T21:39:00.8404671Z If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if 2024-08-20T21:39:00.8405378Z 2024-08-20T21:39:00.8405504Z .. math:: 2024-08-20T21:39:00.8405692Z 2024-08-20T21:39:00.8406396Z \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert 2024-08-20T21:39:00.8407133Z 2024-08-20T21:39:00.8407746Z Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are 2024-08-20T21:39:00.8408721Z only considered equal to each other if ``equal_nan`` is ``True``. 2024-08-20T21:39:00.8409189Z 2024-08-20T21:39:00.8409473Z In addition, they are only considered close if they have the same 2024-08-20T21:39:00.8409930Z 2024-08-20T21:39:00.8410291Z - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``), 2024-08-20T21:39:00.8421588Z - ``dtype`` (if ``check_dtype`` is ``True``), 2024-08-20T21:39:00.8422482Z - ``layout`` (if ``check_layout`` is ``True``), and 2024-08-20T21:39:00.8423081Z - stride (if ``check_stride`` is ``True``). 2024-08-20T21:39:00.8423416Z 2024-08-20T21:39:00.8424158Z If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed. 2024-08-20T21:39:00.8424782Z 2024-08-20T21:39:00.8425350Z If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are 2024-08-20T21:39:00.8426587Z checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR, 2024-08-20T21:39:00.8427624Z or ``ccol_indices`` and ``row_indices`` for CSC and BSC layouts, respectively, 2024-08-20T21:39:00.8428683Z are always checked for equality whereas the values are checked for closeness according to the definition above. 2024-08-20T21:39:00.8429402Z 2024-08-20T21:39:00.8429814Z If ``actual`` and ``expected`` are quantized, they are considered close if they have the same 2024-08-20T21:39:00.8430920Z :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the 2024-08-20T21:39:00.8431725Z definition above. 2024-08-20T21:39:00.8431939Z 2024-08-20T21:39:00.8432510Z ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which 2024-08-20T21:39:00.8433780Z :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types 2024-08-20T21:39:00.8435089Z have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s 2024-08-20T21:39:00.8436464Z or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all 2024-08-20T21:39:00.8437505Z their elements are considered close according to the above definition. 2024-08-20T21:39:00.8438009Z 2024-08-20T21:39:00.8438139Z .. note:: 2024-08-20T21:39:00.8438497Z 2024-08-20T21:39:00.8438983Z Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e. 2024-08-20T21:39:00.8440233Z :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus, 2024-08-20T21:39:00.8441263Z Python scalars of different types can be checked, but require ``check_dtype=False``. 2024-08-20T21:39:00.8441853Z 2024-08-20T21:39:00.8441954Z Args: 2024-08-20T21:39:00.8442250Z actual (Any): Actual input. 2024-08-20T21:39:00.8442803Z expected (Any): Expected input. 2024-08-20T21:39:00.8443620Z allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types 2024-08-20T21:39:00.8444521Z are allowed. Otherwise type equality is required. 2024-08-20T21:39:00.8445422Z rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default 2024-08-20T21:39:00.8446491Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2024-08-20T21:39:00.8447575Z atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default 2024-08-20T21:39:00.8448651Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2024-08-20T21:39:00.8449569Z equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal. 2024-08-20T21:39:00.8450535Z check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same 2024-08-20T21:39:00.8451495Z :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different 2024-08-20T21:39:00.8452463Z :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared. 2024-08-20T21:39:00.8453479Z check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this 2024-08-20T21:39:00.8454765Z check is disabled, tensors with different ``dtype``'s are promoted to a common ``dtype`` (according to 2024-08-20T21:39:00.8455662Z :func:`torch.promote_types`) before being compared. 2024-08-20T21:39:00.8456637Z check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this 2024-08-20T21:39:00.8457909Z check is disabled, tensors with different ``layout``'s are converted to strided tensors before being 2024-08-20T21:39:00.8458671Z compared. 2024-08-20T21:39:00.8459403Z check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride. 2024-08-20T21:39:00.8460587Z msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during 2024-08-20T21:39:00.8461799Z the comparison. Can also passed as callable in which case it will be called with the generated message and 2024-08-20T21:39:00.8462644Z should return the new message. 2024-08-20T21:39:00.8462959Z 2024-08-20T21:39:00.8463062Z Raises: 2024-08-20T21:39:00.8463567Z ValueError: If no :class:`torch.Tensor` can be constructed from an input. 2024-08-20T21:39:00.8464305Z ValueError: If only ``rtol`` or ``atol`` is specified. 2024-08-20T21:39:00.8465145Z AssertionError: If corresponding inputs are not Python scalars and are not directly related. 2024-08-20T21:39:00.8466283Z AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have 2024-08-20T21:39:00.8467088Z different types. 2024-08-20T21:39:00.8467952Z AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match. 2024-08-20T21:39:00.8469261Z AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match. 2024-08-20T21:39:00.8470392Z AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`. 2024-08-20T21:39:00.8471433Z AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same 2024-08-20T21:39:00.8472174Z :attr:`~torch.Tensor.layout`. 2024-08-20T21:39:00.8472795Z AssertionError: If only one of corresponding tensors is quantized. 2024-08-20T21:39:00.8473921Z AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s. 2024-08-20T21:39:00.8475119Z AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same 2024-08-20T21:39:00.8475854Z :attr:`~torch.Tensor.device`. 2024-08-20T21:39:00.8476650Z AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``. 2024-08-20T21:39:00.8477811Z AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride. 2024-08-20T21:39:00.8479128Z AssertionError: If the values of corresponding tensors are not close according to the definition above. 2024-08-20T21:39:00.8479818Z 2024-08-20T21:39:00.8480472Z The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching 2024-08-20T21:39:00.8481447Z ``dtype``'s, the maximum of both tolerances is used. 2024-08-20T21:39:00.8481824Z 2024-08-20T21:39:00.8482070Z +---------------------------+------------+----------+ 2024-08-20T21:39:00.8482582Z | ``dtype`` | ``rtol`` | ``atol`` | 2024-08-20T21:39:00.8483099Z +===========================+============+==========+ 2024-08-20T21:39:00.8483697Z | :attr:`~torch.float16` | ``1e-3`` | ``1e-5`` | 2024-08-20T21:39:00.8484275Z +---------------------------+------------+----------+ 2024-08-20T21:39:00.8484874Z | :attr:`~torch.bfloat16` | ``1.6e-2`` | ``1e-5`` | 2024-08-20T21:39:00.8485466Z +---------------------------+------------+----------+ 2024-08-20T21:39:00.8486045Z | :attr:`~torch.float32` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:39:00.8486636Z +---------------------------+------------+----------+ 2024-08-20T21:39:00.8487321Z | :attr:`~torch.float64` | ``1e-7`` | ``1e-7`` | 2024-08-20T21:39:00.8487896Z +---------------------------+------------+----------+ 2024-08-20T21:39:00.8488494Z | :attr:`~torch.complex32` | ``1e-3`` | ``1e-5`` | 2024-08-20T21:39:00.8489081Z +---------------------------+------------+----------+ 2024-08-20T21:39:00.8489666Z | :attr:`~torch.complex64` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:39:00.8490259Z +---------------------------+------------+----------+ 2024-08-20T21:39:00.8490855Z | :attr:`~torch.complex128` | ``1e-7`` | ``1e-7`` | 2024-08-20T21:39:00.8491447Z +---------------------------+------------+----------+ 2024-08-20T21:39:00.8492025Z | :attr:`~torch.quint8` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:39:00.8492608Z +---------------------------+------------+----------+ 2024-08-20T21:39:00.8493200Z | :attr:`~torch.quint2x4` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:39:00.8493771Z +---------------------------+------------+----------+ 2024-08-20T21:39:00.8494368Z | :attr:`~torch.quint4x2` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:39:00.8494951Z +---------------------------+------------+----------+ 2024-08-20T21:39:00.8495526Z | :attr:`~torch.qint8` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:39:00.8496430Z +---------------------------+------------+----------+ 2024-08-20T21:39:00.8497036Z | :attr:`~torch.qint32` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:39:00.8497605Z +---------------------------+------------+----------+ 2024-08-20T21:39:00.8498119Z | other | ``0.0`` | ``0.0`` | 2024-08-20T21:39:00.8498715Z +---------------------------+------------+----------+ 2024-08-20T21:39:00.8499291Z 2024-08-20T21:39:00.8499410Z .. note:: 2024-08-20T21:39:00.8499592Z 2024-08-20T21:39:00.8500136Z :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged 2024-08-20T21:39:00.8501386Z to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might 2024-08-20T21:39:00.8502469Z define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default: 2024-08-20T21:39:00.8503020Z 2024-08-20T21:39:00.8503143Z >>> import functools 2024-08-20T21:39:00.8503753Z >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0) 2024-08-20T21:39:00.8504665Z >>> assert_equal(1e-9, 1e-10) 2024-08-20T21:39:00.8505095Z Traceback (most recent call last): 2024-08-20T21:39:00.8505525Z ... 2024-08-20T21:39:00.8505862Z AssertionError: Scalars are not equal! 2024-08-20T21:39:00.8506298Z 2024-08-20T21:39:00.8506690Z Expected 1e-10 but got 1e-09. 2024-08-20T21:39:00.8507218Z Absolute difference: 9.000000000000001e-10 2024-08-20T21:39:00.8507681Z Relative difference: 9.0 2024-08-20T21:39:00.8507961Z 2024-08-20T21:39:00.8508066Z Examples: 2024-08-20T21:39:00.8508397Z >>> # tensor to tensor comparison 2024-08-20T21:39:00.8508945Z >>> expected = torch.tensor([1e0, 1e-1, 1e-2]) 2024-08-20T21:39:00.8509485Z >>> actual = torch.acos(torch.cos(expected)) 2024-08-20T21:39:00.8510043Z >>> torch.testing.assert_close(actual, expected) 2024-08-20T21:39:00.8510410Z 2024-08-20T21:39:00.8510570Z >>> # scalar to scalar comparison 2024-08-20T21:39:00.8510994Z >>> import math 2024-08-20T21:39:00.8511350Z >>> expected = math.sqrt(2.0) 2024-08-20T21:39:00.8511783Z >>> actual = 2.0 / math.sqrt(2.0) 2024-08-20T21:39:00.8512273Z >>> torch.testing.assert_close(actual, expected) 2024-08-20T21:39:00.8512652Z 2024-08-20T21:39:00.8512825Z >>> # numpy array to numpy array comparison 2024-08-20T21:39:00.8513310Z >>> import numpy as np 2024-08-20T21:39:00.8513784Z >>> expected = np.array([1e0, 1e-1, 1e-2]) 2024-08-20T21:39:00.8514297Z >>> actual = np.arccos(np.cos(expected)) 2024-08-20T21:39:00.8514943Z >>> torch.testing.assert_close(actual, expected) 2024-08-20T21:39:00.8515310Z 2024-08-20T21:39:00.8515471Z >>> # sequence to sequence comparison 2024-08-20T21:39:00.8515924Z >>> import numpy as np 2024-08-20T21:39:00.8516563Z >>> # The types of the sequences do not have to match. They only have to have the same 2024-08-20T21:39:00.8517295Z >>> # length and their elements have to match. 2024-08-20T21:39:00.8517875Z >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)] 2024-08-20T21:39:00.8518522Z >>> actual = tuple(expected) 2024-08-20T21:39:00.8518999Z >>> torch.testing.assert_close(actual, expected) 2024-08-20T21:39:00.8519382Z 2024-08-20T21:39:00.8519535Z >>> # mapping to mapping comparison 2024-08-20T21:39:00.8520037Z >>> from collections import OrderedDict 2024-08-20T21:39:00.8520503Z >>> import numpy as np 2024-08-20T21:39:00.8520876Z >>> foo = torch.tensor(1.0) 2024-08-20T21:39:00.8521269Z >>> bar = 2.0 2024-08-20T21:39:00.8521604Z >>> baz = np.array(3.0) 2024-08-20T21:39:00.8522222Z >>> # The types and a possible ordering of mappings do not have to match. They only 2024-08-20T21:39:00.8523081Z >>> # have to have the same set of keys and their elements have to match. 2024-08-20T21:39:00.8523857Z >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)]) 2024-08-20T21:39:00.8524505Z >>> actual = {"baz": baz, "bar": bar, "foo": foo} 2024-08-20T21:39:00.8525071Z >>> torch.testing.assert_close(actual, expected) 2024-08-20T21:39:00.8525440Z 2024-08-20T21:39:00.8525630Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2024-08-20T21:39:00.8526109Z >>> actual = expected.clone() 2024-08-20T21:39:00.8526655Z >>> # By default, directly related instances can be compared 2024-08-20T21:39:00.8527382Z >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected) 2024-08-20T21:39:00.8528135Z >>> # This check can be made more strict with allow_subclasses=False 2024-08-20T21:39:00.8528741Z >>> torch.testing.assert_close( 2024-08-20T21:39:00.8529338Z ... torch.nn.Parameter(actual), expected, allow_subclasses=False 2024-08-20T21:39:00.8529897Z ... ) 2024-08-20T21:39:00.8530210Z Traceback (most recent call last): 2024-08-20T21:39:00.8530722Z ... 2024-08-20T21:39:00.8531173Z TypeError: No comparison pair was able to handle inputs of type 2024-08-20T21:39:00.8532028Z and . 2024-08-20T21:39:00.8532853Z >>> # If the inputs are not directly related, they are never considered close 2024-08-20T21:39:00.8533604Z >>> torch.testing.assert_close(actual.numpy(), expected) 2024-08-20T21:39:00.8534148Z Traceback (most recent call last): 2024-08-20T21:39:00.8534573Z ... 2024-08-20T21:39:00.8535256Z TypeError: No comparison pair was able to handle inputs of type 2024-08-20T21:39:00.8536024Z and . 2024-08-20T21:39:00.8536697Z >>> # Exceptions to these rules are Python scalars. They can be checked regardless of 2024-08-20T21:39:00.8537414Z >>> # their type if check_dtype=False. 2024-08-20T21:39:00.8537965Z >>> torch.testing.assert_close(1.0, 1, check_dtype=False) 2024-08-20T21:39:00.8538384Z 2024-08-20T21:39:00.8538515Z >>> # NaN != NaN by default. 2024-08-20T21:39:00.8538978Z >>> expected = torch.tensor(float("Nan")) 2024-08-20T21:39:00.8539447Z >>> actual = expected.clone() 2024-08-20T21:39:00.8539946Z >>> torch.testing.assert_close(actual, expected) 2024-08-20T21:39:00.8540470Z Traceback (most recent call last): 2024-08-20T21:39:00.8540880Z ... 2024-08-20T21:39:00.8541215Z AssertionError: Scalars are not close! 2024-08-20T21:39:00.8541663Z 2024-08-20T21:39:00.8541978Z Expected nan but got nan. 2024-08-20T21:39:00.8542593Z Absolute difference: nan (up to 1e-05 allowed) 2024-08-20T21:39:00.8543235Z Relative difference: nan (up to 1.3e-06 allowed) 2024-08-20T21:39:00.8543884Z >>> torch.testing.assert_close(actual, expected, equal_nan=True) 2024-08-20T21:39:00.8544326Z 2024-08-20T21:39:00.8544501Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2024-08-20T21:39:00.8545023Z >>> actual = torch.tensor([1.0, 4.0, 5.0]) 2024-08-20T21:39:00.8545568Z >>> # The default error message can be overwritten. 2024-08-20T21:39:00.8546331Z >>> torch.testing.assert_close(actual, expected, msg="Argh, the tensors are not close!") 2024-08-20T21:39:00.8547066Z Traceback (most recent call last): 2024-08-20T21:39:00.8547493Z ... 2024-08-20T21:39:00.8547854Z AssertionError: Argh, the tensors are not close! 2024-08-20T21:39:00.8548584Z >>> # If msg is a callable, it can be used to augment the generated message with 2024-08-20T21:39:00.8549241Z >>> # extra information 2024-08-20T21:39:00.8549640Z >>> torch.testing.assert_close( 2024-08-20T21:39:00.8550227Z ... actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter" 2024-08-20T21:39:00.8550788Z ... ) 2024-08-20T21:39:00.8551098Z Traceback (most recent call last): 2024-08-20T21:39:00.8551524Z ... 2024-08-20T21:39:00.8551821Z AssertionError: Header 2024-08-20T21:39:00.8552179Z 2024-08-20T21:39:00.8552557Z Tensor-likes are not close! 2024-08-20T21:39:00.8552956Z 2024-08-20T21:39:00.8553291Z Mismatched elements: 2 / 3 (66.7%) 2024-08-20T21:39:00.8554009Z Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed) 2024-08-20T21:39:00.8554897Z Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed) 2024-08-20T21:39:00.8555483Z 2024-08-20T21:39:00.8555786Z Footer 2024-08-20T21:39:00.8556058Z 2024-08-20T21:39:00.8556612Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:00.8557137Z 2024-08-20T21:39:02.1069968Z 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. 2024-08-20T21:39:02.1072129Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:02.1073242Z Register a container-like type as pytree node. 2024-08-20T21:39:02.1073644Z 2024-08-20T21:39:02.1073747Z Args: 2024-08-20T21:39:02.1074192Z cls (type): A Python type to treat as an internal pytree node. 2024-08-20T21:39:02.1075039Z flatten_fn (callable): A function to be used during flattening, taking an instance of 2024-08-20T21:39:02.1075978Z ``cls`` and returning a pair, with (1) an iterable for the children to be flattened 2024-08-20T21:39:02.1076951Z recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be 2024-08-20T21:39:02.1077718Z passed to the ``unflatten_fn``. 2024-08-20T21:39:02.1078564Z unflatten_fn (callable): A function taking two arguments: the auxiliary data that was 2024-08-20T21:39:02.1079527Z returned by ``flatten_fn`` and stored in the treespec, and the unflattened children. 2024-08-20T21:39:02.1080313Z The function should return an instance of ``cls``. 2024-08-20T21:39:02.1081071Z serialized_type_name (str, optional): A keyword argument used to specify the fully 2024-08-20T21:39:02.1081863Z qualified name used when serializing the tree spec. 2024-08-20T21:39:02.1082707Z to_dumpable_context (callable, optional): An optional keyword argument to custom specify how 2024-08-20T21:39:02.1083735Z to convert the context of the pytree to a custom json dumpable representation. This is 2024-08-20T21:39:02.1084700Z used for json serialization, which is being used in :mod:`torch.export` right now. 2024-08-20T21:39:02.1085815Z from_dumpable_context (callable, optional): An optional keyword argument to custom specify 2024-08-20T21:39:02.1086796Z how to convert the custom json dumpable representation of the context back to the 2024-08-20T21:39:02.1087724Z original context. This is used for json deserialization, which is being used in 2024-08-20T21:39:02.1088434Z :mod:`torch.export` right now. 2024-08-20T21:39:02.1088766Z 2024-08-20T21:39:02.1088907Z Example:: 2024-08-20T21:39:02.1089084Z 2024-08-20T21:39:02.1089223Z >>> # xdoctest: +SKIP 2024-08-20T21:39:02.1089668Z >>> # Registry a Python type with lambda functions 2024-08-20T21:39:02.1090180Z >>> register_pytree_node( 2024-08-20T21:39:02.1090563Z ... set, 2024-08-20T21:39:02.1090926Z ... lambda s: (sorted(s), None, None), 2024-08-20T21:39:02.1091442Z ... lambda children, _: set(children), 2024-08-20T21:39:02.1091892Z ... ) 2024-08-20T21:39:02.1092147Z 2024-08-20T21:39:02.1092758Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:02.1093265Z 2024-08-20T21:39:02.1626311Z msg = Cannot scrape callname=SelectiveCheckpointContext in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py line=1201. 2024-08-20T21:39:02.1627765Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:02.1628325Z 2024-08-20T21:39:02.1628637Z Context passed to policy function during selective checkpointing. 2024-08-20T21:39:02.1629115Z 2024-08-20T21:39:02.1629449Z This class is used to pass relevant metadata to the policy function during 2024-08-20T21:39:02.1630316Z selective checkpointing. The metadata includes whether the current invocation 2024-08-20T21:39:02.1631262Z of the policy function is during recomputation or not. 2024-08-20T21:39:02.1631667Z 2024-08-20T21:39:02.1631772Z Example: 2024-08-20T21:39:02.1632065Z >>> # xdoctest: +SKIP(stub) 2024-08-20T21:39:02.1632422Z >>> 2024-08-20T21:39:02.1632958Z >>> def policy_fn(ctx, op, *args, **kwargs): 2024-08-20T21:39:02.1633803Z >>> print(ctx.is_recompute) 2024-08-20T21:39:02.1634418Z >>> 2024-08-20T21:39:02.1635264Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2024-08-20T21:39:02.1636549Z >>> 2024-08-20T21:39:02.1637516Z >>> out = torch.utils.checkpoint.checkpoint( 2024-08-20T21:39:02.1638472Z >>> fn, x, y, 2024-08-20T21:39:02.1639084Z >>> use_reentrant=False, 2024-08-20T21:39:02.1639581Z >>> context_fn=context_fn, 2024-08-20T21:39:02.1639962Z >>> ) 2024-08-20T21:39:02.1640114Z 2024-08-20T21:39:02.1640593Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:02.1641105Z 2024-08-20T21:39:02.1642177Z 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. 2024-08-20T21:39:02.1643629Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:02.1644167Z 2024-08-20T21:39:02.1644497Z Helper to avoid recomputing certain ops during activation checkpointing. 2024-08-20T21:39:02.1645016Z 2024-08-20T21:39:02.1645325Z Use this with `torch.utils.checkpoint.checkpoint` to control which 2024-08-20T21:39:02.1646023Z operations are recomputed during the backward pass. 2024-08-20T21:39:02.1646404Z 2024-08-20T21:39:02.1646500Z Args: 2024-08-20T21:39:02.1646806Z policy_fn_or_list (Callable or List): 2024-08-20T21:39:02.1647421Z - If a policy function is provided, it should accept a 2024-08-20T21:39:02.1648138Z :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and 2024-08-20T21:39:02.1648955Z kwargs to the op, and return a :class:`CheckpointPolicy` enum value 2024-08-20T21:39:02.1649771Z indicating whether the execution of the op should be recomputed or not. 2024-08-20T21:39:02.1650634Z - If a list of operations is provided, it is equivalent to a policy 2024-08-20T21:39:02.1651492Z returning `CheckpointPolicy.MUST_SAVE` for the specified 2024-08-20T21:39:02.1652232Z operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other 2024-08-20T21:39:02.1652829Z operations. 2024-08-20T21:39:02.1653314Z allow_cache_entry_mutation (bool, optional): By default, an error is 2024-08-20T21:39:02.1654098Z raised if any tensors cached by selective activation checkpoint are 2024-08-20T21:39:02.1654891Z mutated in order to ensure correctness. If set to `True`, this check 2024-08-20T21:39:02.1655477Z is disabled. 2024-08-20T21:39:02.1655788Z Returns: 2024-08-20T21:39:02.1656093Z A tuple of two context managers. 2024-08-20T21:39:02.1656382Z 2024-08-20T21:39:02.1656483Z Example: 2024-08-20T21:39:02.1656779Z >>> # xdoctest: +REQUIRES(LINUX) 2024-08-20T21:39:02.1657199Z >>> import functools 2024-08-20T21:39:02.1657512Z >>> 2024-08-20T21:39:02.1657837Z >>> x = torch.rand(10, 10, requires_grad=True) 2024-08-20T21:39:02.1658358Z >>> y = torch.rand(10, 10, requires_grad=True) 2024-08-20T21:39:02.1658792Z >>> 2024-08-20T21:39:02.1659063Z >>> ops_to_save = [ 2024-08-20T21:39:02.1659435Z >>> torch.ops.aten.mm.default, 2024-08-20T21:39:02.1659826Z >>> ] 2024-08-20T21:39:02.1660086Z >>> 2024-08-20T21:39:02.1660421Z >>> def policy_fn(ctx, op, *args, **kwargs): 2024-08-20T21:39:02.1660884Z >>> if op in ops_to_save: 2024-08-20T21:39:02.1661331Z >>> return CheckpointPolicy.MUST_SAVE 2024-08-20T21:39:02.1661785Z >>> else: 2024-08-20T21:39:02.1662159Z >>> return CheckpointPolicy.PREFER_RECOMPUTE 2024-08-20T21:39:02.1662632Z >>> 2024-08-20T21:39:02.1663153Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2024-08-20T21:39:02.1663786Z >>> 2024-08-20T21:39:02.1664063Z >>> # or equivalently 2024-08-20T21:39:02.1664672Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save) 2024-08-20T21:39:02.1665316Z >>> 2024-08-20T21:39:02.1665580Z >>> def fn(x, y): 2024-08-20T21:39:02.1666068Z >>> return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y 2024-08-20T21:39:02.1666603Z >>> 2024-08-20T21:39:02.1666952Z >>> out = torch.utils.checkpoint.checkpoint( 2024-08-20T21:39:02.1667554Z >>> fn, x, y, 2024-08-20T21:39:02.1667874Z >>> use_reentrant=False, 2024-08-20T21:39:02.1668274Z >>> context_fn=context_fn, 2024-08-20T21:39:02.1668653Z >>> ) 2024-08-20T21:39:02.1668803Z 2024-08-20T21:39:02.1669238Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:02.1669768Z 2024-08-20T21:39:02.1870519Z 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. 2024-08-20T21:39:02.1872050Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:02.1872606Z 2024-08-20T21:39:02.1872837Z Create a :class:`setuptools.Extension` for C++. 2024-08-20T21:39:02.1873199Z 2024-08-20T21:39:02.1873531Z Convenience method that creates a :class:`setuptools.Extension` with the 2024-08-20T21:39:02.1874368Z bare minimum (but often sufficient) arguments to build a C++ extension. 2024-08-20T21:39:02.1874862Z 2024-08-20T21:39:02.1875173Z All arguments are forwarded to the :class:`setuptools.Extension` 2024-08-20T21:39:02.1875874Z constructor. Full list arguments can be found at 2024-08-20T21:39:02.1877096Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2024-08-20T21:39:02.1878080Z 2024-08-20T21:39:02.1878397Z Example: 2024-08-20T21:39:02.1878890Z >>> # xdoctest: +SKIP 2024-08-20T21:39:02.1879681Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-08-20T21:39:02.1880554Z >>> from setuptools import setup 2024-08-20T21:39:02.1881652Z >>> from torch.utils.cpp_extension import BuildExtension, CppExtension 2024-08-20T21:39:02.1882584Z >>> setup( 2024-08-20T21:39:02.1883578Z ... name='extension', 2024-08-20T21:39:02.1884174Z ... ext_modules=[ 2024-08-20T21:39:02.1884761Z ... CppExtension( 2024-08-20T21:39:02.1885562Z ... name='extension', 2024-08-20T21:39:02.1886502Z ... sources=['extension.cpp'], 2024-08-20T21:39:02.1887471Z ... extra_compile_args=['-g'], 2024-08-20T21:39:02.1888593Z ... extra_link_flags=['-Wl,--no-as-needed', '-lm']) 2024-08-20T21:39:02.1889211Z ... ], 2024-08-20T21:39:02.1889492Z ... cmdclass={ 2024-08-20T21:39:02.1889917Z ... 'build_ext': BuildExtension 2024-08-20T21:39:02.1890380Z ... }) 2024-08-20T21:39:02.1890640Z 2024-08-20T21:39:02.1891311Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:02.1892604Z 2024-08-20T21:39:02.1894408Z 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. 2024-08-20T21:39:02.1897015Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:02.1897995Z 2024-08-20T21:39:02.1898391Z Create a :class:`setuptools.Extension` for CUDA/C++. 2024-08-20T21:39:02.1899087Z 2024-08-20T21:39:02.1899707Z Convenience method that creates a :class:`setuptools.Extension` with the 2024-08-20T21:39:02.1900791Z bare minimum (but often sufficient) arguments to build a CUDA/C++ 2024-08-20T21:39:02.1901569Z extension. This includes the CUDA include path, library path and runtime 2024-08-20T21:39:02.1902184Z library. 2024-08-20T21:39:02.1902343Z 2024-08-20T21:39:02.1902646Z All arguments are forwarded to the :class:`setuptools.Extension` 2024-08-20T21:39:02.1903398Z constructor. Full list arguments can be found at 2024-08-20T21:39:02.1904486Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2024-08-20T21:39:02.1905375Z 2024-08-20T21:39:02.1905553Z Example: 2024-08-20T21:39:02.1906014Z >>> # xdoctest: +SKIP 2024-08-20T21:39:02.1906748Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-08-20T21:39:02.1907704Z >>> from setuptools import setup 2024-08-20T21:39:02.1908789Z >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension 2024-08-20T21:39:02.1909387Z >>> setup( 2024-08-20T21:39:02.1910020Z ... name='cuda_extension', 2024-08-20T21:39:02.1910400Z ... ext_modules=[ 2024-08-20T21:39:02.1910763Z ... CUDAExtension( 2024-08-20T21:39:02.1911237Z ... name='cuda_extension', 2024-08-20T21:39:02.1911859Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-08-20T21:39:02.1912519Z ... extra_compile_args={'cxx': ['-g'], 2024-08-20T21:39:02.1913121Z ... 'nvcc': ['-O2']}, 2024-08-20T21:39:02.1913761Z ... extra_link_flags=['-Wl,--no-as-needed', '-lcuda']) 2024-08-20T21:39:02.1914272Z ... ], 2024-08-20T21:39:02.1914568Z ... cmdclass={ 2024-08-20T21:39:02.1914971Z ... 'build_ext': BuildExtension 2024-08-20T21:39:02.1915394Z ... }) 2024-08-20T21:39:02.1915581Z 2024-08-20T21:39:02.1915707Z Compute capabilities: 2024-08-20T21:39:02.1915922Z 2024-08-20T21:39:02.1916376Z By default the extension will be compiled to run on all archs of the cards visible during the 2024-08-20T21:39:02.1917413Z building process of the extension, plus PTX. If down the road a new card is installed the 2024-08-20T21:39:02.1918628Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's 2024-08-20T21:39:02.1919780Z newer than the newest version for which your nvcc can build fully-compiled binaries, Pytorch 2024-08-20T21:39:02.1920805Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does 2024-08-20T21:39:02.1921562Z support (see below for details on PTX). 2024-08-20T21:39:02.1921873Z 2024-08-20T21:39:02.1922879Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which 2024-08-20T21:39:02.1923679Z CCs you want the extension to support: 2024-08-20T21:39:02.1923983Z 2024-08-20T21:39:02.1924241Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py`` 2024-08-20T21:39:02.1925036Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py`` 2024-08-20T21:39:02.1925584Z 2024-08-20T21:39:02.1926057Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified 2024-08-20T21:39:02.1927227Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >= 2024-08-20T21:39:02.1928407Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with 2024-08-20T21:39:02.1929568Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to 2024-08-20T21:39:02.1930718Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on 2024-08-20T21:39:02.1931856Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better 2024-08-20T21:39:02.1932927Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6, 2024-08-20T21:39:02.1934121Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but 2024-08-20T21:39:02.1934870Z "8.0 8.6" would be better. 2024-08-20T21:39:02.1935121Z 2024-08-20T21:39:02.1935649Z Note that while it's possible to include all supported archs, the more archs get included the 2024-08-20T21:39:02.1936698Z slower the building process will be, as it will build a separate kernel image for each arch. 2024-08-20T21:39:02.1937304Z 2024-08-20T21:39:02.1937905Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows. 2024-08-20T21:39:02.1938867Z To workaround the issue, move python binding logic to pure C++ file. 2024-08-20T21:39:02.1939355Z 2024-08-20T21:39:02.1939469Z Example use: 2024-08-20T21:39:02.1939773Z #include 2024-08-20T21:39:02.1940237Z at::Tensor SigmoidAlphaBlendForwardCuda(....) 2024-08-20T21:39:02.1940620Z 2024-08-20T21:39:02.1940725Z Instead of: 2024-08-20T21:39:02.1941029Z #include 2024-08-20T21:39:02.1941577Z torch::Tensor SigmoidAlphaBlendForwardCuda(...) 2024-08-20T21:39:02.1941971Z 2024-08-20T21:39:02.1942356Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460 2024-08-20T21:39:02.1943604Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48 2024-08-20T21:39:02.1944446Z 2024-08-20T21:39:02.1944599Z Relocatable device code linking: 2024-08-20T21:39:02.1944886Z 2024-08-20T21:39:02.1945290Z If you want to reference device symbols across compilation units (across object files), 2024-08-20T21:39:02.1946357Z the object files need to be built with `relocatable device code` (-rdc=true or -dc). 2024-08-20T21:39:02.1947457Z An exception to this rule is "dynamic parallelism" (nested kernel launches) which is not used a lot anymore. 2024-08-20T21:39:02.1948626Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it. 2024-08-20T21:39:02.1949838Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step 2024-08-20T21:39:02.1950785Z help reduce the protentional perf degradation of `-rdc`. 2024-08-20T21:39:02.1951436Z Note that it needs to be used at both steps to be useful. 2024-08-20T21:39:02.1951837Z 2024-08-20T21:39:02.1952507Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step. 2024-08-20T21:39:02.1953553Z There is also a case where `-dlink` is used without `-rdc`: 2024-08-20T21:39:02.1954423Z when an extension is linked against a static lib containing rdc-compiled objects 2024-08-20T21:39:02.1955325Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem). 2024-08-20T21:39:02.1955810Z 2024-08-20T21:39:02.1956103Z Note: Ninja is required to build a CUDA Extension with RDC linking. 2024-08-20T21:39:02.1956569Z 2024-08-20T21:39:02.1956689Z Example: 2024-08-20T21:39:02.1956958Z >>> # xdoctest: +SKIP 2024-08-20T21:39:02.1957401Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-08-20T21:39:02.1957901Z >>> CUDAExtension( 2024-08-20T21:39:02.1958409Z ... name='cuda_extension', 2024-08-20T21:39:02.1959013Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-08-20T21:39:02.1959534Z ... dlink=True, 2024-08-20T21:39:02.1959909Z ... dlink_libraries=["dlink_lib"], 2024-08-20T21:39:02.1960460Z ... extra_compile_args={'cxx': ['-g'], 2024-08-20T21:39:02.1961058Z ... 'nvcc': ['-O2', '-rdc=true']}) 2024-08-20T21:39:02.1961409Z 2024-08-20T21:39:02.1961843Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:02.1962359Z 2024-08-20T21:39:02.1963199Z 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. 2024-08-20T21:39:02.1964503Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:02.1965033Z 2024-08-20T21:39:02.1965300Z Load a PyTorch C++ extension just-in-time (JIT). 2024-08-20T21:39:02.1965659Z 2024-08-20T21:39:02.1965975Z To load an extension, a Ninja build file is emitted, which is used to 2024-08-20T21:39:02.1966728Z compile the given sources into a dynamic library. This library is 2024-08-20T21:39:02.1967496Z subsequently loaded into the current Python process as a module and 2024-08-20T21:39:02.1968143Z returned from this function, ready for use. 2024-08-20T21:39:02.1968476Z 2024-08-20T21:39:02.1968772Z By default, the directory to which the build file is emitted and the 2024-08-20T21:39:02.1969565Z resulting library compiled to is ``/torch_extensions/``, where 2024-08-20T21:39:02.1970366Z ```` is the temporary folder on the current platform and ```` 2024-08-20T21:39:02.1971140Z the name of the extension. This location can be overridden in two ways. 2024-08-20T21:39:02.1971927Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it 2024-08-20T21:39:02.1972811Z replaces ``/torch_extensions`` and all extensions will be compiled 2024-08-20T21:39:02.1973581Z into subfolders of this directory. Second, if the ``build_directory`` 2024-08-20T21:39:02.1974388Z argument to this function is supplied, it overrides the entire path, i.e. 2024-08-20T21:39:02.1975123Z the library will be compiled into that folder directly. 2024-08-20T21:39:02.1975512Z 2024-08-20T21:39:02.1975829Z To compile the sources, the default system compiler (``c++``) is used, 2024-08-20T21:39:02.1976645Z which can be overridden by setting the ``CXX`` environment variable. To pass 2024-08-20T21:39:02.1977471Z additional arguments to the compilation process, ``extra_cflags`` or 2024-08-20T21:39:02.1978266Z ``extra_ldflags`` can be provided. For example, to compile your extension 2024-08-20T21:39:02.1979108Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use 2024-08-20T21:39:02.1979874Z ``extra_cflags`` to pass further include directories. 2024-08-20T21:39:02.1980247Z 2024-08-20T21:39:02.1980578Z CUDA support with mixed compilation is provided. Simply pass CUDA source 2024-08-20T21:39:02.1981358Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be 2024-08-20T21:39:02.1982151Z detected and compiled with nvcc rather than the C++ compiler. This includes 2024-08-20T21:39:02.1982951Z passing the CUDA lib64 directory as a library directory, and linking 2024-08-20T21:39:02.1983631Z ``cudart``. You can pass additional flags to nvcc via 2024-08-20T21:39:02.1984298Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various 2024-08-20T21:39:02.1985089Z heuristics for finding the CUDA install directory are used, which usually 2024-08-20T21:39:02.1986018Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the 2024-08-20T21:39:02.1986618Z safest option. 2024-08-20T21:39:02.1986797Z 2024-08-20T21:39:02.1986907Z Args: 2024-08-20T21:39:02.1987357Z name: The name of the extension to build. This MUST be the same as the 2024-08-20T21:39:02.1987985Z name of the pybind11 module! 2024-08-20T21:39:02.1988569Z sources: A list of relative or absolute paths to C++ source files. 2024-08-20T21:39:02.1989330Z extra_cflags: optional list of compiler flags to forward to the build. 2024-08-20T21:39:02.1990117Z extra_cuda_cflags: optional list of compiler flags to forward to nvcc 2024-08-20T21:39:02.1990729Z when building CUDA sources. 2024-08-20T21:39:02.1991305Z extra_ldflags: optional list of linker flags to forward to the build. 2024-08-20T21:39:02.1992074Z extra_include_paths: optional list of include directories to forward 2024-08-20T21:39:02.1992653Z to the build. 2024-08-20T21:39:02.1993100Z build_directory: optional path to use as build workspace. 2024-08-20T21:39:02.1993763Z verbose: If ``True``, turns on verbose logging of load steps. 2024-08-20T21:39:02.1994489Z with_cuda: Determines whether CUDA headers and libraries are added to 2024-08-20T21:39:02.1995184Z the build. If set to ``None`` (default), this value is 2024-08-20T21:39:02.1996175Z automatically determined based on the existence of ``.cu`` or 2024-08-20T21:39:02.1996895Z ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers 2024-08-20T21:39:02.1997472Z and libraries to be included. 2024-08-20T21:39:02.1998046Z is_python_module: If ``True`` (default), imports the produced shared 2024-08-20T21:39:02.1998871Z library as a Python module. If ``False``, behavior depends on 2024-08-20T21:39:02.1999439Z ``is_standalone``. 2024-08-20T21:39:02.1999960Z is_standalone: If ``False`` (default) loads the constructed extension 2024-08-20T21:39:02.2000734Z into the process as a plain dynamic library. If ``True``, build a 2024-08-20T21:39:02.2001327Z standalone executable. 2024-08-20T21:39:02.2001579Z 2024-08-20T21:39:02.2001678Z Returns: 2024-08-20T21:39:02.2001981Z If ``is_python_module`` is ``True``: 2024-08-20T21:39:02.2002531Z Returns the loaded PyTorch extension as a Python module. 2024-08-20T21:39:02.2003128Z 2024-08-20T21:39:02.2003422Z If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``: 2024-08-20T21:39:02.2004187Z Returns nothing. (The shared library is loaded into the process as 2024-08-20T21:39:02.2004780Z a side effect.) 2024-08-20T21:39:02.2004994Z 2024-08-20T21:39:02.2005147Z If ``is_standalone`` is ``True``. 2024-08-20T21:39:02.2005720Z Return the path to the executable. (On Windows, TORCH_LIB_PATH is 2024-08-20T21:39:02.2006434Z added to the PATH environment variable as a side effect.) 2024-08-20T21:39:02.2006852Z 2024-08-20T21:39:02.2006966Z Example: 2024-08-20T21:39:02.2007236Z >>> # xdoctest: +SKIP 2024-08-20T21:39:02.2007655Z >>> from torch.utils.cpp_extension import load 2024-08-20T21:39:02.2008126Z >>> module = load( 2024-08-20T21:39:02.2008558Z ... name='extension', 2024-08-20T21:39:02.2009093Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-08-20T21:39:02.2009658Z ... extra_cflags=['-O2'], 2024-08-20T21:39:02.2010024Z ... verbose=True) 2024-08-20T21:39:02.2010260Z 2024-08-20T21:39:02.2010675Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:02.2011179Z 2024-08-20T21:39:02.2012067Z 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. 2024-08-20T21:39:02.2013390Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:02.2013928Z 2024-08-20T21:39:02.2014295Z Load a PyTorch C++ extension just-in-time (JIT) from string sources. 2024-08-20T21:39:02.2014867Z 2024-08-20T21:39:02.2015192Z This function behaves exactly like :func:`load`, but takes its sources as 2024-08-20T21:39:02.2016005Z strings rather than filenames. These strings are stored to files in the 2024-08-20T21:39:02.2016771Z build directory, after which the behavior of :func:`load_inline` is 2024-08-20T21:39:02.2017361Z identical to :func:`load`. 2024-08-20T21:39:02.2017601Z 2024-08-20T21:39:02.2017721Z See `the 2024-08-20T21:39:02.2018306Z tests `_ 2024-08-20T21:39:02.2019077Z for good examples of using this function. 2024-08-20T21:39:02.2019391Z 2024-08-20T21:39:02.2019814Z Sources may omit two required parts of a typical non-inline C++ extension: 2024-08-20T21:39:02.2020658Z the necessary header includes, as well as the (pybind11) binding code. More 2024-08-20T21:39:02.2021478Z precisely, strings passed to ``cpp_sources`` are first concatenated into a 2024-08-20T21:39:02.2022252Z single ``.cpp`` file. This file is then prepended with ``#include 2024-08-20T21:39:02.2022816Z ``. 2024-08-20T21:39:02.2023028Z 2024-08-20T21:39:02.2023339Z Furthermore, if the ``functions`` argument is supplied, bindings will be 2024-08-20T21:39:02.2024141Z automatically generated for each function specified. ``functions`` can 2024-08-20T21:39:02.2024953Z either be a list of function names, or a dictionary mapping from function 2024-08-20T21:39:02.2025760Z names to docstrings. If a list is given, the name of each function is used 2024-08-20T21:39:02.2026373Z as its docstring. 2024-08-20T21:39:02.2026563Z 2024-08-20T21:39:02.2026885Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu`` 2024-08-20T21:39:02.2027583Z file and prepended with ``torch/types.h``, ``cuda.h`` and 2024-08-20T21:39:02.2028284Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled 2024-08-20T21:39:02.2029056Z separately, but ultimately linked into a single library. Note that no 2024-08-20T21:39:02.2029862Z bindings are generated for functions in ``cuda_sources`` per se. To bind 2024-08-20T21:39:02.2030675Z to a CUDA kernel, you must create a C++ function that calls it, and either 2024-08-20T21:39:02.2031478Z declare or define this C++ function in one of the ``cpp_sources`` (and 2024-08-20T21:39:02.2032100Z include its name in ``functions``). 2024-08-20T21:39:02.2032466Z 2024-08-20T21:39:02.2032726Z See :func:`load` for a description of arguments omitted below. 2024-08-20T21:39:02.2033169Z 2024-08-20T21:39:02.2033267Z Args: 2024-08-20T21:39:02.2033729Z cpp_sources: A string, or list of strings, containing C++ source code. 2024-08-20T21:39:02.2034512Z cuda_sources: A string, or list of strings, containing CUDA source code. 2024-08-20T21:39:02.2035294Z functions: A list of function names for which to generate function 2024-08-20T21:39:02.2036069Z bindings. If a dictionary is given, it should map function names to 2024-08-20T21:39:02.2036794Z docstrings (which are otherwise just the function names). 2024-08-20T21:39:02.2037526Z with_cuda: Determines whether CUDA headers and libraries are added to 2024-08-20T21:39:02.2038350Z the build. If set to ``None`` (default), this value is 2024-08-20T21:39:02.2039037Z automatically determined based on whether ``cuda_sources`` is 2024-08-20T21:39:02.2039696Z provided. Set it to ``True`` to force CUDA headers 2024-08-20T21:39:02.2040226Z and libraries to be included. 2024-08-20T21:39:02.2040807Z with_pytorch_error_handling: Determines whether pytorch error and 2024-08-20T21:39:02.2041533Z warning macros are handled by pytorch instead of pybind. To do 2024-08-20T21:39:02.2042300Z this, each function ``foo`` is called via an intermediary ``_safe_foo`` 2024-08-20T21:39:02.2043064Z function. This redirection might cause issues in obscure cases 2024-08-20T21:39:02.2043773Z of cpp. This flag should be set to ``False`` when this redirect 2024-08-20T21:39:02.2044440Z causes issues. 2024-08-20T21:39:02.2044648Z 2024-08-20T21:39:02.2044765Z Example: 2024-08-20T21:39:02.2045118Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-08-20T21:39:02.2045710Z >>> from torch.utils.cpp_extension import load_inline 2024-08-20T21:39:02.2046215Z >>> source = """ 2024-08-20T21:39:02.2046623Z at::Tensor sin_add(at::Tensor x, at::Tensor y) { 2024-08-20T21:39:02.2047104Z return x.sin() + y.sin(); 2024-08-20T21:39:02.2047462Z } 2024-08-20T21:39:02.2047696Z """ 2024-08-20T21:39:02.2048129Z >>> module = load_inline(name='inline_extension', 2024-08-20T21:39:02.2048647Z ... cpp_sources=[source], 2024-08-20T21:39:02.2049197Z ... functions=['sin_add']) 2024-08-20T21:39:02.2049541Z 2024-08-20T21:39:02.2049646Z .. note:: 2024-08-20T21:39:02.2050095Z By default, the Ninja backend uses #CPUS + 2 workers to build the 2024-08-20T21:39:02.2050852Z extension. This may use up too many resources on some systems. One 2024-08-20T21:39:02.2051630Z can control the number of workers by setting the `MAX_JOBS` environment 2024-08-20T21:39:02.2052327Z variable to a non-negative number. 2024-08-20T21:39:02.2052624Z 2024-08-20T21:39:02.2053055Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:02.2053565Z 2024-08-20T21:39:02.2106496Z 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. 2024-08-20T21:39:02.2108650Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:02.2109198Z 2024-08-20T21:39:02.2109635Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark. 2024-08-20T21:39:02.2110280Z 2024-08-20T21:39:02.2110689Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible 2024-08-20T21:39:02.2111645Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference 2024-08-20T21:39:02.2112508Z server like load. It can emulate multiple calling threads to a single module 2024-08-20T21:39:02.2113389Z provided. In the future we plan to enhance this component to support inter and 2024-08-20T21:39:02.2114365Z intra-op parallelism as well as multiple models running in a single process. 2024-08-20T21:39:02.2115161Z 2024-08-20T21:39:02.2115527Z Please note that even though nn.Module is supported, it might incur an overhead 2024-08-20T21:39:02.2116408Z from the need to hold GIL every time we execute Python code or pass around 2024-08-20T21:39:02.2117273Z inputs as Python objects. As soon as you have a ScriptModule version of your 2024-08-20T21:39:02.2118111Z model for inference deployment it is better to switch to using it in this 2024-08-20T21:39:02.2118870Z benchmark. 2024-08-20T21:39:02.2119046Z 2024-08-20T21:39:02.2119154Z Example:: 2024-08-20T21:39:02.2119310Z 2024-08-20T21:39:02.2119481Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:39:02.2119997Z >>> from torch.utils import ThroughputBenchmark 2024-08-20T21:39:02.2120541Z >>> bench = ThroughputBenchmark(my_module) 2024-08-20T21:39:02.2121207Z >>> # Pre-populate benchmark's data set with the inputs 2024-08-20T21:39:02.2121707Z >>> for input in inputs: 2024-08-20T21:39:02.2122277Z ... # Both args and kwargs work, same as any PyTorch Module / ScriptModule 2024-08-20T21:39:02.2122956Z ... bench.add_input(input[0], x2=input[1]) 2024-08-20T21:39:02.2123557Z >>> # Inputs supplied above are randomly used during the execution 2024-08-20T21:39:02.2124136Z >>> stats = bench.benchmark( 2024-08-20T21:39:02.2124534Z ... num_calling_threads=4, 2024-08-20T21:39:02.2124927Z ... num_warmup_iters = 100, 2024-08-20T21:39:02.2125326Z ... num_iters = 1000, 2024-08-20T21:39:02.2125673Z ... ) 2024-08-20T21:39:02.2126064Z >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms)) 2024-08-20T21:39:02.2126850Z >>> print("Number of iterations: {}".format(stats.num_iters)) 2024-08-20T21:39:02.2127261Z 2024-08-20T21:39:02.2127711Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:02.2128219Z 2024-08-20T21:39:02.4054745Z 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. 2024-08-20T21:39:02.4057462Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:02.4058289Z Sampler that restricts data loading to a subset of the dataset. 2024-08-20T21:39:02.4058781Z 2024-08-20T21:39:02.4058963Z It is especially useful in conjunction with 2024-08-20T21:39:02.4059685Z :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each 2024-08-20T21:39:02.4060593Z process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a 2024-08-20T21:39:02.4061481Z :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the 2024-08-20T21:39:02.4062170Z original dataset that is exclusive to it. 2024-08-20T21:39:02.4062505Z 2024-08-20T21:39:02.4062649Z .. note:: 2024-08-20T21:39:02.4063166Z Dataset is assumed to be of constant size and that any instance of it always 2024-08-20T21:39:02.4063888Z returns the same elements in the same order. 2024-08-20T21:39:02.4064243Z 2024-08-20T21:39:02.4064355Z Args: 2024-08-20T21:39:02.4064653Z dataset: Dataset used for sampling. 2024-08-20T21:39:02.4065276Z num_replicas (int, optional): Number of processes participating in 2024-08-20T21:39:02.4066094Z distributed training. By default, :attr:`world_size` is retrieved from the 2024-08-20T21:39:02.4066743Z current distributed group. 2024-08-20T21:39:02.4067395Z rank (int, optional): Rank of the current process within :attr:`num_replicas`. 2024-08-20T21:39:02.4068214Z By default, :attr:`rank` is retrieved from the current distributed 2024-08-20T21:39:02.4068767Z group. 2024-08-20T21:39:02.4069283Z shuffle (bool, optional): If ``True`` (default), sampler will shuffle the 2024-08-20T21:39:02.4069889Z indices. 2024-08-20T21:39:02.4070369Z seed (int, optional): random seed used to shuffle the sampler if 2024-08-20T21:39:02.4071093Z :attr:`shuffle=True`. This number should be identical across all 2024-08-20T21:39:02.4072075Z processes in the distributed group. Default: ``0``. 2024-08-20T21:39:02.4072788Z drop_last (bool, optional): if ``True``, then the sampler will drop the 2024-08-20T21:39:02.4073563Z tail of the data to make it evenly divisible across the number of 2024-08-20T21:39:02.4074326Z replicas. If ``False``, the sampler will add extra indices to make 2024-08-20T21:39:02.4075098Z the data evenly divisible across the replicas. Default: ``False``. 2024-08-20T21:39:02.4075574Z 2024-08-20T21:39:02.4075686Z .. warning:: 2024-08-20T21:39:02.4076151Z In distributed mode, calling the :meth:`set_epoch` method at 2024-08-20T21:39:02.4076966Z the beginning of each epoch **before** creating the :class:`DataLoader` iterator 2024-08-20T21:39:02.4077875Z is necessary to make shuffling work properly across multiple epochs. Otherwise, 2024-08-20T21:39:02.4078718Z the same ordering will be always used. 2024-08-20T21:39:02.4079071Z 2024-08-20T21:39:02.4079183Z Example:: 2024-08-20T21:39:02.4079354Z 2024-08-20T21:39:02.4079493Z >>> # xdoctest: +SKIP 2024-08-20T21:39:02.4080035Z >>> sampler = DistributedSampler(dataset) if is_distributed else None 2024-08-20T21:39:02.4080757Z >>> loader = DataLoader(dataset, shuffle=(sampler is None), 2024-08-20T21:39:02.4081328Z ... sampler=sampler) 2024-08-20T21:39:02.4081829Z >>> for epoch in range(start_epoch, n_epochs): 2024-08-20T21:39:02.4082321Z ... if is_distributed: 2024-08-20T21:39:02.4082753Z ... sampler.set_epoch(epoch) 2024-08-20T21:39:02.4083341Z ... train(loader) 2024-08-20T21:39:02.4083691Z 2024-08-20T21:39:02.4084312Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:02.4084820Z 2024-08-20T21:39:02.6103064Z gathering tests 2024-08-20T21:39:02.6117538Z running 695 test(s) 2024-08-20T21:39:02.6125984Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::typename:0, line 980 <- wrt source file 2024-08-20T21:39:02.6135206Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::typename:0 2024-08-20T21:39:02.6137004Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::is_tensor:0, line 1016 <- wrt source file 2024-08-20T21:39:02.6140289Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::is_tensor:0 2024-08-20T21:39:02.6142667Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_device:0, line 1085 <- wrt source file 2024-08-20T21:39:02.6144943Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_device:0 2024-08-20T21:39:02.6147580Z * 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 2024-08-20T21:39:02.6149439Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_tensor_type:0 2024-08-20T21:39:02.6151465Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_dtype:0, line 1171 <- wrt source file 2024-08-20T21:39:02.6153286Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_dtype:0 2024-08-20T21:39:02.6155982Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::use_deterministic_algorithms:0, line 1326 <- wrt source file 2024-08-20T21:39:02.6157943Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::use_deterministic_algorithms:0 2024-08-20T21:39:02.6159726Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::compile:0, line 2412 <- wrt source file 2024-08-20T21:39:02.6161543Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::compile:0 2024-08-20T21:39:02.6163270Z * 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 2024-08-20T21:39:02.6165129Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0 2024-08-20T21:39:02.6167024Z * 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 2024-08-20T21:39:02.6168896Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_C.cpython-312-x86_64-linux-gnu.so::Generator:0 2024-08-20T21:39:02.6170783Z * 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 2024-08-20T21:39:02.6172673Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_C.cpython-312-x86_64-linux-gnu.so::_LinAlgError:0 2024-08-20T21:39:02.6174412Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::custom_op:0, line 55 <- wrt source file 2024-08-20T21:39:02.6176005Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::custom_op:0 2024-08-20T21:39:02.6177706Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl:0, line 137 <- wrt source file 2024-08-20T21:39:02.6179255Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl:0 2024-08-20T21:39:02.6180859Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl_abstract:0, line 206 <- wrt source file 2024-08-20T21:39:02.6437384Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl_abstract:0 2024-08-20T21:39:02.6439483Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_namedtensor_internals.py::update_names:0, line 118 <- wrt source file 2024-08-20T21:39:02.6441270Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_namedtensor_internals.py::update_names:0 2024-08-20T21:39:02.6443021Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_hook:0, line 546 <- wrt source file 2024-08-20T21:39:02.6459210Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_hook:0 2024-08-20T21:39:02.6461061Z * 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 2024-08-20T21:39:02.6557061Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0 2024-08-20T21:39:02.6558987Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.refine_names:0, line 1232 <- wrt source file 2024-08-20T21:39:02.6682311Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.refine_names:0 2024-08-20T21:39:02.6685456Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.align_to:0, line 1277 <- wrt source file 2024-08-20T21:39:02.6692513Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.align_to:0 2024-08-20T21:39:02.6695477Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.rename:0, line 1350 <- wrt source file 2024-08-20T21:39:02.6712972Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.rename:0 2024-08-20T21:39:02.6715267Z * 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 2024-08-20T21:39:02.6819610Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0 2024-08-20T21:39:02.6822545Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.dim_order:0, line 1403 <- wrt source file 2024-08-20T21:39:02.6825581Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.dim_order:0 2024-08-20T21:39:02.6828337Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor_str.py::set_printoptions:0, line 53 <- wrt source file 2024-08-20T21:39:02.6853634Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor_str.py::set_printoptions:0 2024-08-20T21:39:02.6856604Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_tensors:0, line 63 <- wrt source file 2024-08-20T21:39:02.6871921Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_tensors:0 2024-08-20T21:39:02.6875037Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_shapes:0, line 91 <- wrt source file 2024-08-20T21:39:02.6878570Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_shapes:0 2024-08-20T21:39:02.6881497Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::split:0, line 178 <- wrt source file 2024-08-20T21:39:02.6891312Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::split:0 2024-08-20T21:39:02.6892949Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::einsum:0, line 287 <- wrt source file 2024-08-20T21:39:02.7048059Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::einsum:0 2024-08-20T21:39:02.7049781Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_unique_consecutive_impl:0, line 1014 <- wrt source file 2024-08-20T21:39:02.7060501Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_unique_consecutive_impl:0 2024-08-20T21:39:02.7062247Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::tensordot:0, line 1289 <- wrt source file 2024-08-20T21:39:02.7277271Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::tensordot:0 2024-08-20T21:39:02.7279062Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cartesian_prod:0, line 1373 <- wrt source file 2024-08-20T21:39:02.7285112Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cartesian_prod:0 2024-08-20T21:39:02.7286797Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::block_diag:0, line 1407 <- wrt source file 2024-08-20T21:39:02.7294704Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::block_diag:0 2024-08-20T21:39:02.7296567Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cdist:0, line 1458 <- wrt source file 2024-08-20T21:39:02.7310028Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cdist:0 2024-08-20T21:39:02.7311654Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_1d:0, line 1499 <- wrt source file 2024-08-20T21:39:02.7326992Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_1d:0 2024-08-20T21:39:02.7328644Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_2d:0, line 1535 <- wrt source file 2024-08-20T21:39:02.7344937Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_2d:0 2024-08-20T21:39:02.7346605Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_3d:0, line 1573 <- wrt source file 2024-08-20T21:39:02.7367202Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_3d:0 2024-08-20T21:39:02.7368823Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::norm:0, line 1746 <- wrt source file 2024-08-20T21:39:02.7431951Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::norm:0 2024-08-20T21:39:02.7433585Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::unravel_index:0, line 1913 <- wrt source file 2024-08-20T21:39:02.7459631Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::unravel_index:0 2024-08-20T21:39:02.7462179Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::chain_matmul:0, line 2013 <- wrt source file 2024-08-20T21:39:02.7464747Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::chain_matmul:0 2024-08-20T21:39:02.7466402Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_lu_impl:0, line 2113 <- wrt source file 2024-08-20T21:39:02.7467982Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_lu_impl:0 2024-08-20T21:39:02.7470024Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::list:0, line 468 <- wrt source file 2024-08-20T21:39:02.7471467Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::list:0 2024-08-20T21:39:02.7472934Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::help:0, line 528 <- wrt source file 2024-08-20T21:39:02.7474349Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::help:0 2024-08-20T21:39:02.7475848Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::_load_local:0, line 667 <- wrt source file 2024-08-20T21:39:02.7477350Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::_load_local:0 2024-08-20T21:39:02.7479040Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.define:0, line 129 <- wrt source file 2024-08-20T21:39:02.7480684Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.define:0 2024-08-20T21:39:02.7482439Z * 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 2024-08-20T21:39:02.7532297Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library._impl_with_aoti_compile:0 2024-08-20T21:39:02.7534064Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.impl:0, line 268 <- wrt source file 2024-08-20T21:39:02.7537032Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.impl:0 2024-08-20T21:39:02.7538629Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::define:0, line 452 <- wrt source file 2024-08-20T21:39:02.7752552Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::define:0 2024-08-20T21:39:02.7754110Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::impl:0, line 519 <- wrt source file 2024-08-20T21:39:02.7763504Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::impl:0 2024-08-20T21:39:02.7765300Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_kernel:0, line 641 <- wrt source file 2024-08-20T21:39:02.7766956Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_kernel:0 2024-08-20T21:39:02.7768665Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_torch_dispatch:0, line 958 <- wrt source file 2024-08-20T21:39:02.7874208Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_torch_dispatch:0 2024-08-20T21:39:02.7875929Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_vmap:0, line 1047 <- wrt source file 2024-08-20T21:39:02.8020940Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_vmap:0 2024-08-20T21:39:02.8022640Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_ignored_functions:0, line 111 <- wrt source file 2024-08-20T21:39:02.8028351Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_ignored_functions:0 2024-08-20T21:39:02.8030151Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_testing_overrides:0, line 418 <- wrt source file 2024-08-20T21:39:02.8067266Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_testing_overrides:0 2024-08-20T21:39:02.8069302Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::wrap_torch_function:0, line 1564 <- wrt source file 2024-08-20T21:39:02.8071461Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::wrap_torch_function:0 2024-08-20T21:39:02.8073816Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::handle_torch_function:0, line 1699 <- wrt source file 2024-08-20T21:39:02.8075586Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::handle_torch_function:0 2024-08-20T21:39:02.8077396Z * 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 2024-08-20T21:39:02.8104437Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_method_or_property:0 2024-08-20T21:39:02.8106250Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_like:0, line 1966 <- wrt source file 2024-08-20T21:39:02.8111920Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_like:0 2024-08-20T21:39:02.8114618Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/quasirandom.py::SobolEngine:0, line 39 <- wrt source file 2024-08-20T21:39:02.8116510Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/quasirandom.py::SobolEngine:0 2024-08-20T21:39:02.8118305Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::add_safe_globals:0, line 216 <- wrt source file 2024-08-20T21:39:02.8120749Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::add_safe_globals:0 2024-08-20T21:39:02.8123483Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::safe_globals:0, line 241 <- wrt source file 2024-08-20T21:39:02.8125647Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::safe_globals:0 2024-08-20T21:39:02.8127600Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::register_package:0, line 304 <- wrt source file 2024-08-20T21:39:02.8129777Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::register_package:0 2024-08-20T21:39:02.8131469Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::save:0, line 765 <- wrt source file 2024-08-20T21:39:02.8133747Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::save:0 2024-08-20T21:39:02.8136009Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/torch_version.py::TorchVersion:0, line 18 <- wrt source file 2024-08-20T21:39:02.8137671Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/torch_version.py::TorchVersion:0 2024-08-20T21:39:02.8139414Z * 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 2024-08-20T21:39:02.8141173Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_mode_options:0 2024-08-20T21:39:02.8143078Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_options:0, line 164 <- wrt source file 2024-08-20T21:39:02.8144813Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_options:0 2024-08-20T21:39:02.8146705Z * 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 2024-08-20T21:39:02.8148830Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims_common/__init__.py::compute_required_storage_length:0 2024-08-20T21:39:02.8150706Z * 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 2024-08-20T21:39:02.8152436Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::allow_in_graph:0 2024-08-20T21:39:02.8154236Z * 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 2024-08-20T21:39:03.6751054Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::substitute_in_graph:0 2024-08-20T21:39:03.6752962Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::wrap_numpy:0, line 255 <- wrt source file 2024-08-20T21:39:03.6755119Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::wrap_numpy:0 2024-08-20T21:39:03.6756901Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_compiling:0, line 286 <- wrt source file 2024-08-20T21:39:03.6759405Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_compiling:0 2024-08-20T21:39:03.6761980Z * 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 2024-08-20T21:39:03.6764341Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0 2024-08-20T21:39:03.6766897Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::save:0, line 214 <- wrt source file 2024-08-20T21:39:03.6768837Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::save:0 2024-08-20T21:39:03.6770447Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::load:0, line 298 <- wrt source file 2024-08-20T21:39:03.6772022Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::load:0 2024-08-20T21:39:03.6773704Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::register_dataclass:0, line 396 <- wrt source file 2024-08-20T21:39:03.6775458Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::register_dataclass:0 2024-08-20T21:39:03.6777285Z * 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 2024-08-20T21:39:03.6779154Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::Future.add_done_callback:0 2024-08-20T21:39:03.6780991Z * 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 2024-08-20T21:39:03.6782792Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::Future.set_exception:0 2024-08-20T21:39:03.6784775Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::collect_all:0, line 289 <- wrt source file 2024-08-20T21:39:03.6787055Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::collect_all:0 2024-08-20T21:39:03.6788709Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/__init__.py::annotate:0, line 146 <- wrt source file 2024-08-20T21:39:03.6790294Z * SKIPPED: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::check_sparse_tensor_invariants:0 2024-08-20T21:39:03.7104424Z * 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 2024-08-20T21:39:03.7214833Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::as_sparse_gradcheck:0 2024-08-20T21:39:03.7217989Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/decorators.py::substitute_in_graph:0, line 190 <- wrt source file 2024-08-20T21:39:03.7220751Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/decorators.py::substitute_in_graph:0 2024-08-20T21:39:03.7223708Z * 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 2024-08-20T21:39:03.7226727Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/variables/base.py::VariableTracker.python_type:0 2024-08-20T21:39:03.7229582Z * 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 2024-08-20T21:39:03.7512203Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/aot_autograd.py::aot_function:0 2024-08-20T21:39:03.7515118Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/apis.py::grad:0, line 324 <- wrt source file 2024-08-20T21:39:03.7517484Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/apis.py::grad:0 2024-08-20T21:39:03.7520254Z * 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 2024-08-20T21:39:03.7523132Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/benchmark_utils.py::benchmark_utilization:0 2024-08-20T21:39:03.7526011Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::vjp:0, line 271 <- wrt source file 2024-08-20T21:39:03.7625751Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::vjp:0 2024-08-20T21:39:03.7628410Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacrev:0, line 510 <- wrt source file 2024-08-20T21:39:03.7717719Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacrev:0 2024-08-20T21:39:03.7720458Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jvp:0, line 1064 <- wrt source file 2024-08-20T21:39:03.8844841Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jvp:0 2024-08-20T21:39:03.8847506Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0, line 1219 <- wrt source file 2024-08-20T21:39:03.8909539Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0 2024-08-20T21:39:03.8912261Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::hessian:0, line 1384 <- wrt source file 2024-08-20T21:39:03.8928003Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::hessian:0 2024-08-20T21:39:03.8931163Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::functionalize:0, line 1548 <- wrt source file 2024-08-20T21:39:03.8933937Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::functionalize:0 2024-08-20T21:39:03.8936704Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::linearize:0, line 1748 <- wrt source file 2024-08-20T21:39:03.9115937Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::linearize:0 2024-08-20T21:39:03.9118193Z * 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 2024-08-20T21:39:03.9121271Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/functional_call.py::functional_call:0 2024-08-20T21:39:03.9123879Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/fx_minifier.py::minifier:0, line 194 <- wrt source file 2024-08-20T21:39:03.9126091Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/fx_minifier.py::minifier:0 2024-08-20T21:39:03.9128243Z * 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 2024-08-20T21:39:03.9130673Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py::CompilerWrapper.post_compile:0 2024-08-20T21:39:03.9132792Z * 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 2024-08-20T21:39:03.9134765Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/associative_scan.py::associative_scan:0 2024-08-20T21:39:03.9136596Z * 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 2024-08-20T21:39:03.9138248Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/cond.py::cond:0 2024-08-20T21:39:03.9139995Z * 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 2024-08-20T21:39:03.9141804Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/while_loop.py::while_loop:0 2024-08-20T21:39:03.9143764Z * 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 2024-08-20T21:39:03.9145813Z * 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 2024-08-20T21:39:03.9147700Z * 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 2024-08-20T21:39:03.9549112Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::custom_op:0 2024-08-20T21:39:03.9551078Z * 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 2024-08-20T21:39:03.9632399Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.set_kernel_enabled:0 2024-08-20T21:39:03.9634382Z * 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 2024-08-20T21:39:03.9636651Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0 2024-08-20T21:39:03.9638841Z * 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 2024-08-20T21:39:03.9710959Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_fake:0 2024-08-20T21:39:03.9713051Z * 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 2024-08-20T21:39:03.9865275Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0 2024-08-20T21:39:03.9867250Z * 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 2024-08-20T21:39:04.0015918Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_vmap:0 2024-08-20T21:39:04.0018218Z * 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 2024-08-20T21:39:04.0020148Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_class_registry.py::register_fake_class:0 2024-08-20T21:39:04.0022405Z * 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 2024-08-20T21:39:04.0083619Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0 2024-08-20T21:39:04.0085502Z * 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 2024-08-20T21:39:04.0090290Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/infer_schema.py::infer_schema:0 2024-08-20T21:39:04.0092374Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_logging/_internal.py::set_logs:0, line 417 <- wrt source file 2024-08-20T21:39:04.0094053Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_logging/_internal.py::set_logs:0 2024-08-20T21:39:04.0095786Z * 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 2024-08-20T21:39:04.0134975Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_equal:0 2024-08-20T21:39:04.0136890Z * 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 2024-08-20T21:39:04.0138763Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::print_assert_equal:0 2024-08-20T21:39:04.0140618Z * 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 2024-08-20T21:39:04.0188039Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0 2024-08-20T21:39:04.0189923Z * 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 2024-08-20T21:39:04.0191781Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0 2024-08-20T21:39:04.0193881Z * 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 2024-08-20T21:39:04.0298095Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0 2024-08-20T21:39:04.0300146Z * 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 2024-08-20T21:39:04.0302781Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0 2024-08-20T21:39:04.0304766Z * 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 2024-08-20T21:39:04.0307567Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0 2024-08-20T21:39:04.0309390Z * 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 2024-08-20T21:39:04.0311114Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0 2024-08-20T21:39:04.0313544Z * 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 2024-08-20T21:39:04.0316797Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_warns:0 2024-08-20T21:39:04.0319567Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims/context.py::TorchRefsMode:0, line 85 <- wrt source file 2024-08-20T21:39:04.0322001Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims/context.py::TorchRefsMode:0 2024-08-20T21:39:04.0323958Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/amp/grad_scaler.py::GradScaler:0, line 60 <- wrt source file 2024-08-20T21:39:04.0325815Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/amp/grad_scaler.py::GradScaler:0 2024-08-20T21:39:04.0327990Z * 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 2024-08-20T21:39:04.0330318Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0 2024-08-20T21:39:04.0332812Z * 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 2024-08-20T21:39:04.0335368Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0 2024-08-20T21:39:04.0337712Z * 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 2024-08-20T21:39:04.0339980Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0 2024-08-20T21:39:04.0342217Z * 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 2024-08-20T21:39:04.0344534Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0 2024-08-20T21:39:04.0346773Z * 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 2024-08-20T21:39:04.0349184Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0 2024-08-20T21:39:04.0351335Z * 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 2024-08-20T21:39:04.0456030Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0 2024-08-20T21:39:04.0458000Z * 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 2024-08-20T21:39:04.0486218Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0 2024-08-20T21:39:04.0488830Z * 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 2024-08-20T21:39:04.0491997Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0 2024-08-20T21:39:04.0493872Z * 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 2024-08-20T21:39:04.0495705Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0 2024-08-20T21:39:04.0498576Z * 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 2024-08-20T21:39:04.0500660Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0 2024-08-20T21:39:04.0503170Z * 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 2024-08-20T21:39:04.0506116Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/__init__.py::Quantize:0 2024-08-20T21:39:04.0509038Z * 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 2024-08-20T21:39:04.0512298Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/__init__.py::DeQuantize:0 2024-08-20T21:39:04.0514736Z * 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 2024-08-20T21:39:04.0516727Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0 2024-08-20T21:39:04.0519383Z * 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 2024-08-20T21:39:04.0522206Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0 2024-08-20T21:39:04.0524762Z * 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 2024-08-20T21:39:04.0526794Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0 2024-08-20T21:39:04.0528885Z * 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 2024-08-20T21:39:04.0531018Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0 2024-08-20T21:39:04.0533369Z * 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 2024-08-20T21:39:04.0535480Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0 2024-08-20T21:39:04.0537626Z * 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 2024-08-20T21:39:04.0539808Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0 2024-08-20T21:39:04.0542932Z * 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 2024-08-20T21:39:04.0546110Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0 2024-08-20T21:39:04.0549190Z * 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 2024-08-20T21:39:04.0552310Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0 2024-08-20T21:39:04.0555403Z * 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 2024-08-20T21:39:04.0558511Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRU:0 2024-08-20T21:39:04.0561579Z * 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 2024-08-20T21:39:04.0564643Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0 2024-08-20T21:39:04.0567743Z * 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 2024-08-20T21:39:04.0570778Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0 2024-08-20T21:39:04.0573870Z * 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 2024-08-20T21:39:04.0576917Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0 2024-08-20T21:39:04.0579943Z * 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 2024-08-20T21:39:04.0582888Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/activation.py::ReLU6:0 2024-08-20T21:39:04.0585824Z * 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 2024-08-20T21:39:04.0588617Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0 2024-08-20T21:39:04.0591448Z * 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 2024-08-20T21:39:04.0594243Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0 2024-08-20T21:39:04.0597409Z * 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 2024-08-20T21:39:04.0600751Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0 2024-08-20T21:39:04.0603859Z * 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 2024-08-20T21:39:04.0606889Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0 2024-08-20T21:39:04.0609968Z * 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 2024-08-20T21:39:04.0613019Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0 2024-08-20T21:39:04.0616133Z * 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 2024-08-20T21:39:04.0619223Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0 2024-08-20T21:39:04.0622415Z * 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 2024-08-20T21:39:04.0625743Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0 2024-08-20T21:39:04.0629065Z * 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 2024-08-20T21:39:04.0632427Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0 2024-08-20T21:39:04.0635761Z * 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 2024-08-20T21:39:04.0639101Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0 2024-08-20T21:39:04.0642193Z * 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 2024-08-20T21:39:04.0645032Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/linear.py::Linear:0 2024-08-20T21:39:04.0648429Z * 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 2024-08-20T21:39:04.0652264Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py::ActivationSparsifier:0 2024-08-20T21:39:04.0656195Z * 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 2024-08-20T21:39:04.0660145Z * 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 2024-08-20T21:39:04.0663955Z * 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 2024-08-20T21:39:04.0667576Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0 2024-08-20T21:39:04.0671085Z * 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 2024-08-20T21:39:04.0674125Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0 2024-08-20T21:39:04.0677285Z * 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 2024-08-20T21:39:04.0680551Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0 2024-08-20T21:39:04.0683596Z * 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 2024-08-20T21:39:04.0686500Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0 2024-08-20T21:39:04.0689521Z * 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 2024-08-20T21:39:04.0692630Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0 2024-08-20T21:39:04.0696086Z * 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 2024-08-20T21:39:04.0699284Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0 2024-08-20T21:39:04.0702521Z * 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 2024-08-20T21:39:04.0705651Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0 2024-08-20T21:39:04.0708857Z * 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 2024-08-20T21:39:04.0712112Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0 2024-08-20T21:39:04.0715173Z * 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 2024-08-20T21:39:04.0717925Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/observer.py::_with_args:0 2024-08-20T21:39:04.0720911Z * 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 2024-08-20T21:39:04.0723878Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0 2024-08-20T21:39:04.0726790Z * 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 2024-08-20T21:39:04.0729544Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::fuse_fx:0 2024-08-20T21:39:04.0732350Z * 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 2024-08-20T21:39:04.0735189Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0 2024-08-20T21:39:04.0738270Z * 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 2024-08-20T21:39:04.0741187Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0 2024-08-20T21:39:04.0744119Z * 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 2024-08-20T21:39:04.0746932Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0 2024-08-20T21:39:04.0749951Z * 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 2024-08-20T21:39:04.0753060Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0 2024-08-20T21:39:04.0756311Z * 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 2024-08-20T21:39:04.0759706Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::_convert_to_reference_decomposed_fx:0 2024-08-20T21:39:04.0763040Z * 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 2024-08-20T21:39:04.0765981Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_pt2e:0 2024-08-20T21:39:04.0769000Z * 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 2024-08-20T21:39:04.0772007Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0 2024-08-20T21:39:04.0775036Z * 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 2024-08-20T21:39:04.0777964Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0 2024-08-20T21:39:04.0780896Z * 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 2024-08-20T21:39:04.0783729Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0 2024-08-20T21:39:04.0786646Z * 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 2024-08-20T21:39:04.0789548Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0 2024-08-20T21:39:04.0792502Z * 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 2024-08-20T21:39:04.0795411Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0 2024-08-20T21:39:04.0798653Z * 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 2024-08-20T21:39:04.0801676Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0 2024-08-20T21:39:04.0804753Z * 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 2024-08-20T21:39:04.0807584Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0 2024-08-20T21:39:04.0810452Z * 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 2024-08-20T21:39:04.0813297Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0 2024-08-20T21:39:04.0816405Z * 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 2024-08-20T21:39:04.0819772Z * 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 2024-08-20T21:39:04.0823084Z * 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 2024-08-20T21:39:04.0826317Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report.py::ModelReport:0 2024-08-20T21:39:04.0829584Z * 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 2024-08-20T21:39:04.0832954Z * 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 2024-08-20T21:39:04.0836417Z * 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 2024-08-20T21:39:04.0839911Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/pt2e/utils.py::_replace_literals_with_new_placeholders:0 2024-08-20T21:39:04.0843037Z * 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 2024-08-20T21:39:04.0845785Z * 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 2024-08-20T21:39:04.0918603Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::hessian:0 2024-08-20T21:39:04.0921218Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::vhp:0, line 1001 <- wrt source file 2024-08-20T21:39:04.0923848Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::vhp:0 2024-08-20T21:39:04.0926421Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::hvp:0, line 1100 <- wrt source file 2024-08-20T21:39:04.0928912Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::hvp:0 2024-08-20T21:39:04.0931448Z * 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 2024-08-20T21:39:04.0933980Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::no_grad:0 2024-08-20T21:39:04.0936604Z * 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 2024-08-20T21:39:04.0939364Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::enable_grad:0 2024-08-20T21:39:04.0942076Z * 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 2024-08-20T21:39:04.0944787Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::set_grad_enabled:0 2024-08-20T21:39:04.0947517Z * 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 2024-08-20T21:39:04.0950206Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::inference_mode:0 2024-08-20T21:39:04.0952838Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.name:0, line 62 <- wrt source file 2024-08-20T21:39:04.0955337Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.name:0 2024-08-20T21:39:04.0957966Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.register_hook:0, line 112 <- wrt source file 2024-08-20T21:39:04.0960741Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.register_hook:0 2024-08-20T21:39:04.0963510Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.register_prehook:0, line 149 <- wrt source file 2024-08-20T21:39:04.0966361Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.register_prehook:0 2024-08-20T21:39:04.0969105Z * 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 2024-08-20T21:39:04.0971788Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::saved_tensors_hooks:0 2024-08-20T21:39:04.0974450Z * 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 2024-08-20T21:39:04.0976998Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::save_on_cpu:0 2024-08-20T21:39:04.0979729Z * 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 2024-08-20T21:39:04.0982612Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::disable_saved_tensors_hooks:0 2024-08-20T21:39:04.0985486Z * 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 2024-08-20T21:39:04.0988292Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::register_multi_grad_hook:0 2024-08-20T21:39:04.0991187Z * 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 2024-08-20T21:39:04.0994107Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::allow_mutation_on_saved_tensors:0 2024-08-20T21:39:04.0997141Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::profile:0, line 176 <- wrt source file 2024-08-20T21:39:04.0999910Z * SKIPPED: 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DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::scatter_object_list:0, line 3200 <- wrt source file 2024-08-20T21:39:04.1108616Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::scatter_object_list:0 2024-08-20T21:39:04.1111586Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather:0, line 3295 <- wrt source file 2024-08-20T21:39:04.1114459Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather:0 2024-08-20T21:39:04.1117481Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_into_tensor:0, line 3377 <- wrt source file 2024-08-20T21:39:04.1120655Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_into_tensor:0 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* DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/autograd/__init__.py::context:0, line 39 <- wrt source file 2024-08-20T21:39:04.1193723Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/autograd/__init__.py::context:0 2024-08-20T21:39:04.1197019Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/checkpoint_activation.py::checkpoint:0, line 48 <- wrt source file 2024-08-20T21:39:04.1200394Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/checkpoint_activation.py::checkpoint:0 2024-08-20T21:39:04.1203465Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/contract.py::contract:0, line 46 <- wrt source file 2024-08-20T21:39:04.1206361Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/contract.py::contract:0 2024-08-20T21:39:04.1209316Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/replicate.py::replicate:0, line 190 <- wrt source file 2024-08-20T21:39:04.1212235Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/replicate.py::replicate:0 2024-08-20T21:39:04.1215493Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_optim/__init__.py::named_params_with_sharded_tensor:0, line 30 <- wrt source file 2024-08-20T21:39:04.1218898Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_optim/__init__.py::named_params_with_sharded_tensor:0 2024-08-20T21:39:04.1222527Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py::custom_sharded_op_impl:0, line 457 <- wrt source file 2024-08-20T21:39:04.1225842Z * SKIPPED: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::fp16_compress_wrapper:0, line 163 <- wrt source file 2024-08-20T21:39:04.1299006Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::fp16_compress_wrapper:0 2024-08-20T21:39:04.1302697Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::bf16_compress_wrapper:0, line 202 <- wrt source file 2024-08-20T21:39:04.1306208Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::bf16_compress_wrapper:0 2024-08-20T21:39:04.1309789Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py::batched_powerSGD_hook:0, line 705 <- wrt source file 2024-08-20T21:39:04.1313302Z * SKIPPED: 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file 2024-08-20T21:39:04.1367381Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/api.py::StateDictType:0 2024-08-20T21:39:04.1370248Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/api.py::FullStateDictConfig:0, line 301 <- wrt source file 2024-08-20T21:39:04.1373144Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/api.py::FullStateDictConfig:0 2024-08-20T21:39:04.1376401Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel:0, line 140 <- wrt source file 2024-08-20T21:39:04.1379894Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel:0 2024-08-20T21:39:04.1383704Z * DOCTEST : 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/binomial.py::Binomial:0 2024-08-20T21:39:04.1559644Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/categorical.py::Categorical:0, line 40 <- wrt source file 2024-08-20T21:39:04.1562685Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/categorical.py::Categorical:0 2024-08-20T21:39:04.1565544Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/cauchy.py::Cauchy:0, line 23 <- wrt source file 2024-08-20T21:39:04.1568132Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/cauchy.py::Cauchy:0 2024-08-20T21:39:04.1570736Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/chi2.py::Chi2:0, line 15 <- wrt source file 2024-08-20T21:39:04.1573247Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/chi2.py::Chi2:0 2024-08-20T21:39:04.1575939Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/constraints.py::is_dependent:0, line 160 <- wrt source file 2024-08-20T21:39:04.1578785Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/constraints.py::is_dependent:0 2024-08-20T21:39:04.1581728Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/constraints.py::_DependentProperty:0, line 181 <- wrt source file 2024-08-20T21:39:04.1584716Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/constraints.py::_DependentProperty:0 2024-08-20T21:39:04.1587837Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/continuous_bernoulli.py::ContinuousBernoulli:0, line 34 <- wrt source file 2024-08-20T21:39:04.1591009Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/continuous_bernoulli.py::ContinuousBernoulli:0 2024-08-20T21:39:04.1594097Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/dirichlet.py::Dirichlet:0, line 39 <- wrt source file 2024-08-20T21:39:04.1597053Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/dirichlet.py::Dirichlet:0 2024-08-20T21:39:04.1600073Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/exponential.py::Exponential:0, line 19 <- wrt source file 2024-08-20T21:39:04.1602908Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/exponential.py::Exponential:0 2024-08-20T21:39:04.1605845Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/fishersnedecor.py::FisherSnedecor:0, line 21 <- wrt source file 2024-08-20T21:39:04.1608850Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/fishersnedecor.py::FisherSnedecor:0 2024-08-20T21:39:04.1611626Z * DOCTEST : 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23 <- wrt source file 2024-08-20T21:39:04.1630397Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/half_cauchy.py::HalfCauchy:0 2024-08-20T21:39:04.1633406Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/half_normal.py::HalfNormal:0, line 23 <- wrt source file 2024-08-20T21:39:04.1636253Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/half_normal.py::HalfNormal:0 2024-08-20T21:39:04.1640081Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/independent.py::Independent:0, line 23 <- wrt source file 2024-08-20T21:39:04.1643788Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/independent.py::Independent:0 2024-08-20T21:39:04.1647356Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/inverse_gamma.py::InverseGamma:0, line 21 <- wrt source file 2024-08-20T21:39:04.1651163Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/inverse_gamma.py::InverseGamma:0 2024-08-20T21:39:04.1654993Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/kumaraswamy.py::Kumaraswamy:0, line 28 <- wrt source file 2024-08-20T21:39:04.1658714Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/kumaraswamy.py::Kumaraswamy:0 2024-08-20T21:39:04.1662312Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/laplace.py::Laplace:0, line 19 <- wrt source file 2024-08-20T21:39:04.1665779Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/laplace.py::Laplace:0 2024-08-20T21:39:04.1669685Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/lkj_cholesky.py::LKJCholesky:0, line 41 <- wrt source file 2024-08-20T21:39:04.1672677Z * SUCCESS: 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file 2024-08-20T21:39:04.1957036Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/utils.py::reverse_dict:0 2024-08-20T21:39:04.1960122Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/utils.py::freeze:0, line 93 <- wrt source file 2024-08-20T21:39:04.1963047Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/utils.py::freeze:0 2024-08-20T21:39:04.1966118Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/variable.py::variables:0, line 63 <- wrt source file 2024-08-20T21:39:04.1969198Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/variable.py::variables:0 2024-08-20T21:39:04.1972499Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/core.py::dispatch:0, line 19 <- wrt source file 2024-08-20T21:39:04.1975903Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/core.py::dispatch:0 2024-08-20T21:39:04.1979449Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher:0, line 99 <- wrt source file 2024-08-20T21:39:04.1983140Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher:0 2024-08-20T21:39:04.1986856Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.register:0, line 123 <- wrt source file 2024-08-20T21:39:04.1990649Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.register:0 2024-08-20T21:39:04.1994451Z * DOCTEST : 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/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 2024-08-20T21:39:04.2034523Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::reverse_dict:0 2024-08-20T21:39:04.2037996Z * 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 2024-08-20T21:39:04.2041464Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::groupby:0 2024-08-20T21:39:04.2044903Z * 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 2024-08-20T21:39:04.2048330Z * SUCCESS: 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/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 2024-08-20T21:39:04.2087166Z * 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 2024-08-20T21:39:04.2090469Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/_check.py::AttributeTypeIsSupportedChecker:0, line 36 <- wrt source file 2024-08-20T21:39:04.2093391Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/_check.py::AttributeTypeIsSupportedChecker:0 2024-08-20T21:39:04.2096533Z * 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 2024-08-20T21:39:04.2099540Z * SKIPPED: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_input:0, line 32 <- wrt source file 2024-08-20T21:39:04.2975407Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_input:0 2024-08-20T21:39:04.2977855Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_weight:0, line 79 <- wrt source file 2024-08-20T21:39:04.2980707Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_weight:0 2024-08-20T21:39:04.2983121Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_input:0, line 130 <- wrt source file 2024-08-20T21:39:04.3004890Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_input:0 2024-08-20T21:39:04.3007321Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_weight:0, line 177 <- wrt source file 2024-08-20T21:39:04.3011344Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_weight:0 2024-08-20T21:39:04.3014448Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_input:0, line 228 <- wrt source file 2024-08-20T21:39:04.3067414Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_input:0 2024-08-20T21:39:04.3070872Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_weight:0, line 275 <- wrt source file 2024-08-20T21:39:04.3090583Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_weight:0 2024-08-20T21:39:04.3093898Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::calculate_gain:0, line 102 <- wrt source file 2024-08-20T21:39:04.3097401Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::calculate_gain:0 2024-08-20T21:39:04.3100501Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::uniform_:0, line 159 <- wrt source file 2024-08-20T21:39:04.3103260Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::uniform_:0 2024-08-20T21:39:04.3106119Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::normal_:0, line 186 <- wrt source file 2024-08-20T21:39:04.3108280Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::normal_:0 2024-08-20T21:39:04.3109923Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::trunc_normal_:0, line 221 <- wrt source file 2024-08-20T21:39:04.3139619Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::trunc_normal_:0 2024-08-20T21:39:04.3142556Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::constant_:0, line 235 <- wrt source file 2024-08-20T21:39:04.3145481Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::constant_:0 2024-08-20T21:39:04.3148438Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::ones_:0, line 252 <- wrt source file 2024-08-20T21:39:04.3151296Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::ones_:0 2024-08-20T21:39:04.3154100Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::zeros_:0, line 265 <- wrt source file 2024-08-20T21:39:04.3156965Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::zeros_:0 2024-08-20T21:39:04.3159832Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::eye_:0, line 281 <- wrt source file 2024-08-20T21:39:04.3162677Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::eye_:0 2024-08-20T21:39:04.3165330Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::dirac_:0, line 303 <- wrt source file 2024-08-20T21:39:04.3168572Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::dirac_:0 2024-08-20T21:39:04.3171477Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_uniform_:0, line 389 <- wrt source file 2024-08-20T21:39:04.3174597Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_uniform_:0 2024-08-20T21:39:04.3177727Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_normal_:0, line 429 <- wrt source file 2024-08-20T21:39:04.3180735Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_normal_:0 2024-08-20T21:39:04.3183921Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_uniform_:0, line 488 <- wrt source file 2024-08-20T21:39:04.3186958Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_uniform_:0 2024-08-20T21:39:04.3190136Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_normal_:0, line 553 <- wrt source file 2024-08-20T21:39:04.3192809Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_normal_:0 2024-08-20T21:39:04.3196087Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::orthogonal_:0, line 592 <- wrt source file 2024-08-20T21:39:04.3199608Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::orthogonal_:0 2024-08-20T21:39:04.3202571Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::sparse_:0, line 645 <- wrt source file 2024-08-20T21:39:04.3205175Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::sparse_:0 2024-08-20T21:39:04.3208395Z * 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 2024-08-20T21:39:04.3211521Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0 2024-08-20T21:39:04.3214677Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/bias.py::CausalBias:0, line 94 <- wrt source file 2024-08-20T21:39:04.3217910Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/bias.py::CausalBias:0 2024-08-20T21:39:04.3220240Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Threshold:0, line 70 <- wrt source file 2024-08-20T21:39:04.3222080Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Threshold:0 2024-08-20T21:39:04.3223903Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU:0, line 112 <- wrt source file 2024-08-20T21:39:04.3225635Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU:0 2024-08-20T21:39:04.3227405Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::RReLU:0, line 171 <- wrt source file 2024-08-20T21:39:04.3231843Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::RReLU:0 2024-08-20T21:39:04.3234847Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardtanh:0, line 227 <- wrt source file 2024-08-20T21:39:04.3243686Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardtanh:0 2024-08-20T21:39:04.3246614Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU6:0, line 292 <- wrt source file 2024-08-20T21:39:04.3249180Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU6:0 2024-08-20T21:39:04.3251776Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Sigmoid:0, line 320 <- wrt source file 2024-08-20T21:39:04.3254339Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Sigmoid:0 2024-08-20T21:39:04.3257017Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0, line 352 <- wrt source file 2024-08-20T21:39:04.3259680Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0 2024-08-20T21:39:04.3262315Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanh:0, line 385 <- wrt source file 2024-08-20T21:39:04.3264845Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanh:0 2024-08-20T21:39:04.3267387Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SiLU:0, line 418 <- wrt source file 2024-08-20T21:39:04.3269922Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SiLU:0 2024-08-20T21:39:04.3272619Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Mish:0, line 457 <- wrt source file 2024-08-20T21:39:04.3275137Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Mish:0 2024-08-20T21:39:04.3277758Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardswish:0, line 502 <- wrt source file 2024-08-20T21:39:04.3280470Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardswish:0 2024-08-20T21:39:04.3283084Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ELU:0, line 545 <- wrt source file 2024-08-20T21:39:04.3285577Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ELU:0 2024-08-20T21:39:04.3288143Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::CELU:0, line 587 <- wrt source file 2024-08-20T21:39:04.3290643Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::CELU:0 2024-08-20T21:39:04.3293200Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SELU:0, line 640 <- wrt source file 2024-08-20T21:39:04.3295725Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SELU:0 2024-08-20T21:39:04.3298484Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GLU:0, line 678 <- wrt source file 2024-08-20T21:39:04.3301016Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GLU:0 2024-08-20T21:39:04.3303555Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GELU:0, line 720 <- wrt source file 2024-08-20T21:39:04.3306070Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GELU:0 2024-08-20T21:39:04.3308672Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardshrink:0, line 763 <- wrt source file 2024-08-20T21:39:04.3311511Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardshrink:0 2024-08-20T21:39:04.3314194Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LeakyReLU:0, line 812 <- wrt source file 2024-08-20T21:39:04.3316829Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LeakyReLU:0 2024-08-20T21:39:04.3319635Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSigmoid:0, line 848 <- wrt source file 2024-08-20T21:39:04.3322283Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSigmoid:0 2024-08-20T21:39:04.3324955Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softplus:0, line 881 <- wrt source file 2024-08-20T21:39:04.3327544Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softplus:0 2024-08-20T21:39:04.3330209Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softshrink:0, line 924 <- wrt source file 2024-08-20T21:39:04.3332851Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softshrink:0 2024-08-20T21:39:04.3335652Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0, line 1026 <- wrt source file 2024-08-20T21:39:04.3338640Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0 2024-08-20T21:39:04.3341367Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::PReLU:0, line 1489 <- wrt source file 2024-08-20T21:39:04.3343915Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::PReLU:0 2024-08-20T21:39:04.3346539Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softsign:0, line 1531 <- wrt source file 2024-08-20T21:39:04.3349163Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softsign:0 2024-08-20T21:39:04.3352019Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanhshrink:0, line 1554 <- wrt source file 2024-08-20T21:39:04.3354734Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanhshrink:0 2024-08-20T21:39:04.3357474Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmin:0, line 1589 <- wrt source file 2024-08-20T21:39:04.3360208Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmin:0 2024-08-20T21:39:04.3362910Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax:0, line 1647 <- wrt source file 2024-08-20T21:39:04.3365531Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax:0 2024-08-20T21:39:04.3368260Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax2d:0, line 1688 <- wrt source file 2024-08-20T21:39:04.3370976Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax2d:0 2024-08-20T21:39:04.3373707Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSoftmax:0, line 1724 <- wrt source file 2024-08-20T21:39:04.3375529Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSoftmax:0 2024-08-20T21:39:04.3377481Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0, line 330 <- wrt source file 2024-08-20T21:39:04.3385056Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0 2024-08-20T21:39:04.3388294Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0, line 441 <- wrt source file 2024-08-20T21:39:04.3631100Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0 2024-08-20T21:39:04.3634050Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0, line 552 <- wrt source file 2024-08-20T21:39:04.6296512Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0 2024-08-20T21:39:04.6399475Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0, line 21 <- wrt source file 2024-08-20T21:39:04.6420884Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0 2024-08-20T21:39:04.6423703Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential:0, line 86 <- wrt source file 2024-08-20T21:39:04.6426694Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential:0 2024-08-20T21:39:04.6429371Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleList:0, line 292 <- wrt source file 2024-08-20T21:39:04.6432024Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleList:0 2024-08-20T21:39:04.6434683Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleDict:0, line 474 <- wrt source file 2024-08-20T21:39:04.6437300Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleDict:0 2024-08-20T21:39:04.6440109Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterList:0, line 606 <- wrt source file 2024-08-20T21:39:04.6442807Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterList:0 2024-08-20T21:39:04.6445546Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterDict:0, line 758 <- wrt source file 2024-08-20T21:39:04.6448225Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterDict:0 2024-08-20T21:39:04.6450981Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0, line 38 <- wrt source file 2024-08-20T21:39:04.6453709Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0 2024-08-20T21:39:04.6456442Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0, line 77 <- wrt source file 2024-08-20T21:39:04.6459169Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0 2024-08-20T21:39:04.6461833Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout:0, line 60 <- wrt source file 2024-08-20T21:39:04.6477864Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout:0 2024-08-20T21:39:04.6480765Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout1d:0, line 105 <- wrt source file 2024-08-20T21:39:04.6483909Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout1d:0 2024-08-20T21:39:04.6486504Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout2d:0, line 157 <- wrt source file 2024-08-20T21:39:04.6510831Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout2d:0 2024-08-20T21:39:04.6513495Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout3d:0, line 202 <- wrt source file 2024-08-20T21:39:04.6590911Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout3d:0 2024-08-20T21:39:04.6593783Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0, line 245 <- wrt source file 2024-08-20T21:39:04.6596662Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0 2024-08-20T21:39:04.6599503Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0, line 294 <- wrt source file 2024-08-20T21:39:04.6679363Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0 2024-08-20T21:39:04.6682524Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py::Flatten:0, line 30 <- wrt source file 2024-08-20T21:39:04.6688212Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py::Flatten:0 2024-08-20T21:39:04.6691197Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Fold:0, line 111 <- wrt source file 2024-08-20T21:39:04.6696466Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Fold:0 2024-08-20T21:39:04.6699421Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Unfold:0, line 261 <- wrt source file 2024-08-20T21:39:04.6739718Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Unfold:0 2024-08-20T21:39:04.6743202Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0, line 187 <- wrt source file 2024-08-20T21:39:04.6756580Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0 2024-08-20T21:39:04.6760183Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0, line 303 <- wrt source file 2024-08-20T21:39:04.6961739Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0 2024-08-20T21:39:04.6964960Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0, line 419 <- wrt source file 2024-08-20T21:39:04.9616203Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0 2024-08-20T21:39:04.9721007Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0, line 87 <- wrt source file 2024-08-20T21:39:04.9724053Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0 2024-08-20T21:39:04.9727412Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Identity:0, line 34 <- wrt source file 2024-08-20T21:39:04.9732939Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Identity:0 2024-08-20T21:39:04.9735496Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Linear:0, line 80 <- wrt source file 2024-08-20T21:39:04.9741326Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Linear:0 2024-08-20T21:39:04.9743908Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Bilinear:0, line 179 <- wrt source file 2024-08-20T21:39:04.9789772Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Bilinear:0 2024-08-20T21:39:04.9792268Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::L1Loss:0, line 115 <- wrt source file 2024-08-20T21:39:04.9798850Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::L1Loss:0 2024-08-20T21:39:04.9801413Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::NLLLoss:0, line 211 <- wrt source file 2024-08-20T21:39:04.9824327Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::NLLLoss:0 2024-08-20T21:39:04.9827508Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0, line 321 <- wrt source file 2024-08-20T21:39:04.9831824Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0 2024-08-20T21:39:04.9834516Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0, line 406 <- wrt source file 2024-08-20T21:39:04.9846312Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0 2024-08-20T21:39:04.9848956Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::KLDivLoss:0, line 517 <- wrt source file 2024-08-20T21:39:04.9856153Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::KLDivLoss:0 2024-08-20T21:39:04.9858771Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MSELoss:0, line 595 <- wrt source file 2024-08-20T21:39:04.9868460Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MSELoss:0 2024-08-20T21:39:04.9871029Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCELoss:0, line 677 <- wrt source file 2024-08-20T21:39:04.9875630Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCELoss:0 2024-08-20T21:39:04.9878417Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0, line 748 <- wrt source file 2024-08-20T21:39:04.9887926Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0 2024-08-20T21:39:04.9890721Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0, line 941 <- wrt source file 2024-08-20T21:39:04.9897428Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0 2024-08-20T21:39:04.9900395Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0, line 1261 <- wrt source file 2024-08-20T21:39:04.9907359Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0 2024-08-20T21:39:04.9910169Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0, line 1401 <- wrt source file 2024-08-20T21:39:04.9916689Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0 2024-08-20T21:39:04.9919600Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0, line 1466 <- wrt source file 2024-08-20T21:39:04.9924901Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0 2024-08-20T21:39:04.9927653Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0, line 1545 <- wrt source file 2024-08-20T21:39:04.9934348Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0 2024-08-20T21:39:04.9937092Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0, line 1645 <- wrt source file 2024-08-20T21:39:04.9946714Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0 2024-08-20T21:39:04.9949400Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CTCLoss:0, line 1886 <- wrt source file 2024-08-20T21:39:04.9979302Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CTCLoss:0 2024-08-20T21:39:04.9982078Z * 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 2024-08-20T21:39:04.9984973Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.register_buffer:0 2024-08-20T21:39:04.9987824Z * 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 2024-08-20T21:39:05.0000251Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.apply:0 2024-08-20T21:39:05.0002963Z * 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 2024-08-20T21:39:05.0007688Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.to:0 2024-08-20T21:39:05.0010414Z * 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 2024-08-20T21:39:05.0013163Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.state_dict:0 2024-08-20T21:39:05.0015956Z * 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 2024-08-20T21:39:05.0018748Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.parameters:0 2024-08-20T21:39:05.0021628Z * 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 2024-08-20T21:39:05.0024556Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_parameters:0 2024-08-20T21:39:05.0027411Z * 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 2024-08-20T21:39:05.0030391Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.buffers:0 2024-08-20T21:39:05.0033201Z * 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 2024-08-20T21:39:05.0036023Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_buffers:0 2024-08-20T21:39:05.0039017Z * 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 2024-08-20T21:39:05.0041874Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_children:0 2024-08-20T21:39:05.0044721Z * 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 2024-08-20T21:39:05.0063352Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.modules:0 2024-08-20T21:39:05.0066876Z * 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 2024-08-20T21:39:05.0070111Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_modules:0 2024-08-20T21:39:05.0073614Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0, line 38 <- wrt source file 2024-08-20T21:39:05.0076617Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0 2024-08-20T21:39:05.0080127Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LayerNorm:0, line 151 <- wrt source file 2024-08-20T21:39:05.0083428Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LayerNorm:0 2024-08-20T21:39:05.0086153Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::GroupNorm:0, line 262 <- wrt source file 2024-08-20T21:39:05.0105803Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::GroupNorm:0 2024-08-20T21:39:05.0108595Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::RMSNorm:0, line 355 <- wrt source file 2024-08-20T21:39:05.0111724Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::RMSNorm:0 2024-08-20T21:39:05.0114385Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad1d:0, line 69 <- wrt source file 2024-08-20T21:39:05.0119798Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad1d:0 2024-08-20T21:39:05.0122473Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad2d:0, line 120 <- wrt source file 2024-08-20T21:39:05.0141465Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad2d:0 2024-08-20T21:39:05.0144155Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad3d:0, line 184 <- wrt source file 2024-08-20T21:39:05.7027124Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad3d:0 2024-08-20T21:39:05.7232639Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0, line 238 <- wrt source file 2024-08-20T21:39:05.7243862Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0 2024-08-20T21:39:05.7246600Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0, line 291 <- wrt source file 2024-08-20T21:39:05.7250801Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0 2024-08-20T21:39:05.7253532Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0, line 347 <- wrt source file 2024-08-20T21:39:05.7279189Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0 2024-08-20T21:39:05.7281913Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0, line 391 <- wrt source file 2024-08-20T21:39:05.7285398Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0 2024-08-20T21:39:05.7288120Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0, line 435 <- wrt source file 2024-08-20T21:39:05.7292047Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0 2024-08-20T21:39:05.7295063Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0, line 492 <- wrt source file 2024-08-20T21:39:05.7297965Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0 2024-08-20T21:39:05.7300707Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0, line 550 <- wrt source file 2024-08-20T21:39:05.7303618Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0 2024-08-20T21:39:05.7306379Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0, line 593 <- wrt source file 2024-08-20T21:39:05.7311004Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0 2024-08-20T21:39:05.7313768Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0, line 650 <- wrt source file 2024-08-20T21:39:06.2810912Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0 2024-08-20T21:39:06.3016060Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0, line 684 <- wrt source file 2024-08-20T21:39:06.3026727Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0 2024-08-20T21:39:06.3029789Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0, line 739 <- wrt source file 2024-08-20T21:39:06.3035481Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0 2024-08-20T21:39:06.3038734Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0, line 798 <- wrt source file 2024-08-20T21:39:06.3063803Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0 2024-08-20T21:39:06.3070976Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0, line 40 <- wrt source file 2024-08-20T21:39:06.3074122Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0 2024-08-20T21:39:06.3076924Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0, line 93 <- wrt source file 2024-08-20T21:39:06.3079929Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0 2024-08-20T21:39:06.3082693Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0, line 118 <- wrt source file 2024-08-20T21:39:06.3085271Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0 2024-08-20T21:39:06.3087852Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0, line 195 <- wrt source file 2024-08-20T21:39:06.3139775Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0 2024-08-20T21:39:06.3142400Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0, line 278 <- wrt source file 2024-08-20T21:39:06.5536442Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0 2024-08-20T21:39:06.5579680Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0, line 352 <- wrt source file 2024-08-20T21:39:06.5593489Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0 2024-08-20T21:39:06.5596488Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0, line 534 <- wrt source file 2024-08-20T21:39:06.6449147Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0 2024-08-20T21:39:06.6486712Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0, line 622 <- wrt source file 2024-08-20T21:39:06.6499656Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0 2024-08-20T21:39:06.6502746Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0, line 714 <- wrt source file 2024-08-20T21:39:06.6544551Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0 2024-08-20T21:39:06.6547635Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0, line 827 <- wrt source file 2024-08-20T21:39:06.8343315Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0 2024-08-20T21:39:06.8386537Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0, line 917 <- wrt source file 2024-08-20T21:39:06.8442419Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0 2024-08-20T21:39:06.8445279Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0, line 1003 <- wrt source file 2024-08-20T21:39:06.9247325Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0 2024-08-20T21:39:06.9250088Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool1d:0, line 1117 <- wrt source file 2024-08-20T21:39:06.9256688Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool1d:0 2024-08-20T21:39:06.9259750Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool2d:0, line 1168 <- wrt source file 2024-08-20T21:39:06.9316390Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool2d:0 2024-08-20T21:39:06.9319503Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool3d:0, line 1227 <- wrt source file 2024-08-20T21:39:07.1611941Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool3d:0 2024-08-20T21:39:07.1693568Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0, line 1282 <- wrt source file 2024-08-20T21:39:07.1701738Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0 2024-08-20T21:39:07.1705289Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0, line 1316 <- wrt source file 2024-08-20T21:39:07.1714035Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0 2024-08-20T21:39:07.1717671Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0, line 1359 <- wrt source file 2024-08-20T21:39:07.1748880Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0 2024-08-20T21:39:07.1752291Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0, line 1406 <- wrt source file 2024-08-20T21:39:07.1755965Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0 2024-08-20T21:39:07.1759748Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0, line 1437 <- wrt source file 2024-08-20T21:39:07.1766902Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0 2024-08-20T21:39:07.1770532Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0, line 1476 <- wrt source file 2024-08-20T21:39:07.1793278Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0 2024-08-20T21:39:07.1796647Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNN:0, line 589 <- wrt source file 2024-08-20T21:39:07.1808475Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNN:0 2024-08-20T21:39:07.1811856Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTM:0, line 946 <- wrt source file 2024-08-20T21:39:07.2151815Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTM:0 2024-08-20T21:39:07.2154542Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRU:0, line 1284 <- wrt source file 2024-08-20T21:39:07.2171841Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRU:0 2024-08-20T21:39:07.2174357Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNNCell:0, line 1535 <- wrt source file 2024-08-20T21:39:07.2184628Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNNCell:0 2024-08-20T21:39:07.2187599Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTMCell:0, line 1657 <- wrt source file 2024-08-20T21:39:07.2196565Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTMCell:0 2024-08-20T21:39:07.2199208Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRUCell:0, line 1771 <- wrt source file 2024-08-20T21:39:07.2210200Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRUCell:0 2024-08-20T21:39:07.2212908Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding:0, line 69 <- wrt source file 2024-08-20T21:39:07.2224271Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding:0 2024-08-20T21:39:07.2227058Z * 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 2024-08-20T21:39:07.2231036Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0 2024-08-20T21:39:07.2234019Z * 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 2024-08-20T21:39:07.2238328Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0 2024-08-20T21:39:07.2241467Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer:0, line 90 <- wrt source file 2024-08-20T21:39:07.8859753Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer:0 2024-08-20T21:39:07.8878182Z * 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 2024-08-20T21:39:07.8881277Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer.forward:0 2024-08-20T21:39:07.8884286Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0, line 323 <- wrt source file 2024-08-20T21:39:08.0988726Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0 2024-08-20T21:39:08.1025055Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0, line 536 <- wrt source file 2024-08-20T21:39:08.2339444Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0 2024-08-20T21:39:08.2350604Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0, line 657 <- wrt source file 2024-08-20T21:39:08.2581427Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0 2024-08-20T21:39:08.2584823Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0, line 961 <- wrt source file 2024-08-20T21:39:08.2969502Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0 2024-08-20T21:39:08.2972715Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::Upsample:0, line 77 <- wrt source file 2024-08-20T21:39:08.2998719Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::Upsample:0 2024-08-20T21:39:08.3002382Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0, line 223 <- wrt source file 2024-08-20T21:39:08.3013733Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0 2024-08-20T21:39:08.3017112Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0, line 273 <- wrt source file 2024-08-20T21:39:08.3024842Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0 2024-08-20T21:39:08.3028428Z * 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 2024-08-20T21:39:08.3031753Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0 2024-08-20T21:39:08.3034905Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0, line 619 <- wrt source file 2024-08-20T21:39:08.3038440Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0 2024-08-20T21:39:08.3042195Z * 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 2024-08-20T21:39:08.3046328Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0 2024-08-20T21:39:08.3050510Z * 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 2024-08-20T21:39:08.3054615Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0 2024-08-20T21:39:08.3058502Z * 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 2024-08-20T21:39:08.3062716Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1 2024-08-20T21:39:08.3067286Z * 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 2024-08-20T21:39:08.3071715Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0 2024-08-20T21:39:08.3075372Z * 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 2024-08-20T21:39:08.3078722Z * 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 2024-08-20T21:39:08.3082031Z * 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 2024-08-20T21:39:08.3084834Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/init.py::skip_init:0 2024-08-20T21:39:08.3087242Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0, line 265 <- wrt source file 2024-08-20T21:39:08.3089099Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0 2024-08-20T21:39:08.3091221Z * 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 2024-08-20T21:39:08.3093085Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::weight_norm:0 2024-08-20T21:39:08.3094980Z * 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 2024-08-20T21:39:08.3097260Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0 2024-08-20T21:39:08.3099207Z * 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 2024-08-20T21:39:08.3101168Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrize.py::register_parametrization:0 2024-08-20T21:39:08.3102993Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::identity:0, line 845 <- wrt source file 2024-08-20T21:39:08.3104618Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::identity:0 2024-08-20T21:39:08.3106359Z * 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 2024-08-20T21:39:08.3108312Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::random_unstructured:0 2024-08-20T21:39:08.3110087Z * 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 2024-08-20T21:39:08.3111807Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::l1_unstructured:0 2024-08-20T21:39:08.3113516Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::remove:0, line 1191 <- wrt source file 2024-08-20T21:39:08.3120766Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::remove:0 2024-08-20T21:39:08.3123616Z * 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 2024-08-20T21:39:08.3131903Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::is_pruned:0 2024-08-20T21:39:08.3134958Z * 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 2024-08-20T21:39:08.3150387Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pad_packed_sequence:0 2024-08-20T21:39:08.3157660Z * 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 2024-08-20T21:39:08.3160775Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pad_sequence:0 2024-08-20T21:39:08.3163468Z * 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 2024-08-20T21:39:08.3173905Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0 2024-08-20T21:39:08.3177076Z * 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 2024-08-20T21:39:08.3183438Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pack_sequence:0 2024-08-20T21:39:08.3186789Z * 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 2024-08-20T21:39:08.3203481Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0 2024-08-20T21:39:08.3206502Z * 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 2024-08-20T21:39:08.3213345Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0 2024-08-20T21:39:08.3216700Z * 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 2024-08-20T21:39:08.3222660Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0 2024-08-20T21:39:08.3226259Z * 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 2024-08-20T21:39:08.3229533Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/stateless.py::functional_call:0 2024-08-20T21:39:08.3232630Z * 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 2024-08-20T21:39:08.3236990Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0 2024-08-20T21:39:08.3240765Z * 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 2024-08-20T21:39:08.3245306Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0 2024-08-20T21:39:08.3248241Z * 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 2024-08-20T21:39:08.3251131Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0 2024-08-20T21:39:08.3254391Z * 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 2024-08-20T21:39:08.3276815Z * 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 2024-08-20T21:39:08.3282395Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0, line 308 <- wrt source file 2024-08-20T21:39:08.3289658Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0 2024-08-20T21:39:08.3295009Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0, line 410 <- wrt source file 2024-08-20T21:39:08.3301958Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0 2024-08-20T21:39:08.3304596Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::StepLR:0, line 510 <- wrt source file 2024-08-20T21:39:08.3307088Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::StepLR:0 2024-08-20T21:39:08.3309644Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0, line 570 <- wrt source file 2024-08-20T21:39:08.3312258Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0 2024-08-20T21:39:08.3315184Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0, line 635 <- wrt source file 2024-08-20T21:39:08.3317744Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0 2024-08-20T21:39:08.3320393Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LinearLR:0, line 713 <- wrt source file 2024-08-20T21:39:08.3323611Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LinearLR:0 2024-08-20T21:39:08.3326225Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::SequentialLR:0, line 842 <- wrt source file 2024-08-20T21:39:08.3328821Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::SequentialLR:0 2024-08-20T21:39:08.3331980Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0, line 979 <- wrt source file 2024-08-20T21:39:08.3334937Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0 2024-08-20T21:39:08.3337809Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0, line 1135 <- wrt source file 2024-08-20T21:39:08.3340694Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0 2024-08-20T21:39:08.3343769Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ReduceLROnPlateau:0, line 1278 <- wrt source file 2024-08-20T21:39:08.3346711Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ReduceLROnPlateau:0 2024-08-20T21:39:08.3349410Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CyclicLR:0, line 1510 <- wrt source file 2024-08-20T21:39:08.3352131Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CyclicLR:0 2024-08-20T21:39:08.3354971Z * 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 2024-08-20T21:39:08.3358345Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0 2024-08-20T21:39:08.3361461Z * 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 2024-08-20T21:39:08.3364501Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1 2024-08-20T21:39:08.3367592Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::OneCycleLR:0, line 1941 <- wrt source file 2024-08-20T21:39:08.3370159Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::OneCycleLR:0 2024-08-20T21:39:08.3372697Z * 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 2024-08-20T21:39:08.3375183Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py::update_bn:0 2024-08-20T21:39:08.3377688Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/package/glob_group.py::GlobGroup:0, line 21 <- wrt source file 2024-08-20T21:39:08.3380226Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/package/glob_group.py::GlobGroup:0 2024-08-20T21:39:08.3382917Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/profiler/profiler.py::profile:0, line 534 <- wrt source file 2024-08-20T21:39:08.3385408Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/profiler/profiler.py::profile:0 2024-08-20T21:39:08.3388139Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/semi_structured.py::to_sparse_semi_structured:0, line 337 <- wrt source file 2024-08-20T21:39:08.3391064Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/semi_structured.py::to_sparse_semi_structured:0 2024-08-20T21:39:08.3393834Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_creation.py::make_tensor:0, line 113 <- wrt source file 2024-08-20T21:39:08.3396661Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_creation.py::make_tensor:0 2024-08-20T21:39:08.3399497Z * 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 2024-08-20T21:39:08.3402309Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::parametrize:0 2024-08-20T21:39:08.3405138Z * 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 2024-08-20T21:39:08.3408094Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::decorateIf:0 2024-08-20T21:39:08.3411064Z * 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 2024-08-20T21:39:08.3414153Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_symmetric_psd_matrix:0 2024-08-20T21:39:08.3417263Z * 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 2024-08-20T21:39:08.3420340Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_psd_matrix:0 2024-08-20T21:39:08.3423447Z * 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 2024-08-20T21:39:08.3426489Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_pd_matrix:0 2024-08-20T21:39:08.3429482Z * 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 2024-08-20T21:39:08.3432365Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/logging_utils.py::logs_to_string:0 2024-08-20T21:39:08.3435547Z * 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 2024-08-20T21:39:08.3439040Z * 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 2024-08-20T21:39:08.3442495Z * 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 2024-08-20T21:39:08.3445994Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/optests/autograd_registration.py::autograd_registration_check:0 2024-08-20T21:39:08.3449166Z * 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 2024-08-20T21:39:08.3451670Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_flatten:0 2024-08-20T21:39:08.3454298Z * 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 2024-08-20T21:39:08.3456895Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0 2024-08-20T21:39:08.3459456Z * 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 2024-08-20T21:39:08.3461903Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0 2024-08-20T21:39:08.3464442Z * 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 2024-08-20T21:39:08.3466956Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0 2024-08-20T21:39:08.3469545Z * 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 2024-08-20T21:39:08.3472193Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0 2024-08-20T21:39:08.3474751Z * 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 2024-08-20T21:39:08.3477215Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_map:0 2024-08-20T21:39:08.3479878Z * 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 2024-08-20T21:39:08.3482476Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0 2024-08-20T21:39:08.3485024Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::tree_map:0, line 933 <- wrt source file 2024-08-20T21:39:08.3487406Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::tree_map:0 2024-08-20T21:39:08.3490146Z * 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 2024-08-20T21:39:08.3493164Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::rename_privateuse1_backend:0 2024-08-20T21:39:08.3496637Z * 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 2024-08-20T21:39:08.3499932Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::generate_methods_for_privateuse1_backend:0 2024-08-20T21:39:08.3503053Z * 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 2024-08-20T21:39:08.3505950Z * SKIPPED: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/dlpack.py::from_dlpack:0 2024-08-20T21:39:08.3525171Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::IterableDataset:0, line 98 <- wrt source file 2024-08-20T21:39:08.3527822Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::IterableDataset:0 2024-08-20T21:39:08.3530544Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::StackDataset:0, line 223 <- wrt source file 2024-08-20T21:39:08.3533257Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::StackDataset:0 2024-08-20T21:39:08.3536380Z * 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 2024-08-20T21:39:08.3539375Z * SKIPPED: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::BatchSampler:0 2024-08-20T21:39:08.3558337Z * 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 2024-08-20T21:39:08.3561126Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::default_convert:0 2024-08-20T21:39:08.3563881Z * 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 2024-08-20T21:39:08.3566491Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::collate:0 2024-08-20T21:39:08.3569242Z * 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 2024-08-20T21:39:08.3571995Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::default_collate:0 2024-08-20T21:39:08.3574887Z * 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 2024-08-20T21:39:08.3577883Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/datapipe.py::IterDataPipe:0 2024-08-20T21:39:08.3580818Z * 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 2024-08-20T21:39:08.3583666Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/datapipe.py::MapDataPipe:0 2024-08-20T21:39:08.3586767Z * 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 2024-08-20T21:39:08.3589969Z * SKIPPED: 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48 <- wrt source file 2024-08-20T21:39:08.3613120Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0 2024-08-20T21:39:08.3616391Z * 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 2024-08-20T21:39:08.3619603Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ForkerIterDataPipe:0 2024-08-20T21:39:08.3622782Z * 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 2024-08-20T21:39:08.3625908Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::_ChildDataPipe:0 2024-08-20T21:39:08.3629165Z * 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 2024-08-20T21:39:08.3632552Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::DemultiplexerIterDataPipe:0 2024-08-20T21:39:08.3635933Z * 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 2024-08-20T21:39:08.3639354Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::MultiplexerIterDataPipe:0 2024-08-20T21:39:08.3642637Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ZipperIterDataPipe:0, line 681 <- wrt source file 2024-08-20T21:39:08.3645842Z * SKIPPED: 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line 62 <- wrt source file 2024-08-20T21:39:08.3665850Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::BatcherIterDataPipe:0 2024-08-20T21:39:08.3669114Z * 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 2024-08-20T21:39:08.3672334Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::UnBatcherIterDataPipe:0 2024-08-20T21:39:08.3675605Z * 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 2024-08-20T21:39:08.3678995Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::GrouperIterDataPipe:0 2024-08-20T21:39:08.3682265Z * 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 2024-08-20T21:39:08.3685445Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/selecting.py::FilterIterDataPipe:0 2024-08-20T21:39:08.3688808Z * 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 2024-08-20T21:39:08.3692219Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/streamreader.py::StreamReaderIterDataPipe:0 2024-08-20T21:39:08.3695605Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0, line 26 <- wrt source file 2024-08-20T21:39:08.3699086Z * SKIPPED: 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wrt source file 2024-08-20T21:39:08.3718653Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0 2024-08-20T21:39:08.3722068Z * 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 2024-08-20T21:39:08.3725240Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0 2024-08-20T21:39:08.3728418Z * 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 2024-08-20T21:39:08.3731559Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0 2024-08-20T21:39:08.3734791Z * 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 2024-08-20T21:39:08.3738036Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0 2024-08-20T21:39:08.3741230Z * 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 2024-08-20T21:39:08.3744336Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/utils/common.py::validate_input_col:0 2024-08-20T21:39:08.3747604Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/utils/decoder.py::basichandlers:0, line 47 <- wrt source file 2024-08-20T21:39:08.3750647Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/utils/decoder.py::basichandlers:0 2024-08-20T21:39:08.3753608Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0, line 433 <- wrt source file 2024-08-20T21:39:08.3756493Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0 2024-08-20T21:39:08.3759507Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0, line 529 <- wrt source file 2024-08-20T21:39:08.3762429Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0 2024-08-20T21:39:08.3765406Z * 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 2024-08-20T21:39:08.3768368Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0 2024-08-20T21:39:08.3771420Z * 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 2024-08-20T21:39:08.3774471Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0 2024-08-20T21:39:08.3777543Z * 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 2024-08-20T21:39:08.3780545Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0 2024-08-20T21:39:08.3783583Z * 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 2024-08-20T21:39:08.3786591Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0 2024-08-20T21:39:08.3789719Z * 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 2024-08-20T21:39:08.3792702Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_tensor:0 2024-08-20T21:39:08.3795770Z * 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 2024-08-20T21:39:08.3799155Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram:0 2024-08-20T21:39:08.3802319Z * 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 2024-08-20T21:39:08.3805451Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram_raw:0 2024-08-20T21:39:08.3808553Z * 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 2024-08-20T21:39:08.3811523Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0 2024-08-20T21:39:08.3814712Z * 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 2024-08-20T21:39:08.3817708Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_images:0 2024-08-20T21:39:08.3820750Z * 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 2024-08-20T21:39:08.3823754Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0 2024-08-20T21:39:08.3826811Z * 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 2024-08-20T21:39:08.3829875Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0 2024-08-20T21:39:08.3832973Z * 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 2024-08-20T21:39:08.3836021Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0 2024-08-20T21:39:08.3839395Z * 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 2024-08-20T21:39:08.3842810Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0 2024-08-20T21:39:08.3846242Z * 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 2024-08-20T21:39:08.3849634Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_marginchart:0 2024-08-20T21:39:08.3852931Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars:0, line 1108 <- wrt source file 2024-08-20T21:39:08.3856225Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars:0 2024-08-20T21:39:08.3859311Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_mesh:0, line 1154 <- wrt source file 2024-08-20T21:39:08.3862274Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_mesh:0 2024-08-20T21:39:08.3863827Z ============ 2024-08-20T21:39:08.3864321Z Finished doctests 2024-08-20T21:39:08.3864765Z 335 / 695 passed 2024-08-20T21:39:08.3865212Z  2024-08-20T21:39:08.3865754Z === Found 101 parse-time warnings === 2024-08-20T21:39:08.3866584Z --- Parse Warning: 1 / 101 --- 2024-08-20T21:39:08.3869121Z /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. 2024-08-20T21:39:08.3871789Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.3873070Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors. 2024-08-20T21:39:08.3873964Z 2024-08-20T21:39:08.3874553Z This is helpful when you want to visualize data over some 2024-08-20T21:39:08.3875486Z range of inputs. See below for a plotting example. 2024-08-20T21:39:08.3876208Z 2024-08-20T21:39:08.3877005Z Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as 2024-08-20T21:39:08.3878102Z inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`, 2024-08-20T21:39:08.3879331Z this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots 2024-08-20T21:39:08.3880443Z G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where 2024-08-20T21:39:08.3881441Z the output :math:`G_i` is constructed by expanding :math:`T_i` 2024-08-20T21:39:08.3882288Z to the result shape. 2024-08-20T21:39:08.3882812Z 2024-08-20T21:39:08.3883172Z .. note:: 2024-08-20T21:39:08.3883798Z 0D inputs are treated equivalently to 1D inputs of a 2024-08-20T21:39:08.3884564Z single element. 2024-08-20T21:39:08.3885064Z 2024-08-20T21:39:08.3885435Z .. warning:: 2024-08-20T21:39:08.3886129Z `torch.meshgrid(*tensors)` currently has the same behavior 2024-08-20T21:39:08.3887198Z as calling `numpy.meshgrid(*arrays, indexing='ij')`. 2024-08-20T21:39:08.3887937Z 2024-08-20T21:39:08.3888458Z In the future `torch.meshgrid` will transition to 2024-08-20T21:39:08.3889331Z `indexing='xy'` as the default. 2024-08-20T21:39:08.3889942Z 2024-08-20T21:39:08.3890513Z https://github.com/pytorch/pytorch/issues/50276 tracks 2024-08-20T21:39:08.3891602Z this issue with the goal of migrating to NumPy's behavior. 2024-08-20T21:39:08.3892383Z 2024-08-20T21:39:08.3892753Z .. seealso:: 2024-08-20T21:39:08.3893191Z 2024-08-20T21:39:08.3893749Z :func:`torch.cartesian_prod` has the same effect but it 2024-08-20T21:39:08.3894626Z collects the data in a tensor of vectors. 2024-08-20T21:39:08.3895274Z 2024-08-20T21:39:08.3895631Z Args: 2024-08-20T21:39:08.3896655Z tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be 2024-08-20T21:39:08.3897829Z treated as tensors of size :math:`(1,)` automatically 2024-08-20T21:39:08.3898563Z 2024-08-20T21:39:08.3899145Z indexing: (str, optional): the indexing mode, either "xy" 2024-08-20T21:39:08.3900139Z or "ij", defaults to "ij". See warning for future changes. 2024-08-20T21:39:08.3901089Z 2024-08-20T21:39:08.3901636Z If "xy" is selected, the first dimension corresponds 2024-08-20T21:39:08.3902583Z to the cardinality of the second input and the second 2024-08-20T21:39:08.3903542Z dimension corresponds to the cardinality of the first 2024-08-20T21:39:08.3904327Z input. 2024-08-20T21:39:08.3904802Z 2024-08-20T21:39:08.3905324Z If "ij" is selected, the dimensions are in the same 2024-08-20T21:39:08.3906195Z order as the cardinality of the inputs. 2024-08-20T21:39:08.3906868Z 2024-08-20T21:39:08.3907230Z Returns: 2024-08-20T21:39:08.3907862Z seq (sequence of Tensors): If the input has :math:`N` 2024-08-20T21:39:08.3908927Z tensors of size :math:`S_0 \ldots S_{N-1}``, then the 2024-08-20T21:39:08.3909887Z output will also have :math:`N` tensors, where each tensor 2024-08-20T21:39:08.3910888Z is of shape :math:`(S_0, ..., S_{N-1})`. 2024-08-20T21:39:08.3911539Z 2024-08-20T21:39:08.3911904Z Example:: 2024-08-20T21:39:08.3912342Z 2024-08-20T21:39:08.3912779Z >>> x = torch.tensor([1, 2, 3]) 2024-08-20T21:39:08.3913451Z >>> y = torch.tensor([4, 5, 6]) 2024-08-20T21:39:08.3914067Z 2024-08-20T21:39:08.3914777Z Observe the element-wise pairings across the grid, (1, 4), 2024-08-20T21:39:08.3915699Z (1, 5), ..., (3, 6). This is the same thing as the 2024-08-20T21:39:08.3916436Z cartesian product. 2024-08-20T21:39:08.3917433Z >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij') 2024-08-20T21:39:08.3918183Z >>> grid_x 2024-08-20T21:39:08.3918773Z tensor([[1, 1, 1], 2024-08-20T21:39:08.3919335Z [2, 2, 2], 2024-08-20T21:39:08.3919871Z [3, 3, 3]]) 2024-08-20T21:39:08.3920422Z >>> grid_y 2024-08-20T21:39:08.3920940Z tensor([[4, 5, 6], 2024-08-20T21:39:08.3921474Z [4, 5, 6], 2024-08-20T21:39:08.3922019Z [4, 5, 6]]) 2024-08-20T21:39:08.3922547Z 2024-08-20T21:39:08.3923091Z This correspondence can be seen when these grids are 2024-08-20T21:39:08.3923880Z stacked properly. 2024-08-20T21:39:08.3924691Z >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))), 2024-08-20T21:39:08.3925630Z ... torch.cartesian_prod(x, y)) 2024-08-20T21:39:08.3926307Z True 2024-08-20T21:39:08.3926738Z 2024-08-20T21:39:08.3927303Z `torch.meshgrid` is commonly used to produce a grid for 2024-08-20T21:39:08.3928094Z plotting. 2024-08-20T21:39:08.3928692Z >>> # xdoctest: +REQUIRES(module:matplotlib) 2024-08-20T21:39:08.3929495Z >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW) 2024-08-20T21:39:08.3930257Z >>> import matplotlib.pyplot as plt 2024-08-20T21:39:08.3931139Z >>> xs = torch.linspace(-5, 5, steps=100) 2024-08-20T21:39:08.3932007Z >>> ys = torch.linspace(-5, 5, steps=100) 2024-08-20T21:39:08.3932900Z >>> x, y = torch.meshgrid(xs, ys, indexing='xy') 2024-08-20T21:39:08.3933708Z >>> z = torch.sin(torch.sqrt(x * x + y * y)) 2024-08-20T21:39:08.3934558Z >>> ax = plt.axes(projection='3d') 2024-08-20T21:39:08.3935346Z >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy()) 2024-08-20T21:39:08.3936089Z >>> plt.show() 2024-08-20T21:39:08.3936595Z 2024-08-20T21:39:08.3937047Z .. image:: ../_static/img/meshgrid.png 2024-08-20T21:39:08.3937717Z :width: 512 2024-08-20T21:39:08.3938206Z 2024-08-20T21:39:08.3938549Z 2024-08-20T21:39:08.3939429Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.3940454Z 2024-08-20T21:39:08.3940823Z warnings.warn(msg) 2024-08-20T21:39:08.3941297Z 2024-08-20T21:39:08.3941835Z --- Parse Warning: 2 / 101 --- 2024-08-20T21:39:08.3944388Z /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. 2024-08-20T21:39:08.3947068Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.3948814Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> Tuple[Tensor, Tensor, Tensor] 2024-08-20T21:39:08.3949995Z 2024-08-20T21:39:08.3950511Z Returns the unique elements of the input tensor. 2024-08-20T21:39:08.3951216Z 2024-08-20T21:39:08.3952061Z .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that 2024-08-20T21:39:08.3953454Z this function also eliminates non-consecutive duplicate values. 2024-08-20T21:39:08.3987776Z 2024-08-20T21:39:08.3988606Z .. note:: Currently in the CUDA implementation and the CPU implementation, 2024-08-20T21:39:08.3989996Z `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument. 2024-08-20T21:39:08.3991509Z Sorting could be slow, so if your input tensor is already sorted, it is recommended to use 2024-08-20T21:39:08.3992770Z :func:`torch.unique_consecutive` which avoids the sorting. 2024-08-20T21:39:08.3993537Z 2024-08-20T21:39:08.3993884Z Args: 2024-08-20T21:39:08.3994614Z input (Tensor): the input tensor 2024-08-20T21:39:08.3995554Z sorted (bool): Whether to sort the unique elements in ascending order 2024-08-20T21:39:08.3996724Z before returning as output. 2024-08-20T21:39:08.3997613Z return_inverse (bool): Whether to also return the indices for where 2024-08-20T21:39:08.3998915Z elements in the original input ended up in the returned unique list. 2024-08-20T21:39:08.4000113Z return_counts (bool): Whether to also return the counts for each unique 2024-08-20T21:39:08.4000995Z element. 2024-08-20T21:39:08.4001757Z dim (int, optional): the dimension to operate upon. If ``None``, the 2024-08-20T21:39:08.4002905Z unique of the flattened input is returned. Otherwise, each of the 2024-08-20T21:39:08.4004018Z tensors indexed by the given dimension is treated as one of the 2024-08-20T21:39:08.4005169Z elements to apply the unique operation upon. See examples for more 2024-08-20T21:39:08.4006094Z details. Default: ``None`` 2024-08-20T21:39:08.4006675Z 2024-08-20T21:39:08.4007040Z Returns: 2024-08-20T21:39:08.4007898Z (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing 2024-08-20T21:39:08.4008890Z 2024-08-20T21:39:08.4009783Z - **output** (*Tensor*): the output list of unique scalar elements. 2024-08-20T21:39:08.4010850Z - **inverse_indices** (*Tensor*): (optional) if 2024-08-20T21:39:08.4011755Z :attr:`return_inverse` is True, there will be an additional 2024-08-20T21:39:08.4012817Z returned tensor (same shape as input) representing the indices 2024-08-20T21:39:08.4013920Z for where elements in the original input map to in the output; 2024-08-20T21:39:08.4014987Z otherwise, this function will only return a single tensor. 2024-08-20T21:39:08.4015961Z - **counts** (*Tensor*): (optional) if 2024-08-20T21:39:08.4016836Z :attr:`return_counts` is True, there will be an additional 2024-08-20T21:39:08.4017840Z returned tensor (same shape as output or output.size(dim), 2024-08-20T21:39:08.4018880Z if dim was specified) representing the number of occurrences 2024-08-20T21:39:08.4020006Z for each unique value or tensor. 2024-08-20T21:39:08.4020645Z 2024-08-20T21:39:08.4021009Z Example:: 2024-08-20T21:39:08.4021427Z 2024-08-20T21:39:08.4022073Z >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long)) 2024-08-20T21:39:08.4022923Z >>> output 2024-08-20T21:39:08.4023396Z tensor([1, 2, 3]) 2024-08-20T21:39:08.4023888Z 2024-08-20T21:39:08.4024342Z >>> output, inverse_indices = torch.unique( 2024-08-20T21:39:08.4025385Z ... torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True) 2024-08-20T21:39:08.4026334Z >>> output 2024-08-20T21:39:08.4026790Z tensor([1, 2, 3]) 2024-08-20T21:39:08.4027321Z >>> inverse_indices 2024-08-20T21:39:08.4027872Z tensor([0, 2, 1, 2]) 2024-08-20T21:39:08.4028373Z 2024-08-20T21:39:08.4028845Z >>> output, inverse_indices = torch.unique( 2024-08-20T21:39:08.4029900Z ... torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True) 2024-08-20T21:39:08.4030826Z >>> output 2024-08-20T21:39:08.4031297Z tensor([1, 2, 3]) 2024-08-20T21:39:08.4031829Z >>> inverse_indices 2024-08-20T21:39:08.4032357Z tensor([[0, 2], 2024-08-20T21:39:08.4032857Z [1, 2]]) 2024-08-20T21:39:08.4033342Z 2024-08-20T21:39:08.4033717Z >>> a = torch.tensor([ 2024-08-20T21:39:08.4034273Z ... [ 2024-08-20T21:39:08.4034732Z ... [1, 1, 0, 0], 2024-08-20T21:39:08.4035435Z ... [1, 1, 0, 0], 2024-08-20T21:39:08.4036010Z ... [0, 0, 1, 1], 2024-08-20T21:39:08.4036571Z ... ], 2024-08-20T21:39:08.4037005Z ... [ 2024-08-20T21:39:08.4037472Z ... [0, 0, 1, 1], 2024-08-20T21:39:08.4038050Z ... [0, 0, 1, 1], 2024-08-20T21:39:08.4038725Z ... [1, 1, 1, 1], 2024-08-20T21:39:08.4039276Z ... ], 2024-08-20T21:39:08.4039726Z ... [ 2024-08-20T21:39:08.4040172Z ... [1, 1, 0, 0], 2024-08-20T21:39:08.4040745Z ... [1, 1, 0, 0], 2024-08-20T21:39:08.4041316Z ... [0, 0, 1, 1], 2024-08-20T21:39:08.4041850Z ... ], 2024-08-20T21:39:08.4042296Z ... ]) 2024-08-20T21:39:08.4042707Z 2024-08-20T21:39:08.4043381Z >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]` 2024-08-20T21:39:08.4044582Z >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match 2024-08-20T21:39:08.4045584Z >>> # each other, so one of them will be removed. 2024-08-20T21:39:08.4046322Z >>> (a[0, :, :] == a[2, :, :]).all() 2024-08-20T21:39:08.4046960Z tensor(True) 2024-08-20T21:39:08.4047529Z >>> a_unique_dim0 = torch.unique(a, dim=0) 2024-08-20T21:39:08.4048197Z >>> a_unique_dim0 2024-08-20T21:39:08.4048731Z tensor([[[0, 0, 1, 1], 2024-08-20T21:39:08.4049285Z [0, 0, 1, 1], 2024-08-20T21:39:08.4049824Z [1, 1, 1, 1]], 2024-08-20T21:39:08.4050383Z [[1, 1, 0, 0], 2024-08-20T21:39:08.4050923Z [1, 1, 0, 0], 2024-08-20T21:39:08.4051457Z [0, 0, 1, 1]]]) 2024-08-20T21:39:08.4051998Z 2024-08-20T21:39:08.4052849Z >>> # Notice which sub-tensors from `a` match with the sub-tensors from 2024-08-20T21:39:08.4053735Z >>> # `a_unique_dim0`: 2024-08-20T21:39:08.4054394Z >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all() 2024-08-20T21:39:08.4055083Z tensor(True) 2024-08-20T21:39:08.4055663Z >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all() 2024-08-20T21:39:08.4056331Z tensor(True) 2024-08-20T21:39:08.4056790Z 2024-08-20T21:39:08.4057575Z >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are 2024-08-20T21:39:08.4058684Z >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of 2024-08-20T21:39:08.4059555Z >>> # them will be removed. 2024-08-20T21:39:08.4060206Z >>> (a[:, 0, :] == a[:, 1, :]).all() 2024-08-20T21:39:08.4060825Z tensor(True) 2024-08-20T21:39:08.4061336Z >>> torch.unique(a, dim=1) 2024-08-20T21:39:08.4061945Z tensor([[[0, 0, 1, 1], 2024-08-20T21:39:08.4062490Z [1, 1, 0, 0]], 2024-08-20T21:39:08.4063055Z [[1, 1, 1, 1], 2024-08-20T21:39:08.4063612Z [0, 0, 1, 1]], 2024-08-20T21:39:08.4064161Z [[0, 0, 1, 1], 2024-08-20T21:39:08.4064714Z [1, 1, 0, 0]]]) 2024-08-20T21:39:08.4065264Z 2024-08-20T21:39:08.4065919Z >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared. 2024-08-20T21:39:08.4067029Z >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and 2024-08-20T21:39:08.4068056Z >>> # `a[:, :, 3]` match each other as well. So in this case, two of the 2024-08-20T21:39:08.4069027Z >>> # sub-tensors will be removed. 2024-08-20T21:39:08.4069717Z >>> (a[:, :, 0] == a[:, :, 1]).all() 2024-08-20T21:39:08.4070349Z tensor(True) 2024-08-20T21:39:08.4070859Z >>> (a[:, :, 2] == a[:, :, 3]).all() 2024-08-20T21:39:08.4071494Z tensor(True) 2024-08-20T21:39:08.4072010Z >>> torch.unique(a, dim=2) 2024-08-20T21:39:08.4072596Z tensor([[[0, 1], 2024-08-20T21:39:08.4073197Z [0, 1], 2024-08-20T21:39:08.4073698Z [1, 0]], 2024-08-20T21:39:08.4074188Z [[1, 0], 2024-08-20T21:39:08.4074684Z [1, 0], 2024-08-20T21:39:08.4075185Z [1, 1]], 2024-08-20T21:39:08.4075680Z [[0, 1], 2024-08-20T21:39:08.4076167Z [0, 1], 2024-08-20T21:39:08.4076661Z [1, 0]]]) 2024-08-20T21:39:08.4077146Z 2024-08-20T21:39:08.4078012Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.4079035Z 2024-08-20T21:39:08.4079409Z warnings.warn(msg) 2024-08-20T21:39:08.4079882Z 2024-08-20T21:39:08.4080422Z --- Parse Warning: 3 / 101 --- 2024-08-20T21:39:08.4082860Z /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. 2024-08-20T21:39:08.4085410Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.4086347Z 2024-08-20T21:39:08.4086879Z Load a model from a github repo or a local directory. 2024-08-20T21:39:08.4087599Z 2024-08-20T21:39:08.4088317Z Note: Loading a model is the typical use case, but this can also be used to 2024-08-20T21:39:08.4089495Z for loading other objects such as tokenizers, loss functions, etc. 2024-08-20T21:39:08.4090321Z 2024-08-20T21:39:08.4090985Z If ``source`` is 'github', ``repo_or_dir`` is expected to be 2024-08-20T21:39:08.4091950Z of the form ``repo_owner/repo_name[:ref]`` with an optional 2024-08-20T21:39:08.4092723Z ref (a tag or a branch). 2024-08-20T21:39:08.4093225Z 2024-08-20T21:39:08.4093891Z If ``source`` is 'local', ``repo_or_dir`` is expected to be a 2024-08-20T21:39:08.4094687Z path to a local directory. 2024-08-20T21:39:08.4095217Z 2024-08-20T21:39:08.4095586Z Args: 2024-08-20T21:39:08.4096368Z repo_or_dir (str): If ``source`` is 'github', 2024-08-20T21:39:08.4097509Z this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with 2024-08-20T21:39:08.4099212Z an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified, 2024-08-20T21:39:08.4100939Z the default branch is assumed to be ``main`` if it exists, and otherwise ``master``. 2024-08-20T21:39:08.4102367Z If ``source`` is 'local' then it should be a path to a local directory. 2024-08-20T21:39:08.4103494Z model (str): the name of a callable (entrypoint) defined in the 2024-08-20T21:39:08.4104420Z repo/dir's ``hubconf.py``. 2024-08-20T21:39:08.4105264Z *args (optional): the corresponding args for callable ``model``. 2024-08-20T21:39:08.4106406Z source (str, optional): 'github' or 'local'. Specifies how 2024-08-20T21:39:08.4107490Z ``repo_or_dir`` is to be interpreted. Default is 'github'. 2024-08-20T21:39:08.4108581Z trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``. 2024-08-20T21:39:08.4109796Z This parameter was introduced in v1.12 and helps ensuring that users 2024-08-20T21:39:08.4110794Z only run code from repos that they trust. 2024-08-20T21:39:08.4111457Z 2024-08-20T21:39:08.4112198Z - If ``False``, a prompt will ask the user whether the repo should 2024-08-20T21:39:08.4113040Z be trusted. 2024-08-20T21:39:08.4113891Z - If ``True``, the repo will be added to the trusted list and loaded 2024-08-20T21:39:08.4114847Z without requiring explicit confirmation. 2024-08-20T21:39:08.4115854Z - If ``"check"``, the repo will be checked against the list of 2024-08-20T21:39:08.4116903Z trusted repos in the cache. If it is not present in that list, the 2024-08-20T21:39:08.4118143Z behaviour will fall back onto the ``trust_repo=False`` option. 2024-08-20T21:39:08.4119434Z - If ``None``: this will raise a warning, inviting the user to set 2024-08-20T21:39:08.4120471Z ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This 2024-08-20T21:39:08.4121583Z is only present for backward compatibility and will be removed in 2024-08-20T21:39:08.4122455Z v2.0. 2024-08-20T21:39:08.4122854Z 2024-08-20T21:39:08.4123515Z Default is ``None`` and will eventually change to ``"check"`` in v2.0. 2024-08-20T21:39:08.4124663Z force_reload (bool, optional): whether to force a fresh download of 2024-08-20T21:39:08.4125764Z the github repo unconditionally. Does not have any effect if 2024-08-20T21:39:08.4126760Z ``source = 'local'``. Default is ``False``. 2024-08-20T21:39:08.4127706Z verbose (bool, optional): If ``False``, mute messages about hitting 2024-08-20T21:39:08.4128845Z local caches. Note that the message about first download cannot be 2024-08-20T21:39:08.4129991Z muted. Does not have any effect if ``source = 'local'``. 2024-08-20T21:39:08.4130786Z Default is ``True``. 2024-08-20T21:39:08.4131789Z skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit 2024-08-20T21:39:08.4133257Z specified by the ``github`` argument properly belongs to the repo owner. This will make 2024-08-20T21:39:08.4134853Z requests to the GitHub API; you can specify a non-default GitHub token by setting the 2024-08-20T21:39:08.4136085Z ``GITHUB_TOKEN`` environment variable. Default is ``False``. 2024-08-20T21:39:08.4137148Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2024-08-20T21:39:08.4137993Z 2024-08-20T21:39:08.4138353Z Returns: 2024-08-20T21:39:08.4138992Z The output of the ``model`` callable when called with the given 2024-08-20T21:39:08.4139851Z ``*args`` and ``**kwargs``. 2024-08-20T21:39:08.4140401Z 2024-08-20T21:39:08.4140751Z Example: 2024-08-20T21:39:08.4141278Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-08-20T21:39:08.4141991Z >>> # from a github repo 2024-08-20T21:39:08.4142555Z >>> repo = "pytorch/vision" 2024-08-20T21:39:08.4143264Z >>> model = torch.hub.load( 2024-08-20T21:39:08.4144063Z ... repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1" 2024-08-20T21:39:08.4144852Z ... ) 2024-08-20T21:39:08.4145279Z >>> # from a local directory 2024-08-20T21:39:08.4145956Z >>> path = "/some/local/path/pytorch/vision" 2024-08-20T21:39:08.4146651Z >>> # xdoctest: +SKIP 2024-08-20T21:39:08.4147509Z >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT") 2024-08-20T21:39:08.4148440Z 2024-08-20T21:39:08.4149293Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.4150185Z 2024-08-20T21:39:08.4150574Z warnings.warn(msg) 2024-08-20T21:39:08.4151040Z 2024-08-20T21:39:08.4151556Z --- Parse Warning: 4 / 101 --- 2024-08-20T21:39:08.4154134Z /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. 2024-08-20T21:39:08.4156807Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.4157885Z Download object at the given URL to a local path. 2024-08-20T21:39:08.4158680Z 2024-08-20T21:39:08.4159038Z Args: 2024-08-20T21:39:08.4159533Z url (str): URL of the object to download 2024-08-20T21:39:08.4160566Z dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file`` 2024-08-20T21:39:08.4162205Z hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``. 2024-08-20T21:39:08.4163331Z Default: None 2024-08-20T21:39:08.4164221Z progress (bool, optional): whether or not to display a progress bar to stderr 2024-08-20T21:39:08.4165190Z Default: True 2024-08-20T21:39:08.4165672Z 2024-08-20T21:39:08.4166049Z Example: 2024-08-20T21:39:08.4166601Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-08-20T21:39:08.4167354Z >>> # xdoctest: +REQUIRES(POSIX) 2024-08-20T21:39:08.4168060Z >>> torch.hub.download_url_to_file( 2024-08-20T21:39:08.4169159Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth", 2024-08-20T21:39:08.4170088Z ... "/tmp/temporary_file", 2024-08-20T21:39:08.4170678Z ... ) 2024-08-20T21:39:08.4171082Z 2024-08-20T21:39:08.4171423Z 2024-08-20T21:39:08.4172276Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.4173191Z 2024-08-20T21:39:08.4173562Z warnings.warn(msg) 2024-08-20T21:39:08.4174033Z 2024-08-20T21:39:08.4174566Z --- Parse Warning: 5 / 101 --- 2024-08-20T21:39:08.4177155Z /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. 2024-08-20T21:39:08.4180157Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.4181266Z Loads the Torch serialized object at the given URL. 2024-08-20T21:39:08.4181985Z 2024-08-20T21:39:08.4182577Z If downloaded file is a zip file, it will be automatically 2024-08-20T21:39:08.4183363Z decompressed. 2024-08-20T21:39:08.4183810Z 2024-08-20T21:39:08.4184609Z If the object is already present in `model_dir`, it's deserialized and 2024-08-20T21:39:08.4185481Z returned. 2024-08-20T21:39:08.4186209Z The default value of ``model_dir`` is ``/checkpoints`` where 2024-08-20T21:39:08.4187335Z ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`. 2024-08-20T21:39:08.4188149Z 2024-08-20T21:39:08.4188510Z Args: 2024-08-20T21:39:08.4189012Z url (str): URL of the object to download 2024-08-20T21:39:08.4190055Z model_dir (str, optional): directory in which to save the object 2024-08-20T21:39:08.4191495Z map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load) 2024-08-20T21:39:08.4193037Z progress (bool, optional): whether or not to display a progress bar to stderr. 2024-08-20T21:39:08.4194009Z Default: True 2024-08-20T21:39:08.4195081Z check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention 2024-08-20T21:39:08.4196876Z ``filename-.ext`` where ```` is the first eight or more 2024-08-20T21:39:08.4198087Z digits of the SHA256 hash of the contents of the file. The hash is used to 2024-08-20T21:39:08.4199357Z ensure unique names and to verify the contents of the file. 2024-08-20T21:39:08.4200191Z Default: False 2024-08-20T21:39:08.4201280Z file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set. 2024-08-20T21:39:08.4202943Z weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects. 2024-08-20T21:39:08.4204423Z Recommended for untrusted sources. See :func:`~torch.load` for more details. 2024-08-20T21:39:08.4205361Z 2024-08-20T21:39:08.4205715Z Example: 2024-08-20T21:39:08.4206267Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-08-20T21:39:08.4207110Z >>> state_dict = torch.hub.load_state_dict_from_url( 2024-08-20T21:39:08.4208434Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth" 2024-08-20T21:39:08.4209302Z ... ) 2024-08-20T21:39:08.4209714Z 2024-08-20T21:39:08.4210056Z 2024-08-20T21:39:08.4210920Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.4211829Z 2024-08-20T21:39:08.4212197Z warnings.warn(msg) 2024-08-20T21:39:08.4212669Z 2024-08-20T21:39:08.4213204Z --- Parse Warning: 6 / 101 --- 2024-08-20T21:39:08.4215776Z /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. 2024-08-20T21:39:08.4218456Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-08-20T21:39:08.4219711Z Registers the function implementation as the fallback for the given key. 2024-08-20T21:39:08.4220605Z 2024-08-20T21:39:08.4221264Z This function only works for a library with global namespace ("_"). 2024-08-20T21:39:08.4222118Z 2024-08-20T21:39:08.4222477Z Args: 2024-08-20T21:39:08.4223353Z fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel` 2024-08-20T21:39:08.4224455Z to register a fallthrough. 2024-08-20T21:39:08.4225641Z dispatch_key: dispatch key that the input function should be registered for. By default, it uses 2024-08-20T21:39:08.4226929Z the dispatch key that the library was created with. 2024-08-20T21:39:08.4228328Z with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument 2024-08-20T21:39:08.4230110Z to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls. 2024-08-20T21:39:08.4231238Z 2024-08-20T21:39:08.4231638Z Example:: 2024-08-20T21:39:08.4232175Z >>> my_lib = Library("_", "IMPL") 2024-08-20T21:39:08.4232919Z >>> def fallback_kernel(op, *args, **kwargs): 2024-08-20T21:39:08.4233728Z >>> # Handle all autocast ops generically 2024-08-20T21:39:08.4234588Z >>> # ... 2024-08-20T21:39:08.4235398Z >>> my_lib.fallback(fallback_kernel, "Autocast") 2024-08-20T21:39:08.4236089Z 2024-08-20T21:39:08.4237930Z Original Error: IndentationError('expected an indented block after function definition on line 2', ('', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7)) 2024-08-20T21:39:08.4239715Z 2024-08-20T21:39:08.4240170Z my_lib.fallback(fallback_kernel, "Autocast") 2024-08-20T21:39:08.4240827Z ^ 2024-08-20T21:39:08.4241213Z warnings.warn(msg) 2024-08-20T21:39:08.4241669Z 2024-08-20T21:39:08.4242205Z --- Parse Warning: 7 / 101 --- 2024-08-20T21:39:08.4244770Z /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. 2024-08-20T21:39:08.4247369Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-08-20T21:39:08.4248597Z Register a FakeTensor implementation ("fake impl") for this operator. 2024-08-20T21:39:08.4249463Z 2024-08-20T21:39:08.4250019Z Also sometimes known as a "meta kernel", "abstract impl". 2024-08-20T21:39:08.4250780Z 2024-08-20T21:39:08.4251492Z An "FakeTensor implementation" specifies the behavior of this operator on 2024-08-20T21:39:08.4252734Z Tensors that carry no data ("FakeTensor"). Given some input Tensors with 2024-08-20T21:39:08.4253965Z certain properties (sizes/strides/storage_offset/device), it specifies 2024-08-20T21:39:08.4254993Z what the properties of the output Tensors are. 2024-08-20T21:39:08.4255681Z 2024-08-20T21:39:08.4256438Z The FakeTensor implementation has the same signature as the operator. 2024-08-20T21:39:08.4257636Z It is run for both FakeTensors and meta tensors. To write a FakeTensor 2024-08-20T21:39:08.4258804Z implementation, assume that all Tensor inputs to the operator are 2024-08-20T21:39:08.4259917Z regular CPU/CUDA/Meta tensors, but they do not have storage, and 2024-08-20T21:39:08.4261044Z you are trying to return regular CPU/CUDA/Meta tensor(s) as output. 2024-08-20T21:39:08.4262224Z The FakeTensor implementation must consist of only PyTorch operations 2024-08-20T21:39:08.4263362Z (and may not directly access the storage or data of any input or 2024-08-20T21:39:08.4264232Z intermediate Tensors). 2024-08-20T21:39:08.4264751Z 2024-08-20T21:39:08.4265263Z This API may be used as a decorator (see examples). 2024-08-20T21:39:08.4265987Z 2024-08-20T21:39:08.4266476Z For a detailed guide on custom ops, please see 2024-08-20T21:39:08.4267481Z https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html 2024-08-20T21:39:08.4268349Z 2024-08-20T21:39:08.4268720Z Examples: 2024-08-20T21:39:08.4269146Z >>> import torch 2024-08-20T21:39:08.4269686Z >>> import numpy as np 2024-08-20T21:39:08.4270298Z >>> from torch import Tensor 2024-08-20T21:39:08.4270878Z >>> 2024-08-20T21:39:08.4271658Z >>> # Example 1: an operator without data-dependent output shape 2024-08-20T21:39:08.4272777Z >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=()) 2024-08-20T21:39:08.4274049Z >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2024-08-20T21:39:08.4275135Z >>> raise NotImplementedError("Implementation goes here") 2024-08-20T21:39:08.4275916Z >>> 2024-08-20T21:39:08.4276524Z >>> @torch.library.register_fake("mylib::custom_linear") 2024-08-20T21:39:08.4277316Z >>> def _(x, weight, bias): 2024-08-20T21:39:08.4277947Z >>> assert x.dim() == 2 2024-08-20T21:39:08.4278689Z >>> assert weight.dim() == 2 2024-08-20T21:39:08.4279350Z >>> assert bias.dim() == 1 2024-08-20T21:39:08.4280062Z >>> assert x.shape[1] == weight.shape[1] 2024-08-20T21:39:08.4281052Z >>> assert weight.shape[0] == bias.shape[0] 2024-08-20T21:39:08.4281814Z >>> assert x.device == weight.device 2024-08-20T21:39:08.4282463Z >>> 2024-08-20T21:39:08.4282947Z >>> return (x @ weight.t()) + bias 2024-08-20T21:39:08.4283570Z >>> 2024-08-20T21:39:08.4284180Z >>> with torch._subclasses.fake_tensor.FakeTensorMode(): 2024-08-20T21:39:08.4284992Z >>> x = torch.randn(2, 3) 2024-08-20T21:39:08.4285629Z >>> w = torch.randn(3, 3) 2024-08-20T21:39:08.4286267Z >>> b = torch.randn(3) 2024-08-20T21:39:08.4286985Z >>> y = torch.ops.mylib.custom_linear(x, w, b) 2024-08-20T21:39:08.4287667Z >>> 2024-08-20T21:39:08.4288129Z >>> assert y.shape == (2, 3) 2024-08-20T21:39:08.4288718Z >>> 2024-08-20T21:39:08.4289462Z >>> # Example 2: an operator with data-dependent output shape 2024-08-20T21:39:08.4290544Z >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=()) 2024-08-20T21:39:08.4291616Z >>> def custom_nonzero(x: Tensor) -> Tensor: 2024-08-20T21:39:08.4292350Z >>> x_np = x.numpy(force=True) 2024-08-20T21:39:08.4293100Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2024-08-20T21:39:08.4293908Z >>> return torch.tensor(res, device=x.device) 2024-08-20T21:39:08.4294581Z >>> 2024-08-20T21:39:08.4295190Z >>> @torch.library.register_fake("mylib::custom_nonzero") 2024-08-20T21:39:08.4296188Z >>> def _(x): 2024-08-20T21:39:08.4297112Z >>> # Number of nonzero-elements is data-dependent. 2024-08-20T21:39:08.4297997Z >>> # Since we cannot peek at the data in an fake impl, 2024-08-20T21:39:08.4298933Z >>> # we use the ctx object to construct a new symint that 2024-08-20T21:39:08.4299905Z >>> # represents the data-dependent size. 2024-08-20T21:39:08.4300654Z >>> ctx = torch.library.get_ctx() 2024-08-20T21:39:08.4301377Z >>> nnz = ctx.new_dynamic_size() 2024-08-20T21:39:08.4302065Z >>> shape = [nnz, x.dim()] 2024-08-20T21:39:08.4302807Z >>> result = x.new_empty(shape, dtype=torch.int64) 2024-08-20T21:39:08.4303560Z >>> return result 2024-08-20T21:39:08.4304098Z >>> 2024-08-20T21:39:08.4304721Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2024-08-20T21:39:08.4305508Z >>> 2024-08-20T21:39:08.4306003Z >>> x = torch.tensor([0, 1, 2, 3, 4, 0]) 2024-08-20T21:39:08.4307001Z >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x) 2024-08-20T21:39:08.4307993Z >>> trace.print_readable() 2024-08-20T21:39:08.4308580Z >>> 2024-08-20T21:39:08.4309295Z >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x)) 2024-08-20T21:39:08.4310160Z 2024-08-20T21:39:08.4310521Z 2024-08-20T21:39:08.4312066Z Original Error: IndentationError('expected an indented block after function definition on line 37', ('', 38, 1, '_._ = None\n', 38, 2)) 2024-08-20T21:39:08.4313553Z 2024-08-20T21:39:08.4313920Z _._ = None 2024-08-20T21:39:08.4314319Z ^ 2024-08-20T21:39:08.4314694Z warnings.warn(msg) 2024-08-20T21:39:08.4315169Z 2024-08-20T21:39:08.4315705Z --- Parse Warning: 8 / 101 --- 2024-08-20T21:39:08.4318373Z /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. 2024-08-20T21:39:08.4321070Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.4322160Z Register a backward formula for this custom op. 2024-08-20T21:39:08.4322856Z 2024-08-20T21:39:08.4323668Z In order for an operator to work with autograd, you need to register 2024-08-20T21:39:08.4324574Z a backward formula: 2024-08-20T21:39:08.4325379Z 1. You must tell us how to compute gradients during the backward pass 2024-08-20T21:39:08.4326329Z by providing us a "backward" function. 2024-08-20T21:39:08.4327305Z 2. If you need any values from the forward to compute gradients, you can 2024-08-20T21:39:08.4328345Z use `setup_context` to save values for backward. 2024-08-20T21:39:08.4329023Z 2024-08-20T21:39:08.4329706Z ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``: 2024-08-20T21:39:08.4331004Z - ``grads`` is one or more gradients. The number of gradients matches 2024-08-20T21:39:08.4331922Z the number of outputs of the operator. 2024-08-20T21:39:08.4332897Z The ``ctx`` object is `the same ctx object `_ used by 2024-08-20T21:39:08.4334152Z :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the 2024-08-20T21:39:08.4335231Z same as :meth:`torch.autograd.Function.backward`. 2024-08-20T21:39:08.4335951Z 2024-08-20T21:39:08.4336595Z ``setup_context(ctx, inputs, output)`` runs during the forward pass. 2024-08-20T21:39:08.4337747Z Please save quantities needed for backward onto the ``ctx`` object via 2024-08-20T21:39:08.4338976Z either :meth:`torch.autograd.function.FunctionCtx.save_for_backward` 2024-08-20T21:39:08.4340150Z or assigning them as attributes of ``ctx``. If your custom op has 2024-08-20T21:39:08.4341397Z kwarg-only arguments, we expect the signature of ``setup_context`` 2024-08-20T21:39:08.4342595Z to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``. 2024-08-20T21:39:08.4343404Z 2024-08-20T21:39:08.4344078Z Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is, 2024-08-20T21:39:08.4345273Z they may not directly access :meth:`torch.Tensor.data_ptr` and they must 2024-08-20T21:39:08.4346674Z not depend on or mutate global state. If you need a non-traceable backward, 2024-08-20T21:39:08.4347942Z you can make it a separate custom_op that you call inside ``backward_fn``. 2024-08-20T21:39:08.4348833Z 2024-08-20T21:39:08.4349202Z Examples: 2024-08-20T21:39:08.4349642Z >>> import torch 2024-08-20T21:39:08.4350170Z >>> import numpy as np 2024-08-20T21:39:08.4350784Z >>> from torch import Tensor 2024-08-20T21:39:08.4351378Z >>> 2024-08-20T21:39:08.4352044Z >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=()) 2024-08-20T21:39:08.4353045Z >>> def numpy_sin(x: Tensor) -> Tensor: 2024-08-20T21:39:08.4353743Z >>> x_np = x.cpu().numpy() 2024-08-20T21:39:08.4354374Z >>> y_np = np.sin(x_np) 2024-08-20T21:39:08.4355130Z >>> return torch.from_numpy(y_np).to(device=x.device) 2024-08-20T21:39:08.4355855Z >>> 2024-08-20T21:39:08.4356517Z >>> def setup_context(ctx, inputs, output) -> Tensor: 2024-08-20T21:39:08.4357258Z >>> x, = inputs 2024-08-20T21:39:08.4357847Z >>> ctx.save_for_backward(x) 2024-08-20T21:39:08.4358561Z >>> 2024-08-20T21:39:08.4359025Z >>> def backward(ctx, grad): 2024-08-20T21:39:08.4359669Z >>> x, = ctx.saved_tensors 2024-08-20T21:39:08.4360305Z >>> return grad * x.cos() 2024-08-20T21:39:08.4360894Z >>> 2024-08-20T21:39:08.4361390Z >>> torch.library.register_autograd( 2024-08-20T21:39:08.4362268Z ... "mylib::numpy_sin", backward, setup_context=setup_context 2024-08-20T21:39:08.4363041Z ... ) 2024-08-20T21:39:08.4363457Z >>> 2024-08-20T21:39:08.4363970Z >>> x = torch.randn(3, requires_grad=True) 2024-08-20T21:39:08.4364649Z >>> y = numpy_sin(x) 2024-08-20T21:39:08.4365387Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2024-08-20T21:39:08.4366380Z >>> assert torch.allclose(grad_x, x.cos()) 2024-08-20T21:39:08.4367028Z >>> 2024-08-20T21:39:08.4367620Z >>> # Example with a keyword-only arg 2024-08-20T21:39:08.4368530Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2024-08-20T21:39:08.4369598Z >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor: 2024-08-20T21:39:08.4370373Z >>> x_np = x.cpu().numpy() 2024-08-20T21:39:08.4371004Z >>> y_np = x_np * val 2024-08-20T21:39:08.4371721Z >>> return torch.from_numpy(y_np).to(device=x.device) 2024-08-20T21:39:08.4372459Z >>> 2024-08-20T21:39:08.4373308Z >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor: 2024-08-20T21:39:08.4374282Z >>> ctx.val = keyword_only_inputs["val"] 2024-08-20T21:39:08.4374944Z >>> 2024-08-20T21:39:08.4375412Z >>> def backward(ctx, grad): 2024-08-20T21:39:08.4376033Z >>> return grad * ctx.val 2024-08-20T21:39:08.4376617Z >>> 2024-08-20T21:39:08.4377113Z >>> torch.library.register_autograd( 2024-08-20T21:39:08.4377967Z ... "mylib::numpy_mul", backward, setup_context=setup_context 2024-08-20T21:39:08.4378742Z ... ) 2024-08-20T21:39:08.4379155Z >>> 2024-08-20T21:39:08.4379646Z >>> x = torch.randn(3, requires_grad=True) 2024-08-20T21:39:08.4380353Z >>> y = numpy_mul(x, val=3.14) 2024-08-20T21:39:08.4381154Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2024-08-20T21:39:08.4382199Z >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14)) 2024-08-20T21:39:08.4382957Z 2024-08-20T21:39:08.4383319Z 2024-08-20T21:39:08.4384170Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.4385085Z 2024-08-20T21:39:08.4385478Z warnings.warn(msg) 2024-08-20T21:39:08.4385938Z 2024-08-20T21:39:08.4386469Z --- Parse Warning: 9 / 101 --- 2024-08-20T21:39:08.4388983Z /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. 2024-08-20T21:39:08.4391631Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.4392870Z Given an operator and some sample arguments, tests if the operator is 2024-08-20T21:39:08.4393789Z registered correctly. 2024-08-20T21:39:08.4394299Z 2024-08-20T21:39:08.4394971Z That is, when you use the torch.library/TORCH_LIBRARY APIs to create a 2024-08-20T21:39:08.4396486Z custom op, you specified metadata (e.g. mutability info) about the custom op 2024-08-20T21:39:08.4397761Z and these APIs require that the functions you pass them satisfy certain 2024-08-20T21:39:08.4399063Z properties (e.g. no data pointer access in the fake/meta/abstract kernel) 2024-08-20T21:39:08.4400104Z ``opcheck`` tests these metadata and properties. 2024-08-20T21:39:08.4400801Z 2024-08-20T21:39:08.4401217Z Concretely, we test the following: 2024-08-20T21:39:08.4402108Z - test_schema: if the operator's schema is correct. 2024-08-20T21:39:08.4403222Z - test_autograd_registration: if autograd was registered correctly. 2024-08-20T21:39:08.4404365Z - test_faketensor: If the operator has a FakeTensor kernel 2024-08-20T21:39:08.4405363Z (and if it is correct). The FakeTensor kernel is necessary ( 2024-08-20T21:39:08.4406476Z but not sufficient) for the operator to work with PyTorch compilation 2024-08-20T21:39:08.4407420Z APIs (torch.compile/export/FX). 2024-08-20T21:39:08.4408371Z - test_aot_dispatch_dynamic: If the operator has correct behavior 2024-08-20T21:39:08.4409405Z with PyTorch compilation APIs (torch.compile/export/FX). 2024-08-20T21:39:08.4410649Z This checks that the outputs (and gradients, if applicable) are the 2024-08-20T21:39:08.4411748Z same under eager-mode PyTorch and torch.compile. 2024-08-20T21:39:08.4412606Z This test is a superset of ``test_faketensor``. 2024-08-20T21:39:08.4413286Z 2024-08-20T21:39:08.4413889Z For best results, please call ``opcheck`` multiple times with a 2024-08-20T21:39:08.4414924Z representative set of inputs. If your operator supports 2024-08-20T21:39:08.4416028Z autograd, please use ``opcheck`` with inputs with ``requires_grad = True``; 2024-08-20T21:39:08.4417252Z if your operator supports multiple devices (e.g. CPU and CUDA), please 2024-08-20T21:39:08.4418303Z use ``opcheck`` with inputs on all supported devices. 2024-08-20T21:39:08.4419023Z 2024-08-20T21:39:08.4419364Z Args: 2024-08-20T21:39:08.4419996Z op: The operator. Must either be a function decorated with 2024-08-20T21:39:08.4421087Z :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket 2024-08-20T21:39:08.4422242Z found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo) 2024-08-20T21:39:08.4423185Z args: The args to the operator 2024-08-20T21:39:08.4423885Z kwargs: The kwargs to the operator 2024-08-20T21:39:08.4424741Z test_utils: Tests that we should run. Default: all of them. 2024-08-20T21:39:08.4425638Z Example: ("test_schema", "test_faketensor") 2024-08-20T21:39:08.4426573Z raise_exception: If we should raise an exception on the first 2024-08-20T21:39:08.4427721Z error. If False, we will return a dict with information 2024-08-20T21:39:08.4428550Z on if each test passed or not. 2024-08-20T21:39:08.4429170Z 2024-08-20T21:39:08.4429560Z .. warning:: 2024-08-20T21:39:08.4429977Z 2024-08-20T21:39:08.4430654Z opcheck and :func:`torch.autograd.gradcheck` test different things; 2024-08-20T21:39:08.4431827Z opcheck tests if your usage of torch.library APIs is correct while 2024-08-20T21:39:08.4432966Z :func:`torch.autograd.gradcheck` tests if your autograd formula is 2024-08-20T21:39:08.4434117Z mathematically correct. Use both to test custom ops that support 2024-08-20T21:39:08.4435005Z gradient computation. 2024-08-20T21:39:08.4435530Z 2024-08-20T21:39:08.4435896Z Example: 2024-08-20T21:39:08.4436303Z 2024-08-20T21:39:08.4436785Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-20T21:39:08.4437755Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2024-08-20T21:39:08.4438945Z >>> def numpy_add(x: Tensor, y: float) -> Tensor: 2024-08-20T21:39:08.4439702Z >>> x_np = x.numpy(force=True) 2024-08-20T21:39:08.4440350Z >>> z_np = x_np + y 2024-08-20T21:39:08.4441038Z >>> return torch.from_numpy(z_np).to(x.device) 2024-08-20T21:39:08.4441720Z >>> 2024-08-20T21:39:08.4442187Z >>> @numpy_sin.register_fake 2024-08-20T21:39:08.4442807Z >>> def _(x, y): 2024-08-20T21:39:08.4443362Z >>> return torch.empty_like(x) 2024-08-20T21:39:08.4443983Z >>> 2024-08-20T21:39:08.4444491Z >>> def setup_context(ctx, inputs, output): 2024-08-20T21:39:08.4445165Z >>> y, = inputs 2024-08-20T21:39:08.4445698Z >>> ctx.y = y 2024-08-20T21:39:08.4446196Z >>> 2024-08-20T21:39:08.4446636Z >>> def backward(ctx, grad): 2024-08-20T21:39:08.4447295Z >>> return grad * ctx.y, None 2024-08-20T21:39:08.4447912Z >>> 2024-08-20T21:39:08.4448602Z >>> numpy_sin.register_autograd(backward, setup_context=setup_context) 2024-08-20T21:39:08.4449458Z >>> 2024-08-20T21:39:08.4449889Z >>> sample_inputs = [ 2024-08-20T21:39:08.4450572Z >>> (torch.randn(3), 3.14), 2024-08-20T21:39:08.4451411Z >>> (torch.randn(2, 3, device='cuda'), 2.718), 2024-08-20T21:39:08.4452239Z >>> (torch.randn(1, 10, requires_grad=True), 1.234), 2024-08-20T21:39:08.4453350Z >>> (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18), 2024-08-20T21:39:08.4454175Z >>> ] 2024-08-20T21:39:08.4454587Z >>> 2024-08-20T21:39:08.4455051Z >>> for args in sample_inputs: 2024-08-20T21:39:08.4455753Z >>> torch.library.opcheck(foo, args) 2024-08-20T21:39:08.4456401Z 2024-08-20T21:39:08.4456752Z 2024-08-20T21:39:08.4457592Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.4458497Z 2024-08-20T21:39:08.4458874Z warnings.warn(msg) 2024-08-20T21:39:08.4459323Z 2024-08-20T21:39:08.4459854Z --- Parse Warning: 10 / 101 --- 2024-08-20T21:39:08.4462409Z /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. 2024-08-20T21:39:08.4465050Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.4466540Z load(f, map_location=None, pickle_module=pickle, *, weights_only=False, mmap=None, **pickle_load_args) 2024-08-20T21:39:08.4467637Z 2024-08-20T21:39:08.4468200Z Loads an object saved with :func:`torch.save` from a file. 2024-08-20T21:39:08.4468973Z 2024-08-20T21:39:08.4469911Z :func:`torch.load` uses Python's unpickling facilities but treats storages, 2024-08-20T21:39:08.4471134Z which underlie tensors, specially. They are first deserialized on the 2024-08-20T21:39:08.4472345Z CPU and are then moved to the device they were saved from. If this fails 2024-08-20T21:39:08.4473740Z (e.g. because the run time system doesn't have certain devices), an exception 2024-08-20T21:39:08.4475037Z is raised. However, storages can be dynamically remapped to an alternative 2024-08-20T21:39:08.4476129Z set of devices using the :attr:`map_location` argument. 2024-08-20T21:39:08.4476875Z 2024-08-20T21:39:08.4477627Z If :attr:`map_location` is a callable, it will be called once for each serialized 2024-08-20T21:39:08.4478981Z storage with two arguments: storage and location. The storage argument 2024-08-20T21:39:08.4480212Z will be the initial deserialization of the storage, residing on the CPU. 2024-08-20T21:39:08.4481431Z Each serialized storage has a location tag associated with it which 2024-08-20T21:39:08.4482580Z identifies the device it was saved from, and this tag is the second 2024-08-20T21:39:08.4483964Z argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'`` 2024-08-20T21:39:08.4485398Z for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors. 2024-08-20T21:39:08.4486577Z :attr:`map_location` should return either ``None`` or a storage. If 2024-08-20T21:39:08.4487798Z :attr:`map_location` returns a storage, it will be used as the final deserialized 2024-08-20T21:39:08.4489106Z object, already moved to the right device. Otherwise, :func:`torch.load` will 2024-08-20T21:39:08.4490554Z fall back to the default behavior, as if :attr:`map_location` wasn't specified. 2024-08-20T21:39:08.4491476Z 2024-08-20T21:39:08.4518085Z If :attr:`map_location` is a :class:`torch.device` object or a string containing 2024-08-20T21:39:08.4519534Z a device tag, it indicates the location where all tensors should be loaded. 2024-08-20T21:39:08.4520422Z 2024-08-20T21:39:08.4521166Z Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags 2024-08-20T21:39:08.4522395Z appearing in the file (keys), to ones that specify where to put the 2024-08-20T21:39:08.4523586Z storages (values). 2024-08-20T21:39:08.4524034Z 2024-08-20T21:39:08.4524663Z User extensions can register their own location tags and tagging and 2024-08-20T21:39:08.4525874Z deserialization methods using :func:`torch.serialization.register_package`. 2024-08-20T21:39:08.4526779Z 2024-08-20T21:39:08.4527100Z Args: 2024-08-20T21:39:08.4528294Z f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`), 2024-08-20T21:39:08.4529614Z or a string or os.PathLike object containing a file name 2024-08-20T21:39:08.4530907Z map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage 2024-08-20T21:39:08.4531966Z locations 2024-08-20T21:39:08.4532729Z pickle_module: module used for unpickling metadata and objects (has to 2024-08-20T21:39:08.4533781Z match the :attr:`pickle_module` used to serialize file) 2024-08-20T21:39:08.4534807Z weights_only: Indicates whether unpickler should be restricted to 2024-08-20T21:39:08.4535816Z loading only tensors, primitive types, dictionaries 2024-08-20T21:39:08.4536857Z and any types added via :func:`torch.serialization.add_safe_globals`. 2024-08-20T21:39:08.4538286Z mmap: Indicates whether the file should be mmaped rather than loading all the storages into memory. 2024-08-20T21:39:08.4539964Z Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they 2024-08-20T21:39:08.4541811Z are moved to the location that they were tagged with when saving, or specified by ``map_location``. This 2024-08-20T21:39:08.4543755Z second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the 2024-08-20T21:39:08.4545277Z tensor storages from disk to CPU memory in the first step, ``f`` is mmaped. 2024-08-20T21:39:08.4546575Z pickle_load_args: (Python 3 only) optional keyword arguments passed over to 2024-08-20T21:39:08.4547787Z :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g., 2024-08-20T21:39:08.4548660Z :attr:`errors=...`. 2024-08-20T21:39:08.4549179Z 2024-08-20T21:39:08.4549552Z .. warning:: 2024-08-20T21:39:08.4550256Z :func:`torch.load()` unless `weights_only` parameter is set to `True`, 2024-08-20T21:39:08.4551356Z uses ``pickle`` module implicitly, which is known to be insecure. 2024-08-20T21:39:08.4552609Z It is possible to construct malicious pickle data which will execute arbitrary code 2024-08-20T21:39:08.4553943Z during unpickling. Never load data that could have come from an untrusted 2024-08-20T21:39:08.4555337Z source in an unsafe mode, or that could have been tampered with. **Only load data you trust**. 2024-08-20T21:39:08.4556385Z 2024-08-20T21:39:08.4556750Z .. note:: 2024-08-20T21:39:08.4557586Z When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors 2024-08-20T21:39:08.4559252Z will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')`` 2024-08-20T21:39:08.4560703Z and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint. 2024-08-20T21:39:08.4561694Z 2024-08-20T21:39:08.4562059Z .. note:: 2024-08-20T21:39:08.4563018Z By default, we decode byte strings as ``utf-8``. This is to avoid a common error 2024-08-20T21:39:08.4564384Z case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...`` 2024-08-20T21:39:08.4565528Z when loading files saved by Python 2 in Python 3. If this default 2024-08-20T21:39:08.4566795Z is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how 2024-08-20T21:39:08.4568281Z these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them 2024-08-20T21:39:08.4569793Z to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them 2024-08-20T21:39:08.4571066Z as byte arrays which can be decoded later with ``byte_array.decode(...)``. 2024-08-20T21:39:08.4571973Z 2024-08-20T21:39:08.4572327Z Example: 2024-08-20T21:39:08.4572859Z >>> # xdoctest: +SKIP("undefined filepaths") 2024-08-20T21:39:08.4573659Z >>> torch.load("tensors.pt", weights_only=True) 2024-08-20T21:39:08.4574407Z # Load all tensors onto the CPU 2024-08-20T21:39:08.4575396Z >>> torch.load("tensors.pt", map_location=torch.device("cpu"), weights_only=True) 2024-08-20T21:39:08.4576478Z # Load all tensors onto the CPU, using a function 2024-08-20T21:39:08.4577193Z >>> torch.load( 2024-08-20T21:39:08.4578037Z ... "tensors.pt", map_location=lambda storage, loc: storage, weights_only=True 2024-08-20T21:39:08.4578963Z ... ) 2024-08-20T21:39:08.4579420Z # Load all tensors onto GPU 1 2024-08-20T21:39:08.4580043Z >>> torch.load( 2024-08-20T21:39:08.4580555Z ... "tensors.pt", 2024-08-20T21:39:08.4580888Z ... map_location=lambda storage, loc: storage.cuda(1), 2024-08-20T21:39:08.4581075Z ... weights_only=True, 2024-08-20T21:39:08.4581399Z ... ) # type: ignore[attr-defined] 2024-08-20T21:39:08.4581629Z # Map tensors from GPU 1 to GPU 0 2024-08-20T21:39:08.4582155Z >>> torch.load("tensors.pt", map_location={"cuda:1": "cuda:0"}, weights_only=True) 2024-08-20T21:39:08.4582492Z # Load tensor from io.BytesIO object 2024-08-20T21:39:08.4583025Z # Loading from a buffer setting weights_only=False, warning this can be unsafe 2024-08-20T21:39:08.4583270Z >>> with open("tensor.pt", "rb") as f: 2024-08-20T21:39:08.4583490Z ... buffer = io.BytesIO(f.read()) 2024-08-20T21:39:08.4583752Z >>> torch.load(buffer, weights_only=False) 2024-08-20T21:39:08.4584198Z # Load a module with 'ascii' encoding for unpickling 2024-08-20T21:39:08.4584718Z # Loading from a module setting weights_only=False, warning this can be unsafe 2024-08-20T21:39:08.4585118Z >>> torch.load("module.pt", encoding="ascii", weights_only=False) 2024-08-20T21:39:08.4585280Z 2024-08-20T21:39:08.4585905Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.4586040Z 2024-08-20T21:39:08.4586227Z warnings.warn(msg) 2024-08-20T21:39:08.4586365Z 2024-08-20T21:39:08.4586709Z --- Parse Warning: 11 / 101 --- 2024-08-20T21:39:08.4588759Z /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. 2024-08-20T21:39:08.4589384Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-08-20T21:39:08.4589625Z Retrieves the CUDA runtime API module. 2024-08-20T21:39:08.4589763Z 2024-08-20T21:39:08.4589898Z 2024-08-20T21:39:08.4590420Z This function initializes the CUDA runtime environment if it is not already 2024-08-20T21:39:08.4590895Z initialized and returns the CUDA runtime API module (_cudart). The CUDA 2024-08-20T21:39:08.4591354Z runtime API module provides access to various CUDA runtime functions. 2024-08-20T21:39:08.4591506Z 2024-08-20T21:39:08.4591653Z Args: 2024-08-20T21:39:08.4591799Z ``None`` 2024-08-20T21:39:08.4591955Z 2024-08-20T21:39:08.4592105Z Returns: 2024-08-20T21:39:08.4592391Z module: The CUDA runtime API module (_cudart). 2024-08-20T21:39:08.4592542Z 2024-08-20T21:39:08.4592690Z Raises: 2024-08-20T21:39:08.4593303Z RuntimeError: If CUDA cannot be re-initialized in a forked subprocess. 2024-08-20T21:39:08.4594149Z AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable. 2024-08-20T21:39:08.4594287Z 2024-08-20T21:39:08.4594563Z Example of CUDA operations with profiling: 2024-08-20T21:39:08.4594733Z >>> import torch 2024-08-20T21:39:08.4595011Z >>> from torch.cuda import cudart, check_error 2024-08-20T21:39:08.4595192Z >>> import os 2024-08-20T21:39:08.4595338Z >>> 2024-08-20T21:39:08.4595650Z >>> os.environ['CUDA_PROFILE'] = '1' 2024-08-20T21:39:08.4596044Z >>> 2024-08-20T21:39:08.4596340Z >>> def perform_cuda_operations_with_streams(): 2024-08-20T21:39:08.4596569Z >>> stream = torch.cuda.Stream() 2024-08-20T21:39:08.4596827Z >>> with torch.cuda.stream(stream): 2024-08-20T21:39:08.4597208Z >>> x = torch.randn(100, 100, device='cuda') 2024-08-20T21:39:08.4597585Z >>> y = torch.randn(100, 100, device='cuda') 2024-08-20T21:39:08.4597804Z >>> z = torch.mul(x, y) 2024-08-20T21:39:08.4597965Z >>> return z 2024-08-20T21:39:08.4598126Z >>> 2024-08-20T21:39:08.4598452Z >>> torch.cuda.synchronize() 2024-08-20T21:39:08.4598728Z >>> print("====== Start nsys profiling ======") 2024-08-20T21:39:08.4599012Z >>> check_error(cudart().cudaProfilerStart()) 2024-08-20T21:39:08.4599298Z >>> with torch.autograd.profiler.emit_nvtx(): 2024-08-20T21:39:08.4599609Z >>> result = perform_cuda_operations_with_streams() 2024-08-20T21:39:08.4600072Z >>> print("CUDA operations completed.") 2024-08-20T21:39:08.4600405Z >>> check_error(torch.cuda.cudart().cudaProfilerStop()) 2024-08-20T21:39:08.4600663Z >>> print("====== End nsys profiling ======") 2024-08-20T21:39:08.4600823Z 2024-08-20T21:39:08.4601253Z To run this example and save the profiling information, execute: 2024-08-20T21:39:08.4602148Z >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2024-08-20T21:39:08.4602301Z 2024-08-20T21:39:08.4602814Z This command profiles the CUDA operations in the provided script and saves 2024-08-20T21:39:08.4603219Z the profiling information to a file named `trace_name.prof`. 2024-08-20T21:39:08.4603825Z The `--profile-from-start off` option ensures that profiling starts only 2024-08-20T21:39:08.4604129Z after the `cudaProfilerStart` call in the script. 2024-08-20T21:39:08.4604744Z The `--csv` and `--print-summary` options format the profiling output as a 2024-08-20T21:39:08.4605007Z CSV file and print a summary, respectively. 2024-08-20T21:39:08.4605647Z The `-o` option specifies the output file name, and the `-f` option forces the 2024-08-20T21:39:08.4605969Z overwrite of the output file if it already exists. 2024-08-20T21:39:08.4606118Z 2024-08-20T21:39:08.4607694Z 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)) 2024-08-20T21:39:08.4607849Z 2024-08-20T21:39:08.4608718Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2024-08-20T21:39:08.4608878Z ^ 2024-08-20T21:39:08.4609049Z warnings.warn(msg) 2024-08-20T21:39:08.4609187Z 2024-08-20T21:39:08.4609526Z --- Parse Warning: 12 / 101 --- 2024-08-20T21:39:08.4611668Z /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. 2024-08-20T21:39:08.4612326Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.4612587Z 2024-08-20T21:39:08.4613061Z Append the given callback function to this ``Future``, which will be run 2024-08-20T21:39:08.4613498Z when the ``Future`` is completed. Multiple callbacks can be added to 2024-08-20T21:39:08.4613964Z the same ``Future``, but the order in which they will be executed cannot 2024-08-20T21:39:08.4614339Z be guaranteed (to enforce a certain order consider chaining: 2024-08-20T21:39:08.4614798Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which 2024-08-20T21:39:08.4615246Z is the reference to this ``Future``. The callback function can use the 2024-08-20T21:39:08.4615686Z :meth:`value` method to get the value. Note that if this ``Future`` is 2024-08-20T21:39:08.4616159Z already completed, the given callback will be run immediately inline. 2024-08-20T21:39:08.4616298Z 2024-08-20T21:39:08.4616826Z If the ``Future``'s value contains tensors that reside on GPUs, the 2024-08-20T21:39:08.4617312Z callback might be invoked while the async kernels that are populating 2024-08-20T21:39:08.4617902Z those tensors haven't yet finished executing on the device. However, the 2024-08-20T21:39:08.4618334Z callback will be invoked with some dedicated streams set as current 2024-08-20T21:39:08.4618775Z (fetched from a global pool) which will be synchronized with those 2024-08-20T21:39:08.4619243Z kernels. Hence any operation performed by the callback on these tensors 2024-08-20T21:39:08.4619705Z will be scheduled on the device after the kernels complete. In other 2024-08-20T21:39:08.4620252Z words, as long as the callback doesn't switch streams, it can safely 2024-08-20T21:39:08.4620785Z manipulate the result without any additional synchronization. This is 2024-08-20T21:39:08.4621211Z similar to the non-blocking behavior of :meth:`wait`. 2024-08-20T21:39:08.4621348Z 2024-08-20T21:39:08.4621796Z Similarly, if the callback returns a value that contains tensors that 2024-08-20T21:39:08.4622256Z reside on a GPU, it can do so even if the kernels that are producing 2024-08-20T21:39:08.4622712Z these tensors are still running on the device, as long as the callback 2024-08-20T21:39:08.4623239Z didn't change streams during its execution. If one wants to change 2024-08-20T21:39:08.4623815Z streams, one must be careful to re-synchronize them with the original 2024-08-20T21:39:08.4624285Z streams, that is, those that were current when the callback was invoked. 2024-08-20T21:39:08.4624438Z 2024-08-20T21:39:08.4624583Z Args: 2024-08-20T21:39:08.4625001Z callback(``Callable``): a ``Callable`` that takes this ``Future`` as 2024-08-20T21:39:08.4625237Z the only argument. 2024-08-20T21:39:08.4625375Z 2024-08-20T21:39:08.4625523Z Returns: 2024-08-20T21:39:08.4625892Z A new ``Future`` object that holds the return value of the 2024-08-20T21:39:08.4626251Z ``callback`` and will be marked as completed when the given 2024-08-20T21:39:08.4626433Z ``callback`` finishes. 2024-08-20T21:39:08.4626585Z 2024-08-20T21:39:08.4626953Z .. note:: Note that if the callback function throws, either 2024-08-20T21:39:08.4627384Z through the original future being completed with an exception and 2024-08-20T21:39:08.4627803Z calling ``fut.wait()``, or through other code in the callback, the 2024-08-20T21:39:08.4628220Z future returned by ``then`` will be marked appropriately with the 2024-08-20T21:39:08.4628624Z encountered error. However, if this callback later completes 2024-08-20T21:39:08.4629065Z additional futures, those futures are not marked as completed with 2024-08-20T21:39:08.4629520Z an error and the user is responsible for handling completion/waiting 2024-08-20T21:39:08.4629743Z on those futures independently. 2024-08-20T21:39:08.4629882Z 2024-08-20T21:39:08.4630036Z Example:: 2024-08-20T21:39:08.4630354Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2024-08-20T21:39:08.4630612Z >>> def callback(fut): 2024-08-20T21:39:08.4630901Z ... print(f"RPC return value is {fut.wait()}.") 2024-08-20T21:39:08.4631131Z >>> fut = torch.futures.Future() 2024-08-20T21:39:08.4631479Z >>> # The inserted callback will print the return value when 2024-08-20T21:39:08.4631722Z >>> # receiving the response from "worker1" 2024-08-20T21:39:08.4631935Z >>> cb_fut = fut.then(callback) 2024-08-20T21:39:08.4632130Z >>> chain_cb_fut = cb_fut.then( 2024-08-20T21:39:08.4632455Z ... lambda x : print(f"Chained cb done. {x.wait()}") 2024-08-20T21:39:08.4632599Z ... ) 2024-08-20T21:39:08.4632772Z >>> fut.set_result(5) 2024-08-20T21:39:08.4632970Z RPC return value is 5. 2024-08-20T21:39:08.4633148Z Chained cb done. None 2024-08-20T21:39:08.4633285Z 2024-08-20T21:39:08.4633935Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.4634081Z 2024-08-20T21:39:08.4634253Z warnings.warn(msg) 2024-08-20T21:39:08.4634405Z 2024-08-20T21:39:08.4634727Z --- Parse Warning: 13 / 101 --- 2024-08-20T21:39:08.4636908Z /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. 2024-08-20T21:39:08.4637568Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.4637710Z 2024-08-20T21:39:08.4638339Z Set the result for this ``Future``, which will mark this ``Future`` as 2024-08-20T21:39:08.4638811Z completed and trigger all attached callbacks. Note that a ``Future`` 2024-08-20T21:39:08.4639024Z cannot be marked completed twice. 2024-08-20T21:39:08.4639179Z 2024-08-20T21:39:08.4639636Z If the result contains tensors that reside on GPUs, this method can be 2024-08-20T21:39:08.4640066Z called even if the asynchronous kernels that are populating those 2024-08-20T21:39:08.4640662Z tensors haven't yet completed running on the device, provided that the 2024-08-20T21:39:08.4641143Z streams on which those kernels were enqueued are set as the current ones 2024-08-20T21:39:08.4641706Z when this method is called. Put simply, it's safe to call this method 2024-08-20T21:39:08.4642150Z immediately after launching those kernels, without any additional 2024-08-20T21:39:08.4642741Z synchronization, as long as one doesn't change streams in between. This 2024-08-20T21:39:08.4643206Z method will record events on all the relevant current streams and will 2024-08-20T21:39:08.4643649Z use them to ensure proper scheduling for all the consumers of this 2024-08-20T21:39:08.4643802Z ``Future``. 2024-08-20T21:39:08.4643950Z 2024-08-20T21:39:08.4644098Z Args: 2024-08-20T21:39:08.4644421Z result (object): the result object of this ``Future``. 2024-08-20T21:39:08.4644580Z 2024-08-20T21:39:08.4644737Z Example:: 2024-08-20T21:39:08.4645032Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2024-08-20T21:39:08.4645222Z >>> import threading 2024-08-20T21:39:08.4645384Z >>> import time 2024-08-20T21:39:08.4645604Z >>> def slow_set_future(fut, value): 2024-08-20T21:39:08.4645791Z ... time.sleep(0.5) 2024-08-20T21:39:08.4645978Z ... fut.set_result(value) 2024-08-20T21:39:08.4646192Z >>> fut = torch.futures.Future() 2024-08-20T21:39:08.4646397Z >>> t = threading.Thread( 2024-08-20T21:39:08.4646583Z ... target=slow_set_future, 2024-08-20T21:39:08.4646802Z ... args=(fut, torch.ones(2) * 3) 2024-08-20T21:39:08.4646958Z ... ) 2024-08-20T21:39:08.4647113Z >>> t.start() 2024-08-20T21:39:08.4647284Z >>> print(fut.wait()) 2024-08-20T21:39:08.4647466Z tensor([3., 3.]) 2024-08-20T21:39:08.4647616Z >>> t.join() 2024-08-20T21:39:08.4647831Z 2024-08-20T21:39:08.4648483Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.4648621Z 2024-08-20T21:39:08.4648794Z warnings.warn(msg) 2024-08-20T21:39:08.4648947Z 2024-08-20T21:39:08.4649270Z --- Parse Warning: 14 / 101 --- 2024-08-20T21:39:08.4651321Z /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. 2024-08-20T21:39:08.4651976Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.4652308Z Return the sum of each row of the given sparse tensor. 2024-08-20T21:39:08.4652460Z 2024-08-20T21:39:08.4652960Z Returns the sum of each row of the sparse tensor :attr:`input` in the given 2024-08-20T21:39:08.4653356Z dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions, 2024-08-20T21:39:08.4653837Z reduce over all of them. When sum over all ``sparse_dim``, this method 2024-08-20T21:39:08.4654141Z returns a dense tensor instead of a sparse tensor. 2024-08-20T21:39:08.4654279Z 2024-08-20T21:39:08.4654848Z All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output 2024-08-20T21:39:08.4655241Z tensor having :attr:`dim` fewer dimensions than :attr:`input`. 2024-08-20T21:39:08.4655394Z 2024-08-20T21:39:08.4655851Z During backward, only gradients at ``nnz`` locations of :attr:`input` 2024-08-20T21:39:08.4656430Z will propagate back. Note that the gradients of :attr:`input` is coalesced. 2024-08-20T21:39:08.4656586Z 2024-08-20T21:39:08.4656732Z Args: 2024-08-20T21:39:08.4656979Z input (Tensor): the input sparse tensor 2024-08-20T21:39:08.4657605Z dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce 2024-08-20T21:39:08.4657781Z over all dims. 2024-08-20T21:39:08.4658313Z dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. 2024-08-20T21:39:08.4658557Z Default: dtype of :attr:`input`. 2024-08-20T21:39:08.4658695Z 2024-08-20T21:39:08.4658853Z Example:: 2024-08-20T21:39:08.4659007Z 2024-08-20T21:39:08.4659155Z >>> nnz = 3 2024-08-20T21:39:08.4659331Z >>> dims = [5, 5, 2, 3] 2024-08-20T21:39:08.4659675Z >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), 2024-08-20T21:39:08.4660066Z torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) 2024-08-20T21:39:08.4660331Z >>> V = torch.randn(nnz, dims[2], dims[3]) 2024-08-20T21:39:08.4660528Z >>> size = torch.Size(dims) 2024-08-20T21:39:08.4660920Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-08-20T21:39:08.4661188Z >>> S = torch.sparse_coo_tensor(I, V, size) 2024-08-20T21:39:08.4661340Z >>> S 2024-08-20T21:39:08.4661553Z tensor(indices=tensor([[2, 0, 3], 2024-08-20T21:39:08.4661767Z [2, 4, 1]]), 2024-08-20T21:39:08.4662134Z values=tensor([[[-0.6438, -1.6467, 1.4004], 2024-08-20T21:39:08.4662481Z [ 0.3411, 0.0918, -0.2312]], 2024-08-20T21:39:08.4662633Z 2024-08-20T21:39:08.4662975Z [[ 0.5348, 0.0634, -2.0494], 2024-08-20T21:39:08.4663316Z [-0.7125, -1.0646, 2.1844]], 2024-08-20T21:39:08.4663469Z 2024-08-20T21:39:08.4663812Z [[ 0.1276, 0.1874, -0.6334], 2024-08-20T21:39:08.4664169Z [-1.9682, -0.5340, 0.7483]]]), 2024-08-20T21:39:08.4664471Z size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo) 2024-08-20T21:39:08.4664610Z 2024-08-20T21:39:08.4665040Z # when sum over only part of sparse_dims, return a sparse tensor 2024-08-20T21:39:08.4665355Z >>> torch.sparse.sum(S, [1, 3]) 2024-08-20T21:39:08.4665577Z tensor(indices=tensor([[0, 2, 3]]), 2024-08-20T21:39:08.4665912Z values=tensor([[-1.4512, 0.4073], 2024-08-20T21:39:08.4666216Z [-0.8901, 0.2017], 2024-08-20T21:39:08.4666523Z [-0.3183, -1.7539]]), 2024-08-20T21:39:08.4666813Z size=(5, 2), nnz=3, layout=torch.sparse_coo) 2024-08-20T21:39:08.4666950Z 2024-08-20T21:39:08.4667272Z # when sum over all sparse dim, return a dense tensor 2024-08-20T21:39:08.4667497Z # with summed dims squeezed 2024-08-20T21:39:08.4667719Z >>> torch.sparse.sum(S, [0, 1, 3]) 2024-08-20T21:39:08.4667994Z tensor([-2.6596, -1.1450]) 2024-08-20T21:39:08.4668137Z 2024-08-20T21:39:08.4668766Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.4668920Z 2024-08-20T21:39:08.4669096Z warnings.warn(msg) 2024-08-20T21:39:08.4669236Z 2024-08-20T21:39:08.4669571Z --- Parse Warning: 15 / 101 --- 2024-08-20T21:39:08.4671598Z /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. 2024-08-20T21:39:08.4672259Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.4672414Z 2024-08-20T21:39:08.4672877Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that 2024-08-20T21:39:08.4673376Z maps ``func`` over some dimension of the inputs. Semantically, vmap 2024-08-20T21:39:08.4673850Z pushes the map into PyTorch operations called by ``func``, effectively 2024-08-20T21:39:08.4674049Z vectorizing those operations. 2024-08-20T21:39:08.4674202Z 2024-08-20T21:39:08.4674670Z vmap is useful for handling batch dimensions: one can write a function 2024-08-20T21:39:08.4675103Z ``func`` that runs on examples and then lift it to a function that can 2024-08-20T21:39:08.4675563Z take batches of examples with ``vmap(func)``. vmap can also be used to 2024-08-20T21:39:08.4675896Z compute batched gradients when composed with autograd. 2024-08-20T21:39:08.4676037Z 2024-08-20T21:39:08.4676209Z .. note:: 2024-08-20T21:39:08.4676589Z :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for 2024-08-20T21:39:08.4676933Z convenience. Use whichever one you'd like. 2024-08-20T21:39:08.4677090Z 2024-08-20T21:39:08.4677233Z Args: 2024-08-20T21:39:08.4677675Z func (function): A Python function that takes one or more arguments. 2024-08-20T21:39:08.4677914Z Must return one or more Tensors. 2024-08-20T21:39:08.4678454Z in_dims (int or nested structure): Specifies which dimension of the 2024-08-20T21:39:08.4678808Z inputs should be mapped over. ``in_dims`` should have a 2024-08-20T21:39:08.4679221Z structure like the inputs. If the ``in_dim`` for a particular 2024-08-20T21:39:08.4679621Z input is None, then that indicates there is no map dimension. 2024-08-20T21:39:08.4679796Z Default: 0. 2024-08-20T21:39:08.4680214Z out_dims (int or Tuple[int]): Specifies where the mapped dimension 2024-08-20T21:39:08.4680624Z should appear in the outputs. If ``out_dims`` is a Tuple, then 2024-08-20T21:39:08.4680953Z it should have one element per output. Default: 0. 2024-08-20T21:39:08.4681322Z randomness (str): Specifies whether the randomness in this 2024-08-20T21:39:08.4681897Z vmap should be the same or different across batches. If 'different', 2024-08-20T21:39:08.4682443Z the randomness for each batch will be different. If 'same', the 2024-08-20T21:39:08.4683018Z randomness will be the same across batches. If 'error', any calls to 2024-08-20T21:39:08.4683666Z random functions will error. Default: 'error'. WARNING: this flag 2024-08-20T21:39:08.4684090Z only applies to random PyTorch operations and does not apply to 2024-08-20T21:39:08.4684454Z Python's random module or numpy randomness. 2024-08-20T21:39:08.4684969Z chunk_size (None or int): If None (default), apply a single vmap over inputs. 2024-08-20T21:39:08.4685443Z If not None, then compute the vmap :attr:`chunk_size` samples at a time. 2024-08-20T21:39:08.4686138Z Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop. 2024-08-20T21:39:08.4686876Z If you run into memory issues computing the vmap, please try a non-None chunk_size. 2024-08-20T21:39:08.4687017Z 2024-08-20T21:39:08.4687164Z Returns: 2024-08-20T21:39:08.4687573Z Returns a new "batched" function. It takes the same inputs as 2024-08-20T21:39:08.4687952Z ``func``, except each input has an extra dimension at the index 2024-08-20T21:39:08.4688365Z specified by ``in_dims``. It takes returns the same outputs as 2024-08-20T21:39:08.4688752Z ``func``, except each output has an extra dimension at the index 2024-08-20T21:39:08.4688947Z specified by ``out_dims``. 2024-08-20T21:39:08.4689101Z 2024-08-20T21:39:08.4689252Z .. warning: 2024-08-20T21:39:08.4689780Z :func:`vmap` works best with functional-style code. Please do not 2024-08-20T21:39:08.4690279Z perform any side-effects in ``func``, with the exception of 2024-08-20T21:39:08.4690881Z in-place PyTorch operations. Examples of side-effects include mutating 2024-08-20T21:39:08.4691423Z Python data structures and assigning values to variables not captured 2024-08-20T21:39:08.4691597Z in ``func``. 2024-08-20T21:39:08.4691735Z 2024-08-20T21:39:08.4692246Z One example of using :func:`vmap` is to compute batched dot products. PyTorch 2024-08-20T21:39:08.4692837Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully 2024-08-20T21:39:08.4693286Z rummaging through docs, use :func:`vmap` to construct a new function. 2024-08-20T21:39:08.4693443Z 2024-08-20T21:39:08.4693845Z >>> torch.dot # [D], [D] -> [] 2024-08-20T21:39:08.4694380Z >>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N] 2024-08-20T21:39:08.4694653Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2024-08-20T21:39:08.4694827Z >>> batched_dot(x, y) 2024-08-20T21:39:08.4694968Z 2024-08-20T21:39:08.4695479Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler 2024-08-20T21:39:08.4695672Z model authoring experience. 2024-08-20T21:39:08.4696029Z 2024-08-20T21:39:08.4696266Z >>> batch_size, feature_size = 3, 5 2024-08-20T21:39:08.4696610Z >>> weights = torch.randn(feature_size, requires_grad=True) 2024-08-20T21:39:08.4696754Z >>> 2024-08-20T21:39:08.4696963Z >>> def model(feature_vec): 2024-08-20T21:39:08.4697237Z >>> # Very simple linear model with activation 2024-08-20T21:39:08.4697488Z >>> return feature_vec.dot(weights).relu() 2024-08-20T21:39:08.4697650Z >>> 2024-08-20T21:39:08.4697951Z >>> examples = torch.randn(batch_size, feature_size) 2024-08-20T21:39:08.4698190Z >>> result = torch.vmap(model)(examples) 2024-08-20T21:39:08.4698346Z 2024-08-20T21:39:08.4698888Z :func:`vmap` can also help vectorize computations that were previously difficult 2024-08-20T21:39:08.4699516Z or impossible to batch. One example is higher-order gradient computation. 2024-08-20T21:39:08.4700098Z The PyTorch autograd engine computes vjps (vector-Jacobian products). 2024-08-20T21:39:08.4700692Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually 2024-08-20T21:39:08.4701229Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`, 2024-08-20T21:39:08.4701942Z we can vectorize the whole computation, computing the Jacobian in a single 2024-08-20T21:39:08.4702128Z call to ``autograd.grad``. 2024-08-20T21:39:08.4702284Z 2024-08-20T21:39:08.4702437Z >>> # Setup 2024-08-20T21:39:08.4702588Z >>> N = 5 2024-08-20T21:39:08.4702786Z >>> f = lambda x: x ** 2 2024-08-20T21:39:08.4703025Z >>> x = torch.randn(N, requires_grad=True) 2024-08-20T21:39:08.4703178Z >>> y = f(x) 2024-08-20T21:39:08.4703369Z >>> I_N = torch.eye(N) 2024-08-20T21:39:08.4703510Z >>> 2024-08-20T21:39:08.4703698Z >>> # Sequential approach 2024-08-20T21:39:08.4704154Z >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0] 2024-08-20T21:39:08.4704378Z >>> for v in I_N.unbind()] 2024-08-20T21:39:08.4704635Z >>> jacobian = torch.stack(jacobian_rows) 2024-08-20T21:39:08.4704779Z >>> 2024-08-20T21:39:08.4705017Z >>> # vectorized gradient computation 2024-08-20T21:39:08.4705205Z >>> def get_vjp(v): 2024-08-20T21:39:08.4705449Z >>> return torch.autograd.grad(y, x, v) 2024-08-20T21:39:08.4705684Z >>> jacobian = torch.vmap(get_vjp)(I_N) 2024-08-20T21:39:08.4705836Z 2024-08-20T21:39:08.4706407Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions 2024-08-20T21:39:08.4706546Z 2024-08-20T21:39:08.4706967Z >>> torch.dot # [D], [D] -> [] 2024-08-20T21:39:08.4707704Z >>> batched_dot = torch.vmap(torch.vmap(torch.dot)) # [N1, N0, D], [N1, N0, D] -> [N1, N0] 2024-08-20T21:39:08.4708112Z >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5) 2024-08-20T21:39:08.4708386Z >>> batched_dot(x, y) # tensor of size [2, 3] 2024-08-20T21:39:08.4708526Z 2024-08-20T21:39:08.4709064Z If the inputs are not batched along the first dimension, ``in_dims`` specifies 2024-08-20T21:39:08.4709372Z the dimension that each inputs are batched along as 2024-08-20T21:39:08.4709513Z 2024-08-20T21:39:08.4709931Z >>> torch.dot # [N], [N] -> [] 2024-08-20T21:39:08.4710522Z >>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D] 2024-08-20T21:39:08.4710778Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2024-08-20T21:39:08.4711333Z >>> batched_dot(x, y) # output is [5] instead of [2] if batched along the 0th dimension 2024-08-20T21:39:08.4711469Z 2024-08-20T21:39:08.4712020Z If there are multiple inputs each of which is batched along different dimensions, 2024-08-20T21:39:08.4712474Z ``in_dims`` must be a tuple with the batch dimension for each input as 2024-08-20T21:39:08.4712613Z 2024-08-20T21:39:08.4713013Z >>> torch.dot # [D], [D] -> [] 2024-08-20T21:39:08.4713658Z >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N] 2024-08-20T21:39:08.4713902Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2024-08-20T21:39:08.4714603Z >>> batched_dot(x, y) # second arg doesn't have a batch dim because in_dim[1] was None 2024-08-20T21:39:08.4714741Z 2024-08-20T21:39:08.4715263Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct 2024-08-20T21:39:08.4715476Z matching the shape of the input: 2024-08-20T21:39:08.4715611Z 2024-08-20T21:39:08.4715993Z >>> f = lambda dict: torch.dot(dict['x'], dict['y']) 2024-08-20T21:39:08.4716252Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2024-08-20T21:39:08.4716505Z >>> input = {'x': x, 'y': y} 2024-08-20T21:39:08.4716978Z >>> batched_dot = torch.vmap(f, in_dims=({'x': 0, 'y': None},)) 2024-08-20T21:39:08.4717173Z >>> batched_dot(input) 2024-08-20T21:39:08.4717306Z 2024-08-20T21:39:08.4717901Z By default, the output is batched along the first dimension. However, it can be batched 2024-08-20T21:39:08.4718154Z along any dimension by using ``out_dims`` 2024-08-20T21:39:08.4718493Z 2024-08-20T21:39:08.4718675Z >>> f = lambda x: x ** 2 2024-08-20T21:39:08.4718878Z >>> x = torch.randn(2, 5) 2024-08-20T21:39:08.4719125Z >>> batched_pow = torch.vmap(f, out_dims=1) 2024-08-20T21:39:08.4719325Z >>> batched_pow(x) # [5, 2] 2024-08-20T21:39:08.4719463Z 2024-08-20T21:39:08.4720093Z For any function that uses kwargs, the returned function will not batch the kwargs but will 2024-08-20T21:39:08.4720266Z accept kwargs 2024-08-20T21:39:08.4720404Z 2024-08-20T21:39:08.4720593Z >>> x = torch.randn([2, 5]) 2024-08-20T21:39:08.4720786Z >>> def fn(x, scale=4.): 2024-08-20T21:39:08.4720966Z >>> return x * scale 2024-08-20T21:39:08.4721109Z >>> 2024-08-20T21:39:08.4721329Z >>> batched_pow = torch.vmap(fn) 2024-08-20T21:39:08.4721604Z >>> assert torch.allclose(batched_pow(x), x * 4) 2024-08-20T21:39:08.4722087Z >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5] 2024-08-20T21:39:08.4722248Z 2024-08-20T21:39:08.4722418Z .. note:: 2024-08-20T21:39:08.4723008Z vmap does not provide general autobatching or handle variable-length 2024-08-20T21:39:08.4723212Z sequences out of the box. 2024-08-20T21:39:08.4723350Z 2024-08-20T21:39:08.4723967Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.4724118Z 2024-08-20T21:39:08.4724287Z warnings.warn(msg) 2024-08-20T21:39:08.4724438Z 2024-08-20T21:39:08.4724759Z --- Parse Warning: 16 / 101 --- 2024-08-20T21:39:08.4726918Z /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. 2024-08-20T21:39:08.4727583Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.4728103Z Create a custom operator whose implementation is backed by 1+ triton kernels. 2024-08-20T21:39:08.4728247Z 2024-08-20T21:39:08.4728770Z Use this instead of :func:`torch.library.custom_op` when the implementation 2024-08-20T21:39:08.4729232Z consists of 1+ triton kernels. :func:`torch.library.custom_op` treats 2024-08-20T21:39:08.4729558Z custom operators as opaque (:func:`torch.compile` and 2024-08-20T21:39:08.4730063Z :func:`torch.export.export` will never trace into them), but ``triton_op`` 2024-08-20T21:39:08.4730515Z makes the implementation visible to these subsystems, allowing them 2024-08-20T21:39:08.4730743Z to optimize the triton kernel(s). 2024-08-20T21:39:08.4730885Z 2024-08-20T21:39:08.4731411Z Note that ``fn`` must only consist of calls to PyTorch-understood 2024-08-20T21:39:08.4731887Z operators and triton kernels. Any triton kernels called inside ``fn`` 2024-08-20T21:39:08.4732331Z must be wrapped in a call to :func:`torch._library.capture_triton``. 2024-08-20T21:39:08.4732476Z 2024-08-20T21:39:08.4732642Z Args: 2024-08-20T21:39:08.4733132Z name (str): A name for the custom op that looks like "{namespace}::{name}", 2024-08-20T21:39:08.4733722Z e.g. "mylib::my_linear". The name is used as the op's stable identifier 2024-08-20T21:39:08.4734078Z in PyTorch subsystems (e.g. torch.export, FX graphs). 2024-08-20T21:39:08.4734576Z To avoid name collisions, please use your project name as the namespace; 2024-08-20T21:39:08.4735043Z e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace. 2024-08-20T21:39:08.4735623Z mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates. 2024-08-20T21:39:08.4736130Z This MUST be accurate, otherwise, the behavior is undefined. If "unknown", 2024-08-20T21:39:08.4736687Z it pessimistically assumes that all inputs to the operator are being mutated. 2024-08-20T21:39:08.4737184Z schema (None | str): A schema string for the operator. If None 2024-08-20T21:39:08.4737731Z (recommended) we'll infer a schema for the operator from its type 2024-08-20T21:39:08.4738166Z annotations. We recommend letting us infer a schema unless you 2024-08-20T21:39:08.4738390Z have a specific reason not to. 2024-08-20T21:39:08.4738813Z Example: "(Tensor x, int y) -> (Tensor, Tensor)". 2024-08-20T21:39:08.4738955Z 2024-08-20T21:39:08.4739113Z Example:: 2024-08-20T21:39:08.4739268Z 2024-08-20T21:39:08.4739556Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-20T21:39:08.4739731Z >>> import torch 2024-08-20T21:39:08.4740083Z >>> from torch._library import triton_op, capture_triton 2024-08-20T21:39:08.4740230Z >>> 2024-08-20T21:39:08.4740402Z >>> import triton 2024-08-20T21:39:08.4740651Z >>> from triton import language as tl 2024-08-20T21:39:08.4740802Z >>> 2024-08-20T21:39:08.4740964Z >>> @triton.jit 2024-08-20T21:39:08.4741149Z >>> def add_kernel( 2024-08-20T21:39:08.4741311Z >>> in_ptr0, 2024-08-20T21:39:08.4741470Z >>> in_ptr1, 2024-08-20T21:39:08.4741641Z >>> out_ptr, 2024-08-20T21:39:08.4741807Z >>> n_elements, 2024-08-20T21:39:08.4742031Z >>> BLOCK_SIZE: "tl.constexpr", 2024-08-20T21:39:08.4742191Z >>> ): 2024-08-20T21:39:08.4742414Z >>> pid = tl.program_id(axis=0) 2024-08-20T21:39:08.4742649Z >>> block_start = pid * BLOCK_SIZE 2024-08-20T21:39:08.4743061Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2024-08-20T21:39:08.4743281Z >>> mask = offsets < n_elements 2024-08-20T21:39:08.4743564Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2024-08-20T21:39:08.4743826Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2024-08-20T21:39:08.4744009Z >>> output = x + y 2024-08-20T21:39:08.4744331Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2024-08-20T21:39:08.4744479Z >>> 2024-08-20T21:39:08.4744742Z >>> @triton_op("mylib::add", mutates_args={}) 2024-08-20T21:39:08.4745260Z >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: 2024-08-20T21:39:08.4745487Z >>> output = torch.empty_like(x) 2024-08-20T21:39:08.4745711Z >>> n_elements = output.numel() 2024-08-20T21:39:08.4745879Z >>> 2024-08-20T21:39:08.4746057Z >>> def grid(meta): 2024-08-20T21:39:08.4746414Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2024-08-20T21:39:08.4746572Z >>> 2024-08-20T21:39:08.4747004Z >>> # NB: we need to wrap the triton kernel in a call to capture_triton 2024-08-20T21:39:08.4747427Z >>> capture_triton(add_kernel)[grid](x, y, output, n_elements, 16) 2024-08-20T21:39:08.4747606Z >>> return output 2024-08-20T21:39:08.4747752Z >>> 2024-08-20T21:39:08.4747939Z >>> @torch.compile 2024-08-20T21:39:08.4748104Z >>> def f(x, y): 2024-08-20T21:39:08.4748284Z >>> return add(x, y) 2024-08-20T21:39:08.4748449Z >>> 2024-08-20T21:39:08.4748679Z >>> x = torch.randn(3, device="cuda") 2024-08-20T21:39:08.4748908Z >>> y = torch.randn(3, device="cuda") 2024-08-20T21:39:08.4749069Z >>> 2024-08-20T21:39:08.4749229Z >>> z = f(x, y) 2024-08-20T21:39:08.4749462Z >>> assert torch.allclose(z, x + y) 2024-08-20T21:39:08.4749616Z 2024-08-20T21:39:08.4749759Z 2024-08-20T21:39:08.4750391Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.4750545Z 2024-08-20T21:39:08.4750713Z warnings.warn(msg) 2024-08-20T21:39:08.4750926Z 2024-08-20T21:39:08.4751271Z --- Parse Warning: 17 / 101 --- 2024-08-20T21:39:08.4753437Z /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. 2024-08-20T21:39:08.4754109Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.4754497Z Allows capture of a triton kernel into a graph via make_fx or 2024-08-20T21:39:08.4754782Z non-strict export (coming soon). 2024-08-20T21:39:08.4754936Z 2024-08-20T21:39:08.4755404Z These technologies perform Dispatcher-based tracing (via 2024-08-20T21:39:08.4755803Z ``__torch_dispatch__``) and cannot see calls to raw triton kernels. 2024-08-20T21:39:08.4756238Z The ``capture_triton`` API returns a new callable that can actually 2024-08-20T21:39:08.4756422Z be traced into a graph. 2024-08-20T21:39:08.4756564Z 2024-08-20T21:39:08.4756731Z Examples: 2024-08-20T21:39:08.4756872Z 2024-08-20T21:39:08.4757054Z >>> # xdoctest: +SKIP 2024-08-20T21:39:08.4757237Z >>> import torch 2024-08-20T21:39:08.4757405Z >>> import triton 2024-08-20T21:39:08.4757655Z >>> from triton import language as tl 2024-08-20T21:39:08.4758027Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2024-08-20T21:39:08.4758584Z >>> from torch._higher_order_ops.triton_kernel_wrap import capture_triton 2024-08-20T21:39:08.4758748Z >>> 2024-08-20T21:39:08.4758909Z >>> @triton.jit 2024-08-20T21:39:08.4759167Z >>> def add_kernel( 2024-08-20T21:39:08.4759345Z >>> in_ptr0, 2024-08-20T21:39:08.4759505Z >>> in_ptr1, 2024-08-20T21:39:08.4759662Z >>> out_ptr, 2024-08-20T21:39:08.4759842Z >>> n_elements, 2024-08-20T21:39:08.4760067Z >>> BLOCK_SIZE: "tl.constexpr", 2024-08-20T21:39:08.4760219Z >>> ): 2024-08-20T21:39:08.4760454Z >>> pid = tl.program_id(axis=0) 2024-08-20T21:39:08.4760685Z >>> block_start = pid * BLOCK_SIZE 2024-08-20T21:39:08.4760997Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2024-08-20T21:39:08.4761234Z >>> mask = offsets < n_elements 2024-08-20T21:39:08.4761498Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2024-08-20T21:39:08.4761782Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2024-08-20T21:39:08.4761958Z >>> output = x + y 2024-08-20T21:39:08.4762266Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2024-08-20T21:39:08.4762425Z >>> 2024-08-20T21:39:08.4762597Z >>> def add(x, y): 2024-08-20T21:39:08.4762822Z >>> output = torch.empty_like(x) 2024-08-20T21:39:08.4763059Z >>> n_elements = output.numel() 2024-08-20T21:39:08.4763211Z >>> 2024-08-20T21:39:08.4763397Z >>> def grid_fn(meta): 2024-08-20T21:39:08.4763757Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2024-08-20T21:39:08.4763901Z >>> 2024-08-20T21:39:08.4764330Z >>> capture_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16) 2024-08-20T21:39:08.4764521Z >>> return output 2024-08-20T21:39:08.4764669Z >>> 2024-08-20T21:39:08.4764899Z >>> x = torch.randn(3, device="cuda") 2024-08-20T21:39:08.4765136Z >>> y = torch.randn(3, device="cuda") 2024-08-20T21:39:08.4765334Z >>> gm = make_fx(add)(x, y) 2024-08-20T21:39:08.4765505Z >>> print(gm.code) 2024-08-20T21:39:08.4765746Z >>> # def forward(self, x_1, y_1): 2024-08-20T21:39:08.4766246Z >>> # empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False) 2024-08-20T21:39:08.4766736Z >>> # triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation( 2024-08-20T21:39:08.4767097Z >>> # kernel_idx = 0, constant_args_idx = 0, 2024-08-20T21:39:08.4767335Z >>> # grid = [(1, 1, 1)], kwargs = { 2024-08-20T21:39:08.4767830Z >>> # 'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like, 2024-08-20T21:39:08.4768194Z >>> # 'n_elements': 3, 'BLOCK_SIZE': 16 2024-08-20T21:39:08.4768355Z >>> # }) 2024-08-20T21:39:08.4768563Z >>> # return empty_like 2024-08-20T21:39:08.4768705Z 2024-08-20T21:39:08.4768847Z 2024-08-20T21:39:08.4769493Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.4769631Z 2024-08-20T21:39:08.4769802Z warnings.warn(msg) 2024-08-20T21:39:08.4769960Z 2024-08-20T21:39:08.4770284Z --- Parse Warning: 18 / 101 --- 2024-08-20T21:39:08.4772649Z /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. 2024-08-20T21:39:08.4773303Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.4773445Z 2024-08-20T21:39:08.4773887Z Raises an AssertionError if two items are not equal up to desired 2024-08-20T21:39:08.4774042Z precision. 2024-08-20T21:39:08.4774187Z 2024-08-20T21:39:08.4774566Z .. note:: It is recommended to use one of `assert_allclose`, 2024-08-20T21:39:08.4775019Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-08-20T21:39:08.4775413Z instead of this function for more consistent floating point 2024-08-20T21:39:08.4775596Z comparisons. 2024-08-20T21:39:08.4775732Z 2024-08-20T21:39:08.4776182Z The test verifies that the elements of `actual` and `desired` satisfy. 2024-08-20T21:39:08.4776337Z 2024-08-20T21:39:08.4776763Z ``abs(desired-actual) < float64(1.5 * 10**(-decimal))`` 2024-08-20T21:39:08.4776912Z 2024-08-20T21:39:08.4777406Z That is a looser test than originally documented, but agrees with what the 2024-08-20T21:39:08.4777870Z actual implementation in `assert_array_almost_equal` did up to rounding 2024-08-20T21:39:08.4778371Z vagaries. An exception is raised at conflicting values. For ndarrays this 2024-08-20T21:39:08.4778590Z delegates to assert_array_almost_equal 2024-08-20T21:39:08.4778729Z 2024-08-20T21:39:08.4778899Z Parameters 2024-08-20T21:39:08.4779085Z ---------- 2024-08-20T21:39:08.4779259Z actual : array_like 2024-08-20T21:39:08.4779452Z The object to check. 2024-08-20T21:39:08.4779622Z desired : array_like 2024-08-20T21:39:08.4779801Z The expected object. 2024-08-20T21:39:08.4779996Z decimal : int, optional 2024-08-20T21:39:08.4780210Z Desired precision, default is 7. 2024-08-20T21:39:08.4780392Z err_msg : str, optional 2024-08-20T21:39:08.4780721Z The error message to be printed in case of failure. 2024-08-20T21:39:08.4780902Z verbose : bool, optional 2024-08-20T21:39:08.4781332Z If True, the conflicting values are appended to the error message. 2024-08-20T21:39:08.4781485Z 2024-08-20T21:39:08.4781629Z Raises 2024-08-20T21:39:08.4781830Z ------ 2024-08-20T21:39:08.4781992Z AssertionError 2024-08-20T21:39:08.4782396Z If actual and desired are not equal up to specified precision. 2024-08-20T21:39:08.4782551Z 2024-08-20T21:39:08.4782700Z See Also 2024-08-20T21:39:08.4782881Z -------- 2024-08-20T21:39:08.4783373Z assert_allclose: Compare two array_like objects for equality with desired 2024-08-20T21:39:08.4783635Z relative and/or absolute precision. 2024-08-20T21:39:08.4784040Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-08-20T21:39:08.4784194Z 2024-08-20T21:39:08.4784422Z Examples 2024-08-20T21:39:08.4784609Z -------- 2024-08-20T21:39:08.4784948Z >>> from torch._numpy.testing import assert_almost_equal 2024-08-20T21:39:08.4785207Z >>> assert_almost_equal(2.3333333333333, 2.33333334) 2024-08-20T21:39:08.4785548Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) 2024-08-20T21:39:08.4785773Z Traceback (most recent call last): 2024-08-20T21:39:08.4785917Z ... 2024-08-20T21:39:08.4786083Z AssertionError: 2024-08-20T21:39:08.4786342Z Arrays are not almost equal to 10 decimals 2024-08-20T21:39:08.4786511Z ACTUAL: 2.3333333333333 2024-08-20T21:39:08.4786679Z DESIRED: 2.33333334 2024-08-20T21:39:08.4786841Z 2024-08-20T21:39:08.4787130Z >>> assert_almost_equal(np.array([1.0,2.3333333333333]), 2024-08-20T21:39:08.4787408Z ... np.array([1.0,2.33333334]), decimal=9) 2024-08-20T21:39:08.4787614Z Traceback (most recent call last): 2024-08-20T21:39:08.4787758Z ... 2024-08-20T21:39:08.4787938Z AssertionError: 2024-08-20T21:39:08.4788182Z Arrays are not almost equal to 9 decimals 2024-08-20T21:39:08.4788334Z 2024-08-20T21:39:08.4788547Z Mismatched elements: 1 / 2 (50%) 2024-08-20T21:39:08.4788892Z Max absolute difference: 6.666699636781459e-09 2024-08-20T21:39:08.4789242Z Max relative difference: 2.8571569790287484e-09 2024-08-20T21:39:08.4789530Z x: torch.ndarray([1.0000, 2.3333], dtype=float64) 2024-08-20T21:39:08.4789795Z y: torch.ndarray([1.0000, 2.3333], dtype=float64) 2024-08-20T21:39:08.4789927Z 2024-08-20T21:39:08.4790083Z 2024-08-20T21:39:08.4790802Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.4790939Z 2024-08-20T21:39:08.4791127Z warnings.warn(msg) 2024-08-20T21:39:08.4791264Z 2024-08-20T21:39:08.4791591Z --- Parse Warning: 19 / 101 --- 2024-08-20T21:39:08.4793850Z /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. 2024-08-20T21:39:08.4794505Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.4794659Z 2024-08-20T21:39:08.4795119Z Raises an AssertionError if two items are not equal up to significant 2024-08-20T21:39:08.4795265Z digits. 2024-08-20T21:39:08.4795426Z 2024-08-20T21:39:08.4795785Z .. note:: It is recommended to use one of `assert_allclose`, 2024-08-20T21:39:08.4796384Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-08-20T21:39:08.4796798Z instead of this function for more consistent floating point 2024-08-20T21:39:08.4796970Z comparisons. 2024-08-20T21:39:08.4797110Z 2024-08-20T21:39:08.4797497Z Given two numbers, check that they are approximately equal. 2024-08-20T21:39:08.4797943Z Approximately equal is defined as the number of significant digits 2024-08-20T21:39:08.4798098Z that agree. 2024-08-20T21:39:08.4798335Z 2024-08-20T21:39:08.4798489Z Parameters 2024-08-20T21:39:08.4798688Z ---------- 2024-08-20T21:39:08.4798862Z actual : scalar 2024-08-20T21:39:08.4799037Z The object to check. 2024-08-20T21:39:08.4799213Z desired : scalar 2024-08-20T21:39:08.4799393Z The expected object. 2024-08-20T21:39:08.4799582Z significant : int, optional 2024-08-20T21:39:08.4799812Z Desired precision, default is 7. 2024-08-20T21:39:08.4799989Z err_msg : str, optional 2024-08-20T21:39:08.4800309Z The error message to be printed in case of failure. 2024-08-20T21:39:08.4800506Z verbose : bool, optional 2024-08-20T21:39:08.4800934Z If True, the conflicting values are appended to the error message. 2024-08-20T21:39:08.4801073Z 2024-08-20T21:39:08.4801233Z Raises 2024-08-20T21:39:08.4801414Z ------ 2024-08-20T21:39:08.4801776Z AssertionError 2024-08-20T21:39:08.4802199Z If actual and desired are not equal up to specified precision. 2024-08-20T21:39:08.4802341Z 2024-08-20T21:39:08.4802491Z See Also 2024-08-20T21:39:08.4802692Z -------- 2024-08-20T21:39:08.4803167Z assert_allclose: Compare two array_like objects for equality with desired 2024-08-20T21:39:08.4803425Z relative and/or absolute precision. 2024-08-20T21:39:08.4803850Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-08-20T21:39:08.4803987Z 2024-08-20T21:39:08.4804138Z Examples 2024-08-20T21:39:08.4804339Z -------- 2024-08-20T21:39:08.4805050Z >>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20) # doctest: +SKIP 2024-08-20T21:39:08.4805720Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20, # doctest: +SKIP 2024-08-20T21:39:08.4805955Z ... significant=8) 2024-08-20T21:39:08.4806606Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20, # doctest: +SKIP 2024-08-20T21:39:08.4806854Z ... significant=8) 2024-08-20T21:39:08.4807059Z Traceback (most recent call last): 2024-08-20T21:39:08.4807209Z ... 2024-08-20T21:39:08.4807390Z AssertionError: 2024-08-20T21:39:08.4807643Z Items are not equal to 8 significant digits: 2024-08-20T21:39:08.4807875Z ACTUAL: 1.234567e-21 2024-08-20T21:39:08.4808121Z DESIRED: 1.2345672e-21 2024-08-20T21:39:08.4808257Z 2024-08-20T21:39:08.4808572Z the evaluated condition that raises the exception is 2024-08-20T21:39:08.4808728Z 2024-08-20T21:39:08.4809315Z >>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1) 2024-08-20T21:39:08.4809462Z True 2024-08-20T21:39:08.4809615Z 2024-08-20T21:39:08.4809746Z 2024-08-20T21:39:08.4810380Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.4810538Z 2024-08-20T21:39:08.4810720Z warnings.warn(msg) 2024-08-20T21:39:08.4810859Z 2024-08-20T21:39:08.4811195Z --- Parse Warning: 20 / 101 --- 2024-08-20T21:39:08.4813429Z /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. 2024-08-20T21:39:08.4814077Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.4814232Z 2024-08-20T21:39:08.4814660Z Raises an AssertionError if two array_like objects are not equal. 2024-08-20T21:39:08.4814818Z 2024-08-20T21:39:08.4815252Z Given two array_like objects, check that the shape is equal and all 2024-08-20T21:39:08.4815712Z elements of these objects are equal (but see the Notes for the special 2024-08-20T21:39:08.4816162Z handling of a scalar). An exception is raised at shape mismatch or 2024-08-20T21:39:08.4816622Z conflicting values. In contrast to the standard usage in numpy, NaNs 2024-08-20T21:39:08.4817081Z are compared like numbers, no assertion is raised if both objects have 2024-08-20T21:39:08.4817285Z NaNs in the same positions. 2024-08-20T21:39:08.4817427Z 2024-08-20T21:39:08.4817899Z The usual caution for verifying equality with floating point numbers is 2024-08-20T21:39:08.4818069Z advised. 2024-08-20T21:39:08.4818203Z 2024-08-20T21:39:08.4818358Z Parameters 2024-08-20T21:39:08.4818567Z ---------- 2024-08-20T21:39:08.4818725Z x : array_like 2024-08-20T21:39:08.4818920Z The actual object to check. 2024-08-20T21:39:08.4819089Z y : array_like 2024-08-20T21:39:08.4819297Z The desired, expected object. 2024-08-20T21:39:08.4819477Z err_msg : str, optional 2024-08-20T21:39:08.4819807Z The error message to be printed in case of failure. 2024-08-20T21:39:08.4819987Z verbose : bool, optional 2024-08-20T21:39:08.4820432Z If True, the conflicting values are appended to the error message. 2024-08-20T21:39:08.4820689Z strict : bool, optional 2024-08-20T21:39:08.4821116Z If True, raise an AssertionError when either the shape or the data 2024-08-20T21:39:08.4821496Z type of the array_like objects does not match. The special 2024-08-20T21:39:08.4821921Z handling for scalars mentioned in the Notes section is disabled. 2024-08-20T21:39:08.4822058Z 2024-08-20T21:39:08.4822223Z Raises 2024-08-20T21:39:08.4822407Z ------ 2024-08-20T21:39:08.4822571Z AssertionError 2024-08-20T21:39:08.4822859Z If actual and desired objects are not equal. 2024-08-20T21:39:08.4823001Z 2024-08-20T21:39:08.4823155Z See Also 2024-08-20T21:39:08.4823353Z -------- 2024-08-20T21:39:08.4823830Z assert_allclose: Compare two array_like objects for equality with desired 2024-08-20T21:39:08.4824093Z relative and/or absolute precision. 2024-08-20T21:39:08.4824515Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-08-20T21:39:08.4824661Z 2024-08-20T21:39:08.4824808Z Notes 2024-08-20T21:39:08.4825004Z ----- 2024-08-20T21:39:08.4825427Z When one of `x` and `y` is a scalar and the other is array_like, the 2024-08-20T21:39:08.4825906Z function checks that each element of the array_like object is equal to 2024-08-20T21:39:08.4826377Z the scalar. This behaviour can be disabled with the `strict` parameter. 2024-08-20T21:39:08.4826516Z 2024-08-20T21:39:08.4826683Z Examples 2024-08-20T21:39:08.4826869Z -------- 2024-08-20T21:39:08.4827134Z The first assert does not raise an exception: 2024-08-20T21:39:08.4827287Z 2024-08-20T21:39:08.4827663Z >>> np.testing.assert_array_equal([1.0,2.33333,np.nan], 2024-08-20T21:39:08.4827922Z ... [np.exp(0),2.33333, np.nan]) 2024-08-20T21:39:08.4828078Z 2024-08-20T21:39:08.4828553Z Use `assert_allclose` or one of the nulp (number of floating point values) 2024-08-20T21:39:08.4828770Z functions for these cases instead: 2024-08-20T21:39:08.4828925Z 2024-08-20T21:39:08.4829214Z >>> np.testing.assert_allclose([1.0,np.pi,np.nan], 2024-08-20T21:39:08.4829480Z ... [1, np.sqrt(np.pi)**2, np.nan], 2024-08-20T21:39:08.4829824Z ... rtol=1e-10, atol=0) 2024-08-20T21:39:08.4829963Z 2024-08-20T21:39:08.4830387Z As mentioned in the Notes section, `assert_array_equal` has special 2024-08-20T21:39:08.4830874Z handling for scalars. Here the test checks that each value in `x` is 3: 2024-08-20T21:39:08.4831012Z 2024-08-20T21:39:08.4831236Z >>> x = np.full((2, 5), fill_value=3) 2024-08-20T21:39:08.4831459Z >>> np.testing.assert_array_equal(x, 3) 2024-08-20T21:39:08.4831599Z 2024-08-20T21:39:08.4832078Z Use `strict` to raise an AssertionError when comparing a scalar with an 2024-08-20T21:39:08.4832232Z array: 2024-08-20T21:39:08.4832374Z 2024-08-20T21:39:08.4832690Z >>> np.testing.assert_array_equal(x, 3, strict=True) 2024-08-20T21:39:08.4832898Z Traceback (most recent call last): 2024-08-20T21:39:08.4833042Z ... 2024-08-20T21:39:08.4833224Z AssertionError: 2024-08-20T21:39:08.4833397Z Arrays are not equal 2024-08-20T21:39:08.4833549Z 2024-08-20T21:39:08.4833747Z (shapes (2, 5), () mismatch) 2024-08-20T21:39:08.4833942Z x: torch.ndarray([[3, 3, 3, 3, 3], 2024-08-20T21:39:08.4834106Z [3, 3, 3, 3, 3]]) 2024-08-20T21:39:08.4834294Z y: torch.ndarray(3) 2024-08-20T21:39:08.4834431Z 2024-08-20T21:39:08.4834866Z The `strict` parameter also ensures that the array data types match: 2024-08-20T21:39:08.4835027Z 2024-08-20T21:39:08.4835206Z >>> x = np.array([2, 2, 2]) 2024-08-20T21:39:08.4835460Z >>> y = np.array([2., 2., 2.], dtype=np.float32) 2024-08-20T21:39:08.4835769Z >>> np.testing.assert_array_equal(x, y, strict=True) 2024-08-20T21:39:08.4835979Z Traceback (most recent call last): 2024-08-20T21:39:08.4836222Z ... 2024-08-20T21:39:08.4836390Z AssertionError: 2024-08-20T21:39:08.4836565Z Arrays are not equal 2024-08-20T21:39:08.4836734Z 2024-08-20T21:39:08.4837019Z (dtypes dtype("int64"), dtype("float32") mismatch) 2024-08-20T21:39:08.4837209Z x: torch.ndarray([2, 2, 2]) 2024-08-20T21:39:08.4837410Z y: torch.ndarray([2., 2., 2.]) 2024-08-20T21:39:08.4837545Z 2024-08-20T21:39:08.4838180Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.4838468Z 2024-08-20T21:39:08.4838643Z warnings.warn(msg) 2024-08-20T21:39:08.4838782Z 2024-08-20T21:39:08.4839131Z --- Parse Warning: 21 / 101 --- 2024-08-20T21:39:08.4841417Z /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. 2024-08-20T21:39:08.4842068Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.4842223Z 2024-08-20T21:39:08.4842666Z Raises an AssertionError if two objects are not equal up to desired 2024-08-20T21:39:08.4842837Z precision. 2024-08-20T21:39:08.4842972Z 2024-08-20T21:39:08.4843348Z .. note:: It is recommended to use one of `assert_allclose`, 2024-08-20T21:39:08.4843721Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-08-20T21:39:08.4844116Z instead of this function for more consistent floating point 2024-08-20T21:39:08.4844285Z comparisons. 2024-08-20T21:39:08.4844519Z 2024-08-20T21:39:08.4845019Z The test verifies identical shapes and that the elements of ``actual`` and 2024-08-20T21:39:08.4845193Z ``desired`` satisfy. 2024-08-20T21:39:08.4845344Z 2024-08-20T21:39:08.4845693Z ``abs(desired-actual) < 1.5 * 10**(-decimal)`` 2024-08-20T21:39:08.4845836Z 2024-08-20T21:39:08.4846344Z That is a looser test than originally documented, but agrees with what the 2024-08-20T21:39:08.4846834Z actual implementation did up to rounding vagaries. An exception is raised 2024-08-20T21:39:08.4847333Z at shape mismatch or conflicting values. In contrast to the standard usage 2024-08-20T21:39:08.4847822Z in numpy, NaNs are compared like numbers, no assertion is raised if both 2024-08-20T21:39:08.4848063Z objects have NaNs in the same positions. 2024-08-20T21:39:08.4848217Z 2024-08-20T21:39:08.4848371Z Parameters 2024-08-20T21:39:08.4848559Z ---------- 2024-08-20T21:39:08.4848733Z x : array_like 2024-08-20T21:39:08.4848930Z The actual object to check. 2024-08-20T21:39:08.4849087Z y : array_like 2024-08-20T21:39:08.4849300Z The desired, expected object. 2024-08-20T21:39:08.4849481Z decimal : int, optional 2024-08-20T21:39:08.4849692Z Desired precision, default is 6. 2024-08-20T21:39:08.4849881Z err_msg : str, optional 2024-08-20T21:39:08.4850195Z The error message to be printed in case of failure. 2024-08-20T21:39:08.4850374Z verbose : bool, optional 2024-08-20T21:39:08.4850818Z If True, the conflicting values are appended to the error message. 2024-08-20T21:39:08.4850957Z 2024-08-20T21:39:08.4851103Z Raises 2024-08-20T21:39:08.4851296Z ------ 2024-08-20T21:39:08.4851458Z AssertionError 2024-08-20T21:39:08.4851871Z If actual and desired are not equal up to specified precision. 2024-08-20T21:39:08.4852023Z 2024-08-20T21:39:08.4852173Z See Also 2024-08-20T21:39:08.4852356Z -------- 2024-08-20T21:39:08.4852846Z assert_allclose: Compare two array_like objects for equality with desired 2024-08-20T21:39:08.4853109Z relative and/or absolute precision. 2024-08-20T21:39:08.4853530Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-08-20T21:39:08.4853665Z 2024-08-20T21:39:08.4853811Z Examples 2024-08-20T21:39:08.4854105Z -------- 2024-08-20T21:39:08.4854360Z the first assert does not raise an exception 2024-08-20T21:39:08.4854499Z 2024-08-20T21:39:08.4854852Z >>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan], 2024-08-20T21:39:08.4855094Z ... [1.0,2.333,np.nan]) 2024-08-20T21:39:08.4855228Z 2024-08-20T21:39:08.4855595Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2024-08-20T21:39:08.4855866Z ... [1.0,2.33339,np.nan], decimal=5) 2024-08-20T21:39:08.4856078Z Traceback (most recent call last): 2024-08-20T21:39:08.4856240Z ... 2024-08-20T21:39:08.4856416Z AssertionError: 2024-08-20T21:39:08.4856658Z Arrays are not almost equal to 5 decimals 2024-08-20T21:39:08.4856828Z 2024-08-20T21:39:08.4857029Z Mismatched elements: 1 / 3 (33.3%) 2024-08-20T21:39:08.4857374Z Max absolute difference: 5.999999999994898e-05 2024-08-20T21:39:08.4857748Z Max relative difference: 2.5713661239633743e-05 2024-08-20T21:39:08.4858074Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2024-08-20T21:39:08.4858413Z y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64) 2024-08-20T21:39:08.4858551Z 2024-08-20T21:39:08.4858907Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2024-08-20T21:39:08.4859180Z ... [1.0,2.33333, 5], decimal=5) 2024-08-20T21:39:08.4859387Z Traceback (most recent call last): 2024-08-20T21:39:08.4859533Z ... 2024-08-20T21:39:08.4859709Z AssertionError: 2024-08-20T21:39:08.4859944Z Arrays are not almost equal to 5 decimals 2024-08-20T21:39:08.4860167Z 2024-08-20T21:39:08.4860381Z x and y nan location mismatch: 2024-08-20T21:39:08.4860706Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2024-08-20T21:39:08.4861018Z y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64) 2024-08-20T21:39:08.4861171Z 2024-08-20T21:39:08.4861305Z 2024-08-20T21:39:08.4861931Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.4862086Z 2024-08-20T21:39:08.4862261Z warnings.warn(msg) 2024-08-20T21:39:08.4862400Z 2024-08-20T21:39:08.4862743Z --- Parse Warning: 22 / 101 --- 2024-08-20T21:39:08.4865035Z /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. 2024-08-20T21:39:08.4865708Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.4866138Z Context manager that resets warning registry for catching warnings 2024-08-20T21:39:08.4866275Z 2024-08-20T21:39:08.4866797Z Warnings can be slippery, because, whenever a warning is triggered, Python 2024-08-20T21:39:08.4867257Z adds a ``__warningregistry__`` member to the *calling* module. This makes 2024-08-20T21:39:08.4867760Z it impossible to retrigger the warning in this module, whatever you put in 2024-08-20T21:39:08.4868277Z the warnings filters. This context manager accepts a sequence of `modules` 2024-08-20T21:39:08.4868553Z as a keyword argument to its constructor and: 2024-08-20T21:39:08.4868695Z 2024-08-20T21:39:08.4869186Z * stores and removes any ``__warningregistry__`` entries in given `modules` 2024-08-20T21:39:08.4869342Z on entry; 2024-08-20T21:39:08.4869734Z * resets ``__warningregistry__`` to its previous state on exit. 2024-08-20T21:39:08.4869878Z 2024-08-20T21:39:08.4870362Z This makes it possible to trigger any warning afresh inside the context 2024-08-20T21:39:08.4870741Z manager without disturbing the state of warnings outside. 2024-08-20T21:39:08.4870880Z 2024-08-20T21:39:08.4871360Z For compatibility with Python 3.0, please consider all arguments to be 2024-08-20T21:39:08.4871668Z keyword-only. 2024-08-20T21:39:08.4871809Z 2024-08-20T21:39:08.4871965Z Parameters 2024-08-20T21:39:08.4872171Z ---------- 2024-08-20T21:39:08.4872357Z record : bool, optional 2024-08-20T21:39:08.4872732Z Specifies whether warnings should be captured by a custom 2024-08-20T21:39:08.4873232Z implementation of ``warnings.showwarning()`` and be appended to a list 2024-08-20T21:39:08.4873671Z returned by the context manager. Otherwise None is returned by the 2024-08-20T21:39:08.4874152Z context manager. The objects appended to the list are arguments whose 2024-08-20T21:39:08.4874491Z attributes mirror the arguments to ``showwarning()``. 2024-08-20T21:39:08.4874692Z modules : sequence, optional 2024-08-20T21:39:08.4875174Z Sequence of modules for which to reset warnings registry on entry and 2024-08-20T21:39:08.4875710Z restore on exit. To work correctly, all 'ignore' filters should 2024-08-20T21:39:08.4875932Z filter by one of these modules. 2024-08-20T21:39:08.4876082Z 2024-08-20T21:39:08.4876236Z Examples 2024-08-20T21:39:08.4876429Z -------- 2024-08-20T21:39:08.4876616Z >>> import warnings 2024-08-20T21:39:08.4877008Z >>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP 2024-08-20T21:39:08.4877257Z ... modules=[np.core.fromnumeric]): 2024-08-20T21:39:08.4877595Z ... warnings.simplefilter('always') 2024-08-20T21:39:08.4878162Z ... warnings.filterwarnings('ignore', module='np.core.fromnumeric') 2024-08-20T21:39:08.4878721Z ... # do something that raises a warning but ignore those in 2024-08-20T21:39:08.4878933Z ... # np.core.fromnumeric 2024-08-20T21:39:08.4879080Z 2024-08-20T21:39:08.4879730Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.4879876Z 2024-08-20T21:39:08.4880052Z warnings.warn(msg) 2024-08-20T21:39:08.4880207Z 2024-08-20T21:39:08.4880533Z --- Parse Warning: 23 / 101 --- 2024-08-20T21:39:08.4882753Z /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. 2024-08-20T21:39:08.4883428Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.4883863Z Applies a 1D convolution over a quantized input signal composed of 2024-08-20T21:39:08.4884086Z several quantized input planes. 2024-08-20T21:39:08.4884242Z 2024-08-20T21:39:08.4884684Z For details on input arguments, parameters, and implementation see 2024-08-20T21:39:08.4884895Z :class:`~torch.nn.Conv1d`. 2024-08-20T21:39:08.4885034Z 2024-08-20T21:39:08.4885203Z .. note:: 2024-08-20T21:39:08.4885638Z Only `zeros` is supported for the :attr:`padding_mode` argument. 2024-08-20T21:39:08.4885776Z 2024-08-20T21:39:08.4885930Z .. note:: 2024-08-20T21:39:08.4886314Z Only `torch.quint8` is supported for the input data type. 2024-08-20T21:39:08.4886457Z 2024-08-20T21:39:08.4886590Z 2024-08-20T21:39:08.4886763Z Attributes: 2024-08-20T21:39:08.4887197Z weight (Tensor): packed tensor derived from the learnable weight 2024-08-20T21:39:08.4887388Z parameter. 2024-08-20T21:39:08.4887701Z scale (Tensor): scalar for the output scale 2024-08-20T21:39:08.4888036Z zero_point (Tensor): scalar for the output zero point 2024-08-20T21:39:08.4888171Z 2024-08-20T21:39:08.4888498Z See :class:`~torch.nn.Conv1d` for other attributes. 2024-08-20T21:39:08.4888637Z 2024-08-20T21:39:08.4888796Z Examples:: 2024-08-20T21:39:08.4888953Z 2024-08-20T21:39:08.4889253Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) 2024-08-20T21:39:08.4889615Z >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2) 2024-08-20T21:39:08.4889865Z >>> input = torch.randn(20, 16, 100) 2024-08-20T21:39:08.4890074Z >>> # quantize input to quint8 2024-08-20T21:39:08.4890276Z >>> # xdoctest: +SKIP 2024-08-20T21:39:08.4890711Z >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, 2024-08-20T21:39:08.4890982Z ... dtype=torch.quint8) 2024-08-20T21:39:08.4891187Z >>> output = m(q_input) 2024-08-20T21:39:08.4891327Z 2024-08-20T21:39:08.4891475Z 2024-08-20T21:39:08.4892126Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.4892264Z 2024-08-20T21:39:08.4892438Z warnings.warn(msg) 2024-08-20T21:39:08.4892590Z 2024-08-20T21:39:08.4892911Z --- Parse Warning: 24 / 101 --- 2024-08-20T21:39:08.4895098Z /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. 2024-08-20T21:39:08.4895767Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.4896360Z A quantized long short-term memory (LSTM). 2024-08-20T21:39:08.4896521Z 2024-08-20T21:39:08.4897112Z For the description and the argument types, please, refer to :class:`~torch.nn.LSTM` 2024-08-20T21:39:08.4897254Z 2024-08-20T21:39:08.4897428Z Attributes: 2024-08-20T21:39:08.4897869Z layers : instances of the `_LSTMLayer` 2024-08-20T21:39:08.4898008Z 2024-08-20T21:39:08.4898179Z .. note:: 2024-08-20T21:39:08.4898642Z To access the weights and biases, you need to access them per layer. 2024-08-20T21:39:08.4898996Z See examples in :class:`~torch.ao.nn.quantizable.LSTM` 2024-08-20T21:39:08.4899155Z 2024-08-20T21:39:08.4899315Z Examples:: 2024-08-20T21:39:08.4899496Z >>> # xdoctest: +SKIP 2024-08-20T21:39:08.4899715Z >>> custom_module_config = { 2024-08-20T21:39:08.4900101Z ... 'float_to_observed_custom_module_class': { 2024-08-20T21:39:08.4900356Z ... nn.LSTM: nn.quantizable.LSTM, 2024-08-20T21:39:08.4900527Z ... }, 2024-08-20T21:39:08.4900933Z ... 'observed_to_quantized_custom_module_class': { 2024-08-20T21:39:08.4901238Z ... nn.quantizable.LSTM: nn.quantized.LSTM, 2024-08-20T21:39:08.4901394Z ... } 2024-08-20T21:39:08.4901547Z ... } 2024-08-20T21:39:08.4902005Z >>> tq.prepare(model, prepare_custom_module_class=custom_module_config) 2024-08-20T21:39:08.4902443Z >>> tq.convert(model, convert_custom_module_class=custom_module_config) 2024-08-20T21:39:08.4902591Z 2024-08-20T21:39:08.4903234Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.4903372Z 2024-08-20T21:39:08.4903544Z warnings.warn(msg) 2024-08-20T21:39:08.4903701Z 2024-08-20T21:39:08.4904020Z --- Parse Warning: 25 / 101 --- 2024-08-20T21:39:08.4906576Z /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. 2024-08-20T21:39:08.4907235Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.4907581Z Squashes the sparse masks into the appropriate tensors. 2024-08-20T21:39:08.4907739Z 2024-08-20T21:39:08.4908180Z If either the `params_to_keep` or `params_to_keep_per_layer` is set, 2024-08-20T21:39:08.4908567Z the module will have a `sparse_params` dict attached to it. 2024-08-20T21:39:08.4908826Z 2024-08-20T21:39:08.4908979Z Args: 2024-08-20T21:39:08.4909372Z params_to_keep: List of keys to save in the module or a dict 2024-08-20T21:39:08.4909735Z representing the modules and keys that will have 2024-08-20T21:39:08.4909988Z sparsity parameters saved 2024-08-20T21:39:08.4910427Z params_to_keep_per_layer: Dict to specify the params that should be 2024-08-20T21:39:08.4910772Z saved for specific layers. The keys in the dict 2024-08-20T21:39:08.4911116Z should be the module fqn, while the values should 2024-08-20T21:39:08.4911490Z be a list of strings with the names of the variables 2024-08-20T21:39:08.4911749Z to save in the `sparse_params` 2024-08-20T21:39:08.4911888Z 2024-08-20T21:39:08.4912064Z Examples: 2024-08-20T21:39:08.4912343Z >>> # xdoctest: +SKIP("locals are undefined") 2024-08-20T21:39:08.4912665Z >>> # Don't save any sparse params 2024-08-20T21:39:08.4912905Z >>> sparsifier.squash_mask() 2024-08-20T21:39:08.4913290Z >>> hasattr(model.submodule1, 'sparse_params') 2024-08-20T21:39:08.4913444Z False 2024-08-20T21:39:08.4913596Z 2024-08-20T21:39:08.4913830Z >>> # Keep sparse params per layer 2024-08-20T21:39:08.4914041Z >>> sparsifier.squash_mask( 2024-08-20T21:39:08.4914276Z ... params_to_keep_per_layer={ 2024-08-20T21:39:08.4914726Z ... 'submodule1.linear1': ('foo', 'bar'), 2024-08-20T21:39:08.4915085Z ... 'submodule2.linear42': ('baz',) 2024-08-20T21:39:08.4915254Z ... }) 2024-08-20T21:39:08.4915572Z >>> print(model.submodule1.linear1.sparse_params) 2024-08-20T21:39:08.4915843Z {'foo': 42, 'bar': 24} 2024-08-20T21:39:08.4916171Z >>> print(model.submodule2.linear42.sparse_params) 2024-08-20T21:39:08.4916396Z {'baz': 0.1} 2024-08-20T21:39:08.4916545Z 2024-08-20T21:39:08.4916797Z >>> # Keep sparse params for all layers 2024-08-20T21:39:08.4917262Z >>> sparsifier.squash_mask(params_to_keep=('foo', 'bar')) 2024-08-20T21:39:08.4917588Z >>> print(model.submodule1.linear1.sparse_params) 2024-08-20T21:39:08.4917846Z {'foo': 42, 'bar': 24} 2024-08-20T21:39:08.4930823Z >>> print(model.submodule2.linear42.sparse_params) 2024-08-20T21:39:08.4931342Z {'foo': 42, 'bar': 24} 2024-08-20T21:39:08.4931482Z 2024-08-20T21:39:08.4931941Z >>> # Keep some sparse params for all layers, and specific ones for 2024-08-20T21:39:08.4932138Z >>> # some other layers 2024-08-20T21:39:08.4932352Z >>> sparsifier.squash_mask( 2024-08-20T21:39:08.4932711Z ... params_to_keep=('foo', 'bar'), 2024-08-20T21:39:08.4932938Z ... params_to_keep_per_layer={ 2024-08-20T21:39:08.4933297Z ... 'submodule2.linear42': ('baz',) 2024-08-20T21:39:08.4933470Z ... }) 2024-08-20T21:39:08.4933790Z >>> print(model.submodule1.linear1.sparse_params) 2024-08-20T21:39:08.4934048Z {'foo': 42, 'bar': 24} 2024-08-20T21:39:08.4934386Z >>> print(model.submodule2.linear42.sparse_params) 2024-08-20T21:39:08.4934693Z {'foo': 42, 'bar': 24, 'baz': 0.1} 2024-08-20T21:39:08.4934855Z 2024-08-20T21:39:08.4935487Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.4935623Z 2024-08-20T21:39:08.4935811Z warnings.warn(msg) 2024-08-20T21:39:08.4935949Z 2024-08-20T21:39:08.4936274Z --- Parse Warning: 26 / 101 --- 2024-08-20T21:39:08.4939008Z /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. 2024-08-20T21:39:08.4939663Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.4939804Z 2024-08-20T21:39:08.4940341Z Config object that specifies the supported data types passed as arguments to 2024-08-20T21:39:08.4940848Z quantize ops in the reference model spec, for input and output activations, 2024-08-20T21:39:08.4941021Z weights, and biases. 2024-08-20T21:39:08.4941180Z 2024-08-20T21:39:08.4941493Z For example, consider the following reference model: 2024-08-20T21:39:08.4941644Z 2024-08-20T21:39:08.4942055Z quant1 - [dequant1 - fp32_linear - quant2] - dequant2 2024-08-20T21:39:08.4942191Z 2024-08-20T21:39:08.4942658Z The pattern in the square brackets refers to the reference pattern of 2024-08-20T21:39:08.4943131Z statically quantized linear. Setting the input dtype as `torch.quint8` 2024-08-20T21:39:08.4943611Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument 2024-08-20T21:39:08.4944106Z to the first quantize op (quant1). Similarly, setting the output dtype as 2024-08-20T21:39:08.4944564Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to 2024-08-20T21:39:08.4944765Z the second quantize op (quant2). 2024-08-20T21:39:08.4944918Z 2024-08-20T21:39:08.4945380Z Note that the dtype here does not refer to the interface dtypes of the 2024-08-20T21:39:08.4945900Z op. For example, the "input dtype" here is not the dtype of the input 2024-08-20T21:39:08.4946368Z tensor passed to the quantized linear op. Though it can still be the 2024-08-20T21:39:08.4946792Z same as the interface dtype, this is not always the case, e.g. the 2024-08-20T21:39:08.4947261Z interface dtype is fp32 in dynamic quantization but the "input dtype" 2024-08-20T21:39:08.4947712Z specified in the DTypeConfig would still be quint8. The semantics of 2024-08-20T21:39:08.4948161Z dtypes here are the same as the semantics of the dtypes specified in 2024-08-20T21:39:08.4948336Z the observers. 2024-08-20T21:39:08.4948474Z 2024-08-20T21:39:08.4948954Z These dtypes are matched against the ones specified in the user's 2024-08-20T21:39:08.4949431Z QConfig. If there is a match, and the QConfig satisfies the constraints 2024-08-20T21:39:08.4949892Z specified in the DTypeConfig (if any), then we will quantize the given 2024-08-20T21:39:08.4950359Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and 2024-08-20T21:39:08.4950583Z the pattern will not be quantized. 2024-08-20T21:39:08.4950723Z 2024-08-20T21:39:08.4950914Z Example usage:: 2024-08-20T21:39:08.4951065Z 2024-08-20T21:39:08.4951260Z >>> # xdoctest: +SKIP(failing) 2024-08-20T21:39:08.4951490Z >>> dtype_config1 = DTypeConfig( 2024-08-20T21:39:08.4951694Z ... input_dtype=torch.quint8, 2024-08-20T21:39:08.4951899Z ... output_dtype=torch.quint8, 2024-08-20T21:39:08.4952119Z ... weight_dtype=torch.qint8, 2024-08-20T21:39:08.4952311Z ... bias_dtype=torch.float) 2024-08-20T21:39:08.4952452Z 2024-08-20T21:39:08.4952671Z >>> dtype_config2 = DTypeConfig( 2024-08-20T21:39:08.4952921Z ... input_dtype=DTypeWithConstraints( 2024-08-20T21:39:08.4953108Z ... dtype=torch.quint8, 2024-08-20T21:39:08.4953328Z ... quant_min_lower_bound=0, 2024-08-20T21:39:08.4953548Z ... quant_max_upper_bound=255, 2024-08-20T21:39:08.4953701Z ... ), 2024-08-20T21:39:08.4953970Z ... output_dtype=DTypeWithConstraints( 2024-08-20T21:39:08.4954154Z ... dtype=torch.quint8, 2024-08-20T21:39:08.4954358Z ... quant_min_lower_bound=0, 2024-08-20T21:39:08.4954657Z ... quant_max_upper_bound=255, 2024-08-20T21:39:08.4954803Z ... ), 2024-08-20T21:39:08.4955057Z ... weight_dtype=DTypeWithConstraints( 2024-08-20T21:39:08.4955257Z ... dtype=torch.qint8, 2024-08-20T21:39:08.4955568Z ... quant_min_lower_bound=-128, 2024-08-20T21:39:08.4955796Z ... quant_max_upper_bound=127, 2024-08-20T21:39:08.4955946Z ... ), 2024-08-20T21:39:08.4956139Z ... bias_dtype=torch.float) 2024-08-20T21:39:08.4956294Z 2024-08-20T21:39:08.4956491Z >>> dtype_config1.input_dtype 2024-08-20T21:39:08.4956652Z torch.quint8 2024-08-20T21:39:08.4956808Z 2024-08-20T21:39:08.4957013Z >>> dtype_config2.input_dtype 2024-08-20T21:39:08.4957168Z torch.quint8 2024-08-20T21:39:08.4957319Z 2024-08-20T21:39:08.4957583Z >>> dtype_config2.input_dtype_with_constraints 2024-08-20T21:39:08.4958819Z DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None) 2024-08-20T21:39:08.4958979Z 2024-08-20T21:39:08.4959608Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.4959747Z 2024-08-20T21:39:08.4959940Z warnings.warn(msg) 2024-08-20T21:39:08.4960081Z 2024-08-20T21:39:08.4960409Z --- Parse Warning: 27 / 101 --- 2024-08-20T21:39:08.4963432Z /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. 2024-08-20T21:39:08.4964077Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.4964232Z 2024-08-20T21:39:08.4964791Z Takes in optional filter values and generates two tables with desired information. 2024-08-20T21:39:08.4964936Z 2024-08-20T21:39:08.4965483Z The generated tables are presented in both a list-of-lists format 2024-08-20T21:39:08.4965619Z 2024-08-20T21:39:08.4966064Z The reason for the two tables are that they handle different things: 2024-08-20T21:39:08.4966416Z 1.) the first table handles all tensor level information 2024-08-20T21:39:08.4966874Z 2.) the second table handles and displays all channel based information 2024-08-20T21:39:08.4967016Z 2024-08-20T21:39:08.4967761Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2024-08-20T21:39:08.4968633Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2024-08-20T21:39:08.4969403Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2024-08-20T21:39:08.4969539Z 2024-08-20T21:39:08.4969713Z Tensor table columns: 2024-08-20T21:39:08.4970137Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2024-08-20T21:39:08.4970538Z ---- --------- --------- --------- --------- --------- 2024-08-20T21:39:08.4970675Z 2024-08-20T21:39:08.4970950Z Per-Channel table columns: 2024-08-20T21:39:08.4971414Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2024-08-20T21:39:08.4971853Z ---- --------- ------- --------- --------- --------- --------- 2024-08-20T21:39:08.4972005Z 2024-08-20T21:39:08.4972151Z Args: 2024-08-20T21:39:08.4972699Z feature_filter (str, optional): Filters the features presented to only those that 2024-08-20T21:39:08.4972929Z contain this filter substring 2024-08-20T21:39:08.4973264Z Default = "", results in all the features being printed 2024-08-20T21:39:08.4973814Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-08-20T21:39:08.4974425Z Default = "", results in all the modules in the reports to be visible in the table 2024-08-20T21:39:08.4974557Z 2024-08-20T21:39:08.4974782Z Returns a dictionary with two keys: 2024-08-20T21:39:08.4975138Z (Dict[str, Tuple[List, List]]) A dict containing two keys: 2024-08-20T21:39:08.4975378Z "tensor_level_info", "channel_level_info" 2024-08-20T21:39:08.4975606Z Each key maps to a tuple with: 2024-08-20T21:39:08.4975848Z A list of the headers of each table 2024-08-20T21:39:08.4976238Z A list of lists containing the table information row by row 2024-08-20T21:39:08.4976630Z The 0th index row will contain the headers of the columns 2024-08-20T21:39:08.4976882Z The rest of the rows will contain data 2024-08-20T21:39:08.4977042Z 2024-08-20T21:39:08.4977204Z Example Use: 2024-08-20T21:39:08.4977459Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:39:08.4977777Z >>> mod_report_visualizer.generate_filtered_tables( 2024-08-20T21:39:08.4978022Z ... feature_filter = "per_channel_min", 2024-08-20T21:39:08.4978232Z ... module_fqn_filter = "block1" 2024-08-20T21:39:08.4978824Z ... ) # generates table with per_channel_min info for all modules in block 1 of the model 2024-08-20T21:39:08.4978966Z 2024-08-20T21:39:08.4979588Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.4979742Z 2024-08-20T21:39:08.4979912Z warnings.warn(msg) 2024-08-20T21:39:08.4980050Z 2024-08-20T21:39:08.4980390Z --- Parse Warning: 28 / 101 --- 2024-08-20T21:39:08.4983485Z /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. 2024-08-20T21:39:08.4984155Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.4984292Z 2024-08-20T21:39:08.4984859Z Takes in optional filter values and prints out formatted tables of the information. 2024-08-20T21:39:08.4985011Z 2024-08-20T21:39:08.4985774Z The reason for the two tables printed out instead of one large one are that they handle different things: 2024-08-20T21:39:08.4986108Z 1.) the first table handles all tensor level information 2024-08-20T21:39:08.4986584Z 2.) the second table handles and displays all channel based information 2024-08-20T21:39:08.4986726Z 2024-08-20T21:39:08.4987460Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2024-08-20T21:39:08.4988330Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2024-08-20T21:39:08.4989085Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2024-08-20T21:39:08.4989246Z 2024-08-20T21:39:08.4989419Z Tensor table columns: 2024-08-20T21:39:08.4989821Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2024-08-20T21:39:08.4990226Z ---- --------- --------- --------- --------- --------- 2024-08-20T21:39:08.4990365Z 2024-08-20T21:39:08.4990619Z Per-Channel table columns: 2024-08-20T21:39:08.4990772Z 2024-08-20T21:39:08.4991235Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2024-08-20T21:39:08.4991664Z ---- --------- ------- --------- --------- --------- --------- 2024-08-20T21:39:08.4991817Z 2024-08-20T21:39:08.4991964Z Args: 2024-08-20T21:39:08.4992503Z feature_filter (str, optional): Filters the features presented to only those that 2024-08-20T21:39:08.4992730Z contain this filter substring 2024-08-20T21:39:08.4993063Z Default = "", results in all the features being printed 2024-08-20T21:39:08.4993697Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-08-20T21:39:08.4994230Z Default = "", results in all the modules in the reports to be visible in the table 2024-08-20T21:39:08.4994366Z 2024-08-20T21:39:08.4994538Z Example Use: 2024-08-20T21:39:08.4994797Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:39:08.4995121Z >>> mod_report_visualizer.generate_table_visualization( 2024-08-20T21:39:08.4995376Z ... feature_filter = "per_channel_min", 2024-08-20T21:39:08.4995587Z ... module_fqn_filter = "block1" 2024-08-20T21:39:08.4995732Z ... ) 2024-08-20T21:39:08.4996492Z >>> # prints out neatly formatted table with per_channel_min info 2024-08-20T21:39:08.4996753Z >>> # for all modules in block 1 of the model 2024-08-20T21:39:08.4996892Z 2024-08-20T21:39:08.4997553Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.4997698Z 2024-08-20T21:39:08.4997870Z warnings.warn(msg) 2024-08-20T21:39:08.4998021Z 2024-08-20T21:39:08.4998427Z --- Parse Warning: 29 / 101 --- 2024-08-20T21:39:08.5001395Z /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. 2024-08-20T21:39:08.5002040Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5002181Z 2024-08-20T21:39:08.5002937Z Takes in a feature and optional module_filter and plots of the desired data. 2024-08-20T21:39:08.5003074Z 2024-08-20T21:39:08.5003663Z For per channel features, it averages the value across the channels and plots a point 2024-08-20T21:39:08.5004254Z per module. The reason for this is that for models with hundreds of channels, it can 2024-08-20T21:39:08.5004856Z be hard to differentiate one channel line from another, and so the point of generating 2024-08-20T21:39:08.5005455Z a single average point per module is to give a sense of general trends that encourage 2024-08-20T21:39:08.5005632Z further deep dives. 2024-08-20T21:39:08.5005768Z 2024-08-20T21:39:08.5005931Z Note: 2024-08-20T21:39:08.5006511Z Only features in the report that have tensor value data are plottable by this class 2024-08-20T21:39:08.5006840Z When the tensor information is plotted, it will plot: 2024-08-20T21:39:08.5007129Z idx as the x val, feature value as the y_val 2024-08-20T21:39:08.5007479Z When the channel information is plotted, it will plot: 2024-08-20T21:39:08.5008085Z the first idx of each module as the x val, feature value as the y_val [for each channel] 2024-08-20T21:39:08.5008612Z The reason for this is that we want to be able to compare values across the 2024-08-20T21:39:08.5009128Z channels for same layer, and it will be hard if values are staggered by idx 2024-08-20T21:39:08.5009477Z This means each module is represented by only 1 x value 2024-08-20T21:39:08.5009637Z Args: 2024-08-20T21:39:08.5010101Z feature_filter (str): Filters the features presented to only those that 2024-08-20T21:39:08.5010325Z contain this filter substring 2024-08-20T21:39:08.5010862Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-08-20T21:39:08.5011398Z Default = "", results in all the modules in the reports to be visible in the table 2024-08-20T21:39:08.5011556Z 2024-08-20T21:39:08.5011715Z Example Use: 2024-08-20T21:39:08.5011968Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:39:08.5012301Z >>> mod_report_visualizer.generate_plot_visualization( 2024-08-20T21:39:08.5012547Z ... feature_filter = "per_channel_min", 2024-08-20T21:39:08.5012884Z ... module_fqn_filter = "block1" 2024-08-20T21:39:08.5013044Z ... ) 2024-08-20T21:39:08.5013409Z >>> # outputs line plot of per_channel_min information for all 2024-08-20T21:39:08.5013921Z >>> # modules in block1 of model each channel gets it's own line, 2024-08-20T21:39:08.5014432Z >>> # and it's plotted across the in-order modules on the x-axis 2024-08-20T21:39:08.5014574Z 2024-08-20T21:39:08.5015210Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5015350Z 2024-08-20T21:39:08.5015524Z warnings.warn(msg) 2024-08-20T21:39:08.5015675Z 2024-08-20T21:39:08.5016006Z --- Parse Warning: 30 / 101 --- 2024-08-20T21:39:08.5018990Z /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. 2024-08-20T21:39:08.5019650Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5019787Z 2024-08-20T21:39:08.5020374Z Takes in a feature and optional module_filter and plots the histogram of desired data. 2024-08-20T21:39:08.5020525Z 2024-08-20T21:39:08.5020670Z Note: 2024-08-20T21:39:08.5021271Z Only features in the report that have tensor value data can be viewed as a histogram 2024-08-20T21:39:08.5021854Z If you want to plot a histogram from all the channel values of a specific feature for 2024-08-20T21:39:08.5022470Z a specific model, make sure to specify both the model and the feature properly 2024-08-20T21:39:08.5023043Z in the filters and you should be able to see a distribution of the channel data 2024-08-20T21:39:08.5023179Z 2024-08-20T21:39:08.5023323Z Args: 2024-08-20T21:39:08.5023890Z feature_filter (str, optional): Filters the features presented to only those that 2024-08-20T21:39:08.5024098Z contain this filter substring 2024-08-20T21:39:08.5024433Z Default = "", results in all the features being printed 2024-08-20T21:39:08.5024981Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-08-20T21:39:08.5025521Z Default = "", results in all the modules in the reports to be visible in the table 2024-08-20T21:39:08.5026009Z num_bins (int, optional): The number of bins to create the histogram with 2024-08-20T21:39:08.5026409Z Default = 10, the values will be split into 10 equal sized bins 2024-08-20T21:39:08.5026551Z 2024-08-20T21:39:08.5026724Z Example Use: 2024-08-20T21:39:08.5026903Z >>> # xdoctest: +SKIP 2024-08-20T21:39:08.5027497Z >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization( 2024-08-20T21:39:08.5027757Z ... feature_filter = "per_channel_min", 2024-08-20T21:39:08.5027972Z ... module_fqn_filter = "block1" 2024-08-20T21:39:08.5028114Z ... ) 2024-08-20T21:39:08.5028698Z # outputs histogram of per_channel_min information for all modules in block1 of model 2024-08-20T21:39:08.5029239Z information is gathered across all channels for all modules in block 1 for the 2024-08-20T21:39:08.5029694Z per_channel_min and is displayed in a histogram of equally sized bins 2024-08-20T21:39:08.5029848Z 2024-08-20T21:39:08.5030476Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5030630Z 2024-08-20T21:39:08.5030812Z warnings.warn(msg) 2024-08-20T21:39:08.5030949Z 2024-08-20T21:39:08.5031289Z --- Parse Warning: 31 / 101 --- 2024-08-20T21:39:08.5033573Z /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. 2024-08-20T21:39:08.5034268Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-08-20T21:39:08.5034420Z 2024-08-20T21:39:08.5034992Z Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh. 2024-08-20T21:39:08.5035547Z The submesh created consists of the dimensions and the communicators indicated by 2024-08-20T21:39:08.5035721Z ``mesh_dim_names`` 2024-08-20T21:39:08.5035860Z 2024-08-20T21:39:08.5036004Z Args: 2024-08-20T21:39:08.5036524Z mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the 2024-08-20T21:39:08.5036902Z mesh dimension of the DeviceMesh to create the submesh for. 2024-08-20T21:39:08.5037068Z Returns: 2024-08-20T21:39:08.5037263Z A :class:`DeviceMesh` object 2024-08-20T21:39:08.5037401Z 2024-08-20T21:39:08.5038025Z The following program runs on each process/rank in an SPMD manner in a world size of 8. 2024-08-20T21:39:08.5038320Z In the first example: 2024-08-20T21:39:08.5038897Z Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]). 2024-08-20T21:39:08.5039498Z Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of DeviceMesh:([4, 5, 6, 7]). 2024-08-20T21:39:08.5040005Z Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of DeviceMesh:([0, 4]). 2024-08-20T21:39:08.5040511Z Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of DeviceMesh:([1, 5]). 2024-08-20T21:39:08.5041113Z Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of DeviceMesh:([2, 6]). 2024-08-20T21:39:08.5041618Z Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of DeviceMesh:([3, 7]). 2024-08-20T21:39:08.5041771Z 2024-08-20T21:39:08.5041948Z In the second example: 2024-08-20T21:39:08.5042583Z Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]). 2024-08-20T21:39:08.5043224Z Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]). 2024-08-20T21:39:08.5043702Z Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]). 2024-08-20T21:39:08.5044122Z Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]). 2024-08-20T21:39:08.5044230Z 2024-08-20T21:39:08.5044360Z Example:: 2024-08-20T21:39:08.5044503Z >>> # xdoctest: +SKIP("no rank") 2024-08-20T21:39:08.5044757Z >>> from torch.distributed.device_mesh import DeviceMesh 2024-08-20T21:39:08.5044860Z >>> 2024-08-20T21:39:08.5045147Z >>> # Initialize a 2D device mesh as (2, 4) to represent the topology 2024-08-20T21:39:08.5045462Z >>> # of cross-host(dim 0), and within-host (dim 1). 2024-08-20T21:39:08.5045823Z >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2024-08-20T21:39:08.5045971Z >>> tp_mesh = mesh_2d["tp"] 2024-08-20T21:39:08.5046095Z >>> dp_mesh = mesh_2d["dp"] 2024-08-20T21:39:08.5046192Z >>> 2024-08-20T21:39:08.5046336Z >>> # Initialize a 3D mesh. 2024-08-20T21:39:08.5046734Z >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp")) 2024-08-20T21:39:08.5047180Z >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh. 2024-08-20T21:39:08.5047351Z >>> dp_cp_mesh = mesh_3d["dp", "cp"] 2024-08-20T21:39:08.5047503Z >>> cp_dp_mesh = mesh_3d["cp", "dp"] 2024-08-20T21:39:08.5047602Z 2024-08-20T21:39:08.5048726Z 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)) 2024-08-20T21:39:08.5048820Z 2024-08-20T21:39:08.5049182Z mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2024-08-20T21:39:08.5049439Z ^ 2024-08-20T21:39:08.5049562Z warnings.warn(msg) 2024-08-20T21:39:08.5049668Z 2024-08-20T21:39:08.5049895Z --- Parse Warning: 32 / 101 --- 2024-08-20T21:39:08.5051428Z /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. 2024-08-20T21:39:08.5051887Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5051983Z 2024-08-20T21:39:08.5052287Z Gathers picklable objects from the whole group in a single process. 2024-08-20T21:39:08.5052394Z 2024-08-20T21:39:08.5052732Z Similar to :func:`gather`, but Python objects can be passed in. Note that the 2024-08-20T21:39:08.5052953Z object must be picklable in order to be gathered. 2024-08-20T21:39:08.5053046Z 2024-08-20T21:39:08.5053146Z Args: 2024-08-20T21:39:08.5053339Z obj (Any): Input object. Must be picklable. 2024-08-20T21:39:08.5053637Z object_gather_list (list[Any]): Output list. On the ``dst`` rank, it 2024-08-20T21:39:08.5053902Z should be correctly sized as the size of the group for this 2024-08-20T21:39:08.5054298Z collective and will contain the output. Must be ``None`` on non-dst 2024-08-20T21:39:08.5054437Z ranks. (default is ``None``) 2024-08-20T21:39:08.5055049Z dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). (default is 0) 2024-08-20T21:39:08.5055381Z group: (ProcessGroup, optional): The process group to work on. If None, 2024-08-20T21:39:08.5055648Z the default process group will be used. Default is ``None``. 2024-08-20T21:39:08.5055759Z 2024-08-20T21:39:08.5055865Z Returns: 2024-08-20T21:39:08.5056138Z None. On the ``dst`` rank, ``object_gather_list`` will contain the 2024-08-20T21:39:08.5056286Z output of the collective. 2024-08-20T21:39:08.5056380Z 2024-08-20T21:39:08.5056695Z .. note:: Note that this API differs slightly from the gather collective 2024-08-20T21:39:08.5057031Z since it does not provide an async_op handle and thus will be a blocking 2024-08-20T21:39:08.5057131Z call. 2024-08-20T21:39:08.5057224Z 2024-08-20T21:39:08.5057652Z .. note:: For NCCL-based processed groups, internal tensor representations 2024-08-20T21:39:08.5057966Z of objects must be moved to the GPU device before communication takes 2024-08-20T21:39:08.5058170Z place. In this case, the device used is given by 2024-08-20T21:39:08.5058572Z ``torch.cuda.current_device()`` and it is the user's responsiblity to 2024-08-20T21:39:08.5058880Z ensure that this is set so that each rank has an individual GPU, via 2024-08-20T21:39:08.5059031Z ``torch.cuda.set_device()``. 2024-08-20T21:39:08.5059126Z 2024-08-20T21:39:08.5059233Z .. warning:: 2024-08-20T21:39:08.5059527Z :func:`gather_object` uses ``pickle`` module implicitly, which is 2024-08-20T21:39:08.5059849Z known to be insecure. It is possible to construct malicious pickle data 2024-08-20T21:39:08.5060149Z which will execute arbitrary code during unpickling. Only call this 2024-08-20T21:39:08.5060300Z function with data you trust. 2024-08-20T21:39:08.5060394Z 2024-08-20T21:39:08.5060502Z .. warning:: 2024-08-20T21:39:08.5060819Z Calling :func:`gather_object` with GPU tensors is not well supported 2024-08-20T21:39:08.5061224Z and inefficient as it incurs GPU -> CPU transfer since tensors would be 2024-08-20T21:39:08.5061454Z pickled. Please consider using :func:`gather` instead. 2024-08-20T21:39:08.5061563Z 2024-08-20T21:39:08.5061672Z Example:: 2024-08-20T21:39:08.5061925Z >>> # xdoctest: +SKIP("need process group init") 2024-08-20T21:39:08.5062192Z >>> # Note: Process group initialization omitted on each rank. 2024-08-20T21:39:08.5062353Z >>> import torch.distributed as dist 2024-08-20T21:39:08.5062500Z >>> # Assumes world_size of 3. 2024-08-20T21:39:08.5062750Z >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object 2024-08-20T21:39:08.5062920Z >>> output = [None for _ in gather_objects] 2024-08-20T21:39:08.5063058Z >>> dist.gather_object( 2024-08-20T21:39:08.5063218Z ... gather_objects[dist.get_rank()], 2024-08-20T21:39:08.5063411Z ... output if dist.get_rank() == 0 else None, 2024-08-20T21:39:08.5063533Z ... dst=0 2024-08-20T21:39:08.5063631Z ... ) 2024-08-20T21:39:08.5063741Z >>> # On rank 0 2024-08-20T21:39:08.5063860Z >>> output 2024-08-20T21:39:08.5064017Z ['foo', 12, {1: 2}] 2024-08-20T21:39:08.5064111Z 2024-08-20T21:39:08.5064553Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5064648Z 2024-08-20T21:39:08.5064766Z warnings.warn(msg) 2024-08-20T21:39:08.5064872Z 2024-08-20T21:39:08.5065100Z --- Parse Warning: 33 / 101 --- 2024-08-20T21:39:08.5066498Z /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. 2024-08-20T21:39:08.5066937Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5067034Z 2024-08-20T21:39:08.5067287Z Module ``torch.distributed.launch``. 2024-08-20T21:39:08.5067381Z 2024-08-20T21:39:08.5067731Z ``torch.distributed.launch`` is a module that spawns up multiple distributed 2024-08-20T21:39:08.5067947Z training processes on each of the training nodes. 2024-08-20T21:39:08.5068047Z 2024-08-20T21:39:08.5068155Z .. warning:: 2024-08-20T21:39:08.5068261Z 2024-08-20T21:39:08.5068730Z This module is going to be deprecated in favor of :ref:`torchrun `. 2024-08-20T21:39:08.5068826Z 2024-08-20T21:39:08.5069274Z The utility can be used for single-node distributed training, in which one or 2024-08-20T21:39:08.5069615Z more processes per node will be spawned. The utility can be used for either 2024-08-20T21:39:08.5069934Z CPU training or GPU training. If the utility is used for GPU training, 2024-08-20T21:39:08.5070303Z each distributed process will be operating on a single GPU. This can achieve 2024-08-20T21:39:08.5070701Z well-improved single-node training performance. It can also be used in 2024-08-20T21:39:08.5071169Z multi-node distributed training, by spawning up multiple processes on each node 2024-08-20T21:39:08.5071563Z for well-improved multi-node distributed training performance as well. 2024-08-20T21:39:08.5071885Z This will especially be beneficial for systems with multiple Infiniband 2024-08-20T21:39:08.5072344Z interfaces that have direct-GPU support, since all of them can be utilized for 2024-08-20T21:39:08.5072491Z aggregated communication bandwidth. 2024-08-20T21:39:08.5072584Z 2024-08-20T21:39:08.5073020Z In both cases of single-node distributed training or multi-node distributed 2024-08-20T21:39:08.5073347Z training, this utility will launch the given number of processes per node 2024-08-20T21:39:08.5073773Z (``--nproc-per-node``). If used for GPU training, this number needs to be less 2024-08-20T21:39:08.5074108Z or equal to the number of GPUs on the current system (``nproc_per_node``), 2024-08-20T21:39:08.5074399Z and each process will be operating on a single GPU from *GPU 0 to 2024-08-20T21:39:08.5074585Z GPU (nproc_per_node - 1)*. 2024-08-20T21:39:08.5074679Z 2024-08-20T21:39:08.5074804Z **How to use this module:** 2024-08-20T21:39:08.5074911Z 2024-08-20T21:39:08.5075245Z 1. Single-Node multi-process distributed training 2024-08-20T21:39:08.5075339Z 2024-08-20T21:39:08.5075452Z :: 2024-08-20T21:39:08.5075544Z 2024-08-20T21:39:08.5075945Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-08-20T21:39:08.5076298Z YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other 2024-08-20T21:39:08.5076474Z arguments of your training script) 2024-08-20T21:39:08.5076569Z 2024-08-20T21:39:08.5076951Z 2. Multi-Node multi-process distributed training: (e.g. two nodes) 2024-08-20T21:39:08.5077043Z 2024-08-20T21:39:08.5077138Z 2024-08-20T21:39:08.5077363Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)* 2024-08-20T21:39:08.5077458Z 2024-08-20T21:39:08.5077555Z :: 2024-08-20T21:39:08.5077661Z 2024-08-20T21:39:08.5078062Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-08-20T21:39:08.5078505Z --nnodes=2 --node-rank=0 --master-addr="192.168.1.1" 2024-08-20T21:39:08.5078888Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2024-08-20T21:39:08.5079106Z and all other arguments of your training script) 2024-08-20T21:39:08.5079216Z 2024-08-20T21:39:08.5079317Z Node 2: 2024-08-20T21:39:08.5079412Z 2024-08-20T21:39:08.5079526Z :: 2024-08-20T21:39:08.5079618Z 2024-08-20T21:39:08.5080019Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-08-20T21:39:08.5080327Z --nnodes=2 --node-rank=1 --master-addr="192.168.1.1" 2024-08-20T21:39:08.5080760Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2024-08-20T21:39:08.5080979Z and all other arguments of your training script) 2024-08-20T21:39:08.5081087Z 2024-08-20T21:39:08.5081326Z 3. To look up what optional arguments this module offers: 2024-08-20T21:39:08.5081419Z 2024-08-20T21:39:08.5081533Z :: 2024-08-20T21:39:08.5081624Z 2024-08-20T21:39:08.5081867Z python -m torch.distributed.launch --help 2024-08-20T21:39:08.5081973Z 2024-08-20T21:39:08.5082068Z 2024-08-20T21:39:08.5082188Z **Important Notices:** 2024-08-20T21:39:08.5082297Z 2024-08-20T21:39:08.5082627Z 1. This utility and multi-process distributed (single-node or 2024-08-20T21:39:08.5083063Z multi-node) GPU training currently only achieves the best performance using 2024-08-20T21:39:08.5083418Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to 2024-08-20T21:39:08.5083539Z use for GPU training. 2024-08-20T21:39:08.5083647Z 2024-08-20T21:39:08.5084035Z 2. In your training program, you must parse the command-line argument: 2024-08-20T21:39:08.5084429Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module. 2024-08-20T21:39:08.5084772Z If your training program uses GPUs, you should ensure that your code only 2024-08-20T21:39:08.5085066Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by: 2024-08-20T21:39:08.5085160Z 2024-08-20T21:39:08.5085310Z Parsing the local_rank argument 2024-08-20T21:39:08.5085402Z 2024-08-20T21:39:08.5085498Z :: 2024-08-20T21:39:08.5085604Z 2024-08-20T21:39:08.5085726Z >>> # xdoctest: +SKIP 2024-08-20T21:39:08.5085844Z >>> import argparse 2024-08-20T21:39:08.5086033Z >>> parser = argparse.ArgumentParser() 2024-08-20T21:39:08.5086373Z >>> parser.add_argument("--local-rank", "--local_rank", type=int) 2024-08-20T21:39:08.5086527Z >>> args = parser.parse_args() 2024-08-20T21:39:08.5086620Z 2024-08-20T21:39:08.5086796Z Set your device to local rank using either 2024-08-20T21:39:08.5086903Z 2024-08-20T21:39:08.5086999Z :: 2024-08-20T21:39:08.5087091Z 2024-08-20T21:39:08.5087389Z >>> torch.cuda.set_device(args.local_rank) # before your code runs 2024-08-20T21:39:08.5087482Z 2024-08-20T21:39:08.5087643Z or 2024-08-20T21:39:08.5087751Z 2024-08-20T21:39:08.5087848Z :: 2024-08-20T21:39:08.5087941Z 2024-08-20T21:39:08.5088138Z >>> with torch.cuda.device(args.local_rank): 2024-08-20T21:39:08.5088261Z >>> # your code to run 2024-08-20T21:39:08.5088362Z >>> ... 2024-08-20T21:39:08.5088472Z 2024-08-20T21:39:08.5088599Z .. versionchanged:: 2.0.0 2024-08-20T21:39:08.5088694Z 2024-08-20T21:39:08.5089138Z The launcher will passes the ``--local-rank=`` argument to your script. 2024-08-20T21:39:08.5089565Z From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the 2024-08-20T21:39:08.5089818Z previously used underscored ``--local_rank``. 2024-08-20T21:39:08.5089925Z 2024-08-20T21:39:08.5090259Z For backward compatibility, it may be necessary for users to handle both 2024-08-20T21:39:08.5090740Z cases in their argument parsing code. This means including both ``"--local-rank"`` 2024-08-20T21:39:08.5091141Z and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is 2024-08-20T21:39:08.5091493Z provided, the launcher will trigger an error: "error: unrecognized arguments: 2024-08-20T21:39:08.5091914Z --local-rank=". For training code that only supports PyTorch 2.0.0+, 2024-08-20T21:39:08.5092179Z including ``"--local-rank"`` should be sufficient. 2024-08-20T21:39:08.5092274Z 2024-08-20T21:39:08.5092630Z 3. In your training program, you are supposed to call the following function 2024-08-20T21:39:08.5092986Z at the beginning to start the distributed backend. It is strongly recommended 2024-08-20T21:39:08.5093362Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work, 2024-08-20T21:39:08.5093665Z but ``env://`` is the one that is officially supported by this module. 2024-08-20T21:39:08.5093759Z 2024-08-20T21:39:08.5093855Z :: 2024-08-20T21:39:08.5093961Z 2024-08-20T21:39:08.5094324Z >>> torch.distributed.init_process_group(backend='YOUR BACKEND', 2024-08-20T21:39:08.5094606Z >>> init_method='env://') 2024-08-20T21:39:08.5094699Z 2024-08-20T21:39:08.5095044Z 4. In your training program, you can either use regular distributed functions 2024-08-20T21:39:08.5095408Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your 2024-08-20T21:39:08.5095696Z training program uses GPUs for training and you would like to use 2024-08-20T21:39:08.5096257Z :func:`torch.nn.parallel.DistributedDataParallel` module, 2024-08-20T21:39:08.5096406Z here is how to configure it. 2024-08-20T21:39:08.5096505Z 2024-08-20T21:39:08.5096603Z :: 2024-08-20T21:39:08.5096712Z 2024-08-20T21:39:08.5096993Z >>> model = torch.nn.parallel.DistributedDataParallel(model, 2024-08-20T21:39:08.5097209Z >>> device_ids=[args.local_rank], 2024-08-20T21:39:08.5097441Z >>> output_device=args.local_rank) 2024-08-20T21:39:08.5097537Z 2024-08-20T21:39:08.5097898Z Please ensure that ``device_ids`` argument is set to be the only GPU device id 2024-08-20T21:39:08.5098246Z that your code will be operating on. This is generally the local rank of the 2024-08-20T21:39:08.5098588Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``, 2024-08-20T21:39:08.5098914Z and ``output_device`` needs to be ``args.local_rank`` in order to use this 2024-08-20T21:39:08.5099015Z utility 2024-08-20T21:39:08.5099108Z 2024-08-20T21:39:08.5099487Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable 2024-08-20T21:39:08.5099795Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with 2024-08-20T21:39:08.5100199Z ``--use-env=True``. You must adjust the subprocess example above to replace 2024-08-20T21:39:08.5100564Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher 2024-08-20T21:39:08.5101043Z will not pass ``--local-rank`` when you specify this flag. 2024-08-20T21:39:08.5101142Z 2024-08-20T21:39:08.5101266Z .. warning:: 2024-08-20T21:39:08.5101360Z 2024-08-20T21:39:08.5101669Z ``local_rank`` is NOT globally unique: it is only unique per process 2024-08-20T21:39:08.5102024Z on a machine. Thus, don't use it to decide if you should, e.g., 2024-08-20T21:39:08.5102188Z write to a networked filesystem. See 2024-08-20T21:39:08.5102506Z https://github.com/pytorch/pytorch/issues/12042 for an example of 2024-08-20T21:39:08.5102814Z how things can go wrong if you don't do this correctly. 2024-08-20T21:39:08.5102908Z 2024-08-20T21:39:08.5103016Z 2024-08-20T21:39:08.5103110Z 2024-08-20T21:39:08.5103203Z 2024-08-20T21:39:08.5103639Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5103738Z 2024-08-20T21:39:08.5103857Z warnings.warn(msg) 2024-08-20T21:39:08.5103963Z 2024-08-20T21:39:08.5104190Z --- Parse Warning: 34 / 101 --- 2024-08-20T21:39:08.5105863Z /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. 2024-08-20T21:39:08.5106314Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5106408Z 2024-08-20T21:39:08.5106850Z Creates an :class:`ShardedTensor` from local shards and the global metadata. 2024-08-20T21:39:08.5107064Z Needs to be called on all ranks in an SPMD fashion. 2024-08-20T21:39:08.5107158Z 2024-08-20T21:39:08.5107269Z Args: 2024-08-20T21:39:08.5107649Z local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list 2024-08-20T21:39:08.5107892Z of shards that represent the local shards on this rank. 2024-08-20T21:39:08.5108237Z global_size (int...): a list, tuple, or `torch.Size` of integers defining the 2024-08-20T21:39:08.5108402Z shape of the overall sharded tensor. 2024-08-20T21:39:08.5108497Z 2024-08-20T21:39:08.5108625Z Keyword args: 2024-08-20T21:39:08.5108981Z process_group (ProcessGroup, optional): The process group to work on. If None, 2024-08-20T21:39:08.5109156Z the default process group will be used. 2024-08-20T21:39:08.5109409Z init_rrefs (bool, optional): Whether or not to initialize 2024-08-20T21:39:08.5109706Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2024-08-20T21:39:08.5109999Z Need to initialize the RPC Framework if specified as ``True``. 2024-08-20T21:39:08.5110120Z Default: ``False``. 2024-08-20T21:39:08.5110214Z 2024-08-20T21:39:08.5110330Z Returns: 2024-08-20T21:39:08.5110539Z A :class:`ShardedTensor` object handle on this rank 2024-08-20T21:39:08.5110639Z 2024-08-20T21:39:08.5110745Z 2024-08-20T21:39:08.5110850Z Examples: 2024-08-20T21:39:08.5111219Z Suppose we want construct a sharded tensor on two ranks, global size = (10, 5), 2024-08-20T21:39:08.5111503Z each shard have a (5, 5) local tensor, we can do it like below: 2024-08-20T21:39:08.5111597Z 2024-08-20T21:39:08.5111704Z on rank 0: 2024-08-20T21:39:08.5111886Z >>> # xdoctest: +SKIP("not distributed") 2024-08-20T21:39:08.5112054Z >>> local_shard_metadata = ShardMetadata( 2024-08-20T21:39:08.5112187Z >>> shard_offsets=[0, 0], 2024-08-20T21:39:08.5112333Z >>> shard_lengths=[5, 5], 2024-08-20T21:39:08.5112472Z >>> placement="rank:0/cuda:0" 2024-08-20T21:39:08.5112571Z >>> ) 2024-08-20T21:39:08.5112853Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2024-08-20T21:39:08.5113116Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2024-08-20T21:39:08.5113301Z 2024-08-20T21:39:08.5113407Z on rank 1: 2024-08-20T21:39:08.5113571Z >>> # xdoctest: +SKIP("not distributed") 2024-08-20T21:39:08.5113751Z >>> local_shard_metadata = ShardMetadata( 2024-08-20T21:39:08.5113880Z >>> shard_offsets=[5, 0], 2024-08-20T21:39:08.5114008Z >>> shard_lengths=[5, 5], 2024-08-20T21:39:08.5114161Z >>> placement="rank:1/cuda:1" 2024-08-20T21:39:08.5114259Z >>> ) 2024-08-20T21:39:08.5114528Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2024-08-20T21:39:08.5114810Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2024-08-20T21:39:08.5114905Z 2024-08-20T21:39:08.5115337Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5115445Z 2024-08-20T21:39:08.5115564Z warnings.warn(msg) 2024-08-20T21:39:08.5115660Z 2024-08-20T21:39:08.5115904Z --- Parse Warning: 35 / 101 --- 2024-08-20T21:39:08.5117647Z /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. 2024-08-20T21:39:08.5118100Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5118303Z 2024-08-20T21:39:08.5118678Z Initialize a ShardedTensor given only one local tensor, global sharded tensor 2024-08-20T21:39:08.5118844Z size and sharding spec on each rank. 2024-08-20T21:39:08.5119002Z 2024-08-20T21:39:08.5119104Z Args: 2024-08-20T21:39:08.5119439Z local_tensor (Tensor): Single tensor of local shard stored in each rank. 2024-08-20T21:39:08.5119796Z sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): 2024-08-20T21:39:08.5120034Z The specification describing how to shard the Tensor. 2024-08-20T21:39:08.5120285Z global_size (Sequence[int]): Size of the sharded tensor. 2024-08-20T21:39:08.5120628Z process_group (ProcessGroup, optional): The process group to aggregate on. 2024-08-20T21:39:08.5120757Z Default: None 2024-08-20T21:39:08.5120994Z init_rrefs (bool, optional): Whether or not to initialize 2024-08-20T21:39:08.5121286Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2024-08-20T21:39:08.5121582Z Need to initialize the RPC Framework if specified as ``True``. 2024-08-20T21:39:08.5121703Z Default: ``False``. 2024-08-20T21:39:08.5121801Z 2024-08-20T21:39:08.5121919Z Returns: 2024-08-20T21:39:08.5122265Z A :class:`ShardedTensor` sharded based on the given sharding_spec with local 2024-08-20T21:39:08.5122426Z tensor stored in the current rank. 2024-08-20T21:39:08.5122532Z 2024-08-20T21:39:08.5122641Z Examples: 2024-08-20T21:39:08.5122764Z >>> # xdoctest: +SKIP 2024-08-20T21:39:08.5122973Z >>> # All tensors below are of torch.int64 type. 2024-08-20T21:39:08.5123137Z >>> # We have 2 process groups, 2 ranks. 2024-08-20T21:39:08.5123391Z >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank 2024-08-20T21:39:08.5123681Z >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2])) 2024-08-20T21:39:08.5123792Z >>> local_tensor 2024-08-20T21:39:08.5123934Z tensor([[1, 2, 3, 4]]) # Rank 0 2024-08-20T21:39:08.5124061Z tensor([[3, 4, 5, 6]]) # Rank 1 2024-08-20T21:39:08.5124177Z >>> sharding_dim = 0 2024-08-20T21:39:08.5124365Z >>> sharding_spec = ChunkShardingSpec( 2024-08-20T21:39:08.5124487Z dim=sharding_dim, 2024-08-20T21:39:08.5124604Z placements=[ 2024-08-20T21:39:08.5124737Z "rank:0/cuda:0", 2024-08-20T21:39:08.5124857Z "rank:1/cuda:1", 2024-08-20T21:39:08.5125013Z ], 2024-08-20T21:39:08.5125125Z ) 2024-08-20T21:39:08.5125480Z >>> st = ShardedTensor._init_from_local_tensor(local_tensor, sharding_spec, [2, 4]) 2024-08-20T21:39:08.5125581Z >>> st 2024-08-20T21:39:08.5125708Z ShardedTensor( 2024-08-20T21:39:08.5125844Z ShardedTensorMetadata( 2024-08-20T21:39:08.5125965Z shards_metadata=[ 2024-08-20T21:39:08.5126350Z ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0), 2024-08-20T21:39:08.5126706Z ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1), 2024-08-20T21:39:08.5126826Z ], 2024-08-20T21:39:08.5126958Z size=torch.Size([2, 4]) 2024-08-20T21:39:08.5127056Z ) 2024-08-20T21:39:08.5127187Z >>> st.local_tensor() 2024-08-20T21:39:08.5127314Z tensor([1, 2, 3, 4]) # Rank 0 2024-08-20T21:39:08.5127439Z tensor([3, 4, 5, 6]) # Rank 1 2024-08-20T21:39:08.5127548Z 2024-08-20T21:39:08.5127933Z Warning: This API is experimental and subject to change. It lacks of a fully across 2024-08-20T21:39:08.5128281Z rank validations, and we only validate the local shard on the current rank. 2024-08-20T21:39:08.5128630Z We fully rely on the user to ensure local tensor is sharded based on the 2024-08-20T21:39:08.5128748Z sharding spec. 2024-08-20T21:39:08.5128842Z 2024-08-20T21:39:08.5129304Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5129397Z 2024-08-20T21:39:08.5129517Z warnings.warn(msg) 2024-08-20T21:39:08.5129684Z 2024-08-20T21:39:08.5129916Z --- Parse Warning: 36 / 101 --- 2024-08-20T21:39:08.5131587Z /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. 2024-08-20T21:39:08.5132034Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5132128Z 2024-08-20T21:39:08.5132507Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support 2024-08-20T21:39:08.5132624Z single local shard. 2024-08-20T21:39:08.5132717Z 2024-08-20T21:39:08.5133123Z If ``resharding_spec`` is same as the original one, this becomes a no-op. 2024-08-20T21:39:08.5133469Z If only ``resharding_spec`` shares the same sharding dim with the original one, 2024-08-20T21:39:08.5133605Z we swap local shards directly. 2024-08-20T21:39:08.5133996Z For more generic cases, we merge different shards across different ranks and split 2024-08-20T21:39:08.5134363Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API. 2024-08-20T21:39:08.5134469Z 2024-08-20T21:39:08.5134568Z Args: 2024-08-20T21:39:08.5134971Z resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The 2024-08-20T21:39:08.5135209Z specification describing how the tensor is sharded. 2024-08-20T21:39:08.5135303Z 2024-08-20T21:39:08.5135405Z Returns: 2024-08-20T21:39:08.5135702Z A :class:`ShardedTensor` object whose local shards are resharded. 2024-08-20T21:39:08.5135794Z 2024-08-20T21:39:08.5135898Z Examples: 2024-08-20T21:39:08.5136035Z >>> # xdoctest: +SKIP 2024-08-20T21:39:08.5136201Z >>> # We have 2 process groups, 2 ranks. 2024-08-20T21:39:08.5136454Z >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank 2024-08-20T21:39:08.5136639Z >>> tensor = torch.stack([tensor, tensor]) 2024-08-20T21:39:08.5136744Z >>> tensor 2024-08-20T21:39:08.5136914Z tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0 2024-08-20T21:39:08.5137092Z tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1 2024-08-20T21:39:08.5137253Z tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2 2024-08-20T21:39:08.5137500Z tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3 2024-08-20T21:39:08.5137620Z >>> sharding_dim = 0 2024-08-20T21:39:08.5137763Z >>> spec = ChunkShardingSpec( 2024-08-20T21:39:08.5137900Z dim=sharding_dim, 2024-08-20T21:39:08.5138017Z placements=[ 2024-08-20T21:39:08.5138138Z "rank:0/cuda:0", 2024-08-20T21:39:08.5138267Z "rank:1/cuda:1", 2024-08-20T21:39:08.5138381Z "rank:2/cuda:2", 2024-08-20T21:39:08.5138494Z "rank:3/cuda:3", 2024-08-20T21:39:08.5138608Z ], 2024-08-20T21:39:08.5138710Z ) 2024-08-20T21:39:08.5138842Z >>> current_offsets = [0] * 2 2024-08-20T21:39:08.5138998Z >>> current_offsets[0] = rank * 2 2024-08-20T21:39:08.5139151Z >>> shard_metadata = ShardMetadata( 2024-08-20T21:39:08.5139355Z shard_offsets=copy.deepcopy(current_offsets), 2024-08-20T21:39:08.5139517Z shard_sizes=tensor.size(), 2024-08-20T21:39:08.5139683Z placement=spec.placements[rank], 2024-08-20T21:39:08.5139781Z ) 2024-08-20T21:39:08.5139911Z >>> local_shards = [ 2024-08-20T21:39:08.5140013Z Shard( 2024-08-20T21:39:08.5140132Z tensor=tensor, 2024-08-20T21:39:08.5140293Z metadata=shard_metadata, 2024-08-20T21:39:08.5140392Z ) 2024-08-20T21:39:08.5140505Z ] 2024-08-20T21:39:08.5140824Z >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size()) 2024-08-20T21:39:08.5140941Z >>> sharding_dim = 1 2024-08-20T21:39:08.5141185Z >>> resharding_spec = ChunkShardingSpec( 2024-08-20T21:39:08.5141308Z dim=sharding_dim, 2024-08-20T21:39:08.5141424Z placements=[ 2024-08-20T21:39:08.5141554Z "rank:0/cuda:0", 2024-08-20T21:39:08.5141670Z "rank:1/cuda:1", 2024-08-20T21:39:08.5141789Z "rank:2/cuda:2", 2024-08-20T21:39:08.5141917Z "rank:3/cuda:3", 2024-08-20T21:39:08.5142017Z ], 2024-08-20T21:39:08.5142115Z ) 2024-08-20T21:39:08.5142267Z >>> st.reshard(resharding_spec) 2024-08-20T21:39:08.5142423Z >>> tensor = st.local_shards()[0].tensor 2024-08-20T21:39:08.5142525Z >>> tensor 2024-08-20T21:39:08.5142749Z tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0 2024-08-20T21:39:08.5142948Z tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1 2024-08-20T21:39:08.5143153Z tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2 2024-08-20T21:39:08.5143374Z tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3 2024-08-20T21:39:08.5143469Z 2024-08-20T21:39:08.5143919Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5144012Z 2024-08-20T21:39:08.5144129Z warnings.warn(msg) 2024-08-20T21:39:08.5144241Z 2024-08-20T21:39:08.5144469Z --- Parse Warning: 37 / 101 --- 2024-08-20T21:39:08.5146046Z /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. 2024-08-20T21:39:08.5146499Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5146594Z 2024-08-20T21:39:08.5146889Z Representation of a sharding plan, describes how to shard a module 2024-08-20T21:39:08.5147308Z across hosts. `plan` is used to shard module parameters according to the spec provided, 2024-08-20T21:39:08.5147704Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output 2024-08-20T21:39:08.5148093Z layout of a module with a spec, and when to convert back to data parallel fashion. 2024-08-20T21:39:08.5148247Z 2024-08-20T21:39:08.5148347Z Args: 2024-08-20T21:39:08.5148752Z plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`, 2024-08-20T21:39:08.5148983Z :class:`torch.distributed._shard.sharder.Sharder`]): 2024-08-20T21:39:08.5149488Z a dict describes how to shard a module, there're currently two ways to shard a module: 2024-08-20T21:39:08.5149876Z 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of 2024-08-20T21:39:08.5150044Z a parameter to a `ShardingSpec`. 2024-08-20T21:39:08.5150428Z 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module 2024-08-20T21:39:08.5150581Z to a `Sharder` object. 2024-08-20T21:39:08.5151044Z output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional): 2024-08-20T21:39:08.5151532Z a dict specifies the layout of a module's output which produces a ShardedTensor, 2024-08-20T21:39:08.5151894Z keyed by the name of module to ShardingSpec("" in key means the root module). 2024-08-20T21:39:08.5152009Z Default: `None` 2024-08-20T21:39:08.5152384Z return_local_tensor (List[str], optional): a list of string, each element enables 2024-08-20T21:39:08.5152826Z a module's sharded output to be returned as a Tensor from its local shards to 2024-08-20T21:39:08.5153173Z ensure further processing in a data parallel fashion. ("" in list means the 2024-08-20T21:39:08.5153297Z root module). 2024-08-20T21:39:08.5153410Z Default: None 2024-08-20T21:39:08.5153571Z Example: 2024-08-20T21:39:08.5154013Z Suppose we want to shard a module with two linear layers and then run it with DDP, we also 2024-08-20T21:39:08.5154442Z want to convert the output of the second linear layer back to DDP, we can do it as follows: 2024-08-20T21:39:08.5154550Z 2024-08-20T21:39:08.5154792Z >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d) 2024-08-20T21:39:08.5154930Z >>> class MyModule(nn.Module): 2024-08-20T21:39:08.5155142Z >>> def __init__(self) -> None: 2024-08-20T21:39:08.5155268Z >>> super().__init__() 2024-08-20T21:39:08.5155403Z >>> self.fc1 = nn.Linear() 2024-08-20T21:39:08.5155551Z >>> self.gelu = nn.GELU() 2024-08-20T21:39:08.5155686Z >>> self.fc2 = nn.Linear() 2024-08-20T21:39:08.5155823Z >>> self.relu = nn.Linear() 2024-08-20T21:39:08.5155936Z >>> 2024-08-20T21:39:08.5156073Z >>> def forward(self, input): 2024-08-20T21:39:08.5156321Z >>> return self.relu(self.fc2(self.gelu(self.fc1(input)))) 2024-08-20T21:39:08.5156429Z 2024-08-20T21:39:08.5156521Z 2024-08-20T21:39:08.5156709Z >>> # xdoctest: +SKIP("Undefined spec1, spec2) 2024-08-20T21:39:08.5156869Z >>> sharding_plan = ShardingPlan( 2024-08-20T21:39:08.5156978Z >>> plan={ 2024-08-20T21:39:08.5157122Z >>> "fc1.weight": spec1, 2024-08-20T21:39:08.5157249Z >>> "fc2.weight": spec2 2024-08-20T21:39:08.5157350Z >>> }, 2024-08-20T21:39:08.5157482Z >>> output_plan={ 2024-08-20T21:39:08.5157608Z >>> "fc2": output_spec 2024-08-20T21:39:08.5157708Z >>> }, 2024-08-20T21:39:08.5157862Z >>> return_local_tensor=["fc2"] 2024-08-20T21:39:08.5157960Z >>> ) 2024-08-20T21:39:08.5158053Z 2024-08-20T21:39:08.5158634Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5158730Z 2024-08-20T21:39:08.5158854Z warnings.warn(msg) 2024-08-20T21:39:08.5158963Z 2024-08-20T21:39:08.5159197Z --- Parse Warning: 38 / 101 --- 2024-08-20T21:39:08.5160781Z /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. 2024-08-20T21:39:08.5161328Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5161422Z 2024-08-20T21:39:08.5161813Z ``local_map`` is an experimental API that allows users to apply on :class:`DTensors` 2024-08-20T21:39:08.5162120Z a function that is written to be applied on :class:`~torch.Tensors`. 2024-08-20T21:39:08.5162214Z 2024-08-20T21:39:08.5162329Z Args: 2024-08-20T21:39:08.5162626Z func (Callable): the function to be applied on each local shard of 2024-08-20T21:39:08.5162750Z :class:`DTensor`s. 2024-08-20T21:39:08.5163069Z out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]): 2024-08-20T21:39:08.5163527Z the desired placements of the :class:`DTensor`s in ``func``'s flattened output. 2024-08-20T21:39:08.5163880Z If the flattened ``output`` is a single value, the ``out_placements`` should be 2024-08-20T21:39:08.5164244Z of type `PlacementType`. Otherwise if the flattened ``output`` has multiple 2024-08-20T21:39:08.5164590Z values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1 2024-08-20T21:39:08.5164754Z mapping to the flattened ``output``. 2024-08-20T21:39:08.5165057Z Besides, for :class:`Tensor` output, we use `PlacementType` as its 2024-08-20T21:39:08.5165466Z placements (a `Tuple[Placement]` value). For non-:class:`Tensor` output, 2024-08-20T21:39:08.5165646Z the `PlacementType` should be `None`. 2024-08-20T21:39:08.5166047Z Note that the only exception is when no :class:`DTensor` argument is passed 2024-08-20T21:39:08.5166392Z in. In this case, even if `out_placements` is not `None`, the result function 2024-08-20T21:39:08.5166733Z should ignore the desired placements because the application is not on 2024-08-20T21:39:08.5166860Z :class:`DTensors`. 2024-08-20T21:39:08.5167088Z in_placements (Tuple[`PlacementType`, ...], optional): 2024-08-20T21:39:08.5167555Z the required placements of the :class:`DTensor`s in ``func``'s flattened input. 2024-08-20T21:39:08.5167871Z If ``in_placements`` is specified, ``local_map`` would examine whether the 2024-08-20T21:39:08.5168224Z placements of each :class:`DTensor` argument is the same as the required 2024-08-20T21:39:08.5168481Z placements or not. If the placements are not the same and 2024-08-20T21:39:08.5168843Z ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if 2024-08-20T21:39:08.5169187Z ``redistribute_inputs`` is `True`, the argument will be first redistributed to 2024-08-20T21:39:08.5169552Z the required sharding placements before passing its local tensor to ``func``. 2024-08-20T21:39:08.5169891Z The only exception is when required placements are not ``None`` and the 2024-08-20T21:39:08.5170248Z argument is a :class:`torch.Tensor`. In this case, the placements examination 2024-08-20T21:39:08.5170564Z will be skipped and the argument will be directly passed to ``func``. 2024-08-20T21:39:08.5170911Z If ``in_placements`` is ``None``, no placements examination will be performed. 2024-08-20T21:39:08.5171029Z Default: None 2024-08-20T21:39:08.5171231Z device_mesh (:class:`DeviceMesh`, optional): 2024-08-20T21:39:08.5171543Z the device mesh that all the :class:`DTensor`s are placed on. If not 2024-08-20T21:39:08.5171967Z specified, this will be inferred from the input :class:`DTensor`s' device 2024-08-20T21:39:08.5172315Z mesh. `local_map` requires every :class:`DTensor`s to be placed on the same 2024-08-20T21:39:08.5172454Z device mesh. Default: None. 2024-08-20T21:39:08.5172620Z redistribute_inputs (bool, optional): 2024-08-20T21:39:08.5173061Z the bool value indicating whether to reshard the input :class:`DTensor`s when 2024-08-20T21:39:08.5173411Z their placements are different from the required input placements. If this 2024-08-20T21:39:08.5173748Z value is ``False`` and some :class:`DTensor` input has a different placement, 2024-08-20T21:39:08.5173958Z an exception will be raised. Default: False. 2024-08-20T21:39:08.5174055Z 2024-08-20T21:39:08.5174155Z Returns: 2024-08-20T21:39:08.5174547Z A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor` 2024-08-20T21:39:08.5174893Z and returns a :class:`DTensor` constructed from the return value of ``func``. 2024-08-20T21:39:08.5175005Z 2024-08-20T21:39:08.5175103Z Raises: 2024-08-20T21:39:08.5175470Z AssertionError: If the input :class:`DTensor`s are not placed on the same device 2024-08-20T21:39:08.5175842Z mesh, or if they are placed on a different device mesh than the ``device_mesh`` 2024-08-20T21:39:08.5175967Z argument passed in. 2024-08-20T21:39:08.5176060Z 2024-08-20T21:39:08.5176535Z AssertionError: For any non-:class:`DTensor` output, we require its corresponding 2024-08-20T21:39:08.5176899Z output placement in ``out_placements`` be None. An AssertionError will be raised 2024-08-20T21:39:08.5177027Z if this is not the case. 2024-08-20T21:39:08.5177133Z 2024-08-20T21:39:08.5177491Z ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs 2024-08-20T21:39:08.5177696Z a redistribution according to ``in_placements``. 2024-08-20T21:39:08.5177803Z 2024-08-20T21:39:08.5177969Z Example: 2024-08-20T21:39:08.5178143Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:39:08.5178329Z >>> def mm_allreduce_forward(device_mesh, W, X): 2024-08-20T21:39:08.5178499Z >>> partial_sum_tensor = torch.mm(W, X) 2024-08-20T21:39:08.5178845Z >>> reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh) 2024-08-20T21:39:08.5178980Z >>> return reduced_tensor 2024-08-20T21:39:08.5179078Z >>> 2024-08-20T21:39:08.5179273Z >>> W = torch.randn(12, 8, requires_grad=False) 2024-08-20T21:39:08.5179450Z >>> X = torch.randn(8, 16, requires_grad=False) 2024-08-20T21:39:08.5179571Z >>> Y = torch.mm(W, X) 2024-08-20T21:39:08.5179943Z >>> row_wise = [Shard(0)] # row-wise sharding placements on 1-d mesh 2024-08-20T21:39:08.5180294Z >>> col_wise = [Shard(1)] # col-wise sharding placements on 1-d mesh 2024-08-20T21:39:08.5180394Z >>> 2024-08-20T21:39:08.5180789Z >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor convertion 2024-08-20T21:39:08.5180959Z >>> local_mm_allreduce_forward = local_map( 2024-08-20T21:39:08.5181102Z >>> mm_allreduce_forward, 2024-08-20T21:39:08.5181256Z >>> out_placements=[Replicate()], 2024-08-20T21:39:08.5181423Z >>> in_placements=[col_wise, row_wise], 2024-08-20T21:39:08.5181574Z >>> device_mesh=device_mesh, 2024-08-20T21:39:08.5181671Z >>> ) 2024-08-20T21:39:08.5181767Z >>> 2024-08-20T21:39:08.5182248Z >>> W_dt = distribute_tensor(W, device_mesh, (col_wise)) # col-wisely sharded W tensor 2024-08-20T21:39:08.5182714Z >>> X_dt = distribute_tensor(X, device_mesh, (row_wise)) # row-wisely sharded X tensor 2024-08-20T21:39:08.5183198Z >>> Y_dt = local_mm_allreduce_forward(device_mesh, W_dt, X_dt) # apply local_mm_allreduce_forward to DTensors 2024-08-20T21:39:08.5183305Z 2024-08-20T21:39:08.5183581Z NOTE: This API is currently experimental and subject to change 2024-08-20T21:39:08.5183675Z 2024-08-20T21:39:08.5184111Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5184205Z 2024-08-20T21:39:08.5184322Z warnings.warn(msg) 2024-08-20T21:39:08.5184428Z 2024-08-20T21:39:08.5184717Z --- Parse Warning: 39 / 101 --- 2024-08-20T21:39:08.5186422Z /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. 2024-08-20T21:39:08.5186863Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5186957Z 2024-08-20T21:39:08.5187341Z ``register_sharding`` is an experimental API that allows users to register sharding 2024-08-20T21:39:08.5187732Z strategies for an operator when the tensor inputs and outputs are :class:`DTensor`s. 2024-08-20T21:39:08.5188203Z It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``, 2024-08-20T21:39:08.5188585Z e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2) 2024-08-20T21:39:08.5188981Z when users would like to overwrite default sharding strategies of existing operators. 2024-08-20T21:39:08.5189091Z 2024-08-20T21:39:08.5189188Z Args: 2024-08-20T21:39:08.5189364Z op (Union[OpOverload, List[OpOverload]]): 2024-08-20T21:39:08.5189679Z An op or a list of ops to register the customized sharding function. 2024-08-20T21:39:08.5189773Z 2024-08-20T21:39:08.5189874Z Returns: 2024-08-20T21:39:08.5190274Z A function decorator which can be used to wrap a function that defines the sharding 2024-08-20T21:39:08.5190663Z strategy for the operator specified in ``op``. The defined sharding strategy will be 2024-08-20T21:39:08.5191113Z registered to DTensor and will override the default sharding strategy if DTensor has 2024-08-20T21:39:08.5191549Z already implemented the operator. The customized sharding function takes the same inputs 2024-08-20T21:39:08.5191910Z as the original op (except that if an arg is a :class:`torch.Tensor`, it will be 2024-08-20T21:39:08.5192418Z replaced by a tensor-like object that DTensor uses internally). The function should 2024-08-20T21:39:08.5192893Z return a sequence of 2-tuples, each specifying acceptable output placements and its 2024-08-20T21:39:08.5193044Z corresponding intput placements. 2024-08-20T21:39:08.5193150Z 2024-08-20T21:39:08.5193254Z Example: 2024-08-20T21:39:08.5193415Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:39:08.5193610Z >>> @register_sharding(aten._softmax.default) 2024-08-20T21:39:08.5193821Z >>> def custom_softmax_sharding(x, dim, half_to_float): 2024-08-20T21:39:08.5194028Z >>> softmax_dim = dim if dim >= 0 else dim + x.ndim 2024-08-20T21:39:08.5194184Z >>> acceptable_shardings = [] 2024-08-20T21:39:08.5194284Z >>> 2024-08-20T21:39:08.5194536Z >>> all_replicate = ([Replicate()], [Replicate(), None, None]) 2024-08-20T21:39:08.5194747Z >>> acceptable_shardings.append(all_replicate) 2024-08-20T21:39:08.5194849Z >>> 2024-08-20T21:39:08.5195013Z >>> for sharding_dim in range(x.ndim): 2024-08-20T21:39:08.5195184Z >>> if sharding_dim != softmax_dim: 2024-08-20T21:39:08.5195315Z >>> all_sharded = ( 2024-08-20T21:39:08.5195482Z >>> [Shard(sharding_dim)], 2024-08-20T21:39:08.5195666Z >>> [Shard(sharding_dim), None, None], 2024-08-20T21:39:08.5195771Z >>> ) 2024-08-20T21:39:08.5196278Z >>> acceptable_shardings.append(all_sharded) 2024-08-20T21:39:08.5196376Z >>> 2024-08-20T21:39:08.5196524Z >>> return acceptable_shardings 2024-08-20T21:39:08.5196637Z 2024-08-20T21:39:08.5197067Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5197160Z 2024-08-20T21:39:08.5197294Z warnings.warn(msg) 2024-08-20T21:39:08.5197389Z 2024-08-20T21:39:08.5197615Z --- Parse Warning: 40 / 101 --- 2024-08-20T21:39:08.5199696Z /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. 2024-08-20T21:39:08.5200149Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5200260Z 2024-08-20T21:39:08.5200443Z Run post-localSGD algorithm. 2024-08-20T21:39:08.5200535Z 2024-08-20T21:39:08.5200960Z This DDP communication hook is used for running post-localSGD algorithm, 2024-08-20T21:39:08.5201182Z by combining with a model averaging component (e.g., 2024-08-20T21:39:08.5201646Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`) 2024-08-20T21:39:08.5201808Z that runs after the optimizer step. 2024-08-20T21:39:08.5201900Z 2024-08-20T21:39:08.5201997Z Args: 2024-08-20T21:39:08.5202392Z state (PostLocalSGDState): State information to run post-localSGD. 2024-08-20T21:39:08.5202786Z Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD. 2024-08-20T21:39:08.5203498Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2024-08-20T21:39:08.5203878Z Note that since DDP comm hook only supports single process single device mode, 2024-08-20T21:39:08.5204089Z only exactly one tensor is stored in this bucket. 2024-08-20T21:39:08.5204196Z 2024-08-20T21:39:08.5204298Z Returns: 2024-08-20T21:39:08.5204788Z Future handler of the communication, which updates the gradients in place. 2024-08-20T21:39:08.5204897Z 2024-08-20T21:39:08.5205031Z Example:: 2024-08-20T21:39:08.5205153Z >>> # xdoctest: +SKIP 2024-08-20T21:39:08.5205508Z >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup, 2024-08-20T21:39:08.5205681Z start_localSGD_iter=10) 2024-08-20T21:39:08.5205923Z >>> ddp_model.register_comm_hook(state, post_localSGD_hook) 2024-08-20T21:39:08.5206431Z >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``. 2024-08-20T21:39:08.5206938Z >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module. 2024-08-20T21:39:08.5207051Z 2024-08-20T21:39:08.5207477Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5207570Z 2024-08-20T21:39:08.5207705Z warnings.warn(msg) 2024-08-20T21:39:08.5207798Z 2024-08-20T21:39:08.5208023Z --- Parse Warning: 41 / 101 --- 2024-08-20T21:39:08.5209735Z /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. 2024-08-20T21:39:08.5210175Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5210268Z 2024-08-20T21:39:08.5210421Z Implement PowerSGD algorithm. 2024-08-20T21:39:08.5210513Z 2024-08-20T21:39:08.5210824Z This DDP communication hook implements PowerSGD gradient compression 2024-08-20T21:39:08.5211170Z algorithm described in the `paper `_. 2024-08-20T21:39:08.5211507Z Once gradient tensors are aggregated across all workers, this hook applies 2024-08-20T21:39:08.5211650Z compression as follows: 2024-08-20T21:39:08.5211743Z 2024-08-20T21:39:08.5212505Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups: 2024-08-20T21:39:08.5212612Z 2024-08-20T21:39:08.5213210Z 1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth. 2024-08-20T21:39:08.5213368Z 2024-08-20T21:39:08.5213973Z 1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases). 2024-08-20T21:39:08.5214066Z 2024-08-20T21:39:08.5214204Z 2. Handles uncompressed tensors: 2024-08-20T21:39:08.5214311Z 2024-08-20T21:39:08.5215026Z 2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression; 2024-08-20T21:39:08.5215117Z 2024-08-20T21:39:08.5215618Z 2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor. 2024-08-20T21:39:08.5215711Z 2024-08-20T21:39:08.5216054Z 3. Handles the tensors that should be compressed by PowerSGD compression: 2024-08-20T21:39:08.5216147Z 2024-08-20T21:39:08.5216602Z 3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M, 2024-08-20T21:39:08.5217086Z such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized; 2024-08-20T21:39:08.5217179Z 2024-08-20T21:39:08.5217385Z 3.2. Computes each P in Ps, which is equal to MQ; 2024-08-20T21:39:08.5217491Z 2024-08-20T21:39:08.5217629Z 3.3. Allreduces Ps as a batch; 2024-08-20T21:39:08.5217723Z 2024-08-20T21:39:08.5217890Z 3.4. Orthogonalizes each P in Ps; 2024-08-20T21:39:08.5217984Z 2024-08-20T21:39:08.5218278Z 3.5. Computes each Q in Qs, which is approximately equal to M^TP; 2024-08-20T21:39:08.5218389Z 2024-08-20T21:39:08.5218582Z 3.6. Allreduces Qs as a batch; 2024-08-20T21:39:08.5218679Z 2024-08-20T21:39:08.5219141Z 3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T. 2024-08-20T21:39:08.5219238Z 2024-08-20T21:39:08.5219818Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations. 2024-08-20T21:39:08.5220256Z This not only gives the user more control over the tradeoff between speedup and accuracy, 2024-08-20T21:39:08.5220876Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers. 2024-08-20T21:39:08.5220988Z 2024-08-20T21:39:08.5221089Z Args: 2024-08-20T21:39:08.5221681Z state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc. 2024-08-20T21:39:08.5222217Z To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter`` 2024-08-20T21:39:08.5222368Z and ``min_compression_rate``. 2024-08-20T21:39:08.5223093Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2024-08-20T21:39:08.5223478Z Note that since DDP comm hook only supports single process single device mode, 2024-08-20T21:39:08.5223694Z only exactly one tensor is stored in this bucket. 2024-08-20T21:39:08.5223805Z 2024-08-20T21:39:08.5223909Z Returns: 2024-08-20T21:39:08.5224255Z Future handler of the communication, which updates the gradients in place. 2024-08-20T21:39:08.5224366Z 2024-08-20T21:39:08.5224477Z Example:: 2024-08-20T21:39:08.5224601Z >>> # xdoctest: +SKIP 2024-08-20T21:39:08.5224982Z >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, 2024-08-20T21:39:08.5225214Z start_powerSGD_iter=10, min_compression_rate=0.5) 2024-08-20T21:39:08.5225439Z >>> ddp_model.register_comm_hook(state, powerSGD_hook) 2024-08-20T21:39:08.5225549Z 2024-08-20T21:39:08.5225975Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5226071Z 2024-08-20T21:39:08.5226201Z warnings.warn(msg) 2024-08-20T21:39:08.5226353Z 2024-08-20T21:39:08.5226580Z --- Parse Warning: 42 / 101 --- 2024-08-20T21:39:08.5228328Z /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. 2024-08-20T21:39:08.5228766Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5228875Z 2024-08-20T21:39:08.5229186Z Averages parameters periodically after the warm-up stage. 2024-08-20T21:39:08.5229281Z 2024-08-20T21:39:08.5229766Z This can be used for running `post-local SGD `_, 2024-08-20T21:39:08.5230033Z by running :class:`~torch.nn.DistributedDataParallel` (DDP) 2024-08-20T21:39:08.5230362Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`. 2024-08-20T21:39:08.5230469Z 2024-08-20T21:39:08.5230572Z Args: 2024-08-20T21:39:08.5230804Z period (int): The number of steps per model averaging. 2024-08-20T21:39:08.5231207Z Usually the period should be greater than ``1`` to reduce the communication cost. 2024-08-20T21:39:08.5231393Z Otherwise, only DDP needs to be used. 2024-08-20T21:39:08.5231777Z warmup_steps (int): The number of warm-up steps. During this stage, 2024-08-20T21:39:08.5231946Z model averaging is skipped. 2024-08-20T21:39:08.5232272Z process_group: The process group to be used for all-reduce. 2024-08-20T21:39:08.5232549Z If ``None``, the default process group, which 2024-08-20T21:39:08.5232827Z is created by :func:`torch.distributed.init_process_group`, 2024-08-20T21:39:08.5233000Z will be used. (default: ``None``) 2024-08-20T21:39:08.5233113Z 2024-08-20T21:39:08.5233220Z Example:: 2024-08-20T21:39:08.5233319Z 2024-08-20T21:39:08.5233512Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:39:08.5233625Z >>> import torch 2024-08-20T21:39:08.5233784Z >>> import torch.distributed as dist 2024-08-20T21:39:08.5234227Z >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD 2024-08-20T21:39:08.5234601Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2024-08-20T21:39:08.5234741Z >>> import torch.nn as nn 2024-08-20T21:39:08.5234840Z >>> 2024-08-20T21:39:08.5235082Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2024-08-20T21:39:08.5235236Z >>> torch.cuda.set_device(rank) 2024-08-20T21:39:08.5235418Z >>> module = nn.Linear(1, 1, bias=False).cuda() 2024-08-20T21:39:08.5235634Z >>> model = nn.parallel.DistributedDataParallel( 2024-08-20T21:39:08.5235843Z >>> module, device_ids=[rank], output_device=rank 2024-08-20T21:39:08.5235947Z >>> ) 2024-08-20T21:39:08.5236220Z >>> # Register a post-localSGD communication hook. 2024-08-20T21:39:08.5236632Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2024-08-20T21:39:08.5236851Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-08-20T21:39:08.5236950Z >>> 2024-08-20T21:39:08.5237363Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2024-08-20T21:39:08.5237584Z >>> # After 100 steps, run model averaging every 4 steps. 2024-08-20T21:39:08.5238065Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-08-20T21:39:08.5238528Z >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2024-08-20T21:39:08.5238665Z >>> for step in range(0, 200): 2024-08-20T21:39:08.5238814Z >>> optimizer.zero_grad() 2024-08-20T21:39:08.5238966Z >>> loss = loss_fn(output, labels) 2024-08-20T21:39:08.5239156Z >>> loss.backward() 2024-08-20T21:39:08.5239299Z >>> optimizer.step() 2024-08-20T21:39:08.5239574Z >>> # Will average model parameters globally every 4 steps. Thus, 2024-08-20T21:39:08.5239945Z >>> # inter-node communication only occurs every 4 iterations after 2024-08-20T21:39:08.5240130Z >>> # the initial ``warmup_steps`` period. 2024-08-20T21:39:08.5240347Z >>> averager.average_parameters(model.parameters()) 2024-08-20T21:39:08.5240455Z 2024-08-20T21:39:08.5240881Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5240980Z 2024-08-20T21:39:08.5241113Z warnings.warn(msg) 2024-08-20T21:39:08.5241209Z 2024-08-20T21:39:08.5241435Z --- Parse Warning: 43 / 101 --- 2024-08-20T21:39:08.5243426Z /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. 2024-08-20T21:39:08.5243871Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5243965Z 2024-08-20T21:39:08.5244436Z Runs hierarchical model averaging (`hierarchical SGD `_). 2024-08-20T21:39:08.5244531Z 2024-08-20T21:39:08.5244984Z Process groups of different sizes are organized in a hierarchy, and they average parameters 2024-08-20T21:39:08.5245347Z by using different periods concurrently after the warm-up stage. 2024-08-20T21:39:08.5246005Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager` 2024-08-20T21:39:08.5246595Z that supports `post-local SGD `_, which essentially only supports 2024-08-20T21:39:08.5247131Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine 2024-08-20T21:39:08.5247633Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`. 2024-08-20T21:39:08.5248157Z Similarly, the process groups within this class do not have such an intra-machine process 2024-08-20T21:39:08.5248634Z subgroup, which should be embedded by the post-local SGD communication hook instead. 2024-08-20T21:39:08.5248741Z 2024-08-20T21:39:08.5248840Z Args: 2024-08-20T21:39:08.5249211Z period_group_size_dict: An ordered dict mapping keys of model averaging period to 2024-08-20T21:39:08.5249519Z process group size, used for initializing process groups of 2024-08-20T21:39:08.5249844Z different sizes in a hierarchy to average parameters concurrently. 2024-08-20T21:39:08.5250144Z Particularly, at each iteration, there will be at most a single 2024-08-20T21:39:08.5250587Z process group that runs averaging -- the period of such group should 2024-08-20T21:39:08.5250901Z have the largest period which the current step can be divided by. 2024-08-20T21:39:08.5251143Z For example, if the dict has three keys: 2, 4, and 8, 2024-08-20T21:39:08.5251457Z then this means totally three process groups will be created to 2024-08-20T21:39:08.5251755Z average parameters every 2, 4, and 8 iterations, respectively. 2024-08-20T21:39:08.5252059Z At the 4th iteration, only the second process group will run 2024-08-20T21:39:08.5252316Z averaging, because the first process group should be a 2024-08-20T21:39:08.5252644Z subset of the second process group, and no need to execute the first 2024-08-20T21:39:08.5252914Z process group redundantly. 2024-08-20T21:39:08.5253216Z On the other hand, the third process group can only be triggered 2024-08-20T21:39:08.5253549Z every 8 iterations, so it will not be triggered at the 4th iteration. 2024-08-20T21:39:08.5254107Z warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped. 2024-08-20T21:39:08.5254711Z process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging. 2024-08-20T21:39:08.5254998Z If ``None``, the default process group, which is created 2024-08-20T21:39:08.5255309Z by :func:`torch.distributed.init_process_group`, will be used. 2024-08-20T21:39:08.5255485Z (default: ``None``) 2024-08-20T21:39:08.5255593Z 2024-08-20T21:39:08.5255706Z Example:: 2024-08-20T21:39:08.5255929Z >>> # xdoctest: +SKIP('undefined rank') 2024-08-20T21:39:08.5256109Z >>> from collections import OrderedDict 2024-08-20T21:39:08.5256221Z >>> import torch 2024-08-20T21:39:08.5256379Z >>> import torch.distributed as dist 2024-08-20T21:39:08.5256770Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2024-08-20T21:39:08.5256904Z >>> PostLocalSGDState, 2024-08-20T21:39:08.5257043Z >>> post_localSGD_hook, 2024-08-20T21:39:08.5257141Z >>> ) 2024-08-20T21:39:08.5257716Z >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD 2024-08-20T21:39:08.5257857Z >>> import torch.nn as nn 2024-08-20T21:39:08.5257954Z >>> 2024-08-20T21:39:08.5258199Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2024-08-20T21:39:08.5258350Z >>> torch.cuda.set_device(rank) 2024-08-20T21:39:08.5258544Z >>> module = nn.Linear(1, 1, bias=False).to(rank) 2024-08-20T21:39:08.5258764Z >>> model = nn.parallel.DistributedDataParallel( 2024-08-20T21:39:08.5258972Z >>> module, device_ids=[rank], output_device=rank 2024-08-20T21:39:08.5259072Z >>> ) 2024-08-20T21:39:08.5259341Z >>> # Register a post-localSGD communication hook. 2024-08-20T21:39:08.5259863Z >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4. 2024-08-20T21:39:08.5260024Z >>> subgroup, _ = dist.new_subgroups() 2024-08-20T21:39:08.5260463Z >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100) 2024-08-20T21:39:08.5260683Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-08-20T21:39:08.5260784Z >>> 2024-08-20T21:39:08.5261205Z >>> # Average parameters among each group of 8 processes every 4 iterations, and among all 2024-08-20T21:39:08.5261373Z >>> # the 16 processes every 16 iterations. 2024-08-20T21:39:08.5261644Z >>> averager = hierarchicalSGD.HierarchicalModelAverager( 2024-08-20T21:39:08.5261979Z >>> period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100) 2024-08-20T21:39:08.5262447Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-08-20T21:39:08.5262854Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2024-08-20T21:39:08.5263073Z >>> # After 100 steps, run model averaging at two levels. 2024-08-20T21:39:08.5263209Z >>> for step in range(0, 200): 2024-08-20T21:39:08.5263360Z >>> optimizer.zero_grad() 2024-08-20T21:39:08.5263514Z >>> loss = loss_fn(output, labels) 2024-08-20T21:39:08.5263634Z >>> loss.backward() 2024-08-20T21:39:08.5263762Z >>> optimizer.step() 2024-08-20T21:39:08.5270845Z >>> # Average parameters after ``optimizer.step()``. 2024-08-20T21:39:08.5271564Z >>> # Thus, the inter-node communication only occurs periodically after ``warmup_steps``. 2024-08-20T21:39:08.5271793Z >>> averager.average_parameters(model.parameters()) 2024-08-20T21:39:08.5271908Z 2024-08-20T21:39:08.5272030Z .. warning :: 2024-08-20T21:39:08.5272441Z The last group size in the dict must be the size of the provided ``process_group``, 2024-08-20T21:39:08.5272770Z which indicates model averaging at the highest level of the hierarchy. 2024-08-20T21:39:08.5273221Z If ``process_group`` is not provided, then the last group size should be equal to the world size. 2024-08-20T21:39:08.5273342Z 2024-08-20T21:39:08.5273454Z .. warning :: 2024-08-20T21:39:08.5273764Z `HierarchicalModelAverager` is experimental and subject to change. 2024-08-20T21:39:08.5273875Z 2024-08-20T21:39:08.5274306Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5274407Z 2024-08-20T21:39:08.5274543Z warnings.warn(msg) 2024-08-20T21:39:08.5274642Z 2024-08-20T21:39:08.5274877Z --- Parse Warning: 44 / 101 --- 2024-08-20T21:39:08.5276593Z /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. 2024-08-20T21:39:08.5277032Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5277146Z 2024-08-20T21:39:08.5277638Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint 2024-08-20T21:39:08.5278010Z on the coordinator rank, and then broadcast and shard each tensor to all ranks. 2024-08-20T21:39:08.5278125Z 2024-08-20T21:39:08.5278482Z . N.B. Intended to be used with DynamicMetaLoadPlanner 2024-08-20T21:39:08.5278577Z 2024-08-20T21:39:08.5278710Z .. warning:: 2024-08-20T21:39:08.5278943Z Current implementation only supports loading Tensors. 2024-08-20T21:39:08.5279038Z 2024-08-20T21:39:08.5279207Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:39:08.5279327Z >>> sd = {"mode": model} 2024-08-20T21:39:08.5279435Z >>> dcp.load( 2024-08-20T21:39:08.5279554Z >>> sd, 2024-08-20T21:39:08.5279769Z >>> storage_reader=BroadcastingTorchSaveReader(), 2024-08-20T21:39:08.5279939Z >>> planner=DynamicMetaLoadPlanner(), 2024-08-20T21:39:08.5280109Z >>> checkpoint_id="path_to_model.pt" 2024-08-20T21:39:08.5280213Z >>> ) 2024-08-20T21:39:08.5280309Z 2024-08-20T21:39:08.5280758Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5280853Z 2024-08-20T21:39:08.5280991Z warnings.warn(msg) 2024-08-20T21:39:08.5281086Z 2024-08-20T21:39:08.5281313Z --- Parse Warning: 45 / 101 --- 2024-08-20T21:39:08.5282984Z /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. 2024-08-20T21:39:08.5283421Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5283518Z 2024-08-20T21:39:08.5284043Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, 2024-08-20T21:39:08.5284626Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a 2024-08-20T21:39:08.5284798Z metadata file, like Torch Save files. 2024-08-20T21:39:08.5284894Z 2024-08-20T21:39:08.5285156Z . N.B. Intended to be used with BroadcastingTorchSaveReader 2024-08-20T21:39:08.5285269Z 2024-08-20T21:39:08.5285386Z .. warning:: 2024-08-20T21:39:08.5285623Z Current implementation only supports loading Tensors. 2024-08-20T21:39:08.5285807Z 2024-08-20T21:39:08.5285963Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:39:08.5286084Z >>> sd = {"mode": model} 2024-08-20T21:39:08.5286208Z >>> dcp.load( 2024-08-20T21:39:08.5286315Z >>> sd, 2024-08-20T21:39:08.5286530Z >>> storage_reader=BroadcastingTorchSaveReader(), 2024-08-20T21:39:08.5286716Z >>> planner=DynamicMetaLoadPlanner(), 2024-08-20T21:39:08.5286868Z >>> checkpoint_id="path_to_model.pt" 2024-08-20T21:39:08.5286967Z >>> ) 2024-08-20T21:39:08.5287080Z 2024-08-20T21:39:08.5287506Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5287605Z 2024-08-20T21:39:08.5287740Z warnings.warn(msg) 2024-08-20T21:39:08.5287835Z 2024-08-20T21:39:08.5288060Z --- Parse Warning: 46 / 101 --- 2024-08-20T21:39:08.5289769Z /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. 2024-08-20T21:39:08.5290211Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5290324Z 2024-08-20T21:39:08.5290622Z Load a state_dict in conjunction with FSDP sharded optimizer state. 2024-08-20T21:39:08.5290720Z 2024-08-20T21:39:08.5290972Z This is the current recommended way to checkpoint FSDP. 2024-08-20T21:39:08.5291096Z >>> # xdoctest: +SKIP 2024-08-20T21:39:08.5291312Z >>> import torch.distributed.checkpoint as dist_cp 2024-08-20T21:39:08.5291492Z >>> # Save 2024-08-20T21:39:08.5291620Z >>> model: torch.nn.Model 2024-08-20T21:39:08.5291777Z >>> optim_params = model.parameters() 2024-08-20T21:39:08.5291992Z >>> optim = torch.optim.SGD(optim_params, lr=0.01) 2024-08-20T21:39:08.5292097Z >>> # Save 2024-08-20T21:39:08.5292406Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT): 2024-08-20T21:39:08.5292543Z >>> state_dict = { 2024-08-20T21:39:08.5292761Z >>> "optimizer": FSDP.optim_state_dict(model, optim), 2024-08-20T21:39:08.5292922Z >>> "model": model.state_dict() 2024-08-20T21:39:08.5293027Z >>> } 2024-08-20T21:39:08.5293158Z >>> dist_cp.save_state_dict( 2024-08-20T21:39:08.5293308Z >>> state_dict=optim_state, 2024-08-20T21:39:08.5293554Z >>> storage_writer=dist_cp.FileSystemWriter("checkpoint"), 2024-08-20T21:39:08.5293741Z >>> planner=dist_cp.DefaultSavePlanner(), 2024-08-20T21:39:08.5293861Z >>> ) 2024-08-20T21:39:08.5293965Z >>> 2024-08-20T21:39:08.5294067Z >>> # Load 2024-08-20T21:39:08.5294408Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT): 2024-08-20T21:39:08.5294588Z >>> model_state_dict = model_tp.state_dict() 2024-08-20T21:39:08.5294707Z >>> checkpoint = { 2024-08-20T21:39:08.5294866Z >>> "model": model_state_dict 2024-08-20T21:39:08.5294965Z >>> } 2024-08-20T21:39:08.5295098Z >>> dist_cp.load_state_dict( 2024-08-20T21:39:08.5295250Z >>> state_dict=checkpoint, 2024-08-20T21:39:08.5295508Z >>> storage_reader=dist_cp.FileSystemReader(checkpoint_file), 2024-08-20T21:39:08.5295692Z >>> planner=dist_cp.DefaultLoadPlanner(), 2024-08-20T21:39:08.5296060Z >>> ) 2024-08-20T21:39:08.5296269Z >>> model.load_state_dict(checkpoint["model_state"]) 2024-08-20T21:39:08.5296384Z >>> 2024-08-20T21:39:08.5296626Z >>> optim_state = dist_cp.load_sharded_optimizer_state_dict( 2024-08-20T21:39:08.5296752Z >>> model_state_dict, 2024-08-20T21:39:08.5296914Z >>> optimizer_key="optimizer", 2024-08-20T21:39:08.5297158Z >>> storage_reader=dist_cp.FileSystemReader("checkpoint"), 2024-08-20T21:39:08.5297261Z >>> ) 2024-08-20T21:39:08.5297378Z >>> 2024-08-20T21:39:08.5297745Z >>> flattened_osd = FSDP.optim_state_dict_to_load( 2024-08-20T21:39:08.5297922Z >>> model, optim, optim_state["optimizer"] 2024-08-20T21:39:08.5298039Z >>> ) 2024-08-20T21:39:08.5298137Z >>> 2024-08-20T21:39:08.5298308Z >>> optim.load_state_dict(flattened_osd) 2024-08-20T21:39:08.5298418Z 2024-08-20T21:39:08.5298853Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5298948Z 2024-08-20T21:39:08.5299083Z warnings.warn(msg) 2024-08-20T21:39:08.5299180Z 2024-08-20T21:39:08.5299406Z --- Parse Warning: 47 / 101 --- 2024-08-20T21:39:08.5300970Z /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. 2024-08-20T21:39:08.5301410Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5301526Z 2024-08-20T21:39:08.5301931Z Abstract class defining the protocol used by save_state_dict to plan the save process. 2024-08-20T21:39:08.5302029Z 2024-08-20T21:39:08.5302456Z SavePlanners are stateful objects that can be used to customize the whole save process. 2024-08-20T21:39:08.5302554Z 2024-08-20T21:39:08.5302963Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it 2024-08-20T21:39:08.5303132Z will be visible to the whole process. 2024-08-20T21:39:08.5303229Z 2024-08-20T21:39:08.5303629Z A planner subclass can expect the following sequence of calls during save_state_dict: 2024-08-20T21:39:08.5303826Z 2024-08-20T21:39:08.5304049Z 1) set_up_planner - called on all ranks. 2024-08-20T21:39:08.5304222Z Signals the start of a checkpoint save. 2024-08-20T21:39:08.5304334Z 2024-08-20T21:39:08.5304559Z 2) create_local_plan - called on all ranks. 2024-08-20T21:39:08.5304990Z Process the state_dict and produces a `SavePlan` that will be sent for global planning. 2024-08-20T21:39:08.5305084Z 2024-08-20T21:39:08.5305404Z 3) create_global_plan - called on the coordinator rank only. 2024-08-20T21:39:08.5305701Z Takes the SavePlan from all ranks and make any global decision. 2024-08-20T21:39:08.5305794Z 2024-08-20T21:39:08.5305994Z 4) finish_plan - called on all ranks. 2024-08-20T21:39:08.5306323Z This gives each rank a chance to adjust to global planning decisions. 2024-08-20T21:39:08.5306420Z 2024-08-20T21:39:08.5306690Z 5) resolve_data - called multiple times on each rank 2024-08-20T21:39:08.5307014Z Lookups a value on the `state_dict` for the storage layer to write. 2024-08-20T21:39:08.5307111Z 2024-08-20T21:39:08.5307539Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as 2024-08-20T21:39:08.5307816Z most changes can be expressed by changes in a single method. 2024-08-20T21:39:08.5307914Z 2024-08-20T21:39:08.5308083Z There are 3 usual patterns of extension: 2024-08-20T21:39:08.5308195Z 2024-08-20T21:39:08.5308556Z Rewriting state_dict. This is the simplest way to extend the save process as it 2024-08-20T21:39:08.5308962Z doesn't requite understanding the intrincacies of how SavePlan works: 2024-08-20T21:39:08.5309057Z 2024-08-20T21:39:08.5309212Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:39:08.5309411Z >>> class RenamePlanner(DefaultSavePlanner): 2024-08-20T21:39:08.5309537Z >>> def set_up_planner( 2024-08-20T21:39:08.5309644Z >>> self, 2024-08-20T21:39:08.5309816Z >>> state_dict: STATE_DICT_TYPE, 2024-08-20T21:39:08.5309993Z >>> storage_meta: Optional[StorageMeta], 2024-08-20T21:39:08.5310130Z >>> is_coordinator: bool, 2024-08-20T21:39:08.5310293Z >>> ) -> None: 2024-08-20T21:39:08.5310451Z >>> # prefix all keys with `foo_`` 2024-08-20T21:39:08.5310979Z >>> super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator) 2024-08-20T21:39:08.5311090Z 2024-08-20T21:39:08.5311574Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted 2024-08-20T21:39:08.5311673Z 2024-08-20T21:39:08.5311844Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:39:08.5312019Z >>> class FP16Planner(DefaultSavePlanner): 2024-08-20T21:39:08.5312178Z >>> def create_local_plan(self): 2024-08-20T21:39:08.5312342Z >>> plan = super().create_local_plan() 2024-08-20T21:39:08.5312465Z >>> for p in plan: 2024-08-20T21:39:08.5312645Z >>> if p.tensor_data is not None: 2024-08-20T21:39:08.5312874Z >>> p.tensor_data.properties.dtype = torch.float16 2024-08-20T21:39:08.5312991Z >>> return plan 2024-08-20T21:39:08.5313104Z >>> 2024-08-20T21:39:08.5313266Z >>> def resolve_data(self, write_item): 2024-08-20T21:39:08.5313447Z >>> item = super().resolve_data(write_item) 2024-08-20T21:39:08.5313862Z >>> return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16) 2024-08-20T21:39:08.5313959Z 2024-08-20T21:39:08.5314554Z Using the global planning step to make central decisions that can't be made individually by each rank 2024-08-20T21:39:08.5314665Z 2024-08-20T21:39:08.5314819Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:39:08.5314960Z >>> from itertools import islice 2024-08-20T21:39:08.5315122Z >>> from dataclasses import replace 2024-08-20T21:39:08.5315422Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner): 2024-08-20T21:39:08.5315944Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0 2024-08-20T21:39:08.5316196Z >>> # This sample doesn't handle ShardedTensors 2024-08-20T21:39:08.5316375Z >>> def create_global_plan(self, all_plans): 2024-08-20T21:39:08.5316525Z >>> def chunk(it, size): 2024-08-20T21:39:08.5316646Z >>> it = iter(it) 2024-08-20T21:39:08.5316882Z >>> return list(iter(lambda: tuple(islice(it, size)), ())) 2024-08-20T21:39:08.5317017Z >>> all_plans = [ 2024-08-20T21:39:08.5317225Z >>> replace(plan, items=items) for plan, items in 2024-08-20T21:39:08.5317480Z >>> zip(all_plans, chunk(all_plans[0].items, len(all_plans))) 2024-08-20T21:39:08.5317600Z >>> ] 2024-08-20T21:39:08.5317797Z >>> return super().create_global_plan(all_plans) 2024-08-20T21:39:08.5317892Z 2024-08-20T21:39:08.5318435Z Finally, some planners need to save additional metadata in the checkpoint, this is 2024-08-20T21:39:08.5318824Z accomplished by having each rank contribute their data items in the local plan and 2024-08-20T21:39:08.5318988Z the global planner aggregate them: 2024-08-20T21:39:08.5319084Z 2024-08-20T21:39:08.5319236Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:39:08.5319474Z >>> class SaveExtraDataPlanner(DefaultSavePlanner): 2024-08-20T21:39:08.5319723Z >>> def create_local_plan(self) -> SavePlan: 2024-08-20T21:39:08.5319888Z >>> plan = super().create_local_plan() 2024-08-20T21:39:08.5320202Z >>> return replace(plan, planner_data="per-rank-data") 2024-08-20T21:39:08.5320302Z >>> 2024-08-20T21:39:08.5320826Z >>> def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]: 2024-08-20T21:39:08.5321110Z >>> global_plan, metadata = super().create_global_plan(all_plans) 2024-08-20T21:39:08.5321338Z >>> merged_data = [p.planner_data for p in global_plan] 2024-08-20T21:39:08.5321577Z >>> metadata = replace(metadata, planner_data=merged_data) 2024-08-20T21:39:08.5321747Z >>> return global_plan, metadata 2024-08-20T21:39:08.5321842Z 2024-08-20T21:39:08.5322283Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5322448Z 2024-08-20T21:39:08.5322572Z warnings.warn(msg) 2024-08-20T21:39:08.5322683Z 2024-08-20T21:39:08.5322910Z --- Parse Warning: 48 / 101 --- 2024-08-20T21:39:08.5324454Z /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. 2024-08-20T21:39:08.5324912Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5325009Z 2024-08-20T21:39:08.5325415Z Abstract class defining the protocol used by load_state_dict to plan the load process. 2024-08-20T21:39:08.5325527Z 2024-08-20T21:39:08.5325933Z LoadPlanner are stateful objects that can be used to customize the whole load process. 2024-08-20T21:39:08.5326042Z 2024-08-20T21:39:08.5326456Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it 2024-08-20T21:39:08.5326613Z will be visible to the whole process. 2024-08-20T21:39:08.5326727Z 2024-08-20T21:39:08.5327128Z A planner subclass can expect the following sequence of calls during load_state_dict: 2024-08-20T21:39:08.5327225Z 2024-08-20T21:39:08.5327461Z 1) set_up_planner - called on all ranks. 2024-08-20T21:39:08.5327643Z Signals the start of loading a checkpoint. 2024-08-20T21:39:08.5327739Z 2024-08-20T21:39:08.5327982Z 2) create_local_plan - called on all ranks. 2024-08-20T21:39:08.5328460Z Process the state_dict and produces a `LoadPlan` that will be sent for global planning. 2024-08-20T21:39:08.5328557Z 2024-08-20T21:39:08.5328897Z 3) create_global_plan - called on the coordinator rank only. 2024-08-20T21:39:08.5329178Z Takes the LoadPlan from all ranks and make any global decision. 2024-08-20T21:39:08.5329278Z 2024-08-20T21:39:08.5329555Z 4) load_bytes - called multiple times on each rank 2024-08-20T21:39:08.5329861Z This is called once per non-tensor value in state_dict. 2024-08-20T21:39:08.5329976Z 2024-08-20T21:39:08.5330365Z 5) resolve_tensor and commit_tensor - called multiple times on each rank 2024-08-20T21:39:08.5330632Z They are called in pair for each Tensor value in state_dict. 2024-08-20T21:39:08.5330741Z 2024-08-20T21:39:08.5331171Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as 2024-08-20T21:39:08.5331430Z most changes can be expressed by changes in a single method. 2024-08-20T21:39:08.5331542Z 2024-08-20T21:39:08.5331722Z There are two usual patterns of extension: 2024-08-20T21:39:08.5331818Z 2024-08-20T21:39:08.5332197Z Rewriting state_dict. This is the simplest way to extend the load process as it 2024-08-20T21:39:08.5332634Z doesn't requite understanding the intrincacies of how LoadPlan works. We need 2024-08-20T21:39:08.5332970Z to keep a reference to the original state_dict as load happens in place so 2024-08-20T21:39:08.5333161Z we need to be able to perform it in place 2024-08-20T21:39:08.5333258Z 2024-08-20T21:39:08.5333414Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:39:08.5333613Z >>> class RenamePlanner(DefaultLoadPlanner): 2024-08-20T21:39:08.5333737Z >>> def set_up_planner( 2024-08-20T21:39:08.5333859Z >>> self, 2024-08-20T21:39:08.5334012Z >>> state_dict: STATE_DICT_TYPE, 2024-08-20T21:39:08.5334139Z >>> metadata: Metadata, 2024-08-20T21:39:08.5334290Z >>> is_coordinator: bool, 2024-08-20T21:39:08.5334437Z >>> ) -> None: 2024-08-20T21:39:08.5334618Z >>> self.original_state_dict = state_dict 2024-08-20T21:39:08.5334883Z >>> state_dict = {"foo_" + k: v for k, v in state_dict.items()} 2024-08-20T21:39:08.5334982Z >>> 2024-08-20T21:39:08.5335145Z >>> if self.flatten_sharded_tensors: 2024-08-20T21:39:08.5335444Z >>> state_dict = _flatten_sharded_tensors(state_dict) 2024-08-20T21:39:08.5335545Z >>> 2024-08-20T21:39:08.5335695Z >>> if self.flatten_state_dict: 2024-08-20T21:39:08.5335975Z >>> state_dict, self.mappings = flatten_state_dict(state_dict) 2024-08-20T21:39:08.5336074Z >>> 2024-08-20T21:39:08.5336226Z >>> self.state_dict = state_dict 2024-08-20T21:39:08.5336385Z >>> self.metadata = metadata 2024-08-20T21:39:08.5336561Z >>> self.is_coordinator = is_coordinator 2024-08-20T21:39:08.5336661Z >>> 2024-08-20T21:39:08.5336847Z >>> def load_bytes(self, read_item, value): 2024-08-20T21:39:08.5336994Z >>> # Remove the "foo_" prefix 2024-08-20T21:39:08.5337456Z >>> self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False) 2024-08-20T21:39:08.5337552Z 2024-08-20T21:39:08.5337647Z 2024-08-20T21:39:08.5338017Z Modifying resolve_tensor and commit_tensor to handle load time transformation. 2024-08-20T21:39:08.5338119Z 2024-08-20T21:39:08.5338271Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:39:08.5338504Z >>> class MetaModelMaterialize(DefaultSavePlanner): 2024-08-20T21:39:08.5338670Z >>> def resolve_tensor(self, read_item): 2024-08-20T21:39:08.5338859Z >>> tensor = super().resolve_tensor(read_item) 2024-08-20T21:39:08.5339081Z >>> return torch.empty_like(tensor, device="cpu") 2024-08-20T21:39:08.5339180Z >>> 2024-08-20T21:39:08.5339366Z >>> def commit_tensor(self, read_item, tensor): 2024-08-20T21:39:08.5339673Z >>> self.state_dict[read_item.dest_index.fqn] = tensor 2024-08-20T21:39:08.5339769Z 2024-08-20T21:39:08.5340196Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5340308Z 2024-08-20T21:39:08.5340429Z warnings.warn(msg) 2024-08-20T21:39:08.5340539Z 2024-08-20T21:39:08.5340766Z --- Parse Warning: 49 / 101 --- 2024-08-20T21:39:08.5342331Z /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. 2024-08-20T21:39:08.5342786Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5342884Z 2024-08-20T21:39:08.5343083Z Load a distributed ``state_dict`` in SPMD style. 2024-08-20T21:39:08.5343196Z 2024-08-20T21:39:08.5343462Z Each rank will try to read the least amount of data necessary 2024-08-20T21:39:08.5343801Z to fullfill the requested `state_dict`. When loading :class:`ShardedTensor` 2024-08-20T21:39:08.5344172Z or :class:`DTensor` instances, each rank only reads data for their local shards. 2024-08-20T21:39:08.5344267Z 2024-08-20T21:39:08.5344658Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2024-08-20T21:39:08.5345030Z load will first call ``state_dict`` before attempting deserialization, followed by 2024-08-20T21:39:08.5345263Z ``load_state_dict`` once the deserialization is complete. 2024-08-20T21:39:08.5345375Z 2024-08-20T21:39:08.5345497Z .. warning:: 2024-08-20T21:39:08.5345735Z All tensors in ``state_dict`` must be allocated on their 2024-08-20T21:39:08.5345973Z destination device *prior to* calling this function. 2024-08-20T21:39:08.5346069Z 2024-08-20T21:39:08.5346480Z All non-tensor data is loaded using `torch.load()` and modified in place 2024-08-20T21:39:08.5346607Z on state_dict. 2024-08-20T21:39:08.5346706Z 2024-08-20T21:39:08.5346815Z .. warning:: 2024-08-20T21:39:08.5347129Z Users must call `load_state_dict` on the root module to ensure load 2024-08-20T21:39:08.5347447Z pos-processing and non-tensor data properly propagates. 2024-08-20T21:39:08.5347543Z 2024-08-20T21:39:08.5347730Z .. note: 2024-08-20T21:39:08.5348060Z If no process group is initialized, this function will assume the intent 2024-08-20T21:39:08.5348412Z is to load a checkpoint into the local process. This can be useful in the 2024-08-20T21:39:08.5348769Z case of local inference, and when using regular Tensors (as opposed to DTensor 2024-08-20T21:39:08.5348895Z or ShardedTensor) 2024-08-20T21:39:08.5349009Z 2024-08-20T21:39:08.5349113Z .. note: 2024-08-20T21:39:08.5349305Z Rank 0 is assumed to be the coordinator rank. 2024-08-20T21:39:08.5349421Z 2024-08-20T21:39:08.5349523Z Args: 2024-08-20T21:39:08.5349740Z state_dict (Dict[str, Any]): The state_dict to save. 2024-08-20T21:39:08.5349951Z checkpoint_id (Union[str, os.PathLike, None]): 2024-08-20T21:39:08.5350265Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-08-20T21:39:08.5350567Z depends on the storage. It can be a path to a folder or to a file. 2024-08-20T21:39:08.5350915Z It can also be a key if the storage is a key-value store. 2024-08-20T21:39:08.5351036Z (Default: ``None``) 2024-08-20T21:39:08.5351217Z storage_reader (Optional[StorageReader]): 2024-08-20T21:39:08.5351524Z Instance of StorageWriter used to perform reads. If this is not 2024-08-20T21:39:08.5351810Z specified, DCP will automatically infer the reader based on the 2024-08-20T21:39:08.5352103Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-08-20T21:39:08.5352251Z be raised. (Default: ``None``) 2024-08-20T21:39:08.5352401Z planner (Optional[LoadPlanner]): 2024-08-20T21:39:08.5352859Z Instance of LoadPlanner. If this is not specificed, the default 2024-08-20T21:39:08.5353041Z planner will be used. (Default: ``None``) 2024-08-20T21:39:08.5353215Z process_group (Optional[ProcessGroup]): 2024-08-20T21:39:08.5353554Z ProcessGroup to be used for cross-rank synchronization. 2024-08-20T21:39:08.5353678Z (Default: ``None``) 2024-08-20T21:39:08.5353773Z 2024-08-20T21:39:08.5353891Z Returns: 2024-08-20T21:39:08.5353995Z None. 2024-08-20T21:39:08.5354090Z 2024-08-20T21:39:08.5354210Z Examples 2024-08-20T21:39:08.5354334Z >>> # xdoctest: +SKIP 2024-08-20T21:39:08.5354461Z >>> my_model = MyModule() 2024-08-20T21:39:08.5354671Z >>> optimizer = Adagrad(my_model.parameters()) 2024-08-20T21:39:08.5354847Z >>> model_state_dict = my_model.state_dict() 2024-08-20T21:39:08.5355288Z >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader("/checkpoint/1") 2024-08-20T21:39:08.5355391Z 2024-08-20T21:39:08.5355611Z >>> torch.distributed.checkpoint.load_state_dict( 2024-08-20T21:39:08.5355782Z >>> state_dict=model_state_dict, 2024-08-20T21:39:08.5355944Z >>> storage_reader=fs_storage_reader, 2024-08-20T21:39:08.5356043Z >>> ) 2024-08-20T21:39:08.5356160Z 2024-08-20T21:39:08.5356436Z >>> # module.load_state_dict() function might have customized steps 2024-08-20T21:39:08.5356618Z >>> # to flush the state_dict, must call it to 2024-08-20T21:39:08.5356772Z >>> # ensure correct behavior. 2024-08-20T21:39:08.5356953Z >>> my_model.load_state_dict(model_state_dict) 2024-08-20T21:39:08.5357048Z 2024-08-20T21:39:08.5357173Z .. note:: 2024-08-20T21:39:08.5357465Z load_state_dict uses collectives to coordinate reads across ranks. 2024-08-20T21:39:08.5357844Z For NCCL-based process groups, internal tensor representations of 2024-08-20T21:39:08.5358329Z objects must be moved to the GPU device before communication takes place. 2024-08-20T21:39:08.5358659Z In this case, the device used is given by ``torch.cuda.current_device()`` 2024-08-20T21:39:08.5359107Z and it is the user's responsibility to ensure that this is set so that each 2024-08-20T21:39:08.5359460Z rank has an individual GPU, via ``torch.cuda.set_device()``. 2024-08-20T21:39:08.5359557Z 2024-08-20T21:39:08.5359996Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5360090Z 2024-08-20T21:39:08.5360212Z warnings.warn(msg) 2024-08-20T21:39:08.5360326Z 2024-08-20T21:39:08.5360558Z --- Parse Warning: 50 / 101 --- 2024-08-20T21:39:08.5362108Z /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. 2024-08-20T21:39:08.5362566Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5362662Z 2024-08-20T21:39:08.5362842Z Save a distributed model in SPMD style. 2024-08-20T21:39:08.5362936Z 2024-08-20T21:39:08.5363204Z This function is different from ``torch.save()`` as it handles 2024-08-20T21:39:08.5363585Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards. 2024-08-20T21:39:08.5363681Z 2024-08-20T21:39:08.5364054Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2024-08-20T21:39:08.5364279Z save will call ``state_dict`` before serialization. 2024-08-20T21:39:08.5364374Z 2024-08-20T21:39:08.5364490Z .. warning:: 2024-08-20T21:39:08.5364846Z There is no guarantees of Backwards Compatibility across PyTorch versions 2024-08-20T21:39:08.5364973Z for saved state_dicts. 2024-08-20T21:39:08.5365072Z 2024-08-20T21:39:08.5365195Z .. warning:: 2024-08-20T21:39:08.5365564Z If using the `process_group` argument, make sure that only its ranks 2024-08-20T21:39:08.5365862Z call `save_state_dict` and that all data in state_dict belong to it. 2024-08-20T21:39:08.5365974Z 2024-08-20T21:39:08.5366076Z .. note:: 2024-08-20T21:39:08.5366555Z When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of 2024-08-20T21:39:08.5366934Z the shard_group should be calling `save_state_dict` and the corresponding process 2024-08-20T21:39:08.5367072Z group needs to be passed in. 2024-08-20T21:39:08.5367187Z 2024-08-20T21:39:08.5367291Z .. note:: 2024-08-20T21:39:08.5367688Z If no process group is available, this function assumes the intention is to save the 2024-08-20T21:39:08.5367852Z state_dict in the local process. 2024-08-20T21:39:08.5367947Z 2024-08-20T21:39:08.5368049Z .. note: 2024-08-20T21:39:08.5368258Z Rank 0 is assumed to be the coordinator rank. 2024-08-20T21:39:08.5368358Z 2024-08-20T21:39:08.5368453Z 2024-08-20T21:39:08.5368571Z Args: 2024-08-20T21:39:08.5368786Z state_dict (Dict[str, Any]): The state_dict to save. 2024-08-20T21:39:08.5368982Z checkpoint_id (Union[str, os.PathLike, None]): 2024-08-20T21:39:08.5369314Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-08-20T21:39:08.5369620Z depends on the storage. It can be a path to a folder or to a file. 2024-08-20T21:39:08.5369944Z It can also be a key if the storage is a key-value store. 2024-08-20T21:39:08.5370084Z (Default: ``None``) 2024-08-20T21:39:08.5370262Z storage_writer (Optional[StorageWriter]): 2024-08-20T21:39:08.5370577Z Instance of StorageWriter used to perform writes. If this is not 2024-08-20T21:39:08.5370868Z specified, DCP will automatically infer the writer based on the 2024-08-20T21:39:08.5371156Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-08-20T21:39:08.5371321Z be raised. (Default: ``None``) 2024-08-20T21:39:08.5371476Z planner (Optional[SavePlanner]): 2024-08-20T21:39:08.5371764Z Instance of SavePlanner. If this is not specificed, the default 2024-08-20T21:39:08.5371964Z planner will be used. (Default: ``None``) 2024-08-20T21:39:08.5372201Z process_group (Optional[ProcessGroup]): 2024-08-20T21:39:08.5372526Z ProcessGroup to be used for cross-rank synchronization. 2024-08-20T21:39:08.5372665Z (Default: ``None``) 2024-08-20T21:39:08.5372764Z 2024-08-20T21:39:08.5372869Z Returns: 2024-08-20T21:39:08.5373103Z Metadata: Metadata object for the saved checkpoint. 2024-08-20T21:39:08.5373201Z 2024-08-20T21:39:08.5373320Z Example: 2024-08-20T21:39:08.5373445Z >>> # xdoctest: +SKIP 2024-08-20T21:39:08.5373574Z >>> my_model = MyModule() 2024-08-20T21:39:08.5373683Z 2024-08-20T21:39:08.5373840Z >>> state_dict = {"model": my_model} 2024-08-20T21:39:08.5373934Z 2024-08-20T21:39:08.5374375Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2024-08-20T21:39:08.5374562Z >>> torch.distributed.checkpoint.save( 2024-08-20T21:39:08.5374697Z >>> state_dict=state_dict, 2024-08-20T21:39:08.5374878Z >>> storage_writer=fs_storage_writer, 2024-08-20T21:39:08.5374980Z >>> ) 2024-08-20T21:39:08.5375077Z 2024-08-20T21:39:08.5375198Z .. note:: 2024-08-20T21:39:08.5375499Z save_state_dict uses collectives to coordinate writes across ranks. 2024-08-20T21:39:08.5375878Z For NCCL-based process groups, internal tensor representations of 2024-08-20T21:39:08.5376238Z objects must be moved to the GPU device before communication takes place. 2024-08-20T21:39:08.5376567Z In this case, the device used is given by ``torch.cuda.current_device()`` 2024-08-20T21:39:08.5377037Z and it is the user's responsibility to ensure that this is set so that 2024-08-20T21:39:08.5377331Z each rank has an individual GPU, via ``torch.cuda.set_device()``. 2024-08-20T21:39:08.5377427Z 2024-08-20T21:39:08.5377866Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5377965Z 2024-08-20T21:39:08.5378084Z warnings.warn(msg) 2024-08-20T21:39:08.5378199Z 2024-08-20T21:39:08.5378423Z --- Parse Warning: 51 / 101 --- 2024-08-20T21:39:08.5380017Z /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. 2024-08-20T21:39:08.5380473Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5380936Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the 2024-08-20T21:39:08.5381373Z staging storage (defaults to CPU memory), and then calls the `save` in a separate thread. 2024-08-20T21:39:08.5381470Z 2024-08-20T21:39:08.5381581Z .. warning:: 2024-08-20T21:39:08.5381822Z This feature is experimental and subject to change. 2024-08-20T21:39:08.5381916Z 2024-08-20T21:39:08.5382022Z Args: 2024-08-20T21:39:08.5382255Z state_dict (Dict[str, Any]): The state_dict to save. 2024-08-20T21:39:08.5382459Z checkpoint_id (Union[str, os.PathLike, None]): 2024-08-20T21:39:08.5382775Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-08-20T21:39:08.5383096Z depends on the storage. It can be a path to a folder or to a file. 2024-08-20T21:39:08.5383423Z It can also be a key if the storage is a key-value store. 2024-08-20T21:39:08.5383549Z (Default: ``None``) 2024-08-20T21:39:08.5383751Z storage_writer (Optional[StorageWriter]): 2024-08-20T21:39:08.5384139Z Instance of StorageWriter used to perform 'stage' and 'save'. If 2024-08-20T21:39:08.5384510Z this is not specified, DCP will automatically infer the writer based on the 2024-08-20T21:39:08.5384796Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-08-20T21:39:08.5385021Z be raised. (Default: ``None``) 2024-08-20T21:39:08.5385201Z planner (Optional[SavePlanner]): 2024-08-20T21:39:08.5385492Z Instance of SavePlanner. If this is not specificed, the default 2024-08-20T21:39:08.5385675Z planner will be used. (Default: ``None``) 2024-08-20T21:39:08.5385869Z process_group (Optional[ProcessGroup]): 2024-08-20T21:39:08.5386197Z ProcessGroup to be used for cross-rank synchronization. 2024-08-20T21:39:08.5386323Z (Default: ``None``) 2024-08-20T21:39:08.5386435Z 2024-08-20T21:39:08.5386541Z Returns: 2024-08-20T21:39:08.5386846Z Future: A future holding the resultant Metadata object from `save`. 2024-08-20T21:39:08.5386957Z 2024-08-20T21:39:08.5387062Z Example: 2024-08-20T21:39:08.5387188Z >>> # xdoctest: +SKIP 2024-08-20T21:39:08.5387338Z >>> my_model = MyModule() 2024-08-20T21:39:08.5387438Z 2024-08-20T21:39:08.5387612Z >>> state_dict = {"model": my_model} 2024-08-20T21:39:08.5387706Z 2024-08-20T21:39:08.5388135Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2024-08-20T21:39:08.5388446Z >>> checkpoint_future = torch.distributed.checkpoint.async_save( 2024-08-20T21:39:08.5388584Z >>> state_dict=state_dict, 2024-08-20T21:39:08.5388757Z >>> storage_writer=fs_storage_writer, 2024-08-20T21:39:08.5388876Z >>> ) 2024-08-20T21:39:08.5388978Z >>> 2024-08-20T21:39:08.5389109Z >>> # ... do some work ... 2024-08-20T21:39:08.5389286Z >>> 2024-08-20T21:39:08.5389435Z >>> checkpoint_future.result() 2024-08-20T21:39:08.5389532Z 2024-08-20T21:39:08.5389646Z 2024-08-20T21:39:08.5390073Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5390168Z 2024-08-20T21:39:08.5390309Z warnings.warn(msg) 2024-08-20T21:39:08.5390408Z 2024-08-20T21:39:08.5390632Z --- Parse Warning: 52 / 101 --- 2024-08-20T21:39:08.5392325Z /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. 2024-08-20T21:39:08.5392764Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5392878Z 2024-08-20T21:39:08.5393146Z Initialize rendezvous event object and record its operations. 2024-08-20T21:39:08.5393244Z 2024-08-20T21:39:08.5393364Z Args: 2024-08-20T21:39:08.5393544Z run_id (str): The run id of the rendezvous. 2024-08-20T21:39:08.5393750Z message (str): The message describing the event. 2024-08-20T21:39:08.5394129Z node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED). 2024-08-20T21:39:08.5394402Z name (str): Event name. (E.g. Current action being performed). 2024-08-20T21:39:08.5394566Z hostname (str): Hostname of the node. 2024-08-20T21:39:08.5394785Z pid (Optional[int]): The process id of the node. 2024-08-20T21:39:08.5395135Z master_endpoint (str): The master endpoint for the rendezvous store, if known. 2024-08-20T21:39:08.5395548Z local_id (Optional[int]): The local_id of the node, if defined in dynamic_rendezvous.py 2024-08-20T21:39:08.5395766Z rank (Optional[int]): The rank of the node, if known. 2024-08-20T21:39:08.5396172Z Returns: 2024-08-20T21:39:08.5396290Z None 2024-08-20T21:39:08.5396517Z Example: 2024-08-20T21:39:08.5396699Z >>> # See DynamicRendezvousHandler class 2024-08-20T21:39:08.5396835Z >>> def _record( 2024-08-20T21:39:08.5396942Z ... self, 2024-08-20T21:39:08.5397060Z ... message: str, 2024-08-20T21:39:08.5397278Z ... node_state: NodeState = NodeState.RUNNING, 2024-08-20T21:39:08.5397577Z ... rank: Optional[int] = None, 2024-08-20T21:39:08.5397739Z ... ) -> None: 2024-08-20T21:39:08.5397916Z ... construct_and_record_rdzv_event( 2024-08-20T21:39:08.5398154Z ... name=f"{self.__class__.__name__}.{get_method_name()}", 2024-08-20T21:39:08.5398411Z ... run_id=self._settings.run_id, 2024-08-20T21:39:08.5398553Z ... message=message, 2024-08-20T21:39:08.5398689Z ... node_state=node_state, 2024-08-20T21:39:08.5398867Z ... hostname=self._this_node.addr, 2024-08-20T21:39:08.5399021Z ... pid=self._this_node.pid, 2024-08-20T21:39:08.5399194Z ... local_id=self._this_node.local_id, 2024-08-20T21:39:08.5399324Z ... rank=rank, 2024-08-20T21:39:08.5399428Z ... ) 2024-08-20T21:39:08.5399524Z 2024-08-20T21:39:08.5399973Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5400072Z 2024-08-20T21:39:08.5400191Z warnings.warn(msg) 2024-08-20T21:39:08.5400302Z 2024-08-20T21:39:08.5400529Z --- Parse Warning: 53 / 101 --- 2024-08-20T21:39:08.5402005Z /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. 2024-08-20T21:39:08.5402463Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5402559Z 2024-08-20T21:39:08.5402953Z This configures FSDP-native mixed precision training. 2024-08-20T21:39:08.5403066Z 2024-08-20T21:39:08.5403180Z Attributes: 2024-08-20T21:39:08.5403522Z param_dtype (Optional[torch.dtype]): This specifies the dtype for model 2024-08-20T21:39:08.5403800Z parameters during forward and backward and thus the dtype for 2024-08-20T21:39:08.5404116Z forward and backward computation. Outside forward and backward, the 2024-08-20T21:39:08.5404401Z *sharded* parameters are kept in full precision (e.g. for the 2024-08-20T21:39:08.5404696Z optimizer step), and for model checkpointing, the parameters are 2024-08-20T21:39:08.5404916Z always saved in full precision. (Default: ``None``) 2024-08-20T21:39:08.5405231Z reduce_dtype (Optional[torch.dtype]): This specifies the dtype for 2024-08-20T21:39:08.5405618Z gradient reduction (i.e. reduce-scatter or all-reduce). If this is 2024-08-20T21:39:08.5405889Z ``None`` but ``param_dtype`` is not ``None``, then this takes on 2024-08-20T21:39:08.5406187Z the ``param_dtype`` value, still running gradient reduction in low 2024-08-20T21:39:08.5406483Z precision. This is permitted to differ from ``param_dtype``, e.g. 2024-08-20T21:39:08.5406784Z to force gradient reduction to run in full precision. (Default: 2024-08-20T21:39:08.5406895Z ``None``) 2024-08-20T21:39:08.5407192Z buffer_dtype (Optional[torch.dtype]): This specifies the dtype for 2024-08-20T21:39:08.5407500Z buffers. FSDP does not shard buffers. Rather, FSDP casts them to 2024-08-20T21:39:08.5407778Z ``buffer_dtype`` in the first forward pass and keeps them in that 2024-08-20T21:39:08.5408075Z dtype thereafter. For model checkpointing, the buffers are saved 2024-08-20T21:39:08.5408353Z in full precision except for ``LOCAL_STATE_DICT``. (Default: 2024-08-20T21:39:08.5408459Z ``None``) 2024-08-20T21:39:08.5408736Z keep_low_precision_grads (bool): If ``False``, then FSDP upcasts 2024-08-20T21:39:08.5409058Z gradients to full precision after the backward pass in preparation 2024-08-20T21:39:08.5409353Z for the optimizer step. If ``True``, then FSDP keeps the gradients 2024-08-20T21:39:08.5409668Z in the dtype used for gradient reduction, which can save memory if 2024-08-20T21:39:08.5410049Z using a custom optimizer that supports running in low precision. 2024-08-20T21:39:08.5410172Z (Default: ``False``) 2024-08-20T21:39:08.5410481Z cast_forward_inputs (bool): If ``True``, then this FSDP module casts 2024-08-20T21:39:08.5410776Z its forward args and kwargs to ``param_dtype``. This is to ensure 2024-08-20T21:39:08.5411076Z that parameter and input dtypes match for forward computation, as 2024-08-20T21:39:08.5411394Z required by many ops. This may need to be set to ``True`` when only 2024-08-20T21:39:08.5411709Z applying mixed precision to some but not all FSDP modules, in which 2024-08-20T21:39:08.5412104Z case a mixed-precision FSDP submodule needs to recast its inputs. 2024-08-20T21:39:08.5412229Z (Default: ``False``) 2024-08-20T21:39:08.5412531Z cast_root_forward_inputs (bool): If ``True``, then the root FSDP module 2024-08-20T21:39:08.5412839Z casts its forward args and kwargs to ``param_dtype``, overriding 2024-08-20T21:39:08.5413196Z the value of ``cast_forward_inputs``. For non-root FSDP modules, 2024-08-20T21:39:08.5413394Z this does not do anything. (Default: ``True``) 2024-08-20T21:39:08.5413709Z _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies 2024-08-20T21:39:08.5413973Z module classes to ignore for mixed precision when using an 2024-08-20T21:39:08.5414233Z ``auto_wrap_policy``: Modules of these classes will have FSDP 2024-08-20T21:39:08.5414602Z applied to them separately with mixed precision disabled (meaning 2024-08-20T21:39:08.5414901Z that the final FSDP construction would deviate from the specified 2024-08-20T21:39:08.5415189Z policy). If ``auto_wrap_policy`` is not specified, then this does 2024-08-20T21:39:08.5415480Z not do anything. This API is experimental and subject to change. 2024-08-20T21:39:08.5415627Z (Default: ``(_BatchNorm,)``) 2024-08-20T21:39:08.5415738Z 2024-08-20T21:39:08.5415986Z .. note:: This API is experimental and subject to change. 2024-08-20T21:39:08.5416083Z 2024-08-20T21:39:08.5416417Z .. note:: Only floating point tensors are cast to their specified dtypes. 2024-08-20T21:39:08.5416514Z 2024-08-20T21:39:08.5416778Z .. note:: In ``summon_full_params``, parameters are forced to full 2024-08-20T21:39:08.5416943Z precision, but buffers are not. 2024-08-20T21:39:08.5417039Z 2024-08-20T21:39:08.5417340Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when 2024-08-20T21:39:08.5417669Z their inputs are in a low precision like ``float16`` or ``bfloat16``. 2024-08-20T21:39:08.5418076Z Disabling FSDP's mixed precision for those norm modules only means that 2024-08-20T21:39:08.5418391Z the affine parameters are kept in ``float32``. However, this incurs 2024-08-20T21:39:08.5418801Z separate all-gathers and reduce-scatters for those norm modules, which 2024-08-20T21:39:08.5419119Z may be inefficient, so if the workload permits, the user should prefer 2024-08-20T21:39:08.5419338Z to still apply mixed precision to those modules. 2024-08-20T21:39:08.5419434Z 2024-08-20T21:39:08.5419745Z .. note:: By default, if the user passes a model with any ``_BatchNorm`` 2024-08-20T21:39:08.5420054Z modules and specifies an ``auto_wrap_policy``, then the batch norm 2024-08-20T21:39:08.5420372Z modules will have FSDP applied to them separately with mixed precision 2024-08-20T21:39:08.5420638Z disabled. See the ``_module_classes_to_ignore`` argument. 2024-08-20T21:39:08.5420733Z 2024-08-20T21:39:08.5421020Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and 2024-08-20T21:39:08.5421333Z ``cast_forward_inputs=False`` by default. For the root FSDP instance, 2024-08-20T21:39:08.5421645Z its ``cast_root_forward_inputs`` takes precedence over its 2024-08-20T21:39:08.5421960Z ``cast_forward_inputs``. For non-root FSDP instances, their 2024-08-20T21:39:08.5422281Z ``cast_root_forward_inputs`` values are ignored. The default setting is 2024-08-20T21:39:08.5422593Z sufficient for the typical case where each FSDP instance has the same 2024-08-20T21:39:08.5422897Z ``MixedPrecision`` configuration and only needs to cast inputs to the 2024-08-20T21:39:08.5423240Z ``param_dtype`` at the beginning of the model's forward pass. 2024-08-20T21:39:08.5423338Z 2024-08-20T21:39:08.5423656Z .. note:: For nested FSDP instances with different ``MixedPrecision`` 2024-08-20T21:39:08.5423978Z configurations, we recommend setting individual ``cast_forward_inputs`` 2024-08-20T21:39:08.5424304Z values to configure casting inputs or not before each instance's 2024-08-20T21:39:08.5424610Z forward. In such a case, since the casts happen before each FSDP 2024-08-20T21:39:08.5424991Z instance's forward, a parent FSDP instance should have its non-FSDP 2024-08-20T21:39:08.5425319Z submodules run before its FSDP submodules to avoid the activation dtype 2024-08-20T21:39:08.5425641Z being changed due to a different ``MixedPrecision`` configuration. 2024-08-20T21:39:08.5425737Z 2024-08-20T21:39:08.5425850Z Example:: 2024-08-20T21:39:08.5425960Z 2024-08-20T21:39:08.5426147Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:39:08.5426392Z >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3)) 2024-08-20T21:39:08.5426534Z >>> model[1] = FSDP( 2024-08-20T21:39:08.5426709Z >>> model[1], 2024-08-20T21:39:08.5427138Z >>> mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True), 2024-08-20T21:39:08.5427240Z >>> ) 2024-08-20T21:39:08.5427359Z >>> model = FSDP( 2024-08-20T21:39:08.5427495Z >>> model, 2024-08-20T21:39:08.5427909Z >>> mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), 2024-08-20T21:39:08.5428010Z >>> ) 2024-08-20T21:39:08.5428121Z 2024-08-20T21:39:08.5428431Z The above shows a working example. On the other hand, if ``model[1]`` 2024-08-20T21:39:08.5428717Z were replaced with ``model[0]``, meaning that the submodule using 2024-08-20T21:39:08.5429039Z different ``MixedPrecision`` ran its forward first, then ``model[1]`` 2024-08-20T21:39:08.5429340Z would incorrectly see ``float16`` activations instead of ``bfloat16`` 2024-08-20T21:39:08.5429441Z ones. 2024-08-20T21:39:08.5429560Z 2024-08-20T21:39:08.5429654Z 2024-08-20T21:39:08.5430097Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5430191Z 2024-08-20T21:39:08.5430311Z warnings.warn(msg) 2024-08-20T21:39:08.5430423Z 2024-08-20T21:39:08.5430656Z --- Parse Warning: 54 / 101 --- 2024-08-20T21:39:08.5432510Z /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. 2024-08-20T21:39:08.5432968Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5433336Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2024-08-20T21:39:08.5433435Z 2024-08-20T21:39:08.5433920Z Also takes (optional) configuration for the model's and optimizer's state dict. 2024-08-20T21:39:08.5434222Z The target module does not have to be a FSDP module. If the target 2024-08-20T21:39:08.5434549Z module is a FSDP module, its ``state_dict_type`` will also be changed. 2024-08-20T21:39:08.5434648Z 2024-08-20T21:39:08.5435081Z .. note:: This API should be called for only the top-level (root) 2024-08-20T21:39:08.5435204Z module. 2024-08-20T21:39:08.5435299Z 2024-08-20T21:39:08.5435608Z .. note:: This API enables users to transparently use the conventional 2024-08-20T21:39:08.5435901Z ``state_dict`` API to take model checkpoints in cases where the 2024-08-20T21:39:08.5436203Z root FSDP module is wrapped by another ``nn.Module``. For example, 2024-08-20T21:39:08.5436580Z the following will ensure ``state_dict`` is called on all non-FSDP 2024-08-20T21:39:08.5436922Z instances, while dispatching into `sharded_state_dict` implementation 2024-08-20T21:39:08.5437040Z for FSDP: 2024-08-20T21:39:08.5437149Z 2024-08-20T21:39:08.5437266Z Example:: 2024-08-20T21:39:08.5437360Z 2024-08-20T21:39:08.5437567Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:39:08.5437712Z >>> model = DDP(FSDP(...)) 2024-08-20T21:39:08.5437864Z >>> FSDP.set_state_dict_type( 2024-08-20T21:39:08.5437995Z >>> model, 2024-08-20T21:39:08.5438188Z >>> StateDictType.SHARDED_STATE_DICT, 2024-08-20T21:39:08.5438709Z >>> state_dict_config = ShardedStateDictConfig(offload_to_cpu=True), 2024-08-20T21:39:08.5439050Z >>> optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True), 2024-08-20T21:39:08.5439154Z >>> ) 2024-08-20T21:39:08.5439335Z >>> param_state_dict = model.state_dict() 2024-08-20T21:39:08.5439666Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2024-08-20T21:39:08.5439766Z 2024-08-20T21:39:08.5439869Z Args: 2024-08-20T21:39:08.5440066Z module (torch.nn.Module): Root module. 2024-08-20T21:39:08.5440391Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2024-08-20T21:39:08.5440743Z state_dict_config (Optional[StateDictConfig]): the configuration for the 2024-08-20T21:39:08.5440898Z target ``state_dict_type``. 2024-08-20T21:39:08.5441252Z optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration 2024-08-20T21:39:08.5441430Z for the optimizer state dict. 2024-08-20T21:39:08.5441525Z 2024-08-20T21:39:08.5441631Z Returns: 2024-08-20T21:39:08.5441955Z A StateDictSettings that include the previous state_dict type and 2024-08-20T21:39:08.5442111Z configuration for the module. 2024-08-20T21:39:08.5442216Z 2024-08-20T21:39:08.5442668Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5442762Z 2024-08-20T21:39:08.5442882Z warnings.warn(msg) 2024-08-20T21:39:08.5442992Z 2024-08-20T21:39:08.5443220Z --- Parse Warning: 55 / 101 --- 2024-08-20T21:39:08.5445089Z /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. 2024-08-20T21:39:08.5445524Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5445895Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2024-08-20T21:39:08.5446007Z 2024-08-20T21:39:08.5446475Z This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of 2024-08-20T21:39:08.5446660Z :meth:`set_state_dict_type` for the detail. 2024-08-20T21:39:08.5446771Z 2024-08-20T21:39:08.5446890Z Example:: 2024-08-20T21:39:08.5446987Z 2024-08-20T21:39:08.5447195Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:39:08.5447420Z >>> model = DDP(FSDP(...)) 2024-08-20T21:39:08.5447591Z >>> with FSDP.state_dict_type( 2024-08-20T21:39:08.5447703Z >>> model, 2024-08-20T21:39:08.5447895Z >>> StateDictType.SHARDED_STATE_DICT, 2024-08-20T21:39:08.5448015Z >>> ): 2024-08-20T21:39:08.5448190Z >>> checkpoint = model.state_dict() 2024-08-20T21:39:08.5448286Z 2024-08-20T21:39:08.5448404Z Args: 2024-08-20T21:39:08.5448582Z module (torch.nn.Module): Root module. 2024-08-20T21:39:08.5448913Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2024-08-20T21:39:08.5449254Z state_dict_config (Optional[StateDictConfig]): the model ``state_dict`` 2024-08-20T21:39:08.5449479Z configuration for the target ``state_dict_type``. 2024-08-20T21:39:08.5449805Z optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer 2024-08-20T21:39:08.5450105Z ``state_dict`` configuration for the target ``state_dict_type``. 2024-08-20T21:39:08.5450207Z 2024-08-20T21:39:08.5450652Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5450749Z 2024-08-20T21:39:08.5450869Z warnings.warn(msg) 2024-08-20T21:39:08.5450979Z 2024-08-20T21:39:08.5451205Z --- Parse Warning: 56 / 101 --- 2024-08-20T21:39:08.5453114Z /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. 2024-08-20T21:39:08.5453575Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5453670Z 2024-08-20T21:39:08.5454092Z Transform the state-dict of an optimizer corresponding to a sharded model. 2024-08-20T21:39:08.5454208Z 2024-08-20T21:39:08.5454550Z The given state-dict can be transformed to one of three types: 2024-08-20T21:39:08.5454975Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict. 2024-08-20T21:39:08.5455088Z 2024-08-20T21:39:08.5455421Z For full optimizer state_dict, all states are unflattened and not sharded. 2024-08-20T21:39:08.5455756Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to 2024-08-20T21:39:08.5455864Z avoid OOM. 2024-08-20T21:39:08.5455960Z 2024-08-20T21:39:08.5456309Z For sharded optimizer state_dict, all states are unflattened but sharded. 2024-08-20T21:39:08.5456618Z CPU only can be specified via :meth:`state_dict_type` to further save 2024-08-20T21:39:08.5456720Z memory. 2024-08-20T21:39:08.5456831Z 2024-08-20T21:39:08.5457145Z For local state_dict, no transformation will be performed. But a state 2024-08-20T21:39:08.5457494Z will be converted from nn.Tensor to ShardedTensor to represent its sharding 2024-08-20T21:39:08.5457657Z nature (this is not supported yet). 2024-08-20T21:39:08.5457752Z 2024-08-20T21:39:08.5457863Z Example:: 2024-08-20T21:39:08.5457975Z 2024-08-20T21:39:08.5458159Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:39:08.5458494Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2024-08-20T21:39:08.5458736Z >>> from torch.distributed.fsdp import StateDictType 2024-08-20T21:39:08.5458988Z >>> from torch.distributed.fsdp import FullStateDictConfig 2024-08-20T21:39:08.5459292Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2024-08-20T21:39:08.5459419Z >>> # Save a checkpoint 2024-08-20T21:39:08.5459543Z >>> model, optim = ... 2024-08-20T21:39:08.5459698Z >>> FSDP.set_state_dict_type( 2024-08-20T21:39:08.5459806Z >>> model, 2024-08-20T21:39:08.5459974Z >>> StateDictType.FULL_STATE_DICT, 2024-08-20T21:39:08.5460264Z >>> FullStateDictConfig(rank0_only=False), 2024-08-20T21:39:08.5460476Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-08-20T21:39:08.5460579Z >>> ) 2024-08-20T21:39:08.5460749Z >>> state_dict = model.state_dict() 2024-08-20T21:39:08.5460985Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2024-08-20T21:39:08.5461185Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2024-08-20T21:39:08.5461324Z >>> # Load a checkpoint 2024-08-20T21:39:08.5461447Z >>> model, optim = ... 2024-08-20T21:39:08.5461662Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2024-08-20T21:39:08.5461816Z >>> FSDP.set_state_dict_type( 2024-08-20T21:39:08.5461921Z >>> model, 2024-08-20T21:39:08.5462101Z >>> StateDictType.FULL_STATE_DICT, 2024-08-20T21:39:08.5462287Z >>> FullStateDictConfig(rank0_only=False), 2024-08-20T21:39:08.5462498Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-08-20T21:39:08.5462615Z >>> ) 2024-08-20T21:39:08.5462773Z >>> model.load_state_dict(state_dict) 2024-08-20T21:39:08.5462983Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2024-08-20T21:39:08.5463160Z >>> model, optim, optim_state_dict 2024-08-20T21:39:08.5463260Z >>> ) 2024-08-20T21:39:08.5463433Z >>> optim.load_state_dict(optim_state_dict) 2024-08-20T21:39:08.5463547Z 2024-08-20T21:39:08.5463650Z Args: 2024-08-20T21:39:08.5463933Z model (torch.nn.Module): Root module (which may or may not be a 2024-08-20T21:39:08.5464285Z :class:`FullyShardedDataParallel` instance) whose parameters 2024-08-20T21:39:08.5464475Z were passed into the optimizer ``optim``. 2024-08-20T21:39:08.5464772Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2024-08-20T21:39:08.5464904Z parameters. 2024-08-20T21:39:08.5465203Z optim_state_dict (Dict[str, Any]): the target optimizer state_dict to 2024-08-20T21:39:08.5465526Z transform. If the value is None, optim.state_dict() will be used. ( 2024-08-20T21:39:08.5465650Z Default: ``None``) 2024-08-20T21:39:08.5466064Z group (dist.ProcessGroup): Model's process group across which parameters 2024-08-20T21:39:08.5466350Z are sharded or ``None`` if using the default process group. ( 2024-08-20T21:39:08.5466474Z Default: ``None``) 2024-08-20T21:39:08.5466571Z 2024-08-20T21:39:08.5466691Z Returns: 2024-08-20T21:39:08.5466975Z Dict[str, Any]: A :class:`dict` containing the optimizer state for 2024-08-20T21:39:08.5467223Z ``model``. The sharding of the optimizer state is based on 2024-08-20T21:39:08.5467361Z ``state_dict_type``. 2024-08-20T21:39:08.5467457Z 2024-08-20T21:39:08.5467883Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5467995Z 2024-08-20T21:39:08.5468129Z warnings.warn(msg) 2024-08-20T21:39:08.5468240Z 2024-08-20T21:39:08.5468468Z --- Parse Warning: 57 / 101 --- 2024-08-20T21:39:08.5470368Z /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. 2024-08-20T21:39:08.5470822Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5470918Z 2024-08-20T21:39:08.5471551Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model. 2024-08-20T21:39:08.5471661Z 2024-08-20T21:39:08.5471894Z Given a ``optim_state_dict`` that is transformed through 2024-08-20T21:39:08.5472208Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer 2024-08-20T21:39:08.5472582Z state_dict that can be loaded to ``optim`` which is the optimizer for 2024-08-20T21:39:08.5472852Z ``model``. ``model`` must be sharded by FullyShardedDataParallel. 2024-08-20T21:39:08.5472964Z 2024-08-20T21:39:08.5473147Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:39:08.5473481Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2024-08-20T21:39:08.5473722Z >>> from torch.distributed.fsdp import StateDictType 2024-08-20T21:39:08.5473977Z >>> from torch.distributed.fsdp import FullStateDictConfig 2024-08-20T21:39:08.5474258Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2024-08-20T21:39:08.5474404Z >>> # Save a checkpoint 2024-08-20T21:39:08.5474528Z >>> model, optim = ... 2024-08-20T21:39:08.5474667Z >>> FSDP.set_state_dict_type( 2024-08-20T21:39:08.5474791Z >>> model, 2024-08-20T21:39:08.5474955Z >>> StateDictType.FULL_STATE_DICT, 2024-08-20T21:39:08.5475156Z >>> FullStateDictConfig(rank0_only=False), 2024-08-20T21:39:08.5475365Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-08-20T21:39:08.5475465Z >>> ) 2024-08-20T21:39:08.5475628Z >>> state_dict = model.state_dict() 2024-08-20T21:39:08.5475786Z >>> original_osd = optim.state_dict() 2024-08-20T21:39:08.5475965Z >>> optim_state_dict = FSDP.optim_state_dict( 2024-08-20T21:39:08.5476085Z >>> model, 2024-08-20T21:39:08.5476188Z >>> optim, 2024-08-20T21:39:08.5476340Z >>> optim_state_dict=original_osd 2024-08-20T21:39:08.5476449Z >>> ) 2024-08-20T21:39:08.5476707Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2024-08-20T21:39:08.5476830Z >>> # Load a checkpoint 2024-08-20T21:39:08.5476965Z >>> model, optim = ... 2024-08-20T21:39:08.5477175Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2024-08-20T21:39:08.5477309Z >>> FSDP.set_state_dict_type( 2024-08-20T21:39:08.5477430Z >>> model, 2024-08-20T21:39:08.5477591Z >>> StateDictType.FULL_STATE_DICT, 2024-08-20T21:39:08.5477771Z >>> FullStateDictConfig(rank0_only=False), 2024-08-20T21:39:08.5477993Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-08-20T21:39:08.5478090Z >>> ) 2024-08-20T21:39:08.5478382Z >>> model.load_state_dict(state_dict) 2024-08-20T21:39:08.5478593Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2024-08-20T21:39:08.5478747Z >>> model, optim, optim_state_dict 2024-08-20T21:39:08.5478860Z >>> ) 2024-08-20T21:39:08.5479028Z >>> optim.load_state_dict(optim_state_dict) 2024-08-20T21:39:08.5479126Z 2024-08-20T21:39:08.5479241Z Args: 2024-08-20T21:39:08.5479520Z model (torch.nn.Module): Root module (which may or may not be a 2024-08-20T21:39:08.5479796Z :class:`FullyShardedDataParallel` instance) whose parameters 2024-08-20T21:39:08.5479994Z were passed into the optimizer ``optim``. 2024-08-20T21:39:08.5480302Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2024-08-20T21:39:08.5480413Z parameters. 2024-08-20T21:39:08.5480721Z optim_state_dict (Dict[str, Any]): The optimizer states to be loaded. 2024-08-20T21:39:08.5480997Z is_named_optimizer (bool): Is this optimizer a NamedOptimizer or 2024-08-20T21:39:08.5481315Z KeyedOptimizer. Only set to True if ``optim`` is TorchRec's 2024-08-20T21:39:08.5481623Z KeyedOptimizer or torch.distributed's NamedOptimizer. 2024-08-20T21:39:08.5481899Z load_directly (bool): If this is set to True, this API will also 2024-08-20T21:39:08.5482197Z call optim.load_state_dict(result) before returning the result. 2024-08-20T21:39:08.5482501Z Otherwise, users are responsible to call ``optim.load_state_dict()`` 2024-08-20T21:39:08.5482623Z (Default: ``False``) 2024-08-20T21:39:08.5483046Z group (dist.ProcessGroup): Model's process group across which parameters 2024-08-20T21:39:08.5483379Z are sharded or ``None`` if using the default process group. ( 2024-08-20T21:39:08.5483498Z Default: ``None``) 2024-08-20T21:39:08.5483605Z 2024-08-20T21:39:08.5484028Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5484122Z 2024-08-20T21:39:08.5484254Z warnings.warn(msg) 2024-08-20T21:39:08.5484346Z 2024-08-20T21:39:08.5484570Z --- Parse Warning: 58 / 101 --- 2024-08-20T21:39:08.5486183Z /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. 2024-08-20T21:39:08.5486617Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5486724Z 2024-08-20T21:39:08.5487031Z RemoteModule instance can only be created after RPC initialization. 2024-08-20T21:39:08.5487124Z 2024-08-20T21:39:08.5487481Z It creates a user-specified module on a specified remote node. 2024-08-20T21:39:08.5487822Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2024-08-20T21:39:08.5487957Z executed on the remote node. 2024-08-20T21:39:08.5488306Z It takes care of autograd recording to ensure the backward pass propagates 2024-08-20T21:39:08.5488515Z gradients back to the corresponding remote module. 2024-08-20T21:39:08.5489038Z It can be shared across processors using `RPC framework `__, 2024-08-20T21:39:08.5489366Z without incurring any overheads of copying the actual module, 2024-08-20T21:39:08.5489644Z which is equivalent to an :class:`~torch.distributed.rpc.RRef` 2024-08-20T21:39:08.5489794Z pointing to the remote module. 2024-08-20T21:39:08.5489889Z 2024-08-20T21:39:08.5490163Z The arguments of ``forward_async`` and ``forward`` are the same as 2024-08-20T21:39:08.5490468Z the ``forward`` method of the module returned by the ``module_cls``. 2024-08-20T21:39:08.5490563Z 2024-08-20T21:39:08.5491008Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now. 2024-08-20T21:39:08.5491117Z 2024-08-20T21:39:08.5491471Z Particularly, to create a hybrid model, typically the local modules should be 2024-08-20T21:39:08.5492003Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``). 2024-08-20T21:39:08.5492126Z Hybrid Example: 2024-08-20T21:39:08.5492285Z >>> class HybridModel(nn.Module): 2024-08-20T21:39:08.5492513Z >>> def __init__(self) -> None: 2024-08-20T21:39:08.5492667Z >>> nn.Module.__init__(self) 2024-08-20T21:39:08.5492870Z >>> self.remote_embedding = RemoteModule(...) 2024-08-20T21:39:08.5493065Z >>> self.local_linear = nn.Linear(...) 2024-08-20T21:39:08.5493160Z 2024-08-20T21:39:08.5493447Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2024-08-20T21:39:08.5493901Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``, 2024-08-20T21:39:08.5494189Z the generated ``RemoteModule`` will have 2 methods in signature of 2024-08-20T21:39:08.5494422Z ``def forward(input: Tensor) -> Tensor:`` and 2024-08-20T21:39:08.5494724Z ``def forward_async(input: Tensor) -> Future[Tensor]:``. 2024-08-20T21:39:08.5494815Z 2024-08-20T21:39:08.5494928Z .. note:: 2024-08-20T21:39:08.5495148Z If the remote module is placed on a cuda device, 2024-08-20T21:39:08.5495490Z any input CPU tensors will be automatically moved to the same cuda device, 2024-08-20T21:39:08.5496357Z and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend. 2024-08-20T21:39:08.5496610Z 2024-08-20T21:39:08.5496715Z Args: 2024-08-20T21:39:08.5497261Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-08-20T21:39:08.5497703Z The device can be a local device or a remote device specified by one of the following remote 2024-08-20T21:39:08.5497807Z formats: 2024-08-20T21:39:08.5497920Z 2024-08-20T21:39:08.5498119Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2024-08-20T21:39:08.5498333Z 2. "/" (ex: "trainer0/cuda:0"). 2024-08-20T21:39:08.5498445Z 2024-08-20T21:39:08.5498807Z In addition, the device field can be optional and the default value is "cpu". 2024-08-20T21:39:08.5498965Z module_cls (nn.Module): For example, 2024-08-20T21:39:08.5499124Z >>> class MyModule(nn.Module): 2024-08-20T21:39:08.5499257Z >>> def forward(input): 2024-08-20T21:39:08.5499404Z >>> return input + 1 2024-08-20T21:39:08.5499508Z >>> 2024-08-20T21:39:08.5499637Z >>> module_cls = MyModule 2024-08-20T21:39:08.5499922Z args (Sequence, optional): args to be passed to ``module_cls``. 2024-08-20T21:39:08.5500189Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2024-08-20T21:39:08.5500571Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2024-08-20T21:39:08.5500932Z to be created. The type object should be decorated by @torch.jit.interface. 2024-08-20T21:39:08.5501332Z If not provided, the generated RemoteModule is not torchscript-able. 2024-08-20T21:39:08.5501750Z Warning, this is an experimental API and susceptible to frequent changes. 2024-08-20T21:39:08.5501858Z 2024-08-20T21:39:08.5501959Z Returns: 2024-08-20T21:39:08.5502299Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-08-20T21:39:08.5502714Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2024-08-20T21:39:08.5503088Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-08-20T21:39:08.5503365Z on the user-provided module on the remote side. 2024-08-20T21:39:08.5503460Z 2024-08-20T21:39:08.5503572Z Example:: 2024-08-20T21:39:08.5503802Z Run the following code in two different processes: 2024-08-20T21:39:08.5503894Z 2024-08-20T21:39:08.5504053Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:39:08.5504181Z >>> # On worker 0: 2024-08-20T21:39:08.5504293Z >>> import torch 2024-08-20T21:39:08.5504471Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:39:08.5504626Z >>> from torch import nn, Tensor 2024-08-20T21:39:08.5504933Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-08-20T21:39:08.5505034Z >>> 2024-08-20T21:39:08.5505236Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-08-20T21:39:08.5505406Z >>> remote_linear_module = RemoteModule( 2024-08-20T21:39:08.5505596Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-08-20T21:39:08.5505695Z >>> ) 2024-08-20T21:39:08.5505835Z >>> input = torch.randn(128, 20) 2024-08-20T21:39:08.5506063Z >>> ret_fut = remote_linear_module.forward_async(input) 2024-08-20T21:39:08.5506184Z >>> ret = ret_fut.wait() 2024-08-20T21:39:08.5506300Z >>> rpc.shutdown() 2024-08-20T21:39:08.5506406Z 2024-08-20T21:39:08.5506518Z >>> # On worker 1: 2024-08-20T21:39:08.5506629Z >>> import torch 2024-08-20T21:39:08.5506818Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:39:08.5506915Z >>> 2024-08-20T21:39:08.5507103Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-08-20T21:39:08.5507232Z >>> rpc.shutdown() 2024-08-20T21:39:08.5507324Z 2024-08-20T21:39:08.5507750Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5507915Z 2024-08-20T21:39:08.5508032Z warnings.warn(msg) 2024-08-20T21:39:08.5508123Z 2024-08-20T21:39:08.5508363Z --- Parse Warning: 59 / 101 --- 2024-08-20T21:39:08.5510052Z /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. 2024-08-20T21:39:08.5510500Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5510593Z 2024-08-20T21:39:08.5511041Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef. 2024-08-20T21:39:08.5511148Z 2024-08-20T21:39:08.5511593Z This alternate initialization method can be particularly useful if we want to create multiple 2024-08-20T21:39:08.5512029Z RemoteModule instances that share the same underlying module and reduce memory consumption. 2024-08-20T21:39:08.5512139Z 2024-08-20T21:39:08.5512522Z Moreover, this also provides a workaround for passing script RemoteModule over RPC, 2024-08-20T21:39:08.5512771Z which is not supported. The recommended way is as follows: 2024-08-20T21:39:08.5512885Z 2024-08-20T21:39:08.5513047Z 1. the sender creates a RemoteModule; 2024-08-20T21:39:08.5513248Z 2. the sender sends its ``module_rref`` over RPC; 2024-08-20T21:39:08.5513746Z 3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``. 2024-08-20T21:39:08.5513841Z 2024-08-20T21:39:08.5514031Z Example:: 2024-08-20T21:39:08.5514248Z Run the following code in two different processes: 2024-08-20T21:39:08.5514341Z 2024-08-20T21:39:08.5514513Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:39:08.5514632Z >>> # On worker 0: 2024-08-20T21:39:08.5514748Z >>> import torch 2024-08-20T21:39:08.5514943Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:39:08.5515087Z >>> from torch import nn, Tensor 2024-08-20T21:39:08.5515400Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-08-20T21:39:08.5515517Z >>> 2024-08-20T21:39:08.5515708Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-08-20T21:39:08.5515854Z >>> remote_module = RemoteModule( 2024-08-20T21:39:08.5516051Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-08-20T21:39:08.5516152Z >>> ) 2024-08-20T21:39:08.5516249Z >>> 2024-08-20T21:39:08.5516414Z >>> remote_module1 = rpc.rpc_sync( 2024-08-20T21:39:08.5516538Z >>> "worker1/cpu", 2024-08-20T21:39:08.5516710Z >>> RemoteModule.init_from_module_rref, 2024-08-20T21:39:08.5516941Z >>> ("worker1/cpu", remote_module1.get_module_rref()), 2024-08-20T21:39:08.5517041Z >>> ) 2024-08-20T21:39:08.5517173Z >>> rpc.shutdown() 2024-08-20T21:39:08.5517273Z 2024-08-20T21:39:08.5517388Z >>> # On worker 1: 2024-08-20T21:39:08.5517518Z >>> import torch 2024-08-20T21:39:08.5517689Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:39:08.5517785Z >>> 2024-08-20T21:39:08.5517991Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-08-20T21:39:08.5518107Z >>> rpc.shutdown() 2024-08-20T21:39:08.5518327Z 2024-08-20T21:39:08.5518449Z Args: 2024-08-20T21:39:08.5518977Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-08-20T21:39:08.5519428Z The device can be a local device or a remote device specified by one of the following remote 2024-08-20T21:39:08.5519551Z formats: 2024-08-20T21:39:08.5519644Z 2024-08-20T21:39:08.5519841Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2024-08-20T21:39:08.5520069Z 2. "/" (ex: "trainer0/cuda:0"). 2024-08-20T21:39:08.5520229Z 2024-08-20T21:39:08.5520590Z In addition, the device field can be optional and the default value is "cpu". 2024-08-20T21:39:08.5520963Z module_rref (RRef[nn.Module]): The module reference shared by both the caller and 2024-08-20T21:39:08.5521098Z the created remote module. 2024-08-20T21:39:08.5521491Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2024-08-20T21:39:08.5521842Z to be created. The type object should be decorated by @torch.jit.interface. 2024-08-20T21:39:08.5522241Z If not provided, the generated RemoteModule is not torchscript-able. 2024-08-20T21:39:08.5522599Z Warning, this is an experimental API and susceptible to frequent changes. 2024-08-20T21:39:08.5522693Z 2024-08-20T21:39:08.5522792Z Returns: 2024-08-20T21:39:08.5523144Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-08-20T21:39:08.5523553Z user-provided ``module_rref``, it has a blocking ``forward`` method and an 2024-08-20T21:39:08.5523941Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-08-20T21:39:08.5524204Z on the user-provided module on the remote side. 2024-08-20T21:39:08.5524298Z 2024-08-20T21:39:08.5524732Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5524828Z 2024-08-20T21:39:08.5524946Z warnings.warn(msg) 2024-08-20T21:39:08.5525050Z 2024-08-20T21:39:08.5525273Z --- Parse Warning: 60 / 101 --- 2024-08-20T21:39:08.5526896Z /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. 2024-08-20T21:39:08.5527348Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5527445Z 2024-08-20T21:39:08.5527760Z A RemoteModule instance can only be created after RPC initialization. 2024-08-20T21:39:08.5527866Z 2024-08-20T21:39:08.5528219Z It creates a user-specified module on a specified remote node. 2024-08-20T21:39:08.5528573Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2024-08-20T21:39:08.5528706Z executed on the remote node. 2024-08-20T21:39:08.5529046Z It takes care of autograd recording to ensure the backward pass propagates 2024-08-20T21:39:08.5529275Z gradients back to the corresponding remote module. 2024-08-20T21:39:08.5529370Z 2024-08-20T21:39:08.5529681Z It generates two methods ``forward_async`` and ``forward`` based on the 2024-08-20T21:39:08.5530003Z signature of the ``forward`` method of ``module_cls``. ``forward_async`` 2024-08-20T21:39:08.5530349Z runs asynchronously and returns a Future. The arguments of ``forward_async`` 2024-08-20T21:39:08.5530641Z and ``forward`` are the same as the ``forward`` method of the module 2024-08-20T21:39:08.5530790Z returned by the ``module_cls``. 2024-08-20T21:39:08.5530884Z 2024-08-20T21:39:08.5531174Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2024-08-20T21:39:08.5531636Z that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``, 2024-08-20T21:39:08.5531954Z the generated ``RemoteModule`` will have 2 methods with the signatures: 2024-08-20T21:39:08.5532063Z 2024-08-20T21:39:08.5532291Z | ``def forward(input: Tensor) -> Tensor:`` 2024-08-20T21:39:08.5532588Z | ``def forward_async(input: Tensor) -> Future[Tensor]:`` 2024-08-20T21:39:08.5532697Z 2024-08-20T21:39:08.5532797Z Args: 2024-08-20T21:39:08.5533314Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-08-20T21:39:08.5533818Z The format should be "/", where the device field can be parsed as torch.device type. 2024-08-20T21:39:08.5534085Z E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0". 2024-08-20T21:39:08.5534444Z In addition, the device field can be optional and the default value is "cpu". 2024-08-20T21:39:08.5534821Z module_cls (nn.Module): Class for the module to be created remotely. For example, 2024-08-20T21:39:08.5534914Z 2024-08-20T21:39:08.5535071Z >>> class MyModule(nn.Module): 2024-08-20T21:39:08.5535200Z >>> def forward(input): 2024-08-20T21:39:08.5535332Z >>> return input + 1 2024-08-20T21:39:08.5535447Z >>> 2024-08-20T21:39:08.5535580Z >>> module_cls = MyModule 2024-08-20T21:39:08.5535676Z 2024-08-20T21:39:08.5535964Z args (Sequence, optional): args to be passed to ``module_cls``. 2024-08-20T21:39:08.5536233Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2024-08-20T21:39:08.5536326Z 2024-08-20T21:39:08.5536449Z Returns: 2024-08-20T21:39:08.5536790Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-08-20T21:39:08.5537192Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2024-08-20T21:39:08.5537574Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-08-20T21:39:08.5537835Z on the user-provided module on the remote side. 2024-08-20T21:39:08.5537928Z 2024-08-20T21:39:08.5538053Z Example:: 2024-08-20T21:39:08.5538269Z Run the following code in two different processes: 2024-08-20T21:39:08.5538375Z 2024-08-20T21:39:08.5538615Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:39:08.5538731Z >>> # On worker 0: 2024-08-20T21:39:08.5538857Z >>> import torch 2024-08-20T21:39:08.5539031Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:39:08.5539173Z >>> from torch import nn, Tensor 2024-08-20T21:39:08.5539499Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-08-20T21:39:08.5539599Z >>> 2024-08-20T21:39:08.5539786Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-08-20T21:39:08.5539965Z >>> remote_linear_module = RemoteModule( 2024-08-20T21:39:08.5540145Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-08-20T21:39:08.5540247Z >>> ) 2024-08-20T21:39:08.5540402Z >>> input = torch.randn(128, 20) 2024-08-20T21:39:08.5540618Z >>> ret_fut = remote_linear_module.forward_async(input) 2024-08-20T21:39:08.5540742Z >>> ret = ret_fut.wait() 2024-08-20T21:39:08.5540874Z >>> rpc.shutdown() 2024-08-20T21:39:08.5540972Z 2024-08-20T21:39:08.5541086Z >>> # On worker 1: 2024-08-20T21:39:08.5541211Z >>> import torch 2024-08-20T21:39:08.5541381Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:39:08.5541480Z >>> 2024-08-20T21:39:08.5541681Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-08-20T21:39:08.5541799Z >>> rpc.shutdown() 2024-08-20T21:39:08.5541904Z 2024-08-20T21:39:08.5542162Z Furthermore, a more practical example that is combined with 2024-08-20T21:39:08.5542819Z `DistributedDataParallel `__ (DDP) 2024-08-20T21:39:08.5543305Z can be found in this `tutorial `__. 2024-08-20T21:39:08.5543399Z 2024-08-20T21:39:08.5543824Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5543930Z 2024-08-20T21:39:08.5544052Z warnings.warn(msg) 2024-08-20T21:39:08.5544143Z 2024-08-20T21:39:08.5544380Z --- Parse Warning: 61 / 101 --- 2024-08-20T21:39:08.5545956Z /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. 2024-08-20T21:39:08.5546481Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5546574Z 2024-08-20T21:39:08.5546890Z DistributedOptimizer takes remote references to parameters scattered 2024-08-20T21:39:08.5547245Z across workers and applies the given optimizer locally for each parameter. 2024-08-20T21:39:08.5547336Z 2024-08-20T21:39:08.5547681Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order 2024-08-20T21:39:08.5547902Z to retrieve the gradients for specific parameters. 2024-08-20T21:39:08.5547996Z 2024-08-20T21:39:08.5548115Z Concurrent calls to 2024-08-20T21:39:08.5548422Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`, 2024-08-20T21:39:08.5548612Z either from the same or different clients, will 2024-08-20T21:39:08.5549014Z be serialized on each worker -- as each worker's optimizer can only work 2024-08-20T21:39:08.5549344Z on one set of gradients at a time. However, there is no guarantee that 2024-08-20T21:39:08.5549763Z the full forward-backward-optimizer sequence will execute for one client 2024-08-20T21:39:08.5550102Z at a time. This means that the gradients being applied may not correspond 2024-08-20T21:39:08.5550420Z to the latest forward pass executed on a given worker. Also, there is no 2024-08-20T21:39:08.5550565Z guaranteed ordering across workers. 2024-08-20T21:39:08.5550675Z 2024-08-20T21:39:08.5551019Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled 2024-08-20T21:39:08.5551415Z by default, so that optimizer updates are not blocked by the Python Global 2024-08-20T21:39:08.5551787Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed 2024-08-20T21:39:08.5552122Z Model Parallel). This feature is currently enabled for most optimizers. You 2024-08-20T21:39:08.5558715Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support 2024-08-20T21:39:08.5558920Z for your own custom optimizers. 2024-08-20T21:39:08.5559018Z 2024-08-20T21:39:08.5559125Z Args: 2024-08-20T21:39:08.5559425Z optimizer_class (optim.Optimizer): the class of optimizer to 2024-08-20T21:39:08.5559570Z instantiate on each worker. 2024-08-20T21:39:08.5559882Z params_rref (list[RRef]): list of RRefs to local or remote parameters 2024-08-20T21:39:08.5560012Z to optimize. 2024-08-20T21:39:08.5560319Z args: arguments to pass to the optimizer constructor on each worker. 2024-08-20T21:39:08.5560656Z kwargs: arguments to pass to the optimizer constructor on each worker. 2024-08-20T21:39:08.5560753Z 2024-08-20T21:39:08.5560872Z Example:: 2024-08-20T21:39:08.5561049Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:39:08.5561286Z >>> import torch.distributed.autograd as dist_autograd 2024-08-20T21:39:08.5561463Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:39:08.5561616Z >>> from torch import optim 2024-08-20T21:39:08.5561887Z >>> from torch.distributed.optim import DistributedOptimizer 2024-08-20T21:39:08.5561987Z >>> 2024-08-20T21:39:08.5562193Z >>> with dist_autograd.context() as context_id: 2024-08-20T21:39:08.5562318Z >>> # Forward pass. 2024-08-20T21:39:08.5562607Z >>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3)) 2024-08-20T21:39:08.5562907Z >>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1)) 2024-08-20T21:39:08.5563086Z >>> loss = rref1.to_here() + rref2.to_here() 2024-08-20T21:39:08.5563210Z >>> 2024-08-20T21:39:08.5563334Z >>> # Backward pass. 2024-08-20T21:39:08.5563546Z >>> dist_autograd.backward(context_id, [loss.sum()]) 2024-08-20T21:39:08.5563664Z >>> 2024-08-20T21:39:08.5563782Z >>> # Optimizer. 2024-08-20T21:39:08.5563957Z >>> dist_optim = DistributedOptimizer( 2024-08-20T21:39:08.5564215Z >>> optim.SGD, 2024-08-20T21:39:08.5564337Z >>> [rref1, rref2], 2024-08-20T21:39:08.5564446Z >>> lr=0.05, 2024-08-20T21:39:08.5564563Z >>> ) 2024-08-20T21:39:08.5564706Z >>> dist_optim.step(context_id) 2024-08-20T21:39:08.5564801Z 2024-08-20T21:39:08.5565030Z __ https://github.com/pytorch/tutorials/pull/1465 2024-08-20T21:39:08.5565126Z 2024-08-20T21:39:08.5565615Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5565728Z 2024-08-20T21:39:08.5565847Z warnings.warn(msg) 2024-08-20T21:39:08.5565942Z 2024-08-20T21:39:08.5566195Z --- Parse Warning: 62 / 101 --- 2024-08-20T21:39:08.5567882Z /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. 2024-08-20T21:39:08.5568340Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5568437Z 2024-08-20T21:39:08.5569102Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD `_, 2024-08-20T21:39:08.5569335Z This optimizer runs local optimizer at every step. 2024-08-20T21:39:08.5569904Z After the warm-up stage, it averages parameters periodically afer the local optimizer is applied. 2024-08-20T21:39:08.5570003Z 2024-08-20T21:39:08.5570124Z Args: 2024-08-20T21:39:08.5570264Z optim: The local optimizer. 2024-08-20T21:39:08.5570705Z averager: A model averager instance to run post-localSGD algorithm. 2024-08-20T21:39:08.5570819Z 2024-08-20T21:39:08.5570929Z Example:: 2024-08-20T21:39:08.5571024Z 2024-08-20T21:39:08.5571225Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:39:08.5571347Z >>> import torch 2024-08-20T21:39:08.5571512Z >>> import torch.distributed as dist 2024-08-20T21:39:08.5571911Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2024-08-20T21:39:08.5572040Z >>> import torch.nn as nn 2024-08-20T21:39:08.5572330Z >>> from torch.distributed.optim import PostLocalSGDOptimizer 2024-08-20T21:39:08.5572710Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2024-08-20T21:39:08.5572845Z >>> PostLocalSGDState, 2024-08-20T21:39:08.5572988Z >>> post_localSGD_hook, 2024-08-20T21:39:08.5573089Z >>> ) 2024-08-20T21:39:08.5573188Z >>> 2024-08-20T21:39:08.5573433Z >>> model = nn.parallel.DistributedDataParallel( 2024-08-20T21:39:08.5573632Z >>> module, device_ids=[rank], output_device=rank 2024-08-20T21:39:08.5573733Z >>> ) 2024-08-20T21:39:08.5573849Z >>> 2024-08-20T21:39:08.5574118Z >>> # Register a post-localSGD communication hook. 2024-08-20T21:39:08.5574527Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2024-08-20T21:39:08.5574769Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-08-20T21:39:08.5574869Z >>> 2024-08-20T21:39:08.5575254Z >>> # Create a post-localSGD optimizer that wraps a local optimizer. 2024-08-20T21:39:08.5575623Z >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as 2024-08-20T21:39:08.5575858Z >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-08-20T21:39:08.5576174Z >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01) 2024-08-20T21:39:08.5576332Z >>> opt = PostLocalSGDOptimizer( 2024-08-20T21:39:08.5576459Z >>> optim=local_optim, 2024-08-20T21:39:08.5576818Z >>> averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2024-08-20T21:39:08.5576918Z >>> ) 2024-08-20T21:39:08.5577081Z >>> 2024-08-20T21:39:08.5577434Z >>> # In the first 100 steps, DDP runs global gradient averaging at every step. 2024-08-20T21:39:08.5577971Z >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default), 2024-08-20T21:39:08.5578630Z >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer. 2024-08-20T21:39:08.5578772Z >>> for step in range(0, 200): 2024-08-20T21:39:08.5578893Z >>> opt.zero_grad() 2024-08-20T21:39:08.5579065Z >>> loss = loss_fn(output, labels) 2024-08-20T21:39:08.5579187Z >>> loss.backward() 2024-08-20T21:39:08.5579303Z >>> opt.step() 2024-08-20T21:39:08.5579418Z 2024-08-20T21:39:08.5579839Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5579934Z 2024-08-20T21:39:08.5580069Z warnings.warn(msg) 2024-08-20T21:39:08.5580163Z 2024-08-20T21:39:08.5580395Z --- Parse Warning: 63 / 101 --- 2024-08-20T21:39:08.5582135Z /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. 2024-08-20T21:39:08.5582573Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5582690Z 2024-08-20T21:39:08.5583265Z Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group. 2024-08-20T21:39:08.5583359Z 2024-08-20T21:39:08.5583610Z The sharing is done as described by ZeRO_. 2024-08-20T21:39:08.5583706Z 2024-08-20T21:39:08.5583913Z The local optimizer instance in each rank is only 2024-08-20T21:39:08.5584264Z responsible for updating approximately ``1 / world_size`` parameters and 2024-08-20T21:39:08.5584564Z hence only needs to keep ``1 / world_size`` optimizer states. After 2024-08-20T21:39:08.5584909Z parameters are updated locally, each rank will broadcast its parameters to 2024-08-20T21:39:08.5585195Z all other peers to keep all model replicas in the same state. 2024-08-20T21:39:08.5585454Z ``ZeroRedundancyOptimizer`` can be used in conjunction with 2024-08-20T21:39:08.5585909Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak 2024-08-20T21:39:08.5586047Z memory consumption. 2024-08-20T21:39:08.5586142Z 2024-08-20T21:39:08.5586581Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number 2024-08-20T21:39:08.5586924Z of parameters at each rank. Each parameter belongs to a single rank and is 2024-08-20T21:39:08.5587271Z not divided among ranks. The partition is arbitrary and might not match the 2024-08-20T21:39:08.5587463Z the parameter registration or usage order. 2024-08-20T21:39:08.5587557Z 2024-08-20T21:39:08.5587669Z Arguments: 2024-08-20T21:39:08.5587958Z params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s 2024-08-20T21:39:08.5588233Z or :class:`dict` s giving all parameters, which will be sharded 2024-08-20T21:39:08.5588350Z across ranks. 2024-08-20T21:39:08.5588463Z 2024-08-20T21:39:08.5588574Z Keyword Args: 2024-08-20T21:39:08.5588881Z optimizer_class (:class:`torch.nn.Optimizer`): the class of the local 2024-08-20T21:39:08.5589006Z optimizer. 2024-08-20T21:39:08.5589296Z process_group (``ProcessGroup``, optional): ``torch.distributed`` 2024-08-20T21:39:08.5589570Z ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by 2024-08-20T21:39:08.5589789Z :meth:`torch.distributed.init_process_group`). 2024-08-20T21:39:08.5590101Z parameters_as_bucket_view (bool, optional): if ``True``, parameters are 2024-08-20T21:39:08.5590421Z packed into buckets to speed up communication, and ``param.data`` 2024-08-20T21:39:08.5590772Z fields point to bucket views at different offsets; if ``False``, 2024-08-20T21:39:08.5591056Z each individual parameter is communicated separately, and each 2024-08-20T21:39:08.5591283Z ``params.data`` stays intact (default: ``False``). 2024-08-20T21:39:08.5591551Z overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is 2024-08-20T21:39:08.5591908Z overlapped with :class:`DistributedDataParallel` 's gradient 2024-08-20T21:39:08.5592213Z synchronization; this requires (1) either a functional optimizer 2024-08-20T21:39:08.5592482Z for the ``optimizer_class`` argument or one with a functional 2024-08-20T21:39:08.5592742Z equivalent and (2) registering a DDP communication hook 2024-08-20T21:39:08.5593019Z constructed from one of the functions in ``ddp_zero_hook.py``; 2024-08-20T21:39:08.5593247Z parameters are packed into buckets matching those in 2024-08-20T21:39:08.5593495Z :class:`DistributedDataParallel`, meaning that the 2024-08-20T21:39:08.5593704Z ``parameters_as_bucket_view`` argument is ignored. 2024-08-20T21:39:08.5593980Z If ``False``, :meth:`step` runs disjointly after the backward pass 2024-08-20T21:39:08.5594113Z (per normal). 2024-08-20T21:39:08.5594237Z (default: ``False``) 2024-08-20T21:39:08.5594539Z **defaults: any trailing arguments, which are forwarded to the local 2024-08-20T21:39:08.5594665Z optimizer. 2024-08-20T21:39:08.5594761Z 2024-08-20T21:39:08.5594873Z Example:: 2024-08-20T21:39:08.5594985Z 2024-08-20T21:39:08.5595186Z >>> # xdoctest: +SKIP 2024-08-20T21:39:08.5595318Z >>> import torch.nn as nn 2024-08-20T21:39:08.5595620Z >>> from torch.distributed.optim import ZeroRedundancyOptimizer 2024-08-20T21:39:08.5596238Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2024-08-20T21:39:08.5596587Z >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)]) 2024-08-20T21:39:08.5596751Z >>> ddp = DDP(model, device_ids=[rank]) 2024-08-20T21:39:08.5596913Z >>> opt = ZeroRedundancyOptimizer( 2024-08-20T21:39:08.5597058Z >>> ddp.parameters(), 2024-08-20T21:39:08.5597227Z >>> optimizer_class=torch.optim.Adam, 2024-08-20T21:39:08.5597338Z >>> lr=0.01 2024-08-20T21:39:08.5597453Z >>> ) 2024-08-20T21:39:08.5597597Z >>> ddp(inputs).sum().backward() 2024-08-20T21:39:08.5597708Z >>> opt.step() 2024-08-20T21:39:08.5597821Z 2024-08-20T21:39:08.5597930Z .. warning:: 2024-08-20T21:39:08.5598361Z Currently, ``ZeroRedundancyOptimizer`` requires that all of the 2024-08-20T21:39:08.5598656Z passed-in parameters are the same dense type. 2024-08-20T21:39:08.5598753Z 2024-08-20T21:39:08.5598865Z .. warning:: 2024-08-20T21:39:08.5599190Z If you pass ``overlap_with_ddp=True``, be wary of the following: Given 2024-08-20T21:39:08.5599483Z the way that overlapping :class:`DistributedDataParallel` with 2024-08-20T21:39:08.5599818Z :class:`ZeroRedundancyOptimizer` is currently implemented, the first 2024-08-20T21:39:08.5600130Z two or three training iterations do not perform parameter updates in 2024-08-20T21:39:08.5600395Z the optimizer step, depending on if ``static_graph=False`` or 2024-08-20T21:39:08.5600661Z ``static_graph=True``, respectively. This is because it needs 2024-08-20T21:39:08.5600911Z information about the gradient bucketing strategy used by 2024-08-20T21:39:08.5601219Z :class:`DistributedDataParallel`, which is not finalized until the 2024-08-20T21:39:08.5601517Z second forward pass if ``static_graph=False`` or until the third 2024-08-20T21:39:08.5601818Z forward pass if ``static_graph=True``. To adjust for this, one option 2024-08-20T21:39:08.5601958Z is to prepend dummy inputs. 2024-08-20T21:39:08.5602228Z 2024-08-20T21:39:08.5602575Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change. 2024-08-20T21:39:08.5602686Z 2024-08-20T21:39:08.5602865Z .. _ZeRO: https://arxiv.org/abs/1910.02054 2024-08-20T21:39:08.5602960Z 2024-08-20T21:39:08.5603070Z 2024-08-20T21:39:08.5603501Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5603595Z 2024-08-20T21:39:08.5603731Z warnings.warn(msg) 2024-08-20T21:39:08.5603827Z 2024-08-20T21:39:08.5604055Z --- Parse Warning: 64 / 101 --- 2024-08-20T21:39:08.5605650Z /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. 2024-08-20T21:39:08.5606091Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5606192Z 2024-08-20T21:39:08.5606538Z Custom reducer class that can be used to specify a custom operation that 2024-08-20T21:39:08.5606777Z reduces losses of multiple microbatches into one value. 2024-08-20T21:39:08.5606876Z 2024-08-20T21:39:08.5606997Z Example: 2024-08-20T21:39:08.5607120Z >>> # xdoctest: +SKIP 2024-08-20T21:39:08.5607279Z >>> sum_reducer = _CustomReducer( 2024-08-20T21:39:08.5607403Z >>> torch.tensor(0.0), 2024-08-20T21:39:08.5607525Z >>> lambda a, b: a + b 2024-08-20T21:39:08.5607639Z >>> ) 2024-08-20T21:39:08.5607733Z 2024-08-20T21:39:08.5608154Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5608387Z 2024-08-20T21:39:08.5608508Z warnings.warn(msg) 2024-08-20T21:39:08.5608602Z 2024-08-20T21:39:08.5608850Z --- Parse Warning: 65 / 101 --- 2024-08-20T21:39:08.5610346Z /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. 2024-08-20T21:39:08.5610791Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5610902Z 2024-08-20T21:39:08.5611242Z A decorator for a function indicating that the return value of the function 2024-08-20T21:39:08.5611548Z is guaranteed to be a :class:`~torch.futures.Future` object and this 2024-08-20T21:39:08.5611903Z function can run asynchronously on the RPC callee. More specifically, the 2024-08-20T21:39:08.5612243Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped 2024-08-20T21:39:08.5612583Z function and installs subsequent processing steps as a callback to that 2024-08-20T21:39:08.5612917Z :class:`~torch.futures.Future`. The installed callback will read the value 2024-08-20T21:39:08.5613214Z from the :class:`~torch.futures.Future` when completed and send the 2024-08-20T21:39:08.5613496Z value back as the RPC response. That also means the returned 2024-08-20T21:39:08.5613825Z :class:`~torch.futures.Future` only exists on the callee side and is never 2024-08-20T21:39:08.5615264Z sent through RPC. This decorator is useful when the wrapped function's 2024-08-20T21:39:08.5615565Z (``fn``) execution needs to pause and resume due to, e.g., containing 2024-08-20T21:39:08.5615880Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals. 2024-08-20T21:39:08.5615992Z 2024-08-20T21:39:08.5616293Z .. note:: To enable asynchronous execution, applications must pass the 2024-08-20T21:39:08.5616627Z function object returned by this decorator to RPC APIs. If RPC detected 2024-08-20T21:39:08.5616948Z attributes installed by this decorator, it knows that this function 2024-08-20T21:39:08.5617215Z returns a ``Future`` object and will handle that accordingly. 2024-08-20T21:39:08.5617525Z However, this does not mean this decorator has to be outmost one when 2024-08-20T21:39:08.5617938Z defining a function. For example, when combined with ``@staticmethod`` 2024-08-20T21:39:08.5618242Z or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the 2024-08-20T21:39:08.5618577Z inner decorator to allow the target function be recognized as a static 2024-08-20T21:39:08.5618903Z or class function. This target function can still execute asynchronously 2024-08-20T21:39:08.5619231Z because, when accessed, the static or class method preserves attributes 2024-08-20T21:39:08.5619455Z installed by ``@rpc.functions.async_execution``. 2024-08-20T21:39:08.5619555Z 2024-08-20T21:39:08.5619650Z 2024-08-20T21:39:08.5619776Z Example:: 2024-08-20T21:39:08.5620067Z The returned :class:`~torch.futures.Future` object can come from 2024-08-20T21:39:08.5620247Z :meth:`~torch.distributed.rpc.rpc_async`, 2024-08-20T21:39:08.5620578Z :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future` 2024-08-20T21:39:08.5620823Z constructor. The example below shows directly using the 2024-08-20T21:39:08.5621001Z :class:`~torch.futures.Future` returned by 2024-08-20T21:39:08.5621180Z :meth:`~torch.futures.Future.then`. 2024-08-20T21:39:08.5621276Z 2024-08-20T21:39:08.5621455Z >>> from torch.distributed import rpc 2024-08-20T21:39:08.5621557Z >>> 2024-08-20T21:39:08.5621716Z >>> # omitting setup and shutdown RPC 2024-08-20T21:39:08.5621827Z >>> 2024-08-20T21:39:08.5621946Z >>> # On all workers 2024-08-20T21:39:08.5622100Z >>> @rpc.functions.async_execution 2024-08-20T21:39:08.5622333Z >>> def async_add_chained(to, x, y, z): 2024-08-20T21:39:08.5622614Z >>> # This function runs on "worker1" and returns immediately when 2024-08-20T21:39:08.5622887Z >>> # the callback is installed through the `then(cb)` API. In the 2024-08-20T21:39:08.5623180Z >>> # mean time, the `rpc_async` to "worker2" can run concurrently. 2024-08-20T21:39:08.5623411Z >>> # When the return value of that `rpc_async` arrives at 2024-08-20T21:39:08.5623677Z >>> # "worker1", "worker1" will run the lambda function accordingly 2024-08-20T21:39:08.5623968Z >>> # and set the value for the previously returned `Future`, which 2024-08-20T21:39:08.5624235Z >>> # will then trigger RPC to send the result back to "worker0". 2024-08-20T21:39:08.5624488Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-08-20T21:39:08.5624644Z >>> lambda fut: fut.wait() + z 2024-08-20T21:39:08.5624751Z >>> ) 2024-08-20T21:39:08.5624866Z >>> 2024-08-20T21:39:08.5624979Z >>> # On worker0 2024-08-20T21:39:08.5625103Z >>> # xdoctest: +SKIP 2024-08-20T21:39:08.5625240Z >>> ret = rpc.rpc_sync( 2024-08-20T21:39:08.5625354Z >>> "worker1", 2024-08-20T21:39:08.5625481Z >>> async_add_chained, 2024-08-20T21:39:08.5625668Z >>> args=("worker2", torch.ones(2), 1, 1) 2024-08-20T21:39:08.5625770Z >>> ) 2024-08-20T21:39:08.5625935Z >>> print(ret) # prints tensor([3., 3.]) 2024-08-20T21:39:08.5626044Z 2024-08-20T21:39:08.5626362Z When combined with TorchScript decorators, this decorator must be the 2024-08-20T21:39:08.5626471Z outmost one. 2024-08-20T21:39:08.5626582Z 2024-08-20T21:39:08.5626716Z >>> from torch import Tensor 2024-08-20T21:39:08.5626875Z >>> from torch.futures import Future 2024-08-20T21:39:08.5627056Z >>> from torch.distributed import rpc 2024-08-20T21:39:08.5627160Z >>> 2024-08-20T21:39:08.5627318Z >>> # omitting setup and shutdown RPC 2024-08-20T21:39:08.5627429Z >>> 2024-08-20T21:39:08.5627546Z >>> # On all workers 2024-08-20T21:39:08.5627682Z >>> @torch.jit.script 2024-08-20T21:39:08.5627962Z >>> def script_add(x: Tensor, y: Tensor) -> Tensor: 2024-08-20T21:39:08.5628143Z >>> return x + y 2024-08-20T21:39:08.5628256Z >>> 2024-08-20T21:39:08.5628409Z >>> @rpc.functions.async_execution 2024-08-20T21:39:08.5628531Z >>> @torch.jit.script 2024-08-20T21:39:08.5628900Z >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]: 2024-08-20T21:39:08.5629098Z >>> return rpc.rpc_async(to, script_add, (x, y)) 2024-08-20T21:39:08.5629198Z >>> 2024-08-20T21:39:08.5629323Z >>> # On worker0 2024-08-20T21:39:08.5629447Z >>> ret = rpc.rpc_sync( 2024-08-20T21:39:08.5629558Z >>> "worker1", 2024-08-20T21:39:08.5629688Z >>> async_add, 2024-08-20T21:39:08.5629850Z >>> args=("worker2", torch.ones(2), 1) 2024-08-20T21:39:08.5629951Z >>> ) 2024-08-20T21:39:08.5630133Z >>> print(ret) # prints tensor([2., 2.]) 2024-08-20T21:39:08.5630228Z 2024-08-20T21:39:08.5630543Z When combined with static or class method, this decorator must be the 2024-08-20T21:39:08.5630668Z inner one. 2024-08-20T21:39:08.5630761Z 2024-08-20T21:39:08.5630926Z >>> from torch.distributed import rpc 2024-08-20T21:39:08.5631040Z >>> 2024-08-20T21:39:08.5631198Z >>> # omitting setup and shutdown RPC 2024-08-20T21:39:08.5631311Z >>> 2024-08-20T21:39:08.5631429Z >>> # On all workers 2024-08-20T21:39:08.5631574Z >>> class AsyncExecutionClass: 2024-08-20T21:39:08.5631688Z >>> 2024-08-20T21:39:08.5631805Z >>> @staticmethod 2024-08-20T21:39:08.5631968Z >>> @rpc.functions.async_execution 2024-08-20T21:39:08.5632210Z >>> def static_async_add(to, x, y, z): 2024-08-20T21:39:08.5632453Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-08-20T21:39:08.5632611Z >>> lambda fut: fut.wait() + z 2024-08-20T21:39:08.5632728Z >>> ) 2024-08-20T21:39:08.5632827Z >>> 2024-08-20T21:39:08.5632945Z >>> @classmethod 2024-08-20T21:39:08.5633118Z >>> @rpc.functions.async_execution 2024-08-20T21:39:08.5633293Z >>> def class_async_add(cls, to, x, y, z): 2024-08-20T21:39:08.5633470Z >>> ret_fut = torch.futures.Future() 2024-08-20T21:39:08.5633697Z >>> rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-08-20T21:39:08.5633914Z >>> lambda fut: ret_fut.set_result(fut.wait() + z) 2024-08-20T21:39:08.5634018Z >>> ) 2024-08-20T21:39:08.5634158Z >>> return ret_fut 2024-08-20T21:39:08.5634257Z >>> 2024-08-20T21:39:08.5634433Z >>> @rpc.functions.async_execution 2024-08-20T21:39:08.5634609Z >>> def bound_async_add(self, to, x, y, z): 2024-08-20T21:39:08.5634851Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-08-20T21:39:08.5635023Z >>> lambda fut: fut.wait() + z 2024-08-20T21:39:08.5635128Z >>> ) 2024-08-20T21:39:08.5635230Z >>> 2024-08-20T21:39:08.5635359Z >>> # On worker0 2024-08-20T21:39:08.5635481Z >>> ret = rpc.rpc_sync( 2024-08-20T21:39:08.5635592Z >>> "worker1", 2024-08-20T21:39:08.5635802Z >>> AsyncExecutionClass.static_async_add, 2024-08-20T21:39:08.5635970Z >>> args=("worker2", torch.ones(2), 1, 2) 2024-08-20T21:39:08.5636070Z >>> ) 2024-08-20T21:39:08.5636249Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-20T21:39:08.5636347Z >>> 2024-08-20T21:39:08.5636469Z >>> ret = rpc.rpc_sync( 2024-08-20T21:39:08.5636594Z >>> "worker1", 2024-08-20T21:39:08.5636787Z >>> AsyncExecutionClass.class_async_add, 2024-08-20T21:39:08.5636951Z >>> args=("worker2", torch.ones(2), 1, 2) 2024-08-20T21:39:08.5637068Z >>> ) 2024-08-20T21:39:08.5637228Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-20T21:39:08.5637337Z 2024-08-20T21:39:08.5637563Z This decorator also works with RRef helpers, i.e., . 2024-08-20T21:39:08.5637838Z :meth:`torch.distributed.rpc.RRef.rpc_sync`, 2024-08-20T21:39:08.5638072Z :meth:`torch.distributed.rpc.RRef.rpc_async`, and 2024-08-20T21:39:08.5638398Z :meth:`torch.distributed.rpc.RRef.remote`. 2024-08-20T21:39:08.5638497Z 2024-08-20T21:39:08.5638678Z >>> from torch.distributed import rpc 2024-08-20T21:39:08.5638778Z >>> 2024-08-20T21:39:08.5638971Z >>> # reuse the AsyncExecutionClass class above 2024-08-20T21:39:08.5639204Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-08-20T21:39:08.5639511Z >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2) 2024-08-20T21:39:08.5639675Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-20T21:39:08.5639793Z >>> 2024-08-20T21:39:08.5640010Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-08-20T21:39:08.5640349Z >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait() 2024-08-20T21:39:08.5640532Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-20T21:39:08.5640631Z >>> 2024-08-20T21:39:08.5640860Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-08-20T21:39:08.5641193Z >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here() 2024-08-20T21:39:08.5641355Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-20T21:39:08.5641462Z 2024-08-20T21:39:08.5641907Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5642002Z 2024-08-20T21:39:08.5642140Z warnings.warn(msg) 2024-08-20T21:39:08.5642310Z 2024-08-20T21:39:08.5642541Z --- Parse Warning: 66 / 101 --- 2024-08-20T21:39:08.5644253Z /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. 2024-08-20T21:39:08.5644698Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5644810Z 2024-08-20T21:39:08.5645098Z Set device mapping between each RPC caller and callee pair. This 2024-08-20T21:39:08.5645348Z function can be called multiple times to incrementally add 2024-08-20T21:39:08.5645508Z device placement configurations. 2024-08-20T21:39:08.5645602Z 2024-08-20T21:39:08.5645703Z Args: 2024-08-20T21:39:08.5645841Z to (str): Callee name. 2024-08-20T21:39:08.5646123Z device_map (Dict of int, str, or torch.device): Device placement 2024-08-20T21:39:08.5646383Z mappings from this worker to the callee. This map must be 2024-08-20T21:39:08.5646507Z invertible. 2024-08-20T21:39:08.5646603Z 2024-08-20T21:39:08.5646706Z Example: 2024-08-20T21:39:08.5646879Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:39:08.5646997Z >>> # both workers 2024-08-20T21:39:08.5647112Z >>> def add(x, y): 2024-08-20T21:39:08.5647390Z >>> print(x) # tensor([1., 1.], device='cuda:1') 2024-08-20T21:39:08.5647538Z >>> return x + y, (x + y).to(2) 2024-08-20T21:39:08.5647639Z >>> 2024-08-20T21:39:08.5647775Z >>> # on worker 0 2024-08-20T21:39:08.5647969Z >>> options = TensorPipeRpcBackendOptions( 2024-08-20T21:39:08.5648101Z >>> num_worker_threads=8, 2024-08-20T21:39:08.5648271Z >>> device_maps={"worker1": {0: 1}} 2024-08-20T21:39:08.5648529Z >>> # maps worker0's cuda:0 to worker1's cuda:1 2024-08-20T21:39:08.5648649Z >>> ) 2024-08-20T21:39:08.5648827Z >>> options.set_device_map("worker1", {1: 2}) 2024-08-20T21:39:08.5649071Z >>> # maps worker0's cuda:1 to worker1's cuda:2 2024-08-20T21:39:08.5649186Z >>> 2024-08-20T21:39:08.5649301Z >>> rpc.init_rpc( 2024-08-20T21:39:08.5649412Z >>> "worker0", 2024-08-20T21:39:08.5649613Z >>> rank=0, 2024-08-20T21:39:08.5649729Z >>> world_size=2, 2024-08-20T21:39:08.5649910Z >>> backend=rpc.BackendType.TENSORPIPE, 2024-08-20T21:39:08.5650072Z >>> rpc_backend_options=options 2024-08-20T21:39:08.5650171Z >>> ) 2024-08-20T21:39:08.5650270Z >>> 2024-08-20T21:39:08.5650407Z >>> x = torch.ones(2) 2024-08-20T21:39:08.5650635Z >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1)) 2024-08-20T21:39:08.5650901Z >>> # The first argument will be moved to cuda:1 on worker1. When 2024-08-20T21:39:08.5651184Z >>> # sending the return value back, it will follow the invert of 2024-08-20T21:39:08.5651445Z >>> # the device map, and hence will be moved back to cuda:0 and 2024-08-20T21:39:08.5651584Z >>> # cuda:1 on worker0 2024-08-20T21:39:08.5651864Z >>> print(rets[0]) # tensor([2., 2.], device='cuda:0') 2024-08-20T21:39:08.5652138Z >>> print(rets[1]) # tensor([2., 2.], device='cuda:1') 2024-08-20T21:39:08.5652257Z 2024-08-20T21:39:08.5652684Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5652777Z 2024-08-20T21:39:08.5652913Z warnings.warn(msg) 2024-08-20T21:39:08.5653008Z 2024-08-20T21:39:08.5653232Z --- Parse Warning: 67 / 101 --- 2024-08-20T21:39:08.5654868Z /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. 2024-08-20T21:39:08.5655363Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5655458Z 2024-08-20T21:39:08.5656147Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to 2024-08-20T21:39:08.5656591Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``. 2024-08-20T21:39:08.5656709Z 2024-08-20T21:39:08.5656820Z Keyword Args: 2024-08-20T21:39:08.5657085Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2024-08-20T21:39:08.5657586Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2024-08-20T21:39:08.5658126Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2024-08-20T21:39:08.5658284Z as a placeholder. default: None. 2024-08-20T21:39:08.5658610Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2024-08-20T21:39:08.5659173Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2024-08-20T21:39:08.5659761Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2024-08-20T21:39:08.5659943Z input_kwarg_layouts (Dict[str, Placement]): 2024-08-20T21:39:08.5660510Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2024-08-20T21:39:08.5660640Z default: None 2024-08-20T21:39:08.5660856Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2024-08-20T21:39:08.5661407Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2024-08-20T21:39:08.5661633Z have the desired DTensor layouts. default: None. 2024-08-20T21:39:08.5661790Z use_local_output (bool, optional): 2024-08-20T21:39:08.5662330Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2024-08-20T21:39:08.5662435Z Returns: 2024-08-20T21:39:08.5662976Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs. 2024-08-20T21:39:08.5663149Z 2024-08-20T21:39:08.5663263Z Example:: 2024-08-20T21:39:08.5663400Z >>> # xdoctest: +SKIP(failing) 2024-08-20T21:39:08.5663849Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput 2024-08-20T21:39:08.5664117Z >>> from torch.distributed.device_mesh import init_device_mesh 2024-08-20T21:39:08.5664220Z >>> ... 2024-08-20T21:39:08.5664682Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2024-08-20T21:39:08.5664857Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2024-08-20T21:39:08.5664958Z >>> 2024-08-20T21:39:08.5665468Z >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor 2024-08-20T21:39:08.5665674Z >>> # and then redistributed to Replicated DTensor. 2024-08-20T21:39:08.5665813Z >>> parallelize_module( 2024-08-20T21:39:08.5666009Z >>> block, # this can be a submodule or module 2024-08-20T21:39:08.5666123Z >>> tp_mesh, 2024-08-20T21:39:08.5666264Z >>> parallelize_plan={ 2024-08-20T21:39:08.5666430Z >>> "attn": PrepareModuleInput( 2024-08-20T21:39:08.5666628Z >>> input_layouts=(Shard(0), None, None, ...), 2024-08-20T21:39:08.5666881Z >>> desired_input_layouts=(Replicate(), None, None, ...) 2024-08-20T21:39:08.5666988Z >>> ), 2024-08-20T21:39:08.5667089Z >>> } 2024-08-20T21:39:08.5667205Z >>> ) 2024-08-20T21:39:08.5667299Z 2024-08-20T21:39:08.5667782Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5667891Z 2024-08-20T21:39:08.5668011Z warnings.warn(msg) 2024-08-20T21:39:08.5668105Z 2024-08-20T21:39:08.5668347Z --- Parse Warning: 68 / 101 --- 2024-08-20T21:39:08.5669974Z /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. 2024-08-20T21:39:08.5670430Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5670525Z 2024-08-20T21:39:08.5671208Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to 2024-08-20T21:39:08.5671675Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``. 2024-08-20T21:39:08.5671770Z 2024-08-20T21:39:08.5671879Z Keyword Args: 2024-08-20T21:39:08.5672121Z output_layouts (Union[Placement, Tuple[Placement]]): 2024-08-20T21:39:08.5672616Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2024-08-20T21:39:08.5673186Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2024-08-20T21:39:08.5673405Z ``None`` need to be specified as a placeholder. 2024-08-20T21:39:08.5673667Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2024-08-20T21:39:08.5674258Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2024-08-20T21:39:08.5674418Z have the desired DTensor layouts. 2024-08-20T21:39:08.5674567Z use_local_output (bool, optional): 2024-08-20T21:39:08.5675106Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2024-08-20T21:39:08.5675213Z Returns: 2024-08-20T21:39:08.5675713Z A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs. 2024-08-20T21:39:08.5675827Z 2024-08-20T21:39:08.5675937Z Example:: 2024-08-20T21:39:08.5676072Z >>> # xdoctest: +SKIP(failing) 2024-08-20T21:39:08.5676598Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput 2024-08-20T21:39:08.5676866Z >>> from torch.distributed.device_mesh import init_device_mesh 2024-08-20T21:39:08.5676982Z >>> ... 2024-08-20T21:39:08.5677428Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2024-08-20T21:39:08.5677601Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2024-08-20T21:39:08.5677717Z >>> 2024-08-20T21:39:08.5678420Z >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor 2024-08-20T21:39:08.5678619Z >>> # and then redistributed to Sharded DTensor. 2024-08-20T21:39:08.5678762Z >>> parallelize_module( 2024-08-20T21:39:08.5678956Z >>> block, # this can be a submodule or module 2024-08-20T21:39:08.5679069Z >>> tp_mesh, 2024-08-20T21:39:08.5679281Z >>> parallelize_plan = PrepareModuleOutput( 2024-08-20T21:39:08.5679443Z >>> output_layouts=Replicate(), 2024-08-20T21:39:08.5679620Z >>> desired_output_layouts=Shard(0) 2024-08-20T21:39:08.5679726Z >>> ) 2024-08-20T21:39:08.5679826Z >>> ) 2024-08-20T21:39:08.5679937Z 2024-08-20T21:39:08.5680373Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5680470Z 2024-08-20T21:39:08.5680603Z warnings.warn(msg) 2024-08-20T21:39:08.5680696Z 2024-08-20T21:39:08.5680920Z --- Parse Warning: 69 / 101 --- 2024-08-20T21:39:08.5682581Z /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. 2024-08-20T21:39:08.5683026Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5683124Z 2024-08-20T21:39:08.5683441Z The `MixtureSameFamily` distribution implements a (batch of) mixture 2024-08-20T21:39:08.5683777Z distribution where all component are from different parameterizations of 2024-08-20T21:39:08.5684089Z the same distribution type. It is parameterized by a `Categorical` 2024-08-20T21:39:08.5684347Z "selecting distribution" (over `k` component) and a component 2024-08-20T21:39:08.5684636Z distribution, i.e., a `Distribution` with a rightmost batch shape 2024-08-20T21:39:08.5684881Z (equal to `[k]`) which indexes each (batch of) component. 2024-08-20T21:39:08.5684975Z 2024-08-20T21:39:08.5685088Z Examples:: 2024-08-20T21:39:08.5685202Z 2024-08-20T21:39:08.5685368Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:39:08.5685655Z >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally 2024-08-20T21:39:08.5685823Z >>> # weighted normal distributions 2024-08-20T21:39:08.5685986Z >>> mix = D.Categorical(torch.ones(5,)) 2024-08-20T21:39:08.5686198Z >>> comp = D.Normal(torch.randn(5,), torch.rand(5,)) 2024-08-20T21:39:08.5686380Z >>> gmm = MixtureSameFamily(mix, comp) 2024-08-20T21:39:08.5686477Z 2024-08-20T21:39:08.5686759Z >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally 2024-08-20T21:39:08.5686957Z >>> # weighted bivariate normal distributions 2024-08-20T21:39:08.5687120Z >>> mix = D.Categorical(torch.ones(5,)) 2024-08-20T21:39:08.5687282Z >>> comp = D.Independent(D.Normal( 2024-08-20T21:39:08.5687468Z ... torch.randn(5,2), torch.rand(5,2)), 1) 2024-08-20T21:39:08.5687638Z >>> gmm = MixtureSameFamily(mix, comp) 2024-08-20T21:39:08.5687748Z 2024-08-20T21:39:08.5688008Z >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each 2024-08-20T21:39:08.5688294Z >>> # consisting of 5 random weighted bivariate normal distributions 2024-08-20T21:39:08.5688472Z >>> mix = D.Categorical(torch.rand(3,5)) 2024-08-20T21:39:08.5688698Z >>> comp = D.Independent(D.Normal( 2024-08-20T21:39:08.5688893Z ... torch.randn(3,5,2), torch.rand(3,5,2)), 1) 2024-08-20T21:39:08.5689076Z >>> gmm = MixtureSameFamily(mix, comp) 2024-08-20T21:39:08.5689172Z 2024-08-20T21:39:08.5689273Z Args: 2024-08-20T21:39:08.5689643Z mixture_distribution: `torch.distributions.Categorical`-like 2024-08-20T21:39:08.5689901Z instance. Manages the probability of selecting component. 2024-08-20T21:39:08.5690142Z The number of categories must match the rightmost batch 2024-08-20T21:39:08.5690426Z dimension of the `component_distribution`. Must have either 2024-08-20T21:39:08.5690626Z scalar `batch_shape` or `batch_shape` matching 2024-08-20T21:39:08.5690891Z `component_distribution.batch_shape[:-1]` 2024-08-20T21:39:08.5691260Z component_distribution: `torch.distributions.Distribution`-like 2024-08-20T21:39:08.5691578Z instance. Right-most batch dimension indexes component. 2024-08-20T21:39:08.5691687Z 2024-08-20T21:39:08.5692112Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5692206Z 2024-08-20T21:39:08.5692338Z warnings.warn(msg) 2024-08-20T21:39:08.5692433Z 2024-08-20T21:39:08.5692661Z --- Parse Warning: 70 / 101 --- 2024-08-20T21:39:08.5694255Z /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. 2024-08-20T21:39:08.5694747Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5694858Z 2024-08-20T21:39:08.5695106Z Creates a RelaxedBernoulli distribution, parametrized by 2024-08-20T21:39:08.5695367Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits` 2024-08-20T21:39:08.5695706Z (but not both). This is a relaxed version of the `Bernoulli` distribution, 2024-08-20T21:39:08.5696306Z so the values are in (0, 1), and has reparametrizable samples. 2024-08-20T21:39:08.5696404Z 2024-08-20T21:39:08.5696530Z Example:: 2024-08-20T21:39:08.5696624Z 2024-08-20T21:39:08.5696896Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-08-20T21:39:08.5697099Z >>> m = RelaxedBernoulli(torch.tensor([2.2]), 2024-08-20T21:39:08.5697287Z ... torch.tensor([0.1, 0.2, 0.3, 0.99])) 2024-08-20T21:39:08.5697401Z >>> m.sample() 2024-08-20T21:39:08.5697588Z tensor([ 0.2951, 0.3442, 0.8918, 0.9021]) 2024-08-20T21:39:08.5697683Z 2024-08-20T21:39:08.5697783Z Args: 2024-08-20T21:39:08.5697992Z temperature (Tensor): relaxation temperature 2024-08-20T21:39:08.5698228Z probs (Number, Tensor): the probability of sampling `1` 2024-08-20T21:39:08.5698528Z logits (Number, Tensor): the log-odds of sampling `1` 2024-08-20T21:39:08.5698627Z 2024-08-20T21:39:08.5699048Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5699158Z 2024-08-20T21:39:08.5699277Z warnings.warn(msg) 2024-08-20T21:39:08.5699371Z 2024-08-20T21:39:08.5699613Z --- Parse Warning: 71 / 101 --- 2024-08-20T21:39:08.5701254Z /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. 2024-08-20T21:39:08.5701698Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5701814Z 2024-08-20T21:39:08.5702111Z Creates a RelaxedOneHotCategorical distribution parametrized by 2024-08-20T21:39:08.5702379Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`. 2024-08-20T21:39:08.5702898Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so 2024-08-20T21:39:08.5703121Z its samples are on simplex, and are reparametrizable. 2024-08-20T21:39:08.5703232Z 2024-08-20T21:39:08.5703342Z Example:: 2024-08-20T21:39:08.5703438Z 2024-08-20T21:39:08.5703720Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-08-20T21:39:08.5703942Z >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]), 2024-08-20T21:39:08.5704137Z ... torch.tensor([0.1, 0.2, 0.3, 0.4])) 2024-08-20T21:39:08.5704265Z >>> m.sample() 2024-08-20T21:39:08.5704437Z tensor([ 0.1294, 0.2324, 0.3859, 0.2523]) 2024-08-20T21:39:08.5704532Z 2024-08-20T21:39:08.5704650Z Args: 2024-08-20T21:39:08.5704841Z temperature (Tensor): relaxation temperature 2024-08-20T21:39:08.5704998Z probs (Tensor): event probabilities 2024-08-20T21:39:08.5705274Z logits (Tensor): unnormalized log probability for each event 2024-08-20T21:39:08.5705374Z 2024-08-20T21:39:08.5705796Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5705906Z 2024-08-20T21:39:08.5706027Z warnings.warn(msg) 2024-08-20T21:39:08.5706123Z 2024-08-20T21:39:08.5706362Z --- Parse Warning: 72 / 101 --- 2024-08-20T21:39:08.5708002Z /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. 2024-08-20T21:39:08.5708541Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5708817Z Return a new dict with new, potentially nested, key value pair 2024-08-20T21:39:08.5708912Z 2024-08-20T21:39:08.5709118Z >>> purchase = {'name': 'Alice', 2024-08-20T21:39:08.5709373Z ... 'order': {'items': ['Apple', 'Orange'], 2024-08-20T21:39:08.5709609Z ... 'costs': [0.50, 1.25]}, 2024-08-20T21:39:08.5709864Z ... 'credit card': '5555-1234-1234-1234'} 2024-08-20T21:39:08.5710239Z >>> assoc_in(purchase, ['order', 'costs'], [0.25, 1.00]) # doctest: +SKIP 2024-08-20T21:39:08.5710460Z {'credit card': '5555-1234-1234-1234', 2024-08-20T21:39:08.5710612Z 'name': 'Alice', 2024-08-20T21:39:08.5710932Z 'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}} 2024-08-20T21:39:08.5711047Z 2024-08-20T21:39:08.5711467Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5711566Z 2024-08-20T21:39:08.5711699Z warnings.warn(msg) 2024-08-20T21:39:08.5711791Z 2024-08-20T21:39:08.5712015Z --- Parse Warning: 73 / 101 --- 2024-08-20T21:39:08.5713667Z /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. 2024-08-20T21:39:08.5714107Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5714313Z Update value in a (potentially) nested dictionary 2024-08-20T21:39:08.5714421Z 2024-08-20T21:39:08.5714524Z inputs: 2024-08-20T21:39:08.5714739Z d - dictionary on which to operate 2024-08-20T21:39:08.5715140Z keys - list or tuple giving the location of the value to be changed in d 2024-08-20T21:39:08.5715362Z func - function to operate on that value 2024-08-20T21:39:08.5715479Z 2024-08-20T21:39:08.5715788Z If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the 2024-08-20T21:39:08.5716111Z original dictionary with v replaced by func(v), but does not mutate the 2024-08-20T21:39:08.5716250Z original dictionary. 2024-08-20T21:39:08.5716404Z 2024-08-20T21:39:08.5716742Z If k0 is not a key in d, update_in creates nested dictionaries to the depth 2024-08-20T21:39:08.5717066Z specified by the keys, with the innermost value set to func(default). 2024-08-20T21:39:08.5717162Z 2024-08-20T21:39:08.5717290Z >>> inc = lambda x: x + 1 2024-08-20T21:39:08.5717507Z >>> update_in({'a': 0}, ['a'], inc) 2024-08-20T21:39:08.5717644Z {'a': 1} 2024-08-20T21:39:08.5717740Z 2024-08-20T21:39:08.5717949Z >>> transaction = {'name': 'Alice', 2024-08-20T21:39:08.5718349Z ... 'purchase': {'items': ['Apple', 'Orange'], 2024-08-20T21:39:08.5718617Z ... 'costs': [0.50, 1.25]}, 2024-08-20T21:39:08.5718867Z ... 'credit card': '5555-1234-1234-1234'} 2024-08-20T21:39:08.5719245Z >>> update_in(transaction, ['purchase', 'costs'], sum) # doctest: +SKIP 2024-08-20T21:39:08.5719468Z {'credit card': '5555-1234-1234-1234', 2024-08-20T21:39:08.5719623Z 'name': 'Alice', 2024-08-20T21:39:08.5719923Z 'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}} 2024-08-20T21:39:08.5720035Z 2024-08-20T21:39:08.5720204Z >>> # updating a value when k0 is not in d 2024-08-20T21:39:08.5720384Z >>> update_in({}, [1, 2, 3], str, default="bar") 2024-08-20T21:39:08.5720555Z {1: {2: {3: 'bar'}}} 2024-08-20T21:39:08.5720776Z >>> update_in({1: 'foo'}, [2, 3, 4], inc, 0) 2024-08-20T21:39:08.5720944Z {1: 'foo', 2: {3: {4: 1}}} 2024-08-20T21:39:08.5721055Z 2024-08-20T21:39:08.5721476Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5721571Z 2024-08-20T21:39:08.5721766Z warnings.warn(msg) 2024-08-20T21:39:08.5721865Z 2024-08-20T21:39:08.5722092Z --- Parse Warning: 74 / 101 --- 2024-08-20T21:39:08.5723730Z /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. 2024-08-20T21:39:08.5724169Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5724413Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys. 2024-08-20T21:39:08.5724507Z 2024-08-20T21:39:08.5724787Z If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless 2024-08-20T21:39:08.5725081Z ``no_default`` is specified, then it raises KeyError or IndexError. 2024-08-20T21:39:08.5725177Z 2024-08-20T21:39:08.5725482Z ``get_in`` is a generalization of ``operator.getitem`` for nested data 2024-08-20T21:39:08.5725674Z structures such as dictionaries and lists. 2024-08-20T21:39:08.5725770Z 2024-08-20T21:39:08.5725965Z >>> transaction = {'name': 'Alice', 2024-08-20T21:39:08.5726260Z ... 'purchase': {'items': ['Apple', 'Orange'], 2024-08-20T21:39:08.5726506Z ... 'costs': [0.50, 1.25]}, 2024-08-20T21:39:08.5726775Z ... 'credit card': '5555-1234-1234-1234'} 2024-08-20T21:39:08.5727025Z >>> get_in(['purchase', 'items', 0], transaction) 2024-08-20T21:39:08.5727158Z 'Apple' 2024-08-20T21:39:08.5727361Z >>> get_in(['name'], transaction) 2024-08-20T21:39:08.5727491Z 'Alice' 2024-08-20T21:39:08.5727728Z >>> get_in(['purchase', 'total'], transaction) 2024-08-20T21:39:08.5728016Z >>> get_in(['purchase', 'items', 'apple'], transaction) 2024-08-20T21:39:08.5728269Z >>> get_in(['purchase', 'items', 10], transaction) 2024-08-20T21:39:08.5728521Z >>> get_in(['purchase', 'total'], transaction, 0) 2024-08-20T21:39:08.5728635Z 0 2024-08-20T21:39:08.5728836Z >>> get_in(['y'], {}, no_default=True) 2024-08-20T21:39:08.5728985Z Traceback (most recent call last): 2024-08-20T21:39:08.5729103Z ... 2024-08-20T21:39:08.5729247Z KeyError: 'y' 2024-08-20T21:39:08.5729397Z 2024-08-20T21:39:08.5729519Z See Also: 2024-08-20T21:39:08.5729634Z itertoolz.get 2024-08-20T21:39:08.5729755Z operator.getitem 2024-08-20T21:39:08.5729870Z 2024-08-20T21:39:08.5730293Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5730402Z 2024-08-20T21:39:08.5730523Z warnings.warn(msg) 2024-08-20T21:39:08.5730620Z 2024-08-20T21:39:08.5730860Z --- Parse Warning: 75 / 101 --- 2024-08-20T21:39:08.5732501Z /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. 2024-08-20T21:39:08.5732940Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5733108Z Group a collection by a key function 2024-08-20T21:39:08.5733206Z 2024-08-20T21:39:08.5733525Z >>> names = ['Alice', 'Bob', 'Charlie', 'Dan', 'Edith', 'Frank'] 2024-08-20T21:39:08.5733712Z >>> groupby(len, names) # doctest: +SKIP 2024-08-20T21:39:08.5734035Z {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} 2024-08-20T21:39:08.5734130Z 2024-08-20T21:39:08.5734286Z >>> iseven = lambda x: x % 2 == 0 2024-08-20T21:39:08.5734532Z >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP 2024-08-20T21:39:08.5734704Z {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} 2024-08-20T21:39:08.5734799Z 2024-08-20T21:39:08.5735050Z Non-callable keys imply grouping on a member. 2024-08-20T21:39:08.5735243Z 2024-08-20T21:39:08.5735533Z >>> groupby('gender', [{'name': 'Alice', 'gender': 'F'}, 2024-08-20T21:39:08.5735779Z ... {'name': 'Bob', 'gender': 'M'}, 2024-08-20T21:39:08.5736106Z ... {'name': 'Charlie', 'gender': 'M'}]) # doctest:+SKIP 2024-08-20T21:39:08.5736323Z {'F': [{'gender': 'F', 'name': 'Alice'}], 2024-08-20T21:39:08.5736529Z 'M': [{'gender': 'M', 'name': 'Bob'}, 2024-08-20T21:39:08.5736762Z {'gender': 'M', 'name': 'Charlie'}]} 2024-08-20T21:39:08.5736858Z 2024-08-20T21:39:08.5737056Z Not to be confused with ``itertools.groupby`` 2024-08-20T21:39:08.5737166Z 2024-08-20T21:39:08.5737272Z See Also: 2024-08-20T21:39:08.5737379Z countby 2024-08-20T21:39:08.5737492Z 2024-08-20T21:39:08.5737915Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5738011Z 2024-08-20T21:39:08.5738153Z warnings.warn(msg) 2024-08-20T21:39:08.5738248Z 2024-08-20T21:39:08.5738472Z --- Parse Warning: 76 / 101 --- 2024-08-20T21:39:08.5739951Z /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. 2024-08-20T21:39:08.5740396Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5740712Z Applies Batch Normalization over a N-Dimensional input. 2024-08-20T21:39:08.5740808Z 2024-08-20T21:39:08.5741446Z The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper 2024-08-20T21:39:08.5741771Z `Batch Normalization: Accelerating Deep Network Training by Reducing 2024-08-20T21:39:08.5742069Z Internal Covariate Shift `__ . 2024-08-20T21:39:08.5742164Z 2024-08-20T21:39:08.5742298Z .. math:: 2024-08-20T21:39:08.5742391Z 2024-08-20T21:39:08.5742848Z y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta 2024-08-20T21:39:08.5742944Z 2024-08-20T21:39:08.5743347Z The mean and standard-deviation are calculated per-dimension over all 2024-08-20T21:39:08.5743843Z mini-batches of the same process groups. :math:`\gamma` and :math:`\beta` 2024-08-20T21:39:08.5744179Z are learnable parameter vectors of size `C` (where `C` is the input size). 2024-08-20T21:39:08.5744431Z By default, the elements of :math:`\gamma` are sampled from 2024-08-20T21:39:08.5744754Z :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. 2024-08-20T21:39:08.5745199Z The standard-deviation is calculated via the biased estimator, equivalent to 2024-08-20T21:39:08.5745350Z `torch.var(input, unbiased=False)`. 2024-08-20T21:39:08.5745460Z 2024-08-20T21:39:08.5745806Z Also by default, during training this layer keeps running estimates of its 2024-08-20T21:39:08.5746137Z computed mean and variance, which are then used for normalization during 2024-08-20T21:39:08.5746488Z evaluation. The running estimates are kept with a default :attr:`momentum` 2024-08-20T21:39:08.5746597Z of 0.1. 2024-08-20T21:39:08.5746709Z 2024-08-20T21:39:08.5747036Z If :attr:`track_running_stats` is set to ``False``, this layer then does not 2024-08-20T21:39:08.5747343Z keep running estimates, and batch statistics are instead used during 2024-08-20T21:39:08.5747490Z evaluation time as well. 2024-08-20T21:39:08.5747584Z 2024-08-20T21:39:08.5747692Z .. note:: 2024-08-20T21:39:08.5748021Z This :attr:`momentum` argument is different from one used in optimizer 2024-08-20T21:39:08.5748341Z classes and the conventional notion of momentum. Mathematically, the 2024-08-20T21:39:08.5748585Z update rule for running statistics here is 2024-08-20T21:39:08.5749127Z :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, 2024-08-20T21:39:08.5749440Z where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the 2024-08-20T21:39:08.5749578Z new observed value. 2024-08-20T21:39:08.5749676Z 2024-08-20T21:39:08.5750109Z Because the Batch Normalization is done for each channel in the ``C`` dimension, computing 2024-08-20T21:39:08.5750580Z statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch 2024-08-20T21:39:08.5750881Z Normalization or Spatio-temporal Batch Normalization. 2024-08-20T21:39:08.5750976Z 2024-08-20T21:39:08.5751188Z Currently :class:`SyncBatchNorm` only supports 2024-08-20T21:39:08.5751582Z :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use 2024-08-20T21:39:08.5751887Z :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert 2024-08-20T21:39:08.5752191Z :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping 2024-08-20T21:39:08.5752312Z Network with DDP. 2024-08-20T21:39:08.5752410Z 2024-08-20T21:39:08.5752531Z Args: 2024-08-20T21:39:08.5752760Z num_features: :math:`C` from an expected input of size 2024-08-20T21:39:08.5752902Z :math:`(N, C, +)` 2024-08-20T21:39:08.5753181Z eps: a value added to the denominator for numerical stability. 2024-08-20T21:39:08.5753355Z Default: ``1e-5`` 2024-08-20T21:39:08.5753640Z momentum: the value used for the running_mean and running_var 2024-08-20T21:39:08.5753934Z computation. Can be set to ``None`` for cumulative moving average 2024-08-20T21:39:08.5754102Z (i.e. simple average). Default: 0.1 2024-08-20T21:39:08.5754409Z affine: a boolean value that when set to ``True``, this module has 2024-08-20T21:39:08.5754625Z learnable affine parameters. Default: ``True`` 2024-08-20T21:39:08.5754924Z track_running_stats: a boolean value that when set to ``True``, this 2024-08-20T21:39:08.5755271Z module tracks the running mean and variance, and when set to ``False``, 2024-08-20T21:39:08.5755594Z this module does not track such statistics, and initializes statistics 2024-08-20T21:39:08.5755959Z buffers :attr:`running_mean` and :attr:`running_var` as ``None``. 2024-08-20T21:39:08.5756303Z When these buffers are ``None``, this module always uses batch statistics. 2024-08-20T21:39:08.5756520Z in both training and eval modes. Default: ``True`` 2024-08-20T21:39:08.5756873Z process_group: synchronization of stats happen within each process group 2024-08-20T21:39:08.5757184Z individually. Default behavior is synchronization across the whole 2024-08-20T21:39:08.5757291Z world 2024-08-20T21:39:08.5757406Z 2024-08-20T21:39:08.5757509Z Shape: 2024-08-20T21:39:08.5757694Z - Input: :math:`(N, C, +)` 2024-08-20T21:39:08.5757976Z - Output: :math:`(N, C, +)` (same shape as input) 2024-08-20T21:39:08.5758071Z 2024-08-20T21:39:08.5758180Z .. note:: 2024-08-20T21:39:08.5758682Z Synchronization of batchnorm statistics occurs only while training, i.e. 2024-08-20T21:39:08.5758961Z synchronization is disabled when ``model.eval()`` is set or if 2024-08-20T21:39:08.5759153Z ``self.training`` is otherwise ``False``. 2024-08-20T21:39:08.5759249Z 2024-08-20T21:39:08.5759360Z Examples:: 2024-08-20T21:39:08.5759470Z 2024-08-20T21:39:08.5759596Z >>> # xdoctest: +SKIP 2024-08-20T21:39:08.5759748Z >>> # With Learnable Parameters 2024-08-20T21:39:08.5759906Z >>> m = nn.SyncBatchNorm(100) 2024-08-20T21:39:08.5760078Z >>> # creating process group (optional) 2024-08-20T21:39:08.5760341Z >>> # ranks is a list of int identifying rank ids. 2024-08-20T21:39:08.5760495Z >>> ranks = list(range(8)) 2024-08-20T21:39:08.5760644Z >>> r1, r2 = ranks[:4], ranks[4:] 2024-08-20T21:39:08.5760854Z >>> # Note: every rank calls into new_group for every 2024-08-20T21:39:08.5761090Z >>> # process group created, even if that rank is not 2024-08-20T21:39:08.5761222Z >>> # part of the group. 2024-08-20T21:39:08.5761583Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2024-08-20T21:39:08.5761869Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2024-08-20T21:39:08.5762030Z >>> # Without Learnable Parameters 2024-08-20T21:39:08.5762341Z >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group) 2024-08-20T21:39:08.5762516Z >>> input = torch.randn(20, 100, 35, 45, 10) 2024-08-20T21:39:08.5762648Z >>> output = m(input) 2024-08-20T21:39:08.5762756Z 2024-08-20T21:39:08.5762916Z >>> # network is nn.BatchNorm layer 2024-08-20T21:39:08.5763298Z >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group) 2024-08-20T21:39:08.5763544Z >>> # only single gpu per process is currently supported 2024-08-20T21:39:08.5763869Z >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel( 2024-08-20T21:39:08.5764045Z >>> sync_bn_network, 2024-08-20T21:39:08.5764231Z >>> device_ids=[args.local_rank], 2024-08-20T21:39:08.5764418Z >>> output_device=args.local_rank) 2024-08-20T21:39:08.5764529Z 2024-08-20T21:39:08.5764956Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5765048Z 2024-08-20T21:39:08.5765178Z warnings.warn(msg) 2024-08-20T21:39:08.5765271Z 2024-08-20T21:39:08.5765501Z --- Parse Warning: 77 / 101 --- 2024-08-20T21:39:08.5767130Z /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. 2024-08-20T21:39:08.5767633Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5768094Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers. 2024-08-20T21:39:08.5768188Z 2024-08-20T21:39:08.5768288Z Args: 2024-08-20T21:39:08.5768650Z module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers 2024-08-20T21:39:08.5768948Z process_group (optional): process group to scope synchronization, 2024-08-20T21:39:08.5769102Z default is the whole world 2024-08-20T21:39:08.5769210Z 2024-08-20T21:39:08.5769319Z Returns: 2024-08-20T21:39:08.5769685Z The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm` 2024-08-20T21:39:08.5770014Z layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer, 2024-08-20T21:39:08.5770321Z a new :class:`torch.nn.SyncBatchNorm` layer object will be returned 2024-08-20T21:39:08.5770433Z instead. 2024-08-20T21:39:08.5770537Z 2024-08-20T21:39:08.5770646Z Example:: 2024-08-20T21:39:08.5770754Z 2024-08-20T21:39:08.5770928Z >>> # Network with nn.BatchNorm layer 2024-08-20T21:39:08.5771130Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-20T21:39:08.5771308Z >>> module = torch.nn.Sequential( 2024-08-20T21:39:08.5771478Z >>> torch.nn.Linear(20, 100), 2024-08-20T21:39:08.5771655Z >>> torch.nn.BatchNorm1d(100), 2024-08-20T21:39:08.5771852Z >>> ).cuda() 2024-08-20T21:39:08.5772031Z >>> # creating process group (optional) 2024-08-20T21:39:08.5772237Z >>> # ranks is a list of int identifying rank ids. 2024-08-20T21:39:08.5772387Z >>> ranks = list(range(8)) 2024-08-20T21:39:08.5772541Z >>> r1, r2 = ranks[:4], ranks[4:] 2024-08-20T21:39:08.5772755Z >>> # Note: every rank calls into new_group for every 2024-08-20T21:39:08.5772982Z >>> # process group created, even if that rank is not 2024-08-20T21:39:08.5773118Z >>> # part of the group. 2024-08-20T21:39:08.5773301Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:39:08.5773650Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2024-08-20T21:39:08.5773934Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2024-08-20T21:39:08.5774372Z >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group) 2024-08-20T21:39:08.5774468Z 2024-08-20T21:39:08.5774565Z 2024-08-20T21:39:08.5775002Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5775094Z 2024-08-20T21:39:08.5775216Z warnings.warn(msg) 2024-08-20T21:39:08.5775322Z 2024-08-20T21:39:08.5775546Z --- Parse Warning: 78 / 101 --- 2024-08-20T21:39:08.5776970Z /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. 2024-08-20T21:39:08.5777424Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5777518Z 2024-08-20T21:39:08.5777979Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`. 2024-08-20T21:39:08.5778072Z 2024-08-20T21:39:08.5778466Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can 2024-08-20T21:39:08.5778817Z be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively. 2024-08-20T21:39:08.5778911Z 2024-08-20T21:39:08.5779371Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be 2024-08-20T21:39:08.5779842Z a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input; a `NamedShape` 2024-08-20T21:39:08.5780073Z (tuple of `(name, size)` tuples) for `NamedTensor` input. 2024-08-20T21:39:08.5780166Z 2024-08-20T21:39:08.5780281Z Shape: 2024-08-20T21:39:08.5780714Z - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at 2024-08-20T21:39:08.5781085Z dimension :attr:`dim` and :math:`*` means any number of dimensions including none. 2024-08-20T21:39:08.5781501Z - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and 2024-08-20T21:39:08.5781677Z :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`. 2024-08-20T21:39:08.5781783Z 2024-08-20T21:39:08.5781880Z Args: 2024-08-20T21:39:08.5782090Z dim (Union[int, str]): Dimension to be unflattened 2024-08-20T21:39:08.5782582Z unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension 2024-08-20T21:39:08.5782679Z 2024-08-20T21:39:08.5782783Z Examples: 2024-08-20T21:39:08.5782933Z >>> input = torch.randn(2, 50) 2024-08-20T21:39:08.5783057Z >>> # With tuple of ints 2024-08-20T21:39:08.5783179Z >>> m = nn.Sequential( 2024-08-20T21:39:08.5783312Z >>> nn.Linear(50, 50), 2024-08-20T21:39:08.5783451Z >>> nn.Unflatten(1, (2, 5, 5)) 2024-08-20T21:39:08.5783549Z >>> ) 2024-08-20T21:39:08.5783682Z >>> output = m(input) 2024-08-20T21:39:08.5783798Z >>> output.size() 2024-08-20T21:39:08.5783996Z torch.Size([2, 2, 5, 5]) 2024-08-20T21:39:08.5784129Z >>> # With torch.Size 2024-08-20T21:39:08.5784251Z >>> m = nn.Sequential( 2024-08-20T21:39:08.5784388Z >>> nn.Linear(50, 50), 2024-08-20T21:39:08.5784561Z >>> nn.Unflatten(1, torch.Size([2, 5, 5])) 2024-08-20T21:39:08.5784660Z >>> ) 2024-08-20T21:39:08.5784796Z >>> output = m(input) 2024-08-20T21:39:08.5784909Z >>> output.size() 2024-08-20T21:39:08.5785029Z torch.Size([2, 2, 5, 5]) 2024-08-20T21:39:08.5785209Z >>> # With namedshape (tuple of tuples) 2024-08-20T21:39:08.5785493Z >>> input = torch.randn(2, 50, names=('N', 'features')) 2024-08-20T21:39:08.5785869Z >>> unflatten = nn.Unflatten('features', (('C', 2), ('H', 5), ('W', 5))) 2024-08-20T21:39:08.5786019Z >>> output = unflatten(input) 2024-08-20T21:39:08.5786132Z >>> output.size() 2024-08-20T21:39:08.5786254Z torch.Size([2, 2, 5, 5]) 2024-08-20T21:39:08.5786361Z 2024-08-20T21:39:08.5786788Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5786881Z 2024-08-20T21:39:08.5787013Z warnings.warn(msg) 2024-08-20T21:39:08.5787107Z 2024-08-20T21:39:08.5787331Z --- Parse Warning: 79 / 101 --- 2024-08-20T21:39:08.5788904Z /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. 2024-08-20T21:39:08.5789349Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5789637Z Creates a criterion that measures the triplet loss given input 2024-08-20T21:39:08.5789911Z tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor, 2024-08-20T21:39:08.5790208Z positive, and negative examples, respectively), and a nonnegative, 2024-08-20T21:39:08.5790652Z real-valued function ("distance function") used to compute the relationship 2024-08-20T21:39:08.5790960Z between the anchor and positive example ("positive distance") and the 2024-08-20T21:39:08.5791169Z anchor and negative example ("negative distance"). 2024-08-20T21:39:08.5791276Z 2024-08-20T21:39:08.5791716Z The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``) 2024-08-20T21:39:08.5791852Z can be described as: 2024-08-20T21:39:08.5791943Z 2024-08-20T21:39:08.5792051Z .. math:: 2024-08-20T21:39:08.5792270Z \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad 2024-08-20T21:39:08.5792585Z l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} 2024-08-20T21:39:08.5792677Z 2024-08-20T21:39:08.5793150Z where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function 2024-08-20T21:39:08.5793552Z quantifying the closeness of two tensors, referred to as the :attr:`distance_function`; 2024-08-20T21:39:08.5793904Z and :math:`margin` is a nonnegative margin representing the minimum difference 2024-08-20T21:39:08.5794266Z between the positive and negative distances that is required for the loss to 2024-08-20T21:39:08.5794602Z be 0. The input tensors have :math:`N` elements each and can be of any shape 2024-08-20T21:39:08.5794784Z that the distance function can handle. 2024-08-20T21:39:08.5794877Z 2024-08-20T21:39:08.5795089Z If :attr:`reduction` is not ``'none'`` 2024-08-20T21:39:08.5795275Z (default ``'mean'``), then: 2024-08-20T21:39:08.5795368Z 2024-08-20T21:39:08.5795473Z .. math:: 2024-08-20T21:39:08.5795597Z \ell(x, y) = 2024-08-20T21:39:08.5795706Z \begin{cases} 2024-08-20T21:39:08.5796353Z \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ 2024-08-20T21:39:08.5796729Z \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} 2024-08-20T21:39:08.5796992Z \end{cases} 2024-08-20T21:39:08.5797086Z 2024-08-20T21:39:08.5797445Z See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet 2024-08-20T21:39:08.5797805Z loss for input tensors using the :math:`l_p` distance as the distance function. 2024-08-20T21:39:08.5797898Z 2024-08-20T21:39:08.5798016Z Args: 2024-08-20T21:39:08.5798640Z distance_function (Callable, optional): A nonnegative, real-valued function that 2024-08-20T21:39:08.5798904Z quantifies the closeness of two tensors. If not specified, 2024-08-20T21:39:08.5799159Z `nn.PairwiseDistance` will be used. Default: ``None`` 2024-08-20T21:39:08.5799535Z margin (float, optional): A nonnegative margin representing the minimum difference 2024-08-20T21:39:08.5799936Z between the positive and negative distances required for the loss to be 0. Larger 2024-08-20T21:39:08.5800335Z margins penalize cases where the negative examples are not distant enough from the 2024-08-20T21:39:08.5800577Z anchors, relative to the positives. Default: :math:`1`. 2024-08-20T21:39:08.5800948Z swap (bool, optional): Whether to use the distance swap described in the paper 2024-08-20T21:39:08.5801303Z `Learning shallow convolutional feature descriptors with triplet losses` by 2024-08-20T21:39:08.5801675Z V. Balntas, E. Riba et al. If True, and if the positive example is closer to the 2024-08-20T21:39:08.5802076Z negative example than the anchor is, swaps the positive example and the anchor in 2024-08-20T21:39:08.5802261Z the loss computation. Default: ``False``. 2024-08-20T21:39:08.5802667Z reduction (str, optional): Specifies the (optional) reduction to apply to the output: 2024-08-20T21:39:08.5803056Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2024-08-20T21:39:08.5803417Z ``'mean'``: the sum of the output will be divided by the number of 2024-08-20T21:39:08.5803880Z elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'`` 2024-08-20T21:39:08.5803977Z 2024-08-20T21:39:08.5804073Z 2024-08-20T21:39:08.5804192Z Shape: 2024-08-20T21:39:08.5804748Z - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions 2024-08-20T21:39:08.5804927Z as supported by the distance function. 2024-08-20T21:39:08.5805415Z - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar 2024-08-20T21:39:08.5805526Z otherwise. 2024-08-20T21:39:08.5805637Z 2024-08-20T21:39:08.5805751Z Examples:: 2024-08-20T21:39:08.5805847Z 2024-08-20T21:39:08.5805998Z >>> # Initialize embeddings 2024-08-20T21:39:08.5806163Z >>> embedding = nn.Embedding(1000, 128) 2024-08-20T21:39:08.5806341Z >>> anchor_ids = torch.randint(0, 1000, (1,)) 2024-08-20T21:39:08.5806546Z >>> positive_ids = torch.randint(0, 1000, (1,)) 2024-08-20T21:39:08.5806727Z >>> negative_ids = torch.randint(0, 1000, (1,)) 2024-08-20T21:39:08.5806875Z >>> anchor = embedding(anchor_ids) 2024-08-20T21:39:08.5807056Z >>> positive = embedding(positive_ids) 2024-08-20T21:39:08.5807220Z >>> negative = embedding(negative_ids) 2024-08-20T21:39:08.5807321Z >>> 2024-08-20T21:39:08.5807533Z >>> # Built-in Distance Function 2024-08-20T21:39:08.5807658Z >>> triplet_loss = \ 2024-08-20T21:39:08.5808033Z >>> nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance()) 2024-08-20T21:39:08.5808264Z >>> output = triplet_loss(anchor, positive, negative) 2024-08-20T21:39:08.5808390Z >>> output.backward() 2024-08-20T21:39:08.5808505Z >>> 2024-08-20T21:39:08.5808643Z >>> # Custom Distance Function 2024-08-20T21:39:08.5808770Z >>> def l_infinity(x1, x2): 2024-08-20T21:39:08.5809156Z >>> return torch.max(torch.abs(x1 - x2), dim=1).values 2024-08-20T21:39:08.5809260Z >>> 2024-08-20T21:39:08.5809529Z >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time") 2024-08-20T21:39:08.5809663Z >>> triplet_loss = ( 2024-08-20T21:39:08.5810035Z >>> nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5)) 2024-08-20T21:39:08.5810249Z >>> output = triplet_loss(anchor, positive, negative) 2024-08-20T21:39:08.5810386Z >>> output.backward() 2024-08-20T21:39:08.5810483Z >>> 2024-08-20T21:39:08.5810643Z >>> # Custom Distance Function (Lambda) 2024-08-20T21:39:08.5810774Z >>> triplet_loss = ( 2024-08-20T21:39:08.5810959Z >>> nn.TripletMarginWithDistanceLoss( 2024-08-20T21:39:08.5811336Z >>> distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y))) 2024-08-20T21:39:08.5811556Z >>> output = triplet_loss(anchor, positive, negative) 2024-08-20T21:39:08.5811682Z >>> output.backward() 2024-08-20T21:39:08.5811787Z 2024-08-20T21:39:08.5811892Z Reference: 2024-08-20T21:39:08.5812324Z V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses: 2024-08-20T21:39:08.5812598Z http://www.bmva.org/bmvc/2016/papers/paper119/index.html 2024-08-20T21:39:08.5812699Z 2024-08-20T21:39:08.5813126Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17)) 2024-08-20T21:39:08.5813233Z 2024-08-20T21:39:08.5813351Z warnings.warn(msg) 2024-08-20T21:39:08.5813442Z 2024-08-20T21:39:08.5813682Z --- Parse Warning: 80 / 101 --- 2024-08-20T21:39:08.5815125Z /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. 2024-08-20T21:39:08.5815566Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5815782Z Computes a partial inverse of :class:`MaxPool2d`. 2024-08-20T21:39:08.5815876Z 2024-08-20T21:39:08.5816350Z :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost. 2024-08-20T21:39:08.5816519Z 2024-08-20T21:39:08.5816836Z :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d` 2024-08-20T21:39:08.5817191Z including the indices of the maximal values and computes a partial inverse 2024-08-20T21:39:08.5817452Z in which all non-maximal values are set to zero. 2024-08-20T21:39:08.5817546Z 2024-08-20T21:39:08.5817660Z Note: 2024-08-20T21:39:08.5818091Z This operation may behave nondeterministically when the input indices has repeat values. 2024-08-20T21:39:08.5818615Z See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. 2024-08-20T21:39:08.5818723Z 2024-08-20T21:39:08.5819049Z .. note:: :class:`MaxPool2d` can map several input sizes to the same output 2024-08-20T21:39:08.5819303Z sizes. Hence, the inversion process can get ambiguous. 2024-08-20T21:39:08.5819569Z To accommodate this, you can provide the needed output size 2024-08-20T21:39:08.5819871Z as an additional argument :attr:`output_size` in the forward call. 2024-08-20T21:39:08.5820050Z See the Inputs and Example below. 2024-08-20T21:39:08.5820144Z 2024-08-20T21:39:08.5820244Z Args: 2024-08-20T21:39:08.5820510Z kernel_size (int or tuple): Size of the max pooling window. 2024-08-20T21:39:08.5820752Z stride (int or tuple): Stride of the max pooling window. 2024-08-20T21:39:08.5820942Z It is set to :attr:`kernel_size` by default. 2024-08-20T21:39:08.5821222Z padding (int or tuple): Padding that was added to the input 2024-08-20T21:39:08.5821316Z 2024-08-20T21:39:08.5821473Z Inputs: 2024-08-20T21:39:08.5821710Z - `input`: the input Tensor to invert 2024-08-20T21:39:08.5822075Z - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d` 2024-08-20T21:39:08.5822369Z - `output_size` (optional): the targeted output size 2024-08-20T21:39:08.5822465Z 2024-08-20T21:39:08.5822567Z Shape: 2024-08-20T21:39:08.5822938Z - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. 2024-08-20T21:39:08.5823347Z - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where 2024-08-20T21:39:08.5823439Z 2024-08-20T21:39:08.5823562Z .. math:: 2024-08-20T21:39:08.5824108Z H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} 2024-08-20T21:39:08.5824202Z 2024-08-20T21:39:08.5824327Z .. math:: 2024-08-20T21:39:08.5824867Z W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} 2024-08-20T21:39:08.5824960Z 2024-08-20T21:39:08.5825209Z or as given by :attr:`output_size` in the call operator 2024-08-20T21:39:08.5825300Z 2024-08-20T21:39:08.5825409Z Example:: 2024-08-20T21:39:08.5825520Z 2024-08-20T21:39:08.5825751Z >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True) 2024-08-20T21:39:08.5825938Z >>> unpool = nn.MaxUnpool2d(2, stride=2) 2024-08-20T21:39:08.5826126Z >>> input = torch.tensor([[[[ 1., 2., 3., 4.], 2024-08-20T21:39:08.5826289Z [ 5., 6., 7., 8.], 2024-08-20T21:39:08.5826460Z [ 9., 10., 11., 12.], 2024-08-20T21:39:08.5826625Z [13., 14., 15., 16.]]]]) 2024-08-20T21:39:08.5826783Z >>> output, indices = pool(input) 2024-08-20T21:39:08.5826932Z >>> unpool(output, indices) 2024-08-20T21:39:08.5827084Z tensor([[[[ 0., 0., 0., 0.], 2024-08-20T21:39:08.5827222Z [ 0., 6., 0., 8.], 2024-08-20T21:39:08.5827372Z [ 0., 0., 0., 0.], 2024-08-20T21:39:08.5827515Z [ 0., 14., 0., 16.]]]]) 2024-08-20T21:39:08.5827899Z >>> # Now using output_size to resolve an ambiguous size for the inverse 2024-08-20T21:39:08.5828116Z >>> input = torch.tensor([[[[ 1., 2., 3., 4., 5.], 2024-08-20T21:39:08.5828280Z [ 6., 7., 8., 9., 10.], 2024-08-20T21:39:08.5828454Z [11., 12., 13., 14., 15.], 2024-08-20T21:39:08.5828622Z [16., 17., 18., 19., 20.]]]]) 2024-08-20T21:39:08.5828775Z >>> output, indices = pool(input) 2024-08-20T21:39:08.5829033Z >>> # This call will not work without specifying output_size 2024-08-20T21:39:08.5829252Z >>> unpool(output, indices, output_size=input.size()) 2024-08-20T21:39:08.5829398Z tensor([[[[ 0., 0., 0., 0., 0.], 2024-08-20T21:39:08.5829555Z [ 0., 7., 0., 9., 0.], 2024-08-20T21:39:08.5829692Z [ 0., 0., 0., 0., 0.], 2024-08-20T21:39:08.5829838Z [ 0., 17., 0., 19., 0.]]]]) 2024-08-20T21:39:08.5829951Z 2024-08-20T21:39:08.5830046Z 2024-08-20T21:39:08.5830143Z 2024-08-20T21:39:08.5830582Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5830675Z 2024-08-20T21:39:08.5830792Z warnings.warn(msg) 2024-08-20T21:39:08.5830898Z 2024-08-20T21:39:08.5831122Z --- Parse Warning: 81 / 101 --- 2024-08-20T21:39:08.5832632Z /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. 2024-08-20T21:39:08.5833078Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5833631Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings. 2024-08-20T21:39:08.5833741Z 2024-08-20T21:39:08.5834213Z For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`, 2024-08-20T21:39:08.5834353Z and with 2D inputs, this class 2024-08-20T21:39:08.5834467Z 2024-08-20T21:39:08.5834917Z * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``, 2024-08-20T21:39:08.5835391Z * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``, 2024-08-20T21:39:08.5835836Z * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``. 2024-08-20T21:39:08.5835929Z 2024-08-20T21:39:08.5836461Z However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these 2024-08-20T21:39:08.5836568Z operations. 2024-08-20T21:39:08.5836661Z 2024-08-20T21:39:08.5837106Z EmbeddingBag also supports per-sample weights as an argument to the forward 2024-08-20T21:39:08.5837448Z pass. This scales the output of the Embedding before performing a weighted 2024-08-20T21:39:08.5837803Z reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the 2024-08-20T21:39:08.5838156Z only supported ``mode`` is ``"sum"``, which computes a weighted sum according to 2024-08-20T21:39:08.5838408Z :attr:`per_sample_weights`. 2024-08-20T21:39:08.5838502Z 2024-08-20T21:39:08.5838616Z Args: 2024-08-20T21:39:08.5838872Z num_embeddings (int): size of the dictionary of embeddings 2024-08-20T21:39:08.5839120Z embedding_dim (int): the size of each embedding vector 2024-08-20T21:39:08.5839580Z max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm` 2024-08-20T21:39:08.5839810Z is renormalized to have norm :attr:`max_norm`. 2024-08-20T21:39:08.5840461Z norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``. 2024-08-20T21:39:08.5841009Z scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of 2024-08-20T21:39:08.5841354Z the words in the mini-batch. Default ``False``. 2024-08-20T21:39:08.5841640Z Note: this option is not supported when ``mode="max"``. 2024-08-20T21:39:08.5842014Z mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag. 2024-08-20T21:39:08.5842359Z ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights` 2024-08-20T21:39:08.5842674Z into consideration. ``"mean"`` computes the average of the values 2024-08-20T21:39:08.5842947Z in the bag, ``"max"`` computes the max value over each bag. 2024-08-20T21:39:08.5843130Z Default: ``"mean"`` 2024-08-20T21:39:08.5843608Z sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See 2024-08-20T21:39:08.5843979Z Notes for more details regarding sparse gradients. Note: this option is not 2024-08-20T21:39:08.5850389Z supported when ``mode="max"``. 2024-08-20T21:39:08.5850945Z include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element 2024-08-20T21:39:08.5851405Z is equivalent to the size of `indices`. This matches the CSR format. 2024-08-20T21:39:08.5851890Z padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the 2024-08-20T21:39:08.5852281Z gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated 2024-08-20T21:39:08.5852669Z during training, i.e. it remains as a fixed "pad". For a newly constructed 2024-08-20T21:39:08.5853055Z EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all 2024-08-20T21:39:08.5853427Z zeros, but can be updated to another value to be used as the padding vector. 2024-08-20T21:39:08.5853806Z Note that the embedding vector at :attr:`padding_idx` is excluded from the 2024-08-20T21:39:08.5853953Z reduction. 2024-08-20T21:39:08.5854070Z 2024-08-20T21:39:08.5854178Z Attributes: 2024-08-20T21:39:08.5854626Z weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)` 2024-08-20T21:39:08.5854846Z initialized from :math:`\mathcal{N}(0, 1)`. 2024-08-20T21:39:08.5854942Z 2024-08-20T21:39:08.5855063Z Examples:: 2024-08-20T21:39:08.5855168Z 2024-08-20T21:39:08.5855416Z >>> # an EmbeddingBag module containing 10 tensors of size 3 2024-08-20T21:39:08.5855767Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum') 2024-08-20T21:39:08.5855965Z >>> # a batch of 2 samples of 4 indices each 2024-08-20T21:39:08.5856238Z >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long) 2024-08-20T21:39:08.5856462Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2024-08-20T21:39:08.5856733Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-08-20T21:39:08.5856892Z >>> embedding_sum(input, offsets) 2024-08-20T21:39:08.5857116Z tensor([[-0.8861, -5.4350, -0.0523], 2024-08-20T21:39:08.5857321Z [ 1.1306, -2.5798, -1.0044]]) 2024-08-20T21:39:08.5857415Z 2024-08-20T21:39:08.5857573Z >>> # Example with padding_idx 2024-08-20T21:39:08.5858025Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2) 2024-08-20T21:39:08.5858297Z >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long) 2024-08-20T21:39:08.5858515Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2024-08-20T21:39:08.5858670Z >>> embedding_sum(input, offsets) 2024-08-20T21:39:08.5858816Z tensor([[ 0.0000, 0.0000, 0.0000], 2024-08-20T21:39:08.5859033Z [-0.7082, 3.2145, -2.6251]]) 2024-08-20T21:39:08.5859126Z 2024-08-20T21:39:08.5859378Z >>> # An EmbeddingBag can be loaded from an Embedding like so 2024-08-20T21:39:08.5859603Z >>> embedding = nn.Embedding(10, 3, padding_idx=2) 2024-08-20T21:39:08.5859823Z >>> embedding_sum = nn.EmbeddingBag.from_pretrained( 2024-08-20T21:39:08.5859971Z embedding.weight, 2024-08-20T21:39:08.5860147Z padding_idx=embedding.padding_idx, 2024-08-20T21:39:08.5860309Z mode='sum') 2024-08-20T21:39:08.5860419Z 2024-08-20T21:39:08.5860841Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5860934Z 2024-08-20T21:39:08.5861062Z warnings.warn(msg) 2024-08-20T21:39:08.5861154Z 2024-08-20T21:39:08.5861377Z --- Parse Warning: 82 / 101 --- 2024-08-20T21:39:08.5863012Z /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. 2024-08-20T21:39:08.5863507Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5863613Z 2024-08-20T21:39:08.5863942Z Context manager for training with uneven inputs across processes in DDP. 2024-08-20T21:39:08.5864034Z 2024-08-20T21:39:08.5864433Z This context manager will keep track of already-joined DDP processes, 2024-08-20T21:39:08.5864739Z and "shadow" the forward and backward passes by inserting collective 2024-08-20T21:39:08.5865121Z communication operations to match with the ones created by non-joined 2024-08-20T21:39:08.5865458Z DDP processes. This will ensure each collective call has a corresponding 2024-08-20T21:39:08.5865845Z call by already-joined DDP processes, preventing hangs or errors that 2024-08-20T21:39:08.5866114Z would otherwise happen when training with uneven inputs across 2024-08-20T21:39:08.5866441Z processes. Alternatively, if the flag ``throw_on_early_termination`` is 2024-08-20T21:39:08.5866752Z specified to be ``True``, all trainers will throw an error once one rank 2024-08-20T21:39:08.5867055Z runs out of inputs, allowing these errors to be caught and handled 2024-08-20T21:39:08.5867197Z according to application logic. 2024-08-20T21:39:08.5867291Z 2024-08-20T21:39:08.5867615Z Once all DDP processes have joined, the context manager will broadcast 2024-08-20T21:39:08.5867935Z the model corresponding to the last joined process to all processes to 2024-08-20T21:39:08.5868137Z ensure the model is the same across all processes 2024-08-20T21:39:08.5868284Z (which is guaranteed by DDP). 2024-08-20T21:39:08.5868376Z 2024-08-20T21:39:08.5868670Z To use this to enable training with uneven inputs across processes, 2024-08-20T21:39:08.5868997Z simply wrap this context manager around your training loop. No further 2024-08-20T21:39:08.5869229Z modifications to the model or data loading is required. 2024-08-20T21:39:08.5869323Z 2024-08-20T21:39:08.5869446Z .. warning:: 2024-08-20T21:39:08.5869755Z If the model or training loop this context manager is wrapped around 2024-08-20T21:39:08.5870004Z has additional distributed collective operations, such as 2024-08-20T21:39:08.5870346Z ``SyncBatchNorm`` in the model's forward pass, then the flag 2024-08-20T21:39:08.5870700Z ``throw_on_early_termination`` must be enabled. This is because this 2024-08-20T21:39:08.5871081Z context manager is not aware of non-DDP collective communication. 2024-08-20T21:39:08.5871320Z This flag will cause all ranks to throw when any one rank 2024-08-20T21:39:08.5871604Z exhausts inputs, allowing these errors to be caught and recovered 2024-08-20T21:39:08.5871741Z from across all ranks. 2024-08-20T21:39:08.5871836Z 2024-08-20T21:39:08.5871934Z Args: 2024-08-20T21:39:08.5872198Z divide_by_initial_world_size (bool): If ``True``, will divide 2024-08-20T21:39:08.5872486Z gradients by the initial ``world_size`` DDP training was launched 2024-08-20T21:39:08.5872723Z with. If ``False``, will compute the effective world size 2024-08-20T21:39:08.5873005Z (number of ranks that have not depleted their inputs yet) and 2024-08-20T21:39:08.5873205Z divide gradients by that during allreduce. Set 2024-08-20T21:39:08.5873471Z ``divide_by_initial_world_size=True`` to ensure every input 2024-08-20T21:39:08.5873763Z sample including the uneven inputs have equal weight in terms of 2024-08-20T21:39:08.5874002Z how much they contribute to the global gradient. This is 2024-08-20T21:39:08.5874256Z achieved by always dividing the gradient by the initial 2024-08-20T21:39:08.5874518Z ``world_size`` even when we encounter uneven inputs. If you set 2024-08-20T21:39:08.5874763Z this to ``False``, we divide the gradient by the remaining 2024-08-20T21:39:08.5875057Z number of nodes. This ensures parity with training on a smaller 2024-08-20T21:39:08.5875382Z ``world_size`` although it also means the uneven inputs would 2024-08-20T21:39:08.5875649Z contribute more towards the global gradient. Typically, you 2024-08-20T21:39:08.5875934Z would want to set this to ``True`` for cases where the last few 2024-08-20T21:39:08.5876217Z inputs of your training job are uneven. In extreme cases, where 2024-08-20T21:39:08.5876499Z there is a large discrepancy in the number of inputs, setting 2024-08-20T21:39:08.5876698Z this to ``False`` might provide better results. 2024-08-20T21:39:08.5876999Z enable (bool): Whether to enable uneven input detection or not. Pass 2024-08-20T21:39:08.5877260Z in ``enable=False`` to disable in cases where you know that 2024-08-20T21:39:08.5877523Z inputs are even across participating processes. Default is 2024-08-20T21:39:08.5877626Z ``True``. 2024-08-20T21:39:08.5877896Z throw_on_early_termination (bool): Whether to throw an error 2024-08-20T21:39:08.5878142Z or continue training when at least one rank has exhausted 2024-08-20T21:39:08.5878555Z inputs. If ``True``, will throw upon the first rank reaching end 2024-08-20T21:39:08.5878825Z of data. If ``False``, will continue training with a smaller 2024-08-20T21:39:08.5879100Z effective world size until all ranks are joined. Note that if 2024-08-20T21:39:08.5879282Z this flag is specified, then the flag 2024-08-20T21:39:08.5879523Z ``divide_by_initial_world_size`` would be ignored. Default 2024-08-20T21:39:08.5879634Z is ``False``. 2024-08-20T21:39:08.5879744Z 2024-08-20T21:39:08.5879836Z 2024-08-20T21:39:08.5879945Z Example:: 2024-08-20T21:39:08.5880052Z 2024-08-20T21:39:08.5880209Z >>> # xdoctest: +SKIP("Distributed") 2024-08-20T21:39:08.5880319Z >>> import torch 2024-08-20T21:39:08.5880489Z >>> import torch.distributed as dist 2024-08-20T21:39:08.5880599Z >>> import os 2024-08-20T21:39:08.5880768Z >>> import torch.multiprocessing as mp 2024-08-20T21:39:08.5880904Z >>> import torch.nn as nn 2024-08-20T21:39:08.5881034Z >>> # On each spawned worker 2024-08-20T21:39:08.5881152Z >>> def worker(rank): 2024-08-20T21:39:08.5881501Z >>> dist.init_process_group("nccl", rank=rank, world_size=2) 2024-08-20T21:39:08.5881648Z >>> torch.cuda.set_device(rank) 2024-08-20T21:39:08.5881838Z >>> model = nn.Linear(1, 1, bias=False).to(rank) 2024-08-20T21:39:08.5882104Z >>> model = torch.nn.parallel.DistributedDataParallel( 2024-08-20T21:39:08.5882298Z >>> model, device_ids=[rank], output_device=rank 2024-08-20T21:39:08.5882413Z >>> ) 2024-08-20T21:39:08.5882596Z >>> # Rank 1 gets one more input than rank 0. 2024-08-20T21:39:08.5882863Z >>> inputs = [torch.tensor([1]).float() for _ in range(10 + rank)] 2024-08-20T21:39:08.5883004Z >>> with model.join(): 2024-08-20T21:39:08.5883131Z >>> for _ in range(5): 2024-08-20T21:39:08.5883268Z >>> for inp in inputs: 2024-08-20T21:39:08.5883436Z >>> loss = model(inp).sum() 2024-08-20T21:39:08.5883575Z >>> loss.backward() 2024-08-20T21:39:08.5883853Z >>> # Without the join() API, the below synchronization will hang 2024-08-20T21:39:08.5884158Z >>> # blocking for rank 1's allreduce to complete. 2024-08-20T21:39:08.5884330Z >>> torch.cuda.synchronize(device=rank) 2024-08-20T21:39:08.5884428Z 2024-08-20T21:39:08.5884867Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5884960Z 2024-08-20T21:39:08.5885092Z warnings.warn(msg) 2024-08-20T21:39:08.5885184Z 2024-08-20T21:39:08.5885411Z --- Parse Warning: 83 / 101 --- 2024-08-20T21:39:08.5887193Z /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. 2024-08-20T21:39:08.5887634Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5887732Z 2024-08-20T21:39:08.5888183Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction. 2024-08-20T21:39:08.5888274Z 2024-08-20T21:39:08.5888559Z Registers an optimizer with DDP such that the optimization for a 2024-08-20T21:39:08.5888924Z parameter will run immediately when that parameter's gradient is 2024-08-20T21:39:08.5889200Z finished with reduction, instead of waiting for all parameters' 2024-08-20T21:39:08.5889514Z gradients to finish reduction. This can result in a training speedup 2024-08-20T21:39:08.5889826Z depending on your workload since the optimizer can run while gradient 2024-08-20T21:39:08.5890149Z reduction for other parameters are still ongoing. In addition, this has 2024-08-20T21:39:08.5890472Z the potential to reduce peak memory consumption during training, as it 2024-08-20T21:39:08.5890833Z only needs to load the per-parameter optimizer states of a single 2024-08-20T21:39:08.5891210Z parameter at a time, instead of loading all per-parameter optimizer 2024-08-20T21:39:08.5891334Z states at once. 2024-08-20T21:39:08.5891427Z 2024-08-20T21:39:08.5891525Z Args: 2024-08-20T21:39:08.5891816Z optim (Type): a ``torch.optim.Optimizer`` class to be registered 2024-08-20T21:39:08.5891939Z as a fused optimizer. 2024-08-20T21:39:08.5892163Z *args (Sequence[Any]): Arguments to forward to `optim`. 2024-08-20T21:39:08.5892469Z optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters 2024-08-20T21:39:08.5892779Z to optimize, similar to `params` argument of traditional `torch.optim` 2024-08-20T21:39:08.5893080Z Optimizers. If this is omitted, all DDP model parameters will be 2024-08-20T21:39:08.5893186Z optimized. 2024-08-20T21:39:08.5893472Z **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`. 2024-08-20T21:39:08.5893577Z 2024-08-20T21:39:08.5893691Z .. warning :: 2024-08-20T21:39:08.5894048Z _register_fused_optim should only be called once on a DDP instance, 2024-08-20T21:39:08.5894346Z and registering multiple fused optimizers for the same DDP model 2024-08-20T21:39:08.5894519Z is not currently supported. Please ping 2024-08-20T21:39:08.5894842Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-08-20T21:39:08.5894972Z for your use case. 2024-08-20T21:39:08.5895065Z 2024-08-20T21:39:08.5895171Z .. warning :: 2024-08-20T21:39:08.5895448Z _register_fused_optim and register_comm_hook currently do not 2024-08-20T21:39:08.5895745Z compose together, meaning that custom DDP communication hooks are 2024-08-20T21:39:08.5896252Z not supported with overlapped optimizers. Please ping 2024-08-20T21:39:08.5896565Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-08-20T21:39:08.5896683Z for your use case. 2024-08-20T21:39:08.5896797Z 2024-08-20T21:39:08.5896906Z .. warning :: 2024-08-20T21:39:08.5897210Z Gradient accumulation and DDP `no_sync` are currently not supported 2024-08-20T21:39:08.5897396Z with overlapped optimizer. Please ping 2024-08-20T21:39:08.5897708Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-08-20T21:39:08.5897825Z for your use case. 2024-08-20T21:39:08.5897933Z 2024-08-20T21:39:08.5898039Z Example:: 2024-08-20T21:39:08.5898132Z 2024-08-20T21:39:08.5898332Z >>> # xdoctest: +SKIP("No rendezvous handler") 2024-08-20T21:39:08.5899001Z >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...') 2024-08-20T21:39:08.5899297Z >>> net = torch.nn.parallel.DistributedDataParallel(model, pg) 2024-08-20T21:39:08.5899440Z >>> lr = 1e-2 2024-08-20T21:39:08.5899558Z >>> betas = (0.9, 0.99) 2024-08-20T21:39:08.5899702Z >>> eps = 1e-6 2024-08-20T21:39:08.5900013Z >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps) 2024-08-20T21:39:08.5900183Z >>> # Example with subset of parameters 2024-08-20T21:39:08.5900385Z >>> params_to_opt = [list(net.parameters())[0]] 2024-08-20T21:39:08.5900520Z >>> net._register_fused_optim( 2024-08-20T21:39:08.5900855Z ... torch.optim.Adam, lr, optim_params=params_to_opt, betas=betas, eps=eps 2024-08-20T21:39:08.5900968Z ... ) 2024-08-20T21:39:08.5901062Z 2024-08-20T21:39:08.5901487Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5901592Z 2024-08-20T21:39:08.5901707Z warnings.warn(msg) 2024-08-20T21:39:08.5901804Z 2024-08-20T21:39:08.5902040Z --- Parse Warning: 84 / 101 --- 2024-08-20T21:39:08.5903623Z /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. 2024-08-20T21:39:08.5904078Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5904375Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``. 2024-08-20T21:39:08.5904468Z 2024-08-20T21:39:08.5904859Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2024-08-20T21:39:08.5905256Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2024-08-20T21:39:08.5905633Z This function is used to facilitate the computation to adopt NHWC kernels, which 2024-08-20T21:39:08.5906089Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2024-08-20T21:39:08.5906181Z 2024-08-20T21:39:08.5906285Z .. note:: 2024-08-20T21:39:08.5906626Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2024-08-20T21:39:08.5907033Z than the utility function ``convert_conv2d_weight_memory_format``. Any 2024-08-20T21:39:08.5907357Z layer with 4d weight will be affected by ``model.to``, which does not 2024-08-20T21:39:08.5907663Z necessarily benefit from conversion to specified ``memory_format``. 2024-08-20T21:39:08.5907989Z One place we are confident in is that NHWC(channels_last) conversion for 2024-08-20T21:39:08.5908311Z convolution in cuDNN, as it is beneficial to run convolution in NHWC, 2024-08-20T21:39:08.5908610Z even in cases where we have to apply permutation to input tensors. 2024-08-20T21:39:08.5908705Z 2024-08-20T21:39:08.5909046Z Hence our strategy here is to convert only the weight of convolution to 2024-08-20T21:39:08.5909202Z channels_last. This ensures that; 2024-08-20T21:39:08.5909507Z 1. Fast convolution kernels will be used, the benefit of which could 2024-08-20T21:39:08.5909847Z outweigh overhead of permutation (if input is not in the same format). 2024-08-20T21:39:08.5910182Z 2. No unnecessary permutations are applied on layers that do not benefit 2024-08-20T21:39:08.5910343Z from memory_format conversion. 2024-08-20T21:39:08.5910436Z 2024-08-20T21:39:08.5910766Z The optimal case is that, layers between convolution layers are channels 2024-08-20T21:39:08.5911110Z last compatible. Input tensor would be permuted to channels last when it 2024-08-20T21:39:08.5911429Z encounters the first convolution layer and stay in that memory format. 2024-08-20T21:39:08.5911815Z Hence following convolutions will not need to permute its input tensor. 2024-08-20T21:39:08.5911925Z 2024-08-20T21:39:08.5912253Z In case where a channels last incompatible layer is between convolution 2024-08-20T21:39:08.5912563Z layers, we need to permute the input tensor back to contiguous format 2024-08-20T21:39:08.5912902Z for that layer. The input tensor will go through the remaining layers in 2024-08-20T21:39:08.5913221Z contiguous format and be permuted to channels last when it encounters 2024-08-20T21:39:08.5913610Z another convolution layer. There's no point in propagating that 2024-08-20T21:39:08.5913923Z permutation to an earlier layer, as most layers are quite agnostic to 2024-08-20T21:39:08.5914040Z ``memory_format``. 2024-08-20T21:39:08.5914144Z 2024-08-20T21:39:08.5914474Z This claim might change when PyTorch supports fusion of permutation, as 2024-08-20T21:39:08.5914799Z there might have been a better spot to fuse the permutation other than 2024-08-20T21:39:08.5914973Z immediately before a convolution. 2024-08-20T21:39:08.5915066Z 2024-08-20T21:39:08.5915166Z Args: 2024-08-20T21:39:08.5915489Z module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container 2024-08-20T21:39:08.5915627Z ``nn.Module`` 2024-08-20T21:39:08.5915837Z memory_format: user specified ``memory_format``, 2024-08-20T21:39:08.5916109Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2024-08-20T21:39:08.5916201Z 2024-08-20T21:39:08.5916321Z Returns: 2024-08-20T21:39:08.5916519Z The original module with updated ``nn.Conv2d`` 2024-08-20T21:39:08.5916611Z 2024-08-20T21:39:08.5916726Z Example: 2024-08-20T21:39:08.5916923Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-20T21:39:08.5917136Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2024-08-20T21:39:08.5917499Z >>> input = torch.randint(1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda") 2024-08-20T21:39:08.5917637Z >>> model = nn.Sequential( 2024-08-20T21:39:08.5917800Z >>> nn.Conv2d(8, 4, 3)).cuda().half() 2024-08-20T21:39:08.5917946Z >>> # This is identical to: 2024-08-20T21:39:08.5918510Z >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2024-08-20T21:39:08.5918881Z >>> model = nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2024-08-20T21:39:08.5919023Z >>> out = model(input) 2024-08-20T21:39:08.5919120Z 2024-08-20T21:39:08.5919576Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5919669Z 2024-08-20T21:39:08.5919786Z warnings.warn(msg) 2024-08-20T21:39:08.5919898Z 2024-08-20T21:39:08.5920123Z --- Parse Warning: 85 / 101 --- 2024-08-20T21:39:08.5921716Z /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. 2024-08-20T21:39:08.5922168Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5922461Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format`` 2024-08-20T21:39:08.5922839Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2024-08-20T21:39:08.5923255Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2024-08-20T21:39:08.5923626Z This function is used to facilitate the computation to adopt NHWC kernels, which 2024-08-20T21:39:08.5924079Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2024-08-20T21:39:08.5924172Z 2024-08-20T21:39:08.5924282Z .. note:: 2024-08-20T21:39:08.5924724Z Calling ``model.to(memory_format=torch.channels_last_3d)`` is more aggressive 2024-08-20T21:39:08.5925041Z than the utility function ``convert_conv3d_weight_memory_format``. Any 2024-08-20T21:39:08.5925351Z layer with 4d weight will be affected by ``model.to``, which does not 2024-08-20T21:39:08.5925669Z necessarily benefit from conversion to specified ``memory_format``. 2024-08-20T21:39:08.5926022Z One place we are confident in is that NDHWC(channels_last_3d) conversion for 2024-08-20T21:39:08.5926354Z convolution in cuDNN, as it is beneficial to run convolution in NDHWC, 2024-08-20T21:39:08.5926652Z even in cases where we have to apply permutation to input tensors. 2024-08-20T21:39:08.5926745Z 2024-08-20T21:39:08.5927087Z Hence our strategy here is to convert only the weight of convolution to 2024-08-20T21:39:08.5927247Z channels_last_3d. This ensures that; 2024-08-20T21:39:08.5927555Z 1. Fast convolution kernels will be used, the benefit of which could 2024-08-20T21:39:08.5927888Z outweigh overhead of permutation (if input is not in the same format). 2024-08-20T21:39:08.5928218Z 2. No unnecessary permutations are applied on layers that do not benefit 2024-08-20T21:39:08.5928368Z from memory_format conversion. 2024-08-20T21:39:08.5928472Z 2024-08-20T21:39:08.5928802Z The optimal case is that, layers between convolution layers are channels 2024-08-20T21:39:08.5929132Z last compatible. Input tensor would be permuted to channels last when it 2024-08-20T21:39:08.5929466Z encounters the first convolution layer and stay in that memory format. 2024-08-20T21:39:08.5929795Z Hence following convolutions will not need to permute its input tensor. 2024-08-20T21:39:08.5929902Z 2024-08-20T21:39:08.5930224Z In case where a channels last incompatible layer is between convolution 2024-08-20T21:39:08.5930537Z layers, we need to permute the input tensor back to contiguous format 2024-08-20T21:39:08.5930877Z for that layer. The input tensor will go through the remaining layers in 2024-08-20T21:39:08.5931192Z contiguous format and be permuted to channels last when it encounters 2024-08-20T21:39:08.5931623Z another convolution layer. There's no point in propagating that 2024-08-20T21:39:08.5931949Z permutation to an earlier layer, as most layers are quite agnostic to 2024-08-20T21:39:08.5932066Z ``memory_format``. 2024-08-20T21:39:08.5932159Z 2024-08-20T21:39:08.5932502Z This claim might change when PyTorch supports fusion of permutation, as 2024-08-20T21:39:08.5932822Z there might have been a better spot to fuse the permutation other than 2024-08-20T21:39:08.5932996Z immediately before a convolution. 2024-08-20T21:39:08.5933090Z 2024-08-20T21:39:08.5933190Z Args: 2024-08-20T21:39:08.5933519Z module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container 2024-08-20T21:39:08.5933658Z ``nn.Module`` 2024-08-20T21:39:08.5933862Z memory_format: user specified ``memory_format``, 2024-08-20T21:39:08.5934133Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2024-08-20T21:39:08.5934230Z 2024-08-20T21:39:08.5934334Z Returns: 2024-08-20T21:39:08.5934545Z The original module with updated ``nn.Conv3d`` 2024-08-20T21:39:08.5934636Z 2024-08-20T21:39:08.5934739Z Example: 2024-08-20T21:39:08.5934945Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-20T21:39:08.5935159Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2024-08-20T21:39:08.5935526Z >>> input = torch.randint(1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda") 2024-08-20T21:39:08.5935660Z >>> model = nn.Sequential( 2024-08-20T21:39:08.5935877Z >>> nn.Conv3d(8, 4, 3)).cuda().half() 2024-08-20T21:39:08.5936029Z >>> # This is identical to: 2024-08-20T21:39:08.5936379Z >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2024-08-20T21:39:08.5936765Z >>> model = nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2024-08-20T21:39:08.5936905Z >>> out = model(input) 2024-08-20T21:39:08.5937003Z 2024-08-20T21:39:08.5937431Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5937539Z 2024-08-20T21:39:08.5937655Z warnings.warn(msg) 2024-08-20T21:39:08.5937747Z 2024-08-20T21:39:08.5937982Z --- Parse Warning: 86 / 101 --- 2024-08-20T21:39:08.5939426Z /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. 2024-08-20T21:39:08.5939875Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5940195Z Prune tensor by removing random channels along the specified dimension. 2024-08-20T21:39:08.5940289Z 2024-08-20T21:39:08.5940625Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2024-08-20T21:39:08.5940935Z by removing the specified ``amount`` of (currently unpruned) channels 2024-08-20T21:39:08.5941129Z along the specified ``dim`` selected at random. 2024-08-20T21:39:08.5941416Z Modifies module in place (and also return the modified module) 2024-08-20T21:39:08.5941517Z by: 2024-08-20T21:39:08.5941610Z 2024-08-20T21:39:08.5942009Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-08-20T21:39:08.5942317Z binary mask applied to the parameter ``name`` by the pruning method. 2024-08-20T21:39:08.5942637Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-08-20T21:39:08.5942925Z original (unpruned) parameter is stored in a new parameter named 2024-08-20T21:39:08.5943079Z ``name+'_orig'``. 2024-08-20T21:39:08.5943184Z 2024-08-20T21:39:08.5943283Z Args: 2024-08-20T21:39:08.5943594Z module (nn.Module): module containing the tensor to prune 2024-08-20T21:39:08.5943866Z name (str): parameter name within ``module`` on which pruning 2024-08-20T21:39:08.5943978Z will act. 2024-08-20T21:39:08.5944215Z amount (int or float): quantity of parameters to prune. 2024-08-20T21:39:08.5944483Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-08-20T21:39:08.5944765Z fraction of parameters to prune. If ``int``, it represents the 2024-08-20T21:39:08.5944947Z absolute number of parameters to prune. 2024-08-20T21:39:08.5945260Z dim (int): index of the dim along which we define channels to prune. 2024-08-20T21:39:08.5945354Z 2024-08-20T21:39:08.5945456Z Returns: 2024-08-20T21:39:08.5945782Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-08-20T21:39:08.5945873Z 2024-08-20T21:39:08.5945990Z Examples: 2024-08-20T21:39:08.5946118Z >>> # xdoctest: +SKIP 2024-08-20T21:39:08.5946272Z >>> m = prune.random_structured( 2024-08-20T21:39:08.5946555Z ... nn.Linear(5, 3), 'weight', amount=3, dim=1 2024-08-20T21:39:08.5946653Z ... ) 2024-08-20T21:39:08.5946904Z >>> columns_pruned = int(sum(torch.sum(m.weight, dim=0) == 0)) 2024-08-20T21:39:08.5947047Z >>> print(columns_pruned) 2024-08-20T21:39:08.5947144Z 3 2024-08-20T21:39:08.5947240Z 2024-08-20T21:39:08.5947675Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5947768Z 2024-08-20T21:39:08.5947948Z warnings.warn(msg) 2024-08-20T21:39:08.5948053Z 2024-08-20T21:39:08.5948281Z --- Parse Warning: 87 / 101 --- 2024-08-20T21:39:08.5949689Z /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. 2024-08-20T21:39:08.5950141Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5950679Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension. 2024-08-20T21:39:08.5950790Z 2024-08-20T21:39:08.5951108Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2024-08-20T21:39:08.5951418Z by removing the specified ``amount`` of (currently unpruned) channels 2024-08-20T21:39:08.5951746Z along the specified ``dim`` with the lowest L\ ``n``-norm. 2024-08-20T21:39:08.5952026Z Modifies module in place (and also return the modified module) 2024-08-20T21:39:08.5952126Z by: 2024-08-20T21:39:08.5952230Z 2024-08-20T21:39:08.5952613Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-08-20T21:39:08.5952920Z binary mask applied to the parameter ``name`` by the pruning method. 2024-08-20T21:39:08.5953240Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-08-20T21:39:08.5953525Z original (unpruned) parameter is stored in a new parameter named 2024-08-20T21:39:08.5953693Z ``name+'_orig'``. 2024-08-20T21:39:08.5953786Z 2024-08-20T21:39:08.5953885Z Args: 2024-08-20T21:39:08.5954150Z module (nn.Module): module containing the tensor to prune 2024-08-20T21:39:08.5954415Z name (str): parameter name within ``module`` on which pruning 2024-08-20T21:39:08.5954528Z will act. 2024-08-20T21:39:08.5954778Z amount (int or float): quantity of parameters to prune. 2024-08-20T21:39:08.5955034Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-08-20T21:39:08.5955314Z fraction of parameters to prune. If ``int``, it represents the 2024-08-20T21:39:08.5955507Z absolute number of parameters to prune. 2024-08-20T21:39:08.5955939Z n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid 2024-08-20T21:39:08.5956169Z entries for argument ``p`` in :func:`torch.norm`. 2024-08-20T21:39:08.5956467Z dim (int): index of the dim along which we define channels to prune. 2024-08-20T21:39:08.5956784Z importance_scores (torch.Tensor): tensor of importance scores (of same 2024-08-20T21:39:08.5957071Z shape as module parameter) used to compute mask for pruning. 2024-08-20T21:39:08.5957396Z The values in this tensor indicate the importance of the corresponding 2024-08-20T21:39:08.5957581Z elements in the parameter being pruned. 2024-08-20T21:39:08.5957927Z If unspecified or None, the module parameter will be used in its place. 2024-08-20T21:39:08.5958019Z 2024-08-20T21:39:08.5958123Z Returns: 2024-08-20T21:39:08.5958580Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-08-20T21:39:08.5958679Z 2024-08-20T21:39:08.5958788Z Examples: 2024-08-20T21:39:08.5958969Z >>> from torch.nn.utils import prune 2024-08-20T21:39:08.5959111Z >>> m = prune.ln_structured( 2024-08-20T21:39:08.5959495Z ... nn.Conv2d(5, 3, 2), 'weight', amount=0.3, dim=1, n=float('-inf') 2024-08-20T21:39:08.5959597Z ... ) 2024-08-20T21:39:08.5959692Z 2024-08-20T21:39:08.5960127Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5960223Z 2024-08-20T21:39:08.5960340Z warnings.warn(msg) 2024-08-20T21:39:08.5960444Z 2024-08-20T21:39:08.5960741Z --- Parse Warning: 88 / 101 --- 2024-08-20T21:39:08.5962210Z /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. 2024-08-20T21:39:08.5962664Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5962757Z 2024-08-20T21:39:08.5963345Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``. 2024-08-20T21:39:08.5963451Z 2024-08-20T21:39:08.5963584Z Modifies modules in place by: 2024-08-20T21:39:08.5963694Z 2024-08-20T21:39:08.5964072Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-08-20T21:39:08.5964380Z binary mask applied to the parameter ``name`` by the pruning method. 2024-08-20T21:39:08.5964696Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-08-20T21:39:08.5964980Z original (unpruned) parameter is stored in a new parameter named 2024-08-20T21:39:08.5965129Z ``name+'_orig'``. 2024-08-20T21:39:08.5965235Z 2024-08-20T21:39:08.5965336Z Args: 2024-08-20T21:39:08.5965602Z parameters (Iterable of (module, name) tuples): parameters of 2024-08-20T21:39:08.5965902Z the model to prune in a global fashion, i.e. by aggregating all 2024-08-20T21:39:08.5966190Z weights prior to deciding which ones to prune. module must be of 2024-08-20T21:39:08.5966401Z type :class:`nn.Module`, and name must be a string. 2024-08-20T21:39:08.5966717Z pruning_method (function): a valid pruning function from this module, 2024-08-20T21:39:08.5966970Z or a custom one implemented by the user that satisfies the 2024-08-20T21:39:08.5967369Z implementation guidelines and has ``PRUNING_TYPE='unstructured'``. 2024-08-20T21:39:08.5967686Z importance_scores (dict): a dictionary mapping (module, name) tuples to 2024-08-20T21:39:08.5968068Z the corresponding parameter's importance scores tensor. The tensor 2024-08-20T21:39:08.5968392Z should be the same shape as the parameter, and is used for computing 2024-08-20T21:39:08.5968511Z mask for pruning. 2024-08-20T21:39:08.5968873Z If unspecified or None, the parameter will be used in place of its 2024-08-20T21:39:08.5969004Z importance scores. 2024-08-20T21:39:08.5969173Z kwargs: other keyword arguments such as: 2024-08-20T21:39:08.5969461Z amount (int or float): quantity of parameters to prune across the 2024-08-20T21:39:08.5969602Z specified parameters. 2024-08-20T21:39:08.5969853Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-08-20T21:39:08.5970147Z fraction of parameters to prune. If ``int``, it represents the 2024-08-20T21:39:08.5970324Z absolute number of parameters to prune. 2024-08-20T21:39:08.5970418Z 2024-08-20T21:39:08.5970532Z Raises: 2024-08-20T21:39:08.5970802Z TypeError: if ``PRUNING_TYPE != 'unstructured'`` 2024-08-20T21:39:08.5970897Z 2024-08-20T21:39:08.5971009Z Note: 2024-08-20T21:39:08.5971381Z Since global structured pruning doesn't make much sense unless the 2024-08-20T21:39:08.5971679Z norm is normalized by the size of the parameter, we now limit the 2024-08-20T21:39:08.5971898Z scope of global pruning to unstructured methods. 2024-08-20T21:39:08.5971992Z 2024-08-20T21:39:08.5972099Z Examples: 2024-08-20T21:39:08.5972271Z >>> from torch.nn.utils import prune 2024-08-20T21:39:08.5972435Z >>> from collections import OrderedDict 2024-08-20T21:39:08.5972592Z >>> net = nn.Sequential(OrderedDict([ 2024-08-20T21:39:08.5972803Z ... ('first', nn.Linear(10, 4)), 2024-08-20T21:39:08.5972998Z ... ('second', nn.Linear(4, 1)), 2024-08-20T21:39:08.5973649Z ... ])) 2024-08-20T21:39:08.5973796Z >>> parameters_to_prune = ( 2024-08-20T21:39:08.5973978Z ... (net.first, 'weight'), 2024-08-20T21:39:08.5974171Z ... (net.second, 'weight'), 2024-08-20T21:39:08.5974269Z ... ) 2024-08-20T21:39:08.5974407Z >>> prune.global_unstructured( 2024-08-20T21:39:08.5974558Z ... parameters_to_prune, 2024-08-20T21:39:08.5974745Z ... pruning_method=prune.L1Unstructured, 2024-08-20T21:39:08.5974854Z ... amount=10, 2024-08-20T21:39:08.5974967Z ... ) 2024-08-20T21:39:08.5975280Z >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0)) 2024-08-20T21:39:08.5975385Z tensor(10) 2024-08-20T21:39:08.5975490Z 2024-08-20T21:39:08.5975582Z 2024-08-20T21:39:08.5976003Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5976111Z 2024-08-20T21:39:08.5976226Z warnings.warn(msg) 2024-08-20T21:39:08.5976322Z 2024-08-20T21:39:08.5976560Z --- Parse Warning: 89 / 101 --- 2024-08-20T21:39:08.5977996Z /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. 2024-08-20T21:39:08.5978453Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5979120Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``. 2024-08-20T21:39:08.5979215Z 2024-08-20T21:39:08.5979523Z Modifies module in place (and also return the modified module) by: 2024-08-20T21:39:08.5979615Z 2024-08-20T21:39:08.5979997Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-08-20T21:39:08.5980317Z binary mask applied to the parameter ``name`` by the pruning method. 2024-08-20T21:39:08.5980623Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-08-20T21:39:08.5980909Z original (unpruned) parameter is stored in a new parameter named 2024-08-20T21:39:08.5981077Z ``name+'_orig'``. 2024-08-20T21:39:08.5981169Z 2024-08-20T21:39:08.5981268Z Args: 2024-08-20T21:39:08.5981589Z module (nn.Module): module containing the tensor to prune 2024-08-20T21:39:08.5981851Z name (str): parameter name within ``module`` on which pruning 2024-08-20T21:39:08.5981976Z will act. 2024-08-20T21:39:08.5982229Z mask (Tensor): binary mask to be applied to the parameter. 2024-08-20T21:39:08.5982323Z 2024-08-20T21:39:08.5982442Z Returns: 2024-08-20T21:39:08.5982757Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-08-20T21:39:08.5982849Z 2024-08-20T21:39:08.5982968Z Examples: 2024-08-20T21:39:08.5983134Z >>> from torch.nn.utils import prune 2024-08-20T21:39:08.5983285Z >>> m = prune.custom_from_mask( 2024-08-20T21:39:08.5983645Z ... nn.Linear(5, 3), name='bias', mask=torch.tensor([0, 1, 0]) 2024-08-20T21:39:08.5983743Z ... ) 2024-08-20T21:39:08.5983865Z >>> print(m.bias_mask) 2024-08-20T21:39:08.5984000Z tensor([0., 1., 0.]) 2024-08-20T21:39:08.5984092Z 2024-08-20T21:39:08.5984187Z 2024-08-20T21:39:08.5984620Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.5984712Z 2024-08-20T21:39:08.5984829Z warnings.warn(msg) 2024-08-20T21:39:08.5984934Z 2024-08-20T21:39:08.5985158Z --- Parse Warning: 90 / 101 --- 2024-08-20T21:39:08.5986599Z /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. 2024-08-20T21:39:08.5987094Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.5987594Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA). 2024-08-20T21:39:08.5987700Z 2024-08-20T21:39:08.5988031Z Stochastic Weight Averaging was proposed in `Averaging Weights Leads to 2024-08-20T21:39:08.5988342Z Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii 2024-08-20T21:39:08.5988650Z Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson 2024-08-20T21:39:08.5988754Z (UAI 2018). 2024-08-20T21:39:08.5988846Z 2024-08-20T21:39:08.5989148Z Exponential Moving Average is a variation of `Polyak averaging`_, 2024-08-20T21:39:08.5989489Z but using exponential weights instead of equal weights across iterations. 2024-08-20T21:39:08.5989598Z 2024-08-20T21:39:08.5989928Z AveragedModel class creates a copy of the provided module :attr:`model` 2024-08-20T21:39:08.5990259Z on the device :attr:`device` and allows to compute running averages of the 2024-08-20T21:39:08.5990416Z parameters of the :attr:`model`. 2024-08-20T21:39:08.5990508Z 2024-08-20T21:39:08.5990610Z Args: 2024-08-20T21:39:08.5990838Z model (torch.nn.Module): model to use with SWA/EMA 2024-08-20T21:39:08.5991175Z device (torch.device, optional): if provided, the averaged model will be 2024-08-20T21:39:08.5991325Z stored on the :attr:`device` 2024-08-20T21:39:08.5991625Z avg_fn (function, optional): the averaging function used to update 2024-08-20T21:39:08.5991904Z parameters; the function must take in the current value of the 2024-08-20T21:39:08.5992207Z :class:`AveragedModel` parameter, the current value of :attr:`model` 2024-08-20T21:39:08.5992502Z parameter, and the number of models already averaged; if None, 2024-08-20T21:39:08.5992729Z an equally weighted average is used (default: None) 2024-08-20T21:39:08.5993059Z multi_avg_fn (function, optional): the averaging function used to update 2024-08-20T21:39:08.5993388Z parameters inplace; the function must take in the current values of the 2024-08-20T21:39:08.5993760Z :class:`AveragedModel` parameters as a list, the current values of :attr:`model` 2024-08-20T21:39:08.5994174Z parameters as a list, and the number of models already averaged; if None, 2024-08-20T21:39:08.5994400Z an equally weighted average is used (default: None) 2024-08-20T21:39:08.5994697Z use_buffers (bool): if ``True``, it will compute running averages for 2024-08-20T21:39:08.5995031Z both the parameters and the buffers of the model. (default: ``False``) 2024-08-20T21:39:08.5995124Z 2024-08-20T21:39:08.5995227Z Example: 2024-08-20T21:39:08.5995426Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:39:08.5995613Z >>> loader, optimizer, model, loss_fn = ... 2024-08-20T21:39:08.5996137Z >>> swa_model = torch.optim.swa_utils.AveragedModel(model) 2024-08-20T21:39:08.5996452Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 2024-08-20T21:39:08.5996620Z >>> T_max=300) 2024-08-20T21:39:08.5996759Z >>> swa_start = 160 2024-08-20T21:39:08.5996956Z >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05) 2024-08-20T21:39:08.5997084Z >>> for i in range(300): 2024-08-20T21:39:08.5997256Z >>> for input, target in loader: 2024-08-20T21:39:08.5997412Z >>> optimizer.zero_grad() 2024-08-20T21:39:08.5997603Z >>> loss_fn(model(input), target).backward() 2024-08-20T21:39:08.5997759Z >>> optimizer.step() 2024-08-20T21:39:08.5997890Z >>> if i > swa_start: 2024-08-20T21:39:08.5998329Z >>> swa_model.update_parameters(model) 2024-08-20T21:39:08.5998507Z >>> swa_scheduler.step() 2024-08-20T21:39:08.5998614Z >>> else: 2024-08-20T21:39:08.5998763Z >>> scheduler.step() 2024-08-20T21:39:08.5998864Z >>> 2024-08-20T21:39:08.5999087Z >>> # Update bn statistics for the swa_model at the end 2024-08-20T21:39:08.5999332Z >>> torch.optim.swa_utils.update_bn(loader, swa_model) 2024-08-20T21:39:08.5999424Z 2024-08-20T21:39:08.5999863Z You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters. 2024-08-20T21:39:08.6000159Z If no averaging function is provided, the default is to compute 2024-08-20T21:39:08.6000448Z equally-weighted average of the weights (SWA). 2024-08-20T21:39:08.6000541Z 2024-08-20T21:39:08.6000658Z Example: 2024-08-20T21:39:08.6000841Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:39:08.6001133Z >>> # Compute exponential moving averages of the weights and buffers 2024-08-20T21:39:08.6001391Z >>> ema_model = torch.optim.swa_utils.AveragedModel(model, 2024-08-20T21:39:08.6001703Z >>> torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True) 2024-08-20T21:39:08.6001818Z 2024-08-20T21:39:08.6001941Z .. note:: 2024-08-20T21:39:08.6002261Z When using SWA/EMA with models containing Batch Normalization you may 2024-08-20T21:39:08.6002571Z need to update the activation statistics for Batch Normalization. 2024-08-20T21:39:08.6002918Z This can be done either by using the :meth:`torch.optim.swa_utils.update_bn` 2024-08-20T21:39:08.6003250Z or by setting :attr:`use_buffers` to `True`. The first approach updates the 2024-08-20T21:39:08.6003693Z statistics in a post-training step by passing data through the model. The 2024-08-20T21:39:08.6004038Z second does it during the parameter update phase by averaging all buffers. 2024-08-20T21:39:08.6004385Z Empirical evidence has shown that updating the statistics in normalization 2024-08-20T21:39:08.6004712Z layers increases accuracy, but you may wish to empirically test which 2024-08-20T21:39:08.6004927Z approach yields the best results in your problem. 2024-08-20T21:39:08.6005150Z 2024-08-20T21:39:08.6005259Z .. note:: 2024-08-20T21:39:08.6005641Z :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model. 2024-08-20T21:39:08.6005750Z 2024-08-20T21:39:08.6005856Z .. note:: 2024-08-20T21:39:08.6006140Z When :meth:`update_parameters` is called for the first time (i.e. 2024-08-20T21:39:08.6006418Z :attr:`n_averaged` is `0`) the parameters of `model` are copied 2024-08-20T21:39:08.6006709Z to the parameters of :class:`AveragedModel`. For every subsequent 2024-08-20T21:39:08.6006981Z call of :meth:`update_parameters` the function `avg_fn` is used 2024-08-20T21:39:08.6007133Z to update the parameters. 2024-08-20T21:39:08.6007227Z 2024-08-20T21:39:08.6007535Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2024-08-20T21:39:08.6007720Z https://arxiv.org/abs/1803.05407 2024-08-20T21:39:08.6008061Z .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should 2024-08-20T21:39:08.6008179Z Average: 2024-08-20T21:39:08.6008331Z https://arxiv.org/abs/1806.05594 2024-08-20T21:39:08.6008695Z .. _SWALP: Stochastic Weight Averaging in Low-Precision Training: 2024-08-20T21:39:08.6008859Z https://arxiv.org/abs/1904.11943 2024-08-20T21:39:08.6009244Z .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That 2024-08-20T21:39:08.6009364Z Generalizes Well: 2024-08-20T21:39:08.6009526Z https://arxiv.org/abs/2001.02312 2024-08-20T21:39:08.6009705Z .. _Polyak averaging: 2024-08-20T21:39:08.6010009Z https://paperswithcode.com/method/polyak-averaging 2024-08-20T21:39:08.6010117Z 2024-08-20T21:39:08.6010536Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.6010632Z 2024-08-20T21:39:08.6010765Z warnings.warn(msg) 2024-08-20T21:39:08.6010856Z 2024-08-20T21:39:08.6011078Z --- Parse Warning: 91 / 101 --- 2024-08-20T21:39:08.6012476Z /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. 2024-08-20T21:39:08.6012913Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.6013232Z Anneals the learning rate in each parameter group to a fixed value. 2024-08-20T21:39:08.6013326Z 2024-08-20T21:39:08.6013652Z This learning rate scheduler is meant to be used with Stochastic Weight 2024-08-20T21:39:08.6013965Z Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`). 2024-08-20T21:39:08.6014057Z 2024-08-20T21:39:08.6014156Z Args: 2024-08-20T21:39:08.6014402Z optimizer (torch.optim.Optimizer): wrapped optimizer 2024-08-20T21:39:08.6014708Z swa_lrs (float or list): the learning rate value for all param groups 2024-08-20T21:39:08.6014891Z together or separately for each group. 2024-08-20T21:39:08.6015183Z annealing_epochs (int): number of epochs in the annealing phase 2024-08-20T21:39:08.6015298Z (default: 10) 2024-08-20T21:39:08.6015611Z annealing_strategy (str): "cos" or "linear"; specifies the annealing 2024-08-20T21:39:08.6015904Z strategy: "cos" for cosine annealing, "linear" for linear annealing 2024-08-20T21:39:08.6016023Z (default: "cos") 2024-08-20T21:39:08.6016375Z last_epoch (int): the index of the last epoch (default: -1) 2024-08-20T21:39:08.6016467Z 2024-08-20T21:39:08.6016719Z The :class:`SWALR` scheduler can be used together with other 2024-08-20T21:39:08.6017044Z schedulers to switch to a constant learning rate late in the training 2024-08-20T21:39:08.6017171Z as in the example below. 2024-08-20T21:39:08.6017341Z 2024-08-20T21:39:08.6017459Z Example: 2024-08-20T21:39:08.6017640Z >>> # xdoctest: +SKIP("Undefined variables") 2024-08-20T21:39:08.6017800Z >>> loader, optimizer, model = ... 2024-08-20T21:39:08.6017965Z >>> lr_lambda = lambda epoch: 0.9 2024-08-20T21:39:08.6018271Z >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, 2024-08-20T21:39:08.6018423Z >>> lr_lambda=lr_lambda) 2024-08-20T21:39:08.6018662Z >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, 2024-08-20T21:39:08.6018911Z >>> anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05) 2024-08-20T21:39:08.6019045Z >>> swa_start = 160 2024-08-20T21:39:08.6019172Z >>> for i in range(300): 2024-08-20T21:39:08.6019330Z >>> for input, target in loader: 2024-08-20T21:39:08.6019496Z >>> optimizer.zero_grad() 2024-08-20T21:39:08.6019691Z >>> loss_fn(model(input), target).backward() 2024-08-20T21:39:08.6019831Z >>> optimizer.step() 2024-08-20T21:39:08.6019975Z >>> if i > swa_start: 2024-08-20T21:39:08.6020125Z >>> swa_scheduler.step() 2024-08-20T21:39:08.6020233Z >>> else: 2024-08-20T21:39:08.6020379Z >>> scheduler.step() 2024-08-20T21:39:08.6020473Z 2024-08-20T21:39:08.6020782Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2024-08-20T21:39:08.6020951Z https://arxiv.org/abs/1803.05407 2024-08-20T21:39:08.6021046Z 2024-08-20T21:39:08.6021543Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.6021638Z 2024-08-20T21:39:08.6021754Z warnings.warn(msg) 2024-08-20T21:39:08.6021859Z 2024-08-20T21:39:08.6022084Z --- Parse Warning: 92 / 101 --- 2024-08-20T21:39:08.6023548Z /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. 2024-08-20T21:39:08.6024000Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.6024203Z Asserts that ``actual`` and ``expected`` are close. 2024-08-20T21:39:08.6024300Z 2024-08-20T21:39:08.6024954Z If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if 2024-08-20T21:39:08.6025046Z 2024-08-20T21:39:08.6025169Z .. math:: 2024-08-20T21:39:08.6025265Z 2024-08-20T21:39:08.6025947Z \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert 2024-08-20T21:39:08.6026052Z 2024-08-20T21:39:08.6026667Z Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are 2024-08-20T21:39:08.6026956Z only considered equal to each other if ``equal_nan`` is ``True``. 2024-08-20T21:39:08.6027061Z 2024-08-20T21:39:08.6027350Z In addition, they are only considered close if they have the same 2024-08-20T21:39:08.6027442Z 2024-08-20T21:39:08.6027800Z - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``), 2024-08-20T21:39:08.6028035Z - ``dtype`` (if ``check_dtype`` is ``True``), 2024-08-20T21:39:08.6028291Z - ``layout`` (if ``check_layout`` is ``True``), and 2024-08-20T21:39:08.6028531Z - stride (if ``check_stride`` is ``True``). 2024-08-20T21:39:08.6028624Z 2024-08-20T21:39:08.6029073Z If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed. 2024-08-20T21:39:08.6029183Z 2024-08-20T21:39:08.6029720Z If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are 2024-08-20T21:39:08.6030328Z checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR, 2024-08-20T21:39:08.6030657Z or ``ccol_indices`` and ``row_indices`` for CSC and BSC layouts, respectively, 2024-08-20T21:39:08.6031211Z are always checked for equality whereas the values are checked for closeness according to the definition above. 2024-08-20T21:39:08.6031315Z 2024-08-20T21:39:08.6031725Z If ``actual`` and ``expected`` are quantized, they are considered close if they have the same 2024-08-20T21:39:08.6032244Z :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the 2024-08-20T21:39:08.6032375Z definition above. 2024-08-20T21:39:08.6032468Z 2024-08-20T21:39:08.6033038Z ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which 2024-08-20T21:39:08.6033699Z :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types 2024-08-20T21:39:08.6034296Z have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s 2024-08-20T21:39:08.6034975Z or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all 2024-08-20T21:39:08.6035293Z their elements are considered close according to the above definition. 2024-08-20T21:39:08.6035388Z 2024-08-20T21:39:08.6035508Z .. note:: 2024-08-20T21:39:08.6035601Z 2024-08-20T21:39:08.6036143Z Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e. 2024-08-20T21:39:08.6036742Z :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus, 2024-08-20T21:39:08.6037137Z Python scalars of different types can be checked, but require ``check_dtype=False``. 2024-08-20T21:39:08.6037247Z 2024-08-20T21:39:08.6037347Z Args: 2024-08-20T21:39:08.6037483Z actual (Any): Actual input. 2024-08-20T21:39:08.6037646Z expected (Any): Expected input. 2024-08-20T21:39:08.6038157Z allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types 2024-08-20T21:39:08.6038516Z are allowed. Otherwise type equality is required. 2024-08-20T21:39:08.6039046Z rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default 2024-08-20T21:39:08.6039437Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2024-08-20T21:39:08.6039967Z atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default 2024-08-20T21:39:08.6040356Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2024-08-20T21:39:08.6040730Z equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal. 2024-08-20T21:39:08.6041163Z check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same 2024-08-20T21:39:08.6041519Z :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different 2024-08-20T21:39:08.6041957Z :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared. 2024-08-20T21:39:08.6042473Z check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this 2024-08-20T21:39:08.6043082Z check is disabled, tensors with different ``dtype``'s are promoted to a common ``dtype`` (according to 2024-08-20T21:39:08.6043322Z :func:`torch.promote_types`) before being compared. 2024-08-20T21:39:08.6043839Z check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this 2024-08-20T21:39:08.6044497Z check is disabled, tensors with different ``layout``'s are converted to strided tensors before being 2024-08-20T21:39:08.6044621Z compared. 2024-08-20T21:39:08.6045140Z check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride. 2024-08-20T21:39:08.6045650Z msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during 2024-08-20T21:39:08.6046191Z the comparison. Can also passed as callable in which case it will be called with the generated message and 2024-08-20T21:39:08.6046350Z should return the new message. 2024-08-20T21:39:08.6046457Z 2024-08-20T21:39:08.6046558Z Raises: 2024-08-20T21:39:08.6046889Z ValueError: If no :class:`torch.Tensor` can be constructed from an input. 2024-08-20T21:39:08.6047130Z ValueError: If only ``rtol`` or ``atol`` is specified. 2024-08-20T21:39:08.6047581Z AssertionError: If corresponding inputs are not Python scalars and are not directly related. 2024-08-20T21:39:08.6048089Z AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have 2024-08-20T21:39:08.6048223Z different types. 2024-08-20T21:39:08.6048839Z AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match. 2024-08-20T21:39:08.6049484Z AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match. 2024-08-20T21:39:08.6049980Z AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`. 2024-08-20T21:39:08.6050407Z AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same 2024-08-20T21:39:08.6050578Z :attr:`~torch.Tensor.layout`. 2024-08-20T21:39:08.6050882Z AssertionError: If only one of corresponding tensors is quantized. 2024-08-20T21:39:08.6051525Z AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s. 2024-08-20T21:39:08.6051955Z AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same 2024-08-20T21:39:08.6052110Z :attr:`~torch.Tensor.device`. 2024-08-20T21:39:08.6052599Z AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``. 2024-08-20T21:39:08.6053109Z AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride. 2024-08-20T21:39:08.6053612Z AssertionError: If the values of corresponding tensors are not close according to the definition above. 2024-08-20T21:39:08.6053720Z 2024-08-20T21:39:08.6054356Z The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching 2024-08-20T21:39:08.6054637Z ``dtype``'s, the maximum of both tolerances is used. 2024-08-20T21:39:08.6054741Z 2024-08-20T21:39:08.6054976Z +---------------------------+------------+----------+ 2024-08-20T21:39:08.6055164Z | ``dtype`` | ``rtol`` | ``atol`` | 2024-08-20T21:39:08.6055333Z +===========================+============+==========+ 2024-08-20T21:39:08.6055596Z | :attr:`~torch.float16` | ``1e-3`` | ``1e-5`` | 2024-08-20T21:39:08.6055838Z +---------------------------+------------+----------+ 2024-08-20T21:39:08.6056102Z | :attr:`~torch.bfloat16` | ``1.6e-2`` | ``1e-5`` | 2024-08-20T21:39:08.6056325Z +---------------------------+------------+----------+ 2024-08-20T21:39:08.6056596Z | :attr:`~torch.float32` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:39:08.6056820Z +---------------------------+------------+----------+ 2024-08-20T21:39:08.6057150Z | :attr:`~torch.float64` | ``1e-7`` | ``1e-7`` | 2024-08-20T21:39:08.6057386Z +---------------------------+------------+----------+ 2024-08-20T21:39:08.6057647Z | :attr:`~torch.complex32` | ``1e-3`` | ``1e-5`` | 2024-08-20T21:39:08.6057870Z +---------------------------+------------+----------+ 2024-08-20T21:39:08.6058142Z | :attr:`~torch.complex64` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:39:08.6058364Z +---------------------------+------------+----------+ 2024-08-20T21:39:08.6058644Z | :attr:`~torch.complex128` | ``1e-7`` | ``1e-7`` | 2024-08-20T21:39:08.6058869Z +---------------------------+------------+----------+ 2024-08-20T21:39:08.6059125Z | :attr:`~torch.quint8` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:39:08.6059361Z +---------------------------+------------+----------+ 2024-08-20T21:39:08.6059618Z | :attr:`~torch.quint2x4` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:39:08.6059843Z +---------------------------+------------+----------+ 2024-08-20T21:39:08.6060110Z | :attr:`~torch.quint4x2` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:39:08.6060331Z +---------------------------+------------+----------+ 2024-08-20T21:39:08.6060584Z | :attr:`~torch.qint8` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:39:08.6060817Z +---------------------------+------------+----------+ 2024-08-20T21:39:08.6061074Z | :attr:`~torch.qint32` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:39:08.6061313Z +---------------------------+------------+----------+ 2024-08-20T21:39:08.6061489Z | other | ``0.0`` | ``0.0`` | 2024-08-20T21:39:08.6061772Z +---------------------------+------------+----------+ 2024-08-20T21:39:08.6061877Z 2024-08-20T21:39:08.6061992Z .. note:: 2024-08-20T21:39:08.6062084Z 2024-08-20T21:39:08.6062636Z :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged 2024-08-20T21:39:08.6063173Z to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might 2024-08-20T21:39:08.6063553Z define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default: 2024-08-20T21:39:08.6063657Z 2024-08-20T21:39:08.6063778Z >>> import functools 2024-08-20T21:39:08.6064164Z >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0) 2024-08-20T21:39:08.6064360Z >>> assert_equal(1e-9, 1e-10) 2024-08-20T21:39:08.6064524Z Traceback (most recent call last): 2024-08-20T21:39:08.6064645Z ... 2024-08-20T21:39:08.6064822Z AssertionError: Scalars are not equal! 2024-08-20T21:39:08.6064933Z 2024-08-20T21:39:08.6065144Z Expected 1e-10 but got 1e-09. 2024-08-20T21:39:08.6065385Z Absolute difference: 9.000000000000001e-10 2024-08-20T21:39:08.6065523Z Relative difference: 9.0 2024-08-20T21:39:08.6065634Z 2024-08-20T21:39:08.6065741Z Examples: 2024-08-20T21:39:08.6065898Z >>> # tensor to tensor comparison 2024-08-20T21:39:08.6066171Z >>> expected = torch.tensor([1e0, 1e-1, 1e-2]) 2024-08-20T21:39:08.6066357Z >>> actual = torch.acos(torch.cos(expected)) 2024-08-20T21:39:08.6066577Z >>> torch.testing.assert_close(actual, expected) 2024-08-20T21:39:08.6066672Z 2024-08-20T21:39:08.6066828Z >>> # scalar to scalar comparison 2024-08-20T21:39:08.6066960Z >>> import math 2024-08-20T21:39:08.6067102Z >>> expected = math.sqrt(2.0) 2024-08-20T21:39:08.6067252Z >>> actual = 2.0 / math.sqrt(2.0) 2024-08-20T21:39:08.6067470Z >>> torch.testing.assert_close(actual, expected) 2024-08-20T21:39:08.6067566Z 2024-08-20T21:39:08.6067745Z >>> # numpy array to numpy array comparison 2024-08-20T21:39:08.6067886Z >>> import numpy as np 2024-08-20T21:39:08.6068204Z >>> expected = np.array([1e0, 1e-1, 1e-2]) 2024-08-20T21:39:08.6068373Z >>> actual = np.arccos(np.cos(expected)) 2024-08-20T21:39:08.6068585Z >>> torch.testing.assert_close(actual, expected) 2024-08-20T21:39:08.6068680Z 2024-08-20T21:39:08.6068841Z >>> # sequence to sequence comparison 2024-08-20T21:39:08.6068982Z >>> import numpy as np 2024-08-20T21:39:08.6069366Z >>> # The types of the sequences do not have to match. They only have to have the same 2024-08-20T21:39:08.6069565Z >>> # length and their elements have to match. 2024-08-20T21:39:08.6069788Z >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)] 2024-08-20T21:39:08.6069927Z >>> actual = tuple(expected) 2024-08-20T21:39:08.6070142Z >>> torch.testing.assert_close(actual, expected) 2024-08-20T21:39:08.6070235Z 2024-08-20T21:39:08.6070398Z >>> # mapping to mapping comparison 2024-08-20T21:39:08.6070589Z >>> from collections import OrderedDict 2024-08-20T21:39:08.6070713Z >>> import numpy as np 2024-08-20T21:39:08.6070846Z >>> foo = torch.tensor(1.0) 2024-08-20T21:39:08.6070970Z >>> bar = 2.0 2024-08-20T21:39:08.6071093Z >>> baz = np.array(3.0) 2024-08-20T21:39:08.6071468Z >>> # The types and a possible ordering of mappings do not have to match. They only 2024-08-20T21:39:08.6071791Z >>> # have to have the same set of keys and their elements have to match. 2024-08-20T21:39:08.6072081Z >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)]) 2024-08-20T21:39:08.6072338Z >>> actual = {"baz": baz, "bar": bar, "foo": foo} 2024-08-20T21:39:08.6072538Z >>> torch.testing.assert_close(actual, expected) 2024-08-20T21:39:08.6072636Z 2024-08-20T21:39:08.6072831Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2024-08-20T21:39:08.6072979Z >>> actual = expected.clone() 2024-08-20T21:39:08.6073227Z >>> # By default, directly related instances can be compared 2024-08-20T21:39:08.6073544Z >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected) 2024-08-20T21:39:08.6073823Z >>> # This check can be made more strict with allow_subclasses=False 2024-08-20T21:39:08.6073975Z >>> torch.testing.assert_close( 2024-08-20T21:39:08.6074270Z ... torch.nn.Parameter(actual), expected, allow_subclasses=False 2024-08-20T21:39:08.6074368Z ... ) 2024-08-20T21:39:08.6074528Z Traceback (most recent call last): 2024-08-20T21:39:08.6074646Z ... 2024-08-20T21:39:08.6074928Z TypeError: No comparison pair was able to handle inputs of type 2024-08-20T21:39:08.6075335Z and . 2024-08-20T21:39:08.6075668Z >>> # If the inputs are not directly related, they are never considered close 2024-08-20T21:39:08.6075911Z >>> torch.testing.assert_close(actual.numpy(), expected) 2024-08-20T21:39:08.6076083Z Traceback (most recent call last): 2024-08-20T21:39:08.6076182Z ... 2024-08-20T21:39:08.6076697Z TypeError: No comparison pair was able to handle inputs of type 2024-08-20T21:39:08.6076898Z and . 2024-08-20T21:39:08.6077275Z >>> # Exceptions to these rules are Python scalars. They can be checked regardless of 2024-08-20T21:39:08.6077440Z >>> # their type if check_dtype=False. 2024-08-20T21:39:08.6077693Z >>> torch.testing.assert_close(1.0, 1, check_dtype=False) 2024-08-20T21:39:08.6077787Z 2024-08-20T21:39:08.6077924Z >>> # NaN != NaN by default. 2024-08-20T21:39:08.6078113Z >>> expected = torch.tensor(float("Nan")) 2024-08-20T21:39:08.6078384Z >>> actual = expected.clone() 2024-08-20T21:39:08.6078668Z >>> torch.testing.assert_close(actual, expected) 2024-08-20T21:39:08.6078829Z Traceback (most recent call last): 2024-08-20T21:39:08.6078929Z ... 2024-08-20T21:39:08.6079120Z AssertionError: Scalars are not close! 2024-08-20T21:39:08.6079231Z 2024-08-20T21:39:08.6079366Z Expected nan but got nan. 2024-08-20T21:39:08.6079654Z Absolute difference: nan (up to 1e-05 allowed) 2024-08-20T21:39:08.6079923Z Relative difference: nan (up to 1.3e-06 allowed) 2024-08-20T21:39:08.6080200Z >>> torch.testing.assert_close(actual, expected, equal_nan=True) 2024-08-20T21:39:08.6080318Z 2024-08-20T21:39:08.6080497Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2024-08-20T21:39:08.6080665Z >>> actual = torch.tensor([1.0, 4.0, 5.0]) 2024-08-20T21:39:08.6080884Z >>> # The default error message can be overwritten. 2024-08-20T21:39:08.6081286Z >>> torch.testing.assert_close(actual, expected, msg="Argh, the tensors are not close!") 2024-08-20T21:39:08.6081466Z Traceback (most recent call last): 2024-08-20T21:39:08.6081565Z ... 2024-08-20T21:39:08.6081777Z AssertionError: Argh, the tensors are not close! 2024-08-20T21:39:08.6082140Z >>> # If msg is a callable, it can be used to augment the generated message with 2024-08-20T21:39:08.6082267Z >>> # extra information 2024-08-20T21:39:08.6082417Z >>> torch.testing.assert_close( 2024-08-20T21:39:08.6082710Z ... actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter" 2024-08-20T21:39:08.6082864Z ... ) 2024-08-20T21:39:08.6083027Z Traceback (most recent call last): 2024-08-20T21:39:08.6083137Z ... 2024-08-20T21:39:08.6083266Z AssertionError: Header 2024-08-20T21:39:08.6083373Z 2024-08-20T21:39:08.6083571Z Tensor-likes are not close! 2024-08-20T21:39:08.6083682Z 2024-08-20T21:39:08.6083840Z Mismatched elements: 2 / 3 (66.7%) 2024-08-20T21:39:08.6084248Z Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed) 2024-08-20T21:39:08.6084655Z Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed) 2024-08-20T21:39:08.6084777Z 2024-08-20T21:39:08.6084876Z Footer 2024-08-20T21:39:08.6084973Z 2024-08-20T21:39:08.6085409Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.6085503Z 2024-08-20T21:39:08.6085620Z warnings.warn(msg) 2024-08-20T21:39:08.6085730Z 2024-08-20T21:39:08.6085954Z --- Parse Warning: 93 / 101 --- 2024-08-20T21:39:08.6087430Z /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. 2024-08-20T21:39:08.6087888Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.6088131Z Register a container-like type as pytree node. 2024-08-20T21:39:08.6088224Z 2024-08-20T21:39:08.6088337Z Args: 2024-08-20T21:39:08.6088608Z cls (type): A Python type to treat as an internal pytree node. 2024-08-20T21:39:08.6089014Z flatten_fn (callable): A function to be used during flattening, taking an instance of 2024-08-20T21:39:08.6089390Z ``cls`` and returning a pair, with (1) an iterable for the children to be flattened 2024-08-20T21:39:08.6089812Z recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be 2024-08-20T21:39:08.6089983Z passed to the ``unflatten_fn``. 2024-08-20T21:39:08.6090375Z unflatten_fn (callable): A function taking two arguments: the auxiliary data that was 2024-08-20T21:39:08.6090822Z returned by ``flatten_fn`` and stored in the treespec, and the unflattened children. 2024-08-20T21:39:08.6091054Z The function should return an instance of ``cls``. 2024-08-20T21:39:08.6091428Z serialized_type_name (str, optional): A keyword argument used to specify the fully 2024-08-20T21:39:08.6091670Z qualified name used when serializing the tree spec. 2024-08-20T21:39:08.6092098Z to_dumpable_context (callable, optional): An optional keyword argument to custom specify how 2024-08-20T21:39:08.6092513Z to convert the context of the pytree to a custom json dumpable representation. This is 2024-08-20T21:39:08.6092918Z used for json serialization, which is being used in :mod:`torch.export` right now. 2024-08-20T21:39:08.6093331Z from_dumpable_context (callable, optional): An optional keyword argument to custom specify 2024-08-20T21:39:08.6093715Z how to convert the custom json dumpable representation of the context back to the 2024-08-20T21:39:08.6094105Z original context. This is used for json deserialization, which is being used in 2024-08-20T21:39:08.6094262Z :mod:`torch.export` right now. 2024-08-20T21:39:08.6094368Z 2024-08-20T21:39:08.6094482Z Example:: 2024-08-20T21:39:08.6094575Z 2024-08-20T21:39:08.6094712Z >>> # xdoctest: +SKIP 2024-08-20T21:39:08.6094920Z >>> # Registry a Python type with lambda functions 2024-08-20T21:39:08.6095052Z >>> register_pytree_node( 2024-08-20T21:39:08.6095172Z ... set, 2024-08-20T21:39:08.6095407Z ... lambda s: (sorted(s), None, None), 2024-08-20T21:39:08.6095582Z ... lambda children, _: set(children), 2024-08-20T21:39:08.6095694Z ... ) 2024-08-20T21:39:08.6096079Z 2024-08-20T21:39:08.6096516Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.6096635Z 2024-08-20T21:39:08.6096753Z warnings.warn(msg) 2024-08-20T21:39:08.6096845Z 2024-08-20T21:39:08.6097087Z --- Parse Warning: 94 / 101 --- 2024-08-20T21:39:08.6098638Z /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. 2024-08-20T21:39:08.6099087Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.6099179Z 2024-08-20T21:39:08.6099472Z Context passed to policy function during selective checkpointing. 2024-08-20T21:39:08.6099578Z 2024-08-20T21:39:08.6099910Z This class is used to pass relevant metadata to the policy function during 2024-08-20T21:39:08.6100262Z selective checkpointing. The metadata includes whether the current invocation 2024-08-20T21:39:08.6100504Z of the policy function is during recomputation or not. 2024-08-20T21:39:08.6100600Z 2024-08-20T21:39:08.6100702Z Example: 2024-08-20T21:39:08.6100844Z >>> # xdoctest: +SKIP(stub) 2024-08-20T21:39:08.6100944Z >>> 2024-08-20T21:39:08.6101122Z >>> def policy_fn(ctx, op, *args, **kwargs): 2024-08-20T21:39:08.6101268Z >>> print(ctx.is_recompute) 2024-08-20T21:39:08.6101366Z >>> 2024-08-20T21:39:08.6101736Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2024-08-20T21:39:08.6101849Z >>> 2024-08-20T21:39:08.6102048Z >>> out = torch.utils.checkpoint.checkpoint( 2024-08-20T21:39:08.6102171Z >>> fn, x, y, 2024-08-20T21:39:08.6102301Z >>> use_reentrant=False, 2024-08-20T21:39:08.6102431Z >>> context_fn=context_fn, 2024-08-20T21:39:08.6102544Z >>> ) 2024-08-20T21:39:08.6102635Z 2024-08-20T21:39:08.6103060Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.6103321Z 2024-08-20T21:39:08.6103441Z warnings.warn(msg) 2024-08-20T21:39:08.6103536Z 2024-08-20T21:39:08.6103781Z --- Parse Warning: 95 / 101 --- 2024-08-20T21:39:08.6105367Z /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. 2024-08-20T21:39:08.6105808Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.6105922Z 2024-08-20T21:39:08.6106261Z Helper to avoid recomputing certain ops during activation checkpointing. 2024-08-20T21:39:08.6106357Z 2024-08-20T21:39:08.6106687Z Use this with `torch.utils.checkpoint.checkpoint` to control which 2024-08-20T21:39:08.6106904Z operations are recomputed during the backward pass. 2024-08-20T21:39:08.6107016Z 2024-08-20T21:39:08.6107119Z Args: 2024-08-20T21:39:08.6107285Z policy_fn_or_list (Callable or List): 2024-08-20T21:39:08.6107600Z - If a policy function is provided, it should accept a 2024-08-20T21:39:08.6107928Z :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and 2024-08-20T21:39:08.6108235Z kwargs to the op, and return a :class:`CheckpointPolicy` enum value 2024-08-20T21:39:08.6108582Z indicating whether the execution of the op should be recomputed or not. 2024-08-20T21:39:08.6108960Z - If a list of operations is provided, it is equivalent to a policy 2024-08-20T21:39:08.6109310Z returning `CheckpointPolicy.MUST_SAVE` for the specified 2024-08-20T21:39:08.6109631Z operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other 2024-08-20T21:39:08.6109744Z operations. 2024-08-20T21:39:08.6110041Z allow_cache_entry_mutation (bool, optional): By default, an error is 2024-08-20T21:39:08.6110363Z raised if any tensors cached by selective activation checkpoint are 2024-08-20T21:39:08.6110677Z mutated in order to ensure correctness. If set to `True`, this check 2024-08-20T21:39:08.6110806Z is disabled. 2024-08-20T21:39:08.6110909Z Returns: 2024-08-20T21:39:08.6111056Z A tuple of two context managers. 2024-08-20T21:39:08.6111170Z 2024-08-20T21:39:08.6111274Z Example: 2024-08-20T21:39:08.6111417Z >>> # xdoctest: +REQUIRES(LINUX) 2024-08-20T21:39:08.6111556Z >>> import functools 2024-08-20T21:39:08.6111654Z >>> 2024-08-20T21:39:08.6111832Z >>> x = torch.rand(10, 10, requires_grad=True) 2024-08-20T21:39:08.6112027Z >>> y = torch.rand(10, 10, requires_grad=True) 2024-08-20T21:39:08.6112128Z >>> 2024-08-20T21:39:08.6112243Z >>> ops_to_save = [ 2024-08-20T21:39:08.6112409Z >>> torch.ops.aten.mm.default, 2024-08-20T21:39:08.6112508Z >>> ] 2024-08-20T21:39:08.6112605Z >>> 2024-08-20T21:39:08.6112804Z >>> def policy_fn(ctx, op, *args, **kwargs): 2024-08-20T21:39:08.6112934Z >>> if op in ops_to_save: 2024-08-20T21:39:08.6113114Z >>> return CheckpointPolicy.MUST_SAVE 2024-08-20T21:39:08.6113230Z >>> else: 2024-08-20T21:39:08.6113432Z >>> return CheckpointPolicy.PREFER_RECOMPUTE 2024-08-20T21:39:08.6113547Z >>> 2024-08-20T21:39:08.6113915Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2024-08-20T21:39:08.6114019Z >>> 2024-08-20T21:39:08.6114155Z >>> # or equivalently 2024-08-20T21:39:08.6114536Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save) 2024-08-20T21:39:08.6114633Z >>> 2024-08-20T21:39:08.6114757Z >>> def fn(x, y): 2024-08-20T21:39:08.6115033Z >>> return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y 2024-08-20T21:39:08.6115130Z >>> 2024-08-20T21:39:08.6115343Z >>> out = torch.utils.checkpoint.checkpoint( 2024-08-20T21:39:08.6115514Z >>> fn, x, y, 2024-08-20T21:39:08.6115639Z >>> use_reentrant=False, 2024-08-20T21:39:08.6115783Z >>> context_fn=context_fn, 2024-08-20T21:39:08.6115881Z >>> ) 2024-08-20T21:39:08.6115975Z 2024-08-20T21:39:08.6116415Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.6116508Z 2024-08-20T21:39:08.6116627Z warnings.warn(msg) 2024-08-20T21:39:08.6116734Z 2024-08-20T21:39:08.6116960Z --- Parse Warning: 96 / 101 --- 2024-08-20T21:39:08.6118564Z /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. 2024-08-20T21:39:08.6119015Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.6119109Z 2024-08-20T21:39:08.6119321Z Create a :class:`setuptools.Extension` for C++. 2024-08-20T21:39:08.6119415Z 2024-08-20T21:39:08.6119744Z Convenience method that creates a :class:`setuptools.Extension` with the 2024-08-20T21:39:08.6120074Z bare minimum (but often sufficient) arguments to build a C++ extension. 2024-08-20T21:39:08.6120169Z 2024-08-20T21:39:08.6120456Z All arguments are forwarded to the :class:`setuptools.Extension` 2024-08-20T21:39:08.6120669Z constructor. Full list arguments can be found at 2024-08-20T21:39:08.6121213Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2024-08-20T21:39:08.6121307Z 2024-08-20T21:39:08.6121488Z Example: 2024-08-20T21:39:08.6121615Z >>> # xdoctest: +SKIP 2024-08-20T21:39:08.6121833Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-08-20T21:39:08.6121982Z >>> from setuptools import setup 2024-08-20T21:39:08.6122291Z >>> from torch.utils.cpp_extension import BuildExtension, CppExtension 2024-08-20T21:39:08.6122413Z >>> setup( 2024-08-20T21:39:08.6122582Z ... name='extension', 2024-08-20T21:39:08.6122695Z ... ext_modules=[ 2024-08-20T21:39:08.6122830Z ... CppExtension( 2024-08-20T21:39:08.6123012Z ... name='extension', 2024-08-20T21:39:08.6123232Z ... sources=['extension.cpp'], 2024-08-20T21:39:08.6123459Z ... extra_compile_args=['-g'], 2024-08-20T21:39:08.6123744Z ... extra_link_flags=['-Wl,--no-as-needed', '-lm']) 2024-08-20T21:39:08.6123848Z ... ], 2024-08-20T21:39:08.6123972Z ... cmdclass={ 2024-08-20T21:39:08.6124184Z ... 'build_ext': BuildExtension 2024-08-20T21:39:08.6124283Z ... }) 2024-08-20T21:39:08.6124389Z 2024-08-20T21:39:08.6124810Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.6124903Z 2024-08-20T21:39:08.6125041Z warnings.warn(msg) 2024-08-20T21:39:08.6125133Z 2024-08-20T21:39:08.6125367Z --- Parse Warning: 97 / 101 --- 2024-08-20T21:39:08.6126822Z /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. 2024-08-20T21:39:08.6127255Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.6127362Z 2024-08-20T21:39:08.6127582Z Create a :class:`setuptools.Extension` for CUDA/C++. 2024-08-20T21:39:08.6127674Z 2024-08-20T21:39:08.6128021Z Convenience method that creates a :class:`setuptools.Extension` with the 2024-08-20T21:39:08.6128305Z bare minimum (but often sufficient) arguments to build a CUDA/C++ 2024-08-20T21:39:08.6128630Z extension. This includes the CUDA include path, library path and runtime 2024-08-20T21:39:08.6128745Z library. 2024-08-20T21:39:08.6128896Z 2024-08-20T21:39:08.6129183Z All arguments are forwarded to the :class:`setuptools.Extension` 2024-08-20T21:39:08.6129396Z constructor. Full list arguments can be found at 2024-08-20T21:39:08.6129935Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2024-08-20T21:39:08.6130043Z 2024-08-20T21:39:08.6130144Z Example: 2024-08-20T21:39:08.6130265Z >>> # xdoctest: +SKIP 2024-08-20T21:39:08.6130486Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-08-20T21:39:08.6130632Z >>> from setuptools import setup 2024-08-20T21:39:08.6130954Z >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension 2024-08-20T21:39:08.6131075Z >>> setup( 2024-08-20T21:39:08.6131253Z ... name='cuda_extension', 2024-08-20T21:39:08.6131369Z ... ext_modules=[ 2024-08-20T21:39:08.6131510Z ... CUDAExtension( 2024-08-20T21:39:08.6131722Z ... name='cuda_extension', 2024-08-20T21:39:08.6132034Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-08-20T21:39:08.6132296Z ... extra_compile_args={'cxx': ['-g'], 2024-08-20T21:39:08.6132542Z ... 'nvcc': ['-O2']}, 2024-08-20T21:39:08.6132858Z ... extra_link_flags=['-Wl,--no-as-needed', '-lcuda']) 2024-08-20T21:39:08.6132960Z ... ], 2024-08-20T21:39:08.6133075Z ... cmdclass={ 2024-08-20T21:39:08.6133296Z ... 'build_ext': BuildExtension 2024-08-20T21:39:08.6133395Z ... }) 2024-08-20T21:39:08.6133487Z 2024-08-20T21:39:08.6133676Z Compute capabilities: 2024-08-20T21:39:08.6133770Z 2024-08-20T21:39:08.6134216Z By default the extension will be compiled to run on all archs of the cards visible during the 2024-08-20T21:39:08.6134652Z building process of the extension, plus PTX. If down the road a new card is installed the 2024-08-20T21:39:08.6135136Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's 2024-08-20T21:39:08.6135664Z newer than the newest version for which your nvcc can build fully-compiled binaries, Pytorch 2024-08-20T21:39:08.6136098Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does 2024-08-20T21:39:08.6136260Z support (see below for details on PTX). 2024-08-20T21:39:08.6136364Z 2024-08-20T21:39:08.6136813Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which 2024-08-20T21:39:08.6136968Z CCs you want the extension to support: 2024-08-20T21:39:08.6137080Z 2024-08-20T21:39:08.6137336Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py`` 2024-08-20T21:39:08.6137706Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py`` 2024-08-20T21:39:08.6137813Z 2024-08-20T21:39:08.6138266Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified 2024-08-20T21:39:08.6138817Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >= 2024-08-20T21:39:08.6139373Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with 2024-08-20T21:39:08.6139901Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to 2024-08-20T21:39:08.6140456Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on 2024-08-20T21:39:08.6140983Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better 2024-08-20T21:39:08.6141440Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6, 2024-08-20T21:39:08.6142005Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but 2024-08-20T21:39:08.6142127Z "8.0 8.6" would be better. 2024-08-20T21:39:08.6142278Z 2024-08-20T21:39:08.6142823Z Note that while it's possible to include all supported archs, the more archs get included the 2024-08-20T21:39:08.6143254Z slower the building process will be, as it will build a separate kernel image for each arch. 2024-08-20T21:39:08.6143348Z 2024-08-20T21:39:08.6143950Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows. 2024-08-20T21:39:08.6144253Z To workaround the issue, move python binding logic to pure C++ file. 2024-08-20T21:39:08.6144360Z 2024-08-20T21:39:08.6144467Z Example use: 2024-08-20T21:39:08.6144592Z #include 2024-08-20T21:39:08.6144827Z at::Tensor SigmoidAlphaBlendForwardCuda(....) 2024-08-20T21:39:08.6144921Z 2024-08-20T21:39:08.6145024Z Instead of: 2024-08-20T21:39:08.6145172Z #include 2024-08-20T21:39:08.6145388Z torch::Tensor SigmoidAlphaBlendForwardCuda(...) 2024-08-20T21:39:08.6145484Z 2024-08-20T21:39:08.6145882Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460 2024-08-20T21:39:08.6146570Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48 2024-08-20T21:39:08.6146666Z 2024-08-20T21:39:08.6153037Z Relocatable device code linking: 2024-08-20T21:39:08.6153150Z 2024-08-20T21:39:08.6153580Z If you want to reference device symbols across compilation units (across object files), 2024-08-20T21:39:08.6154110Z the object files need to be built with `relocatable device code` (-rdc=true or -dc). 2024-08-20T21:39:08.6154764Z An exception to this rule is "dynamic parallelism" (nested kernel launches) which is not used a lot anymore. 2024-08-20T21:39:08.6155257Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it. 2024-08-20T21:39:08.6155829Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step 2024-08-20T21:39:08.6156147Z help reduce the protentional perf degradation of `-rdc`. 2024-08-20T21:39:08.6156404Z Note that it needs to be used at both steps to be useful. 2024-08-20T21:39:08.6156498Z 2024-08-20T21:39:08.6157178Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step. 2024-08-20T21:39:08.6157509Z There is also a case where `-dlink` is used without `-rdc`: 2024-08-20T21:39:08.6157959Z when an extension is linked against a static lib containing rdc-compiled objects 2024-08-20T21:39:08.6158396Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem). 2024-08-20T21:39:08.6158507Z 2024-08-20T21:39:08.6158803Z Note: Ninja is required to build a CUDA Extension with RDC linking. 2024-08-20T21:39:08.6158896Z 2024-08-20T21:39:08.6159015Z Example: 2024-08-20T21:39:08.6159141Z >>> # xdoctest: +SKIP 2024-08-20T21:39:08.6159350Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-08-20T21:39:08.6159484Z >>> CUDAExtension( 2024-08-20T21:39:08.6159675Z ... name='cuda_extension', 2024-08-20T21:39:08.6159990Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-08-20T21:39:08.6160105Z ... dlink=True, 2024-08-20T21:39:08.6160264Z ... dlink_libraries=["dlink_lib"], 2024-08-20T21:39:08.6160508Z ... extra_compile_args={'cxx': ['-g'], 2024-08-20T21:39:08.6160764Z ... 'nvcc': ['-O2', '-rdc=true']}) 2024-08-20T21:39:08.6160861Z 2024-08-20T21:39:08.6161301Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.6161394Z 2024-08-20T21:39:08.6161512Z warnings.warn(msg) 2024-08-20T21:39:08.6161621Z 2024-08-20T21:39:08.6161848Z --- Parse Warning: 98 / 101 --- 2024-08-20T21:39:08.6163256Z /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. 2024-08-20T21:39:08.6163782Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.6163874Z 2024-08-20T21:39:08.6164147Z Load a PyTorch C++ extension just-in-time (JIT). 2024-08-20T21:39:08.6164240Z 2024-08-20T21:39:08.6164541Z To load an extension, a Ninja build file is emitted, which is used to 2024-08-20T21:39:08.6164847Z compile the given sources into a dynamic library. This library is 2024-08-20T21:39:08.6165156Z subsequently loaded into the current Python process as a module and 2024-08-20T21:39:08.6165331Z returned from this function, ready for use. 2024-08-20T21:39:08.6165441Z 2024-08-20T21:39:08.6165741Z By default, the directory to which the build file is emitted and the 2024-08-20T21:39:08.6166065Z resulting library compiled to is ``/torch_extensions/``, where 2024-08-20T21:39:08.6166381Z ```` is the temporary folder on the current platform and ```` 2024-08-20T21:39:08.6166702Z the name of the extension. This location can be overridden in two ways. 2024-08-20T21:39:08.6167015Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it 2024-08-20T21:39:08.6167315Z replaces ``/torch_extensions`` and all extensions will be compiled 2024-08-20T21:39:08.6167622Z into subfolders of this directory. Second, if the ``build_directory`` 2024-08-20T21:39:08.6167969Z argument to this function is supplied, it overrides the entire path, i.e. 2024-08-20T21:39:08.6168306Z the library will be compiled into that folder directly. 2024-08-20T21:39:08.6168401Z 2024-08-20T21:39:08.6168723Z To compile the sources, the default system compiler (``c++``) is used, 2024-08-20T21:39:08.6169072Z which can be overridden by setting the ``CXX`` environment variable. To pass 2024-08-20T21:39:08.6169383Z additional arguments to the compilation process, ``extra_cflags`` or 2024-08-20T21:39:08.6169712Z ``extra_ldflags`` can be provided. For example, to compile your extension 2024-08-20T21:39:08.6170077Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use 2024-08-20T21:39:08.6170303Z ``extra_cflags`` to pass further include directories. 2024-08-20T21:39:08.6170396Z 2024-08-20T21:39:08.6170725Z CUDA support with mixed compilation is provided. Simply pass CUDA source 2024-08-20T21:39:08.6171017Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be 2024-08-20T21:39:08.6171366Z detected and compiled with nvcc rather than the C++ compiler. This includes 2024-08-20T21:39:08.6171665Z passing the CUDA lib64 directory as a library directory, and linking 2024-08-20T21:39:08.6171889Z ``cudart``. You can pass additional flags to nvcc via 2024-08-20T21:39:08.6172180Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various 2024-08-20T21:39:08.6172514Z heuristics for finding the CUDA install directory are used, which usually 2024-08-20T21:39:08.6172841Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the 2024-08-20T21:39:08.6172950Z safest option. 2024-08-20T21:39:08.6173044Z 2024-08-20T21:39:08.6173159Z Args: 2024-08-20T21:39:08.6173474Z name: The name of the extension to build. This MUST be the same as the 2024-08-20T21:39:08.6173632Z name of the pybind11 module! 2024-08-20T21:39:08.6173924Z sources: A list of relative or absolute paths to C++ source files. 2024-08-20T21:39:08.6174240Z extra_cflags: optional list of compiler flags to forward to the build. 2024-08-20T21:39:08.6174557Z extra_cuda_cflags: optional list of compiler flags to forward to nvcc 2024-08-20T21:39:08.6174694Z when building CUDA sources. 2024-08-20T21:39:08.6174997Z extra_ldflags: optional list of linker flags to forward to the build. 2024-08-20T21:39:08.6175370Z extra_include_paths: optional list of include directories to forward 2024-08-20T21:39:08.6175484Z to the build. 2024-08-20T21:39:08.6175728Z build_directory: optional path to use as build workspace. 2024-08-20T21:39:08.6175995Z verbose: If ``True``, turns on verbose logging of load steps. 2024-08-20T21:39:08.6176298Z with_cuda: Determines whether CUDA headers and libraries are added to 2024-08-20T21:39:08.6176543Z the build. If set to ``None`` (default), this value is 2024-08-20T21:39:08.6176816Z automatically determined based on the existence of ``.cu`` or 2024-08-20T21:39:08.6177078Z ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers 2024-08-20T21:39:08.6177235Z and libraries to be included. 2024-08-20T21:39:08.6177521Z is_python_module: If ``True`` (default), imports the produced shared 2024-08-20T21:39:08.6177793Z library as a Python module. If ``False``, behavior depends on 2024-08-20T21:39:08.6177932Z ``is_standalone``. 2024-08-20T21:39:08.6178225Z is_standalone: If ``False`` (default) loads the constructed extension 2024-08-20T21:39:08.6178515Z into the process as a plain dynamic library. If ``True``, build a 2024-08-20T21:39:08.6178658Z standalone executable. 2024-08-20T21:39:08.6178751Z 2024-08-20T21:39:08.6178853Z Returns: 2024-08-20T21:39:08.6179022Z If ``is_python_module`` is ``True``: 2024-08-20T21:39:08.6179271Z Returns the loaded PyTorch extension as a Python module. 2024-08-20T21:39:08.6179380Z 2024-08-20T21:39:08.6179739Z If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``: 2024-08-20T21:39:08.6180042Z Returns nothing. (The shared library is loaded into the process as 2024-08-20T21:39:08.6180171Z a side effect.) 2024-08-20T21:39:08.6180269Z 2024-08-20T21:39:08.6180409Z If ``is_standalone`` is ``True``. 2024-08-20T21:39:08.6180716Z Return the path to the executable. (On Windows, TORCH_LIB_PATH is 2024-08-20T21:39:08.6180968Z added to the PATH environment variable as a side effect.) 2024-08-20T21:39:08.6181063Z 2024-08-20T21:39:08.6181178Z Example: 2024-08-20T21:39:08.6181298Z >>> # xdoctest: +SKIP 2024-08-20T21:39:08.6181490Z >>> from torch.utils.cpp_extension import load 2024-08-20T21:39:08.6181618Z >>> module = load( 2024-08-20T21:39:08.6181790Z ... name='extension', 2024-08-20T21:39:08.6182079Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-08-20T21:39:08.6182277Z ... extra_cflags=['-O2'], 2024-08-20T21:39:08.6182393Z ... verbose=True) 2024-08-20T21:39:08.6182488Z 2024-08-20T21:39:08.6182923Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.6183018Z 2024-08-20T21:39:08.6183150Z warnings.warn(msg) 2024-08-20T21:39:08.6183246Z 2024-08-20T21:39:08.6183470Z --- Parse Warning: 99 / 101 --- 2024-08-20T21:39:08.6184937Z /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. 2024-08-20T21:39:08.6185372Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.6185468Z 2024-08-20T21:39:08.6185859Z Load a PyTorch C++ extension just-in-time (JIT) from string sources. 2024-08-20T21:39:08.6185952Z 2024-08-20T21:39:08.6186284Z This function behaves exactly like :func:`load`, but takes its sources as 2024-08-20T21:39:08.6186617Z strings rather than filenames. These strings are stored to files in the 2024-08-20T21:39:08.6186913Z build directory, after which the behavior of :func:`load_inline` is 2024-08-20T21:39:08.6187055Z identical to :func:`load`. 2024-08-20T21:39:08.6187213Z 2024-08-20T21:39:08.6187316Z See `the 2024-08-20T21:39:08.6187777Z tests `_ 2024-08-20T21:39:08.6187948Z for good examples of using this function. 2024-08-20T21:39:08.6188040Z 2024-08-20T21:39:08.6188468Z Sources may omit two required parts of a typical non-inline C++ extension: 2024-08-20T21:39:08.6188808Z the necessary header includes, as well as the (pybind11) binding code. More 2024-08-20T21:39:08.6189140Z precisely, strings passed to ``cpp_sources`` are first concatenated into a 2024-08-20T21:39:08.6189428Z single ``.cpp`` file. This file is then prepended with ``#include 2024-08-20T21:39:08.6189550Z ``. 2024-08-20T21:39:08.6189643Z 2024-08-20T21:39:08.6189972Z Furthermore, if the ``functions`` argument is supplied, bindings will be 2024-08-20T21:39:08.6190289Z automatically generated for each function specified. ``functions`` can 2024-08-20T21:39:08.6190635Z either be a list of function names, or a dictionary mapping from function 2024-08-20T21:39:08.6190964Z names to docstrings. If a list is given, the name of each function is used 2024-08-20T21:39:08.6191075Z as its docstring. 2024-08-20T21:39:08.6191184Z 2024-08-20T21:39:08.6191496Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu`` 2024-08-20T21:39:08.6191736Z file and prepended with ``torch/types.h``, ``cuda.h`` and 2024-08-20T21:39:08.6192049Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled 2024-08-20T21:39:08.6192353Z separately, but ultimately linked into a single library. Note that no 2024-08-20T21:39:08.6192745Z bindings are generated for functions in ``cuda_sources`` per se. To bind 2024-08-20T21:39:08.6193088Z to a CUDA kernel, you must create a C++ function that calls it, and either 2024-08-20T21:39:08.6193397Z declare or define this C++ function in one of the ``cpp_sources`` (and 2024-08-20T21:39:08.6193545Z include its name in ``functions``). 2024-08-20T21:39:08.6193658Z 2024-08-20T21:39:08.6193920Z See :func:`load` for a description of arguments omitted below. 2024-08-20T21:39:08.6194028Z 2024-08-20T21:39:08.6194127Z Args: 2024-08-20T21:39:08.6194435Z cpp_sources: A string, or list of strings, containing C++ source code. 2024-08-20T21:39:08.6194763Z cuda_sources: A string, or list of strings, containing CUDA source code. 2024-08-20T21:39:08.6195052Z functions: A list of function names for which to generate function 2024-08-20T21:39:08.6195352Z bindings. If a dictionary is given, it should map function names to 2024-08-20T21:39:08.6195618Z docstrings (which are otherwise just the function names). 2024-08-20T21:39:08.6196254Z with_cuda: Determines whether CUDA headers and libraries are added to 2024-08-20T21:39:08.6196485Z the build. If set to ``None`` (default), this value is 2024-08-20T21:39:08.6196781Z automatically determined based on whether ``cuda_sources`` is 2024-08-20T21:39:08.6196994Z provided. Set it to ``True`` to force CUDA headers 2024-08-20T21:39:08.6197155Z and libraries to be included. 2024-08-20T21:39:08.6197438Z with_pytorch_error_handling: Determines whether pytorch error and 2024-08-20T21:39:08.6197721Z warning macros are handled by pytorch instead of pybind. To do 2024-08-20T21:39:08.6198051Z this, each function ``foo`` is called via an intermediary ``_safe_foo`` 2024-08-20T21:39:08.6198431Z function. This redirection might cause issues in obscure cases 2024-08-20T21:39:08.6198716Z of cpp. This flag should be set to ``False`` when this redirect 2024-08-20T21:39:08.6198847Z causes issues. 2024-08-20T21:39:08.6198940Z 2024-08-20T21:39:08.6199044Z Example: 2024-08-20T21:39:08.6199265Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-08-20T21:39:08.6199645Z >>> from torch.utils.cpp_extension import load_inline 2024-08-20T21:39:08.6199759Z >>> source = """ 2024-08-20T21:39:08.6199991Z at::Tensor sin_add(at::Tensor x, at::Tensor y) { 2024-08-20T21:39:08.6200121Z return x.sin() + y.sin(); 2024-08-20T21:39:08.6200232Z } 2024-08-20T21:39:08.6200329Z """ 2024-08-20T21:39:08.6200613Z >>> module = load_inline(name='inline_extension', 2024-08-20T21:39:08.6200797Z ... cpp_sources=[source], 2024-08-20T21:39:08.6201025Z ... functions=['sin_add']) 2024-08-20T21:39:08.6201119Z 2024-08-20T21:39:08.6201240Z .. note:: 2024-08-20T21:39:08.6201530Z By default, the Ninja backend uses #CPUS + 2 workers to build the 2024-08-20T21:39:08.6201823Z extension. This may use up too many resources on some systems. One 2024-08-20T21:39:08.6202159Z can control the number of workers by setting the `MAX_JOBS` environment 2024-08-20T21:39:08.6202372Z variable to a non-negative number. 2024-08-20T21:39:08.6202466Z 2024-08-20T21:39:08.6202903Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.6202995Z 2024-08-20T21:39:08.6203113Z warnings.warn(msg) 2024-08-20T21:39:08.6203220Z 2024-08-20T21:39:08.6203451Z --- Parse Warning: 100 / 101 --- 2024-08-20T21:39:08.6205013Z /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. 2024-08-20T21:39:08.6205531Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.6205631Z 2024-08-20T21:39:08.6206075Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark. 2024-08-20T21:39:08.6206171Z 2024-08-20T21:39:08.6206578Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible 2024-08-20T21:39:08.6206952Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference 2024-08-20T21:39:08.6207300Z server like load. It can emulate multiple calling threads to a single module 2024-08-20T21:39:08.6207669Z provided. In the future we plan to enhance this component to support inter and 2024-08-20T21:39:08.6208094Z intra-op parallelism as well as multiple models running in a single process. 2024-08-20T21:39:08.6208188Z 2024-08-20T21:39:08.6208568Z Please note that even though nn.Module is supported, it might incur an overhead 2024-08-20T21:39:08.6208905Z from the need to hold GIL every time we execute Python code or pass around 2024-08-20T21:39:08.6209247Z inputs as Python objects. As soon as you have a ScriptModule version of your 2024-08-20T21:39:08.6209593Z model for inference deployment it is better to switch to using it in this 2024-08-20T21:39:08.6209698Z benchmark. 2024-08-20T21:39:08.6209796Z 2024-08-20T21:39:08.6209917Z Example:: 2024-08-20T21:39:08.6210009Z 2024-08-20T21:39:08.6210175Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:39:08.6210386Z >>> from torch.utils import ThroughputBenchmark 2024-08-20T21:39:08.6210562Z >>> bench = ThroughputBenchmark(my_module) 2024-08-20T21:39:08.6210857Z >>> # Pre-populate benchmark's data set with the inputs 2024-08-20T21:39:08.6211002Z >>> for input in inputs: 2024-08-20T21:39:08.6211325Z ... # Both args and kwargs work, same as any PyTorch Module / ScriptModule 2024-08-20T21:39:08.6211520Z ... bench.add_input(input[0], x2=input[1]) 2024-08-20T21:39:08.6211795Z >>> # Inputs supplied above are randomly used during the execution 2024-08-20T21:39:08.6211930Z >>> stats = bench.benchmark( 2024-08-20T21:39:08.6212075Z ... num_calling_threads=4, 2024-08-20T21:39:08.6212205Z ... num_warmup_iters = 100, 2024-08-20T21:39:08.6212432Z ... num_iters = 1000, 2024-08-20T21:39:08.6212544Z ... ) 2024-08-20T21:39:08.6212793Z >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms)) 2024-08-20T21:39:08.6213043Z >>> print("Number of iterations: {}".format(stats.num_iters)) 2024-08-20T21:39:08.6213150Z 2024-08-20T21:39:08.6213572Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.6213665Z 2024-08-20T21:39:08.6213798Z warnings.warn(msg) 2024-08-20T21:39:08.6213891Z 2024-08-20T21:39:08.6214120Z --- Parse Warning: 101 / 101 --- 2024-08-20T21:39:08.6215649Z /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. 2024-08-20T21:39:08.6216088Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:39:08.6216384Z Sampler that restricts data loading to a subset of the dataset. 2024-08-20T21:39:08.6216478Z 2024-08-20T21:39:08.6216661Z It is especially useful in conjunction with 2024-08-20T21:39:08.6217036Z :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each 2024-08-20T21:39:08.6217418Z process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a 2024-08-20T21:39:08.6217740Z :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the 2024-08-20T21:39:08.6217928Z original dataset that is exclusive to it. 2024-08-20T21:39:08.6218022Z 2024-08-20T21:39:08.6218141Z .. note:: 2024-08-20T21:39:08.6218552Z Dataset is assumed to be of constant size and that any instance of it always 2024-08-20T21:39:08.6218745Z returns the same elements in the same order. 2024-08-20T21:39:08.6218854Z 2024-08-20T21:39:08.6218954Z Args: 2024-08-20T21:39:08.6219118Z dataset: Dataset used for sampling. 2024-08-20T21:39:08.6219434Z num_replicas (int, optional): Number of processes participating in 2024-08-20T21:39:08.6219777Z distributed training. By default, :attr:`world_size` is retrieved from the 2024-08-20T21:39:08.6219924Z current distributed group. 2024-08-20T21:39:08.6220283Z rank (int, optional): Rank of the current process within :attr:`num_replicas`. 2024-08-20T21:39:08.6220577Z By default, :attr:`rank` is retrieved from the current distributed 2024-08-20T21:39:08.6220683Z group. 2024-08-20T21:39:08.6221021Z shuffle (bool, optional): If ``True`` (default), sampler will shuffle the 2024-08-20T21:39:08.6221128Z indices. 2024-08-20T21:39:08.6221422Z seed (int, optional): random seed used to shuffle the sampler if 2024-08-20T21:39:08.6221704Z :attr:`shuffle=True`. This number should be identical across all 2024-08-20T21:39:08.6221936Z processes in the distributed group. Default: ``0``. 2024-08-20T21:39:08.6222257Z drop_last (bool, optional): if ``True``, then the sampler will drop the 2024-08-20T21:39:08.6222548Z tail of the data to make it evenly divisible across the number of 2024-08-20T21:39:08.6222835Z replicas. If ``False``, the sampler will add extra indices to make 2024-08-20T21:39:08.6223146Z the data evenly divisible across the replicas. Default: ``False``. 2024-08-20T21:39:08.6223241Z 2024-08-20T21:39:08.6223350Z .. warning:: 2024-08-20T21:39:08.6223630Z In distributed mode, calling the :meth:`set_epoch` method at 2024-08-20T21:39:08.6224001Z the beginning of each epoch **before** creating the :class:`DataLoader` iterator 2024-08-20T21:39:08.6224386Z is necessary to make shuffling work properly across multiple epochs. Otherwise, 2024-08-20T21:39:08.6224552Z the same ordering will be always used. 2024-08-20T21:39:08.6224704Z 2024-08-20T21:39:08.6224829Z Example:: 2024-08-20T21:39:08.6224922Z 2024-08-20T21:39:08.6225048Z >>> # xdoctest: +SKIP 2024-08-20T21:39:08.6225365Z >>> sampler = DistributedSampler(dataset) if is_distributed else None 2024-08-20T21:39:08.6225606Z >>> loader = DataLoader(dataset, shuffle=(sampler is None), 2024-08-20T21:39:08.6225770Z ... sampler=sampler) 2024-08-20T21:39:08.6225973Z >>> for epoch in range(start_epoch, n_epochs): 2024-08-20T21:39:08.6226103Z ... if is_distributed: 2024-08-20T21:39:08.6226262Z ... sampler.set_epoch(epoch) 2024-08-20T21:39:08.6226394Z ... train(loader) 2024-08-20T21:39:08.6226492Z 2024-08-20T21:39:08.6226916Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:39:08.6227027Z 2024-08-20T21:39:08.6227145Z warnings.warn(msg) 2024-08-20T21:39:08.6227263Z 2024-08-20T21:39:08.6227381Z  2024-08-20T21:39:08.6227595Z === Found 9 run-time warnings === 2024-08-20T21:39:08.6227830Z --- Runtime Warning: 1 / 9 --- 2024-08-20T21:39:08.6228188Z example = 2024-08-20T21:39:08.6230387Z /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.) 2024-08-20T21:39:08.6230607Z return super().refine_names(names) 2024-08-20T21:39:08.6230700Z 2024-08-20T21:39:08.6230928Z --- Runtime Warning: 2 / 9 --- 2024-08-20T21:39:08.6231366Z example = 2024-08-20T21:39:08.6232413Z /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. 2024-08-20T21:39:08.6232852Z Overriding a previously registered kernel for the same operator and the same dispatch key 2024-08-20T21:39:08.6233205Z operator: aten::div.Tensor(Tensor self, Tensor other) -> Tensor 2024-08-20T21:39:08.6233622Z registered at /var/lib/jenkins/workspace/build/aten/src/ATen/RegisterSchema.cpp:6 2024-08-20T21:39:08.6233753Z dispatch key: CPU 2024-08-20T21:39:08.6234355Z previous kernel: registered at /var/lib/jenkins/workspace/aten/src/ATen/LegacyBatchingRegistrations.cpp:1079 2024-08-20T21:39:08.6235134Z new kernel: registered at /dev/null:811 (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/core/dispatch/OperatorEntry.cpp:162.) 2024-08-20T21:39:08.6235427Z impl_fn(self.ns, name.split("::")[-1], dispatch_key) 2024-08-20T21:39:08.6235523Z 2024-08-20T21:39:08.6235759Z --- Runtime Warning: 3 / 9 --- 2024-08-20T21:39:08.6236089Z example = 2024-08-20T21:39:08.6237878Z /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.) 2024-08-20T21:39:08.6238338Z return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None) 2024-08-20T21:39:08.6238434Z 2024-08-20T21:39:08.6238668Z --- Runtime Warning: 4 / 9 --- 2024-08-20T21:39:08.6239033Z example = 2024-08-20T21:39:08.6241717Z :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.) 2024-08-20T21:39:08.6241892Z 2024-08-20T21:39:08.6242113Z --- Runtime Warning: 5 / 9 --- 2024-08-20T21:39:08.6242546Z example = 2024-08-20T21:39:08.6244955Z /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 2024-08-20T21:39:08.6245181Z new_node = root_const_gm.graph.get_attr(in_node.target) 2024-08-20T21:39:08.6245289Z 2024-08-20T21:39:08.6245512Z --- Runtime Warning: 6 / 9 --- 2024-08-20T21:39:08.6245918Z example = 2024-08-20T21:39:08.6247683Z /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) 2024-08-20T21:39:08.6247797Z warnings.warn( 2024-08-20T21:39:08.6247906Z 2024-08-20T21:39:08.6248127Z --- Runtime Warning: 7 / 9 --- 2024-08-20T21:39:08.6248645Z example = 2024-08-20T21:39:08.6250399Z /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) 2024-08-20T21:39:08.6250514Z warnings.warn( 2024-08-20T21:39:08.6250624Z 2024-08-20T21:39:08.6250842Z --- Runtime Warning: 8 / 9 --- 2024-08-20T21:39:08.6251231Z example = 2024-08-20T21:39:08.6252570Z /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`. 2024-08-20T21:39:08.6252729Z WeightNorm.apply(module, name, dim) 2024-08-20T21:39:08.6252820Z 2024-08-20T21:39:08.6253058Z --- Runtime Warning: 9 / 9 --- 2024-08-20T21:39:08.6253486Z example = 2024-08-20T21:39:08.6254807Z /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`. 2024-08-20T21:39:08.6254982Z WeightNorm.apply(module, name, dim) 2024-08-20T21:39:08.6255075Z 2024-08-20T21:39:08.6255483Z === 335 passed, 360 skipped, 110 warnings in 13.65 seconds === 2024-08-20T21:39:08.6255735Z Running test_nn 1/2 ... [2024-08-20 21:39:08.367987] 2024-08-20T21:39:08.6255880Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:39:08.6257283Z 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 21:39:08.368422] 2024-08-20T21:47:17.8650461Z 2024-08-20T21:47:17.8672754Z test_nn 1/2 was successful, full logs can be found in artifacts with path test/test-reports/test_nn_1.2_14dfb2c4bdc3ecf1_.log 2024-08-20T21:47:17.9493899Z 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 2024-08-20T21:47:18.0199466Z 2024-08-20T21:47:18.0200061Z Running test_cpp_extensions_jit 1/1 ... [2024-08-20 21:47:17.867276] 2024-08-20T21:47:18.0200687Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:47:18.0202482Z 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 21:47:17.867643] 2024-08-20T21:47:55.2879592Z 2024-08-20T21:47:55.2881582Z 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_89056c0a9eea247f_.log 2024-08-20T21:47:55.2896179Z 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 2024-08-20T21:47:55.2910430Z 2024-08-20T21:47:55.2910787Z Running test_torch 1/1 ... [2024-08-20 21:47:55.288133] 2024-08-20T21:47:55.2911318Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:47:55.2913175Z 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 21:47:55.288494] 2024-08-20T21:53:05.6299926Z 2024-08-20T21:53:05.6301612Z test_torch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_torch_1.1_33c2fa06d052df46_.log 2024-08-20T21:53:05.6737428Z Running 1021 items in this shard: test/test_torch.py::TestBasicVitalSigns::test_basic_vitals, test/test_torch.py::TestBasicVitalSigns::test_basic_vitals_read_write, test/test_torch.py::TestBasicVitalSigns::test_dataloader_vitals, test/test_torch.py::TestTorch::test_RNGState, test/test_torch.py::TestTorch::test_RNGStateAliasing, test/test_torch.py::TestTorch::test_RNG_after_pickle, test/test_torch.py::TestTorch::test_Size, test/test_torch.py::TestTorch::test_Size_iter, test/test_torch.py::TestTorch::test_Size_scalar, test/test_torch.py::TestTorch::test_add_meta_scalar, test/test_torch.py::TestTorch::test_allow_tensor_metadata_change, test/test_torch.py::TestTorch::test_apply, test/test_torch.py::TestTorch::test_as_subclass, test/test_torch.py::TestTorch::test_assert_async, test/test_torch.py::TestTorch::test_backward_hooks_traverse, test/test_torch.py::TestTorch::test_batch_norm_cpu_inference, test/test_torch.py::TestTorch::test_bf16_supported_on_cpu, test/test_torch.py::TestTorch::test_bmm_multithreaded, test/test_torch.py::TestTorch::test_boxMullerState, test/test_torch.py::TestTorch::test_cat_neg_dim, test/test_torch.py::TestTorch::test_check, test/test_torch.py::TestTorch::test_chunk_neg_dim, test/test_torch.py::TestTorch::test_conj_neg_tolist, test/test_torch.py::TestTorch::test_contains, test/test_torch.py::TestTorch::test_copy_broadcast, test/test_torch.py::TestTorch::test_copy_dtypes, test/test_torch.py::TestTorch::test_copy_float16, test/test_torch.py::TestTorch::test_copy_many_to_one, test/test_torch.py::TestTorch::test_copy_transpose, test/test_torch.py::TestTorch::test_cuda_not_built, test/test_torch.py::TestTorch::test_cummax_neg_dim, test/test_torch.py::TestTorch::test_cummin_neg_dim, test/test_torch.py::TestTorch::test_cumprod_neg_dim, test/test_torch.py::TestTorch::test_cumsum_neg_dim, test/test_torch.py::TestTorch::test_cxx_flags, test/test_torch.py::TestTorch::test_data_ptr_of_empty_tensor_with_storage, test/test_torch.py::TestTorch::test_data_ptr_of_empty_view_with_storage, test/test_torch.py::TestTorch::test_deepcopy_gradient, test/test_torch.py::TestTorch::test_deepcopy_parameter, test/test_torch.py::TestTorch::test_deterministic_fill_uninitialized_memory, test/test_torch.py::TestTorch::test_deterministic_flag, test/test_torch.py::TestTorch::test_device, test/test_torch.py::TestTorch::test_dim_order, test/test_torch.py::TestTorch::test_dir, test/test_torch.py::TestTorch::test_doc, test/test_torch.py::TestTorch::test_doc_template, test/test_torch.py::TestTorch::test_dot_data_use, test/test_torch.py::TestTorch::test_dtype_is_signed, test/test_torch.py::TestTorch::test_element_size, test/test_torch.py::TestTorch::test_empty_meta, test/test_torch.py::TestTorch::test_empty_storage_view, test/test_torch.py::TestTorch::test_equal, test/test_torch.py::TestTorch::test_error_msg_type_translation, test/test_torch.py::TestTorch::test_fill_diagonal, test/test_torch.py::TestTorch::test_format_scalar_meta, test/test_torch.py::TestTorch::test_from_buffer, test/test_torch.py::TestTorch::test_from_file, test/test_torch.py::TestTorch::test_gather_neg_dim, test/test_torch.py::TestTorch::test_generator_cpu, test/test_torch.py::TestTorch::test_get_cpu_capability, test/test_torch.py::TestTorch::test_has_internal_overlap, test/test_torch.py::TestTorch::test_has_storage, test/test_torch.py::TestTorch::test_index_add, test/test_torch.py::TestTorch::test_index_add_all_dtypes, test/test_torch.py::TestTorch::test_index_add_cornercase, test/test_torch.py::TestTorch::test_index_add_correctness, test/test_torch.py::TestTorch::test_index_add_neg_dim, test/test_torch.py::TestTorch::test_index_copy_neg_dim, test/test_torch.py::TestTorch::test_index_fill_neg_dim, test/test_torch.py::TestTorch::test_index_select_neg_dim, test/test_torch.py::TestTorch::test_invalid_arg_error_handling, test/test_torch.py::TestTorch::test_invalid_generator_raises, test/test_torch.py::TestTorch::test_is_nonzero, test/test_torch.py::TestTorch::test_is_same_size, test/test_torch.py::TestTorch::test_iter, test/test_torch.py::TestTorch::test_kthvalue_neg_dim, test/test_torch.py::TestTorch::test_linspace_logspace, test/test_torch.py::TestTorch::test_logcumsumexp_neg_dim, test/test_torch.py::TestTorch::test_manual_seed, test/test_torch.py::TestTorch::test_map, test/test_torch.py::TestTorch::test_map2, test/test_torch.py::TestTorch::test_max_neg_dim, test/test_torch.py::TestTorch::test_mean_neg_dim, test/test_torch.py::TestTorch::test_median_neg_dim, test/test_torch.py::TestTorch::test_memory_format, test/test_torch.py::TestTorch::test_memory_format_contiguous_returns_same_tensor_if_already_satisfies, test/test_torch.py::TestTorch::test_memory_format_empty, test/test_torch.py::TestTorch::test_min_neg_dim, test/test_torch.py::TestTorch::test_mode_neg_dim, test/test_torch.py::TestTorch::test_multinomial_invalid_probs, test/test_torch.py::TestTorch::test_nanmedian_neg_dim, test/test_torch.py::TestTorch::test_narrow_neg_dim, test/test_torch.py::TestTorch::test_nbytes, test/test_torch.py::TestTorch::test_ndim, test/test_torch.py::TestTorch::test_new, test/test_torch.py::TestTorch::test_newaxis_numpy_comparison, test/test_torch.py::TestTorch::test_newindex, test/test_torch.py::TestTorch::test_no_cuda_monkeypatch, test/test_torch.py::TestTorch::test_norm_neg_dim, test/test_torch.py::TestTorch::test_normal_shape, test/test_torch.py::TestTorch::test_numel, test/test_torch.py::TestTorch::test_parallel_info, test/test_torch.py::TestTorch::test_parsing_double, test/test_torch.py::TestTorch::test_parsing_int64, test/test_torch.py::TestTorch::test_parsing_intlist, test/test_torch.py::TestTorch::test_permute, test/test_torch.py::TestTorch::test_pickle, test/test_torch.py::TestTorch::test_pickle_dtype, test/test_torch.py::TestTorch::test_pickle_function, test/test_torch.py::TestTorch::test_pickle_generator, test/test_torch.py::TestTorch::test_pickle_parameter, test/test_torch.py::TestTorch::test_pickle_parameter_no_requires_grad, test/test_torch.py::TestTorch::test_pickle_size, test/test_torch.py::TestTorch::test_pin_memory, test/test_torch.py::TestTorch::test_print, test/test_torch.py::TestTorch::test_prod_neg_dim, test/test_torch.py::TestTorch::test_pyobj_preserved, test/test_torch.py::TestTorch::test_qengine, test/test_torch.py::TestTorch::test_renorm_neg_dim, test/test_torch.py::TestTorch::test_resizable, test/test_torch.py::TestTorch::test_reversed, test/test_torch.py::TestTorch::test_scatter_neg_dim, test/test_torch.py::TestTorch::test_select_neg_dim, test/test_torch.py::TestTorch::test_set_flush_denormal, test/test_torch.py::TestTorch::test_setting_real_imag_to_a_number, test/test_torch.py::TestTorch::test_show_config, test/test_torch.py::TestTorch::test_size_neg_dim, test/test_torch.py::TestTorch::test_size_stride, test/test_torch.py::TestTorch::test_sizeof, test/test_torch.py::TestTorch::test_slice, test/test_torch.py::TestTorch::test_slow_test, test/test_torch.py::TestTorch::test_sobolengine_bounds, test/test_torch.py::TestTorch::test_sobolengine_bounds_scrambled, test/test_torch.py::TestTorch::test_sobolengine_continuing, test/test_torch.py::TestTorch::test_sobolengine_continuing_scrambled, test/test_torch.py::TestTorch::test_sobolengine_default_dtype, test/test_torch.py::TestTorch::test_sobolengine_distribution, test/test_torch.py::TestTorch::test_sobolengine_distribution_scrambled, test/test_torch.py::TestTorch::test_sobolengine_draw, test/test_torch.py::TestTorch::test_sobolengine_draw_base2, test/test_torch.py::TestTorch::test_sobolengine_draw_base2_scrambled, test/test_torch.py::TestTorch::test_sobolengine_draw_scrambled, test/test_torch.py::TestTorch::test_sobolengine_fast_forward, test/test_torch.py::TestTorch::test_sobolengine_fast_forward_scrambled, test/test_torch.py::TestTorch::test_sobolengine_first_point, test/test_torch.py::TestTorch::test_sobolengine_high_dim, test/test_torch.py::TestTorch::test_sobolengine_raise, test/test_torch.py::TestTorch::test_sobolengine_reset, test/test_torch.py::TestTorch::test_sobolengine_reset_scrambled, test/test_torch.py::TestTorch::test_sort_neg_dim, test/test_torch.py::TestTorch::test_split_neg_dim, test/test_torch.py::TestTorch::test_split_with_sizes_copy_out, test/test_torch.py::TestTorch::test_squeeze_neg_dim, test/test_torch.py::TestTorch::test_std_neg_dim, test/test_torch.py::TestTorch::test_storage_base_init, test/test_torch.py::TestTorch::test_storage_base_new, test/test_torch.py::TestTorch::test_storage_byteswap, test/test_torch.py::TestTorch::test_storage_casts, test/test_torch.py::TestTorch::test_storage_cycle_via_dict, test/test_torch.py::TestTorch::test_storage_cycle_via_slots, test/test_torch.py::TestTorch::test_storage_dead_weak_ref, test/test_torch.py::TestTorch::test_storage_dealloc, test/test_torch.py::TestTorch::test_storage_dealloc_resurrected, test/test_torch.py::TestTorch::test_storage_dealloc_subclass_resurrected, test/test_torch.py::TestTorch::test_storage_dealloc_subclass_zombie, test/test_torch.py::TestTorch::test_storage_dict_dealloc, test/test_torch.py::TestTorch::test_storage_error, test/test_torch.py::TestTorch::test_storage_error_no_attribute, test/test_torch.py::TestTorch::test_storage_finalizer_dealloc, test/test_torch.py::TestTorch::test_storage_fix_weakref_no_leak, test/test_torch.py::TestTorch::test_storage_from_tensor_dealloc, test/test_torch.py::TestTorch::test_storage_from_tensor_dealloc_resurrected, test/test_torch.py::TestTorch::test_storage_from_tensor_dealloc_zombie, test/test_torch.py::TestTorch::test_storage_preserve_nonhermetic_in_hermetic_context, test/test_torch.py::TestTorch::test_storage_resurrected_weak_ref, test/test_torch.py::TestTorch::test_storage_slot_dealloc, test/test_torch.py::TestTorch::test_storage_weakref_dealloc, test/test_torch.py::TestTorch::test_structseq_repr, test/test_torch.py::TestTorch::test_subclass_preserved, test/test_torch.py::TestTorch::test_subclass_tensors, test/test_torch.py::TestTorch::test_sum_neg_dim, test/test_torch.py::TestTorch::test_swap_basic, test/test_torch.py::TestTorch::test_swap_fail_slots, test/test_torch.py::TestTorch::test_t_not_2d_error, test/test_torch.py::TestTorch::test_tensor_base_init, test/test_torch.py::TestTorch::test_tensor_base_new, test/test_torch.py::TestTorch::test_tensor_ctor_scalar, test/test_torch.py::TestTorch::test_tensor_cycle_via_dict, test/test_torch.py::TestTorch::test_tensor_cycle_via_slots, test/test_torch.py::TestTorch::test_tensor_dead_weak_ref, test/test_torch.py::TestTorch::test_tensor_dict_dealloc, test/test_torch.py::TestTorch::test_tensor_finalizer_dealloc, test/test_torch.py::TestTorch::test_tensor_fix_weakref_no_leak, test/test_torch.py::TestTorch::test_tensor_resurrected_weak_ref, test/test_torch.py::TestTorch::test_tensor_set, test/test_torch.py::TestTorch::test_tensor_set_errors, test/test_torch.py::TestTorch::test_tensor_slot_dealloc, test/test_torch.py::TestTorch::test_tensor_weakref_dealloc, test/test_torch.py::TestTorch::test_tensor_where_scalar, test/test_torch.py::TestTorch::test_tensoriterator_output_setup, test/test_torch.py::TestTorch::test_terminate_handler_on_crash, test/test_torch.py::TestTorch::test_to, test/test_torch.py::TestTorch::test_to_with_tensor, test/test_torch.py::TestTorch::test_topk_neg_dim, test/test_torch.py::TestTorch::test_torch_from_file, test/test_torch.py::TestTorch::test_transpose_neg_dim, test/test_torch.py::TestTorch::test_type, test/test_torch.py::TestTorch::test_type_alias, test/test_torch.py::TestTorch::test_type_conversion_via_dtype_name, test/test_torch.py::TestTorch::test_typed_storage_deprecation_warning, test/test_torch.py::TestTorch::test_typed_storage_internal_no_warning, test/test_torch.py::TestTorch::test_unbind_neg_dim, test/test_torch.py::TestTorch::test_unflatten, test/test_torch.py::TestTorch::test_unfold_neg_dim, test/test_torch.py::TestTorch::test_unsqueeze_neg_dim, test/test_torch.py::TestTorch::test_upsample_nearest1d_meta, test/test_torch.py::TestTorch::test_upsample_nearest2d_meta, test/test_torch.py::TestTorch::test_var_neg_dim, test/test_torch.py::TestTorch::test_warn_types, test/test_torch.py::TestTorch::test_wildcard_import, test/test_torch.py::TestVitalSignsCudaCPU::test_cuda_vitals_gpu_only_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_assertRaisesRegex_ignore_msg_non_native_device_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_edge_cases_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_edge_cases_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_edge_cases_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_p_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_p_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_p_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_p_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_bfloat16_neg_abs_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_bool_tensor_value_change_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_add_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_addcdiv_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_addcmul_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_atan2_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_copy_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_dist_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_div_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_eq_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_fmod_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_ge_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_gt_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_le_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_lerp_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_lt_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_map2_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_map_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_masked_fill_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_masked_scatter_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_masked_select_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_max_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_min_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_mul_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_ne_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_pow_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_remainder_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_sub_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_kstest_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_no_inf_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_no_inf_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_cuda_backward_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_euclidean_large_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_grad_p_lt_1_no_nan_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_large_batch_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_large_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_non_contiguous_batch_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_non_contiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_norm_batch_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_norm_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_same_inputs_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_check_tensor_all_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_check_tensor_internal_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_clone_all_dtypes_and_devices_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_clone_not_memory_dense_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_clone_zero_stride_dim_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_complex_half_experimental_warning_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_constants_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_conv_transposed_backward_agnostic_to_memory_format_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_conv_transposed_large_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_complex32, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_all_dtypes_and_devices_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_math_view_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_mem_overlap_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_transpose_math_view_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_transpose_math_view_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_transpose_math_view_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_corrcoef_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_corrcoef_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_corrcoef_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_cov_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cpp_warnings_have_python_context_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cublas_config_nondeterministic_alert_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cummax_cummin_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cummax_discontiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cummin_discontiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cumprod_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cumsum_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_scalar_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_scalar_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_interpolate_bilinear_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_replication_pad2d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_device_guard_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_dim_function_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_discontiguous_out_cumsum_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_dist_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_dtypetensor_warnings_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_errors_index_copy_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_expected_failure_xla_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_kstest_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_kstest_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_kstest_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_kstest_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_no_zero_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_no_zero_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_gather_backward_deterministic_path_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_gather_backward_one_dim_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_kstest_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scale_will_not_overflow_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaler_deprecated_warning_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaler_pass_itself_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_accumulation_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach0_fused0_AdamW_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach0_fused0_Adam_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach0_fused0_SGD_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach2_fused_True_AdamW_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach2_fused_True_Adam_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach2_fused_True_SGD_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach_True_fused1_AdamW_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach_True_fused1_Adam_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach_True_fused1_SGD_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_clipping_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_clipping_separate_unscale_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_multiple_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_penalty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_state_dict_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_unscale_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_unscale_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_unscale_sparse_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_update_scale_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_all_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_all_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_all_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_extreme_cases_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_extreme_cases_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_extreme_cases_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_spacing_list_length_error_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_spacing_list_length_error_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_spacing_list_length_error_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_type_promotion_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_hook_remove_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_add_deterministic_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_add_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_deterministic_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_int8, 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test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_int8, 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test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_log_normal_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_log_normal_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_log_normal_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_log_normal_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_logcumsumexp_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_lognormal_kstest_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_bool_tensor_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_bfloat16_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_bfloat16_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_bool_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_bool_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_complex128_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_complex128_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_complex64_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_complex64_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_float16_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_float16_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_float32_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_float32_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_float64_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_float64_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_int16_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_int16_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_int32_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_int32_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_int64_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_int64_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_int8_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_int8_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_uint8_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_uint8_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_bool_tensor_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_inplace_noncontiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_large_tensor_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_bfloat16, 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, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_consistency_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_cpu_and_cuda_ops_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_empty_like_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_factory_like_functions_preserve_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_operators_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_preserved_after_permute_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_propagation_rules_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_to_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_type_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_type_shortcuts_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_module_share_memory_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_cpu_cpu_bfloat16, 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, 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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 2024-08-20T21:53:05.7181778Z 2024-08-20T21:53:05.7182360Z Running dynamo/test_trace_rules 1/1 ... [2024-08-20 21:53:05.632833] 2024-08-20T21:53:05.7183006Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:05.7184889Z 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 21:53:05.633259] 2024-08-20T21:53:08.5485012Z 2024-08-20T21:53:08.5486668Z 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_916f29f8ab6b8588_.log 2024-08-20T21:53:08.5487943Z 2024-08-20T21:53:08.5489146Z Running dynamo/test_repros 1/1 ... [2024-08-20 21:53:08.548652] 2024-08-20T21:53:08.5489772Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:08.5493809Z 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 21:53:08.549042] 2024-08-20T21:53:11.5249171Z 2024-08-20T21:53:11.5251019Z dynamo/test_repros 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_repros_1.1_01ce3c7be6162105_.log 2024-08-20T21:53:11.5252131Z 2024-08-20T21:53:11.5253052Z Running dynamo/test_higher_order_ops 1/1 ... [2024-08-20 21:53:11.525092] 2024-08-20T21:53:11.5254015Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:11.5258403Z 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 21:53:11.525501] 2024-08-20T21:53:14.5552464Z 2024-08-20T21:53:14.5554888Z 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_5e1549756b913111_.log 2024-08-20T21:53:14.5556716Z 2024-08-20T21:53:14.5557314Z Running dynamo/test_export 1/1 ... [2024-08-20 21:53:14.555403] 2024-08-20T21:53:14.5558212Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:14.5563493Z 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 21:53:14.555905] 2024-08-20T21:53:17.5805595Z 2024-08-20T21:53:17.5807479Z dynamo/test_export 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_export_1.1_e68774755a77ad70_.log 2024-08-20T21:53:17.5808543Z 2024-08-20T21:53:17.5809632Z Running dynamo/test_exc 1/1 ... [2024-08-20 21:53:17.580672] 2024-08-20T21:53:17.5810348Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:17.5813975Z 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 21:53:17.581027] 2024-08-20T21:53:20.4915568Z 2024-08-20T21:53:20.4917157Z dynamo/test_exc 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_exc_1.1_7bdb32ab87846035_.log 2024-08-20T21:53:20.4918106Z 2024-08-20T21:53:20.4920130Z Running dynamo/test_ctx_manager 1/1 ... [2024-08-20 21:53:20.491751] 2024-08-20T21:53:20.4920751Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:20.4925246Z 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 21:53:20.492157] 2024-08-20T21:53:23.4066811Z 2024-08-20T21:53:23.4068686Z 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_3e66162435a24ff4_.log 2024-08-20T21:53:23.4069725Z 2024-08-20T21:53:23.4071116Z Running dynamo/test_activation_checkpointing 1/1 ... [2024-08-20 21:53:23.406870] 2024-08-20T21:53:23.4071892Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:23.4076484Z 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 21:53:23.407277] 2024-08-20T21:53:26.4161839Z 2024-08-20T21:53:26.4163888Z 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_cfc87fb897d55ddb_.log 2024-08-20T21:53:26.4165204Z 2024-08-20T21:53:26.4165769Z Running dynamo/test_optimizers 1/1 ... [2024-08-20 21:53:26.416353] 2024-08-20T21:53:26.4166385Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:26.4170802Z 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 21:53:26.416743] 2024-08-20T21:53:29.3347282Z 2024-08-20T21:53:29.3349154Z dynamo/test_optimizers 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_optimizers_1.1_252efda0acc1f4f5_.log 2024-08-20T21:53:29.3350359Z 2024-08-20T21:53:29.3350755Z Running dynamo/test_backends 1/1 ... [2024-08-20 21:53:29.334806] 2024-08-20T21:53:29.3351364Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:29.3355518Z 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 21:53:29.335207] 2024-08-20T21:53:32.3591921Z 2024-08-20T21:53:32.3593523Z dynamo/test_backends 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_backends_1.1_cf5b138c69f8d837_.log 2024-08-20T21:53:32.3594583Z 2024-08-20T21:53:32.3595580Z Running dynamo/test_skip_non_tensor 1/1 ... [2024-08-20 21:53:32.359348] 2024-08-20T21:53:32.3596468Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:32.3601178Z 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 21:53:32.359777] 2024-08-20T21:53:35.3061418Z 2024-08-20T21:53:35.3063051Z 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_bd15e859b7b9d593_.log 2024-08-20T21:53:35.3064181Z 2024-08-20T21:53:35.3065165Z Running dynamo/test_python_autograd 1/1 ... [2024-08-20 21:53:35.306310] 2024-08-20T21:53:35.3065815Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:35.3070252Z 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 21:53:35.306685] 2024-08-20T21:53:38.2143375Z 2024-08-20T21:53:38.2145860Z 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_6c072c029d65fe88_.log 2024-08-20T21:53:38.2148099Z 2024-08-20T21:53:38.2148836Z Running dynamo/test_verify_correctness 1/1 ... [2024-08-20 21:53:38.214533] 2024-08-20T21:53:38.2149818Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:38.2154642Z 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 21:53:38.215014] 2024-08-20T21:53:41.1646430Z 2024-08-20T21:53:41.1648457Z 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_fa8b6c5144489830_.log 2024-08-20T21:53:41.1649786Z 2024-08-20T21:53:41.1650176Z Running dynamo/test_exceptions 1/1 ... [2024-08-20 21:53:41.164742] 2024-08-20T21:53:41.1650792Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:41.1655031Z 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 21:53:41.165153] 2024-08-20T21:53:44.1025403Z 2024-08-20T21:53:44.1026996Z dynamo/test_exceptions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_exceptions_1.1_b242f458f9136b91_.log 2024-08-20T21:53:44.1028191Z 2024-08-20T21:53:44.1028882Z Running dynamo/test_base_output 1/1 ... [2024-08-20 21:53:44.102683] 2024-08-20T21:53:44.1029891Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:44.1034053Z 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 21:53:44.103083] 2024-08-20T21:53:47.0415275Z 2024-08-20T21:53:47.0416920Z 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_648962cfdfc9a7db_.log 2024-08-20T21:53:47.0417922Z 2024-08-20T21:53:47.0419344Z Running functorch/test_vmap 2/3 ... [2024-08-20 21:53:47.041698] 2024-08-20T21:53:47.0419922Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:47.0424380Z 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 21:53:47.042120] 2024-08-20T21:53:51.8648200Z 2024-08-20T21:53:51.8650063Z functorch/test_vmap 2/3 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_vmap_2.3_10991a5bff288b61_.log 2024-08-20T21:53:51.8651552Z Running 0 items in this shard: 2024-08-20T21:53:51.8652018Z 2024-08-20T21:53:51.8652728Z Running dynamo/test_structured_trace 1/1 ... [2024-08-20 21:53:51.864996] 2024-08-20T21:53:51.8653362Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:51.8657344Z 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 21:53:51.865360] 2024-08-20T21:53:54.8457854Z 2024-08-20T21:53:54.8460325Z 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_69df6270f8e3e71a_.log 2024-08-20T21:53:54.8462165Z 2024-08-20T21:53:54.8462739Z Running dynamo/test_hooks 1/1 ... [2024-08-20 21:53:54.845988] 2024-08-20T21:53:54.8463626Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:54.8469211Z 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 21:53:54.846493] 2024-08-20T21:53:57.8763272Z 2024-08-20T21:53:57.8765526Z dynamo/test_hooks 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_hooks_1.1_d782c0232090a09c_.log 2024-08-20T21:53:57.8766986Z 2024-08-20T21:53:57.8767691Z Running dynamo/test_profiler 1/1 ... [2024-08-20 21:53:57.876461] 2024-08-20T21:53:57.8768515Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:57.8772956Z 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 21:53:57.876876] 2024-08-20T21:54:00.8266054Z 2024-08-20T21:54:00.8267919Z dynamo/test_profiler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_profiler_1.1_dcd17d4bbe53c660_.log 2024-08-20T21:54:00.8268928Z 2024-08-20T21:54:00.8269762Z Running dynamo/test_recompile_ux 1/1 ... [2024-08-20 21:54:00.826720] 2024-08-20T21:54:00.8270437Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:00.8274813Z 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 21:54:00.827108] 2024-08-20T21:54:03.7803696Z 2024-08-20T21:54:03.7805994Z 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_34a2d04da5b06d40_.log 2024-08-20T21:54:03.7807616Z 2024-08-20T21:54:03.7808053Z Running dynamo/test_deviceguard 1/1 ... [2024-08-20 21:54:03.780487] 2024-08-20T21:54:03.7808685Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:03.7812185Z 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 21:54:03.780854] 2024-08-20T21:54:06.7218843Z 2024-08-20T21:54:06.7220610Z dynamo/test_deviceguard 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_deviceguard_1.1_8e6eac05b6dcf2ee_.log 2024-08-20T21:54:06.7221688Z 2024-08-20T21:54:06.7222408Z Running test_linalg 2/4 ... [2024-08-20 21:54:06.722039] 2024-08-20T21:54:06.7223084Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:06.7227051Z 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 21:54:06.722394] 2024-08-20T21:54:14.8006449Z 2024-08-20T21:54:14.8008073Z test_linalg 2/4 was successful, full logs can be found in artifacts with path test/test-reports/test_linalg_2.4_68e10e7811186e79_.log 2024-08-20T21:54:14.8010217Z Running 2 items in this shard: test/test_linalg.py::TestLinalgCPU::test_svd_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_svd_cpu_float64 2024-08-20T21:54:14.8011131Z 2024-08-20T21:54:14.8011456Z Running test_linalg 3/4 ... [2024-08-20 21:54:14.800767] 2024-08-20T21:54:14.8012079Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:14.8014360Z 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 21:54:14.801114] 2024-08-20T21:54:21.3767016Z 2024-08-20T21:54:21.3769160Z test_linalg 3/4 was successful, full logs can be found in artifacts with path test/test-reports/test_linalg_3.4_7de476417ed9c46b_.log 2024-08-20T21:54:21.3771175Z Running 1 items in this shard: test/test_linalg.py::TestLinalgCPU::test_svd_cpu_float32 2024-08-20T21:54:21.3771776Z 2024-08-20T21:54:21.3772165Z Running test_linalg 4/4 ... [2024-08-20 21:54:21.376805] 2024-08-20T21:54:21.3772789Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:21.3775296Z 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 21:54:21.377172] 2024-08-20T21:54:25.1482362Z 2024-08-20T21:54:25.1483848Z test_linalg 4/4 was successful, full logs can be found in artifacts with path test/test-reports/test_linalg_4.4_c48ffdbdfbe05845_.log 2024-08-20T21:54:25.1484855Z Running 0 items in this shard: 2024-08-20T21:54:25.1485140Z 2024-08-20T21:54:25.1486052Z Running dynamo/test_debug_utils 1/1 ... [2024-08-20 21:54:25.148388] 2024-08-20T21:54:25.1486673Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:25.1490964Z 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 21:54:25.148768] 2024-08-20T21:54:28.1212542Z 2024-08-20T21:54:28.1214886Z 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_95dcc061f4de18f0_.log 2024-08-20T21:54:28.1217140Z 2024-08-20T21:54:28.1217793Z Running test_cuda_multigpu 1/1 ... [2024-08-20 21:54:28.121427] 2024-08-20T21:54:28.1218681Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:28.1223939Z 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 21:54:28.121928] 2024-08-20T21:54:31.3079756Z 2024-08-20T21:54:31.3081399Z test_cuda_multigpu 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_multigpu_1.1_cdc30559002637a6_.log 2024-08-20T21:54:31.3082593Z Running 0 items in this shard: 2024-08-20T21:54:31.3082977Z 2024-08-20T21:54:31.3083347Z Running test_comparison_utils 1/1 ... [2024-08-20 21:54:31.308132] 2024-08-20T21:54:31.3083944Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:31.3088084Z 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 21:54:31.308497] 2024-08-20T21:54:34.4783571Z 2024-08-20T21:54:34.4785479Z test_comparison_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_comparison_utils_1.1_b91a0779d4f60fa0_.log 2024-08-20T21:54:34.4786818Z Running 0 items in this shard: 2024-08-20T21:54:34.4787231Z 2024-08-20T21:54:34.4787853Z Running test_mkl_verbose 1/1 ... [2024-08-20 21:54:34.478503] 2024-08-20T21:54:34.4788437Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:34.4792857Z 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 21:54:34.478891] 2024-08-20T21:54:37.6995016Z 2024-08-20T21:54:37.6997154Z test_mkl_verbose 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mkl_verbose_1.1_f15779bbc81c2e53_.log 2024-08-20T21:54:37.6998317Z Running 0 items in this shard: 2024-08-20T21:54:37.6998704Z 2024-08-20T21:54:37.6999527Z Running test_mkldnn_verbose 1/1 ... [2024-08-20 21:54:37.699695] 2024-08-20T21:54:37.7000120Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:37.7004650Z 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 21:54:37.700087] 2024-08-20T21:54:40.8705944Z 2024-08-20T21:54:40.8707788Z test_mkldnn_verbose 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mkldnn_verbose_1.1_19de1960fac26606_.log 2024-08-20T21:54:40.8709184Z Running 0 items in this shard: 2024-08-20T21:54:40.8709525Z 2024-08-20T21:54:40.8709861Z Running test_custom_ops 1/1 ... [2024-08-20 21:54:40.870721] 2024-08-20T21:54:40.8710428Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:40.8714422Z 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 21:54:40.871085] 2024-08-20T21:54:45.3430218Z 2024-08-20T21:54:45.3432196Z test_custom_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_custom_ops_1.1_d0acd0dc817416a1_.log 2024-08-20T21:54:45.3433562Z Running 0 items in this shard: 2024-08-20T21:54:45.3433839Z 2024-08-20T21:54:45.3434243Z Running test_ao_sparsity 1/1 ... [2024-08-20 21:54:45.343181] 2024-08-20T21:54:45.3434812Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:45.3439770Z 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 21:54:45.343579] 2024-08-20T21:54:48.9141416Z 2024-08-20T21:54:48.9143359Z test_ao_sparsity 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_ao_sparsity_1.1_362189880857ab39_.log 2024-08-20T21:54:48.9145013Z Running 0 items in this shard: 2024-08-20T21:54:48.9145312Z 2024-08-20T21:54:48.9145749Z Running functorch/test_eager_transforms 1/1 ... [2024-08-20 21:54:48.914255] 2024-08-20T21:54:48.9146408Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:48.9149989Z 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 21:54:48.914637] 2024-08-20T21:54:53.5868457Z 2024-08-20T21:54:53.5870417Z 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_f027b31c98faf9ef_.log 2024-08-20T21:54:53.5871666Z Running 0 items in this shard: 2024-08-20T21:54:53.5872068Z 2024-08-20T21:54:53.5872622Z Running test_optim 1/1 ... [2024-08-20 21:54:53.587024] 2024-08-20T21:54:53.5873229Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:53.5877653Z 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 21:54:53.587411] 2024-08-20T21:54:57.8592354Z 2024-08-20T21:54:57.8594367Z test_optim 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_optim_1.1_7a5cf9214e462376_.log 2024-08-20T21:54:57.8596374Z Running 0 items in this shard: 2024-08-20T21:54:57.8596791Z 2024-08-20T21:54:57.8597286Z Running test_masked 1/1 ... [2024-08-20 21:54:57.859420] 2024-08-20T21:54:57.8598132Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:57.8604107Z 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 21:54:57.859965] 2024-08-20T21:55:02.0317699Z 2024-08-20T21:55:02.0319771Z test_masked 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_masked_1.1_80534814bcefa142_.log 2024-08-20T21:55:02.0320883Z Running 0 items in this shard: 2024-08-20T21:55:02.0321160Z 2024-08-20T21:55:02.0321492Z Running test_view_ops 1/1 ... [2024-08-20 21:55:02.031775] 2024-08-20T21:55:02.0322015Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:02.0325972Z 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 21:55:02.032286] 2024-08-20T21:55:05.3024433Z 2024-08-20T21:55:05.3026491Z test_view_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_view_ops_1.1_a2d41f90002efda9_.log 2024-08-20T21:55:05.3027959Z Running 0 items in this shard: 2024-08-20T21:55:05.3028239Z 2024-08-20T21:55:05.3028565Z Running test_indexing 1/1 ... [2024-08-20 21:55:05.302534] 2024-08-20T21:55:05.3029113Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:05.3033407Z 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 21:55:05.302905] 2024-08-20T21:55:08.5730920Z 2024-08-20T21:55:08.5732468Z test_indexing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_indexing_1.1_2c63c03d8b981d19_.log 2024-08-20T21:55:08.5733777Z Running 0 items in this shard: 2024-08-20T21:55:08.5734188Z 2024-08-20T21:55:08.5735237Z Running test_monitor 1/1 ... [2024-08-20 21:55:08.573252] 2024-08-20T21:55:08.5735862Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:08.5739634Z 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 21:55:08.573618] 2024-08-20T21:55:11.7936046Z 2024-08-20T21:55:11.7937784Z test_monitor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_monitor_1.1_01abe30abcac00b4_.log 2024-08-20T21:55:11.7938940Z Running 0 items in this shard: 2024-08-20T21:55:11.7939429Z 2024-08-20T21:55:11.7940149Z Running benchmark_utils/test_benchmark_utils 1/1 ... [2024-08-20 21:55:11.793718] 2024-08-20T21:55:11.7940828Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:11.7944681Z 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 21:55:11.794092] 2024-08-20T21:55:15.3646173Z 2024-08-20T21:55:15.3648238Z 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_04ae0985473f2b9b_.log 2024-08-20T21:55:15.3649684Z Running 0 items in this shard: 2024-08-20T21:55:15.3649964Z 2024-08-20T21:55:15.3650356Z Running test_binary_ufuncs 1/2 ... [2024-08-20 21:55:15.364778] 2024-08-20T21:55:15.3650937Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:15.3655053Z 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 21:55:15.365171] 2024-08-20T21:55:21.8905036Z 2024-08-20T21:55:21.8906897Z test_binary_ufuncs 1/2 was successful, full logs can be found in artifacts with path test/test-reports/test_binary_ufuncs_1.2_d91d6b27b23bf155_.log 2024-08-20T21:55:21.8908051Z Running 0 items in this shard: 2024-08-20T21:55:21.8908427Z 2024-08-20T21:55:21.8908924Z Running test_futures 1/1 ... [2024-08-20 21:55:21.890665] 2024-08-20T21:55:21.8909723Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:21.8914360Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_futures.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:55:21.891077] 2024-08-20T21:55:25.0609786Z 2024-08-20T21:55:25.0611554Z test_futures 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_futures_1.1_d65744851e7bf4de_.log 2024-08-20T21:55:25.0612954Z Running 0 items in this shard: 2024-08-20T21:55:25.0613240Z 2024-08-20T21:55:25.0613601Z Running nn/test_embedding 1/1 ... [2024-08-20 21:55:25.061007] 2024-08-20T21:55:25.0614168Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:25.0617070Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'nn/test_embedding.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:55:25.061382] 2024-08-20T21:55:28.2812482Z 2024-08-20T21:55:28.2814720Z nn/test_embedding 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_embedding_1.1_5dda0f4172fa1127_.log 2024-08-20T21:55:28.2816242Z Running 0 items in this shard: 2024-08-20T21:55:28.2816756Z 2024-08-20T21:55:28.2817159Z Running test_functional_optim 1/1 ... [2024-08-20 21:55:28.281394] 2024-08-20T21:55:28.2817740Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:28.2821319Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_functional_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 21:55:28.281795] 2024-08-20T21:55:31.6520664Z 2024-08-20T21:55:31.6522485Z test_functional_optim 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_functional_optim_1.1_0a50685b3cb33744_.log 2024-08-20T21:55:31.6523908Z Running 0 items in this shard: 2024-08-20T21:55:31.6524235Z 2024-08-20T21:55:31.6524578Z Running test_mkldnn 1/1 ... [2024-08-20 21:55:31.652189] 2024-08-20T21:55:31.6525184Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:31.6529463Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_mkldnn.py', '-m', '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 21:55:31.652605] 2024-08-20T21:55:35.0336259Z 2024-08-20T21:55:35.0337829Z test_mkldnn 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mkldnn_1.1_345364e7d9798a7f_.log 2024-08-20T21:55:35.0339025Z Running 0 items in this shard: 2024-08-20T21:55:35.0339350Z 2024-08-20T21:55:35.0339961Z Running test_weak 1/1 ... [2024-08-20 21:55:35.033788] 2024-08-20T21:55:35.0340585Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:35.0344385Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_weak.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:55:35.034134] 2024-08-20T21:55:38.2041505Z 2024-08-20T21:55:38.2043276Z test_weak 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_weak_1.1_0f69bd313781e318_.log 2024-08-20T21:55:38.2044458Z Running 0 items in this shard: 2024-08-20T21:55:38.2045287Z 2024-08-20T21:55:38.2045647Z Running lazy/test_debug_util 1/1 ... [2024-08-20 21:55:38.204258] 2024-08-20T21:55:38.2046232Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:38.2049831Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'lazy/test_debug_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 21:55:38.204610] 2024-08-20T21:55:41.3745123Z 2024-08-20T21:55:41.3747103Z lazy/test_debug_util 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_debug_util_1.1_1db2d181eb8aa94a_.log 2024-08-20T21:55:41.3748320Z Running 0 items in this shard: 2024-08-20T21:55:41.3748696Z 2024-08-20T21:55:41.3749639Z Running test_sparse_semi_structured 1/1 ... [2024-08-20 21:55:41.374710] 2024-08-20T21:55:41.3750418Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:41.3754821Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_sparse_semi_structured.py', '-m', '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 21:55:41.375092] 2024-08-20T21:55:44.5449332Z 2024-08-20T21:55:44.5451319Z test_sparse_semi_structured 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_sparse_semi_structured_1.1_72d02a919925faa4_.log 2024-08-20T21:55:44.5452579Z Running 0 items in this shard: 2024-08-20T21:55:44.5452851Z 2024-08-20T21:55:44.5453808Z Running nn/test_module_hooks 1/1 ... [2024-08-20 21:55:44.545117] 2024-08-20T21:55:44.5454857Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:44.5458775Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'nn/test_module_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 21:55:44.545525] 2024-08-20T21:55:47.7653607Z 2024-08-20T21:55:47.7655447Z nn/test_module_hooks 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_module_hooks_1.1_609fe9e5c8ff820e_.log 2024-08-20T21:55:47.7656783Z Running 0 items in this shard: 2024-08-20T21:55:47.7657106Z 2024-08-20T21:55:47.7657585Z Running test_functional_autograd_benchmark 1/1 ... [2024-08-20 21:55:47.765516] 2024-08-20T21:55:47.7658246Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:47.7662275Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_functional_autograd_benchmark.py', '-m', '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 21:55:47.765881] 2024-08-20T21:55:50.9356081Z 2024-08-20T21:55:50.9358183Z test_functional_autograd_benchmark 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_functional_autograd_benchmark_1.1_0f2b9893829dcd8f_.log 2024-08-20T21:55:50.9360313Z Running 0 items in this shard: 2024-08-20T21:55:50.9360605Z 2024-08-20T21:55:50.9360964Z Running test_mkldnn_fusion 1/1 ... [2024-08-20 21:55:50.935774] 2024-08-20T21:55:50.9361542Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:50.9364675Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_mkldnn_fusion.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:55:50.936166] 2024-08-20T21:55:54.0560811Z 2024-08-20T21:55:54.0562379Z test_mkldnn_fusion 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mkldnn_fusion_1.1_f7e643ae8ebaee42_.log 2024-08-20T21:55:54.0563527Z Running 0 items in this shard: 2024-08-20T21:55:54.0563795Z 2024-08-20T21:55:54.0564371Z Running test_model_dump 1/1 ... [2024-08-20 21:55:54.056241] 2024-08-20T21:55:54.0565249Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:54.0569242Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_model_dump.py', '-m', '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 21:55:54.056619] 2024-08-20T21:55:57.1761716Z 2024-08-20T21:55:57.1763507Z test_model_dump 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_model_dump_1.1_a71a01502f14955c_.log 2024-08-20T21:55:57.1764739Z Running 0 items in this shard: 2024-08-20T21:55:57.1765019Z 2024-08-20T21:55:57.1765972Z Running test_function_schema 1/1 ... [2024-08-20 21:55:57.176348] 2024-08-20T21:55:57.1766606Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:57.1771206Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_function_schema.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:55:57.176744] 2024-08-20T21:56:00.2965703Z 2024-08-20T21:56:00.2967945Z test_function_schema 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_function_schema_1.1_ac69a8f86acf297c_.log 2024-08-20T21:56:00.2969793Z Running 0 items in this shard: 2024-08-20T21:56:00.2970279Z 2024-08-20T21:56:00.2970922Z Running test_scatter_gather_ops 1/1 ... [2024-08-20 21:56:00.296775] 2024-08-20T21:56:00.2971948Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:00.2977784Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_scatter_gather_ops.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:56:00.297315] 2024-08-20T21:56:03.4675693Z 2024-08-20T21:56:03.4677691Z test_scatter_gather_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_scatter_gather_ops_1.1_02b4a33d3cd6c196_.log 2024-08-20T21:56:03.4679389Z Running 0 items in this shard: 2024-08-20T21:56:03.4679671Z 2024-08-20T21:56:03.4680033Z Running test_schema_check 1/1 ... [2024-08-20 21:56:03.467693] 2024-08-20T21:56:03.4680591Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:03.4684152Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_schema_check.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:56:03.468083] 2024-08-20T21:56:08.5914090Z 2024-08-20T21:56:08.5915685Z test_schema_check 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_schema_check_1.1_2ad0f123379198df_.log 2024-08-20T21:56:08.5917025Z Running 0 items in this shard: 2024-08-20T21:56:08.5917312Z 2024-08-20T21:56:08.5917711Z Running lazy/test_reuse_ir 1/1 ... [2024-08-20 21:56:08.591531] 2024-08-20T21:56:08.5918274Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:08.5922431Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'lazy/test_reuse_ir.py', '-m', '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 21:56:08.591922] 2024-08-20T21:56:11.7618978Z 2024-08-20T21:56:11.7621203Z lazy/test_reuse_ir 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_reuse_ir_1.1_115bc60ac7b20928_.log 2024-08-20T21:56:11.7623138Z Running 0 items in this shard: 2024-08-20T21:56:11.7623566Z 2024-08-20T21:56:11.7624098Z Running test_unary_ufuncs 1/1 ... [2024-08-20 21:56:11.762090] 2024-08-20T21:56:11.7624983Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:11.7630235Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', '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 21:56:11.762582] 2024-08-20T21:56:20.3920335Z 2024-08-20T21:56:20.3921915Z test_unary_ufuncs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_unary_ufuncs_1.1_6c0aa77c659b6172_.log 2024-08-20T21:56:20.3923039Z Running 0 items in this shard: 2024-08-20T21:56:20.3923370Z 2024-08-20T21:56:20.3924389Z Running test_quantization 2/5 ... [2024-08-20 21:56:20.392217] 2024-08-20T21:56:20.3925017Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:20.3930296Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_quantization.py', '-m', 'serial', '--shard-id=2', '--num-shards=5', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:56:20.392618] 2024-08-20T21:56:25.2156171Z 2024-08-20T21:56:25.2158558Z test_quantization 2/5 was successful, full logs can be found in artifacts with path test/test-reports/test_quantization_2.5_9ff2f8ed1bb4e1cf_.log 2024-08-20T21:56:25.2160465Z Running 0 items in this shard: 2024-08-20T21:56:25.2160887Z 2024-08-20T21:56:25.2161464Z Running test_quantization 5/5 ... [2024-08-20 21:56:25.215823] 2024-08-20T21:56:25.2162358Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:25.2168068Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_quantization.py', '-m', 'serial', '--shard-id=5', '--num-shards=5', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:56:25.216335] 2024-08-20T21:56:29.5385728Z 2024-08-20T21:56:29.5387572Z test_quantization 5/5 was successful, full logs can be found in artifacts with path test/test-reports/test_quantization_5.5_4bf58740ec943244_.log 2024-08-20T21:56:29.5388838Z Running 0 items in this shard: 2024-08-20T21:56:29.5389220Z 2024-08-20T21:56:29.5389596Z Running test_package 1/1 ... [2024-08-20 21:56:29.538715] 2024-08-20T21:56:29.5390140Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:29.5394775Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_package.py', '-m', '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 21:56:29.539128] 2024-08-20T21:56:33.0101272Z 2024-08-20T21:56:33.0103018Z test_package 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_package_1.1_2d23990a30543cd3_.log 2024-08-20T21:56:33.0104352Z Running 0 items in this shard: 2024-08-20T21:56:33.0104638Z 2024-08-20T21:56:33.0105121Z Running test_type_promotion 1/1 ... [2024-08-20 21:56:33.010284] 2024-08-20T21:56:33.0105721Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:33.0110626Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_type_promotion.py', '-m', '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 21:56:33.010692] 2024-08-20T21:56:36.2811862Z 2024-08-20T21:56:36.2813790Z test_type_promotion 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_type_promotion_1.1_254250510ca2cfb7_.log 2024-08-20T21:56:36.2815306Z Running 0 items in this shard: 2024-08-20T21:56:36.2815757Z 2024-08-20T21:56:36.2816352Z Running test_sparse_csr 1/1 ... [2024-08-20 21:56:36.281323] 2024-08-20T21:56:36.2816903Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:36.2820523Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_sparse_csr.py', '-m', '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 21:56:36.281694] 2024-08-20T21:56:41.7059617Z 2024-08-20T21:56:41.7062104Z test_sparse_csr 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_sparse_csr_1.1_8122e08aac7e5b97_.log 2024-08-20T21:56:41.7063933Z Running 0 items in this shard: 2024-08-20T21:56:41.7064369Z 2024-08-20T21:56:41.7064903Z Running test_jiterator 1/1 ... [2024-08-20 21:56:41.706169] 2024-08-20T21:56:41.7065749Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:41.7070920Z 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 21:56:41.706653] 2024-08-20T21:56:44.9249130Z 2024-08-20T21:56:44.9250965Z test_jiterator 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jiterator_1.1_8a4b94d1ffc2152f_.log 2024-08-20T21:56:44.9252318Z Running 0 items in this shard: 2024-08-20T21:56:44.9252658Z 2024-08-20T21:56:44.9253099Z Running test_namedtensor 1/1 ... [2024-08-20 21:56:44.925075] 2024-08-20T21:56:44.9253655Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:44.9257653Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_namedtensor.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:56:44.925445] 2024-08-20T21:56:48.1957122Z 2024-08-20T21:56:48.1959014Z test_namedtensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_namedtensor_1.1_a98e6c1312aa9b4b_.log 2024-08-20T21:56:48.1960682Z Running 0 items in this shard: 2024-08-20T21:56:48.1961053Z 2024-08-20T21:56:48.1961640Z Running test_legacy_vmap 1/1 ... [2024-08-20 21:56:48.195903] 2024-08-20T21:56:48.1962292Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:48.1966278Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_legacy_vmap.py', '-m', '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 21:56:48.196274] 2024-08-20T21:56:51.5166834Z 2024-08-20T21:56:51.5168575Z test_legacy_vmap 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_legacy_vmap_1.1_86386718f0a6f54d_.log 2024-08-20T21:56:51.5169810Z Running 0 items in this shard: 2024-08-20T21:56:51.5170117Z 2024-08-20T21:56:51.5170554Z Running test_flop_counter 1/1 ... [2024-08-20 21:56:51.516822] 2024-08-20T21:56:51.5171201Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:51.5175152Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_flop_counter.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:56:51.517193] 2024-08-20T21:56:54.9375830Z 2024-08-20T21:56:54.9377709Z test_flop_counter 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_flop_counter_1.1_88a075dd5ddaac47_.log 2024-08-20T21:56:54.9378952Z Running 0 items in this shard: 2024-08-20T21:56:54.9379295Z 2024-08-20T21:56:54.9379853Z Running functorch/test_ac 1/1 ... [2024-08-20 21:56:54.937765] 2024-08-20T21:56:54.9380412Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:54.9384574Z 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 21:56:54.938135] 2024-08-20T21:56:57.9739763Z 2024-08-20T21:56:57.9741706Z functorch/test_ac 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_ac_1.1_a5e6d73943b3a91e_.log 2024-08-20T21:56:57.9742745Z 2024-08-20T21:56:57.9744120Z Running profiler/test_profiler 1/1 ... [2024-08-20 21:56:57.974152] 2024-08-20T21:56:57.9745133Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:57.9748832Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'profiler/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 21:56:57.974563] 2024-08-20T21:57:07.3055622Z 2024-08-20T21:57:07.3057664Z profiler/test_profiler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_profiler_1.1_0377451791671bbb_.log 2024-08-20T21:57:07.3066581Z Running 10 items in this shard: test/profiler/test_profiler.py::TestProfiler::test_source_multithreaded_basic_work_in_main_thread_False, test/profiler/test_profiler.py::TestProfiler::test_source_multithreaded_basic_work_in_main_thread_True, test/profiler/test_profiler.py::TestProfiler::test_source_multithreaded_close_in_scope_work_in_main_thread_False, test/profiler/test_profiler.py::TestProfiler::test_source_multithreaded_close_in_scope_work_in_main_thread_True, test/profiler/test_profiler.py::TestProfiler::test_source_multithreaded_complex_work_in_main_thread_False, test/profiler/test_profiler.py::TestProfiler::test_source_multithreaded_complex_work_in_main_thread_True, test/profiler/test_profiler.py::TestProfiler::test_source_multithreaded_multiple_preexisting_work_in_main_thread_False, test/profiler/test_profiler.py::TestProfiler::test_source_multithreaded_multiple_preexisting_work_in_main_thread_True, test/profiler/test_profiler.py::TestProfiler::test_source_multithreaded_open_in_scope_work_in_main_thread_False, test/profiler/test_profiler.py::TestProfiler::test_source_multithreaded_open_in_scope_work_in_main_thread_True 2024-08-20T21:57:07.3073987Z 2024-08-20T21:57:07.3074559Z Running torch_np/numpy_tests/core/test_numeric 1/1 ... [2024-08-20 21:57:07.305696] 2024-08-20T21:57:07.3075365Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:57:07.3077655Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_numeric.py', '-m', '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 21:57:07.306086] 2024-08-20T21:57:10.6765770Z 2024-08-20T21:57:10.6767643Z torch_np/numpy_tests/core/test_numeric 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_numeric_1.1_cf78ce58e865e7c5_.log 2024-08-20T21:57:10.6769191Z Running 0 items in this shard: 2024-08-20T21:57:10.6769458Z 2024-08-20T21:57:10.6769827Z Running test_fx_passes 1/1 ... [2024-08-20 21:57:10.676758] 2024-08-20T21:57:10.6770365Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:57:10.6775075Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_fx_passes.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:57:10.677171] 2024-08-20T21:57:13.9477257Z 2024-08-20T21:57:13.9479513Z test_fx_passes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_fx_passes_1.1_745cebde71e0b529_.log 2024-08-20T21:57:13.9481170Z Running 0 items in this shard: 2024-08-20T21:57:13.9481553Z 2024-08-20T21:57:13.9482250Z Running lazy/test_functionalization 1/1 ... [2024-08-20 21:57:13.947694] 2024-08-20T21:57:13.9483208Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:57:13.9486023Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'lazy/test_functionalization.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:57:13.948197] 2024-08-20T21:57:17.1683963Z 2024-08-20T21:57:17.1685941Z lazy/test_functionalization 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_functionalization_1.1_2d3c3d9ebac9523a_.log 2024-08-20T21:57:17.1687931Z Running 0 items in this shard: 2024-08-20T21:57:17.1688230Z 2024-08-20T21:57:17.1688860Z Running torch_np/numpy_tests/lib/test_function_base 1/1 ... [2024-08-20 21:57:17.168509] 2024-08-20T21:57:17.1689669Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:57:17.1692502Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_function_base.py', '-m', '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 21:57:17.168859] 2024-08-20T21:57:20.5392472Z 2024-08-20T21:57:20.5394751Z torch_np/numpy_tests/lib/test_function_base 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_function_base_1.1_faaf6e72a51f0733_.log 2024-08-20T21:57:20.5396739Z Running 0 items in this shard: 2024-08-20T21:57:20.5397269Z 2024-08-20T21:57:20.5397788Z Running torch_np/numpy_tests/lib/test_histograms 1/1 ... [2024-08-20 21:57:20.539412] 2024-08-20T21:57:20.5398619Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:57:20.5402810Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_histograms.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:57:20.539860] 2024-08-20T21:57:23.8102397Z 2024-08-20T21:57:23.8104946Z torch_np/numpy_tests/lib/test_histograms 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_histograms_1.1_57a218c13e9f6e5f_.log 2024-08-20T21:57:23.8106735Z Running 0 items in this shard: 2024-08-20T21:57:23.8107018Z 2024-08-20T21:57:23.8107514Z Running nn/test_load_state_dict 1/1 ... [2024-08-20 21:57:23.810417] 2024-08-20T21:57:23.8108209Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:57:23.8111617Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'nn/test_load_state_dict.py', '-m', '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 21:57:23.810781] 2024-08-20T21:57:27.0810708Z 2024-08-20T21:57:27.0812540Z nn/test_load_state_dict 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_load_state_dict_1.1_56a673294b8d8c0a_.log 2024-08-20T21:57:27.0813768Z Running 0 items in this shard: 2024-08-20T21:57:27.0814199Z 2024-08-20T21:57:27.0814795Z Running test_matmul_cuda 1/1 ... [2024-08-20 21:57:27.081242] 2024-08-20T21:57:27.0815567Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:57:27.0820231Z 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 21:57:27.081660] 2024-08-20T21:57:30.3083164Z 2024-08-20T21:57:30.3084973Z test_matmul_cuda 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_matmul_cuda_1.1_34c74494aabcc062_.log 2024-08-20T21:57:30.3086294Z Running 0 items in this shard: 2024-08-20T21:57:30.3086623Z 2024-08-20T21:57:30.3087159Z Running torch_np/numpy_tests/core/test_indexing 1/1 ... [2024-08-20 21:57:30.308450] 2024-08-20T21:57:30.3087870Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:57:30.3092080Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_indexing.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:57:30.308850] 2024-08-20T21:57:33.5789993Z 2024-08-20T21:57:33.5792121Z torch_np/numpy_tests/core/test_indexing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_indexing_1.1_081c05304622cbe2_.log 2024-08-20T21:57:33.5794033Z Running 0 items in this shard: 2024-08-20T21:57:33.5794352Z 2024-08-20T21:57:33.5794888Z Running torch_np/numpy_tests/lib/test_type_check 1/1 ... [2024-08-20 21:57:33.579171] 2024-08-20T21:57:33.5795577Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:57:33.5799734Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_type_check.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:57:33.579610] 2024-08-20T21:57:36.8497889Z 2024-08-20T21:57:36.8500648Z torch_np/numpy_tests/lib/test_type_check 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_type_check_1.1_e88ab8b2f2daf4fa_.log 2024-08-20T21:57:36.8502974Z Running 0 items in this shard: 2024-08-20T21:57:36.8503433Z 2024-08-20T21:57:36.8504241Z Running torch_np/numpy_tests/core/test_scalarmath 1/1 ... [2024-08-20 21:57:36.850004] 2024-08-20T21:57:36.8505302Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:57:36.8509823Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_scalarmath.py', '-m', '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 21:57:36.850518] 2024-08-20T21:57:40.1209620Z 2024-08-20T21:57:40.1212448Z torch_np/numpy_tests/core/test_scalarmath 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_scalarmath_1.1_13cbe1309de598f0_.log 2024-08-20T21:57:40.1214314Z Running 0 items in this shard: 2024-08-20T21:57:40.1214614Z 2024-08-20T21:57:40.1215104Z Running optim/test_swa_utils 1/1 ... [2024-08-20 21:57:40.121105] 2024-08-20T21:57:40.1215813Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:57:40.1218236Z 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 21:57:40.121447] 2024-08-20T21:57:43.1103105Z 2024-08-20T21:57:43.1105162Z 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_57b2cfeb0433eb35_.log 2024-08-20T21:57:43.1106158Z 2024-08-20T21:57:43.1106562Z Running test_jit_llga_fuser 1/1 ... [2024-08-20 21:57:43.110255] 2024-08-20T21:57:43.1107206Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:57:43.1109837Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_jit_llga_fuser.py', '-m', '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 21:57:43.110652] 2024-08-20T21:57:46.5311749Z 2024-08-20T21:57:46.5313537Z test_jit_llga_fuser 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jit_llga_fuser_1.1_208a240cbe5e4fd6_.log 2024-08-20T21:57:46.5314709Z Running 0 items in this shard: 2024-08-20T21:57:46.5315059Z 2024-08-20T21:57:46.5315490Z Running test_vulkan 1/1 ... [2024-08-20 21:57:46.531339] 2024-08-20T21:57:46.5316506Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:57:46.5321301Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_vulkan.py', '-m', '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 21:57:46.531735] 2024-08-20T21:57:49.7020214Z 2024-08-20T21:57:49.7022488Z test_vulkan 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_vulkan_1.1_7694ed5ee05cf238_.log 2024-08-20T21:57:49.7024599Z Running 0 items in this shard: 2024-08-20T21:57:49.7025009Z 2024-08-20T21:57:49.7025861Z Running torch_np/numpy_tests/core/test_shape_base 1/1 ... [2024-08-20 21:57:49.701964] 2024-08-20T21:57:49.7027150Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:57:49.7029597Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_shape_base.py', '-m', '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 21:57:49.702423] 2024-08-20T21:57:52.9727629Z 2024-08-20T21:57:52.9729838Z torch_np/numpy_tests/core/test_shape_base 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_shape_base_1.1_f989382319e36cd9_.log 2024-08-20T21:57:52.9731401Z Running 0 items in this shard: 2024-08-20T21:57:52.9731844Z 2024-08-20T21:57:52.9732560Z Running torch_np/numpy_tests/core/test_dtype 1/1 ... [2024-08-20 21:57:52.972956] 2024-08-20T21:57:52.9733227Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:57:52.9737464Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/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 21:57:52.973365] 2024-08-20T21:57:56.2434887Z 2024-08-20T21:57:56.2437680Z torch_np/numpy_tests/core/test_dtype 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_dtype_1.1_b4a5b9232a67158b_.log 2024-08-20T21:57:56.2439688Z Running 0 items in this shard: 2024-08-20T21:57:56.2439990Z 2024-08-20T21:57:56.2440500Z Running torch_np/numpy_tests/core/test_getlimits 1/1 ... [2024-08-20 21:57:56.243660] 2024-08-20T21:57:56.2441264Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:57:56.2444051Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_getlimits.py', '-m', '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 21:57:56.244065] 2024-08-20T21:57:59.4641690Z 2024-08-20T21:57:59.4643651Z torch_np/numpy_tests/core/test_getlimits 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_getlimits_1.1_0de4d7a055f8010a_.log 2024-08-20T21:57:59.4645218Z Running 0 items in this shard: 2024-08-20T21:57:59.4645537Z 2024-08-20T21:57:59.4646019Z Running torch_np/test_function_base 1/1 ... [2024-08-20 21:57:59.464329] 2024-08-20T21:57:59.4646660Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:57:59.4650578Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/test_function_base.py', '-m', '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 21:57:59.464728] 2024-08-20T21:58:02.5844926Z 2024-08-20T21:58:02.5847027Z torch_np/test_function_base 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_function_base_1.1_c04897d82b7cc8eb_.log 2024-08-20T21:58:02.5848728Z Running 0 items in this shard: 2024-08-20T21:58:02.5849006Z 2024-08-20T21:58:02.5850608Z Running nn/test_init 1/1 ... [2024-08-20 21:58:02.584663] 2024-08-20T21:58:02.5851299Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:58:02.5854387Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'nn/test_init.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:58:02.585087] 2024-08-20T21:58:06.2060439Z 2024-08-20T21:58:06.2062167Z nn/test_init 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_init_1.1_55b72bebcc44f682_.log 2024-08-20T21:58:06.2063765Z Running 0 items in this shard: 2024-08-20T21:58:06.2064174Z 2024-08-20T21:58:06.2064626Z Running torch_np/test_ndarray_methods 1/1 ... [2024-08-20 21:58:06.206181] 2024-08-20T21:58:06.2065246Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:58:06.2069115Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/test_ndarray_methods.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:58:06.206592] 2024-08-20T21:58:09.4767957Z 2024-08-20T21:58:09.4769958Z torch_np/test_ndarray_methods 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_ndarray_methods_1.1_37e4370d8c70d115_.log 2024-08-20T21:58:09.4771197Z Running 0 items in this shard: 2024-08-20T21:58:09.4771746Z 2024-08-20T21:58:09.4772363Z Running torch_np/numpy_tests/linalg/test_linalg 1/1 ... [2024-08-20 21:58:09.476942] 2024-08-20T21:58:09.4773066Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:58:09.4777113Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/linalg/test_linalg.py', '-m', '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 21:58:09.477336] 2024-08-20T21:58:12.7978036Z 2024-08-20T21:58:12.7980531Z torch_np/numpy_tests/linalg/test_linalg 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.linalg.test_linalg_1.1_de3aa5028c59532f_.log 2024-08-20T21:58:12.7982093Z Running 0 items in this shard: 2024-08-20T21:58:12.7982438Z 2024-08-20T21:58:12.7983024Z Running torch_np/numpy_tests/lib/test_index_tricks 1/1 ... [2024-08-20 21:58:12.797659] 2024-08-20T21:58:12.7983835Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:58:12.7986089Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_index_tricks.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:58:12.798079] 2024-08-20T21:58:16.0683949Z 2024-08-20T21:58:16.0686712Z torch_np/numpy_tests/lib/test_index_tricks 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_index_tricks_1.1_1ace59a1bb431296_.log 2024-08-20T21:58:16.0688959Z Running 0 items in this shard: 2024-08-20T21:58:16.0689462Z 2024-08-20T21:58:16.0690248Z Running torch_np/numpy_tests/lib/test_twodim_base 1/1 ... [2024-08-20 21:58:16.068559] 2024-08-20T21:58:16.0691337Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:58:16.0695150Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_twodim_base.py', '-m', '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 21:58:16.069062] 2024-08-20T21:58:19.2893698Z 2024-08-20T21:58:19.2896672Z torch_np/numpy_tests/lib/test_twodim_base 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_twodim_base_1.1_1929964bcc4d1565_.log 2024-08-20T21:58:19.2898943Z Running 0 items in this shard: 2024-08-20T21:58:19.2899386Z 2024-08-20T21:58:19.2900197Z Running torch_np/numpy_tests/core/test_scalar_methods 1/1 ... [2024-08-20 21:58:19.289542] 2024-08-20T21:58:19.2901367Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:58:19.2905098Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_scalar_methods.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:58:19.290066] 2024-08-20T21:58:22.5102441Z 2024-08-20T21:58:22.5104650Z torch_np/numpy_tests/core/test_scalar_methods 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_scalar_methods_1.1_3b2569384866bf29_.log 2024-08-20T21:58:22.5106299Z Running 0 items in this shard: 2024-08-20T21:58:22.5106582Z 2024-08-20T21:58:22.5106955Z Running lazy/test_generator 1/1 ... [2024-08-20 21:58:22.510399] 2024-08-20T21:58:22.5107533Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:58:22.5111184Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'lazy/test_generator.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:58:22.510778] 2024-08-20T21:58:25.7311105Z 2024-08-20T21:58:25.7313059Z lazy/test_generator 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_generator_1.1_a27fa57ae4fd2453_.log 2024-08-20T21:58:25.7314480Z Running 0 items in this shard: 2024-08-20T21:58:25.7314764Z 2024-08-20T21:58:25.7315356Z Running torch_np/numpy_tests/fft/test_pocketfft 1/1 ... [2024-08-20 21:58:25.731228] 2024-08-20T21:58:25.7316139Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:58:25.7319520Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/fft/test_pocketfft.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:58:25.731583] 2024-08-20T21:58:28.9517536Z 2024-08-20T21:58:28.9519913Z torch_np/numpy_tests/fft/test_pocketfft 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.fft.test_pocketfft_1.1_494cd948a6365d26_.log 2024-08-20T21:58:28.9521676Z Running 0 items in this shard: 2024-08-20T21:58:28.9521980Z 2024-08-20T21:58:28.9522545Z Running torch_np/numpy_tests/core/test_dlpack 1/1 ... [2024-08-20 21:58:28.951819] 2024-08-20T21:58:28.9523360Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:58:28.9525794Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_dlpack.py', '-m', '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 21:58:28.952204] 2024-08-20T21:58:32.1723383Z 2024-08-20T21:58:32.1725691Z torch_np/numpy_tests/core/test_dlpack 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_dlpack_1.1_d94cf1a6f5da3ec2_.log 2024-08-20T21:58:32.1727313Z Running 0 items in this shard: 2024-08-20T21:58:32.1727609Z 2024-08-20T21:58:32.1728172Z Running torch_np/numpy_tests/lib/test_shape_base_ 1/1 ... [2024-08-20 21:58:32.172481] 2024-08-20T21:58:32.1729035Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:58:32.1731861Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_shape_base_.py', '-m', '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 21:58:32.172850] 2024-08-20T21:58:35.3928618Z 2024-08-20T21:58:35.3930890Z torch_np/numpy_tests/lib/test_shape_base_ 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_shape_base__1.1_0673fbb8288b34d5_.log 2024-08-20T21:58:35.3932594Z Running 0 items in this shard: 2024-08-20T21:58:35.3932978Z 2024-08-20T21:58:35.3933471Z Running torch_np/numpy_tests/fft/test_helper 1/1 ... [2024-08-20 21:58:35.392993] 2024-08-20T21:58:35.3934286Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:58:35.3936923Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/fft/test_helper.py', '-m', '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 21:58:35.393351] 2024-08-20T21:58:38.6133639Z 2024-08-20T21:58:38.6135886Z torch_np/numpy_tests/fft/test_helper 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.fft.test_helper_1.1_ef768fa1cc2c5ab2_.log 2024-08-20T21:58:38.6137778Z Running 0 items in this shard: 2024-08-20T21:58:38.6138056Z 2024-08-20T21:58:38.6138561Z Running torch_np/numpy_tests/core/test_numerictypes 1/1 ... [2024-08-20 21:58:38.613510] 2024-08-20T21:58:38.6139320Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:58:38.6142772Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_numerictypes.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:58:38.613896] 2024-08-20T21:58:41.8340316Z 2024-08-20T21:58:41.8342469Z torch_np/numpy_tests/core/test_numerictypes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_numerictypes_1.1_93f15c216f6bfe97_.log 2024-08-20T21:58:41.8344421Z Running 0 items in this shard: 2024-08-20T21:58:41.8344893Z 2024-08-20T21:58:41.8345422Z Running torch_np/numpy_tests/lib/test_arraysetops 1/1 ... [2024-08-20 21:58:41.834191] 2024-08-20T21:58:41.8346154Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:58:41.8350127Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_arraysetops.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:58:41.834603] 2024-08-20T21:58:45.0546206Z 2024-08-20T21:58:45.0548568Z torch_np/numpy_tests/lib/test_arraysetops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_arraysetops_1.1_7fb7b34ffe17341c_.log 2024-08-20T21:58:45.0550278Z Running 0 items in this shard: 2024-08-20T21:58:45.0550577Z 2024-08-20T21:58:45.0551135Z Running torch_np/test_scalars_0D_arrays 1/1 ... [2024-08-20 21:58:45.054730] 2024-08-20T21:58:45.0551896Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:58:45.0554950Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/test_scalars_0D_arrays.py', '-m', '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 21:58:45.055108] 2024-08-20T21:58:48.2749234Z 2024-08-20T21:58:48.2751231Z torch_np/test_scalars_0D_arrays 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_scalars_0D_arrays_1.1_7f7372639add5f30_.log 2024-08-20T21:58:48.2752855Z Running 0 items in this shard: 2024-08-20T21:58:48.2753219Z 2024-08-20T21:58:48.2753642Z Running profiler/test_memory_profiler 1/1 ... [2024-08-20 21:58:48.275091] 2024-08-20T21:58:48.2754291Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:58:48.2758511Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'profiler/test_memory_profiler.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:58:48.275494] 2024-08-20T21:58:51.4955803Z 2024-08-20T21:58:51.4958030Z profiler/test_memory_profiler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_memory_profiler_1.1_87d04663595df07e_.log 2024-08-20T21:58:51.4959939Z Running 0 items in this shard: 2024-08-20T21:58:51.4960220Z 2024-08-20T21:58:51.4960701Z Running torch_np/numpy_tests/lib/test_arraypad 1/1 ... [2024-08-20 21:58:51.495724] 2024-08-20T21:58:51.4961402Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:58:51.4964605Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_arraypad.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:58:51.496096] 2024-08-20T21:58:54.7162005Z 2024-08-20T21:58:54.7164161Z torch_np/numpy_tests/lib/test_arraypad 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_arraypad_1.1_99e3ee7c5a9d34c0_.log 2024-08-20T21:58:54.7165727Z Running 0 items in this shard: 2024-08-20T21:58:54.7166009Z 2024-08-20T21:58:54.7166377Z Running test_jit_autocast 1/1 ... [2024-08-20 21:58:54.716362] 2024-08-20T21:58:54.7166937Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:58:54.7170797Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_jit_autocast.py', '-m', '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 21:58:54.716725] 2024-08-20T21:58:59.9905100Z 2024-08-20T21:58:59.9906901Z test_jit_autocast 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jit_autocast_1.1_fedfb394a3565b7e_.log 2024-08-20T21:58:59.9908049Z Running 0 items in this shard: 2024-08-20T21:58:59.9908323Z 2024-08-20T21:58:59.9909297Z Running profiler/test_profiler_tree 1/1 ... [2024-08-20 21:58:59.990678] 2024-08-20T21:58:59.9910047Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:58:59.9922876Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'profiler/test_profiler_tree.py', '-m', '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 21:58:59.991098] 2024-08-20T21:59:03.2113127Z 2024-08-20T21:59:03.2115123Z profiler/test_profiler_tree 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_profiler_tree_1.1_4145cff92623d624_.log 2024-08-20T21:59:03.2116354Z Running 0 items in this shard: 2024-08-20T21:59:03.2116663Z 2024-08-20T21:59:03.2117196Z Running torch_np/numpy_tests/core/test_scalar_ctors 1/1 ... [2024-08-20 21:59:03.211451] 2024-08-20T21:59:03.2117907Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:59:03.2123346Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_scalar_ctors.py', '-m', '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 21:59:03.211882] 2024-08-20T21:59:06.4320574Z 2024-08-20T21:59:06.4322558Z torch_np/numpy_tests/core/test_scalar_ctors 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_scalar_ctors_1.1_94bf581912f39a68_.log 2024-08-20T21:59:06.4324145Z Running 0 items in this shard: 2024-08-20T21:59:06.4324424Z 2024-08-20T21:59:06.4324875Z Running torch_np/test_reductions 1/1 ... [2024-08-20 21:59:06.432207] 2024-08-20T21:59:06.4325473Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:59:06.4329749Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/test_reductions.py', '-m', '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 21:59:06.432607] 2024-08-20T21:59:09.7529835Z 2024-08-20T21:59:09.7532055Z torch_np/test_reductions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_reductions_1.1_cd38140e05c080f3_.log 2024-08-20T21:59:09.7533362Z Running 0 items in this shard: 2024-08-20T21:59:09.7533746Z 2024-08-20T21:59:09.7534137Z Running profiler/test_torch_tidy 1/1 ... [2024-08-20 21:59:09.753142] 2024-08-20T21:59:09.7534755Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:59:09.7538724Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'profiler/test_torch_tidy.py', '-m', '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 21:59:09.753541] 2024-08-20T21:59:12.8736063Z 2024-08-20T21:59:12.8737904Z profiler/test_torch_tidy 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_torch_tidy_1.1_26355d1dc2ab6648_.log 2024-08-20T21:59:12.8739553Z Running 0 items in this shard: 2024-08-20T21:59:12.8740112Z 2024-08-20T21:59:12.8740565Z Running profiler/test_execution_trace 1/1 ... [2024-08-20 21:59:12.873728] 2024-08-20T21:59:12.8741226Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:59:12.8745250Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'profiler/test_execution_trace.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:59:12.874133] 2024-08-20T21:59:16.0942390Z 2024-08-20T21:59:16.0944192Z profiler/test_execution_trace 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_execution_trace_1.1_a5cf363c3bfa51ba_.log 2024-08-20T21:59:16.0945484Z Running 0 items in this shard: 2024-08-20T21:59:16.0945806Z 2024-08-20T21:59:16.0946683Z Running profiler/test_record_function 1/1 ... [2024-08-20 21:59:16.094425] 2024-08-20T21:59:16.0947415Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:59:16.0951812Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'profiler/test_record_function.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:59:16.094802] 2024-08-20T21:59:19.3150568Z 2024-08-20T21:59:19.3152619Z profiler/test_record_function 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_record_function_1.1_b3d8744d4efdbd12_.log 2024-08-20T21:59:19.3154135Z Running 0 items in this shard: 2024-08-20T21:59:19.3154694Z 2024-08-20T21:59:19.3155055Z Running lazy/test_bindings 1/1 ... [2024-08-20 21:59:19.315216] 2024-08-20T21:59:19.3155647Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:59:19.3159888Z 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 21:59:19.315649] 2024-08-20T21:59:21.1811972Z 2024-08-20T21:59:21.1813891Z lazy/test_bindings 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_bindings_1.1_02f3ae424268106d_.log 2024-08-20T21:59:21.1814847Z 2024-08-20T21:59:21.1925103Z Running dynamo/test_trace_rules 1/1 ... [2024-08-20 21:59:21.192063] 2024-08-20T21:59:21.1925791Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:59:21.1930381Z 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 21:59:21.192617] 2024-08-20T21:59:21.2030789Z Running dynamo/test_repros 1/1 ... [2024-08-20 21:59:21.202586] 2024-08-20T21:59:21.2031400Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:59:21.2035863Z 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 21:59:21.203188] 2024-08-20T21:59:21.2072499Z Running dynamo/test_higher_order_ops 1/1 ... [2024-08-20 21:59:21.206812] 2024-08-20T21:59:21.2073533Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:59:21.2078361Z 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 21:59:21.207457] 2024-08-20T21:59:24.3888254Z 2024-08-20T21:59:24.3890516Z 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_d61562a1fcc79f19_.log 2024-08-20T21:59:24.3891743Z 2024-08-20T21:59:24.5472941Z 2024-08-20T21:59:24.5475297Z dynamo/test_repros 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_repros_1.1_832bb69f9ae5aa42_.log 2024-08-20T21:59:24.5476740Z 2024-08-20T21:59:24.6028376Z 2024-08-20T21:59:24.6030601Z 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_f6e380a6c212e06e_.log 2024-08-20T21:59:24.6031708Z 2024-08-20T21:59:27.6845091Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:59:27.7515290Z Running dynamo/test_export 1/1 ... [2024-08-20 21:59:27.751001] 2024-08-20T21:59:27.7515918Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:59:27.7519699Z 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 21:59:27.751532] 2024-08-20T21:59:27.8419514Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:59:27.8761975Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:59:27.9091620Z Running dynamo/test_exc 1/1 ... [2024-08-20 21:59:27.908597] 2024-08-20T21:59:27.9092596Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:59:27.9096544Z 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 21:59:27.909139] 2024-08-20T21:59:27.9466341Z Running dynamo/test_ctx_manager 1/1 ... [2024-08-20 21:59:27.946058] 2024-08-20T21:59:27.9467443Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:59:27.9470691Z 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 21:59:27.946549] 2024-08-20T21:59:31.1394903Z 2024-08-20T21:59:31.1397065Z dynamo/test_export 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_export_1.1_35897e7182a5223b_.log 2024-08-20T21:59:31.1398028Z 2024-08-20T21:59:31.1864172Z 2024-08-20T21:59:31.1865901Z 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_4db090f37933c45f_.log 2024-08-20T21:59:31.1867006Z 2024-08-20T21:59:31.1950710Z 2024-08-20T21:59:31.1952391Z dynamo/test_exc 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_exc_1.1_e7f8da570faa6db8_.log 2024-08-20T21:59:31.1953292Z 2024-08-20T21:59:34.3870966Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:59:34.4540883Z Running dynamo/test_activation_checkpointing 1/1 ... [2024-08-20 21:59:34.453573] 2024-08-20T21:59:34.4541762Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:59:34.4544425Z 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 21:59:34.454027] 2024-08-20T21:59:34.4945771Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:59:34.5311741Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:59:34.5623076Z Running dynamo/test_optimizers 1/1 ... [2024-08-20 21:59:34.561799] 2024-08-20T21:59:34.5623986Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:59:34.5626629Z 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 21:59:34.562248] 2024-08-20T21:59:34.6027163Z Running dynamo/test_backends 1/1 ... [2024-08-20 21:59:34.602191] 2024-08-20T21:59:34.6028090Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:59:34.6030554Z 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 21:59:34.602645] 2024-08-20T21:59:37.7384890Z 2024-08-20T21:59:37.7387350Z dynamo/test_optimizers 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_optimizers_1.1_8f17d1953aa8a479_.log 2024-08-20T21:59:37.7388581Z 2024-08-20T21:59:37.8237607Z 2024-08-20T21:59:37.8240892Z 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_626c08b214a1e1a8_.log 2024-08-20T21:59:37.8243238Z 2024-08-20T21:59:37.9022269Z 2024-08-20T21:59:37.9023863Z dynamo/test_backends 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_backends_1.1_bfc1cacdf817c95a_.log 2024-08-20T21:59:37.9024860Z 2024-08-20T21:59:40.9646794Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:59:41.0312768Z Running dynamo/test_skip_non_tensor 1/1 ... [2024-08-20 21:59:41.030727] 2024-08-20T21:59:41.0315217Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:59:41.0318936Z 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 21:59:41.031236] 2024-08-20T21:59:41.1183210Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:59:41.1390718Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:59:41.1886805Z Running dynamo/test_python_autograd 1/1 ... [2024-08-20 21:59:41.188111] 2024-08-20T21:59:41.1887861Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:59:41.1891866Z 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 21:59:41.188687] 2024-08-20T21:59:41.2103538Z Running dynamo/test_verify_correctness 1/1 ... [2024-08-20 21:59:41.209830] 2024-08-20T21:59:41.2104332Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:59:41.2107258Z 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 21:59:41.210353] 2024-08-20T21:59:44.1438694Z 2024-08-20T21:59:44.1440785Z 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_fd7437c97c8fbd8b_.log 2024-08-20T21:59:44.1441849Z 2024-08-20T21:59:44.4806392Z 2024-08-20T21:59:44.4809101Z 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_321dbff446aa1589_.log 2024-08-20T21:59:44.4811164Z 2024-08-20T21:59:44.4885318Z 2024-08-20T21:59:44.4887652Z 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_7faf96db0048ac7c_.log 2024-08-20T21:59:47.3799971Z 2024-08-20T21:59:47.3801862Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:59:47.4471424Z Running dynamo/test_exceptions 1/1 ... [2024-08-20 21:59:47.446610] 2024-08-20T21:59:47.4472458Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:59:47.4475587Z 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 21:59:47.447092] 2024-08-20T21:59:47.8093905Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:59:47.8449522Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:59:47.8808642Z Running dynamo/test_base_output 1/1 ... [2024-08-20 21:59:47.880371] 2024-08-20T21:59:47.8809843Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:59:47.8814197Z 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 21:59:47.880918] 2024-08-20T21:59:47.9362147Z Running functorch/test_vmap 2/3 ... [2024-08-20 21:59:47.935473] 2024-08-20T21:59:47.9363568Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:59:47.9371760Z 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 21:59:47.936497] 2024-08-20T21:59:50.6304708Z 2024-08-20T21:59:50.6306675Z dynamo/test_exceptions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_exceptions_1.1_2d07629d20331cf0_.log 2024-08-20T21:59:50.6307675Z 2024-08-20T21:59:51.2266220Z 2024-08-20T21:59:51.2268428Z 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_8bddffd619a3cf93_.log 2024-08-20T21:59:51.2270003Z 2024-08-20T21:59:54.0200389Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:59:54.0887347Z Running dynamo/test_structured_trace 1/1 ... [2024-08-20 21:59:54.088263] 2024-08-20T21:59:54.0888039Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:59:54.0891610Z 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 21:59:54.088768] 2024-08-20T21:59:54.4811360Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:59:54.5537543Z Running dynamo/test_hooks 1/1 ... [2024-08-20 21:59:54.553217] 2024-08-20T21:59:54.5538666Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:59:54.5542196Z 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 21:59:54.553739] 2024-08-20T21:59:57.3657385Z 2024-08-20T21:59:57.3659386Z 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_bb7c33b6a7f0a84c_.log 2024-08-20T21:59:57.3660482Z 2024-08-20T21:59:57.9714713Z 2024-08-20T21:59:57.9716853Z dynamo/test_hooks 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_hooks_1.1_b3fb3b21ebc28783_.log 2024-08-20T21:59:57.9718333Z 2024-08-20T22:00:00.6305724Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:00:00.6986197Z Running dynamo/test_profiler 1/1 ... [2024-08-20 22:00:00.698087] 2024-08-20T22:00:00.6987146Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:00:00.6990927Z 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 22:00:00.698582] 2024-08-20T22:00:01.3025399Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:00:01.3698664Z Running dynamo/test_recompile_ux 1/1 ... [2024-08-20 22:00:01.369249] 2024-08-20T22:00:01.3699750Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:00:01.3702955Z 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 22:00:01.369780] 2024-08-20T22:00:03.8858489Z 2024-08-20T22:00:03.8860808Z dynamo/test_profiler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_profiler_1.1_12fbcbc7b493c513_.log 2024-08-20T22:00:03.8862518Z 2024-08-20T22:00:04.5245801Z 2024-08-20T22:00:04.5247771Z 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_da7b9ecc9e027455_.log 2024-08-20T22:00:04.5248806Z 2024-08-20T22:00:07.0798190Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:00:07.1456074Z Running dynamo/test_deviceguard 1/1 ... [2024-08-20 22:00:07.145086] 2024-08-20T22:00:07.1456817Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:00:07.1460423Z 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 22:00:07.145588] 2024-08-20T22:00:07.8117130Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:00:07.8776809Z Running test_linalg 2/4 ... [2024-08-20 22:00:07.877156] 2024-08-20T22:00:07.8777743Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:00:07.8781136Z 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 22:00:07.877622] 2024-08-20T22:00:10.2526036Z 2024-08-20T22:00:10.2528519Z dynamo/test_deviceguard 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_deviceguard_1.1_26f3af5f1e407e4b_.log 2024-08-20T22:00:10.2530930Z 2024-08-20T22:00:13.3659548Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:00:13.4312820Z Running test_linalg 3/4 ... [2024-08-20 22:00:13.430744] 2024-08-20T22:00:13.4313809Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:00:13.4317544Z 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 22:00:13.431265] 2024-08-20T22:09:00.9542510Z 2024-08-20T22:09:00.9544904Z functorch/test_vmap 2/3 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_vmap_2.3_fcd6f917ca8f9d94_.log 2024-08-20T22:09:01.0325450Z 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, 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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 2024-08-20T22:09:01.1084338Z 2024-08-20T22:09:04.1364960Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:09:04.2094981Z Running test_linalg 4/4 ... [2024-08-20 22:09:04.208938] 2024-08-20T22:09:04.2096620Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:09:04.2099591Z 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 22:09:04.209442] 2024-08-20T22:11:24.1986573Z 2024-08-20T22:11:24.1988711Z test_linalg 3/4 was successful, full logs can be found in artifacts with path test/test-reports/test_linalg_3.4_1b6b9311d18b7717_.log 2024-08-20T22:11:24.2183234Z 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 2024-08-20T22:11:24.2374868Z 2024-08-20T22:11:27.4100151Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:11:27.4853376Z Running dynamo/test_debug_utils 1/1 ... [2024-08-20 22:11:27.484832] 2024-08-20T22:11:27.4854550Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:11:27.4858781Z 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 22:11:27.485333] 2024-08-20T22:11:30.6714874Z 2024-08-20T22:11:30.6716974Z 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_989618ec1414850b_.log 2024-08-20T22:11:30.6718537Z 2024-08-20T22:11:33.8903640Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:11:33.9576633Z Running test_cuda_multigpu 1/1 ... [2024-08-20 22:11:33.957149] 2024-08-20T22:11:33.9577573Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:11:33.9582224Z 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 22:11:33.957678] 2024-08-20T22:11:37.3956838Z 2024-08-20T22:11:37.3959216Z test_cuda_multigpu 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_multigpu_1.1_a8b0db52b3801512_.log 2024-08-20T22:11:37.3961088Z Running 0 items in this shard: 2024-08-20T22:11:37.3961545Z 2024-08-20T22:11:40.5862739Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:11:40.6534172Z Running test_comparison_utils 1/1 ... [2024-08-20 22:11:40.652930] 2024-08-20T22:11:40.6534874Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:11:40.6538303Z 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 22:11:40.653429] 2024-08-20T22:11:44.5401301Z 2024-08-20T22:11:44.5403658Z test_comparison_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_comparison_utils_1.1_85055b35a2c03586_.log 2024-08-20T22:11:44.5406820Z 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 2024-08-20T22:11:44.5409108Z 2024-08-20T22:11:47.7303562Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:11:47.7964188Z Running test_mkl_verbose 1/1 ... [2024-08-20 22:11:47.795937] 2024-08-20T22:11:47.7964790Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:11:47.7969650Z 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 22:11:47.796440] 2024-08-20T22:11:56.1773961Z 2024-08-20T22:11:56.1776137Z test_mkl_verbose 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mkl_verbose_1.1_c15c46523cf105ee_.log 2024-08-20T22:11:56.1778552Z Running 2 items in this shard: test/test_mkl_verbose.py::TestMKLVerbose::test_verbose_off, test/test_mkl_verbose.py::TestMKLVerbose::test_verbose_on 2024-08-20T22:11:56.1779591Z 2024-08-20T22:11:59.4017307Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:11:59.4685974Z Running test_mkldnn_verbose 1/1 ... [2024-08-20 22:11:59.468075] 2024-08-20T22:11:59.4686623Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:11:59.4689875Z 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 22:11:59.468541] 2024-08-20T22:12:07.2983096Z 2024-08-20T22:12:07.2985722Z test_mkldnn_verbose 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mkldnn_verbose_1.1_82b3beded4a7002d_.log 2024-08-20T22:12:07.2988933Z Running 2 items in this shard: test/test_mkldnn_verbose.py::TestMKLDNNVerbose::test_verbose_off, test/test_mkldnn_verbose.py::TestMKLDNNVerbose::test_verbose_on 2024-08-20T22:12:07.2990656Z 2024-08-20T22:12:10.5281049Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:12:10.5963037Z Running test_custom_ops 1/1 ... [2024-08-20 22:12:10.595780] 2024-08-20T22:12:10.5963863Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:12:10.5966600Z 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 22:12:10.596244] 2024-08-20T22:12:57.8592453Z 2024-08-20T22:12:57.8594077Z test_custom_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_custom_ops_1.1_5030888b8dbdefd6_.log 2024-08-20T22:12:57.8728116Z 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 2024-08-20T22:12:57.8835539Z 2024-08-20T22:13:01.2651056Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:13:01.3377585Z Running test_ao_sparsity 1/1 ... [2024-08-20 22:13:01.337261] 2024-08-20T22:13:01.3378417Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:13:01.3382832Z 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 22:13:01.337818] 2024-08-20T22:14:10.0353634Z 2024-08-20T22:14:10.0355899Z test_linalg 2/4 was successful, full logs can be found in artifacts with path test/test-reports/test_linalg_2.4_f9379a578d0731af_.log 2024-08-20T22:14:10.0609868Z 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, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_0_n_32_use_transpose_a_False_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_0_n_32_use_transpose_a_True_use_transpose_b_False_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_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_0_n_32_use_transpose_a_True_use_transpose_b_True_non_contig_type_1_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_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_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_0_cpu, 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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_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_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_32_n_32_use_transpose_a_True_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_k_16_n_16_use_transpose_a_True_use_transpose_b_False_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_k_32_n_16_use_transpose_a_False_use_transpose_b_True_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_k_32_n_16_use_transpose_a_True_use_transpose_b_False_cpu, test/test_linalg.py::TestLinalgCPU::test_addbmm_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_addbmm_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_addmm_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_addmm_relu_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_addmm_sizes_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmv_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmv_rowmajor_colmajor_incx_incy_lda_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addr_float_and_complex_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addr_integral_cpu_int32, test/test_linalg.py::TestLinalgCPU::test_baddbmm_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_baddbmm_nan_input_with_zero_beta_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_blas_alpha_beta_empty_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_blas_alpha_beta_empty_cpu_uint8, test/test_linalg.py::TestLinalgCPU::test_blas_empty_cpu, test/test_linalg.py::TestLinalgCPU::test_blas_nan_out_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_bmm_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_bmm_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_bmm_tunableop_rocm_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_cholesky_errors_and_warnings_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_cholesky_errors_and_warnings_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_cholesky_ex_non_pd_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_cholesky_ex_non_pd_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_cholesky_inverse_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_cholesky_inverse_errors_and_warnings_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_cholesky_inverse_errors_and_warnings_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_out_errors_and_warnings_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_compile_int4_mm_m_32_k_64_n_48_cpu, test/test_linalg.py::TestLinalgCPU::test_compile_int4_mm_m_64_k_32_n_64_cpu, test/test_linalg.py::TestLinalgCPU::test_compile_int4_mm_m_64_k_64_n_48_cpu, test/test_linalg.py::TestLinalgCPU::test_compile_int8_mm_m_64_k_32_n_48_cpu, test/test_linalg.py::TestLinalgCPU::test_compile_int8_mm_m_64_k_64_n_48_cpu, test/test_linalg.py::TestLinalgCPU::test_cond_errors_and_warnings_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_cond_errors_and_warnings_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_cond_errors_and_warnings_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_corner_cases_of_cublasltmatmul_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_corner_cases_of_cublasltmatmul_cpu_int64, test/test_linalg.py::TestLinalgCPU::test_det_logdet_slogdet_batched_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_dot_vs_numpy_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_dot_vs_numpy_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_eig_compare_backends_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_eig_with_nan_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_eigh_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_eigh_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_eigh_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_eigh_errors_and_warnings_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_eigh_errors_and_warnings_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_eigh_svd_illcondition_matrix_input_should_not_crash_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_eigvals_errors_and_warnings_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_eigvals_numpy_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_eigvalsh_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_eigvalsh_errors_and_warnings_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_einsum_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_einsum_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_einsum_random_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_fp16_mv_transposed_first_argument_arm_cpu_m_32_k_36_cpu, test/test_linalg.py::TestLinalgCPU::test_fp16_mv_transposed_first_argument_arm_cpu_m_35_k_32_cpu, test/test_linalg.py::TestLinalgCPU::test_fp16_mv_transposed_first_argument_arm_cpu_m_35_k_35_cpu, test/test_linalg.py::TestLinalgCPU::test_fp16_mv_transposed_first_argument_arm_cpu_m_40_k_32_cpu, test/test_linalg.py::TestLinalgCPU::test_fp16_mv_transposed_first_argument_arm_cpu_m_40_k_64_cpu, test/test_linalg.py::TestLinalgCPU::test_fp16_mv_transposed_first_argument_arm_cpu_m_64_k_36_cpu, test/test_linalg.py::TestLinalgCPU::test_householder_product_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_householder_product_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_inner_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_inv_errors_and_warnings_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_inv_ex_info_device_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_inv_ex_singular_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_inv_ex_singular_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_inverse_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_inverse_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_inverse_errors_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_inverse_errors_large_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_inverse_errors_large_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_inverse_errors_large_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_inverse_many_batches_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_kron_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_kron_empty_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_ldl_factor_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_ldl_solve_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_linalg_cross_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_linalg_lstsq_batch_broadcasting_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_linalg_lstsq_batch_broadcasting_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_linalg_lstsq_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_linalg_lstsq_input_checks_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_linalg_lstsq_input_checks_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_linalg_lu_family_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_linalg_matrix_exp_analytic_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_linalg_matrix_exp_batch_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_linalg_matrix_exp_boundary_cases_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_linalg_matrix_exp_boundary_cases_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_linalg_matrix_exp_compare_with_taylor_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_linalg_solve_triangular_broadcasting_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_linalg_solve_triangular_broadcasting_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_linalg_solve_triangular_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_lobpcg_scipy_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_lu_solve_batched_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_lu_solve_batched_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_lu_solve_batched_many_batches_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_lu_solve_large_matrices_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_lu_solve_large_matrices_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_lu_unpack_check_input_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_matmul_45724_cpu, test/test_linalg.py::TestLinalgCPU::test_matmul_out_kernel_errors_with_autograd_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_matmul_small_brute_force_1d_Nd_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_matmul_small_brute_force_2d_Nd_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_matmul_small_brute_force_tunableop_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_matrix_norm_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_matrix_power_negative_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_matrix_power_non_negative_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_matrix_rank_atol_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_matrix_rank_atol_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_matrix_rank_empty_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_matrix_rank_empty_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_matrix_rank_empty_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_matrix_rank_out_errors_and_warnings_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_mm_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_mm_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_norm_dtype_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_norm_dtype_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_norm_errors_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_norm_fused_type_promotion_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_norm_matrix_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_norm_matrix_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_norm_old_cpu, test/test_linalg.py::TestLinalgCPU::test_norm_vector_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_norm_vector_degenerate_shapes_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_nuclear_norm_axes_small_brute_force_old_cpu, test/test_linalg.py::TestLinalgCPU::test_old_cholesky_batched_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_old_cholesky_batched_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_old_cholesky_batched_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_old_cholesky_batched_upper_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_old_cholesky_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_old_cholesky_empty_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_old_cholesky_empty_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_ormqr_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_ormqr_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_outer_cpu_bool, test/test_linalg.py::TestLinalgCPU::test_outer_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_outer_ger_addr_legacy_tests_cpu, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_bfloat16_bfloat16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_bfloat16_complex64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_bfloat16_int32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_bfloat16_int64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_bfloat16_int8, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_bool_bool, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_bool_complex128, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_bool_int16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex128_bfloat16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex128_bool, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex128_int64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex64_float16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex64_int64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float16_int32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float32_int8, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float64_bool, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float64_complex64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float64_float16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int16_bfloat16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int16_complex128, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int16_int32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int16_int8, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int32_bool, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int32_uint8, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int64_uint8, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int8_float16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int8_int32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int8_int64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_uint8_bool, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_uint8_float32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_uint8_int16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_uint8_int32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_uint8_int8, test/test_linalg.py::TestLinalgCPU::test_pinv_errors_and_warnings_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_pinverse_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_pinverse_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_preferred_blas_library_cpu, test/test_linalg.py::TestLinalgCPU::test_qr_batched_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_qr_batched_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_qr_vs_numpy_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_slogdet_cpu_complex64, 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 2024-08-20T22:14:10.0825470Z 2024-08-20T22:14:13.3327911Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:14:13.4000826Z Running functorch/test_eager_transforms 1/1 ... [2024-08-20 22:14:13.399469] 2024-08-20T22:14:13.4001540Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:14:13.4005693Z 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 22:14:13.400098] 2024-08-20T22:14:22.8751551Z 2024-08-20T22:14:22.8753842Z test_ao_sparsity 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_ao_sparsity_1.1_8b77bc3fbb733284_.log 2024-08-20T22:14:22.8793154Z 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 2024-08-20T22:14:22.8829519Z 2024-08-20T22:14:26.2391120Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:14:26.3074956Z Running test_optim 1/1 ... [2024-08-20 22:14:26.306975] 2024-08-20T22:14:26.3075840Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:14:26.3080416Z 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 22:14:26.307534] 2024-08-20T22:19:26.0610071Z 2024-08-20T22:19:26.0612711Z 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_bfd12c9080d4ba3c_.log 2024-08-20T22:19:26.0823621Z 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, test/functorch/test_eager_transforms.py::TestMakeFunctional::test_make_functional_state_correctly_returned_after_forward_mechanism_functional_call, test/functorch/test_eager_transforms.py::TestMakeFunctional::test_make_functional_state_correctly_returned_after_forward_mechanism_make_functional, test/functorch/test_eager_transforms.py::TestMakeFunctional::test_parameter_tying, test/functorch/test_eager_transforms.py::TestMakeFunctional::test_parameter_tying_ensemble, test/functorch/test_eager_transforms.py::TestMakeFunctional::test_parameter_tying_grad, test/functorch/test_eager_transforms.py::TestMakeFunctional::test_stack_module_state_error, test/functorch/test_eager_transforms.py::TestMakeFunctional::test_stack_module_state_leaf, test/functorch/test_eager_transforms.py::TestMakeFunctional::test_stack_module_state_mismatch_error, test/functorch/test_eager_transforms.py::TestMakeFunctional::test_stack_module_state_smoke, test/functorch/test_eager_transforms.py::TestMakeFunctional::test_using_detach_functional_call_detach_params_False, test/functorch/test_eager_transforms.py::TestMakeFunctional::test_using_detach_functional_call_detach_params_True, test/functorch/test_eager_transforms.py::TestMakeFunctional::test_with_buffers_disable_autograd_tracking_disable_autograd_tracking_False, test/functorch/test_eager_transforms.py::TestMakeFunctional::test_with_buffers_disable_autograd_tracking_disable_autograd_tracking_True, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_advanced_indexing_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_argnums_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_composed_with_autograd_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_composite_complicated_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_composite_simple_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_composite_two_ops_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_conj_bit_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_dtype_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_escaped_wrappers_are_ignored_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_escaped_wrappers_are_marked_as_dead_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_fn_with_kwargs_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_functional_init_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_functional_init_with_buffers_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_grad_aux_pytree_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_grad_aux_tensor_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_grad_of_vjp_composition_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_grad_of_vjp_of_grad_composition_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_grad_pytree_inputs_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_inplace_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_inplace_on_captures_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_inplace_on_view_base_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_inplace_on_view_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_invalid_argnums_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_is_cuda_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_manual_seed_inside_grad_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_negative_argnums_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_nesting_simple_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_no_grad_inside_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_no_grad_mixed_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_no_grad_nested_complicated_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_no_grad_nested_simple_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_no_grad_outside_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_no_grad_outside_vjp_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_no_grad_outside_vjp_fn_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_no_grad_outside_vjp_only_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_no_grad_value_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_numel_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_out_of_order_argnums_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_primitive_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_print_captured_tensor_inside_transform_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_shape_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_tensor_ctor_inside_grad_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_tensor_print_grad_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_tensor_print_grad_grad_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_tensor_print_vmap_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_tensor_print_vmap_grad_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_tensor_print_vmap_vmap_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_unrelated_grad_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_unrelated_hessian_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_unrelated_vjp_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_unrelated_vjp_multiple_inputs_outputs_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_view_inplace_simple_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_views_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_vjp_aux_pytree_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_vjp_aux_tensor_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_vjp_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_vjp_of_grad_composition_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_vjp_outputs_can_any_pytree_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_vjp_pytree_error_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_vjp_pytree_input_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_vjp_pytree_output_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_vjp_two_outputs_cpu, test/functorch/test_eager_transforms.py::TestGradTransformCPU::test_zero_grad_cpu, test/functorch/test_eager_transforms.py::TestVmapOfGradCPU::test_log_softmax_cpu, test/functorch/test_eager_transforms.py::TestVmapOfGradCPU::test_new_empty_materializes_tensor_cpu, test/functorch/test_eager_transforms.py::TestVmapOfGradCPU::test_new_zeros_materializes_tensor_cpu, test/functorch/test_eager_transforms.py::TestVmapOfGradCPU::test_per_sample_grads_embeddingnet_mechanism_functional_call_cpu, test/functorch/test_eager_transforms.py::TestVmapOfGradCPU::test_per_sample_grads_embeddingnet_mechanism_make_functional_cpu, test/functorch/test_eager_transforms.py::TestVmapOfGradCPU::test_per_sample_grads_inplace_view_cpu, test/functorch/test_eager_transforms.py::TestVmapOfGradCPU::test_per_sample_grads_simple_cpu, test/functorch/test_eager_transforms.py::TestJacCPU::test_against_reference_correctness_different_devices_jacfwd_cpu, test/functorch/test_eager_transforms.py::TestJacCPU::test_against_reference_correctness_different_devices_jacrev_cpu, test/functorch/test_eager_transforms.py::TestJacCPU::test_against_reference_default_arg_jacfwd_cpu, test/functorch/test_eager_transforms.py::TestJacCPU::test_against_reference_default_arg_jacrev_cpu, test/functorch/test_eager_transforms.py::TestJacCPU::test_against_reference_multi_input_jacfwd_cpu, test/functorch/test_eager_transforms.py::TestJacCPU::test_against_reference_multi_input_jacrev_cpu, test/functorch/test_eager_transforms.py::TestJacCPU::test_against_reference_multi_input_multi_output_jacfwd_cpu, test/functorch/test_eager_transforms.py::TestJacCPU::test_against_reference_multi_input_multi_output_jacrev_cpu, test/functorch/test_eager_transforms.py::TestJacCPU::test_against_reference_simple_jacfwd_cpu, test/functorch/test_eager_transforms.py::TestJacCPU::test_against_reference_simple_jacrev_cpu, test/functorch/test_eager_transforms.py::TestJacCPU::test_against_reference_unrelated_outputs_jacfwd_cpu, test/functorch/test_eager_transforms.py::TestJacCPU::test_against_reference_unrelated_outputs_jacrev_cpu, test/functorch/test_eager_transforms.py::TestJacCPU::test_against_reference_zero_dim_jacfwd_cpu, test/functorch/test_eager_transforms.py::TestJacCPU::test_against_reference_zero_dim_jacrev_cpu, test/functorch/test_eager_transforms.py::TestJacCPU::test_argnums_defaults_to_zero_jacfwd_cpu, test/functorch/test_eager_transforms.py::TestJacCPU::test_argnums_defaults_to_zero_jacrev_cpu, test/functorch/test_eager_transforms.py::TestJacCPU::test_argnums_effect_on_return_jacfwd_cpu, test/functorch/test_eager_transforms.py::TestJacCPU::test_argnums_effect_on_return_jacrev_cpu, test/functorch/test_eager_transforms.py::TestJacCPU::test_argnums_tuple_jacfwd_cpu, test/functorch/test_eager_transforms.py::TestJacCPU::test_argnums_tuple_jacrev_cpu, test/functorch/test_eager_transforms.py::TestJacCPU::test_aux_pytree_jacfwd_cpu, test/functorch/test_eager_transforms.py::TestJacCPU::test_aux_pytree_jacrev_cpu, test/functorch/test_eager_transforms.py::TestJacCPU::test_aux_tensor_jacfwd_cpu, test/functorch/test_eager_transforms.py::TestJacCPU::test_aux_tensor_jacrev_cpu, test/functorch/test_eager_transforms.py::TestJacCPU::test_chunk_jacrev__preallocate_and_copy_False_cpu, test/functorch/test_eager_transforms.py::TestJacCPU::test_chunk_jacrev__preallocate_and_copy_True_cpu, test/functorch/test_eager_transforms.py::TestJacCPU::test_chunk_jacrev_chunksize_one__preallocate_and_copy_False_cpu, test/functorch/test_eager_transforms.py::TestJacCPU::test_chunk_jacrev_chunksize_one__preallocate_and_copy_True_cpu, test/functorch/test_eager_transforms.py::TestJacCPU::test_chunk_jacrev_composition__preallocate_and_copy_False_cpu, test/functorch/test_eager_transforms.py::TestJacCPU::test_chunk_jacrev_composition__preallocate_and_copy_True_cpu, test/functorch/test_eager_transforms.py::TestJacCPU::test_complex_error_cpu, test/functorch/test_eager_transforms.py::TestJacCPU::test_diff_numel_jacfwd_cpu, test/functorch/test_eager_transforms.py::TestJacCPU::test_diff_numel_jacrev_cpu, test/functorch/test_eager_transforms.py::TestJacCPU::test_dimensionality_jacfwd_cpu, test/functorch/test_eager_transforms.py::TestJacCPU::test_dimensionality_jacrev_cpu, 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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 2024-08-20T22:19:26.1027436Z 2024-08-20T22:19:29.3269960Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:19:29.3941477Z Running test_masked 1/1 ... [2024-08-20 22:19:29.393630] 2024-08-20T22:19:29.3942257Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:19:29.3944866Z 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 22:19:29.394111] 2024-08-20T22:20:29.1596741Z 2024-08-20T22:20:29.1599262Z test_masked 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_masked_1.1_bd94db48f0eb7e72_.log 2024-08-20T22:20:29.1717191Z 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 2024-08-20T22:20:29.1813602Z 2024-08-20T22:20:32.8186546Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:20:32.8846405Z Running test_view_ops 1/1 ... [2024-08-20 22:20:32.884101] 2024-08-20T22:20:32.8847317Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:20:32.8850955Z 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 22:20:32.884599] 2024-08-20T22:21:42.8233024Z 2024-08-20T22:21:42.8236297Z test_view_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_view_ops_1.1_c94ffd45d478be23_.log 2024-08-20T22:21:42.8562963Z 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 2024-08-20T22:21:42.8886424Z 2024-08-20T22:21:45.9928442Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:21:46.0627629Z Running test_indexing 1/1 ... [2024-08-20 22:21:46.062212] 2024-08-20T22:21:46.0628600Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:21:46.0632367Z 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 22:21:46.062748] 2024-08-20T22:22:12.8572578Z 2024-08-20T22:22:12.8574763Z test_linalg 4/4 was successful, full logs can be found in artifacts with path test/test-reports/test_linalg_4.4_98373e1c4235080d_.log 2024-08-20T22:22:12.8831596Z 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, 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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, 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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, 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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 2024-08-20T22:22:12.8977595Z 2024-08-20T22:22:16.0290005Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:22:16.0993603Z Running test_monitor 1/1 ... [2024-08-20 22:22:16.098773] 2024-08-20T22:22:16.0994570Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:22:16.0998225Z 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 22:22:16.099300] 2024-08-20T22:22:19.6703424Z 2024-08-20T22:22:19.6705583Z test_monitor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_monitor_1.1_6a6394d2e77c5d4f_.log 2024-08-20T22:22:19.6710588Z 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 2024-08-20T22:22:19.6714360Z 2024-08-20T22:22:22.8096267Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:22:22.8791149Z Running benchmark_utils/test_benchmark_utils 1/1 ... [2024-08-20 22:22:22.878599] 2024-08-20T22:22:22.8791908Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:22:22.8795504Z 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 22:22:22.879167] 2024-08-20T22:22:28.5552575Z 2024-08-20T22:22:28.5555363Z 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_27fa3dd76dba5d7c_.log 2024-08-20T22:22:28.5561447Z 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 2024-08-20T22:22:28.5565997Z 2024-08-20T22:22:31.7237196Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:22:31.7911614Z Running test_binary_ufuncs 1/2 ... [2024-08-20 22:22:31.790606] 2024-08-20T22:22:31.7912590Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:22:31.7916079Z 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 22:22:31.791127] 2024-08-20T22:23:21.3518200Z 2024-08-20T22:23:21.3519998Z test_optim 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_optim_1.1_17586781585ceb9b_.log 2024-08-20T22:23:21.4017850Z 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, 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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, 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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, 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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_cpu_load_state_dict_impl_capturable_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_cpu_load_state_dict_impl_fused_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_cpu_load_state_dict_impl_fused_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_cpu_load_state_dict_impl_fused_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_cpu_load_state_dict_impl_fused_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_deepcopy_copies_all_public_attrs_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_deepcopy_copies_all_public_attrs_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_deepcopy_copies_all_public_attrs_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_deepcopy_copies_all_public_attrs_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_deepcopy_copies_all_public_attrs_AdamW_cpu_float32, 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test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_second_order_optims_return_consistent_types_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_set_default_dtype_works_with_foreach_ASGD_cpu_float64, test/test_optim.py::TestOptimRenewedCPU::test_set_default_dtype_works_with_foreach_Adadelta_cpu_float64, test/test_optim.py::TestOptimRenewedCPU::test_set_default_dtype_works_with_foreach_Adafactor_cpu_float64, test/test_optim.py::TestOptimRenewedCPU::test_set_default_dtype_works_with_foreach_Adagrad_cpu_float64, test/test_optim.py::TestOptimRenewedCPU::test_set_default_dtype_works_with_foreach_AdamW_cpu_float64, test/test_optim.py::TestOptimRenewedCPU::test_set_default_dtype_works_with_foreach_Adam_cpu_float64, test/test_optim.py::TestOptimRenewedCPU::test_set_default_dtype_works_with_foreach_Adamax_cpu_float64, test/test_optim.py::TestOptimRenewedCPU::test_set_default_dtype_works_with_foreach_NAdam_cpu_float64, test/test_optim.py::TestOptimRenewedCPU::test_set_default_dtype_works_with_foreach_RAdam_cpu_float64, test/test_optim.py::TestOptimRenewedCPU::test_set_default_dtype_works_with_foreach_RMSprop_cpu_float64, test/test_optim.py::TestOptimRenewedCPU::test_set_default_dtype_works_with_foreach_Rprop_cpu_float64, test/test_optim.py::TestOptimRenewedCPU::test_set_default_dtype_works_with_foreach_SGD_cpu_float64, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_post_hook_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_post_hook_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_post_hook_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_post_hook_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_post_hook_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_post_hook_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_post_hook_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_post_hook_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_post_hook_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_post_hook_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_post_hook_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_post_hook_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_post_hook_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_post_hook_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_hook_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_hook_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_hook_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_hook_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_hook_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_hook_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_hook_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_hook_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_hook_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_hook_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_hook_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_hook_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_hook_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_hook_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_post_hook_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_post_hook_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_post_hook_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_post_hook_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_post_hook_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_post_hook_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_post_hook_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_post_hook_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_post_hook_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_post_hook_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_post_hook_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_post_hook_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_post_hook_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_post_hook_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_with_cuda_params_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_with_cuda_params_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_with_cuda_params_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_with_cuda_params_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_with_cuda_params_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_with_cuda_params_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_with_cuda_params_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_with_cuda_params_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_with_cuda_params_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_with_cuda_params_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_with_cuda_params_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_with_cuda_params_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_with_cuda_params_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_with_cuda_params_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_all_hooks_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_all_hooks_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_all_hooks_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_all_hooks_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_all_hooks_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_all_hooks_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_all_hooks_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_all_hooks_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_all_hooks_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_all_hooks_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_all_hooks_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_all_hooks_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_all_hooks_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_all_hooks_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_for_zero_grads_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_for_zero_grads_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_for_zero_grads_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_for_zero_grads_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_for_zero_grads_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_for_zero_grads_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_for_zero_grads_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_for_zero_grads_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_for_zero_grads_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_for_zero_grads_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_for_zero_grads_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_for_zero_grads_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_for_zero_grads_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_for_zero_grads_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_when_params_have_no_grad_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_when_params_have_no_grad_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_when_params_have_no_grad_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_when_params_have_no_grad_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_when_params_have_no_grad_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_when_params_have_no_grad_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_when_params_have_no_grad_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_when_params_have_no_grad_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_when_params_have_no_grad_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_when_params_have_no_grad_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_when_params_have_no_grad_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_when_params_have_no_grad_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_when_params_have_no_grad_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_when_params_have_no_grad_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_post_hook_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_post_hook_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_post_hook_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_post_hook_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_post_hook_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_post_hook_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_post_hook_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_post_hook_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_post_hook_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_post_hook_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_post_hook_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_post_hook_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_post_hook_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_post_hook_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_pre_hook_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_pre_hook_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_pre_hook_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_pre_hook_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_pre_hook_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_pre_hook_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_pre_hook_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_pre_hook_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_pre_hook_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_pre_hook_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_pre_hook_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_pre_hook_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_pre_hook_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_pre_hook_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_AdamW_cpu_float32, 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 2024-08-20T22:23:21.4402172Z 2024-08-20T22:23:24.4920305Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:23:24.5670598Z Running test_futures 1/1 ... [2024-08-20 22:23:24.566529] 2024-08-20T22:23:24.5671656Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:23:24.5675582Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_futures.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:23:24.567044] 2024-08-20T22:23:31.2448829Z 2024-08-20T22:23:31.2450915Z test_futures 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_futures_1.1_195424aedc169d79_.log 2024-08-20T22:23:31.2460471Z Running 22 items in this shard: test/test_futures.py::TestFuture::test_add_done_callback_error_is_ignored, test/test_futures.py::TestFuture::test_add_done_callback_maintains_callback_order, test/test_futures.py::TestFuture::test_add_done_callback_no_arg_error_is_ignored, test/test_futures.py::TestFuture::test_add_done_callback_simple, test/test_futures.py::TestFuture::test_chained_then, test/test_futures.py::TestFuture::test_collect_all, test/test_futures.py::TestFuture::test_done, test/test_futures.py::TestFuture::test_done_exception, test/test_futures.py::TestFuture::test_interleaving_then_and_add_done_callback_maintains_callback_order, test/test_futures.py::TestFuture::test_interleaving_then_and_add_done_callback_propagates_error, test/test_futures.py::TestFuture::test_mark_future_twice, test/test_futures.py::TestFuture::test_pickle_future, test/test_futures.py::TestFuture::test_set_exception, test/test_futures.py::TestFuture::test_set_exception_multithreading, test/test_futures.py::TestFuture::test_then, test/test_futures.py::TestFuture::test_then_no_arg, test/test_futures.py::TestFuture::test_then_raise, test/test_futures.py::TestFuture::test_then_wrong_arg, test/test_futures.py::TestFuture::test_wait, test/test_futures.py::TestFuture::test_wait_all, test/test_futures.py::TestFuture::test_wait_multi_thread, test/test_futures.py::TestFuture::test_wait_none 2024-08-20T22:23:31.2469114Z 2024-08-20T22:23:34.3679452Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:23:34.4373463Z Running nn/test_embedding 1/1 ... [2024-08-20 22:23:34.436834] 2024-08-20T22:23:34.4374089Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:23:34.4377001Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'nn/test_embedding.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:23:34.437350] 2024-08-20T22:23:55.4194622Z 2024-08-20T22:23:55.4197112Z test_indexing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_indexing_1.1_f33828f2e54572de_.log 2024-08-20T22:23:55.4261315Z 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 2024-08-20T22:23:55.4318800Z 2024-08-20T22:23:58.5688559Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:23:58.6360009Z Running test_functional_optim 1/1 ... [2024-08-20 22:23:58.635359] 2024-08-20T22:23:58.6361053Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:23:58.6367833Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_functional_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 22:23:58.635956] 2024-08-20T22:24:03.5612564Z 2024-08-20T22:24:03.5614997Z test_functional_optim 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_functional_optim_1.1_f2fa4aed339c784f_.log 2024-08-20T22:24:03.5620812Z Running 4 items in this shard: test/test_functional_optim.py::TestFunctionalOptimParity::test_functional_optim_parity_adam, test/test_functional_optim.py::TestFunctionalOptimParity::test_functional_optim_parity_adam_w, test/test_functional_optim.py::TestFunctionalOptimParity::test_functional_optim_parity_sgd, test/test_functional_optim.py::TestFunctionalOptimParity::test_functional_optim_registration 2024-08-20T22:24:03.5624968Z 2024-08-20T22:24:06.8862585Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:24:06.9728099Z Running test_mkldnn 1/1 ... [2024-08-20 22:24:06.972189] 2024-08-20T22:24:06.9728974Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:24:06.9738147Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_mkldnn.py', '-m', 'not 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 22:24:06.972689] 2024-08-20T22:26:20.0910954Z 2024-08-20T22:26:20.0998839Z test_mkldnn 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mkldnn_1.1_e862abeb791037f3_.log 2024-08-20T22:26:20.1031733Z Running 83 items in this shard: test/test_mkldnn.py::TestMkldnnCPU::test_0_dimension_tensor_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_adaptive_avg_pool2d_bf16_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_adaptive_avg_pool2d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_add_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_autograd_from_mkldnn_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_autograd_to_mkldnn_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_avg_pool2d_bf16_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_avg_pool2d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_avg_pool2d_stride_none_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_avg_pool3d_bf16_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_avg_pool3d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_batch_norm_2d_bf16_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_batch_norm_2d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_batch_norm_3d_bf16_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_batch_norm_3d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_clone_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_conv1d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_conv1d_functional_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_conv2d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_conv2d_legacy_jit_model_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_conv3d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_conv_deconv_1d_lower_precision_cpu_bfloat16, test/test_mkldnn.py::TestMkldnnCPU::test_conv_deconv_1d_lower_precision_cpu_float16, test/test_mkldnn.py::TestMkldnnCPU::test_conv_deconv_2d_lower_precision_cpu_bfloat16, test/test_mkldnn.py::TestMkldnnCPU::test_conv_deconv_2d_lower_precision_cpu_float16, test/test_mkldnn.py::TestMkldnnCPU::test_conv_deconv_3d_lower_precision_cpu_bfloat16, test/test_mkldnn.py::TestMkldnnCPU::test_conv_deconv_3d_lower_precision_cpu_float16, test/test_mkldnn.py::TestMkldnnCPU::test_conv_nhwc_fp32_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_conv_nhwc_lower_precision_cpu_bfloat16, test/test_mkldnn.py::TestMkldnnCPU::test_conv_nhwc_lower_precision_cpu_float16, test/test_mkldnn.py::TestMkldnnCPU::test_conv_transpose1d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_conv_transpose2d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_conv_transpose3d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_conv_transpose_nhwc_fp32_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_conv_transpose_nhwc_lower_precision_cpu_bfloat16, test/test_mkldnn.py::TestMkldnnCPU::test_conv_transpose_nhwc_lower_precision_cpu_float16, test/test_mkldnn.py::TestMkldnnCPU::test_conversion_byte_char_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_conversion_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_copy_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_detach_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_empty_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_gelu_bf16_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_gelu_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_is_mkldnn_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_is_mkldnn_jit_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_legacy_new_failure_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_linear_backward_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_linear_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_linear_lowp_cpu_bfloat16, test/test_mkldnn.py::TestMkldnnCPU::test_linear_lowp_cpu_float16, test/test_mkldnn.py::TestMkldnnCPU::test_linear_non_contiguous_weight_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_lstm_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_matmul_lower_precision_cpu_bfloat16, test/test_mkldnn.py::TestMkldnnCPU::test_matmul_lower_precision_cpu_float16, test/test_mkldnn.py::TestMkldnnCPU::test_max_pool2d_bf16_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_max_pool2d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_max_pool2d_stride_none_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_max_pool3d_bf16_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_max_pool3d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_max_pool_unsupported_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_mkldnn_conv_shapecheck_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_mul_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_prelu_bf16_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_prelu_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_relu__cpu, test/test_mkldnn.py::TestMkldnnCPU::test_relu_bf16_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_relu_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_relu_inplace_bf16_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_repr_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_reshape_backward_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_reshape_blocked_format_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_reshape_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_resnet18_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_resnext50_32x4d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_set_data_tensorimpl_type_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_sigmoid_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_softmax_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_tanh_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_transpose_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_transpose_invalid_dime_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_unsupported_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_view_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_zero__cpu 2024-08-20T22:26:20.1059738Z 2024-08-20T22:26:23.4381234Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:26:23.5046804Z Running test_weak 1/1 ... [2024-08-20 22:26:23.504136] 2024-08-20T22:26:23.5047835Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:26:23.5051630Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_weak.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:26:23.504624] 2024-08-20T22:26:33.6410757Z 2024-08-20T22:26:33.6414048Z test_weak 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_weak_1.1_63402bd17852cbb7_.log 2024-08-20T22:26:33.6444321Z Running 39 items in this shard: test/test_weak.py::WeakTest::test_make_weak_keyed_dict_from_dict, test/test_weak.py::WeakTest::test_make_weak_keyed_dict_from_weak_keyed_dict, test/test_weak.py::WeakTest::test_make_weak_keyed_dict_repr, test/test_weak.py::WeakTest::test_threaded_weak_key_dict_copy, test/test_weak.py::WeakTest::test_threaded_weak_key_dict_deepcopy, test/test_weak.py::WeakTest::test_weak_keyed_bad_delitem, test/test_weak.py::WeakTest::test_weak_keyed_delitem, test/test_weak.py::WeakTest::test_weak_keyed_dict_popitem, test/test_weak.py::WeakTest::test_weak_keyed_dict_setdefault, test/test_weak.py::WeakTest::test_weak_keyed_dict_update, test/test_weak.py::WeakTest::test_weak_keyed_union_operators, test/test_weak.py::WeakKeyDictionaryTestCase::test_bool, test/test_weak.py::WeakKeyDictionaryTestCase::test_constructor, test/test_weak.py::WeakKeyDictionaryTestCase::test_get, test/test_weak.py::WeakKeyDictionaryTestCase::test_getitem, test/test_weak.py::WeakKeyDictionaryTestCase::test_items, test/test_weak.py::WeakKeyDictionaryTestCase::test_keys, test/test_weak.py::WeakKeyDictionaryTestCase::test_len, test/test_weak.py::WeakKeyDictionaryTestCase::test_pop, test/test_weak.py::WeakKeyDictionaryTestCase::test_popitem, test/test_weak.py::WeakKeyDictionaryTestCase::test_read, test/test_weak.py::WeakKeyDictionaryTestCase::test_setdefault, test/test_weak.py::WeakKeyDictionaryTestCase::test_update, test/test_weak.py::WeakKeyDictionaryTestCase::test_values, test/test_weak.py::WeakKeyDictionaryTestCase::test_write, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_bool, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_constructor, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_get, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_getitem, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_items, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_keys, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_len, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_pop, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_popitem, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_read, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_setdefault, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_update, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_values, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_write 2024-08-20T22:26:33.6473063Z 2024-08-20T22:26:36.8374963Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:26:36.9057335Z Running lazy/test_debug_util 1/1 ... [2024-08-20 22:26:36.904912] 2024-08-20T22:26:36.9058923Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:26:36.9063962Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'lazy/test_debug_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 22:26:36.905417] 2024-08-20T22:26:40.9281392Z 2024-08-20T22:26:40.9283961Z lazy/test_debug_util 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_debug_util_1.1_27e9edfccbedf0be_.log 2024-08-20T22:26:40.9377048Z Running 1 items in this shard: test/lazy/test_debug_util.py::DebugUtilTest::test_get_python_frames 2024-08-20T22:26:40.9378638Z 2024-08-20T22:26:44.2561617Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:26:44.3237427Z Running test_sparse_semi_structured 1/1 ... [2024-08-20 22:26:44.322461] 2024-08-20T22:26:44.3238451Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:26:44.3241579Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_sparse_semi_structured.py', '-m', 'not 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 22:26:44.322948] 2024-08-20T22:26:48.1950618Z 2024-08-20T22:26:48.1953047Z test_sparse_semi_structured 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_sparse_semi_structured_1.1_df148261443576ae_.log 2024-08-20T22:26:48.1958128Z Running 3 items in this shard: test/test_sparse_semi_structured.py::SparseSemiStructuredTensorCompileTest::test_mlp_contiguous_relu_compile_cusparselt, test/test_sparse_semi_structured.py::SparseSemiStructuredTensorCompileTest::test_mlp_contiguous_relu_compile_cutlass, test/test_sparse_semi_structured.py::SparseSemiStructuredTensorCompileTest::test_sp24_compile 2024-08-20T22:26:48.1962459Z 2024-08-20T22:26:51.6068855Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:26:51.6768707Z Running nn/test_module_hooks 1/1 ... [2024-08-20 22:26:51.676312] 2024-08-20T22:26:51.6769714Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:26:51.6772816Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'nn/test_module_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 22:26:51.676843] 2024-08-20T22:27:19.1446448Z 2024-08-20T22:27:19.1448728Z nn/test_module_hooks 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_module_hooks_1.1_640c6fef3633095c_.log 2024-08-20T22:27:19.1481696Z Running 52 items in this shard: test/nn/test_module_hooks.py::TestModuleHooks::test_always_called_forward_hooks, test/nn/test_module_hooks.py::TestModuleHooks::test_bw_hook_warning_for_non_tensor_or_tuple, test/nn/test_module_hooks.py::TestModuleHooks::test_forward_hooks_named_tuple_False, test/nn/test_module_hooks.py::TestModuleHooks::test_forward_hooks_named_tuple_True, test/nn/test_module_hooks.py::TestModuleHooks::test_forward_pre_hooks_named_tuple_False, test/nn/test_module_hooks.py::TestModuleHooks::test_forward_pre_hooks_named_tuple_True, test/nn/test_module_hooks.py::TestModuleHooks::test_full_backward_hooks_named_tuple_False, test/nn/test_module_hooks.py::TestModuleHooks::test_full_backward_hooks_named_tuple_True, test/nn/test_module_hooks.py::TestModuleHooks::test_full_backward_pre_hooks_named_tuple_False, test/nn/test_module_hooks.py::TestModuleHooks::test_full_backward_pre_hooks_named_tuple_True, test/nn/test_module_hooks.py::TestModuleHooks::test_kwarg_hooks, test/nn/test_module_hooks.py::TestModuleHooks::test_mixed_hooks_named_tuple_False, test/nn/test_module_hooks.py::TestModuleHooks::test_mixed_hooks_named_tuple_True, test/nn/test_module_hooks.py::TestModuleHooks::test_remove_kwarg_hooks, test/nn/test_module_hooks.py::TestStateDictHooks::test_load_state_dict_module_pre_hook_swap_False, test/nn/test_module_hooks.py::TestStateDictHooks::test_load_state_dict_module_pre_hook_swap_True, test/nn/test_module_hooks.py::TestStateDictHooks::test_load_state_dict_post_hook_backward_compatibility_swap_False, test/nn/test_module_hooks.py::TestStateDictHooks::test_load_state_dict_post_hook_backward_compatibility_swap_True, test/nn/test_module_hooks.py::TestStateDictHooks::test_load_state_dict_post_hook_swap_False, test/nn/test_module_hooks.py::TestStateDictHooks::test_load_state_dict_post_hook_swap_True, test/nn/test_module_hooks.py::TestStateDictHooks::test_load_state_dict_pre_hook_swap_False, test/nn/test_module_hooks.py::TestStateDictHooks::test_load_state_dict_pre_hook_swap_True, test/nn/test_module_hooks.py::TestStateDictHooks::test_no_extra_ref_to_module, test/nn/test_module_hooks.py::TestStateDictHooks::test_pickled_hook, test/nn/test_module_hooks.py::TestStateDictHooks::test_register_state_dict_post_hook_private_False, test/nn/test_module_hooks.py::TestStateDictHooks::test_register_state_dict_post_hook_private_True, test/nn/test_module_hooks.py::TestStateDictHooks::test_register_state_dict_pre_hook, test/nn/test_module_hooks.py::TestStateDictHooks::test_register_state_dict_pre_hook_backward_compat, test/nn/test_module_hooks.py::TestStateDictHooks::test_register_state_dict_pre_hook_lazy_module, test/nn/test_module_hooks.py::TestModuleGlobalHooks::test_global_and_local_hooks_order, test/nn/test_module_hooks.py::TestModuleGlobalHooks::test_module_backward_global_hook_writeable, test/nn/test_module_hooks.py::TestModuleGlobalHooks::test_module_forward_forward_hook_removable, test/nn/test_module_hooks.py::TestModuleGlobalHooks::test_module_forward_preforward_hook_removable, test/nn/test_module_hooks.py::TestModuleGlobalHooks::test_module_global_forward_preforward_hook_writeable, test/nn/test_module_hooks.py::TestModuleGlobalHooks::test_module_global_hook_invalid_outputs, test/nn/test_module_hooks.py::TestModuleGlobalHooks::test_module_global_hooks, test/nn/test_module_hooks.py::TestModuleHookNN::test_backward_hooks_interaction, test/nn/test_module_hooks.py::TestModuleHookNN::test_hook_backward_size, test/nn/test_module_hooks.py::TestModuleHookNN::test_hook_backward_writeable, test/nn/test_module_hooks.py::TestModuleHookNN::test_hook_buffer_registration, test/nn/test_module_hooks.py::TestModuleHookNN::test_hook_cpp, test/nn/test_module_hooks.py::TestModuleHookNN::test_hook_extra_input, test/nn/test_module_hooks.py::TestModuleHookNN::test_hook_forward_preforward_writable, test/nn/test_module_hooks.py::TestModuleHookNN::test_hook_inplace, test/nn/test_module_hooks.py::TestModuleHookNN::test_hook_invalid_outputs, test/nn/test_module_hooks.py::TestModuleHookNN::test_hook_last_arg_requires_grad, test/nn/test_module_hooks.py::TestModuleHookNN::test_hook_no_requires_grad, test/nn/test_module_hooks.py::TestModuleHookNN::test_hook_non_full_warning, test/nn/test_module_hooks.py::TestModuleHookNN::test_hook_parameter_registration, test/nn/test_module_hooks.py::TestModuleHookNN::test_hook_requires_grad, test/nn/test_module_hooks.py::TestModuleHookNN::test_hook_submodule_registration, test/nn/test_module_hooks.py::TestModuleHookNN::test_hooks 2024-08-20T22:27:19.1506638Z 2024-08-20T22:27:22.6035820Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:27:22.6722533Z Running test_functional_autograd_benchmark 1/1 ... [2024-08-20 22:27:22.671635] 2024-08-20T22:27:22.6724124Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:27:22.6727361Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_functional_autograd_benchmark.py', '-m', 'not 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 22:27:22.672191] 2024-08-20T22:27:48.8429201Z 2024-08-20T22:27:48.8431844Z nn/test_embedding 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_embedding_1.1_fc2a3740c8f98f30_.log 2024-08-20T22:27:48.8617442Z Running 161 items in this shard: test/nn/test_embedding.py::TestEmbeddingNN::test_embedding_bag_from_pretrained, test/nn/test_embedding.py::TestEmbeddingNN::test_embedding_bag_from_pretrained_padding_idx, test/nn/test_embedding.py::TestEmbeddingNN::test_embedding_bag_functional, test/nn/test_embedding.py::TestEmbeddingNN::test_embedding_bag_padding_idx_error, test/nn/test_embedding.py::TestEmbeddingNN::test_embedding_from_pretrained_float32, test/nn/test_embedding.py::TestEmbeddingNN::test_embedding_from_pretrained_float64, test/nn/test_embedding.py::TestEmbeddingNN::test_embedding_from_pretrained_int16, test/nn/test_embedding.py::TestEmbeddingNN::test_embedding_from_pretrained_int32, test/nn/test_embedding.py::TestEmbeddingNN::test_embedding_from_pretrained_int64, test/nn/test_embedding.py::TestEmbeddingNN::test_embedding_from_pretrained_int8, test/nn/test_embedding.py::TestEmbeddingNN::test_embedding_from_pretrained_options, test/nn/test_embedding.py::TestEmbeddingNN::test_embedding_from_pretrained_padding_idx, test/nn/test_embedding.py::TestEmbeddingNN::test_embedding_from_pretrained_uint8, test/nn/test_embedding.py::TestEmbeddingNN::test_embedding_functional, test/nn/test_embedding.py::TestEmbeddingNN::test_embedding_max_norm, test/nn/test_embedding.py::TestEmbeddingNN::test_embedding_max_norm_unsorted_repeating_indices, test/nn/test_embedding.py::TestEmbeddingNN::test_embedding_sparse_basic, test/nn/test_embedding.py::TestEmbeddingNN::test_embedding_sparse_empty_tensor, test/nn/test_embedding.py::TestEmbeddingNN::test_embeddingbag_from_pretrained, test/nn/test_embedding.py::TestEmbeddingNN::test_embeddingbag_from_pretrained_options, test/nn/test_embedding.py::TestEmbeddingNN::test_embeddingbag_include_last_offset, test/nn/test_embedding.py::TestEmbeddingNN::test_large_tensors, test/nn/test_embedding.py::TestEmbeddingNN::test_move_sparse_half_embedding, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_empty_per_sample_weights_and_offsets_cpu_int32_int32_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_empty_per_sample_weights_and_offsets_cpu_int32_int32_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_empty_per_sample_weights_and_offsets_cpu_int32_int32_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_empty_per_sample_weights_and_offsets_cpu_int32_int32_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_empty_per_sample_weights_and_offsets_cpu_int32_int64_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_empty_per_sample_weights_and_offsets_cpu_int32_int64_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_empty_per_sample_weights_and_offsets_cpu_int32_int64_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_empty_per_sample_weights_and_offsets_cpu_int32_int64_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_empty_per_sample_weights_and_offsets_cpu_int64_int32_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_empty_per_sample_weights_and_offsets_cpu_int64_int32_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_empty_per_sample_weights_and_offsets_cpu_int64_int32_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_empty_per_sample_weights_and_offsets_cpu_int64_int32_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_empty_per_sample_weights_and_offsets_cpu_int64_int64_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_empty_per_sample_weights_and_offsets_cpu_int64_int64_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_empty_per_sample_weights_and_offsets_cpu_int64_int64_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_empty_per_sample_weights_and_offsets_cpu_int64_int64_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu_int32_int32_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu_int32_int32_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu_int32_int32_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu_int32_int32_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu_int32_int64_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu_int32_int64_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu_int32_int64_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu_int32_int64_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu_int64_int32_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu_int64_int32_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu_int64_int32_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu_int64_int32_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu_int64_int64_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu_int64_int64_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu_int64_int64_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu_int64_int64_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_no_offsets_cpu_int32_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_no_offsets_cpu_int32_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_no_offsets_cpu_int64_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_no_offsets_cpu_int64_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_offsets_cpu_int32_int32_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_offsets_cpu_int32_int32_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_offsets_cpu_int32_int32_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_offsets_cpu_int32_int32_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_offsets_cpu_int32_int64_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_offsets_cpu_int32_int64_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_offsets_cpu_int32_int64_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_offsets_cpu_int32_int64_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_offsets_cpu_int64_int32_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_offsets_cpu_int64_int32_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_offsets_cpu_int64_int32_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_offsets_cpu_int64_int32_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_offsets_cpu_int64_int64_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_offsets_cpu_int64_int64_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_offsets_cpu_int64_int64_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_offsets_cpu_int64_int64_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_failures_cpu_int32_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_failures_cpu_int32_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_failures_cpu_int64_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_failures_cpu_int64_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_backward_cpu_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_1D_padding_idx_cpu_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_1D_padding_idx_cpu_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_2D_padding_idx_cpu_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_2D_padding_idx_cpu_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_bfloat16_cpu_int32_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_bfloat16_cpu_int32_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_bfloat16_cpu_int64_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_bfloat16_cpu_int64_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_device_cpu_int32_int32_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_device_cpu_int32_int32_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_device_cpu_int32_int32_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_device_cpu_int32_int32_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_device_cpu_int32_int64_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_device_cpu_int32_int64_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_device_cpu_int32_int64_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_device_cpu_int32_int64_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_device_cpu_int64_int32_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_device_cpu_int64_int32_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_device_cpu_int64_int32_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_device_cpu_int64_int32_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_device_cpu_int64_int64_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_device_cpu_int64_int64_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_device_cpu_int64_int64_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_device_cpu_int64_int64_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_dimension_errors_cpu, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_empty_input_cpu_int32_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_empty_input_cpu_int32_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_empty_input_cpu_int64_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_empty_input_cpu_int64_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_half_cpu_int32_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_half_cpu_int32_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_half_cpu_int64_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_half_cpu_int64_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_non_contiguous_weight_cpu_int32_int32_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_non_contiguous_weight_cpu_int32_int32_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_non_contiguous_weight_cpu_int32_int32_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_non_contiguous_weight_cpu_int32_int32_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_non_contiguous_weight_cpu_int32_int64_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_non_contiguous_weight_cpu_int32_int64_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_non_contiguous_weight_cpu_int32_int64_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_non_contiguous_weight_cpu_int32_int64_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_non_contiguous_weight_cpu_int64_int32_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_non_contiguous_weight_cpu_int64_int32_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_non_contiguous_weight_cpu_int64_int32_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_non_contiguous_weight_cpu_int64_int32_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_non_contiguous_weight_cpu_int64_int64_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_non_contiguous_weight_cpu_int64_int64_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_non_contiguous_weight_cpu_int64_int64_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_non_contiguous_weight_cpu_int64_int64_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx0_mode_max_cpu_float32_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx0_mode_max_cpu_float32_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx0_mode_max_cpu_float64_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx0_mode_max_cpu_float64_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx0_mode_mean_cpu_float32_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx0_mode_mean_cpu_float32_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx0_mode_mean_cpu_float64_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx0_mode_mean_cpu_float64_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx0_mode_sum_cpu_float32_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx0_mode_sum_cpu_float32_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx0_mode_sum_cpu_float64_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx0_mode_sum_cpu_float64_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx_0_mode_max_cpu_float32_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx_0_mode_max_cpu_float32_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx_0_mode_max_cpu_float64_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx_0_mode_max_cpu_float64_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx_0_mode_mean_cpu_float32_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx_0_mode_mean_cpu_float32_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx_0_mode_mean_cpu_float64_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx_0_mode_mean_cpu_float64_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx_0_mode_sum_cpu_float32_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx_0_mode_sum_cpu_float32_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx_0_mode_sum_cpu_float64_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx_0_mode_sum_cpu_float64_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_dense_grad_cpu, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_max_norm_backward_cpu_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_max_norm_device_cpu_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_max_norm_device_cpu_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_max_norm_device_cpu_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_max_norm_fwd_AD_cpu_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_padding_idx_cpu_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_scalar_weight_error_cpu 2024-08-20T22:27:48.8798663Z 2024-08-20T22:27:53.7491992Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:27:53.8646852Z Running test_mkldnn_fusion 1/1 ... [2024-08-20 22:27:53.864118] 2024-08-20T22:27:53.8648022Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:27:53.8651487Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_mkldnn_fusion.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:27:53.864590] 2024-08-20T22:28:01.8979695Z 2024-08-20T22:28:01.8982304Z test_mkldnn_fusion 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mkldnn_fusion_1.1_1988b0965eba2b54_.log 2024-08-20T22:28:01.8992832Z Running 8 items in this shard: test/test_mkldnn_fusion.py::TestMkldnnFusion::test_conv_binary_fusion_ops, test/test_mkldnn_fusion.py::TestMkldnnFusion::test_conv_transpose_unary_fusion_ops, test/test_mkldnn_fusion.py::TestMkldnnFusion::test_conv_unary_fusion_nnc, test/test_mkldnn_fusion.py::TestMkldnnFusion::test_conv_unary_fusion_ops, test/test_mkldnn_fusion.py::TestMkldnnFusion::test_linear_binary_fusion_ops, test/test_mkldnn_fusion.py::TestMkldnnFusion::test_linear_unary_fusion_ops, test/test_mkldnn_fusion.py::TestMkldnnFusion::test_single_conv, test/test_mkldnn_fusion.py::TestMkldnnFusion::test_unsupported_conv 2024-08-20T22:28:01.9000888Z 2024-08-20T22:28:06.6442696Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:28:06.7534464Z Running test_model_dump 1/1 ... [2024-08-20 22:28:06.752874] 2024-08-20T22:28:06.7535413Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:28:06.7538799Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_model_dump.py', '-m', 'not 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 22:28:06.753356] 2024-08-20T22:28:08.8587786Z 2024-08-20T22:28:08.8590764Z test_functional_autograd_benchmark 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_functional_autograd_benchmark_1.1_81b813d72c59f2c7_.log 2024-08-20T22:28:08.8594931Z Running 2 items in this shard: test/test_functional_autograd_benchmark.py::TestFunctionalAutogradBenchmark::test_fast_tasks, test/test_functional_autograd_benchmark.py::TestFunctionalAutogradBenchmark::test_slow_tasks 2024-08-20T22:28:08.8597698Z 2024-08-20T22:28:12.0408508Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:28:12.1068013Z Running test_function_schema 1/1 ... [2024-08-20 22:28:12.106247] 2024-08-20T22:28:12.1069096Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:28:12.1072266Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_function_schema.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:28:12.106724] 2024-08-20T22:28:13.6811941Z 2024-08-20T22:28:13.6814402Z test_model_dump 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_model_dump_1.1_bed7d5f928f70a58_.log 2024-08-20T22:28:13.6818855Z Running 9 items in this shard: test/test_model_dump.py::TestModelDump::test_inline_skeleton, test/test_model_dump.py::TestModelDump::test_invalid_json, test/test_model_dump.py::TestModelDump::test_main, test/test_model_dump.py::TestModelDump::test_memory_computation, test/test_model_dump.py::TestModelDump::test_model_with_lists, test/test_model_dump.py::TestModelDump::test_optimized_quantized_model, test/test_model_dump.py::TestModelDump::test_quantized_model, test/test_model_dump.py::TestModelDump::test_scripted_model, test/test_model_dump.py::TestModelDump::test_traced_model 2024-08-20T22:28:13.6822132Z 2024-08-20T22:28:16.4295242Z 2024-08-20T22:28:16.4297944Z test_function_schema 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_function_schema_1.1_86d211874340087d_.log 2024-08-20T22:28:16.4313844Z Running 15 items in this shard: test/test_function_schema.py::TestFunctionSchema::test_backward_compatible_arguments, test/test_function_schema.py::TestFunctionSchema::test_backward_compatible_outputs, test/test_function_schema.py::TestFunctionSchema::test_backward_compatible_structure, test/test_function_schema.py::TestFunctionSchema::test_backward_compatible_with_smart_serialization, test/test_function_schema.py::TestFunctionSchema::test_forward_compatible_arguments_real_use_case, test/test_function_schema.py::TestFunctionSchema::test_forward_compatible_arguments_with_out, test/test_function_schema.py::TestFunctionSchema::test_forward_compatible_arguments_without_out, test/test_function_schema.py::TestFunctionSchema::test_hash_schema, test/test_function_schema.py::TestFunctionSchema::test_out_schema, test/test_function_schema.py::TestFunctionSchema::test_schema_error, test/test_function_schema.py::TestFunctionSchema::test_serialize_and_deserialize, test/test_function_schema.py::TestFunctionSchema::test_string_optional_parameter_default_value, test/test_function_schema.py::TestFunctionSchema::test_sym_int_argument_properly_parsed, test/test_function_schema.py::TestFunctionSchema::test_tensor_list_alias_annotation_properly_parsed, test/test_function_schema.py::TestFunctionSchema::test_tensor_option_arguments_properly_parsed 2024-08-20T22:28:16.4327185Z 2024-08-20T22:28:16.8841113Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:28:16.9553649Z Running test_scatter_gather_ops 1/1 ... [2024-08-20 22:28:16.954817] 2024-08-20T22:28:16.9554652Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:28:16.9558532Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_scatter_gather_ops.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:28:16.955349] 2024-08-20T22:28:19.6785415Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:28:19.7459126Z Running test_schema_check 1/1 ... [2024-08-20 22:28:19.745368] 2024-08-20T22:28:19.7460165Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:28:19.7463433Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_schema_check.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:28:19.745870] 2024-08-20T22:29:55.8001430Z 2024-08-20T22:29:55.8003624Z test_schema_check 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_schema_check_1.1_9ad19acafbc5b8bc_.log 2024-08-20T22:29:56.2099259Z Running 5927 items in this shard: test/test_schema_check.py::TestSchemaCheck::test_alias_check_fail_custom_ops_output_is_input, test/test_schema_check.py::TestSchemaCheck::test_alias_check_fail_custom_ops_secretly_aliasing, test/test_schema_check.py::TestSchemaCheck::test_alias_check_fail_custom_ops_secretly_mutating, test/test_schema_check.py::TestSchemaCheck::test_alias_check_fail_multiple_operators, test/test_schema_check.py::TestSchemaCheck::test_alias_check_fail_multiple_operators_centered, test/test_schema_check.py::TestSchemaCheck::test_alias_check_fail_outputs_unexpectedly_aliasing, test/test_schema_check.py::TestSchemaCheck::test_alias_check_fail_simple, test/test_schema_check.py::TestSchemaCheck::test_is_alias_of_basic, test/test_schema_check.py::TestSchemaCheck::test_is_alias_of_empty_container, test/test_schema_check.py::TestSchemaCheck::test_mutation_check_fail, test/test_schema_check.py::TestSchemaCheck::test_mutation_check_fail_multiple_operators, test/test_schema_check.py::TestSchemaCheck::test_overlaps_basic, test/test_schema_check.py::TestSchemaCheck::test_overlaps_empty_container, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_empty_list_input, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_functionality, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_functionality_aliasing_inputs, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_functionality_default_replaced, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_functionality_device_input, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_functionality_kwarg_tensor, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_functionality_list_input, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_functionality_mutable_inputs, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_functionality_nested_training_op, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_functionality_training_op, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_functionality_wildcard_after, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_functionality_with_multiple_outputs, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_functionality_with_multiple_outputs_aliasing, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_mutated_aliasing_aliasing_inputs, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_mutated_aliasing_aliasing_outputs, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_mutated_aliasing_as_strided, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_mutated_aliasing_multiple_outputs, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_mutated_aliasing_mutation, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_mutated_aliasing_none, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_mutated_aliasing_resize_, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_operator_order, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_operator_order_without_grad, test/test_schema_check.py::TestSchemaCheck::test_schema_info_bind_basic, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_bfloat16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_bool, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_complex128, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_complex32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_complex64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_float16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_float32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_float64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_int16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_int32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_int64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_int8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_uint8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_bfloat16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_bool, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_complex128, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_complex32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_complex64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_float16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_float32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_float64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_int16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_int32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_int64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_int8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_uint8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_bfloat16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_bool, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_complex128, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_complex32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_complex64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_float16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_float32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_float64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_int16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_int32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_int64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_int8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_uint8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___radd___cpu_bfloat16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___radd___cpu_bool, 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test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_cpu_int64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_cpu_int8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_cpu_uint8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_like_cpu_bfloat16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_like_cpu_bool, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_like_cpu_complex128, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_like_cpu_complex32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_like_cpu_complex64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_like_cpu_float16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_like_cpu_float32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_like_cpu_float64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_like_cpu_int16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_like_cpu_int32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_like_cpu_int64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_like_cpu_int8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_like_cpu_uint8 2024-08-20T22:29:56.5580944Z 2024-08-20T22:29:58.9548813Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:29:59.0220224Z Running lazy/test_reuse_ir 1/1 ... [2024-08-20 22:29:59.021427] 2024-08-20T22:29:59.0221266Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:29:59.0224844Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'lazy/test_reuse_ir.py', '-m', 'not 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 22:29:59.021924] 2024-08-20T22:30:03.4460527Z 2024-08-20T22:30:03.4462904Z lazy/test_reuse_ir 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_reuse_ir_1.1_51e6ffe683a0e6e5_.log 2024-08-20T22:30:03.4466955Z Running 4 items in this shard: test/lazy/test_reuse_ir.py::TestLazyReuseIr::testAdd, test/lazy/test_reuse_ir.py::TestLazyReuseIr::testAddSub, test/lazy/test_reuse_ir.py::TestLazyReuseIr::testAddSubFallback, test/lazy/test_reuse_ir.py::TestLazyReuseIr::testBatchNorm 2024-08-20T22:30:03.4469493Z 2024-08-20T22:30:06.6408396Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:30:06.7088922Z Running test_unary_ufuncs 1/1 ... [2024-08-20 22:30:06.708387] 2024-08-20T22:30:06.7089929Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:30:06.7093561Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', '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 22:30:06.708908] 2024-08-20T22:30:26.0203717Z 2024-08-20T22:30:26.0206235Z test_scatter_gather_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_scatter_gather_ops_1.1_62124daca24d4608_.log 2024-08-20T22:30:26.0278675Z Running 81 items in this shard: test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_gather_backward_with_empty_index_tensor_sparse_grad_False_cpu_float32, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_gather_backward_with_empty_index_tensor_sparse_grad_False_cpu_float64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_gather_backward_with_empty_index_tensor_sparse_grad_True_cpu_float32, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_gather_backward_with_empty_index_tensor_sparse_grad_True_cpu_float64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_gather_bool_cpu_bool, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_gather_cpu_complex64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_gather_cpu_float32, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_gather_expanded_index_cpu_bfloat16, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_gather_expanded_index_cpu_float32, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_gather_expanded_index_cpu_float64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter__cpu_complex64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter__cpu_float16, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter__cpu_float32, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter__reductions_cpu_complex64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter__reductions_cpu_float16, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter__reductions_cpu_float32, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter__scalar_cpu_complex64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter__scalar_cpu_float16, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter__scalar_cpu_float32, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_add__cpu_complex64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_add__cpu_float16, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_add__cpu_float32, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_add_mult_index_base_cpu_float32, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_expanded_index_cpu_bfloat16, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_expanded_index_cpu_float16, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_expanded_index_cpu_float32, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_expanded_index_cpu_float64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_amax_cpu_bfloat16, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_amax_cpu_bool, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_amax_cpu_float16, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_amax_cpu_float32, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_amax_cpu_float64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_amax_cpu_int16, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_amax_cpu_int32, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_amax_cpu_int64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_amax_cpu_int8, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_amax_cpu_uint8, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_amin_cpu_bfloat16, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_amin_cpu_bool, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_amin_cpu_float16, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_amin_cpu_float32, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_amin_cpu_float64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_amin_cpu_int16, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_amin_cpu_int32, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_amin_cpu_int64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_amin_cpu_int8, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_amin_cpu_uint8, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_mean_cpu_bfloat16, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_mean_cpu_complex128, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_mean_cpu_complex64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_mean_cpu_float16, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_mean_cpu_float32, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_mean_cpu_float64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_mean_cpu_int16, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_mean_cpu_int32, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_mean_cpu_int64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_mean_cpu_int8, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_mean_cpu_uint8, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_prod_cpu_bfloat16, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_prod_cpu_bool, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_prod_cpu_complex128, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_prod_cpu_complex64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_prod_cpu_float16, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_prod_cpu_float32, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_prod_cpu_float64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_prod_cpu_int16, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_prod_cpu_int32, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_prod_cpu_int64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_prod_cpu_int8, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_prod_cpu_uint8, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_sum_cpu_bfloat16, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_sum_cpu_complex128, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_sum_cpu_complex64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_sum_cpu_float16, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_sum_cpu_float32, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_sum_cpu_float64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_sum_cpu_int16, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_sum_cpu_int32, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_sum_cpu_int64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_sum_cpu_int8, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_sum_cpu_uint8 2024-08-20T22:30:26.0352700Z 2024-08-20T22:30:29.1865643Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:30:29.2536988Z Running test_quantization 2/5 ... [2024-08-20 22:30:29.253213] 2024-08-20T22:30:29.2538023Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:30:29.2542644Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_quantization.py', '-m', 'not serial', '--shard-id=2', '--num-shards=5', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:30:29.253734] 2024-08-20T22:31:07.1947822Z 2024-08-20T22:31:07.1951211Z test_binary_ufuncs 1/2 was successful, full logs can be found in artifacts with path test/test-reports/test_binary_ufuncs_1.2_67422db4fcde6311_.log 2024-08-20T22:31:07.6553715Z 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, 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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, 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test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_fmod_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_fmod_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_fmod_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_gcd_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_ge_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_ge_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_ge_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_ge_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_ge_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_ge_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_gt_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_gt_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_gt_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_gt_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_gt_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_gt_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_heaviside_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_heaviside_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_heaviside_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_heaviside_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_heaviside_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_heaviside_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_hypot_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_igamma_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_igamma_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_igammac_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_igammac_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_isclose_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_isclose_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_isclose_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_isclose_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_isclose_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_isclose_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_cpu_int8, 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_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_special_chebyshev_polynomial_w_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_chebyshev_polynomial_w_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_hermite_polynomial_h_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_hermite_polynomial_h_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_hermite_polynomial_h_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_hermite_polynomial_h_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_hermite_polynomial_h_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_hermite_polynomial_h_cpu_int8, 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test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_shifted_chebyshev_polynomial_t_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_shifted_chebyshev_polynomial_t_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_shifted_chebyshev_polynomial_t_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_shifted_chebyshev_polynomial_t_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_shifted_chebyshev_polynomial_t_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_shifted_chebyshev_polynomial_u_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_special_shifted_chebyshev_polynomial_u_cpu_float64, 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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, 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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_sub_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_sub_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_sub_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_true_divide_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_true_divide_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_true_divide_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_true_divide_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_xlogy_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_xlogy_cpu_float32, 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, 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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, 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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, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_shifted_chebyshev_polynomial_u_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_shifted_chebyshev_polynomial_u_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_shifted_chebyshev_polynomial_u_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_shifted_chebyshev_polynomial_u_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_shifted_chebyshev_polynomial_v_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_shifted_chebyshev_polynomial_v_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_shifted_chebyshev_polynomial_v_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_shifted_chebyshev_polynomial_v_cpu_int8, 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, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_bool_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_bool_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_bool_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_float16_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_float16_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_float16_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_float16_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_float16_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_float32_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_float32_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_float32_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_float32_int16, 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, 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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, 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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 2024-08-20T22:31:08.1143503Z 2024-08-20T22:31:13.1855501Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:31:13.2538085Z Running test_quantization 5/5 ... [2024-08-20 22:31:13.253088] 2024-08-20T22:31:13.2539314Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:31:13.2542483Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_quantization.py', '-m', 'not serial', '--shard-id=5', '--num-shards=5', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:31:13.253678] 2024-08-20T22:38:51.0497990Z 2024-08-20T22:38:51.0499651Z test_unary_ufuncs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_unary_ufuncs_1.1_411deadd0f8d55f1_.log 2024-08-20T22:38:52.5110693Z Running 24500 items in this shard: test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_abs_angle_complex_to_float_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_abs_angle_complex_to_float_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_abs_big_number_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_abs_signed_zero_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_abs_signed_zero_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_abs_zero_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_abs_zero_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_bfloat16_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_bfloat16_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_bfloat16_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_bfloat16_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_bfloat16_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_bfloat16_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_bfloat16_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_bfloat16_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_bfloat16_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_bfloat16_cpu_int32, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_bool_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_bool_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_bool_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_bool_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_bool_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_bool_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_bool_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_byte_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_byte_cpu_bool, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_asin_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_asin_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_asin_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_asin_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_asin_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_asin_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_asin_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_asin_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_asinh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_asinh_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_asinh_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_asinh_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_asinh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_asinh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_asinh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_asinh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_asinh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_asinh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_asinh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_asinh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_atan_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_atan_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_atan_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_atan_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_atan_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_atan_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_atan_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_atan_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_atan_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_atan_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_atan_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_atan_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_atanh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_atanh_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_atanh_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_atanh_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_atanh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_atanh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_atanh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_atanh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_atanh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_atanh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_atanh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_atanh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_bfloat16_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_bfloat16_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_bfloat16_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_bfloat16_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_bfloat16_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_bfloat16_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_bfloat16_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_bfloat16_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_bfloat16_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_bfloat16_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_bfloat16_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_bfloat16_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_bfloat16_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_bitwise_not_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_bitwise_not_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_bitwise_not_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_bitwise_not_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_bitwise_not_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_bitwise_not_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_bool_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_bool_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_bool_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_bool_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_bool_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_bool_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_bool_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_bool_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_bool_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_bool_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_bool_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_bool_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_bool_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_byte_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_byte_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_byte_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_byte_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_byte_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_byte_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_byte_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_byte_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_byte_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_byte_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_byte_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_byte_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cdouble_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cdouble_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cdouble_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cdouble_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cdouble_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cdouble_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cdouble_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cdouble_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cdouble_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cdouble_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cdouble_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cdouble_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cdouble_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_ceil_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_ceil_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_ceil_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_ceil_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_ceil_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_ceil_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_ceil_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_ceil_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_ceil_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cfloat_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cfloat_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cfloat_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cfloat_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cfloat_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cfloat_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cfloat_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cfloat_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cfloat_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cfloat_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cfloat_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cfloat_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cfloat_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_chalf_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_chalf_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_chalf_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_chalf_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_chalf_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_chalf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_chalf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_chalf_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_chalf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_chalf_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_chalf_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_chalf_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_chalf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_char_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_char_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_char_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_char_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_char_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_char_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_char_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_char_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_char_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_char_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_char_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_char_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_char_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_conj_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_conj_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_conj_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_conj_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_conj_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_conj_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_conj_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_conj_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_conj_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_conj_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_conj_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_conj_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_conj_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_conj_physical_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_conj_physical_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_conj_physical_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_conj_physical_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_conj_physical_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_conj_physical_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_conj_physical_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_conj_physical_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_conj_physical_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_conj_physical_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_conj_physical_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_conj_physical_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_conj_physical_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cos_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cos_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cos_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cos_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cos_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cos_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cos_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cos_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cos_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cos_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cos_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cos_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cosh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cosh_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cosh_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cosh_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cosh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cosh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cosh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cosh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cosh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cosh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cosh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cosh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_deg2rad_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_deg2rad_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_deg2rad_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_deg2rad_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_deg2rad_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_deg2rad_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_deg2rad_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_deg2rad_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_deg2rad_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_deg2rad_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_digamma_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_digamma_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_digamma_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_digamma_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_digamma_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_digamma_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_digamma_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_digamma_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_digamma_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_digamma_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_double_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_double_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_double_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_double_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_double_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_double_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_double_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_double_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_double_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_double_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_double_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_double_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_double_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erf_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erf_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erf_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erf_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erf_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erf_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfc_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfc_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfc_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfc_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfc_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfc_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfc_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfc_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfc_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfc_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfinv_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfinv_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfinv_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfinv_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfinv_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfinv_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfinv_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfinv_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfinv_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfinv_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp2_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp2_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp2_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp2_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp2_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp2_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp2_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp2_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp2_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp2_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp2_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp2_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_expm1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_expm1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_expm1_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_expm1_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_expm1_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_expm1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_expm1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_expm1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_expm1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_expm1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_expm1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_expm1_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_fill_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_fill_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_fill_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_fill_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_fill_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_fill_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_fill_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_fill_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_fill_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_fill_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_fill_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_fill_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_fill_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_float_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_float_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_float_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_float_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_float_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_float_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_float_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_float_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_float_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_float_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_float_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_float_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_float_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_floor_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_floor_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_floor_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_floor_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_floor_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_floor_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_floor_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_floor_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_floor_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_frac_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_frac_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_frac_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_frac_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_frexp_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_frexp_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_frexp_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_frexp_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_half_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_half_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_half_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_half_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_half_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_half_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_half_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_half_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_half_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_half_cpu_int64, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_i0_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_imag_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_imag_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_imag_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_int_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_int_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_int_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_int_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_int_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_int_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_int_cpu_float64, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isnan_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isnan_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isnan_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isnan_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isnan_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isnan_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isnan_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isnan_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isnan_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isnan_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isneginf_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isneginf_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isneginf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isneginf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isneginf_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isneginf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isneginf_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isneginf_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isneginf_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isneginf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isposinf_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isposinf_cpu_bool, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isreal_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isreal_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isreal_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isreal_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isreal_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isreal_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isreal_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isreal_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isreal_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isreal_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_jiterator_unary_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_jiterator_unary_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_jiterator_unary_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_jiterator_unary_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_jiterator_unary_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_jiterator_unary_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_jiterator_unary_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_jiterator_unary_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_jiterator_unary_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_jiterator_unary_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_jiterator_unary_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_jiterator_unary_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_lgamma_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_lgamma_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_lgamma_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_lgamma_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_lgamma_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_lgamma_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_lgamma_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_lgamma_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_lgamma_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_lgamma_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log10_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log10_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log10_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log10_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log10_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log10_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log10_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log10_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log10_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log10_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log10_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log10_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log1p_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log1p_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log1p_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log1p_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log1p_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log1p_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log1p_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log1p_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log1p_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log1p_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log1p_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log1p_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log2_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log2_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log2_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log2_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log2_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log2_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log2_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log2_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log2_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log2_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log2_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log2_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log_cpu_int16, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_logit_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_logit_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_logit_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_logit_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_long_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_long_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_long_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_long_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_long_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_long_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_long_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_long_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_long_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_long_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_long_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_long_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_long_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_1_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_1_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_3_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_3_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_3_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_3_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_3_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_3_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_5_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_5_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_5_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_5_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_5_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_5_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_5_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_5_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nan_to_num_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nan_to_num_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nan_to_num_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nan_to_num_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nan_to_num_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nan_to_num_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nan_to_num_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nan_to_num_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nan_to_num_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nan_to_num_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_neg_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_neg_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_neg_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_neg_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_neg_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_neg_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_neg_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_neg_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_neg_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_neg_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_neg_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_neg_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_celu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_celu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_celu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_celu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_elu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_elu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_elu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_elu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_hardshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_hardshrink_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_hardshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_hardshrink_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_hardsigmoid_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_hardsigmoid_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_hardsigmoid_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_hardsigmoid_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_hardtanh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_hardtanh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_hardtanh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_hardtanh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_hardtanh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_hardtanh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_hardtanh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_hardtanh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_logsigmoid_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_logsigmoid_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_logsigmoid_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_logsigmoid_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_mish_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_mish_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_mish_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_mish_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_prelu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_prelu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_prelu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_prelu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_relu6_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_relu6_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_relu6_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_relu6_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_relu6_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_relu6_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_relu6_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_relu6_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_relu6_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_relu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_relu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_relu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_relu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_relu_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_relu_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_relu_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_relu_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_relu_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_rrelu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_rrelu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_rrelu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_selu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_selu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_selu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_selu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_silu_complex_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_silu_complex_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_silu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_silu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_silu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_silu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_softplus_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_softplus_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_softplus_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_softplus_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_softshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_softshrink_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_softshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_softshrink_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_softsign_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_softsign_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_softsign_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_softsign_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_softsign_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_softsign_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_softsign_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_softsign_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_softsign_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_softsign_cpu_int8, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_tanhshrink_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_tanhshrink_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_tanhshrink_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_threshold_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_threshold_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_threshold_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_threshold_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_threshold_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_threshold_cpu_int32, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_log_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_log_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_log_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_log_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_log_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_log_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_logical_not_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_logical_not_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_logical_not_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_logical_not_cpu_complex64, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nan_to_num_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nan_to_num_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nan_to_num_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nan_to_num_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nan_to_num_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nan_to_num_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nan_to_num_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nan_to_num_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nan_to_num_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_neg_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_neg_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_neg_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_neg_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_neg_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_neg_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_neg_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_neg_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_neg_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_neg_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_neg_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_neg_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_celu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_celu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_celu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_celu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_elu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_elu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_elu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_elu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_hardshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_hardshrink_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_hardshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_hardshrink_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_hardtanh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_hardtanh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_hardtanh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_hardtanh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_hardtanh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_hardtanh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_hardtanh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_hardtanh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_mish_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_mish_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_mish_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_mish_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_prelu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_prelu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_prelu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_prelu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu6_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu6_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu6_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu6_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu6_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu6_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu6_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu6_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu6_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_selu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_selu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_selu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_selu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_softplus_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_softplus_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_softplus_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_softplus_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_softshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_softshrink_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_softshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_softshrink_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_tanhshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_tanhshrink_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_tanhshrink_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_tanhshrink_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_tanhshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_tanhshrink_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_tanhshrink_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_tanhshrink_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_tanhshrink_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_tanhshrink_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_tanhshrink_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_threshold_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_threshold_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_threshold_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_threshold_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_threshold_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_threshold_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_threshold_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_threshold_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_threshold_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_positive_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_positive_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_positive_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_positive_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_positive_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_positive_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_positive_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_positive_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_positive_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_positive_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_positive_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_positive_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_rad2deg_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_rad2deg_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_rad2deg_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_rad2deg_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_rad2deg_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_rad2deg_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_rad2deg_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_rad2deg_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_rad2deg_cpu_int8, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acosh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acosh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acosh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acosh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acosh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acosh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acosh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acosh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_angle_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_angle_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_angle_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_angle_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_angle_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_angle_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_angle_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_angle_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_angle_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_angle_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_angle_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_angle_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_asin_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_asin_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_asin_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_asin_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_asin_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_asin_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_asin_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_asin_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_asin_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_asin_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_asin_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_asin_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_asinh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_asinh_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_asinh_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_asinh_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_asinh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_asinh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_asinh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_asinh_cpu_int16, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atanh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atanh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atanh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atanh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atanh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atanh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atanh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atanh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bfloat16_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bfloat16_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bfloat16_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bfloat16_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bfloat16_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bfloat16_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bfloat16_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bfloat16_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bfloat16_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bfloat16_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bfloat16_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bfloat16_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bfloat16_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bitwise_not_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bitwise_not_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bitwise_not_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bitwise_not_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bitwise_not_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bitwise_not_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bool_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bool_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bool_cpu_complex128, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_byte_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_byte_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_byte_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_byte_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_byte_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_byte_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_byte_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_byte_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_byte_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_byte_cpu_int64, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_char_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_char_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_char_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_physical_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_physical_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_physical_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_physical_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_physical_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_physical_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_physical_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_physical_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_physical_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_physical_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_physical_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_physical_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_physical_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cos_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cos_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cos_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cos_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cos_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cos_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cos_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cos_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cos_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cos_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cos_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cos_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cosh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cosh_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cosh_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cosh_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cosh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cosh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cosh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cosh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cosh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cosh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cosh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cosh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_deg2rad_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_deg2rad_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_deg2rad_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_deg2rad_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_deg2rad_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_deg2rad_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_deg2rad_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_deg2rad_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_deg2rad_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_deg2rad_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_digamma_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_digamma_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_digamma_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_digamma_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_digamma_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_digamma_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_digamma_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_digamma_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_digamma_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_digamma_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_double_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_double_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_double_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_double_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_double_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_double_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_double_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_double_cpu_float64, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfc_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfc_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfc_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfinv_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfinv_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfinv_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfinv_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfinv_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfinv_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfinv_cpu_int32, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp2_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp2_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp2_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp2_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp2_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_expm1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_expm1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_expm1_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_expm1_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_expm1_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_expm1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_expm1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_expm1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_expm1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_expm1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_expm1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_expm1_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_fill_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_fill_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_fill_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_fill_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_fill_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_fill_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_fill_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_fill_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_fill_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_fill_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_fill_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_fill_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_fill_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_float_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_float_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_float_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_float_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_float_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_float_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_float_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_float_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_float_cpu_int16, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_floor_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_floor_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_floor_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_frac_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_frac_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_frac_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_frac_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_frexp_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_frexp_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_frexp_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_frexp_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_half_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_half_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_half_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_half_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_half_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_half_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_half_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_half_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_half_cpu_int32, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_i0_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_i0_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_imag_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_imag_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_imag_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_int_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_int_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_int_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_int_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_int_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_int_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_int_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_int_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_int_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_int_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_int_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_int_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isfinite_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isfinite_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isfinite_cpu_complex128, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isnan_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isnan_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isnan_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isnan_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isnan_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isneginf_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isneginf_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isneginf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isneginf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isneginf_cpu_float64, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isreal_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isreal_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isreal_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isreal_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isreal_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isreal_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isreal_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isreal_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_jiterator_unary_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_jiterator_unary_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_jiterator_unary_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_jiterator_unary_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_jiterator_unary_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_jiterator_unary_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_jiterator_unary_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_jiterator_unary_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_jiterator_unary_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_jiterator_unary_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_jiterator_unary_cpu_int8, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_lgamma_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log10_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log10_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log10_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log10_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log10_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log10_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log10_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log10_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log10_cpu_int32, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log2_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log2_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log2_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log2_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log2_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log2_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log2_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logical_not_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logical_not_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logical_not_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logical_not_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logical_not_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logical_not_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logical_not_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logical_not_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logical_not_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logical_not_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logical_not_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logical_not_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logit_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logit_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logit_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logit_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logit_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logit_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logit_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logit_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logit_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logit_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_long_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_long_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_long_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_long_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_long_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_long_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_long_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_long_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_long_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_long_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_long_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_long_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_long_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_1_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_1_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_3_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_3_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_3_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_3_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_3_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_3_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_5_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_5_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_5_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_5_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_5_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_5_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_5_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_5_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nan_to_num_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nan_to_num_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nan_to_num_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nan_to_num_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nan_to_num_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nan_to_num_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nan_to_num_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nan_to_num_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nan_to_num_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nan_to_num_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_neg_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_neg_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_neg_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_neg_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_neg_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_neg_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_neg_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_neg_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_neg_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_neg_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_neg_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_neg_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_celu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_celu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_celu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_celu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_elu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_elu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_elu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_elu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardshrink_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardshrink_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardsigmoid_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardsigmoid_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardsigmoid_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardsigmoid_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardtanh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardtanh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardtanh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardtanh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardtanh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardtanh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardtanh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardtanh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_logsigmoid_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_logsigmoid_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_logsigmoid_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_logsigmoid_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_mish_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_mish_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_mish_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_mish_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_prelu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_prelu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_prelu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_prelu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu6_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu6_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu6_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu6_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu6_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu6_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu6_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu6_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu6_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_rrelu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_rrelu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_rrelu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_selu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_selu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_selu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_selu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_silu_complex_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_silu_complex_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_silu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_silu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_silu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_silu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softplus_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softplus_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softplus_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softplus_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softshrink_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softshrink_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softsign_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softsign_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softsign_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softsign_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softsign_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softsign_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softsign_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softsign_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softsign_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softsign_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softsign_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_tanhshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_tanhshrink_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_tanhshrink_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_tanhshrink_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_tanhshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_tanhshrink_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_tanhshrink_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_tanhshrink_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_tanhshrink_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_tanhshrink_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_tanhshrink_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_threshold_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_threshold_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_threshold_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_threshold_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_threshold_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_threshold_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_threshold_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_threshold_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_threshold_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_0_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_0_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_0_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_0_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_0_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_0_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_0_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_1_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_2_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_2_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_2_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_2_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_2_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_2_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_2_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_2_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_2_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_3_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_3_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_3_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_3_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_3_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_3_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_4_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_4_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_4_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_4_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_4_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_4_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_4_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_4_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_4_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_positive_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_positive_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_positive_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_positive_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_positive_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_positive_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_positive_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_positive_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_positive_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_positive_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_positive_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_positive_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rad2deg_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rad2deg_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rad2deg_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rad2deg_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rad2deg_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rad2deg_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rad2deg_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rad2deg_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rad2deg_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rad2deg_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_real_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_real_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_real_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_real_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_real_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_real_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_real_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_real_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_real_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_real_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_real_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_real_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_real_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_reciprocal_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_reciprocal_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_reciprocal_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_reciprocal_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_reciprocal_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_reciprocal_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_reciprocal_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_reciprocal_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_reciprocal_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_reciprocal_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_reciprocal_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_reciprocal_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_decimals_0_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_decimals_0_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_decimals_0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_decimals_0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_decimals_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_decimals_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_decimals_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_decimals_neg_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_decimals_neg_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_decimals_neg_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rsqrt_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rsqrt_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rsqrt_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rsqrt_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rsqrt_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rsqrt_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rsqrt_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rsqrt_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rsqrt_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rsqrt_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rsqrt_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rsqrt_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sgn_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sgn_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sgn_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sgn_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sgn_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sgn_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sgn_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sgn_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sgn_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sgn_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sgn_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sgn_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sgn_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_short_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_short_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_short_cpu_complex128, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_abs_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_abs_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_abs_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_abs_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_abs_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_abs_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acos_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acos_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acos_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acos_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acos_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acos_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acos_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acos_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acos_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acos_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acos_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acos_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acosh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acosh_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acosh_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acosh_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acosh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acosh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acosh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acosh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acosh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acosh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acosh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acosh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_angle_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_angle_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_angle_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_angle_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_angle_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_angle_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_angle_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_angle_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_angle_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_angle_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_angle_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_angle_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asin_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asin_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asin_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asin_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asin_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asin_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asin_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asin_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asin_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asin_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asin_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asin_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asinh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asinh_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asinh_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asinh_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asinh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asinh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asinh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asinh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asinh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asinh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asinh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asinh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_atan_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_atan_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_atan_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_atan_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_atan_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_atan_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_atan_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_atan_cpu_int16, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_atanh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_atanh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_atanh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_atanh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_atanh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_atanh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bfloat16_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bfloat16_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bfloat16_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bfloat16_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bfloat16_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bfloat16_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bfloat16_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bfloat16_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bfloat16_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bfloat16_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bfloat16_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bfloat16_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bfloat16_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bitwise_not_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bitwise_not_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bitwise_not_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bitwise_not_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bitwise_not_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bitwise_not_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bool_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bool_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bool_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bool_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bool_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bool_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bool_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bool_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bool_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bool_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bool_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bool_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bool_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_byte_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_byte_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_byte_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_byte_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_byte_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_byte_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_byte_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_byte_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_byte_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_byte_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_byte_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_byte_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cdouble_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cdouble_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cdouble_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cdouble_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cdouble_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cdouble_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cdouble_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cdouble_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cdouble_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cdouble_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cdouble_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cdouble_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cdouble_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_ceil_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_ceil_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_ceil_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_ceil_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_ceil_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_ceil_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_ceil_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_ceil_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_ceil_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cfloat_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cfloat_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cfloat_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cfloat_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cfloat_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cfloat_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cfloat_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cfloat_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cfloat_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cfloat_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cfloat_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cfloat_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cfloat_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_chalf_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_chalf_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_chalf_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_chalf_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_chalf_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_chalf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_chalf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_chalf_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_chalf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_chalf_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_chalf_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_chalf_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_chalf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_char_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_char_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_char_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_char_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_char_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_char_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_char_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_char_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_char_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_char_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_char_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_char_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_char_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_physical_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_physical_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_physical_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_physical_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_physical_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_physical_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_physical_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_physical_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_physical_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_physical_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_physical_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_physical_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_physical_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cos_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cos_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cos_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cos_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cos_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cos_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cos_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cos_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cos_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cos_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cos_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cos_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cosh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cosh_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cosh_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cosh_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cosh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cosh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cosh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cosh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cosh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cosh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cosh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cosh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_deg2rad_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_deg2rad_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_deg2rad_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_deg2rad_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_deg2rad_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_deg2rad_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_deg2rad_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_deg2rad_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_deg2rad_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_deg2rad_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_digamma_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_digamma_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_digamma_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_digamma_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_digamma_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_digamma_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_digamma_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_digamma_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_digamma_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_digamma_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erf_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erf_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erf_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erf_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erf_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erf_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfc_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfc_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfc_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfc_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfc_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfc_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfc_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfc_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfc_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfc_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfinv_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfinv_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfinv_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfinv_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfinv_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfinv_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfinv_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfinv_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfinv_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfinv_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp2_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp2_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp2_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp2_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp2_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp2_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp2_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp2_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp2_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp2_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp2_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp2_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_expm1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_expm1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_expm1_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_expm1_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_expm1_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_expm1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_expm1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_expm1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_expm1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_expm1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_expm1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_expm1_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_fill_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_fill_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_fill_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_fill_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_fill_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_fill_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_fill_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_fill_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_fill_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_fill_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_fill_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_fill_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_fill_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_float_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_float_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_float_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_float_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_float_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_float_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_float_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_float_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_float_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_float_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_float_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_float_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_float_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_floor_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_floor_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_floor_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_floor_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_floor_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_floor_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_floor_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_floor_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_floor_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_frac_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_frac_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_frac_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_frac_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_frexp_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_frexp_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_frexp_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_frexp_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_half_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_half_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_half_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_half_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_half_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_half_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_half_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_half_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_half_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_half_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_half_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_half_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_i0_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_i0_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_i0_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_i0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_i0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_i0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_i0_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_i0_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_i0_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_i0_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_imag_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_imag_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_imag_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_int_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_int_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_int_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_int_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_int_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_int_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_int_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_int_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_int_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_int_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_int_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_int_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isinf_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isinf_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isinf_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isinf_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isinf_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isinf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isinf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isinf_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isinf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isinf_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isinf_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isinf_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isinf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isnan_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isnan_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isnan_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isnan_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isnan_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isnan_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isnan_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isnan_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isnan_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isnan_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isnan_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isnan_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isneginf_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isneginf_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isneginf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isneginf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isneginf_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isneginf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isneginf_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isneginf_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isneginf_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isneginf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isposinf_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isposinf_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isposinf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isposinf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isposinf_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isposinf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isposinf_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isposinf_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isposinf_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isposinf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_jiterator_unary_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_jiterator_unary_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_jiterator_unary_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_jiterator_unary_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_jiterator_unary_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_jiterator_unary_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_jiterator_unary_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_jiterator_unary_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_jiterator_unary_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_jiterator_unary_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_jiterator_unary_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_jiterator_unary_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_lgamma_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_lgamma_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_lgamma_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_lgamma_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_lgamma_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_lgamma_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_lgamma_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_lgamma_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_lgamma_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_lgamma_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log10_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log10_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log10_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log10_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log10_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log10_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log10_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log10_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log10_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log10_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log10_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log10_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log1p_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log1p_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log1p_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log1p_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log1p_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log1p_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log1p_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log1p_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log1p_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log1p_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log1p_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log1p_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log2_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log2_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log2_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log2_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log2_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log2_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log2_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log2_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log2_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log2_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log2_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log2_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logical_not_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logical_not_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logical_not_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logical_not_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logical_not_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logical_not_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logical_not_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logical_not_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logical_not_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logical_not_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logical_not_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logical_not_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logit_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logit_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logit_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logit_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logit_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logit_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logit_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logit_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logit_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logit_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_long_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_long_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_long_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_long_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_long_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_long_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_long_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_long_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_long_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_long_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_long_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_long_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_long_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_1_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_1_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_3_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_3_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_3_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_3_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_3_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_3_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_5_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_5_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_5_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_5_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_5_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_5_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_5_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_5_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nan_to_num_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nan_to_num_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nan_to_num_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nan_to_num_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nan_to_num_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nan_to_num_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nan_to_num_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nan_to_num_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nan_to_num_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nan_to_num_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_neg_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_neg_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_neg_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_neg_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_neg_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_neg_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_neg_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_neg_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_neg_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_neg_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_neg_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_neg_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_celu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_celu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_celu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_celu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_elu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_elu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_elu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_elu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardshrink_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardshrink_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardsigmoid_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardsigmoid_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardsigmoid_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardsigmoid_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardtanh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardtanh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardtanh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardtanh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardtanh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardtanh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardtanh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardtanh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_logsigmoid_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_logsigmoid_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_logsigmoid_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_logsigmoid_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_mish_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_mish_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_mish_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_mish_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_prelu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_prelu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_prelu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_prelu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu6_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu6_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu6_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu6_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu6_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu6_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu6_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu6_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu6_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_rrelu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_rrelu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_rrelu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_selu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_selu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_selu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_selu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_silu_complex_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_silu_complex_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_silu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_silu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_silu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_silu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softplus_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softplus_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softplus_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softplus_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softshrink_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softshrink_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softsign_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softsign_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softsign_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softsign_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softsign_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softsign_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softsign_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softsign_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softsign_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softsign_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softsign_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_tanhshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_tanhshrink_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_tanhshrink_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_tanhshrink_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_tanhshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_tanhshrink_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_tanhshrink_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_tanhshrink_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_tanhshrink_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_tanhshrink_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_tanhshrink_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_threshold_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_threshold_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_threshold_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_threshold_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_threshold_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_threshold_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_threshold_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_threshold_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_threshold_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_0_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_0_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_0_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_0_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_0_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_0_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_0_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_1_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_2_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_2_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_2_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_2_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_2_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_2_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_2_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_2_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_2_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_3_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_3_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_3_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_3_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_3_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_3_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_4_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_4_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_4_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_4_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_4_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_4_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_4_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_4_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_4_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_positive_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_positive_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_positive_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_positive_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_positive_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_positive_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_positive_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_positive_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_positive_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_positive_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_positive_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_positive_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rad2deg_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rad2deg_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rad2deg_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rad2deg_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rad2deg_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rad2deg_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rad2deg_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rad2deg_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rad2deg_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rad2deg_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_real_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_real_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_real_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_real_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_real_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_real_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_real_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_real_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_real_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_real_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_real_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_real_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_real_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_reciprocal_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_reciprocal_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_reciprocal_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_reciprocal_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_reciprocal_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_reciprocal_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_reciprocal_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_reciprocal_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_reciprocal_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_reciprocal_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_reciprocal_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_reciprocal_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_decimals_0_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_decimals_0_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_decimals_0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_decimals_0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_decimals_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_decimals_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_decimals_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_decimals_neg_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_decimals_neg_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_decimals_neg_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rsqrt_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rsqrt_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rsqrt_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rsqrt_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rsqrt_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rsqrt_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rsqrt_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rsqrt_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rsqrt_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rsqrt_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rsqrt_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rsqrt_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sgn_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sgn_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sgn_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sgn_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sgn_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sgn_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sgn_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sgn_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sgn_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sgn_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sgn_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sgn_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sgn_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_short_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_short_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_short_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_short_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_short_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_short_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_short_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_short_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_short_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_short_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_short_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_short_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sigmoid_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sigmoid_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sigmoid_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sigmoid_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sigmoid_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sigmoid_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sigmoid_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sigmoid_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sigmoid_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sigmoid_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sigmoid_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sigmoid_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sign_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sign_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sign_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sign_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sign_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sign_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sign_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sign_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sign_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sign_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_signbit_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_signbit_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_signbit_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_signbit_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_signbit_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_signbit_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_signbit_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_signbit_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_signbit_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_signbit_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sin_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sin_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sin_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sin_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sin_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sin_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sin_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sin_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sin_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sin_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sin_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sin_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinc_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinc_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinc_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinc_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinc_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinc_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinc_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinc_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinc_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinc_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinc_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinc_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinh_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinh_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinh_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinh_cpu_uint8, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y0_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y0_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y0_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y0_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y1_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_entr_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_entr_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_entr_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_entr_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_entr_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_entr_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_entr_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_entr_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_entr_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_entr_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_erfcx_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_erfcx_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_erfcx_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_erfcx_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_erfcx_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_erfcx_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_erfcx_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_erfcx_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_i0e_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_i0e_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_i0e_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_i0e_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_i0e_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_i0e_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_i0e_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_i0e_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_i0e_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_i0e_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_i1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_i1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_i1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_i1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_i1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_i1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_i1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_i1_cpu_uint8, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_erfinv_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_erfinv_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_erfinv_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_erfinv_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log10_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log10_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log10_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log10_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log1p_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log1p_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log1p_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log1p_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log2_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log2_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log2_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log2_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_rsqrt_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_rsqrt_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_rsqrt_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_rsqrt_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_logit_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_logit_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_logit_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_logit_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_multigammaln_mvlgamma_p_1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_multigammaln_mvlgamma_p_1_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_multigammaln_mvlgamma_p_1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_multigammaln_mvlgamma_p_1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_multigammaln_mvlgamma_p_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_multigammaln_mvlgamma_p_3_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_multigammaln_mvlgamma_p_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_multigammaln_mvlgamma_p_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_multigammaln_mvlgamma_p_5_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_multigammaln_mvlgamma_p_5_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_multigammaln_mvlgamma_p_5_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_multigammaln_mvlgamma_p_5_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_ndtri_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_ndtri_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_sqrt_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_sqrt_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_sqrt_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_sqrt_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_acos_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_acos_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_acos_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_acos_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_acosh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_acosh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_acosh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_acosh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_asin_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_asin_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_asin_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_asin_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_atanh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_atanh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_atanh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_atanh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_erfinv_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_erfinv_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_erfinv_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_erfinv_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log10_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log10_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log10_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log10_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log1p_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log1p_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log1p_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log1p_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log2_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log2_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log2_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log2_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_logit_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_logit_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_logit_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_logit_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_mvlgamma_mvlgamma_p_1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_mvlgamma_mvlgamma_p_1_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_mvlgamma_mvlgamma_p_1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_mvlgamma_mvlgamma_p_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_mvlgamma_mvlgamma_p_3_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_mvlgamma_mvlgamma_p_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_mvlgamma_mvlgamma_p_5_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_mvlgamma_mvlgamma_p_5_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_mvlgamma_mvlgamma_p_5_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_rsqrt_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_rsqrt_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_rsqrt_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_rsqrt_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_special_ndtri_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_special_ndtri_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_sqrt_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_sqrt_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_sqrt_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_sqrt_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_frexp_assert_raises_cpu, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_frexp_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_frexp_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_frexp_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardshrink_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardshrink_edge_cases_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardshrink_edge_cases_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardshrink_edge_cases_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardsigmoid_backward_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardsigmoid_backward_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardsigmoid_backward_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardsigmoid_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardsigmoid_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardsigmoid_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardswish_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardswish_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardswish_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_i0_range1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_i0_range1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_i0_range1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_i0_range2_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_i0_range2_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_i0_range2_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_i0_range3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_i0_special_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_i0_special_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_i0_special_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_igamma_common_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_igamma_common_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_igamma_common_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_igamma_common_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_igamma_edge_cases_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_igamma_edge_cases_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_igamma_edge_cases_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_igamma_edge_cases_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_igammac_common_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_igammac_common_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_igammac_common_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_igammac_common_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_igammac_edge_cases_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_igammac_edge_cases_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_igammac_edge_cases_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_igammac_edge_cases_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_isposinf_isneginf_non_boolean_output_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_isposinf_isneginf_non_boolean_output_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_isposinf_isneginf_non_boolean_output_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_isposinf_isneginf_non_boolean_output_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_isposinf_isneginf_non_boolean_output_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_isposinf_isneginf_non_boolean_output_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_isposinf_isneginf_non_boolean_output_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_isposinf_isneginf_non_boolean_output_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_isposinf_isneginf_non_boolean_output_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_isposinf_isneginf_non_boolean_output_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_isposinf_isneginf_non_boolean_output_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_log1p_complex_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_log1p_complex_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_mish_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_mish_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_nan_to_num_bfloat16_cpu, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_nan_to_num_complex_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_nan_to_num_complex_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_nan_to_num_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_nan_to_num_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_nan_to_num_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_nan_to_num_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_nan_to_num_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_nan_to_num_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_nan_to_num_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_nan_to_num_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_nan_to_num_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_nan_to_num_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_nan_to_num_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_bfloat16_cpu_bfloat16, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_bfloat16_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_bfloat16_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_bfloat16_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_bfloat16_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_bfloat16_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_bfloat16_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_bfloat16_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_bfloat16_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_bfloat16_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_bfloat16_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_bfloat16_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_bitwise_not_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_bitwise_not_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_bitwise_not_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_bitwise_not_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_bitwise_not_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_bitwise_not_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_bool_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_bool_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_bool_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_bool_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_bool_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_bool_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_bool_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_bool_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_bool_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_bool_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_bool_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_bool_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_bool_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_byte_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_byte_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_byte_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_byte_cpu_complex64, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cdouble_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cdouble_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cdouble_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cdouble_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cdouble_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cdouble_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cdouble_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cdouble_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cdouble_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_ceil_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_ceil_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_ceil_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_ceil_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_ceil_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_ceil_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_ceil_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_ceil_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_ceil_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cfloat_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cfloat_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cfloat_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cfloat_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cfloat_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cfloat_cpu_float16, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_chalf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_chalf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_chalf_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_chalf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_chalf_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_chalf_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_chalf_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_chalf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_char_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_char_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_char_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_char_cpu_complex32, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_conj_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_conj_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_conj_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_conj_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_conj_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_conj_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_conj_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_conj_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_conj_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_conj_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_conj_physical_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_conj_physical_cpu_bool, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cos_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cos_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cos_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cos_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cos_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cos_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cos_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cos_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cos_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cos_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cos_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cos_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cosh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cosh_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cosh_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cosh_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cosh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cosh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cosh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cosh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cosh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cosh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cosh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_cosh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_deg2rad_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_deg2rad_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_deg2rad_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_deg2rad_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_deg2rad_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_deg2rad_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_deg2rad_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_deg2rad_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_deg2rad_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_deg2rad_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_digamma_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_digamma_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_digamma_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_digamma_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_digamma_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_digamma_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_digamma_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_digamma_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_digamma_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_digamma_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_double_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_double_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_double_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_double_cpu_complex32, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_ceil_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cfloat_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cfloat_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cfloat_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cfloat_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cfloat_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cfloat_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cfloat_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cfloat_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cfloat_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cfloat_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cfloat_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cfloat_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cfloat_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_chalf_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_chalf_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_chalf_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_chalf_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_chalf_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_chalf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_chalf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_chalf_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_chalf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_chalf_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_chalf_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_chalf_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_chalf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_char_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_char_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_char_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_char_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_char_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_char_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_char_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_char_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_char_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_char_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_char_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_char_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_char_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_physical_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_physical_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_physical_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_physical_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_physical_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_physical_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_physical_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_physical_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_physical_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_physical_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_physical_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_physical_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_physical_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cos_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cos_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cos_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cos_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cos_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cos_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cos_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cos_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cos_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cos_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cos_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cos_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cosh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cosh_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cosh_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cosh_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cosh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cosh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cosh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cosh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cosh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cosh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cosh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cosh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_deg2rad_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_deg2rad_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_deg2rad_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_deg2rad_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_deg2rad_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_deg2rad_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_deg2rad_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_deg2rad_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_deg2rad_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_deg2rad_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_digamma_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_digamma_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_digamma_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_digamma_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_digamma_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_digamma_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_digamma_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_digamma_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_digamma_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_digamma_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_double_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_double_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_double_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_double_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_double_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_double_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_double_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_double_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_double_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_double_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_double_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_double_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_double_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erf_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erf_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erf_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erf_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erf_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erf_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erfc_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erfc_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erfc_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erfc_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erfc_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erfc_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erfc_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erfc_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erfc_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erfc_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erfinv_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erfinv_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erfinv_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erfinv_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erfinv_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erfinv_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erfinv_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erfinv_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erfinv_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erfinv_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_exp2_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_exp2_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_exp2_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_exp2_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_exp2_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_exp2_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_exp2_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_exp2_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_exp2_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_exp2_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_exp2_cpu_int8, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_exp_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_exp_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_expm1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_expm1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_expm1_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_expm1_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_expm1_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_expm1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_expm1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_expm1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_expm1_cpu_int32, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log10_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log10_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log1p_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log1p_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log1p_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log1p_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log1p_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log1p_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log1p_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log1p_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log1p_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log1p_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log1p_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log1p_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log2_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log2_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log2_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log2_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log2_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log2_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log2_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log2_cpu_int16, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_logical_not_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_logical_not_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_logical_not_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_logical_not_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_logical_not_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_logical_not_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_logit_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_logit_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_logit_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_logit_cpu_float32, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_long_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_long_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_long_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_long_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_long_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_long_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_long_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_long_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_1_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_1_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_3_cpu_float16, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_5_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_5_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_5_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_5_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_5_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_5_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nan_to_num_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nan_to_num_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nan_to_num_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nan_to_num_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nan_to_num_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nan_to_num_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nan_to_num_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nan_to_num_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nan_to_num_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nan_to_num_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_neg_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_neg_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_neg_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_neg_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_neg_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_neg_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_neg_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_neg_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_neg_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_neg_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_neg_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_neg_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_celu_cpu_bfloat16, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardshrink_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardsigmoid_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardsigmoid_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardsigmoid_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardsigmoid_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardtanh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardtanh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardtanh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardtanh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardtanh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardtanh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardtanh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardtanh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_logsigmoid_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_logsigmoid_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_logsigmoid_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_logsigmoid_cpu_float64, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_rrelu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_rrelu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_rrelu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_selu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_selu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_selu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_selu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_silu_complex_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_silu_complex_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_silu_cpu_bfloat16, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_2_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_2_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_2_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_2_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_2_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_2_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_2_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_3_cpu_bool, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_4_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_4_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_4_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_4_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_4_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_4_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_4_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_positive_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_positive_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_positive_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_positive_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_positive_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_positive_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_positive_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_positive_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_positive_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_positive_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_positive_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_positive_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_rad2deg_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_rad2deg_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_rad2deg_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_rad2deg_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_rad2deg_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_rad2deg_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_rad2deg_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_rad2deg_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_rad2deg_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_rad2deg_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_real_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_real_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_real_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_real_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_real_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_real_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_real_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_real_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_real_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_real_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_real_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_real_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_real_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_reciprocal_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_reciprocal_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_reciprocal_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_reciprocal_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_reciprocal_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_reciprocal_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_reciprocal_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_reciprocal_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_reciprocal_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_reciprocal_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_reciprocal_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_reciprocal_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_round_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_round_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_round_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_round_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_round_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_round_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_round_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_round_cpu_int8, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_bessel_j0_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_bessel_j0_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_bessel_j1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_bessel_j1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_bessel_j1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_bessel_j1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_bessel_j1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_bessel_j1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_bessel_j1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_bessel_j1_cpu_uint8, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_mvlgamma_mvlgamma_p_1_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_mvlgamma_mvlgamma_p_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_mvlgamma_mvlgamma_p_3_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_mvlgamma_mvlgamma_p_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_mvlgamma_mvlgamma_p_3_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_mvlgamma_mvlgamma_p_3_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_mvlgamma_mvlgamma_p_3_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_mvlgamma_mvlgamma_p_3_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_mvlgamma_mvlgamma_p_3_cpu_uint8, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nan_to_num_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nan_to_num_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nan_to_num_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nan_to_num_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nan_to_num_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nan_to_num_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nan_to_num_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nan_to_num_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nan_to_num_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_neg_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_neg_cpu_complex128, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_celu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_celu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_elu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_elu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_elu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_elu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_hardshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_hardshrink_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_hardshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_hardshrink_cpu_float64, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_relu6_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_relu6_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_relu6_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_relu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_relu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_relu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_relu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_relu_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_relu_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_relu_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_relu_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_relu_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_rrelu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_rrelu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_rrelu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_selu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_selu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_selu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_selu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_silu_complex_cpu_complex128, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_softsign_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_softsign_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_softsign_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_softsign_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_tanhshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_tanhshrink_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_tanhshrink_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_tanhshrink_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_tanhshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_tanhshrink_cpu_float64, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sqrt_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sqrt_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sqrt_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sqrt_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sqrt_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sqrt_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sqrt_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sqrt_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sqrt_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_square_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_square_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_square_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_square_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_square_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_square_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_square_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_square_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_square_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_square_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_square_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_square_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tan_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tan_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tan_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tan_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tan_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tan_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tan_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tan_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tan_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tan_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tan_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tan_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tanh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tanh_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tanh_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tanh_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tanh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tanh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tanh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tanh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tanh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tanh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tanh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tanh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_trunc_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_trunc_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_trunc_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_trunc_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_trunc_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_trunc_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_trunc_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_trunc_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_trunc_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_silu_complex_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_silu_complex_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_silu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_silu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_sinc_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_special_i0_i1_vs_scipy_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_special_i0_i1_vs_scipy_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_special_i0_i1_vs_scipy_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_special_log_ndtr_vs_scipy_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_special_log_ndtr_vs_scipy_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_special_ndtr_vs_scipy_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_special_ndtr_vs_scipy_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_threshold_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_threshold_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_threshold_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_threshold_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_threshold_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_threshold_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_threshold_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_threshold_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_threshold_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_unary_out_op_mem_overlap_cpu_float64 2024-08-20T22:38:53.8844534Z 2024-08-20T22:38:54.0933497Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:38:54.1558464Z Running test_package 1/1 ... [2024-08-20 22:38:54.155302] 2024-08-20T22:38:54.1559471Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:38:54.1563186Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_package.py', '-m', 'not 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 22:38:54.155796] 2024-08-20T22:42:35.2107326Z 2024-08-20T22:42:35.2109494Z test_quantization 5/5 was successful, full logs can be found in artifacts with path test/test-reports/test_quantization_5.5_62437d1cb324dd6f_.log 2024-08-20T22:42:35.2234490Z Running 271 items in this shard: test/test_quantization.py::TestQuantizedOps::test_avg_pool2d, test/test_quantization.py::TestQuantizedOps::test_empty_batch, test/test_quantization.py::TestQuantizedOps::test_group_norm, test/test_quantization.py::TestQuantizedOps::test_interpolate, test/test_quantization.py::TestQuantizedOps::test_max_pool2d_nhwc, test/test_quantization.py::TestQuantizedOps::test_qadd_relu_different_qparams, test/test_quantization.py::TestQuantizedOps::test_qadd_relu_same_qparams, test/test_quantization.py::TestQuantizedOps::test_qcelu, test/test_quantization.py::TestQuantizedOps::test_qgelu, test/test_quantization.py::TestQuantizedOps::test_qmatmul, test/test_quantization.py::TestQuantizedOps::test_qmul_relu_different_qparams, test/test_quantization.py::TestQuantizedOps::test_qmul_relu_same_qparams, test/test_quantization.py::TestQuantizedOps::test_qtanh, test/test_quantization.py::TestQNNPackOps::test_mean, test/test_quantization.py::TestQNNPackOps::test_qnnpack_maxpool2d, test/test_quantization.py::TestQuantizedLinear::test_qlinear, test/test_quantization.py::TestQuantizedLinear::test_qlinear_add_relu_pt2e, test/test_quantization.py::TestQuantizedLinear::test_qlinear_cudnn, test/test_quantization.py::TestQuantizedLinear::test_qlinear_pt2e, test/test_quantization.py::TestQuantizedLinear::test_qlinear_relu_pt2e, test/test_quantization.py::TestQuantizedConv::test_qconv1d_relu_cudnn, test/test_quantization.py::TestQuantizedConv::test_qconv2d_relu_cudnn, test/test_quantization.py::TestQuantizedConv::test_qconv2d_sum_relu_pt2e, test/test_quantization.py::TestDynamicQuantizedOps::test_dynamic_convtranspose1d, test/test_quantization.py::TestDynamicQuantizedOps::test_dynamic_convtranspose2d, test/test_quantization.py::TestDynamicQuantizedOps::test_linear_prepack_fp16_numerics, test/test_quantization.py::TestDynamicQuantizedOps::test_qlinear_legacy, test/test_quantization.py::TestDynamicQuantizedOps::test_unpacked_qlinear_dynamic_fp16, test/test_quantization.py::TestQuantizedEmbeddingOps::test_embedding, test/test_quantization.py::TestQuantizedFunctionalOps::test_conv1d_api, test/test_quantization.py::TestFakeQuantizeOps::test_backward_per_tensor_cachemask_cpu, test/test_quantization.py::TestFakeQuantizeOps::test_fake_quant_control, test/test_quantization.py::TestFakeQuantizeOps::test_fake_quantize_per_channel_affine_scale_dtypes, test/test_quantization.py::TestFakeQuantizeOps::test_forward_per_tensor_cachemask_cuda, test/test_quantization.py::TestFakeQuantizeOps::test_learnable_backward_per_channel_cuda, test/test_quantization.py::TestFakeQuantizeOps::test_numerical_consistency_per_channel, test/test_quantization.py::TestFakeQuantizeOps::test_numerical_consistency_per_tensor, test/test_quantization.py::TestQuantizedTensor::test_cuda_quantization_does_not_pin_memory, test/test_quantization.py::TestQuantizedTensor::test_decomposed_dequantize_per_tensor, test/test_quantization.py::TestQuantizedTensor::test_decomposed_quantize_per_tensor_bfloat16_input, test/test_quantization.py::TestQuantizedTensor::test_fp16_saturate_op, test/test_quantization.py::TestQuantizedTensor::test_pickle_checkpoint_qtensor, test/test_quantization.py::TestQuantizedTensor::test_qtensor_creation, test/test_quantization.py::TestQuantizedTensor::test_qtensor_cuda, test/test_quantization.py::TestQuantizedTensor::test_qtensor_fill_per_tensor, test/test_quantization.py::TestQuantizedTensor::test_qtensor_index_put_cpu, test/test_quantization.py::TestQuantizedTensor::test_qtensor_index_select_cpu, test/test_quantization.py::TestQuantizedTensor::test_qtensor_load_save, test/test_quantization.py::TestQuantizedTensor::test_qtensor_quant_dequant, test/test_quantization.py::TestQuantizedTensor::test_qtensor_sub_byte_aligned_cols, test/test_quantization.py::TestQuantizedTensor::test_qtensor_view, test/test_quantization.py::TestFakeQuantize::test_fq_serializable_per_channel, test/test_quantization.py::TestObserver::test_observer_scriptable, test/test_quantization.py::TestStaticQuantizedModule::test_batch_norm3d_serialization, test/test_quantization.py::TestStaticQuantizedModule::test_channel_shuffle, test/test_quantization.py::TestStaticQuantizedModule::test_conv2d_api, test/test_quantization.py::TestStaticQuantizedModule::test_group_norm, test/test_quantization.py::TestStaticQuantizedModule::test_instance_norm, test/test_quantization.py::TestStaticQuantizedModule::test_linear, test/test_quantization.py::TestStaticQuantizedModule::test_linear_relu, test/test_quantization.py::TestStaticQuantizedModule::test_pool_api, test/test_quantization.py::TestDynamicQuantizedModule::test_dynamic_conv3d, test/test_quantization.py::TestDynamicQuantizedModule::test_gru_api, test/test_quantization.py::TestReferenceQuantizedModule::test_linear_decomposed_weight_custom_qmin_qmax, test/test_quantization.py::TestRecordHistogramObserver::test_observer_scriptable, test/test_quantization.py::TestHistogramObserver::test_histogram_observer_correct_numel, test/test_quantization.py::TestDistributed::test_fake_quant_preserves_buffers, test/test_quantization.py::TestDistributed::test_qat_data_parallel, test/test_quantization.py::TestFusedObsFakeQuantModule::test_compare_fused_obs_fq_oss_module, test/test_quantization.py::TestFusedObsFakeQuantModule::test_embedding_bag_qat_config, test/test_quantization.py::TestBackendConfig::test_backend_config_to_dict, test/test_quantization.py::TestBackendConfig::test_backend_op_config_set_extra_inputs_getter, test/test_quantization.py::TestBackendConfig::test_backend_op_config_to_dict, test/test_quantization.py::TestBackendConfig::test_dtype_config_from_dict, test/test_quantization.py::TestBackendConfig::test_dtype_config_to_dict, test/test_quantization.py::TestUtils::test_get_fqn_to_example_inputs_complex_args, test/test_quantization.py::TestUtils::test_get_fqn_to_example_inputs_default_kwargs, test/test_quantization.py::TestUtils::test_get_fqn_to_example_inputs_simple, test/test_quantization.py::TestUtils::test_quantize_weight_clamping_per_tensor, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_convtranspose_per_channel_fails_early, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_custom_module_class, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_dequant_stub, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_mha_batch_first_attr_is_copied_in_prepare, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_save_load_state_dict, test/test_quantization.py::TestQuantizeEagerPTQDynamic::test_quantized_rnn_cell, test/test_quantization.py::TestQuantizeEagerQAT::test_add_scalar_uses_input_qparams, test/test_quantization.py::TestQuantizeEagerQAT::test_defused_embedding_bag_linear, test/test_quantization.py::TestQuantizeEagerQAT::test_forward_hooks_preserved, test/test_quantization.py::TestQuantizeEagerQAT::test_mul_scalar_uses_input_qparams, test/test_quantization.py::TestQuantizeEagerQATNumerics::test_conv_bn_folded_vs_unfolded, test/test_quantization.py::TestQuantizeEagerQATNumerics::test_leaky_relu, test/test_quantization.py::TestQuantizeEagerQATNumerics::test_linear_bn_workflow, test/test_quantization.py::TestFuseEager::test_fuse_function_customization, test/test_quantization.py::TestModelNumericsEager::test_fake_quant_true_quant_compare, test/test_quantization.py::TestNumericSuiteEager::test_compare_model_stub_linear_dynamic, test/test_quantization.py::TestNumericSuiteEager::test_compare_model_stub_partial, test/test_quantization.py::TestNumericSuiteEager::test_compare_model_stub_submodule_static, test/test_quantization.py::TestNumericSuiteEager::test_compare_weights_linear_static, test/test_quantization.py::TestNumericSuiteEager::test_mobilenet_v2, test/test_quantization.py::TestEqualizeEager::test_equalize_fused_convrelu, test/test_quantization.py::TestFuseFx::test_fuse_custom_pattern, test/test_quantization.py::TestFuseFx::test_fuse_linear_tanh_for_onednn_backend, test/test_quantization.py::TestFuseFx::test_fusion_pattern_with_matchallnode, test/test_quantization.py::TestFuseFx::test_linear_bn_leaky_relu_not_fused_by_default, test/test_quantization.py::TestQuantizeFx::test__convert_to_reference_decomposed_fx_per_channel_quant, test/test_quantization.py::TestQuantizeFx::test_assert_on_size_after_quant_layer, test/test_quantization.py::TestQuantizeFx::test_change_backend_config_for_fixed_qparam_ops, test/test_quantization.py::TestQuantizeFx::test_conv_linear_reference, test/test_quantization.py::TestQuantizeFx::test_conv_transpose_reference, test/test_quantization.py::TestQuantizeFx::test_conv_transpose_relu_not_reference, test/test_quantization.py::TestQuantizeFx::test_conv_transpose_relu_reference, test/test_quantization.py::TestQuantizeFx::test_custom_module_class, test/test_quantization.py::TestQuantizeFx::test_default_quant_after_none_qconfig, test/test_quantization.py::TestQuantizeFx::test_linear_qint8_activation, test/test_quantization.py::TestQuantizeFx::test_mixed_dtypes, test/test_quantization.py::TestQuantizeFx::test_no_obs_between_unmatched_node_and_copy_node, test/test_quantization.py::TestQuantizeFx::test_non_traceable_module, test/test_quantization.py::TestQuantizeFx::test_packed_weight_fused_op, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_set_non_traceable_module_classes, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_set_standalone_module_class, test/test_quantization.py::TestQuantizeFx::test_propagate_dtypes_for_known_nodes_dict_tuple_args, test/test_quantization.py::TestQuantizeFx::test_qat_prepare_device_affinity, test/test_quantization.py::TestQuantizeFx::test_qconfig_for_call_func, test/test_quantization.py::TestQuantizeFx::test_qconfig_for_call_method, test/test_quantization.py::TestQuantizeFx::test_qconfig_function, test/test_quantization.py::TestQuantizeFx::test_qconfig_mapping_set_module_name, test/test_quantization.py::TestQuantizeFx::test_qconfig_mapping_set_module_name_regex, test/test_quantization.py::TestQuantizeFx::test_qconfig_module_name_object_type_order, test/test_quantization.py::TestQuantizeFx::test_qconfig_module_name_regex, test/test_quantization.py::TestQuantizeFx::test_qconfig_precedence, test/test_quantization.py::TestQuantizeFx::test_ref_linear_module, test/test_quantization.py::TestQuantizeFx::test_repeat_nontensor_args_not_observed, test/test_quantization.py::TestQuantizeFx::test_symmetric_qnnpack_qat_qconfig_mapping, test/test_quantization.py::TestQuantizeFx::test_symmetric_qnnpack_qconfig_mapping, test/test_quantization.py::TestQuantizeFx::test_view_nontensor_args_not_observed, test/test_quantization.py::TestQuantizeFxOps::test_add_relu, test/test_quantization.py::TestQuantizeFxOps::test_clamp, test/test_quantization.py::TestQuantizeFxOps::test_conv_transpose_2d, test/test_quantization.py::TestQuantizeFxOps::test_div, test/test_quantization.py::TestQuantizeFxOps::test_fixed_qparams_ops_wrong_qconfig, test/test_quantization.py::TestQuantizeFxOps::test_gelu_reference, test/test_quantization.py::TestQuantizeFxOps::test_general_shape_ops, test/test_quantization.py::TestQuantizeFxOps::test_getitem, test/test_quantization.py::TestQuantizeFxOps::test_hardswish, test/test_quantization.py::TestQuantizeFxOps::test_multiple_qconfigs_for_single_value, test/test_quantization.py::TestQuantizeFxOps::test_narrow, test/test_quantization.py::TestQuantizeFxOps::test_quantized_conv_relu, test/test_quantization.py::TestQuantizeFxOps::test_rnn_cell, test/test_quantization.py::TestQuantizeFxOps::test_sub, test/test_quantization.py::TestQuantizeFxOps::test_sum, test/test_quantization.py::TestQuantizeFxModels::test_qat_embeddingbag_linear, test/test_quantization.py::TestQuantizeFxModels::test_static_gpu_convert_basic, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_pattern_is_entire_graph, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_placeholder_matching, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_with_oneliner_pattern, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_writer_replace_consecutive_submodules, test/test_quantization.py::TestDuplicateDQPass::test_no_add_quant_duplicate_dq, test/test_quantization.py::TestDuplicateDQPass::test_no_need_for_duplicate_dq, test/test_quantization.py::TestNumericDebugger::test_copy_preserve_handle, test/test_quantization.py::TestNumericDebugger::test_extract_results_from_loggers, test/test_quantization.py::TestQuantizePT2E::test_dont_fold_other_constant, test/test_quantization.py::TestQuantizePT2E::test_embedding_quantizer, test/test_quantization.py::TestQuantizePT2E::test_model_is_exported, test/test_quantization.py::TestQuantizePT2E::test_move_exported_model_dropout_inplace, test/test_quantization.py::TestQuantizePT2E::test_observer_callback, test/test_quantization.py::TestQuantizePT2E::test_quantization_dtype_float32_float8_e4m3fn, test/test_quantization.py::TestQuantizePT2E::test_shared_qspec, test/test_quantization.py::TestQuantizePT2E::test_shared_qspec_transitivity_case_2, test/test_quantization.py::TestQuantizePT2E::test_simple_quantizer, test/test_quantization.py::TestQuantizePT2E::test_wo_annotate_conv_output_quantizer, test/test_quantization.py::TestPT2ERepresentation::test_add_relu, test/test_quantization.py::TestPT2ERepresentation::test_dynamic_linear, test/test_quantization.py::TestPT2ERepresentation::test_maxpool2d, test/test_quantization.py::TestXNNPACKQuantizer::test_add_and_inplace_add, test/test_quantization.py::TestXNNPACKQuantizer::test_conv1d, test/test_quantization.py::TestXNNPACKQuantizer::test_dynamic_linear_with_conv, test/test_quantization.py::TestXNNPACKQuantizer::test_obs_sharing_ops, test/test_quantization.py::TestXNNPACKQuantizer::test_set_module_name_with_underscores, test/test_quantization.py::TestXNNPACKQuantizer::test_set_module_type, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_attention_block, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_conv2d, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_conv2d_serials_binary_unary, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_binary_dynamic_qat, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_binary_unary_qat, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_unary_dynamic, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_maxpool2d_recipe, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_qat_conv2d_binary, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_set_module_name_and_module_type_case1, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_fusion_cuda, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_relu_fusion_cuda, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_bias_derived_qspec, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_fusion, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_transpose_bn_relu, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_per_channel_weight_custom_dtype, test/test_quantization.py::TestFXGraphMatcher::test_matching_failure_node_type, test/test_quantization.py::TestFXGraphMatcher::test_nodes_with_equal_types_get_matched, test/test_quantization.py::TestFXGraphMatcher::test_simple_mod_multi, test/test_quantization.py::TestFXGraphMatcherModels::test_mobilenet_v2_qat, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_add_loggers_cuda, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_add_shadow_loggers_cuda, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_add_shadow_loggers_fun_qat, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_add_shadow_loggers_mod_qat, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_extract_weights_conv_fun_ptq, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_extract_weights_dynamic, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_extract_weights_mod_ptq, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_int8_shadows_int8_fun, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_linear_fp16_vs_linear_fp16_shadow_activations, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_linear_fp16_weights, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_logging_inputs, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_match_activations_fun_qat, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_mul_add_cat_stack_skips_shadowing, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_unsupported_op_copy_skips_shadowing, test/test_quantization.py::TestFXNumericSuiteNShadows::test_add_loggers_linear_mod_quant_quant, test/test_quantization.py::TestFXNumericSuiteNShadows::test_functions, test/test_quantization.py::TestFXNumericSuiteNShadows::test_qconfig_multi_mapping_deduplication, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_compare_activations_linear, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_compare_activations_lstm_dynamic, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_compare_weights_conv, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_sparsenn_compare_activations, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_sparsenn_shadow, test/test_quantization.py::TestFxModelReportObserver::test_observer_after_relu, test/test_quantization.py::TestFxModelReportClass::test_generate_report, test/test_quantization.py::TestFxModelReportClass::test_qconfig_mapping_generation, test/test_quantization.py::TestFxDetectOutliers::test_multiple_run_consistent_spike_outlier_report_gen, test/test_quantization.py::TestFxModelReportVisualizer::test_get_modules_and_features, test/test_quantization.py::TestEqualizeFx::test_input_weight_equalization_convert, test/test_quantization.py::TestSerialization::test_conv2d, test/test_quantization.py::TestSerialization::test_conv2d_graph_v2, test/test_quantization.py::TestSerialization::test_conv2d_nobias_graph_v2, test/test_quantization.py::TestSerialization::test_per_channel_observer, test/test_quantization.py::TestQuantizeJit::test_conv, test/test_quantization.py::TestQuantizeJit::test_nested, test/test_quantization.py::TestQuantizeJitPasses::test_conv_trace, test/test_quantization.py::TestQuantizeJitPasses::test_foldbn_complex_cases, test/test_quantization.py::TestQuantizeJitPasses::test_foldbn_no_fusion, test/test_quantization.py::TestQuantizeJitPasses::test_foldbn_shared_classtype, test/test_quantization.py::TestQuantizeJitPasses::test_foldbn_trivial_nobias, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_interface_unshare_type, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_propagate_observed, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_propagate_observed_for_function, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_shared_class_type, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_skip_values, test/test_quantization.py::TestQuantizeJitPasses::test_interface_with_fork, test/test_quantization.py::TestQuantizeJitPasses::test_module_list, test/test_quantization.py::TestQuantizeJitPasses::test_replicate_dequantize, test/test_quantization.py::TestQuantizeJitPasses::test_skip_dequant_constant_prop, test/test_quantization.py::TestQuantizeJitOps::test_general_shape_ops, test/test_quantization.py::TestQuantizeJitOps::test_group_norm, test/test_quantization.py::TestQuantizeJitOps::test_quantized_add_alpha, test/test_quantization.py::TestQuantizeDynamicJitPasses::test_dynamic_with_if, test/test_quantization.py::TestQuantizeDynamicJitOps::test_embedding_bag_padding_idx_error, test/test_quantization.py::TestDeprecatedJitQuantized::test_erase_class_tensor_shapes, test/test_quantization.py::TestDeprecatedJitQuantized::test_rnn_cell_quantized, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_observer, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_utils, test/test_quantization.py::TestAOMigrationNNQuantized::test_functional_import, test/test_quantization.py::TestAOMigrationNNQuantized::test_import_nn_quantized_dynamic_import, test/test_quantization.py::TestAOMigrationNNQuantized::test_modules_linear, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_import_nn_intrinsic_quantized, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_intrinsic_qat_conv_fused, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_intrinsic_quantized_conv_relu, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_no_import_nn_intrinsic_quantized_dynamic, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_pattern_utils, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_prepare, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_rte_cpu_float8_e4m3fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_subnormals_cpu_float8_e4m3fn, test/test_quantization.py::TestFloat8DtypeCPU::test_empty_cpu_float8_e4m3fn, test/test_quantization.py::TestFloat8DtypeCPU::test_empty_cpu_float8_e5m2fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_type_promotion_fails_cpu_float8_e4m3fn, test/test_quantization.py::TestFloat8DtypeCPU::test_type_promotion_fails_cpu_float8_e4m3fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_type_promotion_fails_cpu_float8_e5m2fnuz 2024-08-20T22:42:35.2365901Z 2024-08-20T22:42:38.7649761Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:42:38.8315790Z Running test_type_promotion 1/1 ... [2024-08-20 22:42:38.830648] 2024-08-20T22:42:38.8317293Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:42:38.8322017Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_type_promotion.py', '-m', 'not 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 22:42:38.831073] 2024-08-20T22:43:43.9427932Z 2024-08-20T22:43:43.9429743Z test_quantization 2/5 was successful, full logs can be found in artifacts with path test/test-reports/test_quantization_2.5_49333950288fddfb_.log 2024-08-20T22:43:43.9543676Z Running 247 items in this shard: test/test_quantization.py::TestQuantizedOps::test_avg_pool3d, test/test_quantization.py::TestQuantizedOps::test_batch_norm, test/test_quantization.py::TestQuantizedOps::test_channel_shuffle, test/test_quantization.py::TestQuantizedOps::test_equal, test/test_quantization.py::TestQuantizedOps::test_instance_norm, test/test_quantization.py::TestQuantizedOps::test_leaky_relu, test/test_quantization.py::TestQuantizedOps::test_max_pool3d, test/test_quantization.py::TestQuantizedOps::test_max_pool3d_nhwc, test/test_quantization.py::TestQuantizedOps::test_mul_scalar_relu, test/test_quantization.py::TestQuantizedOps::test_qlayer_norm, test/test_quantization.py::TestQuantizedOps::test_qrelu, test/test_quantization.py::TestQuantizedOps::test_qsoftmax_qnnpack, test/test_quantization.py::TestQuantizedOps::test_quantized_mean_qnnpack, test/test_quantization.py::TestQuantizedOps::test_std, test/test_quantization.py::TestQNNPackOps::test_hardtanh, test/test_quantization.py::TestQNNPackOps::test_qnnpack_add_broadcast, test/test_quantization.py::TestQNNPackOps::test_qnnpack_relu, test/test_quantization.py::TestQuantizedLinear::test_qlinear_tanh, test/test_quantization.py::TestQuantizedConv::test_qconv1d, test/test_quantization.py::TestQuantizedConv::test_qconv1d_cudnn, test/test_quantization.py::TestQuantizedConv::test_qconv2d_add, test/test_quantization.py::TestQuantizedConv::test_qconv2d_hardtanh_pt2e, test/test_quantization.py::TestQuantizedConv::test_qconv3d_unpack, test/test_quantization.py::TestQuantizedConv::test_qconv_transpose2d, test/test_quantization.py::TestDynamicQuantizedOps::test_dynamic_conv2d, test/test_quantization.py::TestDynamicQuantizedOps::test_wrapped_fbgemm_pack_gemm_matrix_fp16_pt2_compliant, test/test_quantization.py::TestComparatorOps::test_compare_tensor_tensor, test/test_quantization.py::TestPadding::test_constant_padNd, test/test_quantization.py::TestQuantizedEmbeddingOps::test_embedding_2d_indices, test/test_quantization.py::TestQuantizedEmbeddingOps::test_embedding_bag_2bit, test/test_quantization.py::TestQuantizedEmbeddingOps::test_embedding_bag_4bit, test/test_quantization.py::TestQuantizedFunctionalOps::test_conv3d_api, test/test_quantization.py::TestQuantizedFunctionalOps::test_grid_sample, test/test_quantization.py::TestQuantizedFunctionalOps::test_relu_api, test/test_quantization.py::TestFakeQuantizeOps::test_backward_per_tensor, test/test_quantization.py::TestFakeQuantizeOps::test_forward_per_channel, test/test_quantization.py::TestFakeQuantizeOps::test_learnable_backward_per_tensor_cpu, test/test_quantization.py::TestFusedObsFakeQuant::test_fused_obs_fake_quant_backward_op, test/test_quantization.py::TestQuantizedTensor::test_compare_per_channel_device_numerics, test/test_quantization.py::TestQuantizedTensor::test_per_channel_qtensor_to_memory_format, test/test_quantization.py::TestQuantizedTensor::test_per_tensor_qtensor_to_memory_format, test/test_quantization.py::TestQuantizedTensor::test_qtensor_channel_float_assignment, test/test_quantization.py::TestQuantizedTensor::test_qtensor_dtypes, test/test_quantization.py::TestQuantizedTensor::test_qtensor_index_put_cuda, test/test_quantization.py::TestQuantizedTensor::test_qtensor_legacy_new_failure, test/test_quantization.py::TestQuantizedTensor::test_qtensor_permute, test/test_quantization.py::TestQuantizedTensor::test_qtensor_resize, test/test_quantization.py::TestQuantizedTensor::test_qtensor_sub_byte_not_aligned_cols, test/test_quantization.py::TestFakeQuantize::test_fq_module_per_channel, test/test_quantization.py::TestObserver::test_histogram_observer_ignore_infinity, test/test_quantization.py::TestStaticQuantizedModule::test_batch_norm2d, test/test_quantization.py::TestStaticQuantizedModule::test_conv1d_relu_api, test/test_quantization.py::TestStaticQuantizedModule::test_conv3d_api, test/test_quantization.py::TestStaticQuantizedModule::test_dropout_serialization, test/test_quantization.py::TestStaticQuantizedModule::test_hard_swish, test/test_quantization.py::TestStaticQuantizedModule::test_layer_norm, test/test_quantization.py::TestStaticQuantizedModule::test_linear_tanh, test/test_quantization.py::TestStaticQuantizedModule::test_relu, test/test_quantization.py::TestStaticQuantizedModule::test_sigmoid, test/test_quantization.py::TestDynamicQuantizedModule::test_dynamic_convtranspose1d, test/test_quantization.py::TestDynamicQuantizedModule::test_linear_api, test/test_quantization.py::TestDynamicQuantizedModule::test_lstm_api, test/test_quantization.py::TestReferenceQuantizedModule::test_rnn_cell, test/test_quantization.py::TestHistogramObserver::test_histogram_observer_against_reference, test/test_quantization.py::TestHistogramObserver::test_histogram_observer_one_sided, test/test_quantization.py::TestHistogramObserver::test_histogram_observer_single_inputs, test/test_quantization.py::TestHistogramObserver::test_histogram_observer_update_within_range_succeeds, test/test_quantization.py::TestBackendConfig::test_backend_config_set_name, test/test_quantization.py::TestBackendConfig::test_backend_op_config_set_fuser_method, test/test_quantization.py::TestBackendConfig::test_backend_op_config_set_num_tensor_args_to_observation_type, test/test_quantization.py::TestBackendConfig::test_backend_op_config_set_qat_module, test/test_quantization.py::TestQuantizationDocs::test_quantization_doc_custom, test/test_quantization.py::TestQuantizeEagerPTQDynamic::test_forward_hooks_preserved, test/test_quantization.py::TestQuantizeEagerPTQDynamic::test_two_layers, test/test_quantization.py::TestQuantizeEagerOps::test_conv_transpose_1d, test/test_quantization.py::TestQuantizeEagerOps::test_conv_transpose_3d, test/test_quantization.py::TestQuantizeEagerOps::test_leaky_relu, test/test_quantization.py::TestQuantizeEagerQAT::test_conv_linear, test/test_quantization.py::TestQuantizeEagerQAT::test_dynamic_qat_linear, test/test_quantization.py::TestQuantizeEagerQAT::test_eval_only_fake_quant, test/test_quantization.py::TestQuantizeEagerQATNumerics::test_linear_bn_symm_numerics, test/test_quantization.py::TestFuseEager::test_forward_hooks_preserved, test/test_quantization.py::TestFuseEager::test_fuse_modules_with_nested_hooks, test/test_quantization.py::TestFuseEager::test_fusion_convtranspose_bn_eval, test/test_quantization.py::TestNumericSuiteEager::test_compare_model_outputs_functional_static, test/test_quantization.py::TestNumericSuiteEager::test_compare_model_outputs_linear_static, test/test_quantization.py::TestNumericSuiteEager::test_compare_model_outputs_lstm_dynamic, test/test_quantization.py::TestNumericSuiteEager::test_compare_model_stub_conv_static, test/test_quantization.py::TestNumericSuiteEager::test_compare_model_stub_lstm_dynamic, test/test_quantization.py::TestNumericSuiteEager::test_mobilenet_v3, test/test_quantization.py::TestNumericSuiteEager::test_shadow_logger, test/test_quantization.py::TestEqualizeEager::test_cross_layer_equalization, test/test_quantization.py::TestFuseFx::test_fuse_conv_bn_add_relu_lowering, test/test_quantization.py::TestFuseFx::test_fuse_convtranspose_bn_eval, test/test_quantization.py::TestFuseFx::test_fuse_linear_bn_leaky_relu_onednn, test/test_quantization.py::TestFuseFx::test_fuse_module_relu, test/test_quantization.py::TestFuseFx::test_problematic_fuse_example, test/test_quantization.py::TestQuantizeFx::test_channel_shuffle_lowering, test/test_quantization.py::TestQuantizeFx::test_convert_custom_config_set_preserved_attributes, test/test_quantization.py::TestQuantizeFx::test_copy_node_has_shared_actpp_instance, test/test_quantization.py::TestQuantizeFx::test_dict_output, test/test_quantization.py::TestQuantizeFx::test_fuse_custom_config_set_preserved_attributes, test/test_quantization.py::TestQuantizeFx::test_fused_module_qat_swap, test/test_quantization.py::TestQuantizeFx::test_fusion_pattern_unquantized, test/test_quantization.py::TestQuantizeFx::test_get_default_qconfig_valid_backend, test/test_quantization.py::TestQuantizeFx::test_getattr_with_nontensor_result, test/test_quantization.py::TestQuantizeFx::test_linear_bn, test/test_quantization.py::TestQuantizeFx::test_lowering_functional_linear_with_kwargs, test/test_quantization.py::TestQuantizeFx::test_pattern_match, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_set_output_quantized_indexes, test/test_quantization.py::TestQuantizeFx::test_prepare_mode, test/test_quantization.py::TestQuantizeFx::test_preserve_qconfig, test/test_quantization.py::TestQuantizeFx::test_propagate_dtypes_for_known_nodes_dict_args, test/test_quantization.py::TestQuantizeFx::test_propagate_dtypes_for_known_nodes_split_list_args, test/test_quantization.py::TestQuantizeFx::test_qconfig_mapping_set_object_type, test/test_quantization.py::TestQuantizeFx::test_qconfig_mapping_to_dict, test/test_quantization.py::TestQuantizeFx::test_qconfig_none, test/test_quantization.py::TestQuantizeFx::test_qparams_buffers, test/test_quantization.py::TestQuantizeFx::test_quant_output_always_observed, test/test_quantization.py::TestQuantizeFx::test_quantized_model_type, test/test_quantization.py::TestQuantizeFx::test_register_patterns, test/test_quantization.py::TestQuantizeFx::test_reshape_nontensor_args_not_observed, test/test_quantization.py::TestQuantizeFx::test_stack_trace_preserved_linear, test/test_quantization.py::TestQuantizeFx::test_standalone_module_float_interface, test/test_quantization.py::TestQuantizeFx::test_state_dict, test/test_quantization.py::TestQuantizeFx::test_static_lstm, test/test_quantization.py::TestQuantizeFx::test_static_lstm_with_custom_fixed_qparams, test/test_quantization.py::TestQuantizeFx::test_sub_scalar, test/test_quantization.py::TestQuantizeFx::test_torch_unsqueeze_nontensor_args_not_observed, test/test_quantization.py::TestQuantizeFx::test_unsqueeze__nontensor_args_not_observed, test/test_quantization.py::TestQuantizeFx::test_unsqueeze_nontensor_args_not_observed, test/test_quantization.py::TestQuantizeFxOps::test_add, test/test_quantization.py::TestQuantizeFxOps::test_add_relu_multiple_uses_of_relu, test/test_quantization.py::TestQuantizeFxOps::test_elu, test/test_quantization.py::TestQuantizeFxOps::test_embedding, test/test_quantization.py::TestQuantizeFxOps::test_embedding_bag, test/test_quantization.py::TestQuantizeFxOps::test_fixed_qparams_ops, test/test_quantization.py::TestQuantizeFxOps::test_linear_dynamic_fp16, test/test_quantization.py::TestQuantizeFxOps::test_mish_reference, test/test_quantization.py::TestQuantizeFxOps::test_pixel_shuffle_module, test/test_quantization.py::TestQuantizeFxOps::test_prelu, test/test_quantization.py::TestQuantizeFxOps::test_qbatch_norm_relu, test/test_quantization.py::TestQuantizeFxOps::test_quantized_add_qat, test/test_quantization.py::TestQuantizeFxOps::test_rnn, test/test_quantization.py::TestQuantizeFxOps::test_softmax_normal, test/test_quantization.py::TestQuantizeFxModels::test_model_dropout, test/test_quantization.py::TestQuantizeFxModels::test_qat_functional_linear, test/test_quantization.py::TestQuantizeFxModels::test_resnet_base, test/test_quantization.py::TestQuantizeFxModels::test_torchvision, test/test_quantization.py::TestGraphUtils::test_conv_bn_relu, test/test_quantization.py::TestGraphUtils::test_customized_equivalet_types_dict, test/test_quantization.py::TestMetaDataPorting::test_metadata_porting_for_dq, test/test_quantization.py::TestNumericDebugger::test_quantize_pt2e_preserve_handle, test/test_quantization.py::TestNumericDebugger::test_simple, test/test_quantization.py::TestQuantizePT2E::test_composable_quantizer_linear_conv, test/test_quantization.py::TestQuantizePT2E::test_composable_quantizer_transform_for_annotation, test/test_quantization.py::TestQuantizePT2E::test_conv_transpose_bn_relu, test/test_quantization.py::TestQuantizePT2E::test_embedding_conv_linear_quantization, test/test_quantization.py::TestQuantizePT2E::test_fixed_qparams_qspec_ptq, test/test_quantization.py::TestQuantizePT2E::test_fold_quantize_per_channel, test/test_quantization.py::TestQuantizePT2E::test_max_pool2d_quantizer, test/test_quantization.py::TestQuantizePT2E::test_move_exported_model_dropout, test/test_quantization.py::TestQuantizePT2E::test_save_load, test/test_quantization.py::TestQuantizePT2E::test_shared_qspec_transitivity, test/test_quantization.py::TestQuantizePT2E::test_transform_for_annotation, test/test_quantization.py::TestPT2ERepresentation::test_qdq_per_channel, test/test_quantization.py::TestXNNPACKQuantizer::test_conv_linear, test/test_quantization.py::TestXNNPACKQuantizer::test_dynamic_linear, test/test_quantization.py::TestXNNPACKQuantizer::test_dynamic_linear_int4_weight, test/test_quantization.py::TestXNNPACKQuantizer::test_linear_with_dynamic_shape, test/test_quantization.py::TestXNNPACKQuantizer::test_mul_and_inplace_mul, test/test_quantization.py::TestXNNPACKQuantizer::test_set_module_name, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_cat_recipe_same_inputs, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_conv2d_binary_unary, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_flatten_recipe, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_binary_unary_serials, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_qat_conv2d_binary2, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_fold_bn_erases_bn_node, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_bias_derived_qspec, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_fusion_no_conv_bias, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_transpose_bn_relu, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_per_channel_weight_custom_dtype, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_relu_fusion, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_update_shared_qspec, test/test_quantization.py::TestFXGraphMatcher::test_methods, test/test_quantization.py::TestFXGraphMatcher::test_nodes_before_cat, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_extract_weights_conv_fun_qat, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_fp16_shadows_fp32, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_layer_names, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_ops_with_same_fp32_and_int8_signature, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_user_defined_function, test/test_quantization.py::TestFXNumericSuiteNShadows::test_add_loggers_conv_bn_relu_fusion_fp32, test/test_quantization.py::TestFXNumericSuiteNShadows::test_add_loggers_functions, test/test_quantization.py::TestFXNumericSuiteNShadows::test_add_loggers_mobilenet_v2, test/test_quantization.py::TestFXNumericSuiteNShadows::test_extract_weights_linear, test/test_quantization.py::TestFXNumericSuiteNShadows::test_linear_mod, test/test_quantization.py::TestFXNumericSuiteNShadows::test_linear_relu_mod, test/test_quantization.py::TestFXNumericSuiteNShadows::test_logger_enabled_and_save_activations_flags, test/test_quantization.py::TestFXNumericSuiteNShadows::test_mobilenet_v2, test/test_quantization.py::TestFXNumericSuiteNShadows::test_partial_qconfig_mapping, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_compare_weights_linear, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_compare_weights_lstm_dynamic, test/test_quantization.py::TestFxModelReportDetectDynamicStatic::test_nested_detection_case, test/test_quantization.py::TestFxModelReportClass::test_prepare_model_callibration, test/test_quantization.py::TestFxModelReportVisualizer::test_generate_tables_match_with_report, test/test_quantization.py::TestEqualizeFx::test_input_weight_equalization_branching, test/test_quantization.py::TestSerialization::test_conv2d_graph, test/test_quantization.py::TestSerialization::test_conv2d_graph_v3, test/test_quantization.py::TestSerialization::test_conv2d_nobias, test/test_quantization.py::TestSerialization::test_conv2d_relu, test/test_quantization.py::TestSerialization::test_conv3d, test/test_quantization.py::TestSerialization::test_linear_dynamic, test/test_quantization.py::TestQuantizeJit::test_linear_dynamic_fp16, test/test_quantization.py::TestQuantizeJitPasses::test_convtranspose_trace, test/test_quantization.py::TestQuantizeJitPasses::test_dedup_module_uses, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_for_general_ops, test/test_quantization.py::TestQuantizeJitPasses::test_quantize_fork_wait, test/test_quantization.py::TestQuantizeJitOps::test_general_value_ops, test/test_quantization.py::TestQuantizeJitOps::test_hardswish, test/test_quantization.py::TestQuantizeJitOps::test_instance_norm, test/test_quantization.py::TestQuantizeJitOps::test_layer_norm, test/test_quantization.py::TestQuantizeJitOps::test_qbatch_norm_relu_BNFuncInplaceRelu, test/test_quantization.py::TestQuantizeJitOps::test_quantized_add_scalar_relu, test/test_quantization.py::TestQuantizeJitOps::test_quantized_mul_relu, test/test_quantization.py::TestQuantizeDynamicJitPasses::test_dynamic_shared_weights, test/test_quantization.py::TestQuantizeDynamicJitPasses::test_dynamic_weight_observer, test/test_quantization.py::TestQuantizeDynamicJitPasses::test_quantize_dynamic_fp16, test/test_quantization.py::TestQuantizeDynamicJitOps::test_linear, test/test_quantization.py::TestDeprecatedJitQuantized::test_quantization_modules, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_quantization_mappings, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_stubs, test/test_quantization.py::TestAOMigrationNNQuantized::test_import_nn_qat_embedding_ops, test/test_quantization.py::TestAOMigrationNNQuantized::test_import_nn_quantizable_activation, test/test_quantization.py::TestAOMigrationNNQuantized::test_modules_conv, test/test_quantization.py::TestAOMigrationNNQuantized::test_modules_dropout, test/test_quantization.py::TestAOMigrationNNQuantized::test_modules_import, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_intrinsic_quantized_linear_relu, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_nn_intrinsic_fused, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_match_utils, test/test_quantization.py::TestBitsCPU::test_subclass_cpu, test/test_quantization.py::TestBitsCPU::test_types_cpu, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_extremes_cpu_float8_e5m2, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_rte_cpu_float8_e5m2, test/test_quantization.py::TestFloat8DtypeCPU::test_empty_cpu_float8_e5m2, test/test_quantization.py::TestFloat8DtypeCPU::test_special_numbers_cpu_float8_e4m3fnuz, test/test_quantization.py::TestFloat8DtypeCPUOnlyCPU::test_mul_cpu_float8_e4m3fn 2024-08-20T22:43:43.9647295Z 2024-08-20T22:43:47.4005048Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:43:47.4731461Z Running test_sparse_csr 1/1 ... [2024-08-20 22:43:47.472550] 2024-08-20T22:43:47.4732524Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:43:47.4735752Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_sparse_csr.py', '-m', 'not 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 22:43:47.473027] 2024-08-20T22:43:52.7538637Z 2024-08-20T22:43:52.7540978Z test_type_promotion 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_type_promotion_1.1_1f600a20fc26d9e9_.log 2024-08-20T22:43:52.7970715Z Running 423 items in this shard: test/test_type_promotion.py::TestTypePromotionCPU::test_add_wrapped_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_alpha_mismatch_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_alternate_result_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_bfloat16_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_booleans_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_can_cast_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_cat_different_dtypes_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_cat_out_different_dtypes_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_bool_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_bool_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_bool_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_bool_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_float32_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_float32_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_float32_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_float32_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_float64_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_float64_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_float64_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_float64_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_int32_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_int32_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_int32_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_int32_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_bool_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_bool_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_bool_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_bool_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_float32_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_float32_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_float32_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_float32_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_float64_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_float64_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_float64_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_float64_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_int32_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_int32_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_int32_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_int32_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_bool_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_bool_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_bool_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_bool_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_float32_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_float32_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_float32_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_float32_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_float64_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_float64_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_float64_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_float64_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_int32_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_int32_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_int32_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_int32_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_bool_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_bool_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_bool_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_bool_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_float32_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_float32_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_float32_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_float32_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_float64_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_float64_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_float64_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_float64_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_int32_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_int32_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_int32_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_int32_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_comparison_ops_with_type_promotion_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_complex_assertraises_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_complex_half_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_complex_promotion_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_complex_scalar_mult_tensor_promotion_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_computation_ignores_out_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_create_bool_tensors_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_cpu_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_cpu_int16, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_cpu_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_cpu_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_cpu_int8, 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test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_out_cpu_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_out_cpu_int16, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_out_cpu_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_out_cpu_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_out_cpu_int8, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_out_cpu_uint8, test/test_type_promotion.py::TestTypePromotionCPU::test_float_promotion_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_from_issue_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_half_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_indexing_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_indexing_fail_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_inplace_cpu, 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test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_complex64_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_complex64_int16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_complex64_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_complex64_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_complex64_int8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_complex64_uint8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_float16_bfloat16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_float16_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_float16_complex128, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_float16_complex64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_float16_float16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_float16_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_float16_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_float16_int16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_float16_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_float16_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_float16_int8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_float16_uint8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_float32_bfloat16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_float32_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_float32_complex128, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_float32_complex64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_float32_float16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_float32_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_float32_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_float32_int16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_float32_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_float32_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_float32_int8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_float32_uint8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_float64_bfloat16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_float64_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_float64_complex128, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_float64_complex64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_float64_float16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_float64_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_float64_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_float64_int16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_float64_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_float64_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_float64_int8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_float64_uint8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int16_bfloat16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int16_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int16_complex128, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int16_complex64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int16_float16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int16_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int16_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int16_int16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int16_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int16_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int16_int8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int16_uint8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int32_bfloat16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int32_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int32_complex128, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int32_complex64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int32_float16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int32_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int32_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int32_int16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int32_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int32_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int32_int8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int32_uint8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int64_bfloat16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int64_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int64_complex128, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int64_complex64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int64_float16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int64_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int64_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int64_int16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int64_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int64_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int64_int8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int64_uint8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int8_bfloat16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int8_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int8_complex128, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int8_complex64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int8_float16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int8_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int8_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int8_int16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int8_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int8_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int8_int8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int8_uint8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_uint8_bfloat16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_uint8_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_uint8_complex128, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_uint8_complex64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_uint8_float16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_uint8_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_uint8_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_uint8_int16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_uint8_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_uint8_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_uint8_int8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_uint8_uint8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_tensor_vs_scalar_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_sparse_add_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_sparse_div_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_sparse_div_promotion_cpu_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_sparse_div_promotion_cpu_int16, test/test_type_promotion.py::TestTypePromotionCPU::test_sparse_div_promotion_cpu_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_sparse_div_promotion_cpu_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_sparse_div_promotion_cpu_uint8, test/test_type_promotion.py::TestTypePromotionCPU::test_sparse_mul_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_sparse_sub_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_ternary_out_promotion_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_transpose_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_complex128_complex128, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_complex128_complex64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_complex128_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_complex128_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_complex128_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_complex64_complex128, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_complex64_complex64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_complex64_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_complex64_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_complex64_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_float32_complex128, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_float32_complex64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_float32_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_float32_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_float32_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_float64_complex128, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_float64_complex64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_float64_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_float64_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_float64_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_int64_complex128, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_int64_complex64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_int64_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_int64_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_int64_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_unsigned_cpu 2024-08-20T22:43:52.8194440Z 2024-08-20T22:43:55.7587390Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:43:55.8249837Z Running test_jiterator 1/1 ... [2024-08-20 22:43:55.824503] 2024-08-20T22:43:55.8251006Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:43:55.8256247Z 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 22:43:55.825025] 2024-08-20T22:43:56.2546156Z 2024-08-20T22:43:56.2548343Z test_package 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_package_1.1_cb4b347cc1cc30a1_.log 2024-08-20T22:43:56.2714181Z Running 135 items in this shard: test/test_package.py::TestAnalyze::test_trace_dependencies, test/test_package.py::TestDependencyAPI::test_allow_empty_with_error, test/test_package.py::TestDependencyAPI::test_broken_dependency, test/test_package.py::TestDependencyAPI::test_deny, test/test_package.py::TestDependencyAPI::test_deny_glob, test/test_package.py::TestDependencyAPI::test_extern, test/test_package.py::TestDependencyAPI::test_extern_glob, test/test_package.py::TestDependencyAPI::test_extern_glob_allow_empty, test/test_package.py::TestDependencyAPI::test_externing_c_extension, test/test_package.py::TestDependencyAPI::test_implicit_intern, test/test_package.py::TestDependencyAPI::test_intern_error, test/test_package.py::TestDependencyAPI::test_invalid_import, test/test_package.py::TestDependencyAPI::test_mock, test/test_package.py::TestDependencyAPI::test_mock_glob, test/test_package.py::TestDependencyAPI::test_mock_glob_allow_empty, test/test_package.py::TestDependencyAPI::test_pickle_mocked, test/test_package.py::TestDependencyAPI::test_pickle_mocked_all, test/test_package.py::TestDependencyAPI::test_repackage_mocked_module, test/test_package.py::TestDependencyHooks::test_extern_and_mock_hook, test/test_package.py::TestDependencyHooks::test_multiple_extern_hooks, test/test_package.py::TestDependencyHooks::test_multiple_mock_hooks, test/test_package.py::TestDependencyHooks::test_remove_hooks, test/test_package.py::TestDependencyHooks::test_single_hook, test/test_package.py::TestDiGraph::test_all_paths, test/test_package.py::TestDiGraph::test_contains, test/test_package.py::TestDiGraph::test_contains_non_hashable, test/test_package.py::TestDiGraph::test_edges, test/test_package.py::TestDiGraph::test_forward_closure, test/test_package.py::TestDiGraph::test_iter, test/test_package.py::TestDiGraph::test_node_attr_update, test/test_package.py::TestDiGraph::test_node_attrs, test/test_package.py::TestDiGraph::test_predecessor_not_in_graph, test/test_package.py::TestDiGraph::test_predecessors, test/test_package.py::TestDiGraph::test_successor_not_in_graph, test/test_package.py::TestDiGraph::test_successors, test/test_package.py::DirectoryReaderTest::test_importer_access, test/test_package.py::DirectoryReaderTest::test_loading_has_record, test/test_package.py::DirectoryReaderTest::test_loading_module, test/test_package.py::DirectoryReaderTest::test_loading_pickle, test/test_package.py::DirectoryReaderTest::test_package_resource_access, test/test_package.py::DirectoryReaderTest::test_resource_access_by_path, test/test_package.py::DirectoryReaderTest::test_resource_reader, test/test_package.py::DirectoryReaderTest::test_scriptobject_failure_message, test/test_package.py::TestGlobGroup::test_exclude, test/test_package.py::TestGlobGroup::test_exclude_from_all, test/test_package.py::TestGlobGroup::test_invalid_raw, test/test_package.py::TestGlobGroup::test_list_include_exclude, test/test_package.py::TestGlobGroup::test_one_star, test/test_package.py::TestGlobGroup::test_one_star_middle, test/test_package.py::TestGlobGroup::test_one_star_multiple_in_component, test/test_package.py::TestGlobGroup::test_one_star_partial, test/test_package.py::TestGlobGroup::test_one_star_partial_extension, test/test_package.py::TestGlobGroup::test_raw_two_star, test/test_package.py::TestGlobGroup::test_two_star, test/test_package.py::TestGlobGroup::test_two_star_end, test/test_package.py::TestGlobGroup::test_two_star_middle, test/test_package.py::TestGlobGroup::test_two_star_multiple, test/test_package.py::TestImporter::test_ordered_importer_basic, test/test_package.py::TestImporter::test_ordered_importer_whichmodule, test/test_package.py::TestImporter::test_package_importer_whichmodule_no_dunder_module, test/test_package.py::TestImporter::test_single_ordered_importer, test/test_package.py::TestImporter::test_sys_importer, test/test_package.py::TestImporter::test_sys_importer_roundtrip, test/test_package.py::TestLoadBCPackages::test_load_bc_packages_fx_module, test/test_package.py::TestLoadBCPackages::test_load_bc_packages_nn_module, test/test_package.py::TestLoadBCPackages::test_load_bc_packages_torchscript_module, test/test_package.py::TestMangling::test_demangle_base, test/test_package.py::TestMangling::test_demangler_multiple_manglers, test/test_package.py::TestMangling::test_is_mangled, test/test_package.py::TestMangling::test_mangle_empty_errors, test/test_package.py::TestMangling::test_mangle_prefix, test/test_package.py::TestMangling::test_mangler_is_consistent, test/test_package.py::TestMangling::test_package_mangler, test/test_package.py::TestMangling::test_roundtrip_mangling, test/test_package.py::TestMangling::test_unique_manglers, test/test_package.py::TestMangling::test_unique_module_names, test/test_package.py::TestMisc::test_dunder_package_present, test/test_package.py::TestMisc::test_dunder_package_works_from_package, test/test_package.py::TestMisc::test_exporter_content_lists, test/test_package.py::TestMisc::test_file_structure, test/test_package.py::TestMisc::test_file_structure_has_file, test/test_package.py::TestMisc::test_inspect_class, test/test_package.py::TestMisc::test_is_from_package, test/test_package.py::TestMisc::test_load_python_version_from_package, test/test_package.py::TestMisc::test_loaders_that_remap_files_work_ok, test/test_package.py::TestMisc::test_python_version, test/test_package.py::TestMisc::test_std_lib_sys_hackery_checks, test/test_package.py::ModelTest::test_model_save, test/test_package.py::ModelTest::test_resnet, test/test_package.py::ModelTest::test_script_resnet, test/test_package.py::TestPackageFX::test_package_fx_custom_tracer, test/test_package.py::TestPackageFX::test_package_fx_package, test/test_package.py::TestPackageFX::test_package_fx_simple, test/test_package.py::TestPackageFX::test_package_fx_with_imports, test/test_package.py::TestPackageFX::test_package_fx_wrap, test/test_package.py::TestPackageFX::test_package_then_fx, test/test_package.py::TestPackageScript::test_different_package_interface, test/test_package.py::TestPackageScript::test_different_package_script_class, test/test_package.py::TestPackageScript::test_load_shared_scriptmodules, test/test_package.py::TestPackageScript::test_load_shared_tensors, test/test_package.py::TestPackageScript::test_load_shared_tensors_repackaged, test/test_package.py::TestPackageScript::test_mixing_packaged_and_inline_modules, test/test_package.py::TestPackageScript::test_mixing_packaged_and_inline_modules_shared_code, test/test_package.py::TestPackageScript::test_package_interface, test/test_package.py::TestPackageScript::test_package_script_class, test/test_package.py::TestPackageScript::test_package_script_class_referencing_self, test/test_package.py::TestPackageScript::test_save_eager_mods_sharing_scriptmodule, test/test_package.py::TestPackageScript::test_save_independent_scriptmodules, test/test_package.py::TestPackageScript::test_save_repeat_scriptmodules, test/test_package.py::TestPackageScript::test_save_scriptmodule, test/test_package.py::TestPackageScript::test_save_scriptmodule_file, test/test_package.py::TestPackageScript::test_save_scriptmodule_only_necessary_code, test/test_package.py::TestPackageScript::test_save_scriptmodule_with_submods, test/test_package.py::TestPackageScript::test_save_scriptmodules_in_container, test/test_package.py::TestPackageScript::test_save_scriptmodules_submod_redefinition, test/test_package.py::TestPackageScript::test_save_shared_tensors, test/test_package.py::TestPackageScript::test_saving_and_scripting_packaged_mod, test/test_package.py::TestPackageScript::test_scriptmodules_repeat_save, test/test_package.py::TestPackageScript::test_tensor_sharing_pickle, test/test_package.py::TestRepackage::test_repackage_import_indirectly_via_parent_module, test/test_package.py::TestResources::test_importer_access, test/test_package.py::TestResources::test_package_resource_access, test/test_package.py::TestResources::test_resource_access_by_path, test/test_package.py::TestResources::test_resource_reader, test/test_package.py::TestSaveLoad::test_bad_dunder_imports, test/test_package.py::TestSaveLoad::test_dunder_imports, test/test_package.py::TestSaveLoad::test_exporting_mismatched_code, test/test_package.py::TestSaveLoad::test_pickle, test/test_package.py::TestSaveLoad::test_pickle_long_name_with_protocol_4, test/test_package.py::TestSaveLoad::test_save_imported_module, test/test_package.py::TestSaveLoad::test_save_imported_module_using_package_importer, test/test_package.py::TestSaveLoad::test_save_module, test/test_package.py::TestSaveLoad::test_save_module_binary, test/test_package.py::TestSaveLoad::test_saving_source, test/test_package.py::TestSaveLoad::test_saving_string 2024-08-20T22:43:56.2787841Z 2024-08-20T22:43:59.1845592Z 2024-08-20T22:43:59.1847646Z test_jiterator 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jiterator_1.1_cc33e50eda9bac6e_.log 2024-08-20T22:43:59.1849170Z Running 0 items in this shard: 2024-08-20T22:43:59.1849554Z 2024-08-20T22:43:59.4050337Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:43:59.4793203Z Running test_namedtensor 1/1 ... [2024-08-20 22:43:59.478772] 2024-08-20T22:43:59.4794194Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:43:59.4800188Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_namedtensor.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:43:59.479290] 2024-08-20T22:44:02.2316208Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:44:02.2973035Z Running test_legacy_vmap 1/1 ... [2024-08-20 22:44:02.296776] 2024-08-20T22:44:02.2973946Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:44:02.2977359Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_legacy_vmap.py', '-m', 'not 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 22:44:02.297254] 2024-08-20T22:44:14.6418711Z 2024-08-20T22:44:14.6420968Z test_namedtensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_namedtensor_1.1_1e519dcc298ceff6_.log 2024-08-20T22:44:14.6454751Z Running 88 items in this shard: test/test_namedtensor.py::TestNamedTensor::test_aaa_must_run_first_check_experimental_warning, test/test_namedtensor.py::TestNamedTensor::test_addcmul_addcdiv, test/test_namedtensor.py::TestNamedTensor::test_addmm, test/test_namedtensor.py::TestNamedTensor::test_addmv, test/test_namedtensor.py::TestNamedTensor::test_align_as, test/test_namedtensor.py::TestNamedTensor::test_align_tensors, test/test_namedtensor.py::TestNamedTensor::test_align_tensors_two_inputs, test/test_namedtensor.py::TestNamedTensor::test_align_to, test/test_namedtensor.py::TestNamedTensor::test_align_to_ellipsis, test/test_namedtensor.py::TestNamedTensor::test_any_all, test/test_namedtensor.py::TestNamedTensor::test_as_strided, test/test_namedtensor.py::TestNamedTensor::test_as_strided_cuda, test/test_namedtensor.py::TestNamedTensor::test_autograd_ignores_names, test/test_namedtensor.py::TestNamedTensor::test_autograd_smoke, test/test_namedtensor.py::TestNamedTensor::test_autograd_warns_named_grad, test/test_namedtensor.py::TestNamedTensor::test_bernoulli, test/test_namedtensor.py::TestNamedTensor::test_big_tensor_repr_has_names, test/test_namedtensor.py::TestNamedTensor::test_binary_ops, test/test_namedtensor.py::TestNamedTensor::test_bitwise_not, test/test_namedtensor.py::TestNamedTensor::test_bmm, test/test_namedtensor.py::TestNamedTensor::test_cat, test/test_namedtensor.py::TestNamedTensor::test_cdist, test/test_namedtensor.py::TestNamedTensor::test_comparison_ops, test/test_namedtensor.py::TestNamedTensor::test_copy_transpose, test/test_namedtensor.py::TestNamedTensor::test_cummax_cummin, test/test_namedtensor.py::TestNamedTensor::test_detach, test/test_namedtensor.py::TestNamedTensor::test_diagonal, test/test_namedtensor.py::TestNamedTensor::test_dot, test/test_namedtensor.py::TestNamedTensor::test_equal, test/test_namedtensor.py::TestNamedTensor::test_expand, test/test_namedtensor.py::TestNamedTensor::test_factory_coverage, test/test_namedtensor.py::TestNamedTensor::test_factory_edge_cases, test/test_namedtensor.py::TestNamedTensor::test_flatten, test/test_namedtensor.py::TestNamedTensor::test_flatten_index_error, test/test_namedtensor.py::TestNamedTensor::test_flatten_nodims, test/test_namedtensor.py::TestNamedTensor::test_has_names, test/test_namedtensor.py::TestNamedTensor::test_index_fill, test/test_namedtensor.py::TestNamedTensor::test_info_smoke, test/test_namedtensor.py::TestNamedTensor::test_logcumsumexp, test/test_namedtensor.py::TestNamedTensor::test_logical_not, test/test_namedtensor.py::TestNamedTensor::test_logical_ops, test/test_namedtensor.py::TestNamedTensor::test_masked_fill, test/test_namedtensor.py::TestNamedTensor::test_masked_select, test/test_namedtensor.py::TestNamedTensor::test_matmul, test/test_namedtensor.py::TestNamedTensor::test_max_pooling, test/test_namedtensor.py::TestNamedTensor::test_max_pooling_without_names_does_not_warn, test/test_namedtensor.py::TestNamedTensor::test_mm, test/test_namedtensor.py::TestNamedTensor::test_mv, test/test_namedtensor.py::TestNamedTensor::test_no_jit_script_support, test/test_namedtensor.py::TestNamedTensor::test_no_jit_tracer_support, test/test_namedtensor.py::TestNamedTensor::test_no_multiprocessing_support, test/test_namedtensor.py::TestNamedTensor::test_no_pickle_support, test/test_namedtensor.py::TestNamedTensor::test_no_save_support, test/test_namedtensor.py::TestNamedTensor::test_noncontig_contiguous, test/test_namedtensor.py::TestNamedTensor::test_none_names_refcount, test/test_namedtensor.py::TestNamedTensor::test_nyi_dimname_overload_msg, test/test_namedtensor.py::TestNamedTensor::test_out_fn_semantics, test/test_namedtensor.py::TestNamedTensor::test_pow_special, test/test_namedtensor.py::TestNamedTensor::test_py3_ellipsis, test/test_namedtensor.py::TestNamedTensor::test_reduction_fns, test/test_namedtensor.py::TestNamedTensor::test_refine_names, test/test_namedtensor.py::TestNamedTensor::test_rename, test/test_namedtensor.py::TestNamedTensor::test_rename_, test/test_namedtensor.py::TestNamedTensor::test_rename_globber, test/test_namedtensor.py::TestNamedTensor::test_rename_rename_map, test/test_namedtensor.py::TestNamedTensor::test_repr, test/test_namedtensor.py::TestNamedTensor::test_resize, test/test_namedtensor.py::TestNamedTensor::test_select, test/test_namedtensor.py::TestNamedTensor::test_select_cuda, test/test_namedtensor.py::TestNamedTensor::test_set_names_property, test/test_namedtensor.py::TestNamedTensor::test_size, test/test_namedtensor.py::TestNamedTensor::test_split_fns_propagates_names, test/test_namedtensor.py::TestNamedTensor::test_squeeze, test/test_namedtensor.py::TestNamedTensor::test_stride, test/test_namedtensor.py::TestNamedTensor::test_support_device_named_grad, test/test_namedtensor.py::TestNamedTensor::test_tensor_from_lists, test/test_namedtensor.py::TestNamedTensor::test_tensor_from_named_tensor, test/test_namedtensor.py::TestNamedTensor::test_tensor_from_numpy, test/test_namedtensor.py::TestNamedTensor::test_tensor_from_tensor, test/test_namedtensor.py::TestNamedTensor::test_tensor_grad_is_unnamed, test/test_namedtensor.py::TestNamedTensor::test_transpose_variants, test/test_namedtensor.py::TestNamedTensor::test_trivial, test/test_namedtensor.py::TestNamedTensor::test_unary_propagate_names_fns, test/test_namedtensor.py::TestNamedTensor::test_unflatten, test/test_namedtensor.py::TestNamedTensor::test_unsupported_op_error_msg, test/test_namedtensor.py::TestNamedTensor::test_using_seen_interned_string_doesnt_bump_refcount, test/test_namedtensor.py::TestNamedTensor::test_using_unseen_interned_string_bumps_refcount_permanently, test/test_namedtensor.py::TestNamedTensor::test_using_unseen_uninterned_string_refcounts 2024-08-20T22:44:14.6486085Z 2024-08-20T22:44:17.6003687Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:44:17.6649617Z Running test_flop_counter 1/1 ... [2024-08-20 22:44:17.664432] 2024-08-20T22:44:17.6650736Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:44:17.6654169Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_flop_counter.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:44:17.664881] 2024-08-20T22:44:21.1360681Z 2024-08-20T22:44:21.1362576Z test_flop_counter 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_flop_counter_1.1_0c711038b68e0e9e_.log 2024-08-20T22:44:21.1370528Z Running 20 items in this shard: test/test_flop_counter.py::TestFlopCounter::test_addmm_out, test/test_flop_counter.py::TestFlopCounter::test_autograd_op, test/test_flop_counter.py::TestFlopCounter::test_backward, test/test_flop_counter.py::TestFlopCounter::test_backward_reset, test/test_flop_counter.py::TestFlopCounter::test_conv_backwards_as_decomposition, test/test_flop_counter.py::TestFlopCounter::test_conv_transpose_loop, test/test_flop_counter.py::TestFlopCounter::test_convs, test/test_flop_counter.py::TestFlopCounter::test_custom, test/test_flop_counter.py::TestFlopCounter::test_custom_op, test/test_flop_counter.py::TestFlopCounter::test_flop_counter_variety, test/test_flop_counter.py::TestFlopCounter::test_hook_registration, test/test_flop_counter.py::TestFlopCounter::test_module, test/test_flop_counter.py::TestFlopCounter::test_nested_attention_fake_tensors, test/test_flop_counter.py::TestFlopCounter::test_noop, test/test_flop_counter.py::TestFlopCounter::test_op, test/test_flop_counter.py::TestFlopCounter::test_pytrees, test/test_flop_counter.py::TestFlopCounter::test_sdpa, test/test_flop_counter.py::TestFlopCounter::test_sdpa_nested_tensor, test/test_flop_counter.py::TestFlopCounter::test_torchscript, test/test_flop_counter.py::TestFlopCounter::test_warning 2024-08-20T22:44:21.1377742Z 2024-08-20T22:44:24.1150664Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:44:24.1792536Z Running functorch/test_ac 1/1 ... [2024-08-20 22:44:24.178756] 2024-08-20T22:44:24.1793587Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:44:24.1796782Z 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 22:44:24.179219] 2024-08-20T22:44:27.1643997Z 2024-08-20T22:44:27.1646424Z functorch/test_ac 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_ac_1.1_e45d1baea3fc600a_.log 2024-08-20T22:44:27.1647826Z 2024-08-20T22:44:30.3999814Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:44:30.4647874Z Running profiler/test_profiler 1/1 ... [2024-08-20 22:44:30.464251] 2024-08-20T22:44:30.4648865Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:44:30.4651604Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'profiler/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 22:44:30.464726] 2024-08-20T22:44:58.2889859Z 2024-08-20T22:44:58.2892228Z profiler/test_profiler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_profiler_1.1_5ea26477cf9cf410_.log 2024-08-20T22:44:58.2941982Z Running 60 items in this shard: test/profiler/test_profiler.py::TestProfilerCUDA::test_cudagraph_profiling_workaround, test/profiler/test_profiler.py::TestProfilerCUDA::test_custom_module_input_op_ids, test/profiler/test_profiler.py::TestProfilerCUDA::test_mem_leak, test/profiler/test_profiler.py::TestProfilerITT::test_custom_module_input_op_ids, test/profiler/test_profiler.py::TestProfiler::test_basic_chrome_trace, test/profiler/test_profiler.py::TestProfiler::test_concrete_inputs_profiling, test/profiler/test_profiler.py::TestProfiler::test_concrete_inputs_profiling_toggling, test/profiler/test_profiler.py::TestProfiler::test_cpu_annotation_overlap, test/profiler/test_profiler.py::TestProfiler::test_event_list, test/profiler/test_profiler.py::TestProfiler::test_export_stacks, test/profiler/test_profiler.py::TestProfiler::test_flops, test/profiler/test_profiler.py::TestProfiler::test_guarded_record_function_fast, test/profiler/test_profiler.py::TestProfiler::test_high_level_trace, test/profiler/test_profiler.py::TestProfiler::test_is_profiler_enabled, test/profiler/test_profiler.py::TestProfiler::test_kineto, test/profiler/test_profiler.py::TestProfiler::test_kineto_multigpu, test/profiler/test_profiler.py::TestProfiler::test_kineto_profiler_api, test/profiler/test_profiler.py::TestProfiler::test_kineto_profiler_multiple_steppers, test/profiler/test_profiler.py::TestProfiler::test_kineto_profiler_with_environment_variable, test/profiler/test_profiler.py::TestProfiler::test_memory_profiler, test/profiler/test_profiler.py::TestProfiler::test_module_hierarchy, test/profiler/test_profiler.py::TestProfiler::test_nested_tensor_with_shapes, test/profiler/test_profiler.py::TestProfiler::test_oom_tracing, test/profiler/test_profiler.py::TestProfiler::test_profiler_correlation_id, test/profiler/test_profiler.py::TestProfiler::test_profiler_cuda_sync_events, test/profiler/test_profiler.py::TestProfiler::test_profiler_disable_fwd_bwd_link, test/profiler/test_profiler.py::TestProfiler::test_profiler_fwd_bwd_link, test/profiler/test_profiler.py::TestProfiler::test_profiler_metadata, test/profiler/test_profiler.py::TestProfiler::test_profiler_op_event_args, test/profiler/test_profiler.py::TestProfiler::test_profiler_op_event_kwargs, test/profiler/test_profiler.py::TestProfiler::test_profiler_strides, test/profiler/test_profiler.py::TestProfiler::test_profiler_time_scale, test/profiler/test_profiler.py::TestProfiler::test_profiler_tracing, test/profiler/test_profiler.py::TestProfiler::test_profiler_type, test/profiler/test_profiler.py::TestProfiler::test_record_function_fast, test/profiler/test_profiler.py::TestProfiler::test_schedule_function_count, test/profiler/test_profiler.py::TestProfiler::test_source, test/profiler/test_profiler.py::TestProfiler::test_tensorboard_trace_handler, test/profiler/test_profiler.py::TestProfiler::test_user_annotation, test/profiler/test_profiler.py::TestExperimentalUtils::test_bfs, test/profiler/test_profiler.py::TestExperimentalUtils::test_dfs, test/profiler/test_profiler.py::TestExperimentalUtils::test_fuzz_symbolize, test/profiler/test_profiler.py::TestExperimentalUtils::test_profiler_conv2d_bias_followed_by_batchnorm2d_pattern, test/profiler/test_profiler.py::TestExperimentalUtils::test_profiler_extra_cuda_copy_pattern, test/profiler/test_profiler.py::TestExperimentalUtils::test_profiler_extra_cuda_copy_pattern_benchmark, test/profiler/test_profiler.py::TestExperimentalUtils::test_profiler_for_loop_indexing_pattern, test/profiler/test_profiler.py::TestExperimentalUtils::test_profiler_fp32_matmul_pattern, test/profiler/test_profiler.py::TestExperimentalUtils::test_profiler_grad_not_set_to_none_pattern, test/profiler/test_profiler.py::TestExperimentalUtils::test_profiler_matmul_dim_fp16_pattern, test/profiler/test_profiler.py::TestExperimentalUtils::test_profiler_name_pattern, test/profiler/test_profiler.py::TestExperimentalUtils::test_profiler_optimizer_single_tensor_pattern, test/profiler/test_profiler.py::TestExperimentalUtils::test_profiler_pattern_match_helper, test/profiler/test_profiler.py::TestExperimentalUtils::test_profiler_pattern_matcher_json_report, test/profiler/test_profiler.py::TestExperimentalUtils::test_profiler_synchronized_dataloader_pattern, test/profiler/test_profiler.py::TestExperimentalUtils::test_utils_compute_idle_time, test/profiler/test_profiler.py::TestExperimentalUtils::test_utils_compute_queue_depth, test/profiler/test_profiler.py::TestExperimentalUtils::test_utils_compute_queue_depth_when_no_cuda_events, test/profiler/test_profiler.py::TestExperimentalUtils::test_utils_compute_self_time, test/profiler/test_profiler.py::TestExperimentalUtils::test_utils_get_optimizable_events, test/profiler/test_profiler.py::TestExperimentalUtils::test_utils_intervals_overlap 2024-08-20T22:44:58.3077864Z 2024-08-20T22:45:01.4364968Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:45:01.5098276Z Running torch_np/numpy_tests/core/test_numeric 1/1 ... [2024-08-20 22:45:01.509139] 2024-08-20T22:45:01.5100235Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:45:01.5105240Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_numeric.py', '-m', 'not 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 22:45:01.509696] 2024-08-20T22:45:52.2755473Z 2024-08-20T22:45:52.2758613Z torch_np/numpy_tests/core/test_numeric 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_numeric_1.1_7ff44a1850e2e7d9_.log 2024-08-20T22:45:52.2918151Z Running 275 items in this shard: test/torch_np/numpy_tests/core/test_numeric.py::TestResize::test_copies, test/torch_np/numpy_tests/core/test_numeric.py::TestResize::test_negative_resize, test/torch_np/numpy_tests/core/test_numeric.py::TestResize::test_repeats, test/torch_np/numpy_tests/core/test_numeric.py::TestResize::test_reshape_from_zero, test/torch_np/numpy_tests/core/test_numeric.py::TestResize::test_zeroresize, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_choose, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_clip, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_compress, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_count_nonzero, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_cumproduct, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_diagonal, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_accuracy, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype0, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype1, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype2, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype3, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype4, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype5, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype6, 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test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc1_dtype1, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc2_dtype0, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc2_dtype1, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc3_dtype0, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc3_dtype1, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_empty_like, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_filled_like, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_ones_like, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_zeros_like, test/torch_np/numpy_tests/core/test_numeric.py::TestCorrelate::test_complex, test/torch_np/numpy_tests/core/test_numeric.py::TestCorrelate::test_float, test/torch_np/numpy_tests/core/test_numeric.py::TestCorrelate::test_mode, test/torch_np/numpy_tests/core/test_numeric.py::TestCorrelate::test_no_overwrite, test/torch_np/numpy_tests/core/test_numeric.py::TestCorrelate::test_zero_size, test/torch_np/numpy_tests/core/test_numeric.py::TestConvolve::test_mode, test/torch_np/numpy_tests/core/test_numeric.py::TestConvolve::test_no_overwrite, test/torch_np/numpy_tests/core/test_numeric.py::TestConvolve::test_numpy_doc_examples, test/torch_np/numpy_tests/core/test_numeric.py::TestConvolve::test_object, test/torch_np/numpy_tests/core/test_numeric.py::TestDtypePositional::test_dtype_positional, test/torch_np/numpy_tests/core/test_numeric.py::TestArgwhere::test_2D, test/torch_np/numpy_tests/core/test_numeric.py::TestArgwhere::test_list, test/torch_np/numpy_tests/core/test_numeric.py::TestArgwhere::test_nd_nd_0, test/torch_np/numpy_tests/core/test_numeric.py::TestArgwhere::test_nd_nd_1, test/torch_np/numpy_tests/core/test_numeric.py::TestArgwhere::test_nd_nd_2, test/torch_np/numpy_tests/core/test_numeric.py::TestStringFunction::test_set_string_function, test/torch_np/numpy_tests/core/test_numeric.py::TestRoll::test_roll1d, test/torch_np/numpy_tests/core/test_numeric.py::TestRoll::test_roll2d, test/torch_np/numpy_tests/core/test_numeric.py::TestRoll::test_roll_empty, test/torch_np/numpy_tests/core/test_numeric.py::TestRollaxis::test_exceptions, test/torch_np/numpy_tests/core/test_numeric.py::TestRollaxis::test_results, test/torch_np/numpy_tests/core/test_numeric.py::TestMoveaxis::test_errors, test/torch_np/numpy_tests/core/test_numeric.py::TestMoveaxis::test_move_multiples, test/torch_np/numpy_tests/core/test_numeric.py::TestMoveaxis::test_move_new_position, test/torch_np/numpy_tests/core/test_numeric.py::TestMoveaxis::test_move_to_end, test/torch_np/numpy_tests/core/test_numeric.py::TestMoveaxis::test_preserve_order, test/torch_np/numpy_tests/core/test_numeric.py::TestCross::test_2x2, test/torch_np/numpy_tests/core/test_numeric.py::TestCross::test_2x3, test/torch_np/numpy_tests/core/test_numeric.py::TestCross::test_3x3, test/torch_np/numpy_tests/core/test_numeric.py::TestCross::test_broadcasting, test/torch_np/numpy_tests/core/test_numeric.py::TestCross::test_broadcasting_shapes, test/torch_np/numpy_tests/core/test_numeric.py::TestCross::test_uint8_int32_mixed_dtypes, test/torch_np/numpy_tests/core/test_numeric.py::TestOuterMisc::test_outer_out_param, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype0_dims0, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype0_dims1, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype0_dims2, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype1_dims0, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype1_dims1, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype1_dims2, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype2_dims0, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype2_dims1, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype2_dims2, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype3_dims0, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype3_dims1, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype3_dims2, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_scalar_input, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_simple, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_single_input, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_sparse, test/torch_np/numpy_tests/core/test_numeric.py::TestRequire::test_C_and_F_simul, test/torch_np/numpy_tests/core/test_numeric.py::TestRequire::test_non_array_input, test/torch_np/numpy_tests/core/test_numeric.py::TestRequire::test_require_each, test/torch_np/numpy_tests/core/test_numeric.py::TestRequire::test_unknown_requirement, test/torch_np/numpy_tests/core/test_numeric.py::TestBroadcast::test_broadcast_error_kwargs, test/torch_np/numpy_tests/core/test_numeric.py::TestBroadcast::test_broadcast_in_args, test/torch_np/numpy_tests/core/test_numeric.py::TestBroadcast::test_broadcast_single_arg, test/torch_np/numpy_tests/core/test_numeric.py::TestBroadcast::test_number_of_arguments, test/torch_np/numpy_tests/core/test_numeric.py::TestBroadcast::test_shape_mismatch_error_message, test/torch_np/numpy_tests/core/test_numeric.py::TestTensordot::test_zero_dimension, test/torch_np/numpy_tests/core/test_numeric.py::TestTensordot::test_zero_dimension_einsum, test/torch_np/numpy_tests/core/test_numeric.py::TestTensordot::test_zero_dimensional 2024-08-20T22:45:52.3049939Z 2024-08-20T22:45:55.2979786Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:45:55.3626853Z Running test_fx_passes 1/1 ... [2024-08-20 22:45:55.362228] 2024-08-20T22:45:55.3627467Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:45:55.3633613Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_fx_passes.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:45:55.362738] 2024-08-20T22:46:05.5964499Z 2024-08-20T22:46:05.5967074Z test_fx_passes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_fx_passes_1.1_cf675f04bd90aaec_.log 2024-08-20T22:46:05.6013625Z Running 53 items in this shard: test/test_fx_passes.py::TestFXGraphPasses::test_fuser_pass_deep_model, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_partition0, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_partition1, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_partition10, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_partition11, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_partition2, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_partition3, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_partition4, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_partition5, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_partition6, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_partition7, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_partition8, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_partition9, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_xfail_partition0, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_xfail_partition1, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_xfail_partition2, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_xfail_partition3, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn0_expected_partition0_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn10_expected_partition10_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn11_expected_partition11_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn12_expected_partition12_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn13_expected_partition13_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn14_expected_partition14_bookend_non_compute_pass_True, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn15_expected_partition15_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn16_expected_partition16_bookend_non_compute_pass_True, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn17_expected_partition17_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn18_expected_partition18_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn1_expected_partition1_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn2_expected_partition2_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn3_expected_partition3_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn4_expected_partition4_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn5_expected_partition5_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn6_expected_partition6_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn7_expected_partition7_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn8_expected_partition8_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn9_expected_partition9_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_independent_output_fn0_expected_partition0, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model0, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model1, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model10, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model11, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model12, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model13, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model14, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model15, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model2, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model3, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model4, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model5, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model6, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model7, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model8, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model9 2024-08-20T22:46:05.6058565Z 2024-08-20T22:46:08.6862092Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:46:08.7507815Z Running lazy/test_functionalization 1/1 ... [2024-08-20 22:46:08.750230] 2024-08-20T22:46:08.7509000Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:46:08.7512467Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'lazy/test_functionalization.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:46:08.750744] 2024-08-20T22:46:12.4719991Z 2024-08-20T22:46:12.4722652Z lazy/test_functionalization 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_functionalization_1.1_c145d100e3c82706_.log 2024-08-20T22:46:12.4726462Z Running 2 items in this shard: test/lazy/test_functionalization.py::LazyFuncionalizationTest::test_data_assign, test/lazy/test_functionalization.py::LazyFuncionalizationTest::test_lazy_init_with_view 2024-08-20T22:46:12.4728467Z 2024-08-20T22:46:15.4454743Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:46:15.5105397Z Running torch_np/numpy_tests/lib/test_function_base 1/1 ... [2024-08-20 22:46:15.510036] 2024-08-20T22:46:15.5106324Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:46:15.5109946Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_function_base.py', '-m', 'not 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 22:46:15.510568] 2024-08-20T22:47:07.9666231Z 2024-08-20T22:47:07.9694990Z torch_np/numpy_tests/lib/test_function_base 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_function_base_1.1_7c8acc6e134abe3d_.log 2024-08-20T22:47:08.0194241Z Running 530 items in this shard: test/torch_np/numpy_tests/lib/test_function_base.py::TestRot90::test_axes, test/torch_np/numpy_tests/lib/test_function_base.py::TestRot90::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestRot90::test_rotation_axes, test/torch_np/numpy_tests/lib/test_function_base.py::TestFlip::test_3d_swap_axis0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFlip::test_3d_swap_axis1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFlip::test_3d_swap_axis2, test/torch_np/numpy_tests/lib/test_function_base.py::TestFlip::test_4d, test/torch_np/numpy_tests/lib/test_function_base.py::TestFlip::test_axes, test/torch_np/numpy_tests/lib/test_function_base.py::TestFlip::test_basic_lr, test/torch_np/numpy_tests/lib/test_function_base.py::TestFlip::test_basic_ud, test/torch_np/numpy_tests/lib/test_function_base.py::TestFlip::test_default_axis, test/torch_np/numpy_tests/lib/test_function_base.py::TestFlip::test_multiple_axes, test/torch_np/numpy_tests/lib/test_function_base.py::TestAny::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestAny::test_nd, test/torch_np/numpy_tests/lib/test_function_base.py::TestAll::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestAll::test_nd, test/torch_np/numpy_tests/lib/test_function_base.py::TestCopy::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestCopy::test_order, test/torch_np/numpy_tests/lib/test_function_base.py::TestAverage::test_average_class_without_dtype, test/torch_np/numpy_tests/lib/test_function_base.py::TestAverage::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestAverage::test_basic_keepdims_x0_axis0_expected_avg0_weights0_expected_wavg0_expected_wsum0, test/torch_np/numpy_tests/lib/test_function_base.py::TestAverage::test_basic_keepdims_x1_axis_0_expected_avg1_weights1_expected_wavg1_expected_wsum1, test/torch_np/numpy_tests/lib/test_function_base.py::TestAverage::test_returned, test/torch_np/numpy_tests/lib/test_function_base.py::TestAverage::test_upcasting, test/torch_np/numpy_tests/lib/test_function_base.py::TestAverage::test_weights, test/torch_np/numpy_tests/lib/test_function_base.py::TestSelect::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestSelect::test_broadcasting, test/torch_np/numpy_tests/lib/test_function_base.py::TestSelect::test_deprecated_empty, test/torch_np/numpy_tests/lib/test_function_base.py::TestSelect::test_many_arguments, test/torch_np/numpy_tests/lib/test_function_base.py::TestSelect::test_non_bool_deprecation, test/torch_np/numpy_tests/lib/test_function_base.py::TestSelect::test_return_dtype, test/torch_np/numpy_tests/lib/test_function_base.py::TestInsert::test_0d, test/torch_np/numpy_tests/lib/test_function_base.py::TestInsert::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestInsert::test_index_array_copied, test/torch_np/numpy_tests/lib/test_function_base.py::TestInsert::test_index_floats, test/torch_np/numpy_tests/lib/test_function_base.py::TestInsert::test_index_out_of_bounds_idx_-4, test/torch_np/numpy_tests/lib/test_function_base.py::TestInsert::test_index_out_of_bounds_idx_4, test/torch_np/numpy_tests/lib/test_function_base.py::TestInsert::test_multidim, test/torch_np/numpy_tests/lib/test_function_base.py::TestAmax::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestAmin::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestPtp::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestCumsum::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestProd::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestCumprod::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestDiff::test_append, test/torch_np/numpy_tests/lib/test_function_base.py::TestDiff::test_axis, test/torch_np/numpy_tests/lib/test_function_base.py::TestDiff::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestDiff::test_n, test/torch_np/numpy_tests/lib/test_function_base.py::TestDiff::test_nd, test/torch_np/numpy_tests/lib/test_function_base.py::TestDiff::test_prepend, test/torch_np/numpy_tests/lib/test_function_base.py::TestDelete::test_0d, test/torch_np/numpy_tests/lib/test_function_base.py::TestDelete::test_array_order_preserve, test/torch_np/numpy_tests/lib/test_function_base.py::TestDelete::test_fancy, test/torch_np/numpy_tests/lib/test_function_base.py::TestDelete::test_index_floats, test/torch_np/numpy_tests/lib/test_function_base.py::TestDelete::test_single, test/torch_np/numpy_tests/lib/test_function_base.py::TestDelete::test_single_item_array_[1], test/torch_np/numpy_tests/lib/test_function_base.py::TestDelete::test_single_item_array_array([1]), test/torch_np/numpy_tests/lib/test_function_base.py::TestDelete::test_single_item_array_non_int, test/torch_np/numpy_tests/lib/test_function_base.py::TestDelete::test_slices, test/torch_np/numpy_tests/lib/test_function_base.py::TestGradient::test_args, test/torch_np/numpy_tests/lib/test_function_base.py::TestGradient::test_badargs, test/torch_np/numpy_tests/lib/test_function_base.py::TestGradient::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestGradient::test_f_decreasing_unsigned_int_f_dtype0, test/torch_np/numpy_tests/lib/test_function_base.py::TestGradient::test_f_signed_int_big_jump_f_dtype0, test/torch_np/numpy_tests/lib/test_function_base.py::TestGradient::test_f_signed_int_big_jump_f_dtype1, test/torch_np/numpy_tests/lib/test_function_base.py::TestGradient::test_f_signed_int_big_jump_f_dtype2, test/torch_np/numpy_tests/lib/test_function_base.py::TestGradient::test_f_signed_int_big_jump_f_dtype3, test/torch_np/numpy_tests/lib/test_function_base.py::TestGradient::test_inexact_dtypes, test/torch_np/numpy_tests/lib/test_function_base.py::TestGradient::test_second_order_accurate, test/torch_np/numpy_tests/lib/test_function_base.py::TestGradient::test_spacing, test/torch_np/numpy_tests/lib/test_function_base.py::TestGradient::test_specific_axes, test/torch_np/numpy_tests/lib/test_function_base.py::TestGradient::test_values, test/torch_np/numpy_tests/lib/test_function_base.py::TestGradient::test_x_decreasing_unsigned_x_dtype0, test/torch_np/numpy_tests/lib/test_function_base.py::TestGradient::test_x_signed_int_big_jump_x_dtype0, test/torch_np/numpy_tests/lib/test_function_base.py::TestGradient::test_x_signed_int_big_jump_x_dtype1, 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test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_B_expected_dtype0_method_linear_expected_29, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_B_expected_dtype0_method_median_unbiased_expected_27, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_B_expected_dtype0_method_normal_unbiased_expected_27_125, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_B_expected_dtype0_method_weibull_expected_26, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_b_expected_dtype1_method_averaged_inverted_cdf_expected_27_5, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_b_expected_dtype1_method_closest_observation_expected_20, 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test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_b_expected_dtype1_method_weibull_expected_26, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_h_expected_dtype2_method_averaged_inverted_cdf_expected_27_5, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_h_expected_dtype2_method_closest_observation_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_h_expected_dtype2_method_hazen_expected_27_5, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_h_expected_dtype2_method_interpolated_inverted_cdf_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_h_expected_dtype2_method_inverted_cdf_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_h_expected_dtype2_method_linear_expected_29, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_h_expected_dtype2_method_median_unbiased_expected_27, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_h_expected_dtype2_method_normal_unbiased_expected_27_125, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_h_expected_dtype2_method_weibull_expected_26, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_i_expected_dtype3_method_averaged_inverted_cdf_expected_27_5, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_i_expected_dtype3_method_closest_observation_expected_20, 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test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_l_expected_dtype4_method_linear_expected_29, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_l_expected_dtype4_method_median_unbiased_expected_27, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_l_expected_dtype4_method_normal_unbiased_expected_27_125, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_l_expected_dtype4_method_weibull_expected_26, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_nan_1D_dtype_d, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_nan_1D_dtype_e, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_nan_1D_dtype_f, 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test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nearest_dtype_b, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nearest_dtype_d, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nearest_dtype_e, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nearest_dtype_f, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nearest_dtype_g, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nearest_dtype_h, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nearest_dtype_i, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nearest_dtype_l, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nearest_dtype_p, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nearest_dtype_q, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_no_p_overwrite, 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test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_complex, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_correct_quantile_value, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_fraction, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_max_ulp, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_no_p_overwrite, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_monotonic_hypo, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_monotonic_method_averaged_inverted_cdf, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_monotonic_method_closest_observation, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_monotonic_method_hazen, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_monotonic_method_higher, 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test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_preserve_int_type_dtype_B, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_preserve_int_type_dtype_b, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_preserve_int_type_dtype_h, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_preserve_int_type_dtype_i, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_preserve_int_type_dtype_l, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_scalar_nan, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_array_like, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_axis_keyword, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_basic_2, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_empty, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_extended_axis, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_extended_axis_invalid, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_keepdims, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_keepdims_2, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_keepdims_out_axis0, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_keepdims_out_axis2, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_keepdims_out_axis3, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_keepdims_out_axis4, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_keepdims_out_axis_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_nan_behavior, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_nan_behavior_2, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_nan_behavior_3, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_out, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_out_nan, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_overwrite_keyword, test/torch_np/numpy_tests/lib/test_function_base.py::TestSortComplex::test_sort_complex, test/torch_np/numpy_tests/lib/test_function_base.py::TestSortComplex::test_sort_real_type_in_B_type_out_F, test/torch_np/numpy_tests/lib/test_function_base.py::TestSortComplex::test_sort_real_type_in_H_type_out_F, test/torch_np/numpy_tests/lib/test_function_base.py::TestSortComplex::test_sort_real_type_in_b_type_out_F, test/torch_np/numpy_tests/lib/test_function_base.py::TestSortComplex::test_sort_real_type_in_g_type_out_G, test/torch_np/numpy_tests/lib/test_function_base.py::TestSortComplex::test_sort_real_type_in_h_type_out_F, test/torch_np/numpy_tests/lib/test_function_base.py::TestSortComplex::test_sort_real_type_in_l_type_out_D 2024-08-20T22:47:08.0626339Z 2024-08-20T22:47:11.3742631Z 2024-08-20T22:47:11.3745093Z test_legacy_vmap 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_legacy_vmap_1.1_a45835b9709c03df_.log 2024-08-20T22:47:11.3805965Z Running 124 items in this shard: test/test_legacy_vmap.py::TestVmapAPILegacy::test_accepts_nested_inputs, test/test_legacy_vmap.py::TestVmapAPILegacy::test_backward_unsupported_interaction, test/test_legacy_vmap.py::TestVmapAPILegacy::test_batched_gradient_basic, test/test_legacy_vmap.py::TestVmapAPILegacy::test_constant_function, test/test_legacy_vmap.py::TestVmapAPILegacy::test_different_map_dim_size_raises, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_atan2, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_does_not_warn_by_default, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_masked_fill, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_multiple_returns, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_warns_when_warnings_are_enabled, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_with_undefined_grad, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_zero_dim, test/test_legacy_vmap.py::TestVmapAPILegacy::test_func_with_no_inputs, test/test_legacy_vmap.py::TestVmapAPILegacy::test_functools_partial, test/test_legacy_vmap.py::TestVmapAPILegacy::test_grad_unsupported_interaction, test/test_legacy_vmap.py::TestVmapAPILegacy::test_in_dim_not_in_tensor_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_in_dims_wrong_type_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_inplace_fallback_nary_different_levels, test/test_legacy_vmap.py::TestVmapAPILegacy::test_inplace_fallback_nary_same_levels, test/test_legacy_vmap.py::TestVmapAPILegacy::test_inplace_fallback_unary, test/test_legacy_vmap.py::TestVmapAPILegacy::test_integer_in_dim_but_not_tensor_input_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_multiple_inputs, test/test_legacy_vmap.py::TestVmapAPILegacy::test_multiple_out_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_multiple_outputs, test/test_legacy_vmap.py::TestVmapAPILegacy::test_multiple_outputs_error_cases, test/test_legacy_vmap.py::TestVmapAPILegacy::test_nested_non_default_in_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_nested_out_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_nested_with_different_map_dim, test/test_legacy_vmap.py::TestVmapAPILegacy::test_nested_with_same_map_dim, test/test_legacy_vmap.py::TestVmapAPILegacy::test_nn_module, test/test_legacy_vmap.py::TestVmapAPILegacy::test_non_default_in_dims_out_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_non_tensor_output_raises, test/test_legacy_vmap.py::TestVmapAPILegacy::test_non_zero_in_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_none_in_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_nonzero_out_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_noop_in_inner_vmap, test/test_legacy_vmap.py::TestVmapAPILegacy::test_not_enough_in_dims_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_out_dim_out_of_bounds_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_out_dims_and_num_outputs_mismatch_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_out_dims_edge_case, test/test_legacy_vmap.py::TestVmapAPILegacy::test_out_dims_must_be_int_or_tuple_of_int_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_single_input, test/test_legacy_vmap.py::TestVmapAPILegacy::test_unsupported_op_err_msg, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_T_numpy, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_as_strided, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_binary_pointwise_ops, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_bmm, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_cat, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_chunk, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_clamp, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_clone, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_comparison_ops, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_conj, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_contiguous, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_diagonal, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_dot, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_expand_as, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_fill_and_zero_inplace, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_imag, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_is_complex, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_is_contiguous, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_is_floating_point, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_mm, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_movedim, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_mv, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_narrow, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_new_empty, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_new_empty_strided, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_new_zeros, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_no_random_op_support, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_real, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_reshape, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_reshape_as, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_result_type, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_select, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_slice, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_split, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_squeeze, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_stack, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_stride, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_sum_dim, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_t, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_tensor_split, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_to, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_trace, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_transpose, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_unary_pointwise_ops, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_unbind, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_unfold, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_view, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_view_as, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_view_as_complex, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_view_as_real, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_vmap_fallback_check, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_vmap_fallback_check_ok, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_add_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_binary_cross_entropy_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_diagonal_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_div_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_expand_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_index_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_inplace_manyview_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_inplace_on_view_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_lgamma_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_log1p_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_log_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_logsumexp_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_max_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_median_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_min_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_mul_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_permute_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_reshape_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_select_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_sigmoid_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_slice_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_stack_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_sub_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_threshold_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_trace_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_unrelated_output_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_unrelated_output_multiple_grad_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_vmap_fallback_check, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_vmap_fallback_check_ok 2024-08-20T22:47:11.3856208Z 2024-08-20T22:47:11.5475932Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:47:11.6361713Z Running torch_np/numpy_tests/lib/test_histograms 1/1 ... [2024-08-20 22:47:11.635508] 2024-08-20T22:47:11.6368782Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:47:11.6371872Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_histograms.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:47:11.636048] 2024-08-20T22:47:14.8019789Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:47:14.8879364Z Running nn/test_load_state_dict 1/1 ... [2024-08-20 22:47:14.887361] 2024-08-20T22:47:14.8880737Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:47:14.8888621Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'nn/test_load_state_dict.py', '-m', 'not 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 22:47:14.887844] 2024-08-20T22:47:22.6665595Z 2024-08-20T22:47:22.6667695Z nn/test_load_state_dict 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_load_state_dict_1.1_1d423b2c1122eeb0_.log 2024-08-20T22:47:22.6691353Z Running 27 items in this shard: test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_BC_swap_False, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_BC_swap_True, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_assign_meta_swap_False_keep_vars_False, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_assign_meta_swap_False_keep_vars_True, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_assign_meta_swap_True_keep_vars_False, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_assign_meta_swap_True_keep_vars_True, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_assign_shape_stride_swap_False, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_assign_shape_stride_swap_True, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_assign_with_optimizer_swap_False, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_assign_with_optimizer_swap_True, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_child_swap_False, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_child_swap_True, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_custom_swap_False, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_custom_swap_True, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_invalid_swap_False, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_invalid_swap_True, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_ref_cycle_swap_False, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_swap_False, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_swap_True, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_type_swap_False, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_type_swap_True, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_warn_assign_swap_False, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_warn_assign_swap_True, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_with_unexpected_key_swap_False, test/nn/test_load_state_dict.py::TestLoadStateDict::test_load_state_dict_with_unexpected_key_swap_True, test/nn/test_load_state_dict.py::TestLoadStateDictSwap::test_swap_subclass_swap_True_assign_False, test/nn/test_load_state_dict.py::TestLoadStateDictSwap::test_swap_subclass_swap_True_assign_True 2024-08-20T22:47:22.6714061Z 2024-08-20T22:47:23.1383834Z 2024-08-20T22:47:23.1386342Z torch_np/numpy_tests/lib/test_histograms 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_histograms_1.1_e17f27fc8e64f3f1_.log 2024-08-20T22:47:23.1418830Z Running 60 items in this shard: test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_arr_weights_mismatch, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_big_arrays, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_bin_array_dims, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_bin_edge_cases, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_bool_conversion, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_density, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_empty, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_error_binnum_type, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_exotic_weights, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_f32_rounding, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_finite_range, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_histogram_bin_edges, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_invalid_range, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_last_bin_inclusive_range, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_no_side_effects, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_object_array_of_0d, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_one_bin, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_outliers, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_precision, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_signed_overflow_bounds, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_signed_overflow_bounds_2, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_simple, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_some_nan_values, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_type, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_unsigned_monotonicity_check, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_weights, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_empty, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_incorrect_methods, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_limited_variance, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_novariance, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_outlier, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_scott_vs_stone, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_signed_integer_data_bins_auto, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_signed_integer_data_bins_doane, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_signed_integer_data_bins_fd, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_signed_integer_data_bins_rice, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_signed_integer_data_bins_scott, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_signed_integer_data_bins_stone, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_signed_integer_data_bins_sturges, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_simple, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_simple_range, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_simple_weighted, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_small, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_bins_array, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_bins_error_2, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_bins_errors, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_density_non_uniform_1d, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_density_non_uniform_2d, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_edge_dtype, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_empty, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_equal_edges, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_finite_range, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_identical_samples, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_inf_edges, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_large_integers, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_rightmost_binedge, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_shape_3d, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_shape_4d, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_simple, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_weights 2024-08-20T22:47:23.1449251Z 2024-08-20T22:47:25.8438668Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:47:25.9073234Z Running test_matmul_cuda 1/1 ... [2024-08-20 22:47:25.906792] 2024-08-20T22:47:25.9074568Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:47:25.9078484Z 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 22:47:25.907204] 2024-08-20T22:47:26.5214221Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:47:26.5852269Z Running torch_np/numpy_tests/core/test_indexing 1/1 ... [2024-08-20 22:47:26.584683] 2024-08-20T22:47:26.5853604Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:47:26.5857421Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_indexing.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:47:26.585112] 2024-08-20T22:47:29.1668834Z 2024-08-20T22:47:29.1671349Z test_matmul_cuda 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_matmul_cuda_1.1_95f9d93b7322c589_.log 2024-08-20T22:47:29.1673282Z Running 0 items in this shard: 2024-08-20T22:47:29.1673746Z 2024-08-20T22:47:32.5154070Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:47:32.5790698Z Running torch_np/numpy_tests/lib/test_type_check 1/1 ... [2024-08-20 22:47:32.578581] 2024-08-20T22:47:32.5792162Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:47:32.5795582Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_type_check.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:47:32.579012] 2024-08-20T22:47:33.9138272Z 2024-08-20T22:47:33.9141876Z torch_np/numpy_tests/core/test_indexing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_indexing_1.1_b869b5e841ac0ae6_.log 2024-08-20T22:47:33.9182357Z Running 67 items in this shard: test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_boolean_assignment_value_mismatch, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_boolean_indexing_list, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_boolean_indexing_onedim, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_boolean_indexing_twodim, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_boolean_shape_mismatch, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_broaderrors_indexing, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_broken_sequence_not_nd_index, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_ellipsis_index, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_ellipsis_index_2, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_empty_fancy_index, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_empty_tuple_index, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_everything_returns_views, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_index_no_array_to_index, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_index_no_floats, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_indexing_array_negative_strides, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_indexing_array_weird_strides, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_memory_order, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_none_index, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_nontuple_ndindex, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_reverse_strides_and_subspace_bufferinit, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_reversed_strides_result_allocation, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_same_kind_index_casting, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_scalar_array_bool, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_single_bool_index, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_single_int_index, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_slicing_no_floats, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_small_regressions, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index2_num_32_original_ndim_1, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index2_num_32_original_ndim_32, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index2_num_40_original_ndim_1, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index2_num_40_original_ndim_32, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index_False_num_32_original_ndim_1, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index_False_num_32_original_ndim_32, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index_False_num_40_original_ndim_1, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index_False_num_40_original_ndim_32, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index_True_num_32_original_ndim_1, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index_True_num_32_original_ndim_32, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index_True_num_40_original_ndim_1, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index_True_num_40_original_ndim_32, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_fancy_indices_special_case, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_trivial_fancy_not_possible, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_trivial_fancy_out_of_bounds, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_tuple_subclass, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_uncontiguous_subspace_assignment, test/torch_np/numpy_tests/core/test_indexing.py::TestBroadcastedAssignments::test_broadcast_error_reports_correct_shape_index0, test/torch_np/numpy_tests/core/test_indexing.py::TestBroadcastedAssignments::test_broadcast_error_reports_correct_shape_index1, test/torch_np/numpy_tests/core/test_indexing.py::TestBroadcastedAssignments::test_broadcast_error_reports_correct_shape_index2, test/torch_np/numpy_tests/core/test_indexing.py::TestBroadcastedAssignments::test_broadcast_subspace, test/torch_np/numpy_tests/core/test_indexing.py::TestBroadcastedAssignments::test_index_is_larger, test/torch_np/numpy_tests/core/test_indexing.py::TestBroadcastedAssignments::test_prepend_not_one, test/torch_np/numpy_tests/core/test_indexing.py::TestBroadcastedAssignments::test_prepending_ones, test/torch_np/numpy_tests/core/test_indexing.py::TestBroadcastedAssignments::test_simple_broadcasting_errors, test/torch_np/numpy_tests/core/test_indexing.py::TestFancyIndexingCast::test_boolean_index_cast_assign, test/torch_np/numpy_tests/core/test_indexing.py::TestMultiIndexingAutomated::test_1d, test/torch_np/numpy_tests/core/test_indexing.py::TestMultiIndexingAutomated::test_boolean, test/torch_np/numpy_tests/core/test_indexing.py::TestMultiIndexingAutomated::test_multidim, test/torch_np/numpy_tests/core/test_indexing.py::TestFloatNonIntegerArgument::test_non_integer_argument_errors, test/torch_np/numpy_tests/core/test_indexing.py::TestFloatNonIntegerArgument::test_non_integer_sequence_multiplication, test/torch_np/numpy_tests/core/test_indexing.py::TestFloatNonIntegerArgument::test_reduce_axis_float_index, test/torch_np/numpy_tests/core/test_indexing.py::TestFloatNonIntegerArgument::test_valid_indexing, test/torch_np/numpy_tests/core/test_indexing.py::TestFloatNonIntegerArgument::test_valid_slicing, test/torch_np/numpy_tests/core/test_indexing.py::TestBooleanIndexing::test_bool_as_int_argument_errors, test/torch_np/numpy_tests/core/test_indexing.py::TestBooleanIndexing::test_boolean_indexing_fast_path, test/torch_np/numpy_tests/core/test_indexing.py::TestBooleanIndexing::test_boolean_indexing_weirdness, test/torch_np/numpy_tests/core/test_indexing.py::TestArrayToIndexDeprecation::test_array_to_index_error, test/torch_np/numpy_tests/core/test_indexing.py::TestNonIntegerArrayLike::test_basic, test/torch_np/numpy_tests/core/test_indexing.py::TestMultipleEllipsisError::test_basic 2024-08-20T22:47:33.9221309Z 2024-08-20T22:47:36.6840199Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:47:36.7473308Z Running torch_np/numpy_tests/core/test_scalarmath 1/1 ... [2024-08-20 22:47:36.746773] 2024-08-20T22:47:36.7474808Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:47:36.7477251Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_scalarmath.py', '-m', 'not 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 22:47:36.747162] 2024-08-20T22:47:39.0051392Z 2024-08-20T22:47:39.0053921Z torch_np/numpy_tests/lib/test_type_check 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_type_check_1.1_94e284b0a6fbefab_.log 2024-08-20T22:47:39.0076664Z Running 50 items in this shard: test/torch_np/numpy_tests/lib/test_type_check.py::TestCommonType::test_basic, test/torch_np/numpy_tests/lib/test_type_check.py::TestMintypecode::test_default_1, test/torch_np/numpy_tests/lib/test_type_check.py::TestMintypecode::test_default_2, test/torch_np/numpy_tests/lib/test_type_check.py::TestMintypecode::test_default_3, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsscalar::test_basic, test/torch_np/numpy_tests/lib/test_type_check.py::TestReal::test_cmplx, test/torch_np/numpy_tests/lib/test_type_check.py::TestReal::test_real, test/torch_np/numpy_tests/lib/test_type_check.py::TestImag::test_cmplx, test/torch_np/numpy_tests/lib/test_type_check.py::TestImag::test_real, test/torch_np/numpy_tests/lib/test_type_check.py::TestIscomplex::test_fail, test/torch_np/numpy_tests/lib/test_type_check.py::TestIscomplex::test_pass, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsreal::test_fail, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsreal::test_isreal_real, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsreal::test_pass, test/torch_np/numpy_tests/lib/test_type_check.py::TestIscomplexobj::test_basic, test/torch_np/numpy_tests/lib/test_type_check.py::TestIscomplexobj::test_list, test/torch_np/numpy_tests/lib/test_type_check.py::TestIscomplexobj::test_scalar, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsrealobj::test_basic, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsnan::test_complex, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsnan::test_complex1, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsnan::test_goodvalues, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsnan::test_ind, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsnan::test_integer, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsnan::test_neginf, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsnan::test_posinf, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsfinite::test_complex, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsfinite::test_complex1, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsfinite::test_goodvalues, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsfinite::test_ind, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsfinite::test_integer, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsfinite::test_neginf, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsfinite::test_posinf, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsinf::test_goodvalues, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsinf::test_ind, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsinf::test_neginf, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsinf::test_neginf_scalar, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsinf::test_posinf, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsinf::test_posinf_scalar, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsposinf::test_generic, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsneginf::test_generic, test/torch_np/numpy_tests/lib/test_type_check.py::TestNanToNum::test_array, test/torch_np/numpy_tests/lib/test_type_check.py::TestNanToNum::test_complex_bad, test/torch_np/numpy_tests/lib/test_type_check.py::TestNanToNum::test_complex_bad2, test/torch_np/numpy_tests/lib/test_type_check.py::TestNanToNum::test_complex_good, test/torch_np/numpy_tests/lib/test_type_check.py::TestNanToNum::test_do_not_rewrite_previous_keyword, test/torch_np/numpy_tests/lib/test_type_check.py::TestNanToNum::test_float, test/torch_np/numpy_tests/lib/test_type_check.py::TestNanToNum::test_generic, test/torch_np/numpy_tests/lib/test_type_check.py::TestNanToNum::test_integer, test/torch_np/numpy_tests/lib/test_type_check.py::TestRealIfClose::test_basic, test/torch_np/numpy_tests/lib/test_type_check.py::TestArrayConversion::test_asfarray 2024-08-20T22:47:39.0098815Z 2024-08-20T22:47:41.7384209Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:47:41.8012594Z Running optim/test_swa_utils 1/1 ... [2024-08-20 22:47:41.800773] 2024-08-20T22:47:41.8014041Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:47:41.8017868Z 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 22:47:41.801214] 2024-08-20T22:47:44.5040214Z 2024-08-20T22:47:44.5042485Z 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_1323c6c8dac4b06a_.log 2024-08-20T22:47:44.5044145Z 2024-08-20T22:47:47.3284558Z 2024-08-20T22:47:47.3287462Z torch_np/numpy_tests/core/test_scalarmath 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_scalarmath_1.1_77da763bd129c173_.log 2024-08-20T22:47:47.3448622Z Running 228 items in this shard: test/torch_np/numpy_tests/core/test_scalarmath.py::TestTypes::test_leak, test/torch_np/numpy_tests/core/test_scalarmath.py::TestTypes::test_type_add, test/torch_np/numpy_tests/core/test_scalarmath.py::TestTypes::test_type_create, test/torch_np/numpy_tests/core/test_scalarmath.py::TestTypes::test_types, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBaseMath::test_blocked, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBaseMath::test_lower_align, test/torch_np/numpy_tests/core/test_scalarmath.py::TestPower::test_integers_to_negative_integer_power, test/torch_np/numpy_tests/core/test_scalarmath.py::TestPower::test_large_types, test/torch_np/numpy_tests/core/test_scalarmath.py::TestPower::test_mixed_types, test/torch_np/numpy_tests/core/test_scalarmath.py::TestPower::test_modular_power, test/torch_np/numpy_tests/core/test_scalarmath.py::TestPower::test_small_types, test/torch_np/numpy_tests/core/test_scalarmath.py::TestModulus::test_float_modulus_corner_cases_dt_d, test/torch_np/numpy_tests/core/test_scalarmath.py::TestModulus::test_float_modulus_corner_cases_dt_e, test/torch_np/numpy_tests/core/test_scalarmath.py::TestModulus::test_float_modulus_corner_cases_dt_f, test/torch_np/numpy_tests/core/test_scalarmath.py::TestModulus::test_float_modulus_exact, test/torch_np/numpy_tests/core/test_scalarmath.py::TestModulus::test_float_modulus_roundoff, test/torch_np/numpy_tests/core/test_scalarmath.py::TestModulus::test_modulus_basic, test/torch_np/numpy_tests/core/test_scalarmath.py::TestComplexDivision::test_branches, test/torch_np/numpy_tests/core/test_scalarmath.py::TestComplexDivision::test_signed_zeros, test/torch_np/numpy_tests/core/test_scalarmath.py::TestComplexDivision::test_zero_division, test/torch_np/numpy_tests/core/test_scalarmath.py::TestConversion::test_iinfo_long_values_1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestConversion::test_iinfo_long_values_2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestConversion::test_int_from_long, test/torch_np/numpy_tests/core/test_scalarmath.py::TestConversion::test_int_raise_behaviour, test/torch_np/numpy_tests/core/test_scalarmath.py::TestConversion::test_numpy_scalar_relational_operators, test/torch_np/numpy_tests/core/test_scalarmath.py::TestConversion::test_numpy_scalar_relational_operators_2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestConversion::test_scalar_comparison_to_none, test/torch_np/numpy_tests/core/test_scalarmath.py::TestRepr::test_float_repr, test/torch_np/numpy_tests/core/test_scalarmath.py::TestMultiply::test_no_seq_repeat_basic_array_like, test/torch_np/numpy_tests/core/test_scalarmath.py::TestMultiply::test_seq_repeat, test/torch_np/numpy_tests/core/test_scalarmath.py::TestNegative::test_exceptions, test/torch_np/numpy_tests/core/test_scalarmath.py::TestNegative::test_result, test/torch_np/numpy_tests/core/test_scalarmath.py::TestSubtract::test_exceptions, test/torch_np/numpy_tests/core/test_scalarmath.py::TestSubtract::test_result, test/torch_np/numpy_tests/core/test_scalarmath.py::TestAbs::test_builtin_abs_dtype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestAbs::test_builtin_abs_dtype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestAbs::test_builtin_abs_dtype2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestAbs::test_builtin_abs_dtype3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestAbs::test_builtin_abs_dtype4, test/torch_np/numpy_tests/core/test_scalarmath.py::TestAbs::test_builtin_abs_dtype5, test/torch_np/numpy_tests/core/test_scalarmath.py::TestAbs::test_builtin_abs_dtype6, test/torch_np/numpy_tests/core/test_scalarmath.py::TestAbs::test_numpy_abs_dtype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestAbs::test_numpy_abs_dtype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestAbs::test_numpy_abs_dtype2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestAbs::test_numpy_abs_dtype3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestAbs::test_numpy_abs_dtype4, test/torch_np/numpy_tests/core/test_scalarmath.py::TestAbs::test_numpy_abs_dtype5, test/torch_np/numpy_tests/core/test_scalarmath.py::TestAbs::test_numpy_abs_dtype6, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBitShifts::test_shift_all_bits_type_code_B_op0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBitShifts::test_shift_all_bits_type_code_B_op1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBitShifts::test_shift_all_bits_type_code_H_op0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBitShifts::test_shift_all_bits_type_code_H_op1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBitShifts::test_shift_all_bits_type_code_I_op0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBitShifts::test_shift_all_bits_type_code_I_op1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBitShifts::test_shift_all_bits_type_code_L_op0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBitShifts::test_shift_all_bits_type_code_L_op1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBitShifts::test_shift_all_bits_type_code_P_op0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBitShifts::test_shift_all_bits_type_code_P_op1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBitShifts::test_shift_all_bits_type_code_Q_op0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBitShifts::test_shift_all_bits_type_code_Q_op1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBitShifts::test_shift_all_bits_type_code_b_op0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBitShifts::test_shift_all_bits_type_code_b_op1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBitShifts::test_shift_all_bits_type_code_h_op0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBitShifts::test_shift_all_bits_type_code_h_op1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBitShifts::test_shift_all_bits_type_code_i_op0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBitShifts::test_shift_all_bits_type_code_i_op1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBitShifts::test_shift_all_bits_type_code_l_op0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBitShifts::test_shift_all_bits_type_code_l_op1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBitShifts::test_shift_all_bits_type_code_p_op0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBitShifts::test_shift_all_bits_type_code_p_op1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBitShifts::test_shift_all_bits_type_code_q_op0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestBitShifts::test_shift_all_bits_type_code_q_op1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestHash::test_complex_hashes_type_code_D, test/torch_np/numpy_tests/core/test_scalarmath.py::TestHash::test_complex_hashes_type_code_F, test/torch_np/numpy_tests/core/test_scalarmath.py::TestHash::test_complex_hashes_type_code_G, test/torch_np/numpy_tests/core/test_scalarmath.py::TestHash::test_float_and_complex_hashes_type_code_D, test/torch_np/numpy_tests/core/test_scalarmath.py::TestHash::test_float_and_complex_hashes_type_code_F, test/torch_np/numpy_tests/core/test_scalarmath.py::TestHash::test_float_and_complex_hashes_type_code_G, test/torch_np/numpy_tests/core/test_scalarmath.py::TestHash::test_float_and_complex_hashes_type_code_d, test/torch_np/numpy_tests/core/test_scalarmath.py::TestHash::test_float_and_complex_hashes_type_code_e, test/torch_np/numpy_tests/core/test_scalarmath.py::TestHash::test_float_and_complex_hashes_type_code_f, test/torch_np/numpy_tests/core/test_scalarmath.py::TestHash::test_float_and_complex_hashes_type_code_g, test/torch_np/numpy_tests/core/test_scalarmath.py::TestHash::test_integer_hashes_type_code_B, test/torch_np/numpy_tests/core/test_scalarmath.py::TestHash::test_integer_hashes_type_code_H, test/torch_np/numpy_tests/core/test_scalarmath.py::TestHash::test_integer_hashes_type_code_I, test/torch_np/numpy_tests/core/test_scalarmath.py::TestHash::test_integer_hashes_type_code_L, test/torch_np/numpy_tests/core/test_scalarmath.py::TestHash::test_integer_hashes_type_code_P, test/torch_np/numpy_tests/core/test_scalarmath.py::TestHash::test_integer_hashes_type_code_Q, test/torch_np/numpy_tests/core/test_scalarmath.py::TestHash::test_integer_hashes_type_code_b, test/torch_np/numpy_tests/core/test_scalarmath.py::TestHash::test_integer_hashes_type_code_h, test/torch_np/numpy_tests/core/test_scalarmath.py::TestHash::test_integer_hashes_type_code_i, test/torch_np/numpy_tests/core/test_scalarmath.py::TestHash::test_integer_hashes_type_code_l, test/torch_np/numpy_tests/core/test_scalarmath.py::TestHash::test_integer_hashes_type_code_p, test/torch_np/numpy_tests/core/test_scalarmath.py::TestHash::test_integer_hashes_type_code_q, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_divbyzero_dtype_B_operation0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_divbyzero_dtype_B_operation1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_divbyzero_dtype_H_operation0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_divbyzero_dtype_H_operation1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_divbyzero_dtype_I_operation0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_divbyzero_dtype_I_operation1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_divbyzero_dtype_L_operation0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_divbyzero_dtype_L_operation1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_divbyzero_dtype_P_operation0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_divbyzero_dtype_P_operation1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_divbyzero_dtype_Q_operation0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_divbyzero_dtype_Q_operation1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_divbyzero_dtype_b_operation0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_divbyzero_dtype_b_operation1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_divbyzero_dtype_h_operation0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_divbyzero_dtype_h_operation1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_divbyzero_dtype_i_operation0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_divbyzero_dtype_i_operation1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_divbyzero_dtype_l_operation0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_divbyzero_dtype_l_operation1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_divbyzero_dtype_p_operation0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_divbyzero_dtype_p_operation1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_divbyzero_dtype_q_operation0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_divbyzero_dtype_q_operation1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_overflow_dtype_B_operation0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_overflow_dtype_B_operation1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_overflow_dtype_B_operation2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_overflow_dtype_b_operation0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_overflow_dtype_b_operation1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_overflow_dtype_b_operation2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_overflow_dtype_h_operation0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_overflow_dtype_h_operation1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_overflow_dtype_h_operation2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_overflow_dtype_i_operation0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_overflow_dtype_i_operation1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_overflow_dtype_i_operation2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_overflow_dtype_l_operation0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_overflow_dtype_l_operation1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_integer_operation_overflow_dtype_l_operation2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_signed_integer_overflow_dtype_b_operation0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_signed_integer_overflow_dtype_b_operation1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_signed_integer_overflow_dtype_b_operation2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_signed_integer_overflow_dtype_b_operation3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_signed_integer_overflow_dtype_h_operation0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_signed_integer_overflow_dtype_h_operation1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_signed_integer_overflow_dtype_h_operation2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_signed_integer_overflow_dtype_h_operation3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_signed_integer_overflow_dtype_i_operation0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_signed_integer_overflow_dtype_i_operation1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_signed_integer_overflow_dtype_i_operation2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_signed_integer_overflow_dtype_i_operation3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_signed_integer_overflow_dtype_l_operation0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_signed_integer_overflow_dtype_l_operation1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_signed_integer_overflow_dtype_l_operation2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_signed_integer_overflow_dtype_l_operation3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarOpsMisc::test_scalar_unsigned_integer_overflow_dtype_B, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____add_____rop_____radd___op8_cmp_False_subtype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____add_____rop_____radd___op8_cmp_False_subtype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____add_____rop_____radd___op8_cmp_False_subtype2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____add_____rop_____radd___op8_cmp_False_subtype3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____eq_____rop_____eq___op2_cmp_True_subtype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____eq_____rop_____eq___op2_cmp_True_subtype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____eq_____rop_____eq___op2_cmp_True_subtype2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____eq_____rop_____eq___op2_cmp_True_subtype3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____floordiv_____rop_____rfloordiv___op6_cmp_False_subtype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____floordiv_____rop_____rfloordiv___op6_cmp_False_subtype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____floordiv_____rop_____rfloordiv___op6_cmp_False_subtype2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____floordiv_____rop_____rfloordiv___op6_cmp_False_subtype3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____ge_____rop_____le___op5_cmp_True_subtype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____ge_____rop_____le___op5_cmp_True_subtype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____ge_____rop_____le___op5_cmp_True_subtype2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____ge_____rop_____le___op5_cmp_True_subtype3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____gt_____rop_____lt___op4_cmp_True_subtype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____gt_____rop_____lt___op4_cmp_True_subtype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____gt_____rop_____lt___op4_cmp_True_subtype2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____gt_____rop_____lt___op4_cmp_True_subtype3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____le_____rop_____ge___op1_cmp_True_subtype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____le_____rop_____ge___op1_cmp_True_subtype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____le_____rop_____ge___op1_cmp_True_subtype2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____le_____rop_____ge___op1_cmp_True_subtype3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____lt_____rop_____gt___op0_cmp_True_subtype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____lt_____rop_____gt___op0_cmp_True_subtype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____lt_____rop_____gt___op0_cmp_True_subtype2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____lt_____rop_____gt___op0_cmp_True_subtype3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____mod_____rop_____rmod___op9_cmp_False_subtype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____mod_____rop_____rmod___op9_cmp_False_subtype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____mod_____rop_____rmod___op9_cmp_False_subtype2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____mod_____rop_____rmod___op9_cmp_False_subtype3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____mul_____rop_____rmul___op10_cmp_False_subtype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____mul_____rop_____rmul___op10_cmp_False_subtype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____mul_____rop_____rmul___op10_cmp_False_subtype2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____mul_____rop_____rmul___op10_cmp_False_subtype3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____ne_____rop_____ne___op3_cmp_True_subtype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____ne_____rop_____ne___op3_cmp_True_subtype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____ne_____rop_____ne___op3_cmp_True_subtype2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____ne_____rop_____ne___op3_cmp_True_subtype3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____pow_____rop_____rpow___op11_cmp_False_subtype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____pow_____rop_____rpow___op11_cmp_False_subtype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____pow_____rop_____rpow___op11_cmp_False_subtype2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____pow_____rop_____rpow___op11_cmp_False_subtype3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____sub_____rop_____rsub___op12_cmp_False_subtype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____sub_____rop_____rsub___op12_cmp_False_subtype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____sub_____rop_____rsub___op12_cmp_False_subtype2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____sub_____rop_____rsub___op12_cmp_False_subtype3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____truediv_____rop_____rtruediv___op7_cmp_False_subtype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____truediv_____rop_____rtruediv___op7_cmp_False_subtype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____truediv_____rop_____rtruediv___op7_cmp_False_subtype2, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_pyscalar_subclasses___op_____truediv_____rop_____rtruediv___op7_cmp_False_subtype3, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____add_____rop_____radd___op8_cmp_False_sctype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____add_____rop_____radd___op8_cmp_False_sctype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____eq_____rop_____eq___op2_cmp_True_sctype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____eq_____rop_____eq___op2_cmp_True_sctype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____floordiv_____rop_____rfloordiv___op6_cmp_False_sctype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____floordiv_____rop_____rfloordiv___op6_cmp_False_sctype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____ge_____rop_____le___op5_cmp_True_sctype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____ge_____rop_____le___op5_cmp_True_sctype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____gt_____rop_____lt___op4_cmp_True_sctype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____gt_____rop_____lt___op4_cmp_True_sctype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____le_____rop_____ge___op1_cmp_True_sctype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____le_____rop_____ge___op1_cmp_True_sctype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____lt_____rop_____gt___op0_cmp_True_sctype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____lt_____rop_____gt___op0_cmp_True_sctype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____mod_____rop_____rmod___op9_cmp_False_sctype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____mod_____rop_____rmod___op9_cmp_False_sctype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____mul_____rop_____rmul___op10_cmp_False_sctype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____mul_____rop_____rmul___op10_cmp_False_sctype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____ne_____rop_____ne___op3_cmp_True_sctype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____ne_____rop_____ne___op3_cmp_True_sctype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____pow_____rop_____rpow___op11_cmp_False_sctype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____pow_____rop_____rpow___op11_cmp_False_sctype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____sub_____rop_____rsub___op12_cmp_False_sctype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____sub_____rop_____rsub___op12_cmp_False_sctype1, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____truediv_____rop_____rtruediv___op7_cmp_False_sctype0, test/torch_np/numpy_tests/core/test_scalarmath.py::TestScalarSubclassingMisc::test_subclass_deferral___op_____truediv_____rop_____rtruediv___op7_cmp_False_sctype1 2024-08-20T22:47:47.3597545Z 2024-08-20T22:47:47.4734134Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:47:47.5622105Z Running test_jit_llga_fuser 1/1 ... [2024-08-20 22:47:47.561588] 2024-08-20T22:47:47.5623312Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:47:47.5631893Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_jit_llga_fuser.py', '-m', 'not 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 22:47:47.562051] 2024-08-20T22:47:50.2584479Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:47:50.3289014Z Running test_vulkan 1/1 ... [2024-08-20 22:47:50.328393] 2024-08-20T22:47:50.3290018Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:47:50.3293401Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_vulkan.py', '-m', 'not 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 22:47:50.328815] 2024-08-20T22:47:53.4988309Z 2024-08-20T22:47:53.4991201Z test_vulkan 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_vulkan_1.1_05a179ce37b60bd7_.log 2024-08-20T22:47:53.4993743Z Running 1 items in this shard: test/test_vulkan.py::TestVulkanRewritePass::test_conv 2024-08-20T22:47:53.4994869Z 2024-08-20T22:47:56.6673517Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:47:56.7427965Z Running torch_np/numpy_tests/core/test_shape_base 1/1 ... [2024-08-20 22:47:56.742262] 2024-08-20T22:47:56.7429612Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:47:56.7433211Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_shape_base.py', '-m', 'not 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 22:47:56.742787] 2024-08-20T22:48:08.2390435Z 2024-08-20T22:48:08.2394113Z torch_np/numpy_tests/core/test_shape_base 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_shape_base_1.1_4921780856fffd15_.log 2024-08-20T22:48:08.2535387Z Running 119 items in this shard: test/torch_np/numpy_tests/core/test_shape_base.py::TestAtleast1d::test_0D_array, test/torch_np/numpy_tests/core/test_shape_base.py::TestAtleast1d::test_1D_array, test/torch_np/numpy_tests/core/test_shape_base.py::TestAtleast1d::test_2D_array, test/torch_np/numpy_tests/core/test_shape_base.py::TestAtleast1d::test_3D_array, test/torch_np/numpy_tests/core/test_shape_base.py::TestAtleast1d::test_r1array, test/torch_np/numpy_tests/core/test_shape_base.py::TestAtleast2d::test_0D_array, test/torch_np/numpy_tests/core/test_shape_base.py::TestAtleast2d::test_1D_array, test/torch_np/numpy_tests/core/test_shape_base.py::TestAtleast2d::test_2D_array, test/torch_np/numpy_tests/core/test_shape_base.py::TestAtleast2d::test_3D_array, test/torch_np/numpy_tests/core/test_shape_base.py::TestAtleast2d::test_r2array, test/torch_np/numpy_tests/core/test_shape_base.py::TestAtleast3d::test_0D_array, test/torch_np/numpy_tests/core/test_shape_base.py::TestAtleast3d::test_1D_array, test/torch_np/numpy_tests/core/test_shape_base.py::TestAtleast3d::test_2D_array, test/torch_np/numpy_tests/core/test_shape_base.py::TestAtleast3d::test_3D_array, test/torch_np/numpy_tests/core/test_shape_base.py::TestHstack::test_0D_array, test/torch_np/numpy_tests/core/test_shape_base.py::TestHstack::test_1D_array, test/torch_np/numpy_tests/core/test_shape_base.py::TestHstack::test_2D_array, test/torch_np/numpy_tests/core/test_shape_base.py::TestHstack::test_casting_and_dtype, test/torch_np/numpy_tests/core/test_shape_base.py::TestHstack::test_casting_and_dtype_type_error, test/torch_np/numpy_tests/core/test_shape_base.py::TestHstack::test_empty_input, test/torch_np/numpy_tests/core/test_shape_base.py::TestHstack::test_generator, test/torch_np/numpy_tests/core/test_shape_base.py::TestHstack::test_non_iterable, test/torch_np/numpy_tests/core/test_shape_base.py::TestVstack::test_0D_array, test/torch_np/numpy_tests/core/test_shape_base.py::TestVstack::test_1D_array, test/torch_np/numpy_tests/core/test_shape_base.py::TestVstack::test_2D_array, test/torch_np/numpy_tests/core/test_shape_base.py::TestVstack::test_2D_array2, test/torch_np/numpy_tests/core/test_shape_base.py::TestVstack::test_casting_and_dtype, test/torch_np/numpy_tests/core/test_shape_base.py::TestVstack::test_casting_and_dtype_type_error, test/torch_np/numpy_tests/core/test_shape_base.py::TestVstack::test_empty_input, test/torch_np/numpy_tests/core/test_shape_base.py::TestVstack::test_generator, test/torch_np/numpy_tests/core/test_shape_base.py::TestVstack::test_non_iterable, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_bad_out_shape, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_concatenate, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_concatenate_axis_None, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_exceptions, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_large_concatenate_axis_None, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_operator_concat, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_c8_casting_equiv, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_c8_casting_no, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_c8_casting_safe, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_c8_casting_same_kind, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_c8_casting_unsafe, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_f4_casting_equiv, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_f4_casting_no, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_f4_casting_safe, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_f4_casting_same_kind, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_f4_casting_unsafe, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_f8_casting_equiv, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_f8_casting_no, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_f8_casting_safe, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_f8_casting_same_kind, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_f8_casting_unsafe, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_i8_casting_equiv, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_i8_casting_no, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_i8_casting_safe, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_i8_casting_same_kind, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis0_out_dtype_i8_casting_unsafe, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_c8_casting_equiv, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_c8_casting_no, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_c8_casting_safe, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_c8_casting_same_kind, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_c8_casting_unsafe, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_f4_casting_equiv, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_f4_casting_no, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_f4_casting_safe, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_f4_casting_same_kind, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_f4_casting_unsafe, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_f8_casting_equiv, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_f8_casting_no, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_f8_casting_safe, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_f8_casting_same_kind, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_f8_casting_unsafe, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_i8_casting_equiv, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_i8_casting_no, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_i8_casting_safe, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_i8_casting_same_kind, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_axis_0_out_dtype_i8_casting_unsafe, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_out_and_dtype_simple, test/torch_np/numpy_tests/core/test_shape_base.py::TestConcatenate::test_returns_copy, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_c8_casting_equiv, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_c8_casting_no, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_c8_casting_safe, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_c8_casting_same_kind, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_c8_casting_unsafe, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_f4_casting_equiv, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_f4_casting_no, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_f4_casting_safe, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_f4_casting_same_kind, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_f4_casting_unsafe, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_f8_casting_equiv, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_f8_casting_no, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_f8_casting_safe, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_f8_casting_same_kind, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_f8_casting_unsafe, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_i8_casting_equiv, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_i8_casting_no, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_i8_casting_safe, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_i8_casting_same_kind, test/torch_np/numpy_tests/core/test_shape_base.py::TestStackMisc::test_stack_out_and_dtype_axis_0_out_dtype_i8_casting_unsafe, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_3d, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_block_complicated, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_block_memory_order, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_block_mixed_1d_and_2d, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_block_simple_column_wise, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_block_simple_row_wise, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_block_total_size_estimate, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_block_with_1d_arrays_column_wise, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_block_with_1d_arrays_multiple_rows, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_block_with_1d_arrays_row_wise, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_block_with_mismatched_shape, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_different_ndims, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_different_ndims_depths, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_empty_lists, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_invalid_nesting, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_nested, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_no_lists, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_returns_copy, test/torch_np/numpy_tests/core/test_shape_base.py::TestBlock::test_tuple 2024-08-20T22:48:08.2682008Z 2024-08-20T22:48:10.9646826Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:48:11.0272492Z Running torch_np/numpy_tests/core/test_dtype 1/1 ... [2024-08-20 22:48:11.026699] 2024-08-20T22:48:11.0273647Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:48:11.0280227Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/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 22:48:11.027178] 2024-08-20T22:48:16.3523507Z 2024-08-20T22:48:16.3527072Z torch_np/numpy_tests/core/test_dtype 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_dtype_1.1_56fd9f38efe6d156_.log 2024-08-20T22:48:16.3649995Z Running 133 items in this shard: test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_equivalent_dtype_hashing, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_invalid_types, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Bool, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Bytes0, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Complex128, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Complex32, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Complex64, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Datetime64, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Float128, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Float16, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Float32, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Float64, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Int16, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Int32, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Int64, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Int8, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Object0, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Str0, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Timedelta64, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_UInt16, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_UInt32, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_UInt64, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_UInt8, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Uint32, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Uint64, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_numeric_style_types_are_invalid_dtype_Void0, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_richcompare_invalid_dtype_comparison_operation0, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_richcompare_invalid_dtype_comparison_operation1, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_richcompare_invalid_dtype_comparison_operation2, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_richcompare_invalid_dtype_comparison_operation3, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_richcompare_invalid_dtype_equality, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_run_t0, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_run_t1, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_run_t2, test/torch_np/numpy_tests/core/test_dtype.py::TestBuiltin::test_run_t3, test/torch_np/numpy_tests/core/test_dtype.py::TestDtypeAttributeDeletion::test_dtype_non_writable_attributes_deletion, test/torch_np/numpy_tests/core/test_dtype.py::TestDtypeAttributeDeletion::test_dtype_writable_attributes_deletion, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_builtin_t0, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_builtin_t1, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_builtin_t2, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_builtin_t3, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_builtin_t4, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_DType26, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_bool_0, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_bytes_20, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_complex128_18, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_complex256_19, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_complex64_17, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_datetime64_24, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_float128_16, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_float16_13, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_float32_14, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_float64_15, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_int16_2, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_int32_3, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_int64_4, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_int64_5, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_int64_6, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_int8_1, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_object_23, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_str_21, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_timedelta64_25, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_uint16_8, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_uint32_9, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_uint64_10, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_uint64_11, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_uint64_12, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_uint8_7, test/torch_np/numpy_tests/core/test_dtype.py::TestPickling::test_pickle_types_void_22, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_complex_other_value_based_complex64_complex64_None, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_complex_other_value_based_float16_complex64_None, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_complex_other_value_based_float32_complex64_None, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_complex_other_value_based_other_4294967295_expected1_expected_weak1, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_complex_other_value_based_other_65535_expected0_expected_weak0, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_complex_scalar_value_based_other0_expected0, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_complex_scalar_value_based_other1_expected1, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_complex_scalar_value_based_other2_expected2, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_complex_scalar_value_based_other3_expected3, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_complex_scalar_value_based_other4_expected4, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_complex_scalar_value_based_other5_expected5, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_complex_scalar_value_based_other6_expected6, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_permutations_do_not_influence_result_dtypes0_expected0, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_permutations_do_not_influence_result_dtypes1_expected1, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_permutations_do_not_influence_result_dtypes2_expected2, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_permutations_do_not_influence_result_dtypes3_expected3, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_permutations_do_not_influence_result_dtypes4_expected4, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_permutations_do_not_influence_result_dtypes5_expected5, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_permutations_do_not_influence_result_dtypes6_expected6, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_permutations_do_not_influence_result_dtypes7_expected7, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_permutations_do_not_influence_result_dtypes8_expected8, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_permutations_do_not_influence_result_dtypes9_expected9, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_python_integer_promotion_val_18446744073709551616, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_python_integer_promotion_val_2, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_python_integer_promotion_val_200, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_python_integer_promotion_val_4294967296, test/torch_np/numpy_tests/core/test_dtype.py::TestPromotion::test_python_integer_promotion_val_9223372036854775808, test/torch_np/numpy_tests/core/test_dtype.py::TestMisc::test_class_getitem_38, test/torch_np/numpy_tests/core/test_dtype.py::TestMisc::test_dtypes_are_true, test/torch_np/numpy_tests/core/test_dtype.py::TestMisc::test_keyword_argument, test/torch_np/numpy_tests/core/test_dtype.py::TestFromDTypeAttribute::test_recursion, test/torch_np/numpy_tests/core/test_dtype.py::TestFromDTypeAttribute::test_simple, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_?, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_B, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_D, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_F, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_G, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_H, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_I, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_L, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_M, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_O, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_P, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_Q, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_S, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_U, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_V, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_b, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_d, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_e, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_f, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_g, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_h, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_i, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_l, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_m, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_p, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_dtype_subclass_code_q, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_subscript_scalar, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_subscript_tuple_arg_len_0, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_subscript_tuple_arg_len_1, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_subscript_tuple_arg_len_2, test/torch_np/numpy_tests/core/test_dtype.py::TestClassGetItem::test_subscript_tuple_arg_len_3 2024-08-20T22:48:16.3759190Z 2024-08-20T22:48:19.9227852Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:48:20.0082207Z Running torch_np/numpy_tests/core/test_getlimits 1/1 ... [2024-08-20 22:48:20.007539] 2024-08-20T22:48:20.0083510Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:48:20.0089071Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_getlimits.py', '-m', 'not 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 22:48:20.007986] 2024-08-20T22:48:24.7330749Z 2024-08-20T22:48:24.7333789Z torch_np/numpy_tests/core/test_getlimits 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_getlimits_1.1_ac35db3c4cc13cd8_.log 2024-08-20T22:48:24.7532563Z Running 17 items in this shard: test/torch_np/numpy_tests/core/test_getlimits.py::TestPythonFloat::test_singleton, test/torch_np/numpy_tests/core/test_getlimits.py::TestHalf::test_singleton, test/torch_np/numpy_tests/core/test_getlimits.py::TestSingle::test_singleton, test/torch_np/numpy_tests/core/test_getlimits.py::TestDouble::test_singleton, test/torch_np/numpy_tests/core/test_getlimits.py::TestFinfo::test_basic, test/torch_np/numpy_tests/core/test_getlimits.py::TestFinfo::test_basic_missing, test/torch_np/numpy_tests/core/test_getlimits.py::TestIinfo::test_basic, test/torch_np/numpy_tests/core/test_getlimits.py::TestIinfo::test_unsigned_max_T0, test/torch_np/numpy_tests/core/test_getlimits.py::TestIinfo::test_unsigned_max_T1, test/torch_np/numpy_tests/core/test_getlimits.py::TestIinfo::test_unsigned_max_T2, test/torch_np/numpy_tests/core/test_getlimits.py::TestIinfo::test_unsigned_max_T3, test/torch_np/numpy_tests/core/test_getlimits.py::TestRepr::test_finfo_repr, test/torch_np/numpy_tests/core/test_getlimits.py::TestRepr::test_iinfo_repr, test/torch_np/numpy_tests/core/test_getlimits.py::TestMisc::test_instances, test/torch_np/numpy_tests/core/test_getlimits.py::TestMisc::test_known_types, test/torch_np/numpy_tests/core/test_getlimits.py::TestMisc::test_plausible_finfo, test/torch_np/numpy_tests/core/test_getlimits.py::TestMisc::test_subnormal_warning 2024-08-20T22:48:24.7545123Z 2024-08-20T22:48:27.4644805Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:48:27.5281656Z Running torch_np/test_function_base 1/1 ... [2024-08-20 22:48:27.527310] 2024-08-20T22:48:27.5282346Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:48:27.5284633Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/test_function_base.py', '-m', 'not 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 22:48:27.527737] 2024-08-20T22:48:32.2500806Z 2024-08-20T22:48:32.2504023Z torch_np/test_function_base 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_function_base_1.1_5815ffc62db5c9d4_.log 2024-08-20T22:48:32.2507615Z Running 1 items in this shard: test/torch_np/test_function_base.py::TestAppend::test_basic 2024-08-20T22:48:32.2509076Z 2024-08-20T22:48:36.1260083Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:48:36.2408706Z Running nn/test_init 1/1 ... [2024-08-20 22:48:36.240347] 2024-08-20T22:48:36.2410130Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:48:36.2414302Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'nn/test_init.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:48:36.240893] 2024-08-20T22:48:43.2694297Z 2024-08-20T22:48:43.2696498Z nn/test_init 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_init_1.1_713859eb217d805e_.log 2024-08-20T22:48:43.2709072Z Running 30 items in this shard: test/nn/test_init.py::TestNNInit::test_calculate_gain_leaky_relu, test/nn/test_init.py::TestNNInit::test_calculate_gain_leaky_relu_only_accepts_numbers, test/nn/test_init.py::TestNNInit::test_calculate_gain_linear, test/nn/test_init.py::TestNNInit::test_calculate_gain_nonlinear, test/nn/test_init.py::TestNNInit::test_calculate_gain_only_accepts_valid_nonlinearities, test/nn/test_init.py::TestNNInit::test_constant, test/nn/test_init.py::TestNNInit::test_deprecation, test/nn/test_init.py::TestNNInit::test_dirac_identity, test/nn/test_init.py::TestNNInit::test_dirac_only_works_on_3_4_5d_inputs, test/nn/test_init.py::TestNNInit::test_dirac_properties, test/nn/test_init.py::TestNNInit::test_eye, test/nn/test_init.py::TestNNInit::test_eye_only_works_on_2d_inputs, test/nn/test_init.py::TestNNInit::test_kaiming_normal, test/nn/test_init.py::TestNNInit::test_kaiming_normal_errors_on_inputs_smaller_than_2d, test/nn/test_init.py::TestNNInit::test_kaiming_normal_warning_on_0element_tensor, test/nn/test_init.py::TestNNInit::test_kaiming_uniform, test/nn/test_init.py::TestNNInit::test_kaiming_uniform_errors_on_inputs_smaller_than_2d, test/nn/test_init.py::TestNNInit::test_kaiming_uniform_warning_on_0element_tensor, test/nn/test_init.py::TestNNInit::test_normal, test/nn/test_init.py::TestNNInit::test_ones_and_zeros, test/nn/test_init.py::TestNNInit::test_orthogonal, test/nn/test_init.py::TestNNInit::test_sparse_default_std, test/nn/test_init.py::TestNNInit::test_sparse_only_works_on_2d_inputs, test/nn/test_init.py::TestNNInit::test_trunc_normal, test/nn/test_init.py::TestNNInit::test_trunc_normal_generator, test/nn/test_init.py::TestNNInit::test_uniform, test/nn/test_init.py::TestNNInit::test_xavier_normal, test/nn/test_init.py::TestNNInit::test_xavier_normal_errors_on_inputs_smaller_than_2d, test/nn/test_init.py::TestNNInit::test_xavier_uniform, test/nn/test_init.py::TestNNInit::test_xavier_uniform_errors_on_inputs_smaller_than_2d 2024-08-20T22:48:43.2720540Z 2024-08-20T22:48:47.2152804Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:48:47.3292899Z Running torch_np/test_ndarray_methods 1/1 ... [2024-08-20 22:48:47.328671] 2024-08-20T22:48:47.3294106Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:48:47.3297949Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/test_ndarray_methods.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:48:47.329152] 2024-08-20T22:48:50.1128207Z 2024-08-20T22:48:50.1130945Z test_jit_llga_fuser 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jit_llga_fuser_1.1_3328facbba71e507_.log 2024-08-20T22:48:50.1178440Z Running 107 items in this shard: test/test_jit_llga_fuser.py::TestEnableDisableLlgaFuser::test_context_manager, test/test_jit_llga_fuser.py::TestDynamoAOT::test_dynamo_aot_ts_onednn, test/test_jit_llga_fuser.py::TestModel::test_vision_alexnet_bfloat16, test/test_jit_llga_fuser.py::TestModel::test_vision_alexnet_float32, test/test_jit_llga_fuser.py::TestModel::test_vision_densenet121_bfloat16, test/test_jit_llga_fuser.py::TestModel::test_vision_densenet121_float32, test/test_jit_llga_fuser.py::TestModel::test_vision_densenet161_bfloat16, test/test_jit_llga_fuser.py::TestModel::test_vision_densenet161_float32, test/test_jit_llga_fuser.py::TestModel::test_vision_densenet169_bfloat16, test/test_jit_llga_fuser.py::TestModel::test_vision_densenet169_float32, test/test_jit_llga_fuser.py::TestModel::test_vision_densenet201_bfloat16, test/test_jit_llga_fuser.py::TestModel::test_vision_densenet201_float32, test/test_jit_llga_fuser.py::TestModel::test_vision_efficientnet_b0_bfloat16, test/test_jit_llga_fuser.py::TestModel::test_vision_efficientnet_b0_float32, test/test_jit_llga_fuser.py::TestModel::test_vision_efficientnet_b1_bfloat16, test/test_jit_llga_fuser.py::TestModel::test_vision_efficientnet_b1_float32, test/test_jit_llga_fuser.py::TestModel::test_vision_efficientnet_b2_bfloat16, test/test_jit_llga_fuser.py::TestModel::test_vision_efficientnet_b2_float32, test/test_jit_llga_fuser.py::TestModel::test_vision_efficientnet_b3_bfloat16, test/test_jit_llga_fuser.py::TestModel::test_vision_efficientnet_b3_float32, test/test_jit_llga_fuser.py::TestModel::test_vision_efficientnet_b4_bfloat16, test/test_jit_llga_fuser.py::TestModel::test_vision_efficientnet_b4_float32, test/test_jit_llga_fuser.py::TestModel::test_vision_efficientnet_b5_bfloat16, test/test_jit_llga_fuser.py::TestModel::test_vision_efficientnet_b5_float32, test/test_jit_llga_fuser.py::TestModel::test_vision_efficientnet_b6_bfloat16, test/test_jit_llga_fuser.py::TestModel::test_vision_efficientnet_b6_float32, test/test_jit_llga_fuser.py::TestModel::test_vision_efficientnet_b7_bfloat16, test/test_jit_llga_fuser.py::TestModel::test_vision_efficientnet_b7_float32, test/test_jit_llga_fuser.py::TestModel::test_vision_googlenet_bfloat16, test/test_jit_llga_fuser.py::TestModel::test_vision_googlenet_float32, test/test_jit_llga_fuser.py::TestModel::test_vision_mnasnet1_0_bfloat16, test/test_jit_llga_fuser.py::TestModel::test_vision_mnasnet1_0_float32, test/test_jit_llga_fuser.py::TestModel::test_vision_mobilenet_v2_bfloat16, test/test_jit_llga_fuser.py::TestModel::test_vision_mobilenet_v2_float32, test/test_jit_llga_fuser.py::TestModel::test_vision_mobilenet_v3_large_bfloat16, test/test_jit_llga_fuser.py::TestModel::test_vision_mobilenet_v3_large_float32, test/test_jit_llga_fuser.py::TestModel::test_vision_regnet_y_400mf_bfloat16, test/test_jit_llga_fuser.py::TestModel::test_vision_regnet_y_400mf_float32, test/test_jit_llga_fuser.py::TestModel::test_vision_resnet50_bfloat16, test/test_jit_llga_fuser.py::TestModel::test_vision_resnet50_float32, test/test_jit_llga_fuser.py::TestModel::test_vision_resnext101_32x8d_bfloat16, test/test_jit_llga_fuser.py::TestModel::test_vision_resnext101_32x8d_float32, test/test_jit_llga_fuser.py::TestModel::test_vision_resnext50_32x4d_bfloat16, test/test_jit_llga_fuser.py::TestModel::test_vision_resnext50_32x4d_float32, test/test_jit_llga_fuser.py::TestModel::test_vision_shufflenet_v2_x1_0_bfloat16, test/test_jit_llga_fuser.py::TestModel::test_vision_shufflenet_v2_x1_0_float32, test/test_jit_llga_fuser.py::TestModel::test_vision_squeezenet1_0_bfloat16, test/test_jit_llga_fuser.py::TestModel::test_vision_squeezenet1_0_float32, test/test_jit_llga_fuser.py::TestModel::test_vision_vgg16_bfloat16, test/test_jit_llga_fuser.py::TestModel::test_vision_vgg16_float32, test/test_jit_llga_fuser.py::TestModel::test_vision_wide_resnet50_2_bfloat16, test/test_jit_llga_fuser.py::TestModel::test_vision_wide_resnet50_2_float32, test/test_jit_llga_fuser.py::TestFusionPatternCPU::test_bn2d_eltwise_cpu_bfloat16, test/test_jit_llga_fuser.py::TestFusionPatternCPU::test_bn2d_eltwise_cpu_float32, test/test_jit_llga_fuser.py::TestFusionPatternCPU::test_conv2d_bn_cpu_bfloat16, test/test_jit_llga_fuser.py::TestFusionPatternCPU::test_conv2d_bn_cpu_float32, test/test_jit_llga_fuser.py::TestFusionPatternCPU::test_conv2d_bn_relu_cpu_bfloat16, test/test_jit_llga_fuser.py::TestFusionPatternCPU::test_conv2d_bn_relu_cpu_float32, test/test_jit_llga_fuser.py::TestFusionPatternCPU::test_conv2d_clamp_cpu_bfloat16, test/test_jit_llga_fuser.py::TestFusionPatternCPU::test_conv2d_clamp_cpu_float32, test/test_jit_llga_fuser.py::TestFusionPatternCPU::test_conv2d_eltwise_cpu_bfloat16, test/test_jit_llga_fuser.py::TestFusionPatternCPU::test_conv2d_eltwise_cpu_float32, test/test_jit_llga_fuser.py::TestFusionPatternCPU::test_conv2d_silu_cpu_bfloat16, test/test_jit_llga_fuser.py::TestFusionPatternCPU::test_conv2d_silu_cpu_float32, test/test_jit_llga_fuser.py::TestFusionPatternCPU::test_conv2d_sum_cpu_bfloat16, test/test_jit_llga_fuser.py::TestFusionPatternCPU::test_conv2d_sum_cpu_float32, test/test_jit_llga_fuser.py::TestFusionPatternCPU::test_ensure_tensor_is_rewrapped_cpu_bfloat16, test/test_jit_llga_fuser.py::TestFusionPatternCPU::test_ensure_tensor_is_rewrapped_cpu_float32, test/test_jit_llga_fuser.py::TestFusionPatternCPU::test_linear_eltwise_cpu_bfloat16, test/test_jit_llga_fuser.py::TestFusionPatternCPU::test_linear_eltwise_cpu_float32, test/test_jit_llga_fuser.py::TestFusionPatternCPU::test_rewrap_tensor_input_to_pytorch_cpu_bfloat16, test/test_jit_llga_fuser.py::TestFusionPatternCPU::test_rewrap_tensor_input_to_pytorch_cpu_float32, test/test_jit_llga_fuser.py::TestFusionPatternCPU::test_wildcard_cpu_bfloat16, test/test_jit_llga_fuser.py::TestFusionPatternCPU::test_wildcard_cpu_float32, test/test_jit_llga_fuser.py::TestFusionPatternCPU::test_wildcard_unsupported_dtype_cpu_int32, test/test_jit_llga_fuser.py::TestOpCPU::test_add_cpu_bfloat16, test/test_jit_llga_fuser.py::TestOpCPU::test_add_cpu_float32, test/test_jit_llga_fuser.py::TestOpCPU::test_add_scalar_cpu_bfloat16, test/test_jit_llga_fuser.py::TestOpCPU::test_add_scalar_cpu_float32, test/test_jit_llga_fuser.py::TestOpCPU::test_addmm_cpu_bfloat16, test/test_jit_llga_fuser.py::TestOpCPU::test_addmm_cpu_float32, test/test_jit_llga_fuser.py::TestOpCPU::test_avg_pool2d_cpu_bfloat16, test/test_jit_llga_fuser.py::TestOpCPU::test_avg_pool2d_cpu_float32, test/test_jit_llga_fuser.py::TestOpCPU::test_bn2d_cpu_bfloat16, test/test_jit_llga_fuser.py::TestOpCPU::test_bn2d_cpu_float32, test/test_jit_llga_fuser.py::TestOpCPU::test_cat_cpu_bfloat16, test/test_jit_llga_fuser.py::TestOpCPU::test_cat_cpu_float32, test/test_jit_llga_fuser.py::TestOpCPU::test_conv2d_cpu_bfloat16, test/test_jit_llga_fuser.py::TestOpCPU::test_conv2d_cpu_float32, test/test_jit_llga_fuser.py::TestOpCPU::test_eltwise_cpu_bfloat16, test/test_jit_llga_fuser.py::TestOpCPU::test_eltwise_cpu_float32, test/test_jit_llga_fuser.py::TestOpCPU::test_identity_binary_cpu_bfloat16, test/test_jit_llga_fuser.py::TestOpCPU::test_identity_binary_cpu_float32, test/test_jit_llga_fuser.py::TestOpCPU::test_layer_norm_cpu_bfloat16, test/test_jit_llga_fuser.py::TestOpCPU::test_layer_norm_cpu_float32, test/test_jit_llga_fuser.py::TestOpCPU::test_linear_cpu_bfloat16, test/test_jit_llga_fuser.py::TestOpCPU::test_linear_cpu_float32, test/test_jit_llga_fuser.py::TestOpCPU::test_max_pool2d_cpu_bfloat16, test/test_jit_llga_fuser.py::TestOpCPU::test_max_pool2d_cpu_float32, test/test_jit_llga_fuser.py::TestOpCPU::test_mul_cpu_bfloat16, test/test_jit_llga_fuser.py::TestOpCPU::test_mul_cpu_float32, test/test_jit_llga_fuser.py::TestOpCPU::test_softmax_cpu_bfloat16, test/test_jit_llga_fuser.py::TestOpCPU::test_softmax_cpu_float32, test/test_jit_llga_fuser.py::TestOpCPU::test_typecheck_cpu_bfloat16, test/test_jit_llga_fuser.py::TestOpCPU::test_typecheck_cpu_float32, test/test_jit_llga_fuser.py::TestOpCPU::test_variable_kernel_avg_pool2d_cpu_bfloat16, test/test_jit_llga_fuser.py::TestOpCPU::test_variable_kernel_avg_pool2d_cpu_float32 2024-08-20T22:48:50.1220792Z 2024-08-20T22:48:52.8638566Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:48:52.9258868Z Running torch_np/numpy_tests/linalg/test_linalg 1/1 ... [2024-08-20 22:48:52.925359] 2024-08-20T22:48:52.9260273Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:48:52.9264136Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/linalg/test_linalg.py', '-m', 'not 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 22:48:52.925766] 2024-08-20T22:49:13.5417169Z 2024-08-20T22:49:13.5419675Z torch_np/test_ndarray_methods 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_ndarray_methods_1.1_5123783d264e3e0c_.log 2024-08-20T22:49:13.5620552Z Running 342 items in this shard: test/torch_np/test_ndarray_methods.py::TestIndexing::test_indexing_simple, test/torch_np/test_ndarray_methods.py::TestIndexing::test_setitem, test/torch_np/test_ndarray_methods.py::TestReshape::test_reshape_function, test/torch_np/test_ndarray_methods.py::TestReshape::test_reshape_method, test/torch_np/test_ndarray_methods.py::TestTranspose::test_transpose_function, test/torch_np/test_ndarray_methods.py::TestTranspose::test_transpose_method, test/torch_np/test_ndarray_methods.py::TestRavel::test_ravel_function, test/torch_np/test_ndarray_methods.py::TestRavel::test_ravel_method, test/torch_np/test_ndarray_methods.py::TestNonzero::test_array_method, test/torch_np/test_ndarray_methods.py::TestNonzero::test_nonzero_onedim, test/torch_np/test_ndarray_methods.py::TestNonzero::test_nonzero_trivial, test/torch_np/test_ndarray_methods.py::TestNonzero::test_nonzero_twodim, test/torch_np/test_ndarray_methods.py::TestNonzero::test_sparse, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_all_method_max, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_all_method_min, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size0_axis0_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size0_axis0_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size10_axis_-1_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size10_axis_-1_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size11_axis_0_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size11_axis_0_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size12_axis_1_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size12_axis_1_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size13_axis13_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size13_axis13_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size14_axis_-2_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size14_axis_-2_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size15_axis_-1_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size15_axis_-1_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size16_axis_0_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size16_axis_0_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size17_axis_1_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size17_axis_1_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size18_axis18_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size18_axis18_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size19_axis_-3_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size19_axis_-3_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size1_axis_-1_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size1_axis_-1_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size20_axis_-2_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size20_axis_-2_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size21_axis_-1_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size21_axis_-1_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size22_axis_0_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size22_axis_0_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size23_axis_1_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size23_axis_1_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size24_axis_2_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size24_axis_2_method1, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size25_axis25_method0, test/torch_np/test_ndarray_methods.py::TestArgmaxArgminCommon::test_np_argmin_argmax_keepdims_size25_axis25_method1, 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test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data66, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data67, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data68, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data69, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data7, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data70, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data71, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data72, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data73, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data8, test/torch_np/test_ndarray_methods.py::TestArgmax::test_combinations_data9, test/torch_np/test_ndarray_methods.py::TestArgmax::test_maximum_signed_integers, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data0, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data1, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data10, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data11, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data12, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data13, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data14, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data15, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data16, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data17, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data18, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data19, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data2, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data20, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data21, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data22, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data23, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data24, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data25, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data26, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data27, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data28, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data29, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data3, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data30, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data31, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data32, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data33, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data34, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data35, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data36, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data37, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data38, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data39, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data4, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data40, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data41, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data42, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data43, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data44, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data45, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data46, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data47, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data48, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data49, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data5, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data50, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data51, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data52, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data53, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data54, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data55, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data56, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data57, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data58, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data59, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data6, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data60, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data61, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data62, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data63, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data64, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data65, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data66, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data67, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data68, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data69, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data7, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data70, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data71, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data72, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data73, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data8, test/torch_np/test_ndarray_methods.py::TestArgmin::test_combinations_data9, test/torch_np/test_ndarray_methods.py::TestArgmin::test_minimum_signed_integers, test/torch_np/test_ndarray_methods.py::TestAmax::test_basic, test/torch_np/test_ndarray_methods.py::TestAmin::test_basic, test/torch_np/test_ndarray_methods.py::TestContains::test_contains, test/torch_np/test_ndarray_methods.py::TestNoExtraMethods::test_extra_methods_name_fn, test/torch_np/test_ndarray_methods.py::TestNoExtraMethods::test_extra_methods_name_ivar, test/torch_np/test_ndarray_methods.py::TestNoExtraMethods::test_extra_methods_name_method, test/torch_np/test_ndarray_methods.py::TestNoExtraMethods::test_extra_methods_name_name, test/torch_np/test_ndarray_methods.py::TestNoExtraMethods::test_extra_methods_name_plain, test/torch_np/test_ndarray_methods.py::TestNoExtraMethods::test_extra_methods_name_rvar, test/torch_np/test_ndarray_methods.py::TestIter::test_iter_1d, test/torch_np/test_ndarray_methods.py::TestIter::test_iter_2d 2024-08-20T22:49:13.5792973Z 2024-08-20T22:49:16.3524255Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:49:16.4152119Z Running torch_np/numpy_tests/lib/test_index_tricks 1/1 ... [2024-08-20 22:49:16.414659] 2024-08-20T22:49:16.4153390Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:49:16.4157039Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_index_tricks.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:49:16.415070] 2024-08-20T22:49:21.7876467Z 2024-08-20T22:49:21.7879432Z torch_np/numpy_tests/lib/test_index_tricks 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_index_tricks_1.1_83ff0c55765ced0d_.log 2024-08-20T22:49:21.7921671Z Running 47 items in this shard: test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_0d, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_basic, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_big_indices, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_clipmodes, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_dtypes, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_empty_array_ravel_mode_clip, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_empty_array_ravel_mode_raise, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_empty_array_ravel_mode_wrap, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_empty_array_unravel, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_empty_indices, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_writeability, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestGrid::test_accepts_longdouble, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestGrid::test_accepts_npcomplexfloating, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestGrid::test_accepts_npfloating, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestGrid::test_basic, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestGrid::test_linspace_equivalence, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestGrid::test_mgrid_size_none_handling_start0_stop_10_step0_expected0, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestGrid::test_mgrid_size_none_handling_start_-10_stop_20_step1_expected1, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestGrid::test_nd, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestGrid::test_sparse, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestConcatenator::test_0d, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestConcatenator::test_1d, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestConcatenator::test_2d, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestConcatenator::test_complex_step, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestConcatenator::test_mixed_type, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestConcatenator::test_more_mixed_type, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestNdenumerate::test_basic, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestIndexExpression::test_regression_1, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestIndexExpression::test_simple_1, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestIx_::test_1d_only, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestIx_::test_bool, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestIx_::test_regression_1, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestIx_::test_repeated_input, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestIx_::test_shape_and_dtype, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestC::test_c_, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestFillDiagonal::test_basic, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestFillDiagonal::test_hetero_shape_handling, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestFillDiagonal::test_low_dim_handling, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestFillDiagonal::test_operate_4d_array, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestFillDiagonal::test_tall_matrix, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestFillDiagonal::test_tall_matrix_wrap, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestFillDiagonal::test_wide_matrix, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestDiagIndices::test_diag_indices, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestDiagIndicesFrom::test_diag_indices_from, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestDiagIndicesFrom::test_error_shape_mismatch, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestDiagIndicesFrom::test_error_small_input, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestNdIndex::test_ndindex 2024-08-20T22:49:21.7996702Z 2024-08-20T22:49:25.0429574Z 2024-08-20T22:49:25.0432625Z torch_np/numpy_tests/linalg/test_linalg 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.linalg.test_linalg_1.1_f1fbb98e7bfb0fce_.log 2024-08-20T22:49:25.0713931Z Running 268 items in this shard: test/torch_np/numpy_tests/linalg/test_linalg.py::TestSolve::test_0_size, test/torch_np/numpy_tests/linalg/test_linalg.py::TestSolve::test_0_size_k, test/torch_np/numpy_tests/linalg/test_linalg.py::TestSolve::test_empty_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestSolve::test_generalized_empty_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestSolve::test_generalized_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestSolve::test_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestSolve::test_types_dtype0, test/torch_np/numpy_tests/linalg/test_linalg.py::TestSolve::test_types_dtype1, test/torch_np/numpy_tests/linalg/test_linalg.py::TestSolve::test_types_dtype2, test/torch_np/numpy_tests/linalg/test_linalg.py::TestSolve::test_types_dtype3, test/torch_np/numpy_tests/linalg/test_linalg.py::TestInv::test_0_size, test/torch_np/numpy_tests/linalg/test_linalg.py::TestInv::test_empty_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestInv::test_generalized_empty_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestInv::test_generalized_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestInv::test_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestInv::test_types_dtype0, test/torch_np/numpy_tests/linalg/test_linalg.py::TestInv::test_types_dtype1, test/torch_np/numpy_tests/linalg/test_linalg.py::TestInv::test_types_dtype2, test/torch_np/numpy_tests/linalg/test_linalg.py::TestInv::test_types_dtype3, test/torch_np/numpy_tests/linalg/test_linalg.py::TestEigvals::test_0_size, test/torch_np/numpy_tests/linalg/test_linalg.py::TestEigvals::test_empty_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestEigvals::test_generalized_empty_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestEigvals::test_generalized_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestEigvals::test_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestEigvals::test_types_dtype0, test/torch_np/numpy_tests/linalg/test_linalg.py::TestEigvals::test_types_dtype1, test/torch_np/numpy_tests/linalg/test_linalg.py::TestEigvals::test_types_dtype2, test/torch_np/numpy_tests/linalg/test_linalg.py::TestEigvals::test_types_dtype3, test/torch_np/numpy_tests/linalg/test_linalg.py::TestEig::test_0_size, test/torch_np/numpy_tests/linalg/test_linalg.py::TestEig::test_empty_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestEig::test_generalized_empty_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestEig::test_generalized_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestEig::test_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestEig::test_types_dtype0, 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test/torch_np/numpy_tests/linalg/test_linalg.py::TestQR::test_stacked_inputs_size4_outer_size1_dt3, test/torch_np/numpy_tests/linalg/test_linalg.py::TestQR::test_stacked_inputs_size4_outer_size2_dt0, test/torch_np/numpy_tests/linalg/test_linalg.py::TestQR::test_stacked_inputs_size4_outer_size2_dt1, test/torch_np/numpy_tests/linalg/test_linalg.py::TestQR::test_stacked_inputs_size4_outer_size2_dt2, test/torch_np/numpy_tests/linalg/test_linalg.py::TestQR::test_stacked_inputs_size4_outer_size2_dt3, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_0_size, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape0_dtype0, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape0_dtype1, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape0_dtype2, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape0_dtype3, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape1_dtype0, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape1_dtype1, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape1_dtype2, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape1_dtype3, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape2_dtype0, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape2_dtype1, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape2_dtype2, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape2_dtype3, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape3_dtype0, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape3_dtype1, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape3_dtype2, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape3_dtype3, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape4_dtype0, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape4_dtype1, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape4_dtype2, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape4_dtype3, test/torch_np/numpy_tests/linalg/test_linalg.py::TestMisc::test_byteorder_check, test/torch_np/numpy_tests/linalg/test_linalg.py::TestMisc::test_generalized_raise_multiloop, test/torch_np/numpy_tests/linalg/test_linalg.py::TestMisc::test_sdot_bug_8577, test/torch_np/numpy_tests/linalg/test_linalg.py::TestMisc::test_xerbla_override, test/torch_np/numpy_tests/linalg/test_linalg.py::TestMultiDot::test_basic_function_with_dynamic_programming_optimization, test/torch_np/numpy_tests/linalg/test_linalg.py::TestMultiDot::test_basic_function_with_three_arguments, test/torch_np/numpy_tests/linalg/test_linalg.py::TestMultiDot::test_basic_function_with_two_arguments, test/torch_np/numpy_tests/linalg/test_linalg.py::TestMultiDot::test_dynamic_programming_logic, test/torch_np/numpy_tests/linalg/test_linalg.py::TestMultiDot::test_dynamic_programming_optimization_and_out, test/torch_np/numpy_tests/linalg/test_linalg.py::TestMultiDot::test_three_arguments_and_out, test/torch_np/numpy_tests/linalg/test_linalg.py::TestMultiDot::test_too_few_input_arrays, test/torch_np/numpy_tests/linalg/test_linalg.py::TestMultiDot::test_two_arguments_and_out, test/torch_np/numpy_tests/linalg/test_linalg.py::TestMultiDot::test_vector_as_first_and_last_argument, test/torch_np/numpy_tests/linalg/test_linalg.py::TestMultiDot::test_vector_as_first_argument, test/torch_np/numpy_tests/linalg/test_linalg.py::TestMultiDot::test_vector_as_last_argument, test/torch_np/numpy_tests/linalg/test_linalg.py::TestTensorinv::test_non_square_handling_arr0_ind_2, test/torch_np/numpy_tests/linalg/test_linalg.py::TestTensorinv::test_non_square_handling_arr1_ind_1, test/torch_np/numpy_tests/linalg/test_linalg.py::TestTensorinv::test_tensorinv_ind_limit_ind_-2, test/torch_np/numpy_tests/linalg/test_linalg.py::TestTensorinv::test_tensorinv_ind_limit_ind_0, test/torch_np/numpy_tests/linalg/test_linalg.py::TestTensorinv::test_tensorinv_result, test/torch_np/numpy_tests/linalg/test_linalg.py::TestTensorinv::test_tensorinv_shape_shape0_ind_2, test/torch_np/numpy_tests/linalg/test_linalg.py::TestTensorinv::test_tensorinv_shape_shape1_ind_1, test/torch_np/numpy_tests/linalg/test_linalg.py::TestTensorsolve::test_non_square_handling_a0_axes0, test/torch_np/numpy_tests/linalg/test_linalg.py::TestTensorsolve::test_non_square_handling_a1_axes1, test/torch_np/numpy_tests/linalg/test_linalg.py::TestTensorsolve::test_tensorsolve_result_shape0, test/torch_np/numpy_tests/linalg/test_linalg.py::TestTensorsolve::test_tensorsolve_result_shape1, test/torch_np/numpy_tests/linalg/test_linalg.py::TestTensorsolve::test_tensorsolve_result_shape2, test/torch_np/numpy_tests/linalg/test_linalg.py::TestMisc2::test_blas64_dot, test/torch_np/numpy_tests/linalg/test_linalg.py::TestMisc2::test_blas64_geqrf_lwork_smoketest, test/torch_np/numpy_tests/linalg/test_linalg.py::TestMisc2::test_unsupported_commontype 2024-08-20T22:49:25.0962346Z 2024-08-20T22:49:25.0963875Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:49:25.1550359Z Running torch_np/numpy_tests/lib/test_twodim_base 1/1 ... [2024-08-20 22:49:25.154411] 2024-08-20T22:49:25.1551744Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:49:25.1557085Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_twodim_base.py', '-m', 'not 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 22:49:25.154887] 2024-08-20T22:49:28.2028251Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:49:28.2888318Z Running torch_np/numpy_tests/core/test_scalar_methods 1/1 ... [2024-08-20 22:49:28.288212] 2024-08-20T22:49:28.2889425Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:49:28.2893109Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_scalar_methods.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:49:28.288642] 2024-08-20T22:49:31.4316225Z 2024-08-20T22:49:31.4320653Z torch_np/numpy_tests/lib/test_twodim_base 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_twodim_base_1.1_a65870486cff6897_.log 2024-08-20T22:49:31.4352405Z Running 34 items in this shard: test/torch_np/numpy_tests/lib/test_twodim_base.py::TestEye::test_2d, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestEye::test_basic, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestEye::test_bool, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestEye::test_diag, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestEye::test_diag2d, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestEye::test_eye_bounds, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestEye::test_order, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestDiag::test_diag_bounds, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestDiag::test_failure, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestDiag::test_fortran_order, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestDiag::test_matrix, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestDiag::test_vector, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestFliplr::test_basic, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestFlipud::test_basic, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestHistogram2d::test_all_outliers, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestHistogram2d::test_asym, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestHistogram2d::test_bad_length_x_len_10_y_len_11, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestHistogram2d::test_bad_length_x_len_20_y_len_19, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestHistogram2d::test_binparameter_combination, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestHistogram2d::test_density, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestHistogram2d::test_empty, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestHistogram2d::test_simple, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestTri::test_dtype, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestTri::test_mask_indices, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestTri::test_tril_indices, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestTri::test_tril_triu_dtype, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestTri::test_tril_triu_ndim2, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestTri::test_tril_triu_ndim3, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestTri::test_tril_triu_with_inf, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestTriuIndices::test_triu_indices, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestTrilIndicesFrom::test_exceptions, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestTriuIndicesFrom::test_exceptions, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestVander::test_basic, test/torch_np/numpy_tests/lib/test_twodim_base.py::TestVander::test_dtypes 2024-08-20T22:49:31.4380997Z 2024-08-20T22:49:31.9105102Z 2024-08-20T22:49:31.9108854Z torch_np/numpy_tests/core/test_scalar_methods 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_scalar_methods_1.1_3c1888089ca37949_.log 2024-08-20T22:49:31.9174289Z Running 97 items in this shard: test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_against_known_values, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_errors_ftype0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_errors_ftype1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_errors_ftype2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_roundtrip_ftype0_frac_vals0_exp_vals0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_roundtrip_ftype1_frac_vals1_exp_vals1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_roundtrip_ftype2_frac_vals2_exp_vals2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_simple_fractions_ftype0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_simple_fractions_ftype1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_simple_fractions_ftype2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype0_f_-0_875_ratio1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype0_f_0_0_ratio2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype0_f_0_875_ratio0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype0_f_11_5_ratio3, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype1_f_-0_875_ratio1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype1_f_0_0_ratio2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype1_f_0_875_ratio0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype1_f_11_5_ratio3, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype2_f_-0_875_ratio1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype2_f_0_0_ratio2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype2_f_0_875_ratio0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype2_f_11_5_ratio3, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_false_code_b, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_false_code_h, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_false_code_i, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_false_code_l, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_special_str_value_inf_code_d, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_special_str_value_inf_code_e, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_special_str_value_inf_code_f, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_special_str_value_inf_code_g, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_special_str_value_nan_code_d, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_special_str_value_nan_code_e, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_special_str_value_nan_code_f, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_special_str_value_nan_code_g, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_true_code_B, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_true_code_b, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_true_code_d, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_true_code_e, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_true_code_f, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_true_code_h, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_true_code_i, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_true_code_l, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_cls0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_cls1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_cls2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_cls3, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_cls4, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_cls5, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_complexfloating, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_complexfloating_subscript_tuple_arg_len_0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_complexfloating_subscript_tuple_arg_len_1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_complexfloating_subscript_tuple_arg_len_2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_complexfloating_subscript_tuple_arg_len_3, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_non_numeric_cls0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_?, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_B, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_D, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_F, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_G, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_H, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_I, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_L, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_M, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_O, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_P, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_Q, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_S, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_U, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_V, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_b, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_d, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_e, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_f, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_g, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_h, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_i, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_l, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_m, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_p, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_q, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_subscript_scalar, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_subscript_tuple_arg_len_0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_subscript_tuple_arg_len_1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_subscript_tuple_arg_len_2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_subscript_tuple_arg_len_3, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetitemMisc::test_class_getitem_38_cls0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetitemMisc::test_class_getitem_38_cls1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetitemMisc::test_class_getitem_38_cls2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestBitCount::test_bit_count, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestBitCount::test_small_itype0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestBitCount::test_small_itype1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestBitCount::test_small_itype2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestBitCount::test_small_itype3, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestBitCount::test_small_itype4, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestBitCount::test_small_itype5, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestBitCount::test_small_itype6, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestBitCount::test_small_itype7 2024-08-20T22:49:31.9227016Z 2024-08-20T22:49:34.3201352Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:49:34.3830131Z Running lazy/test_generator 1/1 ... [2024-08-20 22:49:34.382527] 2024-08-20T22:49:34.3831094Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:49:34.3835955Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'lazy/test_generator.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:49:34.382963] 2024-08-20T22:49:34.9721433Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:49:35.0341820Z Running torch_np/numpy_tests/fft/test_pocketfft 1/1 ... [2024-08-20 22:49:35.033634] 2024-08-20T22:49:35.0346064Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:49:35.0351605Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/fft/test_pocketfft.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:49:35.034104] 2024-08-20T22:49:38.1124708Z 2024-08-20T22:49:38.1127180Z lazy/test_generator 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_generator_1.1_09db22cf0ae77a52_.log 2024-08-20T22:49:38.1130461Z Running 2 items in this shard: test/lazy/test_generator.py::LazyGeneratorTest::test_generator, test/lazy/test_generator.py::LazyGeneratorTest::test_generator_causes_multiple_compiles 2024-08-20T22:49:38.1132460Z 2024-08-20T22:49:41.0374301Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:49:41.1006480Z Running torch_np/numpy_tests/core/test_dlpack 1/1 ... [2024-08-20 22:49:41.099502] 2024-08-20T22:49:41.1007997Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:49:41.1012225Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_dlpack.py', '-m', 'not 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 22:49:41.100015] 2024-08-20T22:49:45.2718113Z 2024-08-20T22:49:45.2720961Z torch_np/numpy_tests/core/test_dlpack 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_dlpack_1.1_141c73713bd931d5_.log 2024-08-20T22:49:45.2748018Z Running 53 items in this shard: test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dlpack_destructor_exception, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dlpack_device, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dtype_passthrough_dtype0, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dtype_passthrough_dtype1, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dtype_passthrough_dtype2, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dtype_passthrough_dtype3, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dtype_passthrough_dtype4, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dtype_passthrough_dtype5, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dtype_passthrough_dtype6, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dtype_passthrough_dtype7, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dtype_passthrough_dtype8, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dtype_passthrough_dtype9, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dunder_dlpack_refcount, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_dunder_dlpack_stream, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_from_dlpack_refcount, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_from_torch, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_0, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_1, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_10, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_11, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_12, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_13, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_14, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_15, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_16, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_17, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_18, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_19, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_2, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_20, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_21, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_22, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_23, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_24, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_25, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_26, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_27, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_28, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_29, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_3, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_30, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_31, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_32, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_4, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_5, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_6, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_7, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_8, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_higher_dims_ndim_9, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_ndim0, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_non_contiguous, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_readonly, test/torch_np/numpy_tests/core/test_dlpack.py::TestDLPack::test_to_torch 2024-08-20T22:49:45.2772890Z 2024-08-20T22:49:48.0837537Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:49:48.2028767Z Running torch_np/numpy_tests/lib/test_shape_base_ 1/1 ... [2024-08-20 22:49:48.202377] 2024-08-20T22:49:48.2029869Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:49:48.2035306Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_shape_base_.py', '-m', 'not 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 22:49:48.202871] 2024-08-20T22:49:56.2317453Z 2024-08-20T22:49:56.2351238Z torch_np/numpy_tests/lib/test_shape_base_ 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_shape_base__1.1_792d8c2a921abaac_.log 2024-08-20T22:49:56.2422912Z Running 73 items in this shard: test/torch_np/numpy_tests/lib/test_shape_base_.py::TestTakeAlongAxis::test_argequivalent, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestTakeAlongAxis::test_broadcast, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestTakeAlongAxis::test_empty, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestTakeAlongAxis::test_invalid, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestPutAlongAxis::test_broadcast, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestPutAlongAxis::test_replace_max, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestApplyAlongAxis::test_0d_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestApplyAlongAxis::test_3d, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestApplyAlongAxis::test_axis_insertion, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestApplyAlongAxis::test_axis_insertion_ma, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestApplyAlongAxis::test_empty, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestApplyAlongAxis::test_scalar_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestApplyAlongAxis::test_simple, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestApplyAlongAxis::test_simple101, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestApplyAlongAxis::test_tuple_func1d, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestApplyAlongAxis::test_with_iterable_object, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestApplyOverAxes::test_simple, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestExpandDims::test_axis_out_of_range, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestExpandDims::test_axis_tuple, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestExpandDims::test_functionality, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestExpandDims::test_repeated_axis, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestArraySplit::test_index_split_high_bound, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestArraySplit::test_index_split_low_bound, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestArraySplit::test_index_split_simple, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestArraySplit::test_integer_0_split, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestArraySplit::test_integer_split, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestArraySplit::test_integer_split_2D_cols, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestArraySplit::test_integer_split_2D_default, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestArraySplit::test_integer_split_2D_rows, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestArraySplit::test_integer_split_2D_rows_greater_max_int32, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestSplit::test_equal_split, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestSplit::test_unequal_split, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestColumnStack::test_1D_arrays, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestColumnStack::test_2D_arrays, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestColumnStack::test_generator, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestColumnStack::test_non_iterable, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestDstack::test_0D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestDstack::test_1D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestDstack::test_2D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestDstack::test_2D_array2, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestDstack::test_generator, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestDstack::test_non_iterable, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestHsplit::test_0D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestHsplit::test_1D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestHsplit::test_2D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestHsplit::test_non_iterable, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestVsplit::test_0D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestVsplit::test_1D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestVsplit::test_2D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestVsplit::test_non_iterable, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestDsplit::test_0D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestDsplit::test_1D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestDsplit::test_2D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestDsplit::test_3D_array, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestDsplit::test_non_iterable, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestSqueeze::test_basic, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestSqueeze::test_basic_2, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestSqueeze::test_squeeze_axis, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestSqueeze::test_squeeze_axis_handling, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestSqueeze::test_squeeze_contiguous, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestSqueeze::test_squeeze_type, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestKron::test_basic, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestKron::test_kron_shape_shape_a0_shape_b0, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestKron::test_kron_shape_shape_a1_shape_b1, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestKron::test_kron_shape_shape_a2_shape_b2, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestKron::test_kron_shape_shape_a3_shape_b3, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestKron::test_kron_shape_shape_a4_shape_b4, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestKron::test_kron_shape_shape_a5_shape_b5, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestTile::test_basic, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestTile::test_empty, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestTile::test_kroncompare, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestTile::test_tile_one_repetition_on_array_gh4679, test/torch_np/numpy_tests/lib/test_shape_base_.py::TestMayShareMemory::test_basic 2024-08-20T22:49:56.2542454Z 2024-08-20T22:49:58.9604230Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:49:59.0229392Z Running torch_np/numpy_tests/fft/test_helper 1/1 ... [2024-08-20 22:49:59.022408] 2024-08-20T22:49:59.0230690Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:49:59.0234316Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/fft/test_helper.py', '-m', 'not 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 22:49:59.022860] 2024-08-20T22:50:02.9935400Z 2024-08-20T22:50:02.9937799Z torch_np/numpy_tests/fft/test_helper 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.fft.test_helper_1.1_560e167beeccfbf9_.log 2024-08-20T22:50:02.9943749Z Running 8 items in this shard: test/torch_np/numpy_tests/fft/test_helper.py::TestFFTShift::test_axes_keyword, test/torch_np/numpy_tests/fft/test_helper.py::TestFFTShift::test_definition, test/torch_np/numpy_tests/fft/test_helper.py::TestFFTShift::test_equal_to_original, test/torch_np/numpy_tests/fft/test_helper.py::TestFFTShift::test_inverse, test/torch_np/numpy_tests/fft/test_helper.py::TestFFTShift::test_uneven_dims, test/torch_np/numpy_tests/fft/test_helper.py::TestFFTFreq::test_definition, test/torch_np/numpy_tests/fft/test_helper.py::TestRFFTFreq::test_definition, test/torch_np/numpy_tests/fft/test_helper.py::TestIRFFTN::test_not_last_axis_success 2024-08-20T22:50:02.9947478Z 2024-08-20T22:50:05.7516122Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:50:05.8137079Z Running torch_np/numpy_tests/core/test_numerictypes 1/1 ... [2024-08-20 22:50:05.813206] 2024-08-20T22:50:05.8138211Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:50:05.8140645Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_numerictypes.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:50:05.813592] 2024-08-20T22:50:09.5341456Z 2024-08-20T22:50:09.5344292Z torch_np/numpy_tests/core/test_numerictypes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_numerictypes_1.1_736ef3d143e1553a_.log 2024-08-20T22:50:09.5378952Z Running 34 items in this shard: test/torch_np/numpy_tests/core/test_numerictypes.py::TestCommonType::test_scalar_loses1, test/torch_np/numpy_tests/core/test_numerictypes.py::TestCommonType::test_scalar_loses2, test/torch_np/numpy_tests/core/test_numerictypes.py::TestCommonType::test_scalar_wins, test/torch_np/numpy_tests/core/test_numerictypes.py::TestCommonType::test_scalar_wins2, test/torch_np/numpy_tests/core/test_numerictypes.py::TestCommonType::test_scalar_wins3, test/torch_np/numpy_tests/core/test_numerictypes.py::TestIsSubDType::test_both_abstract, test/torch_np/numpy_tests/core/test_numerictypes.py::TestIsSubDType::test_nondtype_nonscalartype, test/torch_np/numpy_tests/core/test_numerictypes.py::TestIsSubDType::test_same, test/torch_np/numpy_tests/core/test_numerictypes.py::TestIsSubDType::test_sibling_class, test/torch_np/numpy_tests/core/test_numerictypes.py::TestIsSubDType::test_subclass, test/torch_np/numpy_tests/core/test_numerictypes.py::TestIsSubDType::test_subclass_backwards, test/torch_np/numpy_tests/core/test_numerictypes.py::TestBitName::test_abstract, test/torch_np/numpy_tests/core/test_numerictypes.py::TestDocStrings::test_platform_dependent_aliases, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t0, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t1, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t2, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t3, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t4, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t5, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t6, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t7, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t8, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t9, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_unique, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t0, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t1, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t2, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t3, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t4, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t5, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t6, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t7, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t8, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t9 2024-08-20T22:50:09.5412997Z 2024-08-20T22:50:12.2407427Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:50:12.3106304Z Running torch_np/numpy_tests/lib/test_arraysetops 1/1 ... [2024-08-20 22:50:12.310059] 2024-08-20T22:50:12.3107798Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:50:12.3109904Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_arraysetops.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:50:12.310464] 2024-08-20T22:50:20.0876473Z 2024-08-20T22:50:20.0878916Z torch_np/numpy_tests/lib/test_arraysetops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_arraysetops_1.1_f88b729432ea47d0_.log 2024-08-20T22:50:20.0917659Z Running 62 items in this shard: test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_ediff1d, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_ediff1d_forbidden_type_casts_ary0_prepend0_append_nan_expected_to_end, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_ediff1d_forbidden_type_casts_ary1_prepend1_append1_expected_to_begin, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_ediff1d_forbidden_type_casts_ary2_prepend_nan_append_nan_expected_to_begin, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_ediff1d_scalar_handling_ary0_prepend_65536_append_65540_expected0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_ediff1d_scalar_handling_ary1_prepend1_append1_expected1, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_ediff1d_scalar_handling_ary2_prepend_0_append_0_expected2, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_ediff1d_scalar_handling_ary3_prepend_3_append_-9_expected3, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_boolean_kind0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_boolean_kind_sort, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_boolean_kind_table, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_both_arrays_are_object, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_both_arrays_have_structured_dtype, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_char_array, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_errors, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_first_array_is_object, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_hit_alternate_algorithm, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_invert_kind0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_invert_kind_sort, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_invert_kind_table, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_kind0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_kind_sort, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_kind_table, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_mixed_boolean_kind0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_mixed_boolean_kind_sort, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_mixed_boolean_kind_table, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_mixed_dtype_dtype10_dtype20_kind0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_mixed_dtype_dtype10_dtype20_kind_sort, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_mixed_dtype_dtype10_dtype20_kind_table, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_mixed_dtype_dtype11_dtype21_kind0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_mixed_dtype_dtype11_dtype21_kind_sort, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_mixed_dtype_dtype11_dtype21_kind_table, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_ravel_kind0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_ravel_kind_sort, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_ravel_kind_table, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_second_array_is_object, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_table_timedelta_fails, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_timedelta_kind0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_timedelta_kind_sort, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_with_arrays_containing_tuples, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_intersect1d, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_intersect1d_array_like, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_intersect1d_indices, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_isin_kind0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_isin_kind_sort, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_isin_kind_table, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_manyways, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_setdiff1d, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_setdiff1d_char_array, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_setdiff1d_unique, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_setxor1d, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_union1d, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_1d, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_1d_2, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_1d_with_axis_axis_-1, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_1d_with_axis_axis_0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_axis, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_axis_errors, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_axis_list, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_axis_zeros, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_nanequals, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_sort_order_with_axis 2024-08-20T22:50:20.0950316Z 2024-08-20T22:50:22.8047495Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:50:22.8672244Z Running torch_np/test_scalars_0D_arrays 1/1 ... [2024-08-20 22:50:22.866684] 2024-08-20T22:50:22.8673554Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:50:22.8677646Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/test_scalars_0D_arrays.py', '-m', 'not 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 22:50:22.867081] 2024-08-20T22:50:26.9377411Z 2024-08-20T22:50:26.9379899Z torch_np/test_scalars_0D_arrays 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_scalars_0D_arrays_1.1_97635335adcf3d1e_.log 2024-08-20T22:50:26.9399093Z Running 33 items in this shard: test/torch_np/test_scalars_0D_arrays.py::TestArrayScalars::test_array_scalar_basic_array, test/torch_np/test_scalars_0D_arrays.py::TestArrayScalars::test_array_scalar_basic_asarray, test/torch_np/test_scalars_0D_arrays.py::TestArrayScalars::test_array_scalar_basic_asarray_int, test/torch_np/test_scalars_0D_arrays.py::TestArrayScalars::test_array_scalar_basic_int64, test/torch_np/test_scalars_0D_arrays.py::TestArrayScalars::test_conversion_to_int_array, test/torch_np/test_scalars_0D_arrays.py::TestArrayScalars::test_conversion_to_int_asarray, test/torch_np/test_scalars_0D_arrays.py::TestArrayScalars::test_conversion_to_int_asarray_int, test/torch_np/test_scalars_0D_arrays.py::TestArrayScalars::test_conversion_to_int_int64, test/torch_np/test_scalars_0D_arrays.py::TestArrayScalars::test_decay_to_py_scalar_array, test/torch_np/test_scalars_0D_arrays.py::TestArrayScalars::test_decay_to_py_scalar_asarray, test/torch_np/test_scalars_0D_arrays.py::TestArrayScalars::test_decay_to_py_scalar_asarray_int, test/torch_np/test_scalars_0D_arrays.py::TestArrayScalars::test_decay_to_py_scalar_int64, test/torch_np/test_scalars_0D_arrays.py::TestArrayScalars::test_scalar_comparisons, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_not_scalar_value0, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_not_scalar_value1, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_not_scalar_value10, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_not_scalar_value11, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_not_scalar_value4, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_not_scalar_value5, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_not_scalar_value6, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_not_scalar_value7, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_not_scalar_value8, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_not_scalar_value9, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_not_scalar_value_s, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_not_scalar_value_string, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_scalar_array_0D, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_scalar_array_1D, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_scalar_array_2D, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_scalar_float32, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_scalar_int, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_scalar_list, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_scalar_list-list, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_scalar_literal 2024-08-20T22:50:26.9414107Z 2024-08-20T22:50:29.6469973Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:50:29.7093497Z Running profiler/test_memory_profiler 1/1 ... [2024-08-20 22:50:29.708839] 2024-08-20T22:50:29.7094677Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:50:29.7098518Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'profiler/test_memory_profiler.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:50:29.709284] 2024-08-20T22:50:32.7280882Z 2024-08-20T22:50:32.7283418Z profiler/test_memory_profiler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_memory_profiler_1.1_6fd433c5b47fcabf_.log 2024-08-20T22:50:32.7317524Z Running 33 items in this shard: test/profiler/test_memory_profiler.py::TestMemoryProfiler::test_config_check, test/profiler/test_memory_profiler.py::TestIdentifyGradients::test_extract_gradients_from_module, test/profiler/test_memory_profiler.py::TestIdentifyGradients::test_extract_gradients_from_module_and_optimizer, test/profiler/test_memory_profiler.py::TestIdentifyGradients::test_extract_gradients_from_optimizer, test/profiler/test_memory_profiler.py::TestIdentifyGradients::test_extract_gradients_from_optimizer_set_to_none, test/profiler/test_memory_profiler.py::TestIdentifyGradients::test_extract_gradients_low_level, test/profiler/test_memory_profiler.py::TestDataFlow::test_data_flow_graph_complicated, test/profiler/test_memory_profiler.py::TestDataFlow::test_data_flow_graph_non_op_allocations, test/profiler/test_memory_profiler.py::TestDataFlow::test_data_flow_graph_simple, test/profiler/test_memory_profiler.py::TestDataFlow::test_data_flow_graph_simple_backward, test/profiler/test_memory_profiler.py::TestDataFlow::test_data_flow_graph_simple_inplace, test/profiler/test_memory_profiler.py::TestDataFlow::test_data_flow_graph_stacked, test/profiler/test_memory_profiler.py::TestDataFlow::test_data_flow_graph_with_annotations, test/profiler/test_memory_profiler.py::TestDataFlow::test_match_schemas, test/profiler/test_memory_profiler.py::TestDataFlow::test_match_schemas_backward, test/profiler/test_memory_profiler.py::TestDataFlow::test_match_schemas_tensorlist, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_categories_e2e_sequential_fwd, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_categories_e2e_sequential_fwd_bwd, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_categories_e2e_simple_fwd, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_categories_e2e_simple_fwd_bwd, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_categories_e2e_simple_fwd_bwd_step, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_categories_e2e_simple_module_fwd, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_categories_e2e_simple_module_fwd_bwd, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_categories_e2e_simple_module_fwd_bwd_step, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_inputs_fwd, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_inputs_fwd_bwd, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_inputs_fwd_lazy, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_lazily_initialized, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_manual_optimizer_step, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_memory_timeline, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_memory_timeline_no_id, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_parameters_and_gradients, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_parameters_and_gradients_set_to_none 2024-08-20T22:50:32.7349009Z 2024-08-20T22:50:35.4143303Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:50:35.4768466Z Running torch_np/numpy_tests/lib/test_arraypad 1/1 ... [2024-08-20 22:50:35.476405] 2024-08-20T22:50:35.4769348Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:50:35.4771983Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_arraypad.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:50:35.476824] 2024-08-20T22:50:39.0968701Z 2024-08-20T22:50:39.0971821Z torch_np/numpy_tests/lib/test_arraypad 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_arraypad_1.1_035cfa687a267287_.log 2024-08-20T22:50:39.0983011Z Running 9 items in this shard: test/torch_np/numpy_tests/lib/test_arraypad.py::TestConstant::test_check_constant, test/torch_np/numpy_tests/lib/test_arraypad.py::TestConstant::test_check_constant_float, test/torch_np/numpy_tests/lib/test_arraypad.py::TestConstant::test_check_constant_float2, test/torch_np/numpy_tests/lib/test_arraypad.py::TestConstant::test_check_constant_float3, test/torch_np/numpy_tests/lib/test_arraypad.py::TestConstant::test_check_constant_odd_pad_amount, test/torch_np/numpy_tests/lib/test_arraypad.py::TestConstant::test_check_constant_pad_2d, test/torch_np/numpy_tests/lib/test_arraypad.py::TestConstant::test_check_constant_zeros, test/torch_np/numpy_tests/lib/test_arraypad.py::TestConstant::test_check_large_integers, test/torch_np/numpy_tests/lib/test_arraypad.py::TestConstant::test_pad_empty_dimension 2024-08-20T22:50:39.0992067Z 2024-08-20T22:50:41.7742303Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:50:41.8367828Z Running test_jit_autocast 1/1 ... [2024-08-20 22:50:41.836267] 2024-08-20T22:50:41.8368879Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:50:41.8372377Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_jit_autocast.py', '-m', 'not 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 22:50:41.836690] 2024-08-20T22:50:46.4083165Z 2024-08-20T22:50:46.4085445Z test_jit_autocast 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jit_autocast_1.1_557c7119d853859b_.log 2024-08-20T22:50:46.4107012Z Running 54 items in this shard: test/test_jit_autocast.py::TestAutocast::test_autocast_api, test/test_jit_autocast.py::TestAutocast::test_autocast_api_not_supported, test/test_jit_autocast.py::TestAutocast::test_autocast_autodiff, test/test_jit_autocast.py::TestAutocast::test_autocast_decorator, test/test_jit_autocast.py::TestAutocast::test_autocast_decorator_outside_jit, test/test_jit_autocast.py::TestAutocast::test_autocast_mixed_dtypes, test/test_jit_autocast.py::TestAutocast::test_callees, test/test_jit_autocast.py::TestAutocast::test_callees_with_autocast_off, test/test_jit_autocast.py::TestAutocast::test_callees_with_autocast_on, test/test_jit_autocast.py::TestAutocast::test_conditional_autocast, test/test_jit_autocast.py::TestAutocast::test_control_flow, test/test_jit_autocast.py::TestAutocast::test_divergent_autocast, test/test_jit_autocast.py::TestAutocast::test_divergent_types, test/test_jit_autocast.py::TestAutocast::test_duplicate_inputs, test/test_jit_autocast.py::TestAutocast::test_eager_and_script, test/test_jit_autocast.py::TestAutocast::test_explicit_casts, test/test_jit_autocast.py::TestAutocast::test_fp32_policy, test/test_jit_autocast.py::TestAutocast::test_fp32_policy_with_fp64, test/test_jit_autocast.py::TestAutocast::test_fp32_set_opt_dtype_policy, test/test_jit_autocast.py::TestAutocast::test_fp32_set_opt_dtype_policy_fp64, test/test_jit_autocast.py::TestAutocast::test_ignore_amp, test/test_jit_autocast.py::TestAutocast::test_implicitly_nested_autocast, test/test_jit_autocast.py::TestAutocast::test_inplace, test/test_jit_autocast.py::TestAutocast::test_jit_autocast_softmax_cpu, test/test_jit_autocast.py::TestAutocast::test_jit_autocast_softmax_gpu, test/test_jit_autocast.py::TestAutocast::test_jit_call_method_under_autocast, test/test_jit_autocast.py::TestAutocast::test_jit_executor_under_autocast, test/test_jit_autocast.py::TestAutocast::test_jit_freeze_autocast_basic, test/test_jit_autocast.py::TestAutocast::test_jit_freeze_autocast_constants, test/test_jit_autocast.py::TestAutocast::test_jit_generic_autocast, test/test_jit_autocast.py::TestAutocast::test_linear_bf16, test/test_jit_autocast.py::TestAutocast::test_minimal, test/test_jit_autocast.py::TestAutocast::test_minimal_cpu, test/test_jit_autocast.py::TestAutocast::test_minimal_off, test/test_jit_autocast.py::TestAutocast::test_nested_autocast, test/test_jit_autocast.py::TestAutocast::test_promote_policy, test/test_jit_autocast.py::TestAutocast::test_promote_policy_fp64, test/test_jit_autocast.py::TestAutocast::test_reused_autocast, test/test_jit_autocast.py::TestAutocast::test_reused_autocast_expr, test/test_jit_autocast.py::TestAutocast::test_runtime_autocast_state, test/test_jit_autocast.py::TestAutocast::test_runtime_autocast_state_expr, test/test_jit_autocast.py::TestAutocast::test_script_and_tracing, test/test_jit_autocast.py::TestAutocast::test_script_and_tracing_with_autocast, test/test_jit_autocast.py::TestAutocast::test_script_module, test/test_jit_autocast.py::TestAutocast::test_tracing_and_script, test/test_jit_autocast.py::TestAutocast::test_tracing_with_autocast_and_script, test/test_jit_autocast.py::TestJitTraceAutocast::test_cat_promote, test/test_jit_autocast.py::TestJitTraceAutocast::test_generate_autocast_jit_trace_model, test/test_jit_autocast.py::TestJitTraceAutocast::test_nchw_autocast_jit_trace_model, test/test_jit_autocast.py::TestJitTraceAutocast::test_nhwc_autocast_jit_trace_model, test/test_jit_autocast.py::TestJitTraceAutocast::test_script_autocast_cpu, test/test_jit_autocast.py::TestJitTraceAutocast::test_script_autocast_cuda, test/test_jit_autocast.py::TestJitTraceAutocast::test_script_autocast_enable_and_check, test/test_jit_autocast.py::TestJitTraceAutocast::test_scripted_aliasing 2024-08-20T22:50:46.4127200Z 2024-08-20T22:50:49.1358012Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:50:49.1982427Z Running profiler/test_profiler_tree 1/1 ... [2024-08-20 22:50:49.197771] 2024-08-20T22:50:49.1983378Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:50:49.1985877Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'profiler/test_profiler_tree.py', '-m', 'not 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 22:50:49.198182] 2024-08-20T22:50:53.4692838Z 2024-08-20T22:50:53.4695035Z profiler/test_profiler_tree 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_profiler_tree_1.1_ce99ab9e056f37eb_.log 2024-08-20T22:50:53.4702220Z Running 10 items in this shard: test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree, test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree_cuda, test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree_cuda_detailed, test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree_cuda_with_stream, test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree_with_memory, test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree_with_memory_and_stack, test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree_with_record_function, test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree_with_stack_and_modules, test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree_with_stack_and_torch_dispatch, test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree_with_stack_and_torch_function 2024-08-20T22:50:53.4708671Z 2024-08-20T22:50:56.2058187Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:50:56.2676830Z Running torch_np/numpy_tests/core/test_scalar_ctors 1/1 ... [2024-08-20 22:50:56.267164] 2024-08-20T22:50:56.2678128Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:50:56.2681236Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_scalar_ctors.py', '-m', 'not 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 22:50:56.267579] 2024-08-20T22:51:01.2926542Z 2024-08-20T22:51:01.2929729Z torch_np/numpy_tests/core/test_scalar_ctors 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_scalar_ctors_1.1_16c807a6cd4ffde1_.log 2024-08-20T22:51:01.2966913Z Running 65 items in this shard: test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestFromString::test_bool, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestFromString::test_floating, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestFromString::test_floating_overflow, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestFromInt::test_intp, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestFromInt::test_uint64_from_negative, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_complex_t10_t20, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_complex_t10_t21, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_complex_t10_t22, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_complex_t11_t20, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_complex_t11_t21, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_complex_t11_t22, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_byte_np_byte, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_byte_np_int_, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_byte_np_intc, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_byte_np_longlong, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_byte_np_short, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_byte_t25, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_byte_t26, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_int__np_byte, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_int__np_int_, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_int__np_intc, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_int__np_longlong, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_int__np_short, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_int__t25, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_int__t26, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_intc_np_byte, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_intc_np_int_, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_intc_np_intc, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_intc_np_longlong, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_intc_np_short, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_intc_t25, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_intc_t26, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_longlong_np_byte, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_longlong_np_int_, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_longlong_np_intc, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_longlong_np_longlong, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_longlong_np_short, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_longlong_t25, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_longlong_t26, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_short_np_byte, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_short_np_int_, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_short_np_intc, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_short_np_longlong, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_short_np_short, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_short_t25, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_short_t26, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_t15_np_byte, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_t15_np_int_, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_t15_np_intc, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_t15_np_longlong, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_t15_np_short, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_t15_t25, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_t15_t26, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_reals_t10_t20, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_reals_t10_t21, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_reals_t10_t22, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_reals_t10_t23, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_reals_t11_t20, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_reals_t11_t21, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_reals_t11_t22, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_reals_t11_t23, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_reals_t12_t20, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_reals_t12_t21, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_reals_t12_t22, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_reals_t12_t23 2024-08-20T22:51:01.3002688Z 2024-08-20T22:51:04.0388897Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:51:04.1021372Z Running torch_np/test_reductions 1/1 ... [2024-08-20 22:51:04.101642] 2024-08-20T22:51:04.1022398Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:51:04.1025276Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/test_reductions.py', '-m', 'not 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 22:51:04.102056] 2024-08-20T22:51:15.4480382Z 2024-08-20T22:51:15.4482661Z test_sparse_csr 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_sparse_csr_1.1_35d795cee23ef1b7_.log 2024-08-20T22:51:15.8470429Z Running 4731 items in this shard: test/test_sparse_csr.py::TestSparseCSRSampler::test_make_crow_indices, test/test_sparse_csr.py::TestSparseCSRCPU::test_add_SparseCSC_cpu_float32, test/test_sparse_csr.py::TestSparseCSRCPU::test_add_SparseCSC_cpu_float64, test/test_sparse_csr.py::TestSparseCSRCPU::test_add_SparseCSR_cpu_float32, test/test_sparse_csr.py::TestSparseCSRCPU::test_add_SparseCSR_cpu_float64, test/test_sparse_csr.py::TestSparseCSRCPU::test_addmm_all_sparse_csr_SparseCSC_cpu_complex128, test/test_sparse_csr.py::TestSparseCSRCPU::test_addmm_all_sparse_csr_SparseCSC_cpu_complex64, test/test_sparse_csr.py::TestSparseCSRCPU::test_addmm_all_sparse_csr_SparseCSC_cpu_float32, test/test_sparse_csr.py::TestSparseCSRCPU::test_addmm_all_sparse_csr_SparseCSC_cpu_float64, test/test_sparse_csr.py::TestSparseCSRCPU::test_addmm_all_sparse_csr_SparseCSR_cpu_complex128, test/test_sparse_csr.py::TestSparseCSRCPU::test_addmm_all_sparse_csr_SparseCSR_cpu_complex64, test/test_sparse_csr.py::TestSparseCSRCPU::test_addmm_all_sparse_csr_SparseCSR_cpu_float32, test/test_sparse_csr.py::TestSparseCSRCPU::test_addmm_all_sparse_csr_SparseCSR_cpu_float64, test/test_sparse_csr.py::TestSparseCSRCPU::test_addmm_dense_result_SparseCSC_cpu_complex128, test/test_sparse_csr.py::TestSparseCSRCPU::test_addmm_dense_result_SparseCSC_cpu_complex64, test/test_sparse_csr.py::TestSparseCSRCPU::test_addmm_dense_result_SparseCSC_cpu_float32, test/test_sparse_csr.py::TestSparseCSRCPU::test_addmm_dense_result_SparseCSC_cpu_float64, test/test_sparse_csr.py::TestSparseCSRCPU::test_addmm_dense_result_SparseCSR_cpu_complex128, 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test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseBSC_cpu_float16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseBSC_cpu_float32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseBSC_cpu_float64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseBSC_cpu_int16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseBSC_cpu_int32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseBSC_cpu_int64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseBSC_cpu_int8, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseBSC_cpu_uint8, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseBSR_cpu_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseBSR_cpu_bool, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseBSR_cpu_complex128, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseBSR_cpu_complex64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseBSR_cpu_float16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseBSR_cpu_float32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseBSR_cpu_float64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseBSR_cpu_int16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseBSR_cpu_int32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseBSR_cpu_int64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseBSR_cpu_int8, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseBSR_cpu_uint8, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSC_cpu_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSC_cpu_bool, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSC_cpu_complex128, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSC_cpu_complex64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSC_cpu_float16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSC_cpu_float32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSC_cpu_float64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSC_cpu_int16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSC_cpu_int32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSC_cpu_int64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSC_cpu_int8, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSC_cpu_uint8, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSR_cpu_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSR_cpu_bool, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSR_cpu_complex128, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSR_cpu_complex64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSR_cpu_float16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSR_cpu_float32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSR_cpu_float64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSR_cpu_int16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSR_cpu_int32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSR_cpu_int64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSR_cpu_int8, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSR_cpu_uint8, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCPU::test_TensorAsKey, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCPU::test_triton_bsr_dense_addmm_meta, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCPU::test_triton_bsr_dense_bmm, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCPU::test_triton_bsr_dense_bmm_error_messages, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCPU::test_triton_bsr_scatter_mm, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCPU::test_triton_bsr_softmax, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCPU::test_triton_kernel, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCPU::test_triton_sampled_addmm, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCPU::test_triton_scaled_dot_product_attention, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCPU::test_triton_scatter_mm, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCPU::test_triton_tune 2024-08-20T22:51:16.0923026Z 2024-08-20T22:51:18.2144171Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:51:18.2770685Z Running profiler/test_torch_tidy 1/1 ... [2024-08-20 22:51:18.276567] 2024-08-20T22:51:18.2771706Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:51:18.2774999Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'profiler/test_torch_tidy.py', '-m', 'not 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 22:51:18.277033] 2024-08-20T22:51:24.2984514Z 2024-08-20T22:51:24.2986401Z torch_np/test_reductions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_reductions_1.1_b5d1166aafb3bef8_.log 2024-08-20T22:51:24.4044365Z Running 966 items in this shard: test/torch_np/test_reductions.py::TestFlatnonzero::test_basic, test/torch_np/test_reductions.py::TestAny::test_basic, test/torch_np/test_reductions.py::TestAny::test_method_vs_function, test/torch_np/test_reductions.py::TestAny::test_nd, test/torch_np/test_reductions.py::TestAll::test_basic, test/torch_np/test_reductions.py::TestAll::test_method_vs_function, test/torch_np/test_reductions.py::TestAll::test_nd, test/torch_np/test_reductions.py::TestMean::test_mean, test/torch_np/test_reductions.py::TestMean::test_mean_float16, test/torch_np/test_reductions.py::TestMean::test_mean_values, test/torch_np/test_reductions.py::TestMean::test_mean_where, test/torch_np/test_reductions.py::TestSum::test_sum, test/torch_np/test_reductions.py::TestSum::test_sum_boolean, test/torch_np/test_reductions.py::TestSum::test_sum_complex_1_dt0, test/torch_np/test_reductions.py::TestSum::test_sum_complex_1_dt1, test/torch_np/test_reductions.py::TestSum::test_sum_complex_2_dt0, test/torch_np/test_reductions.py::TestSum::test_sum_complex_2_dt1, test/torch_np/test_reductions.py::TestSum::test_sum_dtypes_2, test/torch_np/test_reductions.py::TestSum::test_sum_dtypes_warnings, test/torch_np/test_reductions.py::TestSum::test_sum_initial, test/torch_np/test_reductions.py::TestSum::test_sum_stability, test/torch_np/test_reductions.py::TestSum::test_sum_where, test/torch_np/test_reductions.py::TestGenericReductions::test_array_axis_func0, test/torch_np/test_reductions.py::TestGenericReductions::test_array_axis_func1, test/torch_np/test_reductions.py::TestGenericReductions::test_array_axis_func10, test/torch_np/test_reductions.py::TestGenericReductions::test_array_axis_func11, test/torch_np/test_reductions.py::TestGenericReductions::test_array_axis_func2, test/torch_np/test_reductions.py::TestGenericReductions::test_array_axis_func3, test/torch_np/test_reductions.py::TestGenericReductions::test_array_axis_func4, test/torch_np/test_reductions.py::TestGenericReductions::test_array_axis_func5, test/torch_np/test_reductions.py::TestGenericReductions::test_array_axis_func6, test/torch_np/test_reductions.py::TestGenericReductions::test_array_axis_func7, test/torch_np/test_reductions.py::TestGenericReductions::test_array_axis_func8, test/torch_np/test_reductions.py::TestGenericReductions::test_array_axis_func9, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_bad_tuple_func0, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_bad_tuple_func1, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_bad_tuple_func10, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_bad_tuple_func11, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_bad_tuple_func2, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_bad_tuple_func3, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_bad_tuple_func4, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_bad_tuple_func5, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_bad_tuple_func6, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_bad_tuple_func7, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_bad_tuple_func8, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_bad_tuple_func9, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_empty_generic_func0, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_empty_generic_func1, 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test/torch_np/test_reductions.py::TestGenericReductions::test_bad_axis_func1, test/torch_np/test_reductions.py::TestGenericReductions::test_bad_axis_func10, test/torch_np/test_reductions.py::TestGenericReductions::test_bad_axis_func11, test/torch_np/test_reductions.py::TestGenericReductions::test_bad_axis_func2, test/torch_np/test_reductions.py::TestGenericReductions::test_bad_axis_func3, test/torch_np/test_reductions.py::TestGenericReductions::test_bad_axis_func4, test/torch_np/test_reductions.py::TestGenericReductions::test_bad_axis_func5, test/torch_np/test_reductions.py::TestGenericReductions::test_bad_axis_func6, test/torch_np/test_reductions.py::TestGenericReductions::test_bad_axis_func7, test/torch_np/test_reductions.py::TestGenericReductions::test_bad_axis_func8, test/torch_np/test_reductions.py::TestGenericReductions::test_bad_axis_func9, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis5_func0, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis5_func1, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis5_func10, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis5_func11, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis5_func2, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis5_func3, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis5_func4, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis5_func5, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis5_func6, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis5_func7, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis5_func8, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis5_func9, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis6_func0, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis6_func1, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis6_func10, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis6_func11, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis6_func2, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis6_func3, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis6_func4, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis6_func5, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis6_func6, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis6_func7, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis6_func8, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis6_func9, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis7_func0, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis7_func1, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis7_func10, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis7_func11, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis7_func2, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis7_func3, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis7_func4, 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test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_float64_func9_axis5, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_float64_func9_axis6, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_float64_func9_axis7, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_float64_func9_axis8, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_float64_func9_axis_-1, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_float64_func9_axis_-2, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_float64_func9_axis_0, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_float64_func9_axis_1, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_float64_func9_axis_2, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func0_axis5, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func0_axis6, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func0_axis7, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func0_axis8, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func0_axis_-1, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func0_axis_-2, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func0_axis_0, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func0_axis_1, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func0_axis_2, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func10_axis5, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func10_axis6, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func10_axis7, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func10_axis8, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func10_axis_-1, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func10_axis_-2, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func10_axis_0, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func10_axis_1, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func10_axis_2, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func11_axis5, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func11_axis6, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func11_axis7, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func11_axis8, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func11_axis_-1, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func11_axis_-2, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func11_axis_0, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func11_axis_1, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func11_axis_2, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func1_axis5, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func1_axis6, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func1_axis7, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func1_axis8, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func1_axis_-1, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func1_axis_-2, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func1_axis_0, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func1_axis_1, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func1_axis_2, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func2_axis5, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func2_axis6, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func2_axis7, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func2_axis8, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func2_axis_-1, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func2_axis_-2, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func2_axis_0, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func2_axis_1, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func2_axis_2, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func3_axis5, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func3_axis6, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func3_axis7, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func3_axis8, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func3_axis_-1, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func3_axis_-2, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func3_axis_0, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func3_axis_1, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func3_axis_2, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func4_axis5, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func4_axis6, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func4_axis7, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func4_axis8, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func4_axis_-1, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func4_axis_-2, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func4_axis_0, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func4_axis_1, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func4_axis_2, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func5_axis5, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func5_axis6, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func5_axis7, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func5_axis8, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func5_axis_-1, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func5_axis_-2, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func5_axis_0, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func5_axis_1, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func5_axis_2, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func6_axis5, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func6_axis6, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func6_axis7, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func6_axis8, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func6_axis_-1, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func6_axis_-2, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func6_axis_0, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func6_axis_1, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func6_axis_2, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func7_axis5, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func7_axis6, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func7_axis7, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func7_axis8, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func7_axis_-1, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func7_axis_-2, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func7_axis_0, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func7_axis_1, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func7_axis_2, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func8_axis5, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func8_axis6, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func8_axis7, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func8_axis8, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func8_axis_-1, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func8_axis_-2, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func8_axis_0, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func8_axis_1, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func8_axis_2, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func9_axis5, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func9_axis6, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func9_axis7, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func9_axis8, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func9_axis_-1, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func9_axis_-2, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func9_axis_0, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func9_axis_1, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func9_axis_2, test/torch_np/test_reductions.py::TestGenericReductions::test_out_scalar_func0, test/torch_np/test_reductions.py::TestGenericReductions::test_out_scalar_func1, test/torch_np/test_reductions.py::TestGenericReductions::test_out_scalar_func10, test/torch_np/test_reductions.py::TestGenericReductions::test_out_scalar_func11, test/torch_np/test_reductions.py::TestGenericReductions::test_out_scalar_func2, test/torch_np/test_reductions.py::TestGenericReductions::test_out_scalar_func3, test/torch_np/test_reductions.py::TestGenericReductions::test_out_scalar_func4, test/torch_np/test_reductions.py::TestGenericReductions::test_out_scalar_func5, test/torch_np/test_reductions.py::TestGenericReductions::test_out_scalar_func6, test/torch_np/test_reductions.py::TestGenericReductions::test_out_scalar_func7, test/torch_np/test_reductions.py::TestGenericReductions::test_out_scalar_func8, test/torch_np/test_reductions.py::TestGenericReductions::test_out_scalar_func9, test/torch_np/test_reductions.py::TestGenericCumSumProd::test_array_axis_func0, test/torch_np/test_reductions.py::TestGenericCumSumProd::test_array_axis_func1, test/torch_np/test_reductions.py::TestGenericCumSumProd::test_axis_bad_tuple_func0, test/torch_np/test_reductions.py::TestGenericCumSumProd::test_axis_bad_tuple_func1, test/torch_np/test_reductions.py::TestGenericCumSumProd::test_axis_empty_generic_func0, test/torch_np/test_reductions.py::TestGenericCumSumProd::test_axis_empty_generic_func1, test/torch_np/test_reductions.py::TestGenericCumSumProd::test_bad_axis_func0, test/torch_np/test_reductions.py::TestGenericCumSumProd::test_bad_axis_func1 2024-08-20T22:51:24.4966019Z 2024-08-20T22:51:27.2125445Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:51:27.2779453Z Running profiler/test_execution_trace 1/1 ... [2024-08-20 22:51:27.277414] 2024-08-20T22:51:27.2780663Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:51:27.2784246Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'profiler/test_execution_trace.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:51:27.277818] 2024-08-20T22:51:27.9068729Z 2024-08-20T22:51:27.9072840Z profiler/test_torch_tidy 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_torch_tidy_1.1_10f8f54c72b70c5a_.log 2024-08-20T22:51:27.9085954Z Running 22 items in this shard: test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_allocation_id_uniqueness, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_allocation_ids, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_allocation_ids_with_other_ops, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_allocations, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_extra_fields, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_impl_reuse, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_mkldnn_tensors, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_module_and_optimizer_ids, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_nnmodule_params, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_optimizer, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_optimizer_parameters_adam, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_optimizer_parameters_sgd, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_pointers_and_ids, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_refcounts, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_scalar_ins, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_sparse_tensors, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_tensor_lists, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_tensor_properties, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_tensorimpl_invalidation_full, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_tensorimpl_invalidation_keep_alive, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_tensorimpl_invalidation_scalar_args, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_tensorimpl_invalidation_set 2024-08-20T22:51:27.9098147Z 2024-08-20T22:51:30.9720952Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:51:31.0402754Z Running profiler/test_record_function 1/1 ... [2024-08-20 22:51:31.039745] 2024-08-20T22:51:31.0403818Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:51:31.0406984Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'profiler/test_record_function.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:51:31.040215] 2024-08-20T22:51:32.1011584Z 2024-08-20T22:51:32.1013834Z profiler/test_execution_trace 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_execution_trace_1.1_1fd9ff4494baab94_.log 2024-08-20T22:51:32.1020197Z Running 7 items in this shard: test/profiler/test_execution_trace.py::TestExecutionTrace::test_execution_trace_alone, test/profiler/test_execution_trace.py::TestExecutionTrace::test_execution_trace_nested_tensor, test/profiler/test_execution_trace.py::TestExecutionTrace::test_execution_trace_no_capture, test/profiler/test_execution_trace.py::TestExecutionTrace::test_execution_trace_repeat_in_loop, test/profiler/test_execution_trace.py::TestExecutionTrace::test_execution_trace_start_stop, test/profiler/test_execution_trace.py::TestExecutionTrace::test_execution_trace_with_kineto, test/profiler/test_execution_trace.py::TestExecutionTrace::test_execution_trace_with_pt2 2024-08-20T22:51:32.1024368Z 2024-08-20T22:51:34.8614680Z 2024-08-20T22:51:34.8617506Z profiler/test_record_function 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_record_function_1.1_4d61439fc596e405_.log 2024-08-20T22:51:34.8623904Z Running 4 items in this shard: test/profiler/test_record_function.py::TestRecordFunction::test_datapipe_delegation_with_profiler, test/profiler/test_record_function.py::TestRecordFunction::test_datapipe_with_record_function, test/profiler/test_record_function.py::TestRecordFunction::test_datapipe_with_record_function_fork, test/profiler/test_record_function.py::TestRecordFunction::test_record_function 2024-08-20T22:51:34.8628746Z 2024-08-20T22:51:35.1112816Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:51:35.1756038Z Running lazy/test_bindings 1/1 ... [2024-08-20 22:51:35.175049] 2024-08-20T22:51:35.1757224Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:51:35.1761116Z 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 22:51:35.175517] 2024-08-20T22:51:37.0439226Z 2024-08-20T22:51:37.0440986Z lazy/test_bindings 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_bindings_1.1_ea8c24080c2ee681_.log 2024-08-20T22:51:37.0450400Z 2024-08-20T22:51:37.9107377Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:51:40.0098251Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:52:37.7585470Z 2024-08-20T22:52:37.7588212Z torch_np/numpy_tests/fft/test_pocketfft 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.fft.test_pocketfft_1.1_6ee5f15c220d9b42_.log 2024-08-20T22:52:37.7695345Z Running 79 items in this shard: test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFTShift::test_fft_n, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_all_1d_norm_preserving, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_axes_op0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_axes_op1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_axes_op2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_axes_op3, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_dtypes_dtype0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_dtypes_dtype1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_dtypes_dtype2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_F_fft0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_F_fft1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_F_fft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_F_fft3, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_F_fft4, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_F_fft5, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_non-contiguous_fft0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_non-contiguous_fft1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_non-contiguous_fft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_non-contiguous_fft3, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_non-contiguous_fft4, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_non-contiguous_fft5, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_F_fft0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_F_fft1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_F_fft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_F_fft3, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_F_fft4, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_F_fft5, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_non-contiguous_fft0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_non-contiguous_fft1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_non-contiguous_fft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_non-contiguous_fft3, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_non-contiguous_fft4, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_non-contiguous_fft5, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_F_fft0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_F_fft1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_F_fft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_F_fft3, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_F_fft4, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_F_fft5, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_non-contiguous_fft0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_non-contiguous_fft1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_non-contiguous_fft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_non-contiguous_fft3, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_non-contiguous_fft4, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_non-contiguous_fft5, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_F_fft0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_F_fft1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_F_fft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_F_fft3, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_F_fft4, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_F_fft5, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_non-contiguous_fft0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_non-contiguous_fft1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_non-contiguous_fft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_non-contiguous_fft3, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_non-contiguous_fft4, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_non-contiguous_fft5, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fftn, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_hfft, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_identity, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_ifft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_ifft_norm0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_ifft_norm_backward, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_ifft_norm_forward, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_ifft_norm_ortho, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_ifftn, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_ihfft, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_irfft, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_irfft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_irfftn, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_rfft, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_rfft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_rfftn, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFTThreadSafe::test_fft, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFTThreadSafe::test_ifft, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFTThreadSafe::test_irfft, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFTThreadSafe::test_rfft 2024-08-20T22:52:37.7782532Z 2024-08-20T22:52:38.3901561Z Running test batch 'tests to run' cost 4423.77 seconds 2024-08-20T22:52:39.2356304Z 2024-08-20T22:52:39.2357085Z real 73m48.037s 2024-08-20T22:52:39.2357489Z user 233m59.053s 2024-08-20T22:52:39.2357866Z sys 27m25.720s 2024-08-20T22:52:39.2358187Z + assert_git_not_dirty 2024-08-20T22:52:39.2359037Z + [[ linux-focal-py3.12-clang10 != *rocm* ]] 2024-08-20T22:52:39.2359677Z + [[ linux-focal-py3.12-clang10 != *xla* ]] 2024-08-20T22:52:39.2365309Z ++ git status --porcelain 2024-08-20T22:52:39.2365800Z ++ grep -v '?? third_party' 2024-08-20T22:53:17.1765192Z ++ true 2024-08-20T22:53:17.1766613Z + git_status= 2024-08-20T22:53:17.1767260Z + [[ -n '' ]] 2024-08-20T22:53:17.1769187Z + [[ 2 == 1 ]] 2024-08-20T22:53:17.1769925Z + cleanup_workspace 2024-08-20T22:53:17.1771201Z + echo 'sudo may print the following warning message that can be ignored. The chown command will still run.' 2024-08-20T22:53:17.1772822Z sudo may print the following warning message that can be ignored. The chown command will still run. 2024-08-20T22:53:17.1774423Z + echo ' sudo: setrlimit(RLIMIT_STACK): Operation not permitted' 2024-08-20T22:53:17.1775090Z sudo: setrlimit(RLIMIT_STACK): Operation not permitted 2024-08-20T22:53:17.1775973Z + echo 'For more details refer to https://github.com/sudo-project/sudo/issues/42' 2024-08-20T22:53:17.1776926Z For more details refer to https://github.com/sudo-project/sudo/issues/42 2024-08-20T22:53:17.1777668Z + sudo chown -R 1000 /var/lib/jenkins/workspace 2024-08-20T22:53:17.7253076Z ##[group]Run cat test/**/*_toprint.log || true 2024-08-20T22:53:17.7253605Z cat test/**/*_toprint.log || true 2024-08-20T22:53:17.7263624Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T22:53:17.7264149Z env: 2024-08-20T22:53:17.7264405Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:53:17.7265064Z DOCKER_CONTAINER_ID: c78658a3dfdfbcf27d398914d87deab2e05563bfce93bca4b69e2ba0b625e59d 2024-08-20T22:53:17.7265786Z ##[endgroup] 2024-08-20T22:53:17.7350360Z cat: 'test/**/*_toprint.log': No such file or directory 2024-08-20T22:53:17.7386240Z ##[group]Run kill "$MONITOR_SCRIPT_PID" 2024-08-20T22:53:17.7386739Z kill "$MONITOR_SCRIPT_PID" 2024-08-20T22:53:17.7393897Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T22:53:17.7394400Z env: 2024-08-20T22:53:17.7394677Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:53:17.7395341Z DOCKER_CONTAINER_ID: c78658a3dfdfbcf27d398914d87deab2e05563bfce93bca4b69e2ba0b625e59d 2024-08-20T22:53:17.7396433Z MONITOR_SCRIPT_PID: 39723 2024-08-20T22:53:17.7396793Z ##[endgroup] 2024-08-20T22:53:17.7616943Z Prepare all required actions 2024-08-20T22:53:17.7617456Z Getting action download info 2024-08-20T22:53:17.9762851Z Download action repository 'actions/upload-artifact@v3' (SHA:a8a3f3ad30e3422c9c7b888a15615d19a852ae32) 2024-08-20T22:53:18.1368223Z ##[group]Run ./.github/actions/upload-test-artifacts 2024-08-20T22:53:18.1368712Z with: 2024-08-20T22:53:18.1369153Z file-suffix: test-dynamo-2-3-amz2023.linux.2xlarge_29025268893 2024-08-20T22:53:18.1369733Z s3-bucket: gha-artifacts 2024-08-20T22:53:18.1370172Z env: 2024-08-20T22:53:18.1370440Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:53:18.1371096Z DOCKER_CONTAINER_ID: c78658a3dfdfbcf27d398914d87deab2e05563bfce93bca4b69e2ba0b625e59d 2024-08-20T22:53:18.1371787Z ##[endgroup] 2024-08-20T22:53:18.1401794Z ##[group]Run # Remove any previous test jsons if they exist 2024-08-20T22:53:18.1402447Z # Remove any previous test jsons if they exist 2024-08-20T22:53:18.1402975Z rm -f test-jsons-*.zip 2024-08-20T22:53:18.1403555Z zip -r "test-jsons-${FILE_SUFFIX}.zip" test -i '*.json' 2024-08-20T22:53:18.1411020Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T22:53:18.1411539Z env: 2024-08-20T22:53:18.1411802Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:53:18.1412456Z DOCKER_CONTAINER_ID: c78658a3dfdfbcf27d398914d87deab2e05563bfce93bca4b69e2ba0b625e59d 2024-08-20T22:53:18.1413448Z FILE_SUFFIX: test-dynamo-2-3-amz2023.linux.2xlarge_29025268893 2024-08-20T22:53:18.1413994Z ##[endgroup] 2024-08-20T22:53:18.1828874Z adding: test/allowlist_for_publicAPI.json (deflated 79%) 2024-08-20T22:53:18.1857836Z adding: test/benchmark_utils/callgrind_artifacts.json (deflated 92%) 2024-08-20T22:53:18.1858578Z adding: test/minioptest_failures_dict.json (deflated 70%) 2024-08-20T22:53:18.1864524Z adding: test/profiler/profiler_utils_mock_events.json (deflated 87%) 2024-08-20T22:53:18.1870321Z adding: test/slow_tests.json (deflated 82%) 2024-08-20T22:53:18.1872999Z adding: test/test-reports/td_exclusions-9ec3e6f3a7774496cf9d.json (deflated 81%) 2024-08-20T22:53:18.1884985Z adding: test/.pytorch-disabled-tests.json (deflated 88%) 2024-08-20T22:53:18.1918786Z ##[group]Run # Remove any previous test reports if they exist 2024-08-20T22:53:18.1919487Z # Remove any previous test reports if they exist 2024-08-20T22:53:18.1920053Z rm -f test-reports-*.zip 2024-08-20T22:53:18.1920677Z zip -r "test-reports-${FILE_SUFFIX}.zip" test -i '*.xml' -i '*.csv' 2024-08-20T22:53:18.1927292Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T22:53:18.1927807Z env: 2024-08-20T22:53:18.1928080Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:53:18.1928719Z DOCKER_CONTAINER_ID: c78658a3dfdfbcf27d398914d87deab2e05563bfce93bca4b69e2ba0b625e59d 2024-08-20T22:53:18.1929596Z FILE_SUFFIX: test-dynamo-2-3-amz2023.linux.2xlarge_29025268893 2024-08-20T22:53:18.1930154Z ##[endgroup] 2024-08-20T22:53:18.2204771Z adding: test/test-reports/python-pytest/test_nn/test_nn-553348d077bc3af9.xml (deflated 95%) 2024-08-20T22:53:18.2206521Z adding: test/test-reports/python-pytest/test_cpp_extensions_jit/test_cpp_extensions_jit-3e9377a3ecdf8c26.xml (deflated 88%) 2024-08-20T22:53:18.2242940Z adding: test/test-reports/python-pytest/test_torch/test_torch-b17c6e13f6ffa5c7.xml (deflated 95%) 2024-08-20T22:53:18.2244349Z adding: 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##[group]Run # Remove any previous usage logs if they exist 2024-08-20T22:53:18.3670923Z # Remove any previous usage logs if they exist 2024-08-20T22:53:18.3671434Z rm -f logs-*.zip 2024-08-20T22:53:18.3672145Z # this workflow is also run in bazel build test, but we dont generate usage reports for it 2024-08-20T22:53:18.3672978Z # so check to see if the file exists first 2024-08-20T22:53:18.3673496Z if [ -f 'usage_log.txt' ]; then 2024-08-20T22:53:18.3674137Z  zip "logs-${FILE_SUFFIX}.zip" 'usage_log.txt' 2024-08-20T22:53:18.3674641Z fi 2024-08-20T22:53:18.3675010Z if ls test/**/*.log 1> /dev/null 2>&1; then 2024-08-20T22:53:18.3675595Z  zip -r "logs-${FILE_SUFFIX}.zip" test -i '*.log' 2024-08-20T22:53:18.3676106Z fi 2024-08-20T22:53:18.3682538Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T22:53:18.3683039Z env: 2024-08-20T22:53:18.3683316Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:53:18.3683978Z DOCKER_CONTAINER_ID: c78658a3dfdfbcf27d398914d87deab2e05563bfce93bca4b69e2ba0b625e59d 2024-08-20T22:53:18.3684849Z FILE_SUFFIX: test-dynamo-2-3-amz2023.linux.2xlarge_29025268893 2024-08-20T22:53:18.3685408Z ##[endgroup] 2024-08-20T22:53:18.3772563Z adding: usage_log.txt (deflated 92%) 2024-08-20T22:53:18.4074227Z adding: test/test-reports/test_nn_1.2_14dfb2c4bdc3ecf1_.log (deflated 93%) 2024-08-20T22:53:18.4086916Z adding: test/test-reports/test_type_promotion_1.1_1f600a20fc26d9e9_.log (deflated 93%) 2024-08-20T22:53:18.4089497Z adding: test/test-reports/test_cpp_extensions_jit_1.1_89056c0a9eea247f_.log (deflated 88%) 2024-08-20T22:53:18.4130752Z adding: test/test-reports/test_torch_1.1_33c2fa06d052df46_.log (deflated 92%) 2024-08-20T22:53:18.4135082Z adding: test/test-reports/test_package_1.1_cb4b347cc1cc30a1_.log (deflated 86%) 2024-08-20T22:53:18.4136173Z adding: test/test-reports/dynamo.test_trace_rules_1.1_916f29f8ab6b8588_.log (stored 0%) 2024-08-20T22:53:18.4137229Z adding: 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test/test-reports/test_unary_ufuncs_1.1_411deadd0f8d55f1_.log (deflated 97%) 2024-08-20T22:53:18.5295032Z adding: test/test-reports/dynamo.test_trace_rules_1.1_d61562a1fcc79f19_.log (stored 0%) 2024-08-20T22:53:18.5296341Z adding: test/test-reports/dynamo.test_repros_1.1_832bb69f9ae5aa42_.log (stored 0%) 2024-08-20T22:53:18.5297421Z adding: test/test-reports/dynamo.test_higher_order_ops_1.1_f6e380a6c212e06e_.log (stored 0%) 2024-08-20T22:53:18.5298617Z adding: test/test-reports/dynamo.test_activation_checkpointing_1.1_626c08b214a1e1a8_.log (stored 0%) 2024-08-20T22:53:18.5299808Z adding: test/test-reports/dynamo.test_skip_non_tensor_1.1_fd7437c97c8fbd8b_.log (stored 0%) 2024-08-20T22:53:18.5300847Z adding: test/test-reports/dynamo.test_export_1.1_35897e7182a5223b_.log (stored 0%) 2024-08-20T22:53:18.5301891Z adding: test/test-reports/dynamo.test_ctx_manager_1.1_4db090f37933c45f_.log (stored 0%) 2024-08-20T22:53:18.5302922Z adding: 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test/test-reports/lazy.test_functionalization_1.1_c145d100e3c82706_.log (deflated 60%) 2024-08-20T22:53:18.5456723Z adding: test/test-reports/torch_np.numpy_tests.lib.test_function_base_1.1_7c8acc6e134abe3d_.log (deflated 92%) 2024-08-20T22:53:18.5459554Z adding: test/test-reports/torch_np.numpy_tests.lib.test_histograms_1.1_e17f27fc8e64f3f1_.log (deflated 86%) 2024-08-20T22:53:18.5463588Z adding: test/test-reports/torch_np.numpy_tests.core.test_indexing_1.1_b869b5e841ac0ae6_.log (deflated 86%) 2024-08-20T22:53:18.5465331Z adding: test/test-reports/torch_np.numpy_tests.lib.test_type_check_1.1_94e284b0a6fbefab_.log (deflated 86%) 2024-08-20T22:53:18.5473944Z adding: test/test-reports/torch_np.numpy_tests.core.test_scalarmath_1.1_77da763bd129c173_.log (deflated 92%) 2024-08-20T22:53:18.5479582Z adding: test/test-reports/torch_np.numpy_tests.core.test_shape_base_1.1_4921780856fffd15_.log (deflated 91%) 2024-08-20T22:53:18.5483419Z adding: test/test-reports/torch_np.numpy_tests.core.test_dtype_1.1_56fd9f38efe6d156_.log (deflated 91%) 2024-08-20T22:53:18.5485434Z adding: test/test-reports/torch_np.numpy_tests.core.test_getlimits_1.1_ac35db3c4cc13cd8_.log (deflated 77%) 2024-08-20T22:53:18.5486688Z adding: test/test-reports/torch_np.test_function_base_1.1_5815ffc62db5c9d4_.log (deflated 52%) 2024-08-20T22:53:18.5493002Z adding: test/test-reports/torch_np.test_ndarray_methods_1.1_5123783d264e3e0c_.log (deflated 93%) 2024-08-20T22:53:18.5494743Z adding: test/test-reports/torch_np.numpy_tests.lib.test_index_tricks_1.1_83ff0c55765ced0d_.log (deflated 85%) 2024-08-20T22:53:18.5510076Z adding: test/test-reports/torch_np.numpy_tests.linalg.test_linalg_1.1_f1fbb98e7bfb0fce_.log (deflated 92%) 2024-08-20T22:53:18.5511868Z adding: test/test-reports/torch_np.numpy_tests.lib.test_twodim_base_1.1_a65870486cff6897_.log (deflated 83%) 2024-08-20T22:53:18.5514785Z adding: test/test-reports/torch_np.numpy_tests.core.test_scalar_methods_1.1_3c1888089ca37949_.log (deflated 93%) 2024-08-20T22:53:18.5519816Z adding: test/test-reports/torch_np.numpy_tests.core.test_dlpack_1.1_141c73713bd931d5_.log (deflated 96%) 2024-08-20T22:53:18.5524565Z adding: test/test-reports/torch_np.numpy_tests.lib.test_shape_base__1.1_792d8c2a921abaac_.log (deflated 89%) 2024-08-20T22:53:18.5526135Z adding: test/test-reports/torch_np.numpy_tests.fft.test_helper_1.1_560e167beeccfbf9_.log (deflated 75%) 2024-08-20T22:53:18.5527880Z adding: test/test-reports/torch_np.numpy_tests.core.test_numerictypes_1.1_736ef3d143e1553a_.log (deflated 90%) 2024-08-20T22:53:18.5529740Z adding: test/test-reports/torch_np.numpy_tests.lib.test_arraysetops_1.1_f88b729432ea47d0_.log (deflated 86%) 2024-08-20T22:53:18.5531406Z adding: test/test-reports/torch_np.test_scalars_0D_arrays_1.1_97635335adcf3d1e_.log (deflated 82%) 2024-08-20T22:53:18.5533362Z adding: test/test-reports/profiler.test_memory_profiler_1.1_6fd433c5b47fcabf_.log (deflated 88%) 2024-08-20T22:53:18.5535164Z adding: test/test-reports/torch_np.numpy_tests.lib.test_arraypad_1.1_035cfa687a267287_.log (deflated 73%) 2024-08-20T22:53:18.5537148Z adding: test/test-reports/profiler.test_profiler_tree_1.1_ce99ab9e056f37eb_.log (deflated 81%) 2024-08-20T22:53:18.5538919Z adding: test/test-reports/torch_np.numpy_tests.core.test_scalar_ctors_1.1_16c807a6cd4ffde1_.log (deflated 90%) 2024-08-20T22:53:18.5540205Z adding: test/test-reports/profiler.test_execution_trace_1.1_1fd9ff4494baab94_.log (deflated 72%) 2024-08-20T22:53:18.5541407Z adding: test/test-reports/profiler.test_record_function_1.1_4d61439fc596e405_.log (deflated 68%) 2024-08-20T22:53:18.5574025Z ##[group]Run # Remove any previous debugging artifacts if they exist 2024-08-20T22:53:18.5574943Z # Remove any previous debugging artifacts if they exist 2024-08-20T22:53:18.5575523Z rm -f debug-*.zip 2024-08-20T22:53:18.5575915Z if [ -d 'test/debug' ]; then 2024-08-20T22:53:18.5576450Z  zip -r "debug-${FILE_SUFFIX}.zip" test/debug 2024-08-20T22:53:18.5576932Z fi 2024-08-20T22:53:18.5583252Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T22:53:18.5583788Z env: 2024-08-20T22:53:18.5584054Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:53:18.5584721Z DOCKER_CONTAINER_ID: c78658a3dfdfbcf27d398914d87deab2e05563bfce93bca4b69e2ba0b625e59d 2024-08-20T22:53:18.5585711Z FILE_SUFFIX: test-dynamo-2-3-amz2023.linux.2xlarge_29025268893 2024-08-20T22:53:18.5586256Z ##[endgroup] 2024-08-20T22:53:18.5683905Z ##[group]Run seemethere/upload-artifact-s3@v5 2024-08-20T22:53:18.5684349Z with: 2024-08-20T22:53:18.5684630Z s3-bucket: gha-artifacts 2024-08-20T22:53:18.5685074Z s3-prefix: pytorch/pytorch/10479309237/1/artifact 2024-08-20T22:53:18.5685548Z retention-days: 14 2024-08-20T22:53:18.5685894Z if-no-files-found: warn 2024-08-20T22:53:18.5686251Z path: test-jsons-*.zip 2024-08-20T22:53:18.5686576Z name: artifact 2024-08-20T22:53:18.5686874Z region: us-east-1 2024-08-20T22:53:18.5687171Z env: 2024-08-20T22:53:18.5687421Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:53:18.5688076Z DOCKER_CONTAINER_ID: c78658a3dfdfbcf27d398914d87deab2e05563bfce93bca4b69e2ba0b625e59d 2024-08-20T22:53:18.5688908Z ##[endgroup] 2024-08-20T22:53:19.0224020Z NOTE: s3-prefix specified, ignoring name parameter 2024-08-20T22:53:19.0225100Z With the provided path, there will be 1 file uploaded 2024-08-20T22:53:19.0225736Z Uploading to s3 prefix: pytorch/pytorch/10479309237/1/artifact 2024-08-20T22:53:19.2326120Z Starting upload of test-jsons-test-dynamo-2-3-amz2023.linux.2xlarge_29025268893.zip 2024-08-20T22:53:19.3706938Z Finished upload of test-jsons-test-dynamo-2-3-amz2023.linux.2xlarge_29025268893.zip 2024-08-20T22:53:19.4421861Z ##[group]Run seemethere/upload-artifact-s3@v5 2024-08-20T22:53:19.4422326Z with: 2024-08-20T22:53:19.4422623Z s3-bucket: gha-artifacts 2024-08-20T22:53:19.4423052Z s3-prefix: pytorch/pytorch/10479309237/1/artifact 2024-08-20T22:53:19.4423543Z retention-days: 14 2024-08-20T22:53:19.4423880Z if-no-files-found: error 2024-08-20T22:53:19.4424234Z path: test-reports-*.zip 2024-08-20T22:53:19.4424586Z name: artifact 2024-08-20T22:53:19.4424892Z region: us-east-1 2024-08-20T22:53:19.4425177Z env: 2024-08-20T22:53:19.4425446Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:53:19.4426104Z DOCKER_CONTAINER_ID: c78658a3dfdfbcf27d398914d87deab2e05563bfce93bca4b69e2ba0b625e59d 2024-08-20T22:53:19.4426796Z ##[endgroup] 2024-08-20T22:53:19.8245821Z NOTE: s3-prefix specified, ignoring name parameter 2024-08-20T22:53:19.8247134Z With the provided path, there will be 1 file uploaded 2024-08-20T22:53:19.8248252Z Uploading to s3 prefix: pytorch/pytorch/10479309237/1/artifact 2024-08-20T22:53:19.8293662Z Starting upload of test-reports-test-dynamo-2-3-amz2023.linux.2xlarge_29025268893.zip 2024-08-20T22:53:20.0151346Z Finished upload of test-reports-test-dynamo-2-3-amz2023.linux.2xlarge_29025268893.zip 2024-08-20T22:53:20.0367505Z ##[group]Run seemethere/upload-artifact-s3@v5 2024-08-20T22:53:20.0367956Z with: 2024-08-20T22:53:20.0368241Z s3-bucket: gha-artifacts 2024-08-20T22:53:20.0368694Z s3-prefix: pytorch/pytorch/10479309237/1/artifact 2024-08-20T22:53:20.0369176Z retention-days: 14 2024-08-20T22:53:20.0369520Z if-no-files-found: ignore 2024-08-20T22:53:20.0369886Z path: logs-*.zip 2024-08-20T22:53:20.0370187Z name: artifact 2024-08-20T22:53:20.0370492Z region: us-east-1 2024-08-20T22:53:20.0370798Z env: 2024-08-20T22:53:20.0371061Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:53:20.0371726Z DOCKER_CONTAINER_ID: c78658a3dfdfbcf27d398914d87deab2e05563bfce93bca4b69e2ba0b625e59d 2024-08-20T22:53:20.0372445Z ##[endgroup] 2024-08-20T22:53:20.4147020Z NOTE: s3-prefix specified, ignoring name parameter 2024-08-20T22:53:20.4148184Z With the provided path, there will be 1 file uploaded 2024-08-20T22:53:20.4149250Z Uploading to s3 prefix: pytorch/pytorch/10479309237/1/artifact 2024-08-20T22:53:20.4191660Z Starting upload of logs-test-dynamo-2-3-amz2023.linux.2xlarge_29025268893.zip 2024-08-20T22:53:20.5988578Z Finished upload of logs-test-dynamo-2-3-amz2023.linux.2xlarge_29025268893.zip 2024-08-20T22:53:20.6204088Z ##[group]Run seemethere/upload-artifact-s3@v5 2024-08-20T22:53:20.6204564Z with: 2024-08-20T22:53:20.6204831Z s3-bucket: gha-artifacts 2024-08-20T22:53:20.6205282Z s3-prefix: pytorch/pytorch/10479309237/1/artifact 2024-08-20T22:53:20.6205869Z retention-days: 14 2024-08-20T22:53:20.6206194Z if-no-files-found: ignore 2024-08-20T22:53:20.6206565Z path: debug-*.zip 2024-08-20T22:53:20.6206878Z name: artifact 2024-08-20T22:53:20.6207168Z region: us-east-1 2024-08-20T22:53:20.6207473Z env: 2024-08-20T22:53:20.6207747Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:53:20.6208385Z DOCKER_CONTAINER_ID: c78658a3dfdfbcf27d398914d87deab2e05563bfce93bca4b69e2ba0b625e59d 2024-08-20T22:53:20.6209105Z ##[endgroup] 2024-08-20T22:53:20.9946475Z No files were found with the provided path: debug-*.zip. No artifacts will be uploaded. 2024-08-20T22:53:21.0161839Z ##[group]Run # shellcheck disable=SC2156 2024-08-20T22:53:21.0162350Z # shellcheck disable=SC2156 2024-08-20T22:53:21.0163204Z find . -iname "core.[1-9]*" -exec docker exec "${DOCKER_CONTAINER_ID}" sh -c "gdb python {} -ex 'bt' -ex 'q'" \; 2024-08-20T22:53:21.0169817Z shell: /usr/bin/bash -e {0} 2024-08-20T22:53:21.0170185Z env: 2024-08-20T22:53:21.0170461Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:53:21.0171104Z DOCKER_CONTAINER_ID: c78658a3dfdfbcf27d398914d87deab2e05563bfce93bca4b69e2ba0b625e59d 2024-08-20T22:53:21.0171817Z ##[endgroup] 2024-08-20T22:53:21.2631509Z ##[group]Run pytorch/test-infra/.github/actions/teardown-linux@main 2024-08-20T22:53:21.2632088Z with: 2024-08-20T22:53:21.2632339Z env: 2024-08-20T22:53:21.2632606Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:53:21.2633257Z DOCKER_CONTAINER_ID: c78658a3dfdfbcf27d398914d87deab2e05563bfce93bca4b69e2ba0b625e59d 2024-08-20T22:53:21.2633963Z ##[endgroup] 2024-08-20T22:53:21.2657130Z ##[group]Run set -eou pipefail 2024-08-20T22:53:21.2657538Z set -eou pipefail 2024-08-20T22:53:21.2658204Z  2024-08-20T22:53:21.2658745Z echo "Holding runner for 2 hours until all ssh sessions have logged out" 2024-08-20T22:53:21.2659416Z for _ in $(seq 1440); do 2024-08-20T22:53:21.2659905Z  # Break if no ssh session exists anymore 2024-08-20T22:53:21.2660426Z  if [ "$(who)" = "" ]; then 2024-08-20T22:53:21.2660847Z  break 2024-08-20T22:53:21.2661144Z  fi 2024-08-20T22:53:21.2661493Z  echo "." 2024-08-20T22:53:21.2661812Z  sleep 5 2024-08-20T22:53:21.2662130Z done 2024-08-20T22:53:21.2672063Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T22:53:21.2672568Z env: 2024-08-20T22:53:21.2672846Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:53:21.2673507Z DOCKER_CONTAINER_ID: c78658a3dfdfbcf27d398914d87deab2e05563bfce93bca4b69e2ba0b625e59d 2024-08-20T22:53:21.2674198Z ##[endgroup] 2024-08-20T22:53:21.2701634Z Holding runner for 2 hours until all ssh sessions have logged out 2024-08-20T22:53:21.2787946Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2024-08-20T22:53:21.2788934Z # ignore expansion of "docker ps -q" since it could be empty 2024-08-20T22:53:21.2789557Z # shellcheck disable=SC2046 2024-08-20T22:53:21.2790043Z docker stop $(docker ps -q) || true 2024-08-20T22:53:21.2790523Z # Prune all of the docker images 2024-08-20T22:53:21.2790991Z docker system prune -af 2024-08-20T22:53:21.2797612Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T22:53:21.2798117Z env: 2024-08-20T22:53:21.2798511Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:53:21.2799178Z DOCKER_CONTAINER_ID: c78658a3dfdfbcf27d398914d87deab2e05563bfce93bca4b69e2ba0b625e59d 2024-08-20T22:53:21.2799873Z ##[endgroup] 2024-08-20T22:53:21.9212805Z c78658a3dfdf 2024-08-20T22:53:22.2921052Z Deleted Containers: 2024-08-20T22:53:22.2921828Z c78658a3dfdfbcf27d398914d87deab2e05563bfce93bca4b69e2ba0b625e59d 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[command]/usr/bin/git version 2024-08-20T22:53:29.9578597Z git version 2.40.1 2024-08-20T22:53:29.9619905Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/07ec15bb-9da2-4586-ade7-e6177ed55903' before making global git config changes 2024-08-20T22:53:29.9621436Z Adding repository directory to the temporary git global config as a safe directory 2024-08-20T22:53:29.9625707Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-08-20T22:53:29.9671094Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2024-08-20T22:53:29.9710420Z [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' || :" 2024-08-20T22:53:30.0091580Z Entering 'android/libs/fbjni' 2024-08-20T22:53:30.0151636Z Entering 'third_party/FP16' 2024-08-20T22:53:30.0208560Z Entering 'third_party/FXdiv' 2024-08-20T22:53:30.0266354Z Entering 'third_party/NNPACK' 2024-08-20T22:53:30.0324585Z Entering 'third_party/VulkanMemoryAllocator' 2024-08-20T22:53:30.0382904Z Entering 'third_party/XNNPACK' 2024-08-20T22:53:30.0461280Z Entering 'third_party/benchmark' 2024-08-20T22:53:30.0523134Z Entering 'third_party/cpp-httplib' 2024-08-20T22:53:30.0579101Z Entering 'third_party/cpuinfo' 2024-08-20T22:53:30.0637741Z Entering 'third_party/cudnn_frontend' 2024-08-20T22:53:30.0697342Z Entering 'third_party/cutlass' 2024-08-20T22:53:30.0763900Z Entering 'third_party/eigen' 2024-08-20T22:53:30.0824243Z Entering 'third_party/fbgemm' 2024-08-20T22:53:30.0881973Z Entering 'third_party/fbgemm/third_party/asmjit' 2024-08-20T22:53:30.0967175Z Entering 'third_party/fbgemm/third_party/cpuinfo' 2024-08-20T22:53:30.0999551Z Entering 'third_party/fbgemm/third_party/cutlass' 2024-08-20T22:53:30.1061746Z Entering 'third_party/fbgemm/third_party/googletest' 2024-08-20T22:53:30.1118893Z Entering 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'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-08-20T22:53:30.3215699Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2024-08-20T22:53:30.3274101Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2024-08-20T22:53:30.3334706Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-08-20T22:53:30.3413942Z Entering 'third_party/pocketfft' 2024-08-20T22:53:30.3472888Z Entering 'third_party/protobuf' 2024-08-20T22:53:30.3534449Z Entering 'third_party/protobuf/third_party/benchmark' 2024-08-20T22:53:30.3591206Z Entering 'third_party/protobuf/third_party/googletest' 2024-08-20T22:53:30.3650144Z Entering 'third_party/psimd' 2024-08-20T22:53:30.3708889Z Entering 'third_party/pthreadpool' 2024-08-20T22:53:30.3766253Z Entering 'third_party/pybind11' 2024-08-20T22:53:30.3824413Z Entering 'third_party/python-peachpy' 2024-08-20T22:53:30.3882548Z Entering 'third_party/sleef' 2024-08-20T22:53:30.3939562Z Entering 'third_party/tensorpipe' 2024-08-20T22:53:30.3997261Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-08-20T22:53:30.4053264Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-08-20T22:53:30.4111574Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-08-20T22:53:30.4167908Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-08-20T22:53:30.4222335Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-08-20T22:53:30.4300760Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2024-08-20T22:53:30.4331490Z http.https://github.com/.extraheader 2024-08-20T22:53:30.4341170Z [command]/usr/bin/git config --local --unset-all http.https://github.com/.extraheader 2024-08-20T22:53:30.4380792Z [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' || :" 2024-08-20T22:53:30.4695595Z Entering 'android/libs/fbjni' 2024-08-20T22:53:30.4734686Z http.https://github.com/.extraheader 2024-08-20T22:53:30.4769171Z Entering 'third_party/FP16' 2024-08-20T22:53:30.4808745Z http.https://github.com/.extraheader 2024-08-20T22:53:30.4844561Z Entering 'third_party/FXdiv' 2024-08-20T22:53:30.4884432Z http.https://github.com/.extraheader 2024-08-20T22:53:30.4921694Z Entering 'third_party/NNPACK' 2024-08-20T22:53:30.4961088Z http.https://github.com/.extraheader 2024-08-20T22:53:30.4997995Z Entering 'third_party/VulkanMemoryAllocator' 2024-08-20T22:53:30.5036867Z http.https://github.com/.extraheader 2024-08-20T22:53:30.5074150Z Entering 'third_party/XNNPACK' 2024-08-20T22:53:30.5112564Z http.https://github.com/.extraheader 2024-08-20T22:53:30.5168846Z Entering 'third_party/benchmark' 2024-08-20T22:53:30.5209166Z http.https://github.com/.extraheader 2024-08-20T22:53:30.5245668Z Entering 'third_party/cpp-httplib' 2024-08-20T22:53:30.5286947Z http.https://github.com/.extraheader 2024-08-20T22:53:30.5322957Z Entering 'third_party/cpuinfo' 2024-08-20T22:53:30.5361903Z http.https://github.com/.extraheader 2024-08-20T22:53:30.5398681Z Entering 'third_party/cudnn_frontend' 2024-08-20T22:53:30.5437334Z http.https://github.com/.extraheader 2024-08-20T22:53:30.5473285Z Entering 'third_party/cutlass' 2024-08-20T22:53:30.5513042Z http.https://github.com/.extraheader 2024-08-20T22:53:30.5558670Z Entering 'third_party/eigen' 2024-08-20T22:53:30.5600416Z http.https://github.com/.extraheader 2024-08-20T22:53:30.5639178Z Entering 'third_party/fbgemm' 2024-08-20T22:53:30.5679262Z http.https://github.com/.extraheader 2024-08-20T22:53:30.5715062Z Entering 'third_party/fbgemm/third_party/asmjit' 2024-08-20T22:53:30.5753732Z http.https://github.com/.extraheader 2024-08-20T22:53:30.5789803Z Entering 'third_party/fbgemm/third_party/cpuinfo' 2024-08-20T22:53:30.5828807Z http.https://github.com/.extraheader 2024-08-20T22:53:30.5864364Z Entering 'third_party/fbgemm/third_party/cutlass' 2024-08-20T22:53:30.5903289Z http.https://github.com/.extraheader 2024-08-20T22:53:30.5945363Z Entering 'third_party/fbgemm/third_party/googletest' 2024-08-20T22:53:30.5984255Z http.https://github.com/.extraheader 2024-08-20T22:53:30.6020646Z Entering 'third_party/fbgemm/third_party/hipify_torch' 2024-08-20T22:53:30.6059219Z http.https://github.com/.extraheader 2024-08-20T22:53:30.6096316Z Entering 'third_party/flatbuffers' 2024-08-20T22:53:30.6136764Z http.https://github.com/.extraheader 2024-08-20T22:53:30.6175417Z Entering 'third_party/fmt' 2024-08-20T22:53:30.6215008Z http.https://github.com/.extraheader 2024-08-20T22:53:30.6250439Z Entering 'third_party/gemmlowp/gemmlowp' 2024-08-20T22:53:30.6288977Z http.https://github.com/.extraheader 2024-08-20T22:53:30.6325552Z Entering 'third_party/gloo' 2024-08-20T22:53:30.6363711Z http.https://github.com/.extraheader 2024-08-20T22:53:30.6399802Z Entering 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'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2024-08-20T22:53:30.6969959Z http.https://github.com/.extraheader 2024-08-20T22:53:30.7006196Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2024-08-20T22:53:30.7043021Z http.https://github.com/.extraheader 2024-08-20T22:53:30.7078846Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-08-20T22:53:30.7116792Z http.https://github.com/.extraheader 2024-08-20T22:53:30.7151750Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2024-08-20T22:53:30.7190987Z http.https://github.com/.extraheader 2024-08-20T22:53:30.7228811Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2024-08-20T22:53:30.7267147Z http.https://github.com/.extraheader 2024-08-20T22:53:30.7307406Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2024-08-20T22:53:30.7346105Z http.https://github.com/.extraheader 2024-08-20T22:53:30.7382742Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2024-08-20T22:53:30.7421709Z http.https://github.com/.extraheader 2024-08-20T22:53:30.7457708Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2024-08-20T22:53:30.7496152Z http.https://github.com/.extraheader 2024-08-20T22:53:30.7535245Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2024-08-20T22:53:30.7572784Z http.https://github.com/.extraheader 2024-08-20T22:53:30.7609351Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2024-08-20T22:53:30.7646287Z http.https://github.com/.extraheader 2024-08-20T22:53:30.7683924Z Entering 'third_party/mimalloc' 2024-08-20T22:53:30.7723344Z http.https://github.com/.extraheader 2024-08-20T22:53:30.7759478Z Entering 'third_party/nccl/nccl' 2024-08-20T22:53:30.7797151Z http.https://github.com/.extraheader 2024-08-20T22:53:30.7833271Z Entering 'third_party/nlohmann' 2024-08-20T22:53:30.7872390Z http.https://github.com/.extraheader 2024-08-20T22:53:30.7909519Z Entering 'third_party/onnx' 2024-08-20T22:53:30.7947351Z http.https://github.com/.extraheader 2024-08-20T22:53:30.8000776Z Entering 'third_party/onnx/third_party/benchmark' 2024-08-20T22:53:30.8039886Z http.https://github.com/.extraheader 2024-08-20T22:53:30.8076769Z Entering 'third_party/onnx/third_party/pybind11' 2024-08-20T22:53:30.8114809Z http.https://github.com/.extraheader 2024-08-20T22:53:30.8153560Z Entering 'third_party/opentelemetry-cpp' 2024-08-20T22:53:30.8192923Z http.https://github.com/.extraheader 2024-08-20T22:53:30.8233338Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2024-08-20T22:53:30.8270407Z http.https://github.com/.extraheader 2024-08-20T22:53:30.8307354Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2024-08-20T22:53:30.8344448Z http.https://github.com/.extraheader 2024-08-20T22:53:30.8379483Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2024-08-20T22:53:30.8418134Z http.https://github.com/.extraheader 2024-08-20T22:53:30.8453424Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2024-08-20T22:53:30.8491271Z http.https://github.com/.extraheader 2024-08-20T22:53:30.8528903Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2024-08-20T22:53:30.8565570Z http.https://github.com/.extraheader 2024-08-20T22:53:30.8601723Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-08-20T22:53:30.8638820Z http.https://github.com/.extraheader 2024-08-20T22:53:30.8674582Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-08-20T22:53:30.8712150Z http.https://github.com/.extraheader 2024-08-20T22:53:30.8747268Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2024-08-20T22:53:30.8785556Z http.https://github.com/.extraheader 2024-08-20T22:53:30.8824796Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2024-08-20T22:53:30.8862663Z http.https://github.com/.extraheader 2024-08-20T22:53:30.8900492Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-08-20T22:53:30.8939006Z http.https://github.com/.extraheader 2024-08-20T22:53:30.8998794Z Entering 'third_party/pocketfft' 2024-08-20T22:53:30.9038662Z http.https://github.com/.extraheader 2024-08-20T22:53:30.9075232Z Entering 'third_party/protobuf' 2024-08-20T22:53:30.9115085Z http.https://github.com/.extraheader 2024-08-20T22:53:30.9155257Z Entering 'third_party/protobuf/third_party/benchmark' 2024-08-20T22:53:30.9192866Z http.https://github.com/.extraheader 2024-08-20T22:53:30.9229734Z Entering 'third_party/protobuf/third_party/googletest' 2024-08-20T22:53:30.9267675Z http.https://github.com/.extraheader 2024-08-20T22:53:30.9305436Z Entering 'third_party/psimd' 2024-08-20T22:53:30.9344508Z http.https://github.com/.extraheader 2024-08-20T22:53:30.9380565Z Entering 'third_party/pthreadpool' 2024-08-20T22:53:30.9419222Z http.https://github.com/.extraheader 2024-08-20T22:53:30.9454909Z Entering 'third_party/pybind11' 2024-08-20T22:53:30.9494127Z http.https://github.com/.extraheader 2024-08-20T22:53:30.9530686Z Entering 'third_party/python-peachpy' 2024-08-20T22:53:30.9568189Z http.https://github.com/.extraheader 2024-08-20T22:53:30.9604816Z Entering 'third_party/sleef' 2024-08-20T22:53:30.9643063Z http.https://github.com/.extraheader 2024-08-20T22:53:30.9678767Z Entering 'third_party/tensorpipe' 2024-08-20T22:53:30.9716577Z http.https://github.com/.extraheader 2024-08-20T22:53:30.9752735Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-08-20T22:53:30.9789622Z http.https://github.com/.extraheader 2024-08-20T22:53:30.9827077Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-08-20T22:53:30.9863856Z http.https://github.com/.extraheader 2024-08-20T22:53:30.9899129Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-08-20T22:53:30.9937674Z http.https://github.com/.extraheader 2024-08-20T22:53:30.9973001Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-08-20T22:53:31.0011433Z http.https://github.com/.extraheader 2024-08-20T22:53:31.0045936Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-08-20T22:53:31.0083907Z http.https://github.com/.extraheader 2024-08-20T22:53:31.0214423Z A job completed hook has been configured by the self-hosted runner administrator 2024-08-20T22:53:31.0234832Z ##[group]Run '/home/ec2-user/runner-scripts/after_job.sh' 2024-08-20T22:53:31.0240659Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T22:53:31.0241194Z ##[endgroup] 2024-08-20T22:53:39.1162993Z Cleaning up orphan processes