2024-06-26T04:53:54.1544817Z Current runner version: '2.317.0' 2024-06-26T04:53:54.1551350Z Runner name: 'i-000220d454490c80e' 2024-06-26T04:53:54.1552066Z Runner group name: 'Default' 2024-06-26T04:53:54.1552953Z Machine name: 'ip-10-0-57-28' 2024-06-26T04:53:54.1557329Z ##[group]GITHUB_TOKEN Permissions 2024-06-26T04:53:54.1559368Z Actions: read 2024-06-26T04:53:54.1559930Z Attestations: read 2024-06-26T04:53:54.1560441Z Checks: read 2024-06-26T04:53:54.1560988Z Contents: read 2024-06-26T04:53:54.1561475Z Deployments: read 2024-06-26T04:53:54.1561985Z Discussions: read 2024-06-26T04:53:54.1562455Z Issues: read 2024-06-26T04:53:54.1562958Z Metadata: read 2024-06-26T04:53:54.1563439Z Packages: read 2024-06-26T04:53:54.1563894Z Pages: read 2024-06-26T04:53:54.1564380Z PullRequests: read 2024-06-26T04:53:54.1564907Z RepositoryProjects: read 2024-06-26T04:53:54.1565454Z SecurityEvents: read 2024-06-26T04:53:54.1566011Z Statuses: read 2024-06-26T04:53:54.1566494Z ##[endgroup] 2024-06-26T04:53:54.1570068Z Secret source: Actions 2024-06-26T04:53:54.1570858Z Prepare workflow directory 2024-06-26T04:53:54.5141817Z Prepare all required actions 2024-06-26T04:53:54.5325909Z Getting action download info 2024-06-26T04:53:54.7026710Z Download action repository 'pytorch/test-infra@main' (SHA:43a2ce341cc31288e9a38b65ce600a7f43021bd5) 2024-06-26T04:53:55.0246379Z Download action repository 'pytorch/pytorch@main' (SHA:6181e65cd81725efc6bc5d64ef3be607b0aa3ca1) 2024-06-26T04:53:57.7531269Z Download action repository 'aws-actions/configure-aws-credentials@v3' (SHA:50ac8dd1e1b10d09dac7b8727528b91bed831ac0) 2024-06-26T04:53:57.8710156Z Download action repository 'seemethere/upload-artifact-s3@v5' (SHA:baba72d0712b404f646cebe0730933554ebce96a) 2024-06-26T04:53:58.1363909Z Getting action download info 2024-06-26T04:53:58.2255442Z Download action repository 'malfet/checkout@silent-checkout' (SHA:e07af140b3ccefc05679e3755b9db68f4ee4589c) 2024-06-26T04:53:58.3896710Z Getting action download info 2024-06-26T04:53:58.4787813Z Download action repository 'nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482' (SHA:3e91a01664abd3c5cd539100d10d33b9c5b68482) 2024-06-26T04:53:58.6234994Z Uses: pytorch/pytorch/.github/workflows/_linux-test.yml@refs/pull/129470/merge (4b51b1a62a63a1add1b3a0f7882f5c7dc66b8f8d) 2024-06-26T04:53:58.6237550Z ##[group] Inputs 2024-06-26T04:53:58.6238016Z build-environment: linux-focal-py3.12-clang10 2024-06-26T04:53:58.6240334Z test-matrix: {"include": [{"config": "default", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "default", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "default", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge"}]} 2024-06-26T04:53:58.6243290Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:53:58.6244349Z sync-tag: 2024-06-26T04:53:58.6245203Z timeout-minutes: 600 2024-06-26T04:53:58.6245552Z use-gha: 2024-06-26T04:53:58.6245866Z dashboard-tag: 2024-06-26T04:53:58.6246214Z s3-bucket: gha-artifacts 2024-06-26T04:53:58.6246587Z aws-role-to-assume: 2024-06-26T04:53:58.6246946Z ##[endgroup] 2024-06-26T04:53:58.6247737Z Complete job name: linux-focal-py3.12-clang10 / test (dynamo, 1, 3, linux.2xlarge) 2024-06-26T04:53:58.6815020Z A job started hook has been configured by the self-hosted runner administrator 2024-06-26T04:53:58.6960303Z ##[group]Run '/home/ec2-user/runner-scripts/cleanup.sh' 2024-06-26T04:53:58.6971815Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T04:53:58.6972386Z ##[endgroup] 2024-06-26T04:53:59.5347368Z ##[group]Run pytorch/test-infra/.github/actions/setup-ssh@main 2024-06-26T04:53:59.5347976Z with: 2024-06-26T04:53:59.5348785Z github-secret: *** 2024-06-26T04:53:59.5349809Z instructions: All testing is done inside the container, to start an interactive session run: docker exec -it $(docker container ps --format '{{.ID}}') bash 2024-06-26T04:53:59.5351049Z activate-with-label: false 2024-06-26T04:53:59.5351429Z label: with-ssh 2024-06-26T04:53:59.5351781Z remove-existing-keys: true 2024-06-26T04:53:59.5352182Z fail-silently: true 2024-06-26T04:53:59.5352517Z env: 2024-06-26T04:53:59.5352819Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:53:59.5353195Z ##[endgroup] 2024-06-26T04:53:59.6228085Z Please see https://github.com/pytorch/pytorch/wiki/Debugging-using-with-ssh-for-Github-Actions for more info. 2024-06-26T04:53:59.8821400Z Grabbing public ssh keys from https://github.com/leslie-fang-intel.keys 2024-06-26T04:53:59.9496331Z No SSH keys found for user leslie-fang-intel 2024-06-26T04:53:59.9497133Z Grabbing public ssh keys from https://github.com/leslie-fang-intel.keys 2024-06-26T04:54:00.0149928Z No SSH keys found for user leslie-fang-intel 2024-06-26T04:54:00.0261835Z ##[group]Run pytorch/pytorch/.github/actions/checkout-pytorch@main 2024-06-26T04:54:00.0262425Z with: 2024-06-26T04:54:00.0262735Z submodules: recursive 2024-06-26T04:54:00.0263097Z fetch-depth: 0 2024-06-26T04:54:00.0263396Z env: 2024-06-26T04:54:00.0263695Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:54:00.0264069Z ##[endgroup] 2024-06-26T04:54:00.0452689Z ##[group]Run retry () { 2024-06-26T04:54:00.0453117Z retry () { 2024-06-26T04:54:00.0453826Z  $* || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*) 2024-06-26T04:54:00.0454423Z } 2024-06-26T04:54:00.0454744Z echo "${GITHUB_WORKSPACE}" 2024-06-26T04:54:00.0455199Z if [ -z "${NO_SUDO}" ]; then 2024-06-26T04:54:00.0455699Z  retry sudo rm -rf "${GITHUB_WORKSPACE}" 2024-06-26T04:54:00.0456172Z else 2024-06-26T04:54:00.0456541Z  retry rm -rf "${GITHUB_WORKSPACE}" 2024-06-26T04:54:00.0456990Z fi 2024-06-26T04:54:00.0457358Z mkdir "${GITHUB_WORKSPACE}" 2024-06-26T04:54:00.0465357Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T04:54:00.0465900Z env: 2024-06-26T04:54:00.0466204Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:54:00.0466560Z NO_SUDO: 2024-06-26T04:54:00.0466870Z ##[endgroup] 2024-06-26T04:54:00.0489882Z /home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-06-26T04:54:02.6593247Z ##[group]Run malfet/checkout@silent-checkout 2024-06-26T04:54:02.6593725Z with: 2024-06-26T04:54:02.6594082Z ref: b8c4c54d347aa776934c60784e35936878ef18dc 2024-06-26T04:54:02.6594543Z fetch-depth: 0 2024-06-26T04:54:02.6594885Z submodules: recursive 2024-06-26T04:54:02.6595254Z quiet-checkout: true 2024-06-26T04:54:02.6595618Z repository: pytorch/pytorch 2024-06-26T04:54:02.6596142Z token: *** 2024-06-26T04:54:02.6596457Z ssh-strict: true 2024-06-26T04:54:02.6596799Z persist-credentials: true 2024-06-26T04:54:02.6597186Z clean: true 2024-06-26T04:54:02.6597545Z sparse-checkout-cone-mode: true 2024-06-26T04:54:02.6597962Z lfs: false 2024-06-26T04:54:02.6598293Z set-safe-directory: true 2024-06-26T04:54:02.6598660Z env: 2024-06-26T04:54:02.6598945Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:54:02.6599314Z ##[endgroup] 2024-06-26T04:54:02.7635984Z Syncing repository: pytorch/pytorch 2024-06-26T04:54:02.7637782Z ##[group]Getting Git version info 2024-06-26T04:54:02.7638564Z Working directory is '/home/ec2-user/actions-runner/_work/pytorch/pytorch' 2024-06-26T04:54:02.7639491Z [command]/usr/bin/git version 2024-06-26T04:54:02.7639899Z git version 2.40.1 2024-06-26T04:54:02.7641425Z ##[endgroup] 2024-06-26T04:54:02.7653008Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/734388c2-1721-4aff-a947-e079af0b7520' before making global git config changes 2024-06-26T04:54:02.7654486Z Adding repository directory to the temporary git global config as a safe directory 2024-06-26T04:54:02.7655696Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-06-26T04:54:02.7682844Z Deleting the contents of '/home/ec2-user/actions-runner/_work/pytorch/pytorch' 2024-06-26T04:54:02.7686884Z ##[group]Initializing the repository 2024-06-26T04:54:02.7689850Z [command]/usr/bin/git init /home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-06-26T04:54:02.7770407Z hint: Using 'master' as the name for the initial branch. This default branch name 2024-06-26T04:54:02.7771498Z hint: is subject to change. To configure the initial branch name to use in all 2024-06-26T04:54:02.7772348Z hint: of your new repositories, which will suppress this warning, call: 2024-06-26T04:54:02.7773020Z hint: 2024-06-26T04:54:02.7773688Z hint: git config --global init.defaultBranch 2024-06-26T04:54:02.7774198Z hint: 2024-06-26T04:54:02.7774735Z hint: Names commonly chosen instead of 'master' are 'main', 'trunk' and 2024-06-26T04:54:02.7775622Z hint: 'development'. The just-created branch can be renamed via this command: 2024-06-26T04:54:02.7776545Z hint: 2024-06-26T04:54:02.7776910Z hint: git branch -m 2024-06-26T04:54:02.7777692Z Initialized empty Git repository in /home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/ 2024-06-26T04:54:02.7779823Z [command]/usr/bin/git remote add origin https://github.com/pytorch/pytorch 2024-06-26T04:54:02.7805573Z ##[endgroup] 2024-06-26T04:54:02.7806213Z ##[group]Disabling automatic garbage collection 2024-06-26T04:54:02.7809201Z [command]/usr/bin/git config --local gc.auto 0 2024-06-26T04:54:02.7834456Z ##[endgroup] 2024-06-26T04:54:02.7835021Z ##[group]Setting up auth 2024-06-26T04:54:02.7840323Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2024-06-26T04:54:02.7866990Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'core\.sshCommand' && git config --local --unset-all 'core.sshCommand' || :" 2024-06-26T04:54:02.8104209Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2024-06-26T04:54:02.8131792Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'http\.https\:\/\/github\.com\/\.extraheader' && git config --local --unset-all 'http.https://github.com/.extraheader' || :" 2024-06-26T04:54:02.8365347Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic *** 2024-06-26T04:54:02.8407458Z ##[endgroup] 2024-06-26T04:54:02.8408057Z ##[group]Fetching the repository 2024-06-26T04:54:02.8414165Z [command]/usr/bin/git -c protocol.version=2 fetch --prune --progress --no-recurse-submodules --quiet origin +refs/heads/*:refs/remotes/origin/* +refs/tags/*:refs/tags/* 2024-06-26T04:54:05.0109863Z remote: Enumerating objects: 987778 2024-06-26T04:54:05.0110473Z remote: Enumerating objects: 989963, done. 2024-06-26T04:54:05.0117010Z remote: 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2024-06-26T04:54:05.0206417Z remote: Counting objects: 99% (2164/2185) 2024-06-26T04:54:05.0206908Z remote: Counting objects: 100% (2185/2185) 2024-06-26T04:54:05.0207437Z remote: Counting objects: 100% (2185/2185), done. 2024-06-26T04:54:05.0250339Z remote: Compressing objects: 0% (1/895) 2024-06-26T04:54:05.0540693Z remote: Compressing objects: 1% (9/895) 2024-06-26T04:54:05.0851803Z remote: Compressing objects: 2% (18/895) 2024-06-26T04:54:05.1123466Z remote: Compressing objects: 3% (27/895) 2024-06-26T04:54:05.1407810Z remote: Compressing objects: 4% (36/895) 2024-06-26T04:54:05.1531671Z remote: Compressing objects: 5% (45/895) 2024-06-26T04:54:05.1682022Z remote: Compressing objects: 6% (54/895) 2024-06-26T04:54:05.1839266Z remote: Compressing objects: 7% (63/895) 2024-06-26T04:54:05.1977671Z remote: Compressing objects: 8% (72/895) 2024-06-26T04:54:05.2295692Z remote: Compressing objects: 9% (81/895) 2024-06-26T04:54:05.2999130Z remote: Compressing objects: 10% (90/895) 2024-06-26T04:54:05.3547712Z remote: Compressing objects: 11% (99/895) 2024-06-26T04:54:05.4278458Z remote: Compressing objects: 12% (108/895) 2024-06-26T04:54:05.4480444Z remote: Compressing objects: 13% (117/895) 2024-06-26T04:54:05.4606404Z remote: Compressing objects: 14% (126/895) 2024-06-26T04:54:05.4705003Z remote: Compressing objects: 15% (135/895) 2024-06-26T04:54:05.4804543Z remote: Compressing objects: 16% (144/895) 2024-06-26T04:54:05.4847943Z remote: Compressing objects: 17% (153/895) 2024-06-26T04:54:05.4852626Z remote: Compressing objects: 18% (162/895) 2024-06-26T04:54:05.4856587Z remote: Compressing objects: 19% (171/895) 2024-06-26T04:54:05.4881241Z remote: Compressing objects: 20% (179/895) 2024-06-26T04:54:05.4896200Z remote: Compressing objects: 21% (188/895) 2024-06-26T04:54:05.4907519Z remote: Compressing objects: 22% (197/895) 2024-06-26T04:54:05.4919150Z remote: Compressing objects: 23% (206/895) 2024-06-26T04:54:05.4933051Z remote: Compressing objects: 24% (215/895) 2024-06-26T04:54:05.4946875Z remote: Compressing objects: 25% (224/895) 2024-06-26T04:54:05.4957298Z remote: Compressing objects: 26% (233/895) 2024-06-26T04:54:05.4960181Z remote: Compressing objects: 27% (242/895) 2024-06-26T04:54:05.4972204Z remote: Compressing objects: 28% (251/895) 2024-06-26T04:54:05.4973231Z remote: Compressing objects: 29% (260/895) 2024-06-26T04:54:05.4978033Z remote: Compressing objects: 30% (269/895) 2024-06-26T04:54:05.4981992Z remote: Compressing objects: 31% (278/895) 2024-06-26T04:54:05.4985523Z remote: Compressing objects: 32% (287/895) 2024-06-26T04:54:05.4993374Z remote: Compressing objects: 33% (296/895) 2024-06-26T04:54:05.4994688Z remote: Compressing objects: 34% (305/895) 2024-06-26T04:54:05.5004777Z remote: Compressing objects: 35% (314/895) 2024-06-26T04:54:05.5009636Z remote: Compressing objects: 36% (323/895) 2024-06-26T04:54:05.5011513Z remote: Compressing objects: 37% (332/895) 2024-06-26T04:54:05.5030789Z remote: Compressing objects: 38% (341/895) 2024-06-26T04:54:05.5037548Z remote: Compressing objects: 39% (350/895) 2024-06-26T04:54:05.5045925Z remote: Compressing objects: 40% (358/895) 2024-06-26T04:54:05.5063420Z remote: Compressing objects: 41% (367/895) 2024-06-26T04:54:05.5076569Z remote: Compressing objects: 42% (376/895) 2024-06-26T04:54:05.5087875Z remote: Compressing objects: 43% (385/895) 2024-06-26T04:54:05.5099382Z remote: Compressing objects: 44% (394/895) 2024-06-26T04:54:05.5111154Z remote: Compressing objects: 45% (403/895) 2024-06-26T04:54:05.5129274Z remote: Compressing objects: 46% (412/895) 2024-06-26T04:54:05.5144114Z remote: Compressing objects: 47% (421/895) 2024-06-26T04:54:05.5158846Z remote: Compressing objects: 48% (430/895) 2024-06-26T04:54:05.5169648Z remote: Compressing objects: 49% (439/895) 2024-06-26T04:54:05.5174220Z remote: Compressing objects: 50% (448/895) 2024-06-26T04:54:05.5178838Z remote: Compressing objects: 51% (457/895) 2024-06-26T04:54:05.5179358Z remote: Compressing objects: 52% (466/895) 2024-06-26T04:54:05.5181559Z remote: Compressing objects: 53% (475/895) 2024-06-26T04:54:05.5195203Z remote: Compressing objects: 54% (484/895) 2024-06-26T04:54:05.5206520Z remote: Compressing objects: 55% (493/895) 2024-06-26T04:54:05.5212071Z remote: Compressing objects: 56% (502/895) 2024-06-26T04:54:05.5222603Z remote: Compressing objects: 57% (511/895) 2024-06-26T04:54:05.5231402Z remote: Compressing objects: 58% (520/895) 2024-06-26T04:54:05.5244453Z remote: Compressing objects: 59% (529/895) 2024-06-26T04:54:05.5258087Z remote: Compressing objects: 60% (537/895) 2024-06-26T04:54:05.5266396Z remote: Compressing objects: 61% (546/895) 2024-06-26T04:54:05.5277604Z remote: Compressing objects: 62% (555/895) 2024-06-26T04:54:05.5281166Z remote: Compressing objects: 63% (564/895) 2024-06-26T04:54:05.5292978Z remote: Compressing objects: 64% (573/895) 2024-06-26T04:54:05.5301232Z remote: Compressing objects: 65% (582/895) 2024-06-26T04:54:05.5313987Z remote: Compressing objects: 66% (591/895) 2024-06-26T04:54:05.5323067Z remote: Compressing objects: 67% (600/895) 2024-06-26T04:54:05.5332993Z remote: Compressing objects: 68% (609/895) 2024-06-26T04:54:05.5338290Z remote: Compressing objects: 69% (618/895) 2024-06-26T04:54:05.5348127Z remote: Compressing objects: 70% (627/895) 2024-06-26T04:54:05.5350174Z remote: Compressing objects: 71% (636/895) 2024-06-26T04:54:05.5359315Z remote: Compressing objects: 72% (645/895) 2024-06-26T04:54:05.5368465Z remote: Compressing objects: 73% (654/895) 2024-06-26T04:54:05.5374631Z remote: Compressing objects: 74% (663/895) 2024-06-26T04:54:05.5380102Z remote: Compressing objects: 75% (672/895) 2024-06-26T04:54:05.5381585Z remote: Compressing objects: 76% (681/895) 2024-06-26T04:54:05.5384513Z remote: Compressing objects: 77% (690/895) 2024-06-26T04:54:05.5385853Z remote: Compressing objects: 78% (699/895) 2024-06-26T04:54:05.5386575Z remote: Compressing objects: 79% (708/895) 2024-06-26T04:54:05.5387284Z remote: Compressing objects: 80% (716/895) 2024-06-26T04:54:05.5387892Z remote: Compressing objects: 81% (725/895) 2024-06-26T04:54:05.5389580Z remote: Compressing objects: 82% (734/895) 2024-06-26T04:54:05.5396324Z remote: Compressing objects: 83% (743/895) 2024-06-26T04:54:05.5401660Z remote: Compressing objects: 84% (752/895) 2024-06-26T04:54:05.5403456Z remote: Compressing objects: 85% (761/895) 2024-06-26T04:54:05.5403979Z remote: Compressing objects: 86% (770/895) 2024-06-26T04:54:05.5404794Z remote: Compressing objects: 87% (779/895) 2024-06-26T04:54:05.5406469Z remote: Compressing objects: 88% (788/895) 2024-06-26T04:54:05.5407694Z remote: Compressing objects: 89% (797/895) 2024-06-26T04:54:05.5408989Z remote: Compressing objects: 90% (806/895) 2024-06-26T04:54:05.5410292Z remote: Compressing objects: 91% (815/895) 2024-06-26T04:54:05.5411514Z remote: Compressing objects: 92% (824/895) 2024-06-26T04:54:05.5413191Z remote: Compressing objects: 93% (833/895) 2024-06-26T04:54:05.5414213Z remote: Compressing objects: 94% (842/895) 2024-06-26T04:54:05.5415197Z remote: Compressing objects: 95% (851/895) 2024-06-26T04:54:05.5416365Z remote: Compressing objects: 96% (860/895) 2024-06-26T04:54:05.5417749Z remote: Compressing objects: 97% (869/895) 2024-06-26T04:54:05.5418641Z remote: Compressing objects: 98% (878/895) 2024-06-26T04:54:05.5419540Z remote: Compressing objects: 99% (887/895) 2024-06-26T04:54:05.5420871Z remote: Compressing objects: 100% (895/895) 2024-06-26T04:54:05.5421743Z remote: Compressing objects: 100% (895/895), done. 2024-06-26T04:54:25.2550171Z remote: Total 989963 (delta 1765), reused 1554 (delta 1289), pack-reused 987778 2024-06-26T04:54:51.3418446Z [command]/usr/bin/git rev-parse --verify --quiet b8c4c54d347aa776934c60784e35936878ef18dc^{object} 2024-06-26T04:54:51.3443611Z b8c4c54d347aa776934c60784e35936878ef18dc 2024-06-26T04:54:51.3447866Z ##[endgroup] 2024-06-26T04:54:51.3448376Z ##[group]Determining the checkout info 2024-06-26T04:54:51.3450519Z ##[endgroup] 2024-06-26T04:54:51.3451213Z ##[group]Checking out the ref 2024-06-26T04:54:51.3452809Z [command]/usr/bin/git checkout --quiet --force b8c4c54d347aa776934c60784e35936878ef18dc 2024-06-26T04:54:52.6619572Z ##[endgroup] 2024-06-26T04:54:52.6635321Z ##[group]Setting up auth for fetching submodules 2024-06-26T04:54:52.6636391Z [command]/usr/bin/git config --global http.https://github.com/.extraheader AUTHORIZATION: basic *** 2024-06-26T04:54:52.6666755Z [command]/usr/bin/git config --global --unset-all url.https://github.com/.insteadOf 2024-06-26T04:54:52.6695457Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf git@github.com: 2024-06-26T04:54:52.6722717Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf org-21003710@github.com: 2024-06-26T04:54:52.6745875Z ##[endgroup] 2024-06-26T04:54:52.6746379Z ##[group]Fetching submodules 2024-06-26T04:54:52.6749455Z [command]/usr/bin/git submodule sync --recursive 2024-06-26T04:54:52.6998806Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --recursive 2024-06-26T04:54:52.7236489Z Submodule 'android/libs/fbjni' (https://github.com/facebookincubator/fbjni.git) registered for path 'android/libs/fbjni' 2024-06-26T04:54:52.7238321Z Submodule 'third_party/NNPACK_deps/FP16' (https://github.com/Maratyszcza/FP16.git) registered for path 'third_party/FP16' 2024-06-26T04:54:52.7239984Z Submodule 'third_party/NNPACK_deps/FXdiv' (https://github.com/Maratyszcza/FXdiv.git) registered for path 'third_party/FXdiv' 2024-06-26T04:54:52.7241824Z Submodule 'third_party/NNPACK' (https://github.com/Maratyszcza/NNPACK.git) registered for path 'third_party/NNPACK' 2024-06-26T04:54:52.7244177Z Submodule 'third_party/VulkanMemoryAllocator' (https://github.com/GPUOpen-LibrariesAndSDKs/VulkanMemoryAllocator.git) registered for path 'third_party/VulkanMemoryAllocator' 2024-06-26T04:54:52.7246314Z Submodule 'third_party/XNNPACK' (https://github.com/google/XNNPACK.git) registered for path 'third_party/XNNPACK' 2024-06-26T04:54:52.7248074Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/benchmark' 2024-06-26T04:54:52.7250336Z Submodule 'third_party/cpp-httplib' (https://github.com/yhirose/cpp-httplib.git) registered for path 'third_party/cpp-httplib' 2024-06-26T04:54:52.7252876Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo.git) registered for path 'third_party/cpuinfo' 2024-06-26T04:54:52.7255884Z Submodule 'third_party/cudnn_frontend' (https://github.com/NVIDIA/cudnn-frontend.git) registered for path 'third_party/cudnn_frontend' 2024-06-26T04:54:52.7258454Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/cutlass' 2024-06-26T04:54:52.7261345Z Submodule 'third_party/eigen' (https://gitlab.com/libeigen/eigen.git) registered for path 'third_party/eigen' 2024-06-26T04:54:52.7264270Z Submodule 'third_party/fbgemm' (https://github.com/pytorch/fbgemm) registered for path 'third_party/fbgemm' 2024-06-26T04:54:52.7267438Z Submodule 'third_party/flatbuffers' (https://github.com/google/flatbuffers.git) registered for path 'third_party/flatbuffers' 2024-06-26T04:54:52.7270408Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/fmt' 2024-06-26T04:54:52.7273647Z Submodule 'third_party/foxi' (https://github.com/houseroad/foxi.git) registered for path 'third_party/foxi' 2024-06-26T04:54:52.7277190Z Submodule 'third_party/gemmlowp/gemmlowp' (https://github.com/google/gemmlowp.git) registered for path 'third_party/gemmlowp/gemmlowp' 2024-06-26T04:54:52.7280401Z Submodule 'third_party/gloo' (https://github.com/facebookincubator/gloo) registered for path 'third_party/gloo' 2024-06-26T04:54:52.7284145Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/googletest' 2024-06-26T04:54:52.7287599Z Submodule 'third_party/ideep' (https://github.com/intel/ideep) registered for path 'third_party/ideep' 2024-06-26T04:54:52.7291333Z Submodule 'third_party/ittapi' (https://github.com/intel/ittapi.git) registered for path 'third_party/ittapi' 2024-06-26T04:54:52.7295390Z Submodule 'third_party/kineto' (https://github.com/pytorch/kineto) registered for path 'third_party/kineto' 2024-06-26T04:54:52.7299488Z Submodule 'third_party/mimalloc' (https://github.com/microsoft/mimalloc.git) registered for path 'third_party/mimalloc' 2024-06-26T04:54:52.7303443Z Submodule 'third_party/nccl/nccl' (https://github.com/NVIDIA/nccl) registered for path 'third_party/nccl/nccl' 2024-06-26T04:54:52.7307617Z Submodule 'third_party/nlohmann' (https://github.com/nlohmann/json.git) registered for path 'third_party/nlohmann' 2024-06-26T04:54:52.7311720Z Submodule 'third_party/onnx' (https://github.com/onnx/onnx.git) registered for path 'third_party/onnx' 2024-06-26T04:54:52.7316427Z Submodule 'third_party/opentelemetry-cpp' (https://github.com/open-telemetry/opentelemetry-cpp.git) registered for path 'third_party/opentelemetry-cpp' 2024-06-26T04:54:52.7320595Z Submodule 'third_party/pocketfft' (https://github.com/mreineck/pocketfft) registered for path 'third_party/pocketfft' 2024-06-26T04:54:52.7325280Z Submodule 'third_party/protobuf' (https://github.com/protocolbuffers/protobuf.git) registered for path 'third_party/protobuf' 2024-06-26T04:54:52.7329809Z Submodule 'third_party/NNPACK_deps/psimd' (https://github.com/Maratyszcza/psimd.git) registered for path 'third_party/psimd' 2024-06-26T04:54:52.7335664Z Submodule 'third_party/NNPACK_deps/pthreadpool' (https://github.com/Maratyszcza/pthreadpool.git) registered for path 'third_party/pthreadpool' 2024-06-26T04:54:52.7340144Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/pybind11' 2024-06-26T04:54:52.7345002Z Submodule 'third_party/python-peachpy' (https://github.com/malfet/PeachPy.git) registered for path 'third_party/python-peachpy' 2024-06-26T04:54:52.7349822Z Submodule 'third_party/sleef' (https://github.com/shibatch/sleef) registered for path 'third_party/sleef' 2024-06-26T04:54:52.7355072Z Submodule 'third_party/tensorpipe' (https://github.com/pytorch/tensorpipe.git) registered for path 'third_party/tensorpipe' 2024-06-26T04:54:52.7379130Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/android/libs/fbjni'... 2024-06-26T04:54:52.9983525Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FP16'... 2024-06-26T04:54:53.1879267Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FXdiv'... 2024-06-26T04:54:53.4014035Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/NNPACK'... 2024-06-26T04:54:53.6445421Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/VulkanMemoryAllocator'... 2024-06-26T04:54:55.7989100Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/XNNPACK'... 2024-06-26T04:55:07.8974150Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/benchmark'... 2024-06-26T04:55:08.2475984Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpp-httplib'... 2024-06-26T04:55:08.7831078Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpuinfo'... 2024-06-26T04:55:09.3999446Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cudnn_frontend'... 2024-06-26T04:55:11.5660575Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cutlass'... 2024-06-26T04:55:13.3534077Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/eigen'... 2024-06-26T04:55:18.8401892Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm'... 2024-06-26T04:55:20.2212273Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flatbuffers'... 2024-06-26T04:55:22.0076946Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fmt'... 2024-06-26T04:55:23.3725779Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/foxi'... 2024-06-26T04:55:23.5350186Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gemmlowp/gemmlowp'... 2024-06-26T04:55:24.0397136Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gloo'... 2024-06-26T04:55:24.3647466Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/googletest'... 2024-06-26T04:55:25.4661896Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep'... 2024-06-26T04:55:25.8167381Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ittapi'... 2024-06-26T04:55:26.0583311Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto'... 2024-06-26T04:55:27.8776817Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/mimalloc'... 2024-06-26T04:55:28.6285691Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/nccl/nccl'... 2024-06-26T04:55:29.2797719Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/nlohmann'... 2024-06-26T04:55:35.5411098Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx'... 2024-06-26T04:55:38.2069199Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp'... 2024-06-26T04:55:45.3301055Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pocketfft'... 2024-06-26T04:55:45.5432780Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/protobuf'... 2024-06-26T04:55:54.5825395Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/psimd'... 2024-06-26T04:55:54.7568142Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pthreadpool'... 2024-06-26T04:55:54.9740860Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pybind11'... 2024-06-26T04:55:56.0655390Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/python-peachpy'... 2024-06-26T04:55:56.3298556Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/sleef'... 2024-06-26T04:55:56.9393893Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe'... 2024-06-26T04:55:57.3431759Z Submodule path 'android/libs/fbjni': checked out '7e1e1fe3858c63c251c637ae41a20de425dde96f' 2024-06-26T04:55:57.3534416Z Submodule path 'third_party/FP16': checked out '4dfe081cf6bcd15db339cf2680b9281b8451eeb3' 2024-06-26T04:55:57.3610761Z Submodule path 'third_party/FXdiv': checked out 'b408327ac2a15ec3e43352421954f5b1967701d1' 2024-06-26T04:55:57.3819621Z Submodule path 'third_party/NNPACK': checked out 'c07e3a0400713d546e0dea2d5466dd22ea389c73' 2024-06-26T04:55:57.4172154Z Submodule path 'third_party/VulkanMemoryAllocator': checked out 'a6bfc237255a6bac1513f7c1ebde6d8aed6b5191' 2024-06-26T04:55:58.2908313Z Submodule path 'third_party/XNNPACK': checked out 'fcbf55af6cf28a4627bcd1f703ab7ad843f0f3a2' 2024-06-26T04:55:58.3114733Z Submodule path 'third_party/benchmark': checked out '0d98dba29d66e93259db7daa53a9327df767a415' 2024-06-26T04:55:58.3478688Z Submodule path 'third_party/cpp-httplib': checked out '3b6597bba913d51161383657829b7e644e59c006' 2024-06-26T04:55:58.4391159Z Submodule path 'third_party/cpuinfo': checked out '3c8b1533ac03dd6531ab6e7b9245d488f13a82a5' 2024-06-26T04:55:58.4699101Z Submodule path 'third_party/cudnn_frontend': checked out 'aa3abd4bc689d6412979c7f55f9cd132848c9c6a' 2024-06-26T04:55:58.9407577Z Submodule path 'third_party/cutlass': checked out 'bbe579a9e3beb6ea6626d9227ec32d0dae119a49' 2024-06-26T04:55:59.1709344Z Submodule path 'third_party/eigen': checked out '3147391d946bb4b6c68edd901f2add6ac1f31f8c' 2024-06-26T04:55:59.2397188Z Submodule path 'third_party/fbgemm': checked out 'dbc3157bf256f1339b3fa1fef2be89ac4078be0e' 2024-06-26T04:55:59.2411164Z Submodule 'third_party/asmjit' (https://github.com/asmjit/asmjit.git) registered for path 'third_party/fbgemm/third_party/asmjit' 2024-06-26T04:55:59.2412871Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo) registered for path 'third_party/fbgemm/third_party/cpuinfo' 2024-06-26T04:55:59.2415163Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/fbgemm/third_party/cutlass' 2024-06-26T04:55:59.2417270Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/fbgemm/third_party/googletest' 2024-06-26T04:55:59.2419799Z Submodule 'third_party/hipify_torch' (https://github.com/ROCmSoftwarePlatform/hipify_torch.git) registered for path 'third_party/fbgemm/third_party/hipify_torch' 2024-06-26T04:55:59.2440877Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/asmjit'... 2024-06-26T04:56:00.2869702Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/cpuinfo'... 2024-06-26T04:56:00.8722353Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/cutlass'... 2024-06-26T04:56:02.6826427Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/googletest'... 2024-06-26T04:56:03.8316435Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/hipify_torch'... 2024-06-26T04:56:04.1559395Z Submodule path 'third_party/fbgemm/third_party/asmjit': checked out 'd3fbf7c9bc7c1d1365a94a45614b91c5a3706b81' 2024-06-26T04:56:04.2443575Z Submodule path 'third_party/fbgemm/third_party/cpuinfo': checked out 'ed8b86a253800bafdb7b25c5c399f91bff9cb1f3' 2024-06-26T04:56:04.6149562Z Submodule path 'third_party/fbgemm/third_party/cutlass': checked out 'fc9ebc645b63f3a6bc80aaefde5c063fb72110d6' 2024-06-26T04:56:04.6743286Z Submodule path 'third_party/fbgemm/third_party/googletest': checked out 'cbf019de22c8dd37b2108da35b2748fd702d1796' 2024-06-26T04:56:04.6856092Z Submodule path 'third_party/fbgemm/third_party/hipify_torch': checked out '23f53b025b466d8ec3c45d52290d3442f7fbe6b1' 2024-06-26T04:56:04.7906089Z Submodule path 'third_party/flatbuffers': checked out '01834de25e4bf3975a9a00e816292b1ad0fe184b' 2024-06-26T04:56:04.8232350Z Submodule path 'third_party/fmt': checked out 'e69e5f977d458f2650bb346dadf2ad30c5320281' 2024-06-26T04:56:04.8315015Z Submodule path 'third_party/foxi': checked out 'c278588e34e535f0bb8f00df3880d26928038cad' 2024-06-26T04:56:04.8677751Z Submodule path 'third_party/gemmlowp/gemmlowp': checked out '3fb5c176c17c765a3492cd2f0321b0dab712f350' 2024-06-26T04:56:04.8891228Z Submodule path 'third_party/gloo': checked out '5354032ea08eadd7fc4456477f7f7c6308818509' 2024-06-26T04:56:04.9295178Z Submodule path 'third_party/googletest': checked out 'e2239ee6043f73722e7aa812a459f54a28552929' 2024-06-26T04:56:04.9402798Z Submodule path 'third_party/ideep': checked out '55ca0191687aaf19aca5cdb7881c791e3bea442b' 2024-06-26T04:56:04.9414678Z Submodule 'mkl-dnn' (https://github.com/intel/mkl-dnn.git) registered for path 'third_party/ideep/mkl-dnn' 2024-06-26T04:56:04.9434897Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep/mkl-dnn'... 2024-06-26T04:56:17.8739866Z Submodule path 'third_party/ideep/mkl-dnn': checked out '1137e04ec0b5251ca2b4400a4fd3c667ce843d67' 2024-06-26T04:56:17.8894819Z Submodule path 'third_party/ittapi': checked out '5b8a7d7422611c3a0d799fb5fc5dd4abfae35b42' 2024-06-26T04:56:17.9749539Z Submodule path 'third_party/kineto': checked out '8681ff11e1fa54da39023076c5c43eddd87b7a8a' 2024-06-26T04:56:17.9763766Z Submodule 'libkineto/third_party/dynolog' (https://github.com/facebookincubator/dynolog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog' 2024-06-26T04:56:17.9766611Z Submodule 'libkineto/third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/fmt' 2024-06-26T04:56:17.9768786Z Submodule 'libkineto/third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/googletest' 2024-06-26T04:56:17.9789761Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog'... 2024-06-26T04:56:18.5371950Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/fmt'... 2024-06-26T04:56:19.9495810Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/googletest'... 2024-06-26T04:56:21.1682805Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog': checked out '7d04a0053a845370ae06ce317a22a48e9edcc74e' 2024-06-26T04:56:21.1695655Z Submodule 'third_party/DCGM' (https://github.com/NVIDIA/DCGM.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2024-06-26T04:56:21.1697606Z Submodule 'third_party/cpr' (https://github.com/libcpr/cpr.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2024-06-26T04:56:21.1699446Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2024-06-26T04:56:21.1701623Z Submodule 'third_party/gflags' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-06-26T04:56:21.1703788Z Submodule 'third_party/glog' (https://github.com/google/glog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2024-06-26T04:56:21.1706222Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2024-06-26T04:56:21.1708664Z Submodule 'third_party/json' (https://github.com/nlohmann/json.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2024-06-26T04:56:21.1711244Z Submodule 'third_party/pfs' (https://github.com/dtrugman/pfs.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2024-06-26T04:56:21.1734456Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'... 2024-06-26T04:56:22.1876421Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'... 2024-06-26T04:56:22.5888409Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'... 2024-06-26T04:56:23.9313193Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'... 2024-06-26T04:56:24.2044283Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/glog'... 2024-06-26T04:56:24.7139075Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'... 2024-06-26T04:56:25.8414843Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/json'... 2024-06-26T04:56:33.2113234Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'... 2024-06-26T04:56:33.6076837Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM': checked out 'ffde4e54bc7249a6039a5e6b45b395141e1217f9' 2024-06-26T04:56:33.6232920Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr': checked out '871ed52d350214a034f6ef8a3b8f51c5ce1bd400' 2024-06-26T04:56:33.6568389Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt': checked out 'cd4af11efc9c622896a3e4cb599fa28668ca3d05' 2024-06-26T04:56:33.6679481Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags': checked out 'e171aa2d15ed9eb17054558e0b3a6a413bb01067' 2024-06-26T04:56:33.6692131Z Submodule 'doc' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2024-06-26T04:56:33.6712993Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'... 2024-06-26T04:56:33.9584048Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc': checked out '8411df715cf522606e3b1aca386ddfc0b63d34b4' 2024-06-26T04:56:33.9748866Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog': checked out 'b33e3bad4c46c8a6345525fd822af355e5ef9446' 2024-06-26T04:56:34.0122698Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest': checked out '58d77fa8070e8cec2dc1ed015d66b454c8d78850' 2024-06-26T04:56:34.1094364Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json': checked out '4f8fba14066156b73f1189a2b8bd568bde5284c5' 2024-06-26T04:56:34.1241463Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs': checked out 'f68a2fa8ea36c783bdd760371411fcb495aa3150' 2024-06-26T04:56:34.1581942Z Submodule path 'third_party/kineto/libkineto/third_party/fmt': checked out 'a33701196adfad74917046096bf5a2aa0ab0bb50' 2024-06-26T04:56:34.2131366Z Submodule path 'third_party/kineto/libkineto/third_party/googletest': checked out '7aca84427f224eeed3144123d5230d5871e93347' 2024-06-26T04:56:34.2469600Z Submodule path 'third_party/mimalloc': checked out 'b66e3214d8a104669c2ec05ae91ebc26a8f5ab78' 2024-06-26T04:56:34.2694444Z Submodule path 'third_party/nccl/nccl': checked out '48bb7fec7953112ff37499a272317f6663f8f600' 2024-06-26T04:56:34.3681368Z Submodule path 'third_party/nlohmann': checked out '87cda1d6646592ac5866dc703c8e1839046a6806' 2024-06-26T04:56:34.6760262Z Submodule path 'third_party/onnx': checked out '990217f043af7222348ca8f0301e17fa7b841781' 2024-06-26T04:56:34.6793450Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/onnx/third_party/benchmark' 2024-06-26T04:56:34.6795243Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/onnx/third_party/pybind11' 2024-06-26T04:56:34.6819510Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx/third_party/benchmark'... 2024-06-26T04:56:35.0918936Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx/third_party/pybind11'... 2024-06-26T04:56:36.1696405Z Submodule path 'third_party/onnx/third_party/benchmark': checked out '2dd015dfef425c866d9a43f2c67d8b52d709acb6' 2024-06-26T04:56:36.2002314Z Submodule path 'third_party/onnx/third_party/pybind11': checked out '5b0a6fc2017fcc176545afe3e09c9f9885283242' 2024-06-26T04:56:36.2585271Z Submodule path 'third_party/opentelemetry-cpp': checked out 'a799f4aed9c94b765dcdaabaeab7d5e7e2310878' 2024-06-26T04:56:36.2602062Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark) registered for path 'third_party/opentelemetry-cpp/third_party/benchmark' 2024-06-26T04:56:36.2603770Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/opentelemetry-cpp/third_party/googletest' 2024-06-26T04:56:36.2605462Z Submodule 'third_party/ms-gsl' (https://github.com/microsoft/GSL) registered for path 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2024-06-26T04:56:36.2607436Z Submodule 'third_party/nlohmann-json' (https://github.com/nlohmann/json) registered for path 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2024-06-26T04:56:36.2609923Z Submodule 'third_party/opentelemetry-proto' (https://github.com/open-telemetry/opentelemetry-proto) registered for path 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2024-06-26T04:56:36.2612195Z Submodule 'third_party/opentracing-cpp' (https://github.com/opentracing/opentracing-cpp.git) registered for path 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-06-26T04:56:36.2614876Z Submodule 'third_party/prometheus-cpp' (https://github.com/jupp0r/prometheus-cpp) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-06-26T04:56:36.2617320Z Submodule 'tools/vcpkg' (https://github.com/Microsoft/vcpkg) registered for path 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-06-26T04:56:36.2641267Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/benchmark'... 2024-06-26T04:56:36.6540344Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/googletest'... 2024-06-26T04:56:37.7834067Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/ms-gsl'... 2024-06-26T04:56:38.0856864Z Cloning into 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file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/googletest/config remote.origin.url 2024-06-26T04:57:02.9440506Z Entering 'third_party/ideep' 2024-06-26T04:57:02.9481598Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/config remote.origin.url 2024-06-26T04:57:02.9496630Z Entering 'third_party/ideep/mkl-dnn' 2024-06-26T04:57:02.9535974Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/modules/mkl-dnn/config remote.origin.url 2024-06-26T04:57:02.9559043Z Entering 'third_party/ittapi' 2024-06-26T04:57:02.9599646Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ittapi/config remote.origin.url 2024-06-26T04:57:02.9615439Z Entering 'third_party/kineto' 2024-06-26T04:57:02.9654886Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/config remote.origin.url 2024-06-26T04:57:02.9669753Z Entering 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file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/fmt/config remote.origin.url 2024-06-26T04:57:02.9895483Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-06-26T04:57:02.9936555Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/gflags/config remote.origin.url 2024-06-26T04:57:02.9950873Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2024-06-26T04:57:02.9991430Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/gflags/modules/doc/config remote.origin.url 2024-06-26T04:57:03.0007128Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2024-06-26T04:57:03.0047674Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/glog/config remote.origin.url 2024-06-26T04:57:03.0062143Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2024-06-26T04:57:03.0102690Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/googletest/config remote.origin.url 2024-06-26T04:57:03.0118037Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2024-06-26T04:57:03.0159569Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/json/config remote.origin.url 2024-06-26T04:57:03.0175743Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2024-06-26T04:57:03.0215596Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/pfs/config remote.origin.url 2024-06-26T04:57:03.0232286Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2024-06-26T04:57:03.0273188Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/fmt/config remote.origin.url 2024-06-26T04:57:03.0288665Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2024-06-26T04:57:03.0329495Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/googletest/config remote.origin.url 2024-06-26T04:57:03.0345321Z Entering 'third_party/mimalloc' 2024-06-26T04:57:03.0386235Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/mimalloc/config remote.origin.url 2024-06-26T04:57:03.0400984Z Entering 'third_party/nccl/nccl' 2024-06-26T04:57:03.0441869Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/nccl/nccl/config remote.origin.url 2024-06-26T04:57:03.0456983Z Entering 'third_party/nlohmann' 2024-06-26T04:57:03.0497771Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/nlohmann/config remote.origin.url 2024-06-26T04:57:03.0513943Z Entering 'third_party/onnx' 2024-06-26T04:57:03.0554432Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/config remote.origin.url 2024-06-26T04:57:03.0587741Z Entering 'third_party/onnx/third_party/benchmark' 2024-06-26T04:57:03.0629102Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/modules/third_party/benchmark/config remote.origin.url 2024-06-26T04:57:03.0644690Z Entering 'third_party/onnx/third_party/pybind11' 2024-06-26T04:57:03.0686429Z 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file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/opentracing-cpp/config remote.origin.url 2024-06-26T04:57:03.1095451Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-06-26T04:57:03.1135320Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/config remote.origin.url 2024-06-26T04:57:03.1149065Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2024-06-26T04:57:03.1190318Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/modules/civetweb/config remote.origin.url 2024-06-26T04:57:03.1206983Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2024-06-26T04:57:03.1247701Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/modules/googletest/config remote.origin.url 2024-06-26T04:57:03.1263536Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-06-26T04:57:03.1302313Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/tools/vcpkg/config remote.origin.url 2024-06-26T04:57:03.1336965Z Entering 'third_party/pocketfft' 2024-06-26T04:57:03.1376625Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/pocketfft/config remote.origin.url 2024-06-26T04:57:03.1391246Z Entering 'third_party/protobuf' 2024-06-26T04:57:03.1430915Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/config remote.origin.url 2024-06-26T04:57:03.1448426Z Entering 'third_party/protobuf/third_party/benchmark' 2024-06-26T04:57:03.1488075Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/modules/third_party/benchmark/config remote.origin.url 2024-06-26T04:57:03.1503789Z Entering 'third_party/protobuf/third_party/googletest' 2024-06-26T04:57:03.1543348Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/modules/third_party/googletest/config remote.origin.url 2024-06-26T04:57:03.1559046Z Entering 'third_party/psimd' 2024-06-26T04:57:03.1598865Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/psimd/config remote.origin.url 2024-06-26T04:57:03.1613105Z Entering 'third_party/pthreadpool' 2024-06-26T04:57:03.1651750Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/pthreadpool/config remote.origin.url 2024-06-26T04:57:03.1666593Z Entering 'third_party/pybind11' 2024-06-26T04:57:03.1705153Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/pybind11/config remote.origin.url 2024-06-26T04:57:03.1719836Z Entering 'third_party/python-peachpy' 2024-06-26T04:57:03.1758032Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/python-peachpy/config remote.origin.url 2024-06-26T04:57:03.1772162Z Entering 'third_party/sleef' 2024-06-26T04:57:03.1811599Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/sleef/config remote.origin.url 2024-06-26T04:57:03.1826386Z Entering 'third_party/tensorpipe' 2024-06-26T04:57:03.1864025Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/config remote.origin.url 2024-06-26T04:57:03.1878822Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-06-26T04:57:03.1918301Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/googletest/config remote.origin.url 2024-06-26T04:57:03.1932273Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-06-26T04:57:03.1970764Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libnop/config remote.origin.url 2024-06-26T04:57:03.1984741Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-06-26T04:57:03.2022602Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libuv/config remote.origin.url 2024-06-26T04:57:03.2037602Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-06-26T04:57:03.2076345Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/pybind11/config remote.origin.url 2024-06-26T04:57:03.2089881Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-06-26T04:57:03.2128909Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/pybind11/modules/tools/clang/config remote.origin.url 2024-06-26T04:57:03.2941338Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'git@github.com:' 2024-06-26T04:57:03.3199128Z Entering 'android/libs/fbjni' 2024-06-26T04:57:03.3232416Z Entering 'third_party/FP16' 2024-06-26T04:57:03.3265880Z Entering 'third_party/FXdiv' 2024-06-26T04:57:03.3299880Z Entering 'third_party/NNPACK' 2024-06-26T04:57:03.3333067Z Entering 'third_party/VulkanMemoryAllocator' 2024-06-26T04:57:03.3367529Z Entering 'third_party/XNNPACK' 2024-06-26T04:57:03.3417854Z Entering 'third_party/benchmark' 2024-06-26T04:57:03.3451348Z Entering 'third_party/cpp-httplib' 2024-06-26T04:57:03.3485324Z Entering 'third_party/cpuinfo' 2024-06-26T04:57:03.3519510Z Entering 'third_party/cudnn_frontend' 2024-06-26T04:57:03.3552756Z Entering 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[command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'org-21003710@github.com:' 2024-06-26T04:57:03.5938726Z Entering 'android/libs/fbjni' 2024-06-26T04:57:03.5971306Z Entering 'third_party/FP16' 2024-06-26T04:57:03.6004127Z Entering 'third_party/FXdiv' 2024-06-26T04:57:03.6037074Z Entering 'third_party/NNPACK' 2024-06-26T04:57:03.6070822Z Entering 'third_party/VulkanMemoryAllocator' 2024-06-26T04:57:03.6104033Z Entering 'third_party/XNNPACK' 2024-06-26T04:57:03.6153208Z Entering 'third_party/benchmark' 2024-06-26T04:57:03.6187320Z Entering 'third_party/cpp-httplib' 2024-06-26T04:57:03.6219870Z Entering 'third_party/cpuinfo' 2024-06-26T04:57:03.6255026Z Entering 'third_party/cudnn_frontend' 2024-06-26T04:57:03.6286658Z Entering 'third_party/cutlass' 2024-06-26T04:57:03.6327393Z Entering 'third_party/eigen' 2024-06-26T04:57:03.6363109Z Entering 'third_party/fbgemm' 2024-06-26T04:57:03.6396886Z Entering 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2024-06-26T04:57:03.6910453Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2024-06-26T04:57:03.6943556Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2024-06-26T04:57:03.6977480Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2024-06-26T04:57:03.7010018Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2024-06-26T04:57:03.7042942Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-06-26T04:57:03.7074989Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2024-06-26T04:57:03.7109393Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2024-06-26T04:57:03.7142531Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2024-06-26T04:57:03.7175381Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2024-06-26T04:57:03.7209258Z Entering 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2024-06-26T04:57:03.7669304Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2024-06-26T04:57:03.7703427Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2024-06-26T04:57:03.7736164Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-06-26T04:57:03.7768530Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-06-26T04:57:03.7800398Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2024-06-26T04:57:03.7834414Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2024-06-26T04:57:03.7868991Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-06-26T04:57:03.7922022Z Entering 'third_party/pocketfft' 2024-06-26T04:57:03.7956380Z Entering 'third_party/protobuf' 2024-06-26T04:57:03.7994569Z Entering 'third_party/protobuf/third_party/benchmark' 2024-06-26T04:57:03.8027155Z Entering 'third_party/protobuf/third_party/googletest' 2024-06-26T04:57:03.8062664Z Entering 'third_party/psimd' 2024-06-26T04:57:03.8095893Z Entering 'third_party/pthreadpool' 2024-06-26T04:57:03.8129263Z Entering 'third_party/pybind11' 2024-06-26T04:57:03.8163944Z Entering 'third_party/python-peachpy' 2024-06-26T04:57:03.8197732Z Entering 'third_party/sleef' 2024-06-26T04:57:03.8231492Z Entering 'third_party/tensorpipe' 2024-06-26T04:57:03.8265160Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-06-26T04:57:03.8298252Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-06-26T04:57:03.8330500Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-06-26T04:57:03.8363292Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-06-26T04:57:03.8396643Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-06-26T04:57:03.8441474Z ##[endgroup] 2024-06-26T04:57:03.8476217Z [command]/usr/bin/git log -1 --format='%H' 2024-06-26T04:57:03.8500013Z 'b8c4c54d347aa776934c60784e35936878ef18dc' 2024-06-26T04:57:03.8675884Z Prepare all required actions 2024-06-26T04:57:03.8676456Z Getting action download info 2024-06-26T04:57:04.0486419Z ##[group]Run ./.github/actions/setup-linux 2024-06-26T04:57:04.0486851Z env: 2024-06-26T04:57:04.0487116Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:57:04.0487428Z ##[endgroup] 2024-06-26T04:57:04.0552279Z ##[group]Run set -euo pipefail 2024-06-26T04:57:04.0552710Z set -euo pipefail 2024-06-26T04:57:04.0553067Z function get_ec2_metadata() { 2024-06-26T04:57:04.0553587Z  # Pulled from instance metadata endpoint for EC2 2024-06-26T04:57:04.0554630Z  # see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html 2024-06-26T04:57:04.0555395Z  category=$1 2024-06-26T04:57:04.0555905Z  # If it is GCP runner (runner name contains gcp), do not run this 2024-06-26T04:57:04.0556516Z  runner_name_str=i-000220d454490c80e 2024-06-26T04:57:04.0557000Z  if [[ -f /.inarc ]]; then 2024-06-26T04:57:04.0557519Z  echo "ARC Runner, no info on ec2 metadata" 2024-06-26T04:57:04.0558052Z  elif [[ $runner_name_str == *"gcp"* ]]; then 2024-06-26T04:57:04.0558725Z  echo "Runner is from Google Cloud Platform, No info on ec2 metadata" 2024-06-26T04:57:04.0559315Z  else 2024-06-26T04:57:04.0559770Z  curl -fsSL "http://169.254.169.254/latest/meta-data/${category}" 2024-06-26T04:57:04.0560328Z  fi 2024-06-26T04:57:04.0560701Z } 2024-06-26T04:57:04.0561034Z echo "ami-id: $(get_ec2_metadata ami-id)" 2024-06-26T04:57:04.0561600Z echo "instance-id: $(get_ec2_metadata instance-id)" 2024-06-26T04:57:04.0562238Z echo "instance-type: $(get_ec2_metadata instance-type)" 2024-06-26T04:57:04.0562778Z echo "system info $(uname -a)" 2024-06-26T04:57:04.0570627Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T04:57:04.0571113Z env: 2024-06-26T04:57:04.0571366Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:57:04.0571689Z ##[endgroup] 2024-06-26T04:57:04.0657215Z ami-id: ami-0ce0c36d7a00b20e2 2024-06-26T04:57:04.0717589Z instance-id: i-000220d454490c80e 2024-06-26T04:57:04.0776774Z instance-type: c5.2xlarge 2024-06-26T04:57:04.0784427Z system info Linux ip-10-0-57-28.ec2.internal 4.14.336-257.562.amzn2.x86_64 #1 SMP Sat Feb 24 09:50:35 UTC 2024 x86_64 x86_64 x86_64 GNU/Linux 2024-06-26T04:57:04.0808225Z ##[group]Run echo "IN_ARC_RUNNER=$([ -f /.inarc ] && echo true || echo false)" >> $GITHUB_OUTPUT 2024-06-26T04:57:04.0809168Z echo "IN_ARC_RUNNER=$([ -f /.inarc ] && echo true || echo false)" >> $GITHUB_OUTPUT 2024-06-26T04:57:04.0817190Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T04:57:04.0817679Z env: 2024-06-26T04:57:04.0817959Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:57:04.0818276Z ##[endgroup] 2024-06-26T04:57:04.0926015Z ##[group]Run if systemctl is-active --quiet docker; then 2024-06-26T04:57:04.0926598Z if systemctl is-active --quiet docker; then 2024-06-26T04:57:04.0927119Z  echo "Docker daemon is running..."; 2024-06-26T04:57:04.0927547Z else 2024-06-26T04:57:04.0928006Z  echo "Starting docker deamon..." && sudo systemctl start docker; 2024-06-26T04:57:04.0928575Z fi 2024-06-26T04:57:04.0936229Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T04:57:04.0936724Z env: 2024-06-26T04:57:04.0936977Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:57:04.0937288Z ##[endgroup] 2024-06-26T04:57:04.0976837Z Docker daemon is running... 2024-06-26T04:57:04.1022862Z ##[group]Run nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482 2024-06-26T04:57:04.1023412Z with: 2024-06-26T04:57:04.1023656Z shell: bash 2024-06-26T04:57:04.1023914Z timeout_minutes: 5 2024-06-26T04:57:04.1024207Z max_attempts: 3 2024-06-26T04:57:04.1024493Z retry_wait_seconds: 30 2024-06-26T04:57:04.1026039Z command: AWS_ACCOUNT_ID=$(aws sts get-caller-identity|grep Account|cut -f4 -d\") aws ecr get-login-password --region "$AWS_DEFAULT_REGION" | docker login --username AWS \ --password-stdin "$AWS_ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com" 2024-06-26T04:57:04.1027515Z polling_interval_seconds: 1 2024-06-26T04:57:04.1027868Z warning_on_retry: true 2024-06-26T04:57:04.1028196Z continue_on_error: false 2024-06-26T04:57:04.1028494Z env: 2024-06-26T04:57:04.1028745Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:57:04.1029121Z AWS_RETRY_MODE: standard 2024-06-26T04:57:04.1029433Z AWS_MAX_ATTEMPTS: 5 2024-06-26T04:57:04.1029849Z AWS_DEFAULT_REGION: us-east-1 2024-06-26T04:57:04.1030203Z ##[endgroup] 2024-06-26T04:57:04.9876251Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2024-06-26T04:57:04.9877547Z Configure a credential helper to remove this warning. See 2024-06-26T04:57:04.9878389Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2024-06-26T04:57:04.9878925Z 2024-06-26T04:57:04.9879056Z Login Succeeded 2024-06-26T04:57:05.1551593Z Command completed after 1 attempt(s). 2024-06-26T04:57:05.1594929Z ##[group]Run env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2024-06-26T04:57:05.1595643Z env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2024-06-26T04:57:05.1596564Z env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2024-06-26T04:57:05.1604156Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T04:57:05.1604674Z env: 2024-06-26T04:57:05.1604929Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:57:05.1605261Z ##[endgroup] 2024-06-26T04:57:05.1671344Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2024-06-26T04:57:05.1672113Z # ignore expansion of "docker ps -q" since it could be empty 2024-06-26T04:57:05.1672688Z # shellcheck disable=SC2046 2024-06-26T04:57:05.1673125Z docker stop $(docker ps -q) || true 2024-06-26T04:57:05.1673579Z # Prune all of the docker images 2024-06-26T04:57:05.1674004Z docker system prune -af 2024-06-26T04:57:05.1681184Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T04:57:05.1681668Z env: 2024-06-26T04:57:05.1681926Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:57:05.1682234Z ##[endgroup] 2024-06-26T04:57:05.1928596Z "docker stop" requires at least 1 argument. 2024-06-26T04:57:05.1929502Z See 'docker stop --help'. 2024-06-26T04:57:05.1929726Z 2024-06-26T04:57:05.1929960Z Usage: docker stop [OPTIONS] CONTAINER [CONTAINER...] 2024-06-26T04:57:05.1930323Z 2024-06-26T04:57:05.1930489Z Stop one or more running containers 2024-06-26T04:57:05.2095099Z Total reclaimed space: 0B 2024-06-26T04:57:05.2148022Z ##[group]Run set +e 2024-06-26T04:57:05.2148664Z set +e 2024-06-26T04:57:05.2149198Z set -x 2024-06-26T04:57:05.2149740Z  2024-06-26T04:57:05.2150362Z PT_DOMAIN=download.pytorch.org 2024-06-26T04:57:05.2151863Z # TODO: Flaky access to download.pytorch.org https://github.com/pytorch/pytorch/issues/100400, 2024-06-26T04:57:05.2153903Z # cleaning this up once the issue is fixed. There are more than one resolved IP here, the last 2024-06-26T04:57:05.2155300Z # one is returned at random 2024-06-26T04:57:05.2156311Z RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" | tail -n1) 2024-06-26T04:57:05.2157312Z  2024-06-26T04:57:05.2157919Z if [ -z "${RESOLVED_IP}" ]; then 2024-06-26T04:57:05.2158973Z  echo "Couldn't resolve ${PT_DOMAIN}, retrying with Google DNS..." 2024-06-26T04:57:05.2160332Z  RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" @8.8.8.8 | tail -n1) 2024-06-26T04:57:05.2161451Z  2024-06-26T04:57:05.2162076Z  if [ -z "${RESOLVED_IP}" ]; then 2024-06-26T04:57:05.2163118Z  echo "Couldn't resolve ${PT_DOMAIN}, exiting..." 2024-06-26T04:57:05.2164109Z  exit 1 2024-06-26T04:57:05.2164722Z  fi 2024-06-26T04:57:05.2165281Z fi 2024-06-26T04:57:05.2166063Z  2024-06-26T04:57:05.2166769Z if grep -r "${PT_DOMAIN}" /etc/hosts; then 2024-06-26T04:57:05.2167743Z  # Clean up any old records first 2024-06-26T04:57:05.2168646Z  sudo sed -i "/${PT_DOMAIN}/d" /etc/hosts 2024-06-26T04:57:05.2169512Z fi 2024-06-26T04:57:05.2170025Z  2024-06-26T04:57:05.2170838Z echo "${RESOLVED_IP} ${PT_DOMAIN}" | sudo tee -a /etc/hosts 2024-06-26T04:57:05.2171763Z cat /etc/hosts 2024-06-26T04:57:05.2209399Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T04:57:05.2210073Z env: 2024-06-26T04:57:05.2210383Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:57:05.2210774Z ##[endgroup] 2024-06-26T04:57:05.2242578Z + PT_DOMAIN=download.pytorch.org 2024-06-26T04:57:05.2248310Z ++ dig -4 +short download.pytorch.org 2024-06-26T04:57:05.2248794Z ++ tail -n1 2024-06-26T04:57:05.2631139Z + RESOLVED_IP=18.160.10.76 2024-06-26T04:57:05.2632035Z + '[' -z 18.160.10.76 ']' 2024-06-26T04:57:05.2632656Z + grep -r download.pytorch.org /etc/hosts 2024-06-26T04:57:05.2639504Z 18.160.10.36 download.pytorch.org 2024-06-26T04:57:05.2640372Z + sudo sed -i /download.pytorch.org/d /etc/hosts 2024-06-26T04:57:05.2752310Z + echo '18.160.10.76 download.pytorch.org' 2024-06-26T04:57:05.2753445Z + sudo tee -a /etc/hosts 2024-06-26T04:57:05.3094066Z 18.160.10.76 download.pytorch.org 2024-06-26T04:57:05.3108072Z + cat /etc/hosts 2024-06-26T04:57:05.3114868Z 127.0.0.1 localhost localhost.localdomain localhost4 localhost4.localdomain4 2024-06-26T04:57:05.3128488Z ::1 localhost6 localhost6.localdomain6 2024-06-26T04:57:05.3129076Z 18.160.10.76 download.pytorch.org 2024-06-26T04:57:05.3302041Z ##[group]Run pytorch/test-infra/.github/actions/calculate-docker-image@main 2024-06-26T04:57:05.3302686Z with: 2024-06-26T04:57:05.3303621Z docker-image-name: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:57:05.3304719Z docker-build-dir: .ci/docker 2024-06-26T04:57:05.3305129Z working-directory: . 2024-06-26T04:57:05.3305615Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-06-26T04:57:05.3306184Z force-push: false 2024-06-26T04:57:05.3306512Z env: 2024-06-26T04:57:05.3306798Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:57:05.3307174Z ##[endgroup] 2024-06-26T04:57:05.3340474Z ##[group]Run set -ex 2024-06-26T04:57:05.3340883Z set -ex 2024-06-26T04:57:05.3341206Z  2024-06-26T04:57:05.3341825Z # If the docker build directory or the build script doesn't exist, the action will 2024-06-26T04:57:05.3342916Z # gracefully return the docker image name as it is. Pulling docker image in Linux 2024-06-26T04:57:05.3343750Z # job could then download the pre-built image as usual 2024-06-26T04:57:05.3344527Z if [[ ! -d "${DOCKER_BUILD_DIR}" ]] || [[ ! -f "${DOCKER_BUILD_DIR}/build.sh" ]]; then 2024-06-26T04:57:05.3345241Z  echo "skip=true" >> "${GITHUB_OUTPUT}" 2024-06-26T04:57:05.3345910Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2024-06-26T04:57:05.3346496Z  2024-06-26T04:57:05.3347044Z  echo "There is no Docker build script in ${REPO_NAME} repo, skipping..." 2024-06-26T04:57:05.3347704Z  exit 0 2024-06-26T04:57:05.3348017Z else 2024-06-26T04:57:05.3348406Z  echo "skip=false" >> "${GITHUB_OUTPUT}" 2024-06-26T04:57:05.3348880Z fi 2024-06-26T04:57:05.3349170Z  2024-06-26T04:57:05.3349675Z if [[ "${DOCKER_IMAGE_NAME}" == *"${DOCKER_REGISTRY}/${REPO_NAME}"* ]]; then 2024-06-26T04:57:05.3350573Z  # The docker image name already includes the ECR prefix and tag, so we can just 2024-06-26T04:57:05.3351378Z  # use it as it is, but first let's extract the tag 2024-06-26T04:57:05.3352308Z  DOCKER_TAG=$(echo "${DOCKER_IMAGE_NAME}" | awk -F '[:,]' '{print $2}') 2024-06-26T04:57:05.3353077Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2024-06-26T04:57:05.3353910Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2024-06-26T04:57:05.3354487Z else 2024-06-26T04:57:05.3354952Z  DOCKER_TAG=$(git rev-parse HEAD:"${DOCKER_BUILD_DIR}") 2024-06-26T04:57:05.3355650Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2024-06-26T04:57:05.3356584Z  echo "docker-image=${DOCKER_REGISTRY}/${REPO_NAME}/${DOCKER_IMAGE_NAME}:${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2024-06-26T04:57:05.3357554Z fi 2024-06-26T04:57:05.3365096Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T04:57:05.3365623Z env: 2024-06-26T04:57:05.3365932Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:57:05.3366319Z REPO_NAME: pytorch 2024-06-26T04:57:05.3367290Z DOCKER_IMAGE_NAME: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:57:05.3368485Z DOCKER_BUILD_DIR: .ci/docker 2024-06-26T04:57:05.3369111Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-06-26T04:57:05.3369668Z ##[endgroup] 2024-06-26T04:57:05.3393132Z + [[ ! -d .ci/docker ]] 2024-06-26T04:57:05.3393851Z + [[ ! -f .ci/docker/build.sh ]] 2024-06-26T04:57:05.3394324Z + echo skip=false 2024-06-26T04:57:05.3395971Z + [[ 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 == *\3\0\8\5\3\5\3\8\5\1\1\4\.\d\k\r\.\e\c\r\.\u\s\-\e\a\s\t\-\1\.\a\m\a\z\o\n\a\w\s\.\c\o\m\/\p\y\t\o\r\c\h* ]] 2024-06-26T04:57:05.3399701Z ++ echo 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:57:05.3400929Z ++ awk -F '[:,]' '{print $2}' 2024-06-26T04:57:05.3454477Z + DOCKER_TAG=91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:57:05.3455822Z + echo docker-tag=91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:57:05.3457760Z + echo docker-image=308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:57:05.3505145Z ##[group]Run set +e 2024-06-26T04:57:05.3505544Z set +e 2024-06-26T04:57:05.3505927Z set -x 2024-06-26T04:57:05.3506235Z  2024-06-26T04:57:05.3506548Z login() { 2024-06-26T04:57:05.3507241Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2024-06-26T04:57:05.3508009Z } 2024-06-26T04:57:05.3508311Z  2024-06-26T04:57:05.3508615Z retry () { 2024-06-26T04:57:05.3509025Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2024-06-26T04:57:05.3509500Z } 2024-06-26T04:57:05.3509824Z  2024-06-26T04:57:05.3510155Z retry login "${DOCKER_REGISTRY}" 2024-06-26T04:57:05.3510595Z  2024-06-26T04:57:05.3511095Z # Check if image already exists, if it does then skip building it 2024-06-26T04:57:05.3511841Z if docker manifest inspect "${DOCKER_IMAGE}"; then 2024-06-26T04:57:05.3512562Z  exit 0 2024-06-26T04:57:05.3512895Z fi 2024-06-26T04:57:05.3513203Z  2024-06-26T04:57:05.3513719Z # NB: This part requires a full checkout. Otherwise, the merge base will 2024-06-26T04:57:05.3514594Z # be empty. The default action would be to continue rebuild the image 2024-06-26T04:57:05.3515375Z if [[ "$BASE_REVISION" = "$(git rev-parse HEAD)" ]]; then 2024-06-26T04:57:05.3516076Z  # if we're on the base branch then use the parent commit 2024-06-26T04:57:05.3516706Z  MERGE_BASE=$(git rev-parse HEAD~) 2024-06-26T04:57:05.3517166Z else 2024-06-26T04:57:05.3517648Z  # otherwise we're on a PR, so use the most recent base commit 2024-06-26T04:57:05.3518373Z  MERGE_BASE=$(git merge-base HEAD "$BASE_REVISION") 2024-06-26T04:57:05.3518911Z fi 2024-06-26T04:57:05.3519199Z  2024-06-26T04:57:05.3519542Z if [[ -z "${MERGE_BASE}" ]]; then 2024-06-26T04:57:05.3520073Z  echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2024-06-26T04:57:05.3520627Z  2024-06-26T04:57:05.3521307Z  echo "Finding merge base only works with full checkout, please set fetch-depth to 0, continuing ..." 2024-06-26T04:57:05.3522117Z  exit 0 2024-06-26T04:57:05.3522450Z fi 2024-06-26T04:57:05.3522828Z  2024-06-26T04:57:05.3523299Z if ! git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}"; then 2024-06-26T04:57:05.3524526Z  echo "Directory '${DOCKER_BUILD_DIR}' not found in commit $MERGE_BASE, you should rebase onto a more recent commit" 2024-06-26T04:57:05.3525372Z  exit 1 2024-06-26T04:57:05.3525703Z fi 2024-06-26T04:57:05.3526011Z  2024-06-26T04:57:05.3526614Z PREVIOUS_DOCKER_TAG=$(git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}") 2024-06-26T04:57:05.3527768Z # If no image exists but the hash is the same as the previous hash then we should error out here 2024-06-26T04:57:05.3528685Z if [[ "${PREVIOUS_DOCKER_TAG}" == "${DOCKER_TAG}" ]]; then 2024-06-26T04:57:05.3529703Z  echo "WARNING: Something has gone wrong and the previous image isn't available for the merge-base of your branch" 2024-06-26T04:57:05.3530845Z  echo " Will re-build docker image to store in local cache, TTS may be longer" 2024-06-26T04:57:05.3531536Z fi 2024-06-26T04:57:05.3531873Z  2024-06-26T04:57:05.3532366Z echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2024-06-26T04:57:05.3540223Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T04:57:05.3540760Z env: 2024-06-26T04:57:05.3541055Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:57:05.3541449Z DOCKER_BUILD_DIR: .ci/docker 2024-06-26T04:57:05.3541945Z BASE_REVISION: 4b9c9a9cc9c9283380a011310ba180c105c3dcb9 2024-06-26T04:57:05.3543067Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:57:05.3544174Z DOCKER_TAG: 91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:57:05.3561476Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-06-26T04:57:05.3562121Z ##[endgroup] 2024-06-26T04:57:05.3584688Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-06-26T04:57:05.3585455Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-06-26T04:57:05.3587404Z + aws ecr get-login-password --region us-east-1 2024-06-26T04:57:05.3588291Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-06-26T04:57:05.7594819Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2024-06-26T04:57:05.7596391Z Configure a credential helper to remove this warning. See 2024-06-26T04:57:05.7597726Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2024-06-26T04:57:05.7598298Z 2024-06-26T04:57:05.7598426Z Login Succeeded 2024-06-26T04:57:05.7606720Z + docker manifest inspect 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:57:05.9573940Z { 2024-06-26T04:57:05.9574385Z "schemaVersion": 2, 2024-06-26T04:57:05.9575002Z "mediaType": "application/vnd.docker.distribution.manifest.v2+json", 2024-06-26T04:57:05.9575944Z "config": { 2024-06-26T04:57:05.9576813Z "mediaType": "application/vnd.docker.container.image.v1+json", 2024-06-26T04:57:05.9577506Z "size": 43620, 2024-06-26T04:57:05.9578414Z "digest": "sha256:c557aad093b3e05d2c865978f4d0f2394ea4fa88c1e2f3e78bbc35645582fa29" 2024-06-26T04:57:05.9579586Z }, 2024-06-26T04:57:05.9579858Z "layers": [ 2024-06-26T04:57:05.9580156Z { 2024-06-26T04:57:05.9580621Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9581210Z "size": 28584223, 2024-06-26T04:57:05.9581815Z "digest": "sha256:560c024910bebac6b404791af28ebd48a8289303b8377d17b67ffdfe52754f2a" 2024-06-26T04:57:05.9582473Z }, 2024-06-26T04:57:05.9582731Z { 2024-06-26T04:57:05.9583191Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9583782Z "size": 1822, 2024-06-26T04:57:05.9584352Z "digest": "sha256:d56c75e5f4d98716b0a5d7d6c0683f689a92d377a19b52e2b1fe4ea37d275ffb" 2024-06-26T04:57:05.9585010Z }, 2024-06-26T04:57:05.9585282Z { 2024-06-26T04:57:05.9585732Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9587125Z "size": 313359083, 2024-06-26T04:57:05.9587992Z + exit 0 2024-06-26T04:57:05.9590909Z "digest": "sha256:402182948e899a656a2f36e5dac9a5e518bfbb09b7ac88a68b8cd56fa0fb1f5d" 2024-06-26T04:57:05.9591890Z }, 2024-06-26T04:57:05.9592298Z { 2024-06-26T04:57:05.9592940Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9593915Z "size": 804, 2024-06-26T04:57:05.9594444Z "digest": "sha256:a07c3239eb667d17b1272a8fe8ee6e0a16fb5311e35a2edb710754b1b2204d6e" 2024-06-26T04:57:05.9595055Z }, 2024-06-26T04:57:05.9595277Z { 2024-06-26T04:57:05.9595679Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9596224Z "size": 79404485, 2024-06-26T04:57:05.9596775Z "digest": "sha256:cadd53e932c09e175e1dd71459e5df3e2fd0f06d364dd9ac85681f529dcdfc3c" 2024-06-26T04:57:05.9597380Z }, 2024-06-26T04:57:05.9597601Z { 2024-06-26T04:57:05.9598014Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9598559Z "size": 546, 2024-06-26T04:57:05.9599244Z "digest": "sha256:b9491f8e87658bf406a9098a6af3453d8ef49e3282ce6f56309d77eb8a9c09c7" 2024-06-26T04:57:05.9599850Z }, 2024-06-26T04:57:05.9600057Z { 2024-06-26T04:57:05.9600468Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9601090Z "size": 1283, 2024-06-26T04:57:05.9601609Z "digest": "sha256:9f184530f2e2aad5d626add328eda59c1c202a68475c8b37b4ba8d40da7de02e" 2024-06-26T04:57:05.9614279Z }, 2024-06-26T04:57:05.9614685Z { 2024-06-26T04:57:05.9615614Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9616391Z "size": 485, 2024-06-26T04:57:05.9617088Z "digest": "sha256:c6150e458a983b4722772b390dd307ae7db526c878eab9d12735ae73bc79be32" 2024-06-26T04:57:05.9618078Z }, 2024-06-26T04:57:05.9618440Z { 2024-06-26T04:57:05.9619121Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9620152Z "size": 110, 2024-06-26T04:57:05.9621113Z "digest": "sha256:bfaf1c5525d9cd0c4bfbd66acfb4408dd6fa22607e67875d19f67be0a47f2a16" 2024-06-26T04:57:05.9622186Z }, 2024-06-26T04:57:05.9622585Z { 2024-06-26T04:57:05.9623313Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9624246Z "size": 3686, 2024-06-26T04:57:05.9625124Z "digest": "sha256:869aa8bd3e9d0685ef206b56581e69a41a74b27a9540495c8e80f7db2fb6d94c" 2024-06-26T04:57:05.9626165Z }, 2024-06-26T04:57:05.9626576Z { 2024-06-26T04:57:05.9627340Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9628318Z "size": 1905, 2024-06-26T04:57:05.9629179Z "digest": "sha256:db1ea1e5664a7f25a65ffc51ae30639b7dd4bdf025aec490c770da0f511369fd" 2024-06-26T04:57:05.9630223Z }, 2024-06-26T04:57:05.9630632Z { 2024-06-26T04:57:05.9631386Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9632419Z "size": 700, 2024-06-26T04:57:05.9633330Z "digest": "sha256:f4fc602d79e2d965373aaaf2c8cfe6534d2b90c33a7c655586c1b70e0f6b4845" 2024-06-26T04:57:05.9634347Z }, 2024-06-26T04:57:05.9634692Z { 2024-06-26T04:57:05.9635416Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9636434Z "size": 2665563591, 2024-06-26T04:57:05.9637479Z "digest": "sha256:28d6bdf30db3cec4cb73a388385ec3e7ae3a8e668a35d7d3a7efb9e2f5402239" 2024-06-26T04:57:05.9638438Z }, 2024-06-26T04:57:05.9638819Z { 2024-06-26T04:57:05.9639593Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9640674Z "size": 32, 2024-06-26T04:57:05.9641664Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-06-26T04:57:05.9642687Z }, 2024-06-26T04:57:05.9643013Z { 2024-06-26T04:57:05.9643616Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9644482Z "size": 381, 2024-06-26T04:57:05.9645001Z "digest": "sha256:96c42d4a99b046ae057ee19b9ef481e45bbbbb21455e2a258b1680a2bdf97111" 2024-06-26T04:57:05.9645846Z }, 2024-06-26T04:57:05.9646124Z { 2024-06-26T04:57:05.9646665Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9647208Z "size": 104, 2024-06-26T04:57:05.9647729Z "digest": "sha256:19990b8b1757bd175f09eecf7749f395a6c1b4a897cb5e29cf162ba637cafe3a" 2024-06-26T04:57:05.9648323Z }, 2024-06-26T04:57:05.9648542Z { 2024-06-26T04:57:05.9648956Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9649484Z "size": 231, 2024-06-26T04:57:05.9650008Z "digest": "sha256:807599da5b1a88e6bbf37a02869d0fcd366dadf13b600d32369b410e25ca2a11" 2024-06-26T04:57:05.9650653Z }, 2024-06-26T04:57:05.9651020Z { 2024-06-26T04:57:05.9651434Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9651975Z "size": 2839017, 2024-06-26T04:57:05.9652501Z "digest": "sha256:3bc392ea68959eb298ae507bbc36afaf35192f3f5416bf81124b60bdbb94ccb6" 2024-06-26T04:57:05.9653118Z }, 2024-06-26T04:57:05.9653338Z { 2024-06-26T04:57:05.9654076Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9654634Z "size": 1989, 2024-06-26T04:57:05.9655160Z "digest": 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"sha256:f4fc602d79e2d965373aaaf2c8cfe6534d2b90c33a7c655586c1b70e0f6b4845" 2024-06-26T04:57:05.9776807Z }, 2024-06-26T04:57:05.9777015Z { 2024-06-26T04:57:05.9777428Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9777968Z "size": 139, 2024-06-26T04:57:05.9778616Z "digest": "sha256:c5f8bd342f184c62bdcd81b70f09e0e68f90c12e194ef32c0a1fc63455d2915f" 2024-06-26T04:57:05.9779242Z }, 2024-06-26T04:57:05.9779768Z { 2024-06-26T04:57:05.9780320Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9780869Z "size": 32, 2024-06-26T04:57:05.9781397Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-06-26T04:57:05.9782156Z }, 2024-06-26T04:57:05.9782376Z { 2024-06-26T04:57:05.9782789Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9783442Z "size": 158, 2024-06-26T04:57:05.9783968Z "digest": "sha256:63417469eaf6ea97e6fc339931afea4109714caadc4fbd7d2e347ff3434c95d2" 2024-06-26T04:57:05.9784667Z }, 2024-06-26T04:57:05.9784875Z { 2024-06-26T04:57:05.9785293Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9785831Z "size": 908, 2024-06-26T04:57:05.9786412Z "digest": "sha256:3b971402f4833579e12f197dc43266852344dcc3fb423e6548f6779c0807689c" 2024-06-26T04:57:05.9786999Z }, 2024-06-26T04:57:05.9787217Z { 2024-06-26T04:57:05.9787763Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9788489Z "size": 700, 2024-06-26T04:57:05.9789014Z "digest": "sha256:f4fc602d79e2d965373aaaf2c8cfe6534d2b90c33a7c655586c1b70e0f6b4845" 2024-06-26T04:57:05.9789610Z }, 2024-06-26T04:57:05.9789934Z { 2024-06-26T04:57:05.9790441Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9790976Z "size": 134, 2024-06-26T04:57:05.9791508Z "digest": "sha256:c9ba29c920afe8702e5392d6afe72b7b6f916eefdc433bd3e2820653273a114c" 2024-06-26T04:57:05.9792224Z }, 2024-06-26T04:57:05.9792430Z { 2024-06-26T04:57:05.9792842Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9793382Z "size": 32, 2024-06-26T04:57:05.9793898Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-06-26T04:57:05.9794598Z }, 2024-06-26T04:57:05.9794818Z { 2024-06-26T04:57:05.9795217Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9796154Z "size": 156, 2024-06-26T04:57:05.9796727Z "digest": "sha256:d706890df26378f388ccfcfde99f4b922e07c64147857bf2d11cc3da2a595453" 2024-06-26T04:57:05.9797321Z }, 2024-06-26T04:57:05.9797540Z { 2024-06-26T04:57:05.9797950Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9798477Z "size": 1579, 2024-06-26T04:57:05.9799012Z "digest": "sha256:3b1f57fd0104cbcbd9d7c8ec985cbc371f6f92249a01fe6698314459b9b969a9" 2024-06-26T04:57:05.9799624Z }, 2024-06-26T04:57:05.9799941Z { 2024-06-26T04:57:05.9800398Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9801014Z "size": 32, 2024-06-26T04:57:05.9801530Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-06-26T04:57:05.9802270Z }, 2024-06-26T04:57:05.9802490Z { 2024-06-26T04:57:05.9802892Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9803551Z "size": 128, 2024-06-26T04:57:05.9804066Z "digest": "sha256:565227b9e7141060948b22cc43c4d8a33df043204402bb61b9be3ecd0d97f89d" 2024-06-26T04:57:05.9804645Z }, 2024-06-26T04:57:05.9804865Z { 2024-06-26T04:57:05.9805279Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9805922Z "size": 379, 2024-06-26T04:57:05.9806495Z "digest": 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"sha256:48eeae0fe99ddcb85faf46544e9adcae44005d6daa9c5ccb3a0a2ae6a8eb16ae" 2024-06-26T04:57:05.9822483Z }, 2024-06-26T04:57:05.9822687Z { 2024-06-26T04:57:05.9823100Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9823645Z "size": 7944, 2024-06-26T04:57:05.9824160Z "digest": "sha256:80d1fccb27712d5d0857704d19ce3079e31961bccaad65c8f4a6b0838d2a783e" 2024-06-26T04:57:05.9824950Z }, 2024-06-26T04:57:05.9825300Z { 2024-06-26T04:57:05.9825740Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9826284Z "size": 8065, 2024-06-26T04:57:05.9827008Z "digest": "sha256:cdd9e4d7a0b1ae2d382df45cd7edb679c42951b3e8aeacbab693fa0fd6c14291" 2024-06-26T04:57:05.9827674Z }, 2024-06-26T04:57:05.9827894Z { 2024-06-26T04:57:05.9828496Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9829025Z "size": 301, 2024-06-26T04:57:05.9829559Z "digest": "sha256:f14381b4af2d3d15ac41db40423af7ce0d42df396771ed3a5fe5ec74e6eab1a7" 2024-06-26T04:57:05.9830172Z }, 2024-06-26T04:57:05.9830383Z { 2024-06-26T04:57:05.9830794Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9831332Z "size": 32, 2024-06-26T04:57:05.9831844Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-06-26T04:57:05.9832451Z }, 2024-06-26T04:57:05.9832665Z { 2024-06-26T04:57:05.9833061Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9833599Z "size": 108, 2024-06-26T04:57:05.9834106Z "digest": "sha256:f0c45732a66338f7cecf3516738862709286aa73f07ff2584e54c1f6d808e7ef" 2024-06-26T04:57:05.9834803Z }, 2024-06-26T04:57:05.9835021Z { 2024-06-26T04:57:05.9835439Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9835972Z "size": 54145775, 2024-06-26T04:57:05.9836509Z "digest": "sha256:60298c8310b13acaf88912bfae3a4ce350982bcc020cf95291453458642600aa" 2024-06-26T04:57:05.9837113Z }, 2024-06-26T04:57:05.9837321Z { 2024-06-26T04:57:05.9837730Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:57:05.9838489Z "size": 32, 2024-06-26T04:57:05.9839000Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-06-26T04:57:05.9839802Z } 2024-06-26T04:57:05.9840021Z ] 2024-06-26T04:57:05.9840225Z } 2024-06-26T04:57:05.9950271Z ##[group]Run tag=${ECR_DOCKER_IMAGE##*/} 2024-06-26T04:57:05.9950739Z tag=${ECR_DOCKER_IMAGE##*/} 2024-06-26T04:57:05.9951268Z echo "docker pull ghcr.io/pytorch/ci-image:${tag/:/-}" 2024-06-26T04:57:05.9959609Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T04:57:05.9960102Z env: 2024-06-26T04:57:05.9960353Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:57:05.9961368Z ECR_DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:57:05.9962359Z ##[endgroup] 2024-06-26T04:57:05.9987216Z docker pull ghcr.io/pytorch/ci-image:pytorch-linux-focal-py3.12-clang10-91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:57:06.0045112Z ##[group]Run pytorch/test-infra/.github/actions/pull-docker-image@main 2024-06-26T04:57:06.0045672Z with: 2024-06-26T04:57:06.0046551Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:57:06.0047685Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-06-26T04:57:06.0048171Z env: 2024-06-26T04:57:06.0048420Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:57:06.0048742Z ##[endgroup] 2024-06-26T04:57:06.0068126Z ##[group]Run set -x 2024-06-26T04:57:06.0068450Z set -x 2024-06-26T04:57:06.0068728Z set +e 2024-06-26T04:57:06.0068997Z  2024-06-26T04:57:06.0069238Z login() { 2024-06-26T04:57:06.0069890Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2024-06-26T04:57:06.0070601Z } 2024-06-26T04:57:06.0070846Z  2024-06-26T04:57:06.0071160Z retry () { 2024-06-26T04:57:06.0071517Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2024-06-26T04:57:06.0071943Z } 2024-06-26T04:57:06.0072195Z  2024-06-26T04:57:06.0072471Z retry login "${DOCKER_REGISTRY}" 2024-06-26T04:57:06.0072867Z  2024-06-26T04:57:06.0073116Z set -e 2024-06-26T04:57:06.0073555Z # ignore output since only exit code is used for conditional 2024-06-26T04:57:06.0074242Z # only pull docker image if it's not available locally 2024-06-26T04:57:06.0075008Z if ! docker inspect --type=image "${DOCKER_IMAGE}" >/dev/null 2>/dev/null; then 2024-06-26T04:57:06.0075684Z  retry docker pull "${DOCKER_IMAGE}" 2024-06-26T04:57:06.0076087Z fi 2024-06-26T04:57:06.0083289Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T04:57:06.0083769Z env: 2024-06-26T04:57:06.0084011Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:57:06.0084956Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:57:06.0086064Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-06-26T04:57:06.0086552Z ##[endgroup] 2024-06-26T04:57:06.0107500Z + set +e 2024-06-26T04:57:06.0108227Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-06-26T04:57:06.0108904Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-06-26T04:57:06.0111104Z + aws ecr get-login-password --region us-east-1 2024-06-26T04:57:06.0112260Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-06-26T04:57:06.4081248Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2024-06-26T04:57:06.4082571Z Configure a credential helper to remove this warning. See 2024-06-26T04:57:06.4083670Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2024-06-26T04:57:06.4084208Z 2024-06-26T04:57:06.4084317Z Login Succeeded 2024-06-26T04:57:06.4091228Z + set -e 2024-06-26T04:57:06.4092908Z + docker inspect --type=image 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:57:06.4220917Z + retry docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:57:06.4222683Z + docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:57:06.6212177Z 91382da70d5719cd7007b6b80b71d2f48398f6b7: Pulling from pytorch/pytorch-linux-focal-py3.12-clang10 2024-06-26T04:57:06.6225825Z 560c024910be: Pulling fs layer 2024-06-26T04:57:06.6226457Z d56c75e5f4d9: Pulling fs layer 2024-06-26T04:57:06.6227076Z 402182948e89: Pulling fs layer 2024-06-26T04:57:06.6227687Z a07c3239eb66: Pulling fs layer 2024-06-26T04:57:06.6228278Z cadd53e932c0: Pulling fs layer 2024-06-26T04:57:06.6229182Z b9491f8e8765: Pulling fs layer 2024-06-26T04:57:06.6229813Z 9f184530f2e2: Pulling fs layer 2024-06-26T04:57:06.6230423Z c6150e458a98: Pulling fs layer 2024-06-26T04:57:06.6231030Z bfaf1c5525d9: Pulling fs layer 2024-06-26T04:57:06.6231678Z 869aa8bd3e9d: Pulling fs layer 2024-06-26T04:57:06.6232300Z db1ea1e5664a: Pulling fs layer 2024-06-26T04:57:06.6232982Z f4fc602d79e2: Pulling fs layer 2024-06-26T04:57:06.6233598Z 28d6bdf30db3: Pulling fs layer 2024-06-26T04:57:06.6234188Z 4f4fb700ef54: Pulling fs layer 2024-06-26T04:57:06.6234802Z 96c42d4a99b0: Pulling fs layer 2024-06-26T04:57:06.6235401Z 19990b8b1757: Pulling fs layer 2024-06-26T04:57:06.6235982Z 807599da5b1a: Pulling fs layer 2024-06-26T04:57:06.6236569Z 3bc392ea6895: Pulling fs layer 2024-06-26T04:57:06.6237156Z 51d0409ee5a5: Pulling fs layer 2024-06-26T04:57:06.6237769Z 2ab4a482ebb6: Pulling fs layer 2024-06-26T04:57:06.6238335Z 7bd34ce32c4e: Pulling fs layer 2024-06-26T04:57:06.6239028Z a07c3239eb66: Waiting 2024-06-26T04:57:06.6239641Z bc7869b23905: Pulling fs layer 2024-06-26T04:57:06.6240291Z 5309dec9ef4b: Pulling fs layer 2024-06-26T04:57:06.6240988Z 869aa8bd3e9d: Waiting 2024-06-26T04:57:06.6241537Z b9491f8e8765: Waiting 2024-06-26T04:57:06.6242105Z f97cbbc932b6: Pulling fs layer 2024-06-26T04:57:06.6242728Z db1ea1e5664a: Waiting 2024-06-26T04:57:06.6243205Z c6150e458a98: Waiting 2024-06-26T04:57:06.6243480Z 9f184530f2e2: Waiting 2024-06-26T04:57:06.6243849Z 98cb4a93632e: Pulling fs layer 2024-06-26T04:57:06.6244187Z bfaf1c5525d9: Waiting 2024-06-26T04:57:06.6244466Z f4fc602d79e2: Waiting 2024-06-26T04:57:06.6244765Z cadd53e932c0: Waiting 2024-06-26T04:57:06.6245073Z 3fe82dcf5152: Pulling fs layer 2024-06-26T04:57:06.6245417Z 28d6bdf30db3: Waiting 2024-06-26T04:57:06.6245715Z 4f4fb700ef54: Waiting 2024-06-26T04:57:06.6246021Z ab9c373a715d: Pulling fs layer 2024-06-26T04:57:06.6246359Z 96c42d4a99b0: Waiting 2024-06-26T04:57:06.6246655Z 5309dec9ef4b: Waiting 2024-06-26T04:57:06.6246970Z aa2d7818db84: Pulling fs layer 2024-06-26T04:57:06.6247306Z 19990b8b1757: Waiting 2024-06-26T04:57:06.6247714Z 7bd34ce32c4e: Waiting 2024-06-26T04:57:06.6248160Z f733c4f50868: Pulling fs layer 2024-06-26T04:57:06.6248572Z a84efe42f3a3: Pulling fs layer 2024-06-26T04:57:06.6248909Z 3bc392ea6895: Waiting 2024-06-26T04:57:06.6249231Z 796fdea9be06: Pulling fs layer 2024-06-26T04:57:06.6249577Z 58e9d24c5602: Pulling fs layer 2024-06-26T04:57:06.6249918Z 2ab4a482ebb6: Waiting 2024-06-26T04:57:06.6250210Z 51d0409ee5a5: Waiting 2024-06-26T04:57:06.6250510Z 448b0bc764da: Pulling fs layer 2024-06-26T04:57:06.6251047Z 807599da5b1a: Waiting 2024-06-26T04:57:06.6251377Z f97cbbc932b6: Waiting 2024-06-26T04:57:06.6251656Z bc7869b23905: Waiting 2024-06-26T04:57:06.6251963Z ab1f0809c531: Pulling fs layer 2024-06-26T04:57:06.6252304Z 796fdea9be06: Waiting 2024-06-26T04:57:06.6252596Z 403d548a9d3f: Pulling fs layer 2024-06-26T04:57:06.6252936Z 3fe82dcf5152: Waiting 2024-06-26T04:57:06.6253230Z 58e9d24c5602: Waiting 2024-06-26T04:57:06.6253684Z 431f6d892eb4: Pulling fs layer 2024-06-26T04:57:06.6254050Z 5df7e38e5d56: Pulling fs layer 2024-06-26T04:57:06.6254390Z 98cb4a93632e: Waiting 2024-06-26T04:57:06.6254672Z 448b0bc764da: Waiting 2024-06-26T04:57:06.6254983Z ed9cdcecda09: Pulling fs layer 2024-06-26T04:57:06.6255318Z ab9c373a715d: Waiting 2024-06-26T04:57:06.6255633Z e1bf0caebceb: Pulling fs layer 2024-06-26T04:57:06.6255975Z 431f6d892eb4: Waiting 2024-06-26T04:57:06.6256324Z fb2d6dc9808d: Pulling fs layer 2024-06-26T04:57:06.6256681Z 9068f3d11281: Pulling fs layer 2024-06-26T04:57:06.6257013Z a84efe42f3a3: Waiting 2024-06-26T04:57:06.6257313Z e01067f03626: Pulling fs layer 2024-06-26T04:57:06.6257683Z ab1f0809c531: Waiting 2024-06-26T04:57:06.6257980Z 5df7e38e5d56: Waiting 2024-06-26T04:57:06.6258258Z 403d548a9d3f: Waiting 2024-06-26T04:57:06.6258652Z e1bf0caebceb: Waiting 2024-06-26T04:57:06.6259112Z be74a2d30609: Pulling fs layer 2024-06-26T04:57:06.6259699Z 9068f3d11281: Waiting 2024-06-26T04:57:06.6260270Z bfb36cacae9a: Pulling fs layer 2024-06-26T04:57:06.6261017Z 144221b84a63: Pulling fs layer 2024-06-26T04:57:06.6261366Z 6c30dfdcf5a2: Pulling fs layer 2024-06-26T04:57:06.6261728Z a78edc01be60: Pulling fs layer 2024-06-26T04:57:06.6262065Z e01067f03626: Waiting 2024-06-26T04:57:06.6262360Z b92d914a069c: Pulling fs layer 2024-06-26T04:57:06.6262703Z be74a2d30609: Waiting 2024-06-26T04:57:06.6263000Z fb2d6dc9808d: Waiting 2024-06-26T04:57:06.6263319Z ef77b35f5994: Pulling fs layer 2024-06-26T04:57:06.6263697Z bfb36cacae9a: Waiting 2024-06-26T04:57:06.6264124Z 144221b84a63: Waiting 2024-06-26T04:57:06.6264647Z ddd5132eddf6: Pulling fs layer 2024-06-26T04:57:06.6265205Z 6c30dfdcf5a2: Waiting 2024-06-26T04:57:06.6265507Z a78edc01be60: Waiting 2024-06-26T04:57:06.6265831Z 733134e2ad51: Pulling fs layer 2024-06-26T04:57:06.6266177Z b92d914a069c: Waiting 2024-06-26T04:57:06.6266493Z 2fb71352883c: Pulling fs layer 2024-06-26T04:57:06.6266853Z ddd5132eddf6: Waiting 2024-06-26T04:57:06.6267201Z 2fb71352883c: Waiting 2024-06-26T04:57:06.6267630Z 733134e2ad51: Waiting 2024-06-26T04:57:06.6268142Z f3c560e868a6: Pulling fs layer 2024-06-26T04:57:06.6268646Z ef77b35f5994: Waiting 2024-06-26T04:57:06.6268957Z 440771933ac6: Pulling fs layer 2024-06-26T04:57:06.6269409Z 526b68d3127a: Pulling fs layer 2024-06-26T04:57:06.6269819Z db7fe98fd9ce: Pulling fs layer 2024-06-26T04:57:06.6270233Z 998e82e7f434: Pulling fs layer 2024-06-26T04:57:06.6270580Z 526b68d3127a: Waiting 2024-06-26T04:57:06.6270871Z 440771933ac6: Waiting 2024-06-26T04:57:06.6271167Z c5f8bd342f18: Pulling fs layer 2024-06-26T04:57:06.6271512Z f3c560e868a6: Waiting 2024-06-26T04:57:06.6271832Z 998e82e7f434: Waiting 2024-06-26T04:57:06.6272146Z 63417469eaf6: Pulling fs layer 2024-06-26T04:57:06.6272527Z c5f8bd342f18: Waiting 2024-06-26T04:57:06.6272843Z 3b971402f483: Pulling fs layer 2024-06-26T04:57:06.6273195Z 63417469eaf6: Waiting 2024-06-26T04:57:06.6273495Z c9ba29c920af: Pulling fs layer 2024-06-26T04:57:06.6273847Z db7fe98fd9ce: Waiting 2024-06-26T04:57:06.6274159Z d706890df263: Pulling fs layer 2024-06-26T04:57:06.6274489Z 3b971402f483: Waiting 2024-06-26T04:57:06.6274787Z c9ba29c920af: Waiting 2024-06-26T04:57:06.6275096Z 3b1f57fd0104: Pulling fs layer 2024-06-26T04:57:06.6275456Z d706890df263: Waiting 2024-06-26T04:57:06.6275807Z 3b1f57fd0104: Waiting 2024-06-26T04:57:06.6276115Z 565227b9e714: Pulling fs layer 2024-06-26T04:57:06.6276494Z f7be3bf4a877: Pulling fs layer 2024-06-26T04:57:06.6276884Z 85b9d4888a5f: Pulling fs layer 2024-06-26T04:57:06.6277229Z f7be3bf4a877: Waiting 2024-06-26T04:57:06.6277578Z 565227b9e714: Waiting 2024-06-26T04:57:06.6277884Z c48375725932: Pulling fs layer 2024-06-26T04:57:06.6278511Z 2ee46f0bbde3: Pulling fs layer 2024-06-26T04:57:06.6278859Z e1d9c59b8ecb: Pulling fs layer 2024-06-26T04:57:06.6279297Z 48eeae0fe99d: Pulling fs layer 2024-06-26T04:57:06.6279637Z 85b9d4888a5f: Waiting 2024-06-26T04:57:06.6279977Z c48375725932: Waiting 2024-06-26T04:57:06.6280324Z 2ee46f0bbde3: Waiting 2024-06-26T04:57:06.6280778Z 80d1fccb2771: Pulling fs layer 2024-06-26T04:57:06.6281108Z 48eeae0fe99d: Waiting 2024-06-26T04:57:06.6281415Z e1d9c59b8ecb: Waiting 2024-06-26T04:57:06.6281827Z 80d1fccb2771: Waiting 2024-06-26T04:57:06.6282127Z cdd9e4d7a0b1: Pulling fs layer 2024-06-26T04:57:06.6282560Z f14381b4af2d: Pulling fs layer 2024-06-26T04:57:06.6282906Z cdd9e4d7a0b1: Waiting 2024-06-26T04:57:06.6283256Z f14381b4af2d: Waiting 2024-06-26T04:57:06.6283580Z f0c45732a663: Pulling fs layer 2024-06-26T04:57:06.6283987Z 60298c8310b1: Pulling fs layer 2024-06-26T04:57:06.6284319Z f0c45732a663: Waiting 2024-06-26T04:57:06.6284610Z 60298c8310b1: Waiting 2024-06-26T04:57:06.6941716Z d56c75e5f4d9: Verifying Checksum 2024-06-26T04:57:06.6942242Z d56c75e5f4d9: Download complete 2024-06-26T04:57:06.7687470Z a07c3239eb66: Download complete 2024-06-26T04:57:06.9656573Z 560c024910be: Verifying Checksum 2024-06-26T04:57:06.9657155Z 560c024910be: Download complete 2024-06-26T04:57:07.0274173Z b9491f8e8765: Verifying Checksum 2024-06-26T04:57:07.0274926Z b9491f8e8765: Download complete 2024-06-26T04:57:07.0966009Z 9f184530f2e2: Download complete 2024-06-26T04:57:07.1726257Z c6150e458a98: Download complete 2024-06-26T04:57:07.2444607Z bfaf1c5525d9: Verifying Checksum 2024-06-26T04:57:07.2445073Z bfaf1c5525d9: Download complete 2024-06-26T04:57:07.3274668Z 869aa8bd3e9d: Download complete 2024-06-26T04:57:07.4342224Z db1ea1e5664a: Verifying Checksum 2024-06-26T04:57:07.4343246Z db1ea1e5664a: Download complete 2024-06-26T04:57:07.5065195Z f4fc602d79e2: Verifying Checksum 2024-06-26T04:57:07.5065668Z f4fc602d79e2: Download complete 2024-06-26T04:57:07.6110469Z cadd53e932c0: Verifying Checksum 2024-06-26T04:57:07.6110959Z cadd53e932c0: Download complete 2024-06-26T04:57:07.6208262Z 4f4fb700ef54: Verifying Checksum 2024-06-26T04:57:07.6209027Z 4f4fb700ef54: Download complete 2024-06-26T04:57:07.7000911Z 96c42d4a99b0: Verifying Checksum 2024-06-26T04:57:07.7001455Z 96c42d4a99b0: Download complete 2024-06-26T04:57:07.7699882Z 19990b8b1757: Verifying Checksum 2024-06-26T04:57:07.7700624Z 19990b8b1757: Download complete 2024-06-26T04:57:07.8698169Z 560c024910be: Pull complete 2024-06-26T04:57:07.9116720Z 807599da5b1a: Verifying Checksum 2024-06-26T04:57:07.9117369Z 807599da5b1a: Download complete 2024-06-26T04:57:07.9147614Z d56c75e5f4d9: Pull complete 2024-06-26T04:57:08.0192917Z 3bc392ea6895: Verifying Checksum 2024-06-26T04:57:08.0193432Z 3bc392ea6895: Download complete 2024-06-26T04:57:08.0935176Z 51d0409ee5a5: Verifying Checksum 2024-06-26T04:57:08.0935641Z 51d0409ee5a5: Download complete 2024-06-26T04:57:08.1562568Z 2ab4a482ebb6: Verifying Checksum 2024-06-26T04:57:08.1563063Z 2ab4a482ebb6: Download complete 2024-06-26T04:57:08.2245098Z 7bd34ce32c4e: Verifying Checksum 2024-06-26T04:57:08.2245738Z 7bd34ce32c4e: Download complete 2024-06-26T04:57:08.2963302Z bc7869b23905: Verifying Checksum 2024-06-26T04:57:08.3805819Z bc7869b23905: Download complete 2024-06-26T04:57:08.3806406Z 5309dec9ef4b: Download complete 2024-06-26T04:57:09.6474118Z f97cbbc932b6: Verifying Checksum 2024-06-26T04:57:09.6474616Z f97cbbc932b6: Download complete 2024-06-26T04:57:09.7148587Z 98cb4a93632e: Verifying Checksum 2024-06-26T04:57:09.7149425Z 98cb4a93632e: Download complete 2024-06-26T04:57:09.8016977Z 3fe82dcf5152: Verifying Checksum 2024-06-26T04:57:09.8017476Z 3fe82dcf5152: Download complete 2024-06-26T04:57:09.8145986Z 402182948e89: Verifying Checksum 2024-06-26T04:57:09.8146435Z 402182948e89: Download complete 2024-06-26T04:57:09.8779005Z ab9c373a715d: Download complete 2024-06-26T04:57:09.8836297Z aa2d7818db84: Verifying Checksum 2024-06-26T04:57:09.8836895Z aa2d7818db84: Download complete 2024-06-26T04:57:09.9511527Z f733c4f50868: Download complete 2024-06-26T04:57:10.0140060Z 796fdea9be06: Download complete 2024-06-26T04:57:10.0892388Z 58e9d24c5602: Verifying Checksum 2024-06-26T04:57:10.0893153Z 58e9d24c5602: Download complete 2024-06-26T04:57:10.1876589Z 448b0bc764da: Download complete 2024-06-26T04:57:10.2573043Z ab1f0809c531: Verifying Checksum 2024-06-26T04:57:10.2573853Z ab1f0809c531: Download complete 2024-06-26T04:57:10.3316860Z 403d548a9d3f: Verifying Checksum 2024-06-26T04:57:10.3318004Z 403d548a9d3f: Download complete 2024-06-26T04:57:10.3951315Z 431f6d892eb4: Verifying Checksum 2024-06-26T04:57:10.3952026Z 431f6d892eb4: Download complete 2024-06-26T04:57:10.4686607Z 5df7e38e5d56: Verifying Checksum 2024-06-26T04:57:10.4687429Z 5df7e38e5d56: Download complete 2024-06-26T04:57:10.5532097Z ed9cdcecda09: Verifying Checksum 2024-06-26T04:57:10.5532554Z ed9cdcecda09: Download complete 2024-06-26T04:57:10.6245654Z e1bf0caebceb: Verifying Checksum 2024-06-26T04:57:10.6246286Z e1bf0caebceb: Download complete 2024-06-26T04:57:10.6939887Z fb2d6dc9808d: Download complete 2024-06-26T04:57:10.7623951Z 9068f3d11281: Download complete 2024-06-26T04:57:10.8343527Z e01067f03626: Verifying Checksum 2024-06-26T04:57:10.8344300Z e01067f03626: Download complete 2024-06-26T04:57:11.3268540Z be74a2d30609: Verifying Checksum 2024-06-26T04:57:11.3269312Z be74a2d30609: Download complete 2024-06-26T04:57:11.4019890Z bfb36cacae9a: Download complete 2024-06-26T04:57:11.4643356Z 144221b84a63: Verifying Checksum 2024-06-26T04:57:11.4644067Z 144221b84a63: Download complete 2024-06-26T04:57:11.5352649Z 6c30dfdcf5a2: Verifying Checksum 2024-06-26T04:57:11.5353257Z 6c30dfdcf5a2: Download complete 2024-06-26T04:57:11.6074725Z a78edc01be60: Download complete 2024-06-26T04:57:11.8650229Z b92d914a069c: Verifying Checksum 2024-06-26T04:57:11.8651008Z b92d914a069c: Download complete 2024-06-26T04:57:11.9466087Z ef77b35f5994: Verifying Checksum 2024-06-26T04:57:11.9466944Z ef77b35f5994: Download complete 2024-06-26T04:57:12.0223884Z ddd5132eddf6: Verifying Checksum 2024-06-26T04:57:12.0224715Z ddd5132eddf6: Download complete 2024-06-26T04:57:12.0939654Z 733134e2ad51: Verifying Checksum 2024-06-26T04:57:12.0940459Z 733134e2ad51: Download complete 2024-06-26T04:57:12.1728226Z 2fb71352883c: Verifying Checksum 2024-06-26T04:57:12.1728847Z 2fb71352883c: Download complete 2024-06-26T04:57:12.2508145Z f3c560e868a6: Download complete 2024-06-26T04:57:12.3227361Z 440771933ac6: Verifying Checksum 2024-06-26T04:57:12.3228025Z 440771933ac6: Download complete 2024-06-26T04:57:12.3952916Z 526b68d3127a: Download complete 2024-06-26T04:57:12.4727824Z db7fe98fd9ce: Verifying Checksum 2024-06-26T04:57:12.4728613Z db7fe98fd9ce: Download complete 2024-06-26T04:57:12.5478623Z 998e82e7f434: Download complete 2024-06-26T04:57:12.6210913Z c5f8bd342f18: Download complete 2024-06-26T04:57:12.6930617Z 63417469eaf6: Verifying Checksum 2024-06-26T04:57:12.6931216Z 63417469eaf6: Download complete 2024-06-26T04:57:12.7566281Z 3b971402f483: Verifying Checksum 2024-06-26T04:57:12.7567317Z 3b971402f483: Download complete 2024-06-26T04:57:12.8261475Z c9ba29c920af: Download complete 2024-06-26T04:57:12.9017522Z d706890df263: Verifying Checksum 2024-06-26T04:57:12.9018155Z d706890df263: Download complete 2024-06-26T04:57:12.9669494Z 3b1f57fd0104: Download complete 2024-06-26T04:57:13.0482256Z 565227b9e714: Verifying Checksum 2024-06-26T04:57:13.0482968Z 565227b9e714: Download complete 2024-06-26T04:57:13.1173862Z f7be3bf4a877: Verifying Checksum 2024-06-26T04:57:13.1174531Z f7be3bf4a877: Download complete 2024-06-26T04:57:13.1966128Z 85b9d4888a5f: Download complete 2024-06-26T04:57:13.2753775Z c48375725932: Verifying Checksum 2024-06-26T04:57:13.2754546Z c48375725932: Download complete 2024-06-26T04:57:13.4036283Z 2ee46f0bbde3: Verifying Checksum 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2024-06-26T04:58:48.4082159Z f0c45732a663: Pull complete 2024-06-26T04:58:50.0198018Z 60298c8310b1: Pull complete 2024-06-26T04:58:50.0906022Z Digest: sha256:7b399ecc0d0cb44dc26da833aa04b6714aa8a8efdabb4a4d766be163ffdd83c4 2024-06-26T04:58:50.0941937Z Status: Downloaded newer image for 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:58:50.0986150Z 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:58:50.1031210Z ##[group]Run echo "IN_ARC_RUNNER=$([ -f /.inarc ] && echo true || echo false)" >> "$GITHUB_OUTPUT" 2024-06-26T04:58:50.1032122Z echo "IN_ARC_RUNNER=$([ -f /.inarc ] && echo true || echo false)" >> "$GITHUB_OUTPUT" 2024-06-26T04:58:50.1221528Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T04:58:50.1222023Z env: 2024-06-26T04:58:50.1222274Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:58:50.1222605Z ##[endgroup] 2024-06-26T04:58:50.1286663Z ##[group]Run python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 2024-06-26T04:58:50.1287405Z python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 2024-06-26T04:58:50.1288091Z python3 -m tools.stats.monitor > usage_log.txt 2>&1 & 2024-06-26T04:58:50.1288736Z echo "monitor-script-pid=${!}" >> "${GITHUB_OUTPUT}" 2024-06-26T04:58:50.1295942Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T04:58:50.1296417Z env: 2024-06-26T04:58:50.1296674Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:58:50.1297001Z ##[endgroup] 2024-06-26T04:58:50.7151010Z Defaulting to user installation because normal site-packages is not writeable 2024-06-26T04:58:50.7424085Z Requirement already satisfied: psutil==5.9.1 in /home/ec2-user/.local/lib/python3.7/site-packages (5.9.1) 2024-06-26T04:58:50.7539795Z Requirement already satisfied: nvidia-ml-py==11.525.84 in /home/ec2-user/.local/lib/python3.7/site-packages (11.525.84) 2024-06-26T04:58:50.8757168Z Prepare all required actions 2024-06-26T04:58:50.8757645Z Getting action download info 2024-06-26T04:58:50.9864723Z Download action repository 'seemethere/download-artifact-s3@v4' (SHA:1da556a7aa0a088e3153970611f6c432d58e80e6) 2024-06-26T04:58:51.1760148Z Download action repository 'actions/download-artifact@v3' (SHA:9bc31d5ccc31df68ecc42ccf4149144866c47d8a) 2024-06-26T04:58:51.2984756Z ##[group]Run ./.github/actions/download-build-artifacts 2024-06-26T04:58:51.2985230Z with: 2024-06-26T04:58:51.2985492Z name: linux-focal-py3.12-clang10 2024-06-26T04:58:51.2985876Z s3-bucket: gha-artifacts 2024-06-26T04:58:51.2986190Z env: 2024-06-26T04:58:51.2986425Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:58:51.2986743Z ##[endgroup] 2024-06-26T04:58:51.3036097Z ##[group]Run seemethere/download-artifact-s3@v4 2024-06-26T04:58:51.3036515Z with: 2024-06-26T04:58:51.3036794Z name: linux-focal-py3.12-clang10 2024-06-26T04:58:51.3037182Z s3-bucket: gha-artifacts 2024-06-26T04:58:51.3037551Z region: us-east-1 2024-06-26T04:58:51.3037814Z env: 2024-06-26T04:58:51.3038062Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:58:51.3038383Z ##[endgroup] 2024-06-26T04:58:51.7696143Z (node:31827) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2024-06-26T04:58:51.7696882Z 2024-06-26T04:58:51.7697127Z Please migrate your code to use AWS SDK for JavaScript (v3). 2024-06-26T04:58:51.7698104Z For more information, check the migration guide at https://a.co/7PzMCcy 2024-06-26T04:58:51.7699007Z (Use `node --trace-warnings ...` to show where the warning was created) 2024-06-26T04:58:51.8434896Z Found 1 objects with prefix pytorch/pytorch/9673645538/linux-focal-py3.12-clang10/ 2024-06-26T04:58:51.8435907Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip 2024-06-26T04:58:58.7892051Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip 2024-06-26T04:58:58.7898429Z Artifact download has finished successfully 2024-06-26T04:58:58.8052412Z ##[group]Run unzip -o artifacts.zip 2024-06-26T04:58:58.8052877Z unzip -o artifacts.zip 2024-06-26T04:58:58.8060559Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T04:58:58.8061036Z env: 2024-06-26T04:58:58.8061299Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:58:58.8061624Z ##[endgroup] 2024-06-26T04:58:58.8123454Z Archive: artifacts.zip 2024-06-26T04:58:58.8124933Z creating: dist/ 2024-06-26T04:58:59.7347152Z inflating: dist/torch-2.5.0a0+gitb8c4c54-cp312-cp312-linux_x86_64.whl 2024-06-26T04:58:59.7348306Z creating: build/custom_test_artifacts/ 2024-06-26T04:58:59.7349300Z creating: build/custom_test_artifacts/custom-op-build/ 2024-06-26T04:58:59.7350611Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/ 2024-06-26T04:58:59.7352246Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/CMakeOutput.log 2024-06-26T04:58:59.7353952Z creating: 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build/bin/vec_test_all_types_AVX512 2024-06-26T04:59:04.3959900Z inflating: build/bin/TCPStoreTest 2024-06-26T04:59:04.4008822Z inflating: build/bin/FileStoreTest 2024-06-26T04:59:04.4057730Z inflating: build/bin/HashStoreTest 2024-06-26T04:59:04.4120245Z inflating: build/bin/ProcessGroupGlooTest 2024-06-26T04:59:04.4123467Z inflating: build/bin/example_allreduce 2024-06-26T04:59:04.4174195Z inflating: build/bin/test_dist_autograd 2024-06-26T04:59:04.4176810Z inflating: build/bin/parallel_benchmark 2024-06-26T04:59:04.4240280Z inflating: build/bin/test_cpp_rpc 2024-06-26T04:59:04.4303846Z inflating: build/bin/test_mobile_nnc 2024-06-26T04:59:04.4313561Z inflating: build/bin/aot_model_compiler_test 2024-06-26T04:59:04.4358798Z inflating: build/bin/op_allowlist_test 2024-06-26T04:59:04.4409982Z inflating: build/bin/backend_fallback_test 2024-06-26T04:59:04.4503376Z inflating: build/bin/make_boxed_from_unboxed_functor_test 2024-06-26T04:59:04.4560181Z inflating: build/bin/kernel_stackbased_test 2024-06-26T04:59:04.4652198Z inflating: build/bin/kernel_function_test 2024-06-26T04:59:04.4776474Z inflating: build/bin/kernel_function_legacy_test 2024-06-26T04:59:04.4830414Z inflating: build/bin/IListRef_test 2024-06-26T04:59:04.4889461Z inflating: build/bin/KernelFunction_test 2024-06-26T04:59:04.5206355Z inflating: build/bin/test_lazy 2024-06-26T04:59:04.5252981Z inflating: build/bin/xla_tensor_test 2024-06-26T04:59:04.5319713Z inflating: build/bin/legacy_vmap_test 2024-06-26T04:59:04.5367554Z inflating: build/bin/type_ptr_test 2024-06-26T04:59:04.5423580Z inflating: build/bin/type_test 2024-06-26T04:59:04.5498329Z inflating: build/bin/tensor_iterator_test 2024-06-26T04:59:04.5545881Z inflating: build/bin/stride_properties_test 2024-06-26T04:59:04.5592254Z inflating: build/bin/StorageUtils_test 2024-06-26T04:59:04.5645541Z inflating: build/bin/apply_utils_test 2024-06-26T04:59:04.5692172Z inflating: build/bin/weakref_test 2024-06-26T04:59:04.5743328Z inflating: build/bin/NamedTensor_test 2024-06-26T04:59:04.5796785Z inflating: build/bin/scalar_test 2024-06-26T04:59:04.5862762Z inflating: build/bin/Dict_test 2024-06-26T04:59:04.5921564Z inflating: build/bin/basic 2024-06-26T04:59:04.5974172Z inflating: build/bin/cpu_generator_test 2024-06-26T04:59:04.6023920Z inflating: build/bin/broadcast_test 2024-06-26T04:59:04.6084135Z inflating: build/bin/MaybeOwned_test 2024-06-26T04:59:04.6132611Z inflating: build/bin/test_parallel 2024-06-26T04:59:04.6231037Z inflating: build/bin/kernel_lambda_test 2024-06-26T04:59:04.6279488Z inflating: build/bin/cpu_profiling_allocator_test 2024-06-26T04:59:04.6327694Z inflating: build/bin/half_test 2024-06-26T04:59:04.6374372Z inflating: build/bin/cpu_allocator_test 2024-06-26T04:59:04.6375914Z inflating: build/bin/verify_api_visibility 2024-06-26T04:59:04.6664225Z inflating: build/bin/static_runtime_test 2024-06-26T04:59:04.6718248Z inflating: build/bin/atest 2024-06-26T04:59:04.6805261Z inflating: build/bin/cpu_rng_test 2024-06-26T04:59:04.6853135Z inflating: build/bin/memory_overlapping_test 2024-06-26T04:59:04.6899049Z inflating: build/bin/dispatch_key_set_test 2024-06-26T04:59:04.6955574Z inflating: build/bin/inline_container_test 2024-06-26T04:59:04.8236975Z inflating: build/bin/test_api 2024-06-26T04:59:04.8238933Z inflating: build/bin/thread_init_test 2024-06-26T04:59:04.8333714Z inflating: build/bin/List_test 2024-06-26T04:59:04.8380132Z inflating: build/bin/operators_test 2024-06-26T04:59:04.8426882Z inflating: build/bin/wrapdim_test 2024-06-26T04:59:04.8473063Z inflating: build/bin/operator_name_test 2024-06-26T04:59:04.8519266Z inflating: build/bin/dlconvertor_test 2024-06-26T04:59:04.8574087Z inflating: build/bin/extension_backend_test 2024-06-26T04:59:04.8621357Z inflating: build/bin/undefined_tensor_test 2024-06-26T04:59:04.8666899Z inflating: build/bin/lazy_tensor_test 2024-06-26T04:59:04.8712641Z inflating: build/bin/CppSignature_test 2024-06-26T04:59:04.8764873Z inflating: build/bin/scalar_tensor_test 2024-06-26T04:59:04.8855801Z inflating: build/bin/ivalue_test 2024-06-26T04:59:04.8904434Z inflating: build/bin/mobile_memory_cleanup 2024-06-26T04:59:04.9217019Z inflating: build/bin/op_registration_test 2024-06-26T04:59:04.9265440Z inflating: build/bin/math_kernel_test 2024-06-26T04:59:04.9313605Z inflating: build/bin/memory_format_test 2024-06-26T04:59:04.9365861Z inflating: build/bin/native_test 2024-06-26T04:59:04.9411960Z inflating: build/bin/reduce_ops_test 2024-06-26T04:59:04.9459168Z inflating: build/bin/packedtensoraccessor_test 2024-06-26T04:59:04.9526011Z inflating: build/bin/pow_test 2024-06-26T04:59:04.9578273Z inflating: build/bin/quantized_test 2024-06-26T04:59:04.9624717Z inflating: build/bin/reportMemoryUsage_test 2024-06-26T04:59:04.9673204Z inflating: build/bin/test_edge_op_registration 2024-06-26T04:59:04.9677109Z inflating: build/bin/torch_shm_manager 2024-06-26T04:59:04.9693917Z inflating: build/bin/tutorial_tensorexpr 2024-06-26T04:59:05.0728481Z inflating: build/bin/test_tensorexpr 2024-06-26T04:59:05.1298729Z inflating: build/bin/test_jit 2024-06-26T04:59:05.1299219Z creating: .additional_ci_files/ 2024-06-26T04:59:05.1353299Z inflating: .additional_ci_files/test-times.json 2024-06-26T04:59:05.1569233Z inflating: .additional_ci_files/test-class-times.json 2024-06-26T04:59:05.1610963Z ##[group]Run rm artifacts.zip 2024-06-26T04:59:05.1611629Z rm artifacts.zip 2024-06-26T04:59:05.1622334Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T04:59:05.1623153Z env: 2024-06-26T04:59:05.1623613Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:59:05.1624173Z ##[endgroup] 2024-06-26T04:59:05.2063443Z ##[group]Run df -H 2024-06-26T04:59:05.2063743Z df -H 2024-06-26T04:59:05.2070954Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T04:59:05.2071426Z env: 2024-06-26T04:59:05.2071686Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:59:05.2072012Z ##[endgroup] 2024-06-26T04:59:05.2434792Z Filesystem Size Used Avail Use% Mounted on 2024-06-26T04:59:05.2435645Z devtmpfs 8.2G 0 8.2G 0% /dev 2024-06-26T04:59:05.2436552Z tmpfs 8.2G 56M 8.2G 1% /dev/shm 2024-06-26T04:59:05.2437330Z tmpfs 8.2G 410k 8.2G 1% /run 2024-06-26T04:59:05.2437777Z tmpfs 8.2G 0 8.2G 0% /sys/fs/cgroup 2024-06-26T04:59:05.2438407Z /dev/nvme0n1p1 162G 19G 143G 12% / 2024-06-26T04:59:05.2619341Z Prepare all required actions 2024-06-26T04:59:05.2619777Z Getting action download info 2024-06-26T04:59:05.3741807Z ##[group]Run ./.github/actions/download-td-artifacts 2024-06-26T04:59:05.3742266Z with: 2024-06-26T04:59:05.3742482Z env: 2024-06-26T04:59:05.3742731Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:59:05.3743050Z ##[endgroup] 2024-06-26T04:59:05.3899536Z ##[group]Run seemethere/download-artifact-s3@v4 2024-06-26T04:59:05.3899979Z with: 2024-06-26T04:59:05.3900227Z name: td_results 2024-06-26T04:59:05.3900508Z s3-bucket: gha-artifacts 2024-06-26T04:59:05.3900843Z region: us-east-1 2024-06-26T04:59:05.3901117Z env: 2024-06-26T04:59:05.3901351Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:59:05.3901668Z ##[endgroup] 2024-06-26T04:59:05.8683520Z (node:31849) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2024-06-26T04:59:05.8684303Z 2024-06-26T04:59:05.8684635Z Please migrate your code to use AWS SDK for JavaScript (v3). 2024-06-26T04:59:05.8685583Z For more information, check the migration guide at https://a.co/7PzMCcy 2024-06-26T04:59:05.8686878Z (Use `node --trace-warnings ...` to show where the warning was created) 2024-06-26T04:59:05.9468062Z Found 1 objects with prefix pytorch/pytorch/9673645538/td_results/ 2024-06-26T04:59:05.9469165Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/td_results.json 2024-06-26T04:59:06.0040622Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/td_results.json 2024-06-26T04:59:06.0046491Z Artifact download has finished successfully 2024-06-26T04:59:06.0175477Z ##[group]Run mkdir -p .additional_ci_files 2024-06-26T04:59:06.0175954Z mkdir -p .additional_ci_files 2024-06-26T04:59:06.0176496Z mv td_results.json .additional_ci_files/td_results.json 2024-06-26T04:59:06.0183916Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T04:59:06.0184388Z env: 2024-06-26T04:59:06.0184674Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:59:06.0185002Z ##[endgroup] 2024-06-26T04:59:06.0486713Z ##[group]Run .github/scripts/parse_ref.py 2024-06-26T04:59:06.0487302Z .github/scripts/parse_ref.py 2024-06-26T04:59:06.0494595Z shell: /usr/bin/bash -e {0} 2024-06-26T04:59:06.0494927Z env: 2024-06-26T04:59:06.0495181Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:59:06.0495493Z ##[endgroup] 2024-06-26T04:59:06.1063780Z Prepare all required actions 2024-06-26T04:59:06.1154254Z ##[group]Run ./.github/actions/get-workflow-job-id 2024-06-26T04:59:06.1154706Z with: 2024-06-26T04:59:06.1155296Z github-token: *** 2024-06-26T04:59:06.1155569Z env: 2024-06-26T04:59:06.1155820Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:59:06.1156144Z ##[endgroup] 2024-06-26T04:59:06.1243369Z ##[group]Run set -eux 2024-06-26T04:59:06.1243690Z set -eux 2024-06-26T04:59:06.1244262Z python3 .github/scripts/get_workflow_job_id.py "${GITHUB_RUN_ID}" "${RUNNER_NAME}" 2024-06-26T04:59:06.1252067Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T04:59:06.1252536Z env: 2024-06-26T04:59:06.1252791Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:59:06.1253309Z GITHUB_TOKEN: *** 2024-06-26T04:59:06.1253858Z ##[endgroup] 2024-06-26T04:59:06.1274901Z + python3 .github/scripts/get_workflow_job_id.py 9673645538 i-000220d454490c80e 2024-06-26T04:59:08.2430095Z setting job-id=26688259850 2024-06-26T04:59:08.2431469Z setting job-name=linux-focal-py3.12-clang10 / test (dynamo, 1, 3, linux.2xlarge) 2024-06-26T04:59:08.2830519Z Prepare all required actions 2024-06-26T04:59:08.2830937Z Getting action download info 2024-06-26T04:59:08.4348939Z ##[group]Run ./.github/actions/filter-test-configs 2024-06-26T04:59:08.4349393Z with: 2024-06-26T04:59:08.4349843Z github-token: *** 2024-06-26T04:59:08.4351933Z test-matrix: {"include": [{"config": "default", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "default", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "default", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge"}]} 2024-06-26T04:59:08.4354598Z job-name: linux-focal-py3.12-clang10 / test (dynamo, 1, 3, linux.2xlarge) 2024-06-26T04:59:08.4355186Z env: 2024-06-26T04:59:08.4355442Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:59:08.4355803Z ##[endgroup] 2024-06-26T04:59:08.4449899Z ##[group]Run nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482 2024-06-26T04:59:08.4450444Z with: 2024-06-26T04:59:08.4450685Z shell: bash 2024-06-26T04:59:08.4450945Z timeout_minutes: 10 2024-06-26T04:59:08.4451244Z max_attempts: 5 2024-06-26T04:59:08.4451528Z retry_wait_seconds: 30 2024-06-26T04:59:08.4452647Z command: set -eux # PyYAML 6.0 doesn't work with MacOS x86 anymore # This must run on Python-3.7 (AmazonLinux2) so can't use request=3.32.2 python3 -m pip install requests==2.27.1 pyyaml==6.0.1 2024-06-26T04:59:08.4454179Z polling_interval_seconds: 1 2024-06-26T04:59:08.4454529Z warning_on_retry: true 2024-06-26T04:59:08.4454852Z continue_on_error: false 2024-06-26T04:59:08.4455152Z env: 2024-06-26T04:59:08.4455396Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:59:08.4455898Z GITHUB_TOKEN: *** 2024-06-26T04:59:08.4456171Z ##[endgroup] 2024-06-26T04:59:08.5082108Z + python3 -m pip install requests==2.27.1 pyyaml==6.0.1 2024-06-26T04:59:08.7262727Z Defaulting to user installation because normal site-packages is not writeable 2024-06-26T04:59:08.7436973Z Requirement already satisfied: requests==2.27.1 in /home/ec2-user/.local/lib/python3.7/site-packages (2.27.1) 2024-06-26T04:59:08.7574210Z Requirement already satisfied: pyyaml==6.0.1 in /home/ec2-user/.local/lib/python3.7/site-packages (6.0.1) 2024-06-26T04:59:08.7583037Z Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/ec2-user/.local/lib/python3.7/site-packages (from requests==2.27.1) (1.26.19) 2024-06-26T04:59:08.7765939Z Requirement already satisfied: charset-normalizer~=2.0.0; python_version >= "3" in /home/ec2-user/.local/lib/python3.7/site-packages (from requests==2.27.1) (2.0.12) 2024-06-26T04:59:08.7786634Z Requirement already satisfied: certifi>=2017.4.17 in /home/ec2-user/.local/lib/python3.7/site-packages (from requests==2.27.1) (2024.6.2) 2024-06-26T04:59:08.7795594Z Requirement already satisfied: idna<4,>=2.5; python_version >= "3" in /home/ec2-user/.local/lib/python3.7/site-packages (from requests==2.27.1) (3.7) 2024-06-26T04:59:09.5079622Z Command completed after 1 attempt(s). 2024-06-26T04:59:09.5128034Z ##[group]Run set -x 2024-06-26T04:59:09.5128349Z set -x 2024-06-26T04:59:09.5128622Z  2024-06-26T04:59:09.5129150Z # Use relative path here as this could be checked out anywhere, not necessarily 2024-06-26T04:59:09.5129806Z # in runner workspace 2024-06-26T04:59:09.5130319Z python3 "${GITHUB_ACTION_PATH}/../../scripts/parse_ref.py" 2024-06-26T04:59:09.5138020Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T04:59:09.5138487Z env: 2024-06-26T04:59:09.5138742Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:59:09.5139067Z ##[endgroup] 2024-06-26T04:59:09.5160738Z + python3 /home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/filter-test-configs/../../scripts/parse_ref.py 2024-06-26T04:59:09.5368353Z ##[group]Run echo "Workflow: ${GITHUB_WORKFLOW}" 2024-06-26T04:59:09.5368987Z echo "Workflow: ${GITHUB_WORKFLOW}" 2024-06-26T04:59:09.5369429Z echo "Job name: ${JOB_NAME}" 2024-06-26T04:59:09.5369790Z  2024-06-26T04:59:09.5370313Z # Use relative path here as this could be checked out anywhere, not necessarily 2024-06-26T04:59:09.5370978Z # in runner workspace 2024-06-26T04:59:09.5371523Z python3 "${GITHUB_ACTION_PATH}/../../scripts/filter_test_configs.py" \ 2024-06-26T04:59:09.5372153Z  --workflow "${GITHUB_WORKFLOW}" \ 2024-06-26T04:59:09.5372697Z  --job-name "${JOB_NAME}" \ 2024-06-26T04:59:09.5375081Z  --test-matrix "{"include": [{"config": "default", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "default", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "default", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge"}]}" \ 2024-06-26T04:59:09.5377326Z  --selected-test-configs "" \ 2024-06-26T04:59:09.5377748Z  --pr-number "${PR_NUMBER}" \ 2024-06-26T04:59:09.5378148Z  --tag "${TAG}" \ 2024-06-26T04:59:09.5378510Z  --event-name "${EVENT_NAME}" \ 2024-06-26T04:59:09.5378919Z  --schedule "${SCHEDULE}" \ 2024-06-26T04:59:09.5379318Z  --branch "${HEAD_BRANCH}" 2024-06-26T04:59:09.5386520Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T04:59:09.5386989Z env: 2024-06-26T04:59:09.5387242Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:59:09.5387760Z GITHUB_TOKEN: *** 2024-06-26T04:59:09.5388247Z JOB_NAME: linux-focal-py3.12-clang10 / test (dynamo, 1, 3, linux.2xlarge) 2024-06-26T04:59:09.5388841Z PR_NUMBER: 129470 2024-06-26T04:59:09.5389122Z TAG: 2024-06-26T04:59:09.5389362Z EVENT_NAME: pull_request 2024-06-26T04:59:09.5389690Z SCHEDULE: 2024-06-26T04:59:09.5389951Z HEAD_BRANCH: 2024-06-26T04:59:09.5390208Z ##[endgroup] 2024-06-26T04:59:09.5409400Z Workflow: pull 2024-06-26T04:59:09.5410052Z Job name: linux-focal-py3.12-clang10 / test (dynamo, 1, 3, linux.2xlarge) 2024-06-26T04:59:09.7571957Z INFO:root:Found no test-config label on the PR, so all test configs are included 2024-06-26T04:59:10.3485676Z ##[group]Run echo "Filtered matrix:" 2024-06-26T04:59:10.3486278Z echo "Filtered matrix:" 2024-06-26T04:59:10.3488521Z echo "{"include": [{"config": "default", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "default", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "default", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge"}]}" 2024-06-26T04:59:10.3490669Z  2024-06-26T04:59:10.3490900Z echo 2024-06-26T04:59:10.3491252Z echo "Is the current job unstable? False" 2024-06-26T04:59:10.3491689Z  2024-06-26T04:59:10.3491921Z echo 2024-06-26T04:59:10.3492250Z echo "Is keep-going label set? False" 2024-06-26T04:59:10.3492667Z  2024-06-26T04:59:10.3492897Z echo 2024-06-26T04:59:10.3493184Z echo "Renabled issues? " 2024-06-26T04:59:10.3500679Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T04:59:10.3501145Z env: 2024-06-26T04:59:10.3501400Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:59:10.3501725Z ##[endgroup] 2024-06-26T04:59:10.3522058Z Filtered matrix: 2024-06-26T04:59:10.3525144Z {include: [{config: default, shard: 1, num_shards: 3, runner: linux.2xlarge}, {config: default, shard: 2, num_shards: 3, runner: linux.2xlarge}, {config: default, shard: 3, num_shards: 3, runner: linux.2xlarge}, {config: dynamo, shard: 1, num_shards: 3, runner: linux.2xlarge}, {config: dynamo, shard: 2, num_shards: 3, runner: linux.2xlarge}, {config: dynamo, shard: 3, num_shards: 3, runner: linux.2xlarge}]} 2024-06-26T04:59:10.3527118Z 2024-06-26T04:59:10.3527262Z Is the current job unstable? False 2024-06-26T04:59:10.3527529Z 2024-06-26T04:59:10.3527796Z Is keep-going label set? False 2024-06-26T04:59:10.3528038Z 2024-06-26T04:59:10.3528144Z Renabled issues? 2024-06-26T04:59:10.3570183Z ##[group]Run echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2024-06-26T04:59:10.3570996Z echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2024-06-26T04:59:10.3578490Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T04:59:10.3578956Z env: 2024-06-26T04:59:10.3579212Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:59:10.3579541Z JOB_TIMEOUT: 600 2024-06-26T04:59:10.3579809Z ##[endgroup] 2024-06-26T04:59:10.3652113Z ##[group]Run set -x 2024-06-26T04:59:10.3652493Z set -x 2024-06-26T04:59:10.3652766Z  2024-06-26T04:59:10.3653095Z if [[ $TEST_CONFIG == 'multigpu' ]]; then 2024-06-26T04:59:10.3653778Z  TEST_COMMAND=.ci/pytorch/multigpu-test.sh 2024-06-26T04:59:10.3654330Z elif [[ $BUILD_ENVIRONMENT == *onnx* ]]; then 2024-06-26T04:59:10.3654828Z  TEST_COMMAND=.ci/onnx/test.sh 2024-06-26T04:59:10.3655211Z else 2024-06-26T04:59:10.3655527Z  TEST_COMMAND=.ci/pytorch/test.sh 2024-06-26T04:59:10.3655928Z fi 2024-06-26T04:59:10.3656177Z  2024-06-26T04:59:10.3656621Z # detached container should get cleaned up by teardown_ec2_linux 2024-06-26T04:59:10.3657370Z # TODO: Stop building test binaries as part of the build phase 2024-06-26T04:59:10.3658017Z # Used for GPU_FLAG since that doesn't play nice 2024-06-26T04:59:10.3658603Z # shellcheck disable=SC2086,SC2090 2024-06-26T04:59:10.3659032Z container_name=$(docker run \ 2024-06-26T04:59:10.3659429Z  ${GPU_FLAG:-} \ 2024-06-26T04:59:10.3659784Z  -e BUILD_ENVIRONMENT \ 2024-06-26T04:59:10.3660150Z  -e PR_NUMBER \ 2024-06-26T04:59:10.3660489Z  -e GITHUB_ACTIONS \ 2024-06-26T04:59:10.3660857Z  -e GITHUB_REPOSITORY \ 2024-06-26T04:59:10.3661229Z  -e GITHUB_WORKFLOW \ 2024-06-26T04:59:10.3661586Z  -e GITHUB_JOB \ 2024-06-26T04:59:10.3661918Z  -e GITHUB_RUN_ID \ 2024-06-26T04:59:10.3662272Z  -e GITHUB_RUN_NUMBER \ 2024-06-26T04:59:10.3662648Z  -e GITHUB_RUN_ATTEMPT \ 2024-06-26T04:59:10.3663016Z  -e JOB_ID \ 2024-06-26T04:59:10.3663327Z  -e JOB_NAME \ 2024-06-26T04:59:10.3663634Z  -e BASE_SHA \ 2024-06-26T04:59:10.3663947Z  -e BRANCH \ 2024-06-26T04:59:10.3664251Z  -e SHA1 \ 2024-06-26T04:59:10.3664555Z  -e AWS_DEFAULT_REGION \ 2024-06-26T04:59:10.3664938Z  -e IN_WHEEL_TEST \ 2024-06-26T04:59:10.3665298Z  -e SHARD_NUMBER \ 2024-06-26T04:59:10.3665634Z  -e TEST_CONFIG \ 2024-06-26T04:59:10.3665982Z  -e NUM_TEST_SHARDS \ 2024-06-26T04:59:10.3666355Z  -e REENABLED_ISSUES \ 2024-06-26T04:59:10.3666735Z  -e CONTINUE_THROUGH_ERROR \ 2024-06-26T04:59:10.3667146Z  -e VERBOSE_TEST_LOGS \ 2024-06-26T04:59:10.3667529Z  -e NO_TEST_TIMEOUT \ 2024-06-26T04:59:10.3667872Z  -e NO_TD \ 2024-06-26T04:59:10.3668193Z  -e TD_DISTRIBUTED \ 2024-06-26T04:59:10.3668559Z  -e PR_LABELS \ 2024-06-26T04:59:10.3668929Z  -e MAX_JOBS="$(nproc --ignore=2)" \ 2024-06-26T04:59:10.3669367Z  -e SCCACHE_BUCKET \ 2024-06-26T04:59:10.3669743Z  -e SCCACHE_S3_KEY_PREFIX \ 2024-06-26T04:59:10.3670119Z  -e XLA_CUDA \ 2024-06-26T04:59:10.3670502Z  -e XLA_CLANG_CACHE_S3_BUCKET_NAME \ 2024-06-26T04:59:10.3670994Z  -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK \ 2024-06-26T04:59:10.3671485Z  -e PYTORCH_TEST_RERUN_DISABLED_TESTS \ 2024-06-26T04:59:10.3671975Z  -e SKIP_SCCACHE_INITIALIZATION=1 \ 2024-06-26T04:59:10.3672421Z  -e HUGGING_FACE_HUB_TOKEN \ 2024-06-26T04:59:10.3672805Z  -e DASHBOARD_TAG \ 2024-06-26T04:59:10.3673259Z  --env-file="/tmp/github_env_${GITHUB_RUN_ID}" \ 2024-06-26T04:59:10.3673793Z  --security-opt seccomp=unconfined \ 2024-06-26T04:59:10.3674220Z  --cap-add=SYS_PTRACE \ 2024-06-26T04:59:10.3674751Z  --ipc=host \ 2024-06-26T04:59:10.3675086Z  --shm-size="${SHM_SIZE}" \ 2024-06-26T04:59:10.3675456Z  --tty \ 2024-06-26T04:59:10.3675730Z  --detach \ 2024-06-26T04:59:10.3676060Z  --name="${container_name}" \ 2024-06-26T04:59:10.3676456Z  --user jenkins \ 2024-06-26T04:59:10.3676990Z  -v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \ 2024-06-26T04:59:10.3677536Z  -w /var/lib/jenkins/workspace \ 2024-06-26T04:59:10.3677951Z  "${DOCKER_IMAGE}" 2024-06-26T04:59:10.3678264Z ) 2024-06-26T04:59:10.3678646Z # Propagate download.pytorch.org IP to container 2024-06-26T04:59:10.3679571Z grep download.pytorch.org /etc/hosts | docker exec -i "${container_name}" sudo bash -c "/bin/cat >> /etc/hosts" 2024-06-26T04:59:10.3680528Z echo "DOCKER_CONTAINER_ID=${container_name}" >> "${GITHUB_ENV}" 2024-06-26T04:59:10.3681522Z docker exec -t "${container_name}" sh -c "pip install $(echo dist/*.whl)[opt-einsum] && ${TEST_COMMAND}" 2024-06-26T04:59:10.3688647Z shell: /usr/bin/bash -e {0} 2024-06-26T04:59:10.3688964Z env: 2024-06-26T04:59:10.3689222Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:59:10.3689617Z BUILD_ENVIRONMENT: linux-focal-py3.12-clang10 2024-06-26T04:59:10.3690041Z PR_NUMBER: 129470 2024-06-26T04:59:10.3690368Z GITHUB_REPOSITORY: pytorch/pytorch 2024-06-26T04:59:10.3690760Z GITHUB_WORKFLOW: pull 2024-06-26T04:59:10.3691058Z GITHUB_JOB: test 2024-06-26T04:59:10.3691344Z GITHUB_RUN_ID: 9673645538 2024-06-26T04:59:10.3691676Z GITHUB_RUN_NUMBER: 219936 2024-06-26T04:59:10.3691993Z GITHUB_RUN_ATTEMPT: 1 2024-06-26T04:59:10.3692294Z JOB_ID: 26688259850 2024-06-26T04:59:10.3692793Z JOB_NAME: linux-focal-py3.12-clang10 / test (dynamo, 1, 3, linux.2xlarge) 2024-06-26T04:59:10.3693384Z BRANCH: pull/129470 2024-06-26T04:59:10.3693880Z SHA1: b8c4c54d347aa776934c60784e35936878ef18dc 2024-06-26T04:59:10.3694377Z BASE_SHA: 4b9c9a9cc9c9283380a011310ba180c105c3dcb9 2024-06-26T04:59:10.3694828Z TEST_CONFIG: dynamo 2024-06-26T04:59:10.3695112Z SHARD_NUMBER: 1 2024-06-26T04:59:10.3695392Z NUM_TEST_SHARDS: 3 2024-06-26T04:59:10.3695685Z REENABLED_ISSUES: 2024-06-26T04:59:10.3695981Z CONTINUE_THROUGH_ERROR: False 2024-06-26T04:59:10.3696336Z VERBOSE_TEST_LOGS: False 2024-06-26T04:59:10.3696671Z NO_TEST_TIMEOUT: False 2024-06-26T04:59:10.3696963Z NO_TD: False 2024-06-26T04:59:10.3697234Z TD_DISTRIBUTED: False 2024-06-26T04:59:10.3697624Z SCCACHE_BUCKET: ossci-compiler-cache-circleci-v2 2024-06-26T04:59:10.3698079Z SCCACHE_S3_KEY_PREFIX: pull 2024-06-26T04:59:10.3698412Z SHM_SIZE: 1g 2024-06-26T04:59:10.3699284Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:59:10.3700243Z XLA_CUDA: 2024-06-26T04:59:10.3700703Z XLA_CLANG_CACHE_S3_BUCKET_NAME: ossci-compiler-clang-cache-circleci-xla 2024-06-26T04:59:10.3701304Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK: 0 2024-06-26T04:59:10.3701701Z PYTORCH_TEST_RERUN_DISABLED_TESTS: 0 2024-06-26T04:59:10.3702081Z DASHBOARD_TAG: 2024-06-26T04:59:10.3702366Z HUGGING_FACE_HUB_TOKEN: 2024-06-26T04:59:10.3702669Z ##[endgroup] 2024-06-26T04:59:10.3722564Z + [[ dynamo == \m\u\l\t\i\g\p\u ]] 2024-06-26T04:59:10.3723445Z + [[ linux-focal-py3.12-clang10 == *onnx* ]] 2024-06-26T04:59:10.3723906Z + TEST_COMMAND=.ci/pytorch/test.sh 2024-06-26T04:59:10.3731044Z +++ nproc --ignore=2 2024-06-26T04:59:10.3762936Z ++ docker run -e BUILD_ENVIRONMENT -e PR_NUMBER -e GITHUB_ACTIONS -e GITHUB_REPOSITORY -e GITHUB_WORKFLOW -e GITHUB_JOB -e GITHUB_RUN_ID -e GITHUB_RUN_NUMBER -e GITHUB_RUN_ATTEMPT -e JOB_ID -e JOB_NAME -e BASE_SHA -e BRANCH -e SHA1 -e AWS_DEFAULT_REGION -e IN_WHEEL_TEST -e SHARD_NUMBER -e TEST_CONFIG -e NUM_TEST_SHARDS -e REENABLED_ISSUES -e CONTINUE_THROUGH_ERROR -e VERBOSE_TEST_LOGS -e NO_TEST_TIMEOUT -e NO_TD -e TD_DISTRIBUTED -e PR_LABELS -e MAX_JOBS=6 -e SCCACHE_BUCKET -e SCCACHE_S3_KEY_PREFIX -e XLA_CUDA -e XLA_CLANG_CACHE_S3_BUCKET_NAME -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK -e PYTORCH_TEST_RERUN_DISABLED_TESTS -e SKIP_SCCACHE_INITIALIZATION=1 -e HUGGING_FACE_HUB_TOKEN -e DASHBOARD_TAG --env-file=/tmp/github_env_9673645538 --security-opt seccomp=unconfined --cap-add=SYS_PTRACE --ipc=host --shm-size=1g --tty --detach --name= --user jenkins -v /home/ec2-user/actions-runner/_work/pytorch/pytorch:/var/lib/jenkins/workspace -w /var/lib/jenkins/workspace 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:59:14.3470470Z + container_name=dece8529f48a7f61fcf0b1a68620de6340e6da1a183eee9f3a1750c5234902db 2024-06-26T04:59:14.3473216Z + grep download.pytorch.org /etc/hosts 2024-06-26T04:59:14.3474618Z + docker exec -i dece8529f48a7f61fcf0b1a68620de6340e6da1a183eee9f3a1750c5234902db sudo bash -c '/bin/cat >> /etc/hosts' 2024-06-26T04:59:14.4602414Z + echo DOCKER_CONTAINER_ID=dece8529f48a7f61fcf0b1a68620de6340e6da1a183eee9f3a1750c5234902db 2024-06-26T04:59:14.4607018Z ++ echo dist/torch-2.5.0a0+gitb8c4c54-cp312-cp312-linux_x86_64.whl 2024-06-26T04:59:14.4608647Z + docker exec -t dece8529f48a7f61fcf0b1a68620de6340e6da1a183eee9f3a1750c5234902db sh -c 'pip install dist/torch-2.5.0a0+gitb8c4c54-cp312-cp312-linux_x86_64.whl[opt-einsum] && .ci/pytorch/test.sh' 2024-06-26T04:59:15.0467335Z Processing ./dist/torch-2.5.0a0+gitb8c4c54-cp312-cp312-linux_x86_64.whl (from torch==2.5.0a0+gitb8c4c54) 2024-06-26T04:59:15.4676968Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+gitb8c4c54->torch==2.5.0a0+gitb8c4c54) (3.13.1) 2024-06-26T04:59:15.4685483Z Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+gitb8c4c54->torch==2.5.0a0+gitb8c4c54) (4.12.2) 2024-06-26T04:59:15.4695698Z Requirement already satisfied: sympy in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+gitb8c4c54->torch==2.5.0a0+gitb8c4c54) (1.12) 2024-06-26T04:59:15.4703755Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+gitb8c4c54->torch==2.5.0a0+gitb8c4c54) (2.8.8) 2024-06-26T04:59:15.4711156Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+gitb8c4c54->torch==2.5.0a0+gitb8c4c54) (3.1.4) 2024-06-26T04:59:15.4720517Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+gitb8c4c54->torch==2.5.0a0+gitb8c4c54) (2024.2.0) 2024-06-26T04:59:15.4729807Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+gitb8c4c54->torch==2.5.0a0+gitb8c4c54) (69.5.1) 2024-06-26T04:59:15.4802617Z Requirement already satisfied: opt-einsum>=3.3 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+gitb8c4c54->torch==2.5.0a0+gitb8c4c54) (3.3.0) 2024-06-26T04:59:15.4854443Z 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+gitb8c4c54->torch==2.5.0a0+gitb8c4c54) (1.26.0) 2024-06-26T04:59:15.5150473Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from jinja2->torch==2.5.0a0+gitb8c4c54->torch==2.5.0a0+gitb8c4c54) (2.1.5) 2024-06-26T04:59:15.5543218Z Requirement already satisfied: mpmath>=0.19 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from sympy->torch==2.5.0a0+gitb8c4c54->torch==2.5.0a0+gitb8c4c54) (1.2.1) 2024-06-26T04:59:16.1891673Z Installing collected packages: torch 2024-06-26T04:59:23.9107817Z Successfully installed torch-2.5.0a0+gitb8c4c54 2024-06-26T04:59:24.0228798Z ++ dirname .ci/pytorch/test.sh 2024-06-26T04:59:24.0243368Z + source .ci/pytorch/common.sh 2024-06-26T04:59:24.0246491Z +++ dirname .ci/pytorch/common.sh 2024-06-26T04:59:24.0251937Z ++ source .ci/pytorch/common_utils.sh 2024-06-26T04:59:24.0253978Z +++ declare -f -t trap_add 2024-06-26T04:59:24.0259499Z ++ set -ex 2024-06-26T04:59:24.0259929Z ++ [[ linux-focal-py3.12-clang10 == *rocm* ]] 2024-06-26T04:59:24.0260502Z ++ BUILD_TEST_LIBTORCH=0 2024-06-26T04:59:24.0261633Z + [[ linux-focal-py3.12-clang10 != *rocm* ]] 2024-06-26T04:59:24.0263409Z ++ stat -c %u /var/lib/jenkins/workspace 2024-06-26T04:59:24.0285788Z + WORKSPACE_ORIGINAL_OWNER_ID=1000 2024-06-26T04:59:24.0286585Z + trap_add cleanup_workspace EXIT 2024-06-26T04:59:24.0287317Z + trap_add_cmd=cleanup_workspace 2024-06-26T04:59:24.0287880Z + shift 2024-06-26T04:59:24.0288138Z + for trap_add_name in "$@" 2024-06-26T04:59:24.0292511Z +++ trap -p EXIT 2024-06-26T04:59:24.0295072Z ++ eval 'extract_trap_cmd ' 2024-06-26T04:59:24.0295730Z +++ extract_trap_cmd 2024-06-26T04:59:24.0296159Z +++ printf '%s\n' '' 2024-06-26T04:59:24.0308509Z ++ printf '%s\n' cleanup_workspace 2024-06-26T04:59:24.0309221Z + trap -- ' 2024-06-26T04:59:24.0309534Z cleanup_workspace' EXIT 2024-06-26T04:59:24.0309998Z + sudo chown -R jenkins /var/lib/jenkins/workspace 2024-06-26T04:59:25.9289080Z + git config --global --add safe.directory /var/lib/jenkins/workspace 2024-06-26T04:59:26.0405865Z + echo 'Environment variables:' 2024-06-26T04:59:26.0406320Z Environment variables: 2024-06-26T04:59:26.0406612Z + env 2024-06-26T04:59:26.0423194Z INSTALLED_DB=yes 2024-06-26T04:59:26.0424303Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-06-26T04:59:26.0424893Z CONTINUE_THROUGH_ERROR=False 2024-06-26T04:59:26.0425348Z BUILD_ENVIRONMENT=linux-focal-py3.12-clang10 2024-06-26T04:59:26.0425767Z HOSTNAME=dece8529f48a 2024-06-26T04:59:26.0426621Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_1f386446-d3ba-41cc-9ba5-abe7c28e47af 2024-06-26T04:59:26.0427435Z GITHUB_ACTION=__self 2024-06-26T04:59:26.0427746Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2024-06-26T04:59:26.0428128Z GITHUB_RUN_NUMBER=219936 2024-06-26T04:59:26.0428446Z TEST_CONFIG=dynamo 2024-06-26T04:59:26.0428752Z GITHUB_REPOSITORY_OWNER_ID=21003710 2024-06-26T04:59:26.0429205Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all 2024-06-26T04:59:26.0429686Z GITHUB_TRIGGERING_ACTOR=leslie-fang-intel 2024-06-26T04:59:26.0430088Z GITHUB_REF_TYPE=branch 2024-06-26T04:59:26.0430440Z TORCH_CUDA_ARCH_LIST=Maxwell 2024-06-26T04:59:26.0430839Z BASE_SHA=4b9c9a9cc9c9283380a011310ba180c105c3dcb9 2024-06-26T04:59:26.0431256Z XLA_CUDA= 2024-06-26T04:59:26.0431514Z HUGGING_FACE_HUB_TOKEN= 2024-06-26T04:59:26.0432202Z *** 2024-06-26T04:59:26.0432446Z GITHUB_REPOSITORY_ID=65600975 2024-06-26T04:59:26.0432789Z GITHUB_ACTIONS=true 2024-06-26T04:59:26.0433130Z SHA1=b8c4c54d347aa776934c60784e35936878ef18dc 2024-06-26T04:59:26.0433609Z GITHUB_SHA=4b51b1a62a63a1add1b3a0f7882f5c7dc66b8f8d 2024-06-26T04:59:26.0434321Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/pull/129470/merge 2024-06-26T04:59:26.0434948Z UCC_HOME=/usr 2024-06-26T04:59:26.0435207Z VERBOSE_TEST_LOGS=False 2024-06-26T04:59:26.0435531Z GITHUB_REF=refs/pull/129470/merge 2024-06-26T04:59:26.0435893Z SHARD_NUMBER=1 2024-06-26T04:59:26.0436168Z GITHUB_REF_PROTECTED=false 2024-06-26T04:59:26.0436498Z HOME=/var/lib/jenkins 2024-06-26T04:59:26.0436842Z GITHUB_API_URL=https://api.github.com 2024-06-26T04:59:26.0437247Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2024-06-26T04:59:26.0437612Z UCX_COMMIT= 2024-06-26T04:59:26.0437888Z SCCACHE_S3_KEY_PREFIX=pull 2024-06-26T04:59:26.0438200Z NUM_TEST_SHARDS=3 2024-06-26T04:59:26.0438478Z UCX_HOME=/usr 2024-06-26T04:59:26.0439323Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_1f386446-d3ba-41cc-9ba5-abe7c28e47af 2024-06-26T04:59:26.0440404Z JOB_NAME=linux-focal-py3.12-clang10 / test (dynamo, 1, 3, linux.2xlarge) 2024-06-26T04:59:26.0441667Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_1f386446-d3ba-41cc-9ba5-abe7c28e47af 2024-06-26T04:59:26.0442805Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json 2024-06-26T04:59:26.0443725Z GITHUB_EVENT_NAME=pull_request 2024-06-26T04:59:26.0444075Z DASHBOARD_TAG= 2024-06-26T04:59:26.0444356Z GITHUB_RUN_ID=9673645538 2024-06-26T04:59:26.0445259Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_1f386446-d3ba-41cc-9ba5-abe7c28e47af 2024-06-26T04:59:26.0446191Z GITHUB_ACTOR=leslie-fang-intel 2024-06-26T04:59:26.0446696Z PR_NUMBER=129470 2024-06-26T04:59:26.0446963Z DESIRED_CUDA= 2024-06-26T04:59:26.0447237Z GITHUB_RUN_ATTEMPT=1 2024-06-26T04:59:26.0447617Z ANACONDA_PYTHON_VERSION=3.12 2024-06-26T04:59:26.0448027Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2024-06-26T04:59:26.0448471Z TERM=xterm 2024-06-26T04:59:26.0448730Z INSTALLED_VISION=yes 2024-06-26T04:59:26.0449013Z BRANCH=pull/129470 2024-06-26T04:59:26.0449313Z OPENSSL_ROOT_DIR=/opt/openssl 2024-06-26T04:59:26.0449663Z CUDA_PATH=/usr/local/cuda 2024-06-26T04:59:26.0450391Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux 2024-06-26T04:59:26.0451152Z GITHUB_SERVER_URL=https://github.com 2024-06-26T04:59:26.0451531Z UCC_COMMIT= 2024-06-26T04:59:26.0451777Z REENABLED_ISSUES= 2024-06-26T04:59:26.0452045Z DOCS= 2024-06-26T04:59:26.0452286Z INSTALLED_ANDROID= 2024-06-26T04:59:26.0452547Z SHLVL=1 2024-06-26T04:59:26.0452779Z MAX_JOBS=6 2024-06-26T04:59:26.0453038Z GITHUB_ACTOR_ID=53841472 2024-06-26T04:59:26.0453470Z GITHUB_WORKFLOW_SHA=4b51b1a62a63a1add1b3a0f7882f5c7dc66b8f8d 2024-06-26T04:59:26.0454480Z GITHUB_REF_NAME=129470/merge 2024-06-26T04:59:26.0455502Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla 2024-06-26T04:59:26.0456416Z GITHUB_JOB=test 2024-06-26T04:59:26.0456884Z NO_TEST_TIMEOUT=False 2024-06-26T04:59:26.0457418Z TD_DISTRIBUTED=False 2024-06-26T04:59:26.0457962Z GITHUB_REPOSITORY=pytorch/pytorch 2024-06-26T04:59:26.0458500Z GITHUB_RETENTION_DAYS=90 2024-06-26T04:59:26.0458829Z OPENSSL_DIR=/opt/openssl 2024-06-26T04:59:26.0459143Z GITHUB_ACTION_REPOSITORY= 2024-06-26T04:59:26.0460209Z PATH=/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.12/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2024-06-26T04:59:26.0461496Z GITHUB_BASE_REF=gh/leslie-fang-intel/126/base 2024-06-26T04:59:26.0461906Z INSTALLED_ACL= 2024-06-26T04:59:26.0462175Z CI=true 2024-06-26T04:59:26.0462438Z GITHUB_REPOSITORY_OWNER=pytorch 2024-06-26T04:59:26.0462789Z JOB_ID=26688259850 2024-06-26T04:59:26.0463085Z INSTALLED_PROTOBUF=yes 2024-06-26T04:59:26.0463497Z GITHUB_HEAD_REF=gh/leslie-fang-intel/126/head 2024-06-26T04:59:26.0463902Z GITHUB_ACTION_REF= 2024-06-26T04:59:26.0464311Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2 2024-06-26T04:59:26.0464762Z GITHUB_WORKFLOW=pull 2024-06-26T04:59:26.0465064Z DEBIAN_FRONTEND=noninteractive 2024-06-26T04:59:26.0465980Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_1f386446-d3ba-41cc-9ba5-abe7c28e47af 2024-06-26T04:59:26.0466810Z NO_TD=False 2024-06-26T04:59:26.0467078Z SKIP_SCCACHE_INITIALIZATION=1 2024-06-26T04:59:26.0467430Z _=/usr/bin/env 2024-06-26T04:59:26.0467901Z ++ python -c 'import site; print(site.getsitepackages()[0])' 2024-06-26T04:59:26.0629323Z + TORCH_INSTALL_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch 2024-06-26T04:59:26.0630733Z + TORCH_BIN_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/bin 2024-06-26T04:59:26.0632165Z + TORCH_LIB_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib 2024-06-26T04:59:26.0633718Z + TORCH_TEST_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/test 2024-06-26T04:59:26.0634735Z + BUILD_DIR=build 2024-06-26T04:59:26.0635039Z + BUILD_RENAMED_DIR=build_renamed 2024-06-26T04:59:26.0635408Z + BUILD_BIN_DIR=build/bin 2024-06-26T04:59:26.0635714Z + SHARD_NUMBER=1 2024-06-26T04:59:26.0635994Z + NUM_TEST_SHARDS=3 2024-06-26T04:59:26.0636294Z + export VALGRIND=ON 2024-06-26T04:59:26.0636623Z + VALGRIND=ON 2024-06-26T04:59:26.0637016Z + [[ linux-focal-py3.12-clang10 == *clang9* ]] 2024-06-26T04:59:26.0637769Z + [[ 0 == \1 ]] 2024-06-26T04:59:26.0638034Z + [[ False == \1 ]] 2024-06-26T04:59:26.0638424Z + [[ linux-focal-py3.12-clang10 != *bazel* ]] 2024-06-26T04:59:26.0638887Z ++ realpath build/custom_test_artifacts 2024-06-26T04:59:26.0657192Z + CUSTOM_TEST_ARTIFACT_BUILD_DIR=/var/lib/jenkins/workspace/build/custom_test_artifacts 2024-06-26T04:59:26.0658060Z + [[ -n '' ]] 2024-06-26T04:59:26.0658574Z + echo 'Environment variables' 2024-06-26T04:59:26.0658923Z Environment variables 2024-06-26T04:59:26.0659216Z + env 2024-06-26T04:59:26.0663596Z INSTALLED_DB=yes 2024-06-26T04:59:26.0664426Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-06-26T04:59:26.0665383Z CONTINUE_THROUGH_ERROR=False 2024-06-26T04:59:26.0666231Z BUILD_ENVIRONMENT=linux-focal-py3.12-clang10 2024-06-26T04:59:26.0666682Z HOSTNAME=dece8529f48a 2024-06-26T04:59:26.0667681Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_1f386446-d3ba-41cc-9ba5-abe7c28e47af 2024-06-26T04:59:26.0668518Z GITHUB_ACTION=__self 2024-06-26T04:59:26.0668845Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2024-06-26T04:59:26.0669209Z GITHUB_RUN_NUMBER=219936 2024-06-26T04:59:26.0669525Z TEST_CONFIG=dynamo 2024-06-26T04:59:26.0669834Z GITHUB_REPOSITORY_OWNER_ID=21003710 2024-06-26T04:59:26.0670269Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all 2024-06-26T04:59:26.0670755Z GITHUB_TRIGGERING_ACTOR=leslie-fang-intel 2024-06-26T04:59:26.0671169Z GITHUB_REF_TYPE=branch 2024-06-26T04:59:26.0671471Z TORCH_CUDA_ARCH_LIST=Maxwell 2024-06-26T04:59:26.0675888Z BASE_SHA=4b9c9a9cc9c9283380a011310ba180c105c3dcb9 2024-06-26T04:59:26.0676767Z XLA_CUDA= 2024-06-26T04:59:26.0677227Z HUGGING_FACE_HUB_TOKEN= 2024-06-26T04:59:26.0677966Z *** 2024-06-26T04:59:26.0678222Z GITHUB_REPOSITORY_ID=65600975 2024-06-26T04:59:26.0678556Z GITHUB_ACTIONS=true 2024-06-26T04:59:26.0678902Z SHA1=b8c4c54d347aa776934c60784e35936878ef18dc 2024-06-26T04:59:26.0679396Z GITHUB_SHA=4b51b1a62a63a1add1b3a0f7882f5c7dc66b8f8d 2024-06-26T04:59:26.0680102Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/pull/129470/merge 2024-06-26T04:59:26.0680827Z UCC_HOME=/usr 2024-06-26T04:59:26.0681104Z VERBOSE_TEST_LOGS=False 2024-06-26T04:59:26.0681421Z GITHUB_REF=refs/pull/129470/merge 2024-06-26T04:59:26.0681777Z SHARD_NUMBER=1 2024-06-26T04:59:26.0682060Z GITHUB_REF_PROTECTED=false 2024-06-26T04:59:26.0682375Z HOME=/var/lib/jenkins 2024-06-26T04:59:26.0682728Z GITHUB_API_URL=https://api.github.com 2024-06-26T04:59:26.0683158Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2024-06-26T04:59:26.0683521Z UCX_COMMIT= 2024-06-26T04:59:26.0697611Z SCCACHE_S3_KEY_PREFIX=pull 2024-06-26T04:59:26.0698097Z NUM_TEST_SHARDS=3 2024-06-26T04:59:26.0698653Z UCX_HOME=/usr 2024-06-26T04:59:26.0699702Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_1f386446-d3ba-41cc-9ba5-abe7c28e47af 2024-06-26T04:59:26.0700855Z JOB_NAME=linux-focal-py3.12-clang10 / test (dynamo, 1, 3, linux.2xlarge) 2024-06-26T04:59:26.0701969Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_1f386446-d3ba-41cc-9ba5-abe7c28e47af 2024-06-26T04:59:26.0703102Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json 2024-06-26T04:59:26.0703765Z GITHUB_EVENT_NAME=pull_request 2024-06-26T04:59:26.0704109Z DASHBOARD_TAG= 2024-06-26T04:59:26.0704375Z GITHUB_RUN_ID=9673645538 2024-06-26T04:59:26.0705308Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_1f386446-d3ba-41cc-9ba5-abe7c28e47af 2024-06-26T04:59:26.0706238Z GITHUB_ACTOR=leslie-fang-intel 2024-06-26T04:59:26.0706575Z PR_NUMBER=129470 2024-06-26T04:59:26.0706853Z DESIRED_CUDA= 2024-06-26T04:59:26.0707129Z GITHUB_RUN_ATTEMPT=1 2024-06-26T04:59:26.0707409Z VALGRIND=ON 2024-06-26T04:59:26.0707682Z ANACONDA_PYTHON_VERSION=3.12 2024-06-26T04:59:26.0708110Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2024-06-26T04:59:26.0708546Z TERM=xterm 2024-06-26T04:59:26.0708808Z INSTALLED_VISION=yes 2024-06-26T04:59:26.0709110Z BRANCH=pull/129470 2024-06-26T04:59:26.0709636Z OPENSSL_ROOT_DIR=/opt/openssl 2024-06-26T04:59:26.0709990Z CUDA_PATH=/usr/local/cuda 2024-06-26T04:59:26.0710738Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux 2024-06-26T04:59:26.0711487Z GITHUB_SERVER_URL=https://github.com 2024-06-26T04:59:26.0711865Z UCC_COMMIT= 2024-06-26T04:59:26.0712233Z REENABLED_ISSUES= 2024-06-26T04:59:26.0712493Z DOCS= 2024-06-26T04:59:26.0712735Z INSTALLED_ANDROID= 2024-06-26T04:59:26.0713009Z SHLVL=1 2024-06-26T04:59:26.0713228Z MAX_JOBS=6 2024-06-26T04:59:26.0713486Z GITHUB_ACTOR_ID=53841472 2024-06-26T04:59:26.0713928Z GITHUB_WORKFLOW_SHA=4b51b1a62a63a1add1b3a0f7882f5c7dc66b8f8d 2024-06-26T04:59:26.0714426Z GITHUB_REF_NAME=129470/merge 2024-06-26T04:59:26.0715019Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla 2024-06-26T04:59:26.0715593Z GITHUB_JOB=test 2024-06-26T04:59:26.0715860Z NO_TEST_TIMEOUT=False 2024-06-26T04:59:26.0716170Z TD_DISTRIBUTED=False 2024-06-26T04:59:26.0716505Z GITHUB_REPOSITORY=pytorch/pytorch 2024-06-26T04:59:26.0716869Z GITHUB_RETENTION_DAYS=90 2024-06-26T04:59:26.0717194Z OPENSSL_DIR=/opt/openssl 2024-06-26T04:59:26.0717520Z GITHUB_ACTION_REPOSITORY= 2024-06-26T04:59:26.0718559Z PATH=/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.12/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2024-06-26T04:59:26.0719939Z GITHUB_BASE_REF=gh/leslie-fang-intel/126/base 2024-06-26T04:59:26.0720368Z INSTALLED_ACL= 2024-06-26T04:59:26.0720623Z CI=true 2024-06-26T04:59:26.0720964Z GITHUB_REPOSITORY_OWNER=pytorch 2024-06-26T04:59:26.0721327Z JOB_ID=26688259850 2024-06-26T04:59:26.0721602Z INSTALLED_PROTOBUF=yes 2024-06-26T04:59:26.0722018Z GITHUB_HEAD_REF=gh/leslie-fang-intel/126/head 2024-06-26T04:59:26.0722440Z GITHUB_ACTION_REF= 2024-06-26T04:59:26.0722838Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2 2024-06-26T04:59:26.0723293Z GITHUB_WORKFLOW=pull 2024-06-26T04:59:26.0723611Z DEBIAN_FRONTEND=noninteractive 2024-06-26T04:59:26.0724524Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_1f386446-d3ba-41cc-9ba5-abe7c28e47af 2024-06-26T04:59:26.0725359Z NO_TD=False 2024-06-26T04:59:26.0725638Z SKIP_SCCACHE_INITIALIZATION=1 2024-06-26T04:59:26.0725966Z _=/usr/bin/env 2024-06-26T04:59:26.0726285Z + echo 'Testing pytorch' 2024-06-26T04:59:26.0726612Z Testing pytorch 2024-06-26T04:59:26.0726897Z + export LANG=C.UTF-8 2024-06-26T04:59:26.0727216Z + LANG=C.UTF-8 2024-06-26T04:59:26.0747913Z + PR_NUMBER=129470 2024-06-26T04:59:26.0748517Z + [[ dynamo == \d\e\f\a\u\l\t ]] 2024-06-26T04:59:26.0749256Z + [[ dynamo == \d\i\s\t\r\i\b\u\t\e\d ]] 2024-06-26T04:59:26.0749815Z + [[ dynamo == \s\l\o\w ]] 2024-06-26T04:59:26.0750591Z + [[ linux-focal-py3.12-clang10 == *slow-gradcheck* ]] 2024-06-26T04:59:26.0751357Z + [[ linux-focal-py3.12-clang10 == *cuda* ]] 2024-06-26T04:59:26.0751878Z + [[ linux-focal-py3.12-clang10 == *rocm* ]] 2024-06-26T04:59:26.0752375Z + [[ linux-focal-py3.12-clang10 == *xpu* ]] 2024-06-26T04:59:26.0752832Z + [[ dynamo == *crossref* ]] 2024-06-26T04:59:26.0753270Z + [[ linux-focal-py3.12-clang10 == *rocm* ]] 2024-06-26T04:59:26.0753776Z + [[ linux-focal-py3.12-clang10 == *xpu* ]] 2024-06-26T04:59:26.0754285Z + [[ linux-focal-py3.12-clang10 != *-bazel-* ]] 2024-06-26T04:59:26.0754779Z + pip_install --user ninja==1.10.2 2024-06-26T04:59:26.0755312Z + pip install --progress-bar off --user ninja==1.10.2 2024-06-26T04:59:26.5638505Z Collecting ninja==1.10.2 2024-06-26T04:59:26.5794555Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl.metadata (5.0 kB) 2024-06-26T04:59:26.5949982Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (108 kB) 2024-06-26T04:59:27.1844556Z Installing collected packages: ninja 2024-06-26T04:59:27.1919067Z  WARNING: The script ninja is installed in '/var/lib/jenkins/.local/bin' which is not on PATH. 2024-06-26T04:59:27.1920450Z Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. 2024-06-26T04:59:27.1961789Z Successfully installed ninja-1.10.2 2024-06-26T04:59:27.3191969Z + export PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.12/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2024-06-26T04:59:27.3196051Z + PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.12/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2024-06-26T04:59:27.3198655Z + [[ linux-focal-py3.12-clang10 == *aarch64* ]] 2024-06-26T04:59:27.3199396Z + install_tlparse 2024-06-26T04:59:27.3199972Z + pip_install --user tlparse==0.3.7 2024-06-26T04:59:27.3200945Z + pip install --progress-bar off --user tlparse==0.3.7 2024-06-26T04:59:27.7533848Z Collecting tlparse==0.3.7 2024-06-26T04:59:27.7673679Z Downloading tlparse-0.3.7-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (346 bytes) 2024-06-26T04:59:27.7782343Z Downloading tlparse-0.3.7-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.2 MB) 2024-06-26T04:59:28.3928304Z Installing collected packages: tlparse 2024-06-26T04:59:28.4308528Z Successfully installed tlparse-0.3.7 2024-06-26T04:59:28.5483809Z ++ python -m site --user-base 2024-06-26T04:59:28.5654602Z + PATH=/var/lib/jenkins/.local/bin:/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.12/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2024-06-26T04:59:28.5656949Z + [[ linux-focal-py3.12-clang10 == *asan* ]] 2024-06-26T04:59:28.5657613Z + [[ linux-focal-py3.12-clang10 == *-debug* ]] 2024-06-26T04:59:28.5658403Z + [[ linux-focal-py3.12-clang10 != *-bazel-* ]] 2024-06-26T04:59:28.5659228Z + echo 'We are not in debug mode: linux-focal-py3.12-clang10. Expect the assertion to pass' 2024-06-26T04:59:28.5660787Z We are not in debug mode: linux-focal-py3.12-clang10. Expect the assertion to pass 2024-06-26T04:59:28.5661809Z + cd test 2024-06-26T04:59:28.5662655Z + python -c 'import torch; torch._C._crash_if_debug_asserts_fail(424242)' 2024-06-26T04:59:29.9453238Z + [[ dynamo == \n\o\g\p\u\_\N\O\_\A\V\X\2 ]] 2024-06-26T04:59:29.9454174Z + [[ dynamo == \n\o\g\p\u\_\A\V\X\5\1\2 ]] 2024-06-26T04:59:29.9454817Z + [[ linux-focal-py3.12-clang10 != *-bazel-* ]] 2024-06-26T04:59:29.9455315Z + pushd test 2024-06-26T04:59:29.9455630Z ~/workspace/test ~/workspace 2024-06-26T04:59:29.9456366Z ++ python -c 'import torch; print(torch.version.cuda)' 2024-06-26T04:59:31.2959894Z + CUDA_VERSION=None 2024-06-26T04:59:31.2960702Z + '[' None == 12.4 ']' 2024-06-26T04:59:31.2961358Z + ISCUDA124= 2024-06-26T04:59:31.2961844Z + popd 2024-06-26T04:59:31.2962279Z ~/workspace 2024-06-26T04:59:31.2966925Z + DYNAMO_BENCHMARK_FLAGS=() 2024-06-26T04:59:31.2967761Z + [[ dynamo == *dynamo_eager* ]] 2024-06-26T04:59:31.2968446Z + [[ dynamo == *aot_eager* ]] 2024-06-26T04:59:31.2969121Z + [[ dynamo == *aot_inductor* ]] 2024-06-26T04:59:31.2969805Z + [[ dynamo == *inductor* ]] 2024-06-26T04:59:31.2970448Z + [[ dynamo == *dynamic* ]] 2024-06-26T04:59:31.2971090Z + [[ dynamo == *cpu_inductor* ]] 2024-06-26T04:59:31.2971970Z + DYNAMO_BENCHMARK_FLAGS+=(--device cuda) 2024-06-26T04:59:31.2998992Z + [[ linux-focal-py3.12-clang10 == *libtorch* ]] 2024-06-26T04:59:31.2999603Z + [[ linux-focal-py3.12-clang10 == *-bazel-* ]] 2024-06-26T04:59:31.3001143Z + cd test 2024-06-26T04:59:31.3001698Z + python -c 'import torch; print(torch.__config__.show())' 2024-06-26T04:59:32.4322906Z PyTorch built with: 2024-06-26T04:59:32.4323512Z - GCC 4.2 2024-06-26T04:59:32.4323822Z - C++ Version: 201703 2024-06-26T04:59:32.4324246Z - clang 10.0.0 2024-06-26T04:59:32.4325113Z - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications 2024-06-26T04:59:32.4326261Z - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) 2024-06-26T04:59:32.4327277Z - OpenMP 201511 (a.k.a. OpenMP 4.5) 2024-06-26T04:59:32.4327794Z - LAPACK is enabled (usually provided by MKL) 2024-06-26T04:59:32.4328325Z - NNPACK is enabled 2024-06-26T04:59:32.4328688Z - CPU capability usage: AVX512 2024-06-26T04:59:32.4338769Z - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CXX_COMPILER=/opt/cache/bin/clang++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=1 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=braced-scalar-init -Werror=range-loop-construct -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wvla-extension -Wnewline-eof -Winconsistent-missing-override -Winconsistent-missing-destructor-override -Wno-pass-failed -Wno-error=pedantic -Wno-error=old-style-cast -Wno-error=inconsistent-missing-override -Wno-error=inconsistent-missing-destructor-override -Wconstant-conversion -Wno-invalid-partial-specialization -Wno-missing-braces -Qunused-arguments -fcolor-diagnostics -faligned-new -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, 2024-06-26T04:59:32.4347939Z 2024-06-26T04:59:32.6365370Z + cd test 2024-06-26T04:59:32.6366038Z + python -c 'import torch; print(torch.__config__.parallel_info())' 2024-06-26T04:59:33.7736220Z ATen/Parallel: 2024-06-26T04:59:33.7736934Z at::get_num_threads() : 4 2024-06-26T04:59:33.7737367Z at::get_num_interop_threads() : 4 2024-06-26T04:59:33.7737786Z OpenMP 201511 (a.k.a. OpenMP 4.5) 2024-06-26T04:59:33.7738159Z omp_get_max_threads() : 4 2024-06-26T04:59:33.7739141Z Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications 2024-06-26T04:59:33.7739933Z mkl_get_max_threads() : 4 2024-06-26T04:59:33.7740550Z Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) 2024-06-26T04:59:33.7741169Z std::thread::hardware_concurrency() : 8 2024-06-26T04:59:33.7741562Z Environment variables: 2024-06-26T04:59:33.7741887Z OMP_NUM_THREADS : [not set] 2024-06-26T04:59:33.7742233Z MKL_NUM_THREADS : [not set] 2024-06-26T04:59:33.7742573Z ATen parallel backend: OpenMP 2024-06-26T04:59:33.7742826Z 2024-06-26T04:59:34.0244916Z + [[ linux-focal-py3.12-clang10 == *aarch64* ]] 2024-06-26T04:59:34.0245715Z + [[ dynamo == *backward* ]] 2024-06-26T04:59:34.0246327Z + [[ dynamo == *xla* ]] 2024-06-26T04:59:34.0246674Z + [[ dynamo == *executorch* ]] 2024-06-26T04:59:34.0247061Z + [[ dynamo == \j\i\t\_\l\e\g\a\c\y ]] 2024-06-26T04:59:34.0247559Z + [[ linux-focal-py3.12-clang10 == *libtorch* ]] 2024-06-26T04:59:34.0248155Z + [[ dynamo == distributed ]] 2024-06-26T04:59:34.0248727Z + [[ dynamo == *inductor_distributed* ]] 2024-06-26T04:59:34.0249222Z + [[ dynamo == *inductor-halide* ]] 2024-06-26T04:59:34.0249709Z + [[ dynamo == *inductor-micro-benchmark* ]] 2024-06-26T04:59:34.0250148Z + [[ dynamo == *huggingface* ]] 2024-06-26T04:59:34.0250496Z + [[ dynamo == *timm* ]] 2024-06-26T04:59:34.0250885Z + [[ dynamo == *torchbench* ]] 2024-06-26T04:59:34.0251325Z + [[ dynamo == *inductor_cpp_wrapper_abi_compatible* ]] 2024-06-26T04:59:34.0251787Z + [[ dynamo == *inductor* ]] 2024-06-26T04:59:34.0252132Z + [[ dynamo == *inductor* ]] 2024-06-26T04:59:34.0252475Z + [[ dynamo == *dynamo* ]] 2024-06-26T04:59:34.0252777Z + [[ 1 == 1 ]] 2024-06-26T04:59:34.0253072Z + [[ 3 -gt 1 ]] 2024-06-26T04:59:34.0253358Z + install_torchvision 2024-06-26T04:59:34.0253873Z + local orig_preload 2024-06-26T04:59:34.0254467Z + local commit 2024-06-26T04:59:34.0254762Z ++ get_pinned_commit vision 2024-06-26T04:59:34.0255116Z ++ cat .github/ci_commit_pins/vision.txt 2024-06-26T04:59:34.0262096Z + commit=d23a6e1664d20707c11781299611436e1f0c104f 2024-06-26T04:59:34.0262891Z + orig_preload= 2024-06-26T04:59:34.0263243Z + '[' -n '' ']' 2024-06-26T04:59:34.0264420Z + pip_install --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2024-06-26T04:59:34.0265914Z + pip install --progress-bar off --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2024-06-26T04:59:34.4361507Z Collecting git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2024-06-26T04:59:34.4367248Z Cloning https://github.com/pytorch/vision.git (to revision d23a6e1664d20707c11781299611436e1f0c104f) to /tmp/pip-req-build-fkv5nzcc 2024-06-26T04:59:34.4389478Z Running command git clone --filter=blob:none --quiet https://github.com/pytorch/vision.git /tmp/pip-req-build-fkv5nzcc 2024-06-26T04:59:36.0232018Z Running command git rev-parse -q --verify 'sha^d23a6e1664d20707c11781299611436e1f0c104f' 2024-06-26T04:59:36.0247688Z Running command git fetch -q https://github.com/pytorch/vision.git d23a6e1664d20707c11781299611436e1f0c104f 2024-06-26T04:59:37.3192658Z Running command git checkout -q d23a6e1664d20707c11781299611436e1f0c104f 2024-06-26T04:59:37.5016188Z Resolved https://github.com/pytorch/vision.git to commit d23a6e1664d20707c11781299611436e1f0c104f 2024-06-26T04:59:39.6436378Z Preparing metadata (setup.py) ... [?25l- \ done 2024-06-26T04:59:39.6490987Z [?25hRequirement already satisfied: numpy in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torchvision==0.19.0a0+d23a6e1) (1.26.0) 2024-06-26T04:59:39.6504659Z Requirement already satisfied: torch in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torchvision==0.19.0a0+d23a6e1) (2.5.0a0+gitb8c4c54) 2024-06-26T04:59:39.6517163Z Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torchvision==0.19.0a0+d23a6e1) (10.3.0) 2024-06-26T04:59:39.6901246Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (3.13.1) 2024-06-26T04:59:39.6911758Z Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (4.12.2) 2024-06-26T04:59:39.6921706Z Requirement already satisfied: sympy in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (1.12) 2024-06-26T04:59:39.6930398Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (2.8.8) 2024-06-26T04:59:39.6937109Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (3.1.4) 2024-06-26T04:59:39.6946493Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (2024.2.0) 2024-06-26T04:59:39.6955692Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (69.5.1) 2024-06-26T04:59:39.7264818Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from jinja2->torch->torchvision==0.19.0a0+d23a6e1) (2.1.5) 2024-06-26T04:59:39.7664356Z Requirement already satisfied: mpmath>=0.19 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from sympy->torch->torchvision==0.19.0a0+d23a6e1) (1.2.1) 2024-06-26T04:59:39.7723463Z Building wheels for collected packages: torchvision 2024-06-26T05:00:43.9275740Z Building wheel for torchvision (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ done 2024-06-26T05:00:43.9311331Z [?25h Created wheel for torchvision: filename=torchvision-0.19.0a0+d23a6e1-cp312-cp312-linux_x86_64.whl size=1115254 sha256=388456334a77ea7732c3d20da998470f631475cf7a0d71f9c37b51d9107c3a07 2024-06-26T05:00:43.9313841Z Stored in directory: /var/lib/jenkins/.cache/pip/wheels/b9/aa/81/39d3509ec629531316195ffac7a7b05ff7603f393064d63ec9 2024-06-26T05:00:43.9347500Z Successfully built torchvision 2024-06-26T05:00:44.5014588Z Installing collected packages: torchvision 2024-06-26T05:00:44.9374316Z Successfully installed torchvision-0.19.0a0+d23a6e1 2024-06-26T05:00:45.0837173Z + '[' -n '' ']' 2024-06-26T05:00:45.0837519Z + test_dynamo_shard 1 2024-06-26T05:00:45.0839813Z + [[ -z 3 ]] 2024-06-26T05:00:45.0840180Z + python tools/dynamo/verify_dynamo.py 2024-06-26T05:00:46.2291517Z Python version: 3.12.4 2024-06-26T05:00:46.2291966Z `torch` version: 2.5.0a0+gitb8c4c54 2024-06-26T05:00:46.2292407Z CUDA version: None 2024-06-26T05:00:46.2292740Z ROCM version: None 2024-06-26T05:00:46.2292918Z 2024-06-26T05:00:46.9182971Z CUDA not available -- skipping CUDA check on eager backend 2024-06-26T05:00:46.9183389Z 2024-06-26T05:00:47.3978733Z CUDA not available -- skipping CUDA check on aot_eager backend 2024-06-26T05:00:47.3979763Z 2024-06-26T05:00:52.4091744Z CUDA not available -- skipping CUDA check on inductor backend 2024-06-26T05:00:52.4092495Z 2024-06-26T05:00:52.4092682Z All required checks passed 2024-06-26T05:00:52.9547543Z + python test/run_test.py --dynamo --exclude-inductor-tests --exclude-jit-executor --exclude-distributed-tests --exclude-torch-export-tests --shard 1 3 --verbose 2024-06-26T05:00:53.0558996Z /var/lib/jenkins/workspace/test/run_test.py:21: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html 2024-06-26T05:00:53.0561141Z import pkg_resources 2024-06-26T05:00:54.9378967Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:00:55.8685747Z Downloading https://ossci-metrics.s3.amazonaws.com/slow-tests.json to /var/lib/jenkins/workspace/test/.pytorch-slow-tests.json 2024-06-26T05:00:55.9213046Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2024-06-26T05:00:55.9530105Z Ignoring disabled issues: [''] 2024-06-26T05:00:55.9630071Z Found test times from artifacts 2024-06-26T05:00:56.0016186Z Found test times from artifacts 2024-06-26T05:00:56.0029092Z Running all tests 2024-06-26T05:00:56.0126234Z Running parallel tests on 3 processes 2024-06-26T05:00:56.0129408Z Name: tests to run (est. time: 51.66min) 2024-06-26T05:00:56.0130149Z Serial tests (34): 2024-06-26T05:00:56.0130588Z test_nn 1/2 2024-06-26T05:00:56.0130881Z test_cpp_api_parity 1/1 2024-06-26T05:00:56.0131206Z test_torch 1/1 2024-06-26T05:00:56.0131576Z test_ci_sanity_check_fail 1/1 2024-06-26T05:00:56.0132255Z test_show_pickle 1/1 2024-06-26T05:00:56.0132746Z test_autocast 1/1 2024-06-26T05:00:56.0133122Z test_utils 1/1 2024-06-26T05:00:56.0133834Z test_tensorexpr 1/1 2024-06-26T05:00:56.0134295Z test_autograd_fallback 1/1 2024-06-26T05:00:56.0134786Z test_python_dispatch 1/1 2024-06-26T05:00:56.0135510Z test_cpp_extensions_stream_and_event 1/1 2024-06-26T05:00:56.0136050Z test_cpp_extensions_mtia_backend 1/1 2024-06-26T05:00:56.0136452Z test_overrides 1/1 2024-06-26T05:00:56.0136762Z test_jit_disabled 1/1 2024-06-26T05:00:56.0137069Z test_native_mha 1/1 2024-06-26T05:00:56.0137400Z test_cpp_extensions_jit 1/1 2024-06-26T05:00:56.0137830Z test_cpp_extensions_open_device_registration 1/1 2024-06-26T05:00:56.0138277Z test_sort_and_select 1/1 2024-06-26T05:00:56.0138628Z test_multiprocessing 1/1 2024-06-26T05:00:56.0138982Z test_mobile_optimizer 1/1 2024-06-26T05:00:56.0139315Z nn/test_pooling 1/1 2024-06-26T05:00:56.0139639Z test_tensor_creation_ops 1/1 2024-06-26T05:00:56.0142886Z test_reductions 1/1 2024-06-26T05:00:56.0143710Z test_cuda_primary_ctx 1/1 2024-06-26T05:00:56.0144501Z test_dispatch 1/1 2024-06-26T05:00:56.0145194Z test_cuda_trace 1/1 2024-06-26T05:00:56.0145957Z test_multiprocessing_spawn 1/1 2024-06-26T05:00:56.0146815Z test_cuda_nvml_based_avail 1/1 2024-06-26T05:00:56.0147955Z test_spectral_ops 1/1 2024-06-26T05:00:56.0148787Z distributions/test_distributions 1/2 2024-06-26T05:00:56.0149770Z distributions/test_distributions 2/2 2024-06-26T05:00:56.0150669Z doctests 1/1 2024-06-26T05:00:56.0151360Z test_cpp_extensions_aot_no_ninja 1/1 2024-06-26T05:00:56.0152299Z test_cpp_extensions_aot_ninja 1/1 2024-06-26T05:00:56.0153206Z Parallel tests (26): 2024-06-26T05:00:56.0153956Z dynamo/test_dynamic_shapes 1/1 2024-06-26T05:00:56.0154830Z dynamo/test_fx_passes_pre_grad 1/1 2024-06-26T05:00:56.0155732Z dynamo/test_frame_init 1/1 2024-06-26T05:00:56.0156515Z dynamo/test_sdpa 1/1 2024-06-26T05:00:56.0157266Z dynamo/test_exceptions 1/1 2024-06-26T05:00:56.0158062Z dynamo/test_repros 1/1 2024-06-26T05:00:56.0158803Z dynamo/test_nops 1/1 2024-06-26T05:00:56.0159510Z dynamo/test_config 1/1 2024-06-26T05:00:56.0160252Z test_jiterator 1/1 2024-06-26T05:00:56.0161043Z test_matmul_cuda 1/1 2024-06-26T05:00:56.0161779Z dynamo/test_sources 1/1 2024-06-26T05:00:56.0162519Z xpu/test_conv 1/1 2024-06-26T05:00:56.0163185Z test_cuda 1/1 2024-06-26T05:00:56.0163863Z dynamo/test_verify_correctness 1/1 2024-06-26T05:00:56.0164778Z dynamo/test_profiler 1/1 2024-06-26T05:00:56.0165572Z dynamo/test_reorder_logs 1/1 2024-06-26T05:00:56.0166351Z test_hub 1/1 2024-06-26T05:00:56.0166994Z dynamo/test_minifier 1/1 2024-06-26T05:00:56.0167871Z dynamo/test_activation_checkpointing 1/1 2024-06-26T05:00:56.0168850Z dynamo/test_recompile_ux 1/1 2024-06-26T05:00:56.0169681Z dynamo/test_subclasses 1/1 2024-06-26T05:00:56.0170539Z lazy/test_extract_compiled_graph 1/1 2024-06-26T05:00:56.0171482Z dynamo/test_aot_autograd_cache 1/1 2024-06-26T05:00:56.0172363Z test_cuda_multigpu 1/1 2024-06-26T05:00:56.0173087Z test_linalg 3/3 2024-06-26T05:00:56.0173859Z dynamo/test_python_autograd 1/1 2024-06-26T05:00:56.0174609Z Name: excluded (est. time: 0.0min) 2024-06-26T05:00:56.0175077Z Serial tests (0): 2024-06-26T05:00:56.0175366Z Parallel tests (0): 2024-06-26T05:00:56.0176020Z Starting test batch 'tests to run' 0.0 seconds after initiating testing 2024-06-26T05:00:56.0265446Z Running test_nn 1/2 ... [2024-06-26 05:00:56.026212] 2024-06-26T05:00:56.0270190Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_nn.py', '--shard-id=1', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:00:56.026611] 2024-06-26T05:00:58.0373323Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:00:58.0742710Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:00:58.0862440Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:07:09.9887657Z 2024-06-26T05:07:09.9888982Z test_nn 1/2 was successful, full logs can be found in artifacts with path test/test-reports/test_nn_1.2_65c608ac0184beaf_.log 2024-06-26T05:07:10.0652823Z Running 1041 items in this shard: test/test_nn.py::TestNN::test_AdaptiveLogSoftmax, test/test_nn.py::TestNN::test_AdaptiveLogSoftmax_cuda, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_BCELoss_no_reduce_cuda, test/test_nn.py::TestNN::test_BCELoss_weights_no_reduce, test/test_nn.py::TestNN::test_BCELoss_weights_no_reduce_cuda, test/test_nn.py::TestNN::test_BCELoss_weights_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_legacy_enum_cuda, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_CELU_no_batch_dim, test/test_nn.py::TestNN::test_CELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_CTCLoss_zero_lengths, test/test_nn.py::TestNN::test_Conv1d, test/test_nn.py::TestNN::test_Conv1d_circular_stride2_pad2_cuda, 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test/test_nn.py::TestNN::test_ReplicationPad3d_cuda, test/test_nn.py::TestNN::test_ReplicationPad3d_no_batch_dim_cuda, test/test_nn.py::TestNN::test_SELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Sequential_extend, test/test_nn.py::TestNN::test_Sequential_getitem, test/test_nn.py::TestNN::test_Sequential_iadd, test/test_nn.py::TestNN::test_Sequential_rmul, test/test_nn.py::TestNN::test_SiLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Sigmoid_no_batch_dim_cuda, test/test_nn.py::TestNN::test_SmoothL1Loss_beta, test/test_nn.py::TestNN::test_SmoothL1Loss_beta_cuda, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_none, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_SmoothL1Loss_no_reduce, test/test_nn.py::TestNN::test_SmoothL1Loss_no_reduce_cuda, test/test_nn.py::TestNN::test_SmoothL1Loss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_SmoothL1Loss_zero_beta_cuda, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_SoftMarginLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_Softplus_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Softshrink_no_batch_dim, test/test_nn.py::TestNN::test_Softsign_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Tanhshrink_no_batch_dim_cuda, test/test_nn.py::TestNN::test_TransformerDecoderLayer_relu_activation_cuda, test/test_nn.py::TestNN::test_TransformerEncoderLayer_gelu_activation, test/test_nn.py::TestNN::test_TransformerEncoderLayer_relu_activation_cuda, test/test_nn.py::TestNN::test_Transformer_cell, test/test_nn.py::TestNN::test_Transformer_multilayer_coder, test/test_nn.py::TestNN::test_Transformer_multilayer_coder_cuda, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_Unfold, test/test_nn.py::TestNN::test_Unfold_cuda, test/test_nn.py::TestNN::test_add_module_raises_error_if_attr_exists, test/test_nn.py::TestNN::test_affine_grid_3d, test/test_nn.py::TestNN::test_affine_grid_backward_cl_cf_consistency_device_cpu_nd_2, test/test_nn.py::TestNN::test_affine_grid_error_checking, test/test_nn.py::TestNN::test_assignment, test/test_nn.py::TestNN::test_batch_norm_update_stats, test/test_nn.py::TestNN::test_batchnorm_buffer_update_when_stats_are_not_tracked, test/test_nn.py::TestNN::test_batchnorm_cudnn_half, test/test_nn.py::TestNN::test_batchnorm_cudnn_nhwc, test/test_nn.py::TestNN::test_batchnorm_load_state_dict, test/test_nn.py::TestNN::test_batchnorm_nhwc_cpu, test/test_nn.py::TestNN::test_batchnorm_nhwc_cuda, test/test_nn.py::TestNN::test_batchnorm_non_contig_cpu_BatchNorm2d, test/test_nn.py::TestNN::test_batchnorm_nonaffine_cuda_half_input, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_bias_is_not_same_size_as_input, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_less_than_one_value_per_channel, test/test_nn.py::TestNN::test_bce_with_logits_broadcasts_weights, test/test_nn.py::TestNN::test_bce_with_logits_gives_same_result_as_sigmoid_and_bce_loss, test/test_nn.py::TestNN::test_bce_with_logits_gives_same_result_as_sigmoid_and_bce_loss_large_tensors_with_grad, test/test_nn.py::TestNN::test_bce_with_logits_has_correct_forward_grad, test/test_nn.py::TestNN::test_bce_with_logits_ones_in_pos_weights_are_the_same_as_none, test/test_nn.py::TestNN::test_bce_with_logits_with_pos_weight_has_correct_grad_at_zero, test/test_nn.py::TestNN::test_bilinear, test/test_nn.py::TestNN::test_broadcast_double_backwards_gpu, test/test_nn.py::TestNN::test_broadcast_no_grad, test/test_nn.py::TestNN::test_buffer_not_persistent_load, test/test_nn.py::TestNN::test_buffers_and_named_buffers, test/test_nn.py::TestNN::test_cosine_embedding_loss_margin_no_reduce, test/test_nn.py::TestNN::test_cosine_embedding_loss_no_reduce, test/test_nn.py::TestNN::test_cosine_embedding_loss_with_diff_type, test/test_nn.py::TestNN::test_cross_entropy_loss_precision, test/test_nn.py::TestNN::test_cudnn_rnn_dropout_states_device, test/test_nn.py::TestNN::test_cudnn_weight_tying, test/test_nn.py::TestNN::test_extra_state, test/test_nn.py::TestNN::test_extra_state_missing_set_extra_state, test/test_nn.py::TestNN::test_fb_fc_packed, test/test_nn.py::TestNN::test_flatten, test/test_nn.py::TestNN::test_fractional_max_pool2d_invalid_output_ratio, test/test_nn.py::TestNN::test_gaussian_nll_loss_args, test/test_nn.py::TestNN::test_gaussian_nll_loss_broadcasting, test/test_nn.py::TestNN::test_get_buffer, test/test_nn.py::TestNN::test_grid_sample, test/test_nn.py::TestNN::test_grid_sample_3d, test/test_nn.py::TestNN::test_grid_sample_error_checking, test/test_nn.py::TestNN::test_hardtanh_backward, test/test_nn.py::TestNN::test_hardtanh_inplace_gradgrad, test/test_nn.py::TestNN::test_huber_loss_invalid_delta, test/test_nn.py::TestNN::test_inplace_thnn, test/test_nn.py::TestNN::test_interpolate_bicubic_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_2d_zero_dim, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_shared_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_shared_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_skewed_2d_align_corners, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_skewed_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_skewed_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_tuple_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_tuple_2d_align_corners, test/test_nn.py::TestNN::test_interpolate_bicubic_tuple_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_tuple_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_2d, test/test_nn.py::TestNN::test_interpolate_bilinear_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_2d_zero_dim, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_skewed_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_skewed_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_tuple_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_linear_1d_align_corners, test/test_nn.py::TestNN::test_interpolate_linear_1d_cuda, test/test_nn.py::TestNN::test_interpolate_linear_1d_zero_dim, test/test_nn.py::TestNN::test_interpolate_linear_1d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_linear_scale_1d_align_corners, test/test_nn.py::TestNN::test_interpolate_linear_scale_1d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_linear_tuple_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_1d_zero_dim, test/test_nn.py::TestNN::test_interpolate_nearest_2d_launch_configs, test/test_nn.py::TestNN::test_interpolate_nearest_2d_launch_configs_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_2d_zero_dim, test/test_nn.py::TestNN::test_interpolate_nearest_3d, test/test_nn.py::TestNN::test_interpolate_nearest_3d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_3d_zero_dim, test/test_nn.py::TestNN::test_interpolate_nearest_3d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_scale_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_scale_2d, test/test_nn.py::TestNN::test_interpolate_nearest_scale_2d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_scale_3d, test/test_nn.py::TestNN::test_interpolate_nearest_scale_3d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_1d, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_2d, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_3d_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_3d, test/test_nn.py::TestNN::test_interpolate_trilinear_3d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_scale_3d_align_corners, test/test_nn.py::TestNN::test_interpolate_trilinear_tuple_3d_align_corners, test/test_nn.py::TestNN::test_interpolate_trilinear_tuple_3d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_tuple_3d_cuda, test/test_nn.py::TestNN::test_interpolate_undefined_behavior_casting, test/test_nn.py::TestNN::test_l1_loss_correct, test/test_nn.py::TestNN::test_layer_norm_grads_with_create_graph_flag, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_bias_weightCSR, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_nobias_weightCOO, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_nobias_weightStrided, test/test_nn.py::TestNN::test_log_softmax_scalar, test/test_nn.py::TestNN::test_log_softmax_spatial_special_cuda, test/test_nn.py::TestNN::test_loss_equal_input_target_shape, test/test_nn.py::TestNN::test_margin_ranking_loss_no_reduce, test/test_nn.py::TestNN::test_module_backcompat, test/test_nn.py::TestNN::test_module_super_init, test/test_nn.py::TestNN::test_modules, test/test_nn.py::TestNN::test_multimarginloss_1d_input_0d_target_no_reduce, test/test_nn.py::TestNN::test_named_modules, test/test_nn.py::TestNN::test_named_parameters_remove_duplicate, test/test_nn.py::TestNN::test_nested_tensor_from_mask, test/test_nn.py::TestNN::test_overwrite_module_params_on_conversion, test/test_nn.py::TestNN::test_pack_sequence_batch_sizes_throw, test/test_nn.py::TestNN::test_padding_list, test/test_nn.py::TestNN::test_parameterlistdict_setting_attributes, test/test_nn.py::TestNN::test_pdist_empty_col, test/test_nn.py::TestNN::test_pixel_shuffle_nhwc_cpu, test/test_nn.py::TestNN::test_pixel_shuffle_unshuffle, test/test_nn.py::TestNN::test_pointwise_loss_broadcast, test/test_nn.py::TestNN::test_projections_errors_on_gru_and_rnn, test/test_nn.py::TestNN::test_projections_lstm_args_check, test/test_nn.py::TestNN::test_projections_lstm_initial_hidden_state, test/test_nn.py::TestNN::test_register_buffer_allows_overwriting_with_same_name, test/test_nn.py::TestNN::test_register_buffer_raises_error_if_attr_exists, test/test_nn.py::TestNN::test_register_parameter_raises_error_if_name_is_not_string, test/test_nn.py::TestNN::test_relu_inplace_on_view, test/test_nn.py::TestNN::test_rnn_check_device, test/test_nn.py::TestNN::test_rnn_initial_hidden_state, test/test_nn.py::TestNN::test_rnn_weight_norm, test/test_nn.py::TestNN::test_smoothl1loss_intergral_target, test/test_nn.py::TestNN::test_softmax_functional_dim0, test/test_nn.py::TestNN::test_softmax_functional_dim0_cuda, test/test_nn.py::TestNN::test_softmax_functional_dim3, test/test_nn.py::TestNN::test_softmax_lastdim, test/test_nn.py::TestNN::test_softmax_lastdim_dtype, test/test_nn.py::TestNN::test_softmax_spatial, test/test_nn.py::TestNN::test_softmax_spatial_dtype_cuda, test/test_nn.py::TestNN::test_softmax_spatial_special, test/test_nn.py::TestNN::test_softmin, test/test_nn.py::TestNN::test_spectral_norm, test/test_nn.py::TestNN::test_spectral_norm_dim, test/test_nn.py::TestNN::test_spectral_norm_forward, test/test_nn.py::TestNN::test_spectral_norm_load_state_dict, test/test_nn.py::TestNN::test_spectral_norm_pickle, test/test_nn.py::TestNN::test_state_dict, test/test_nn.py::TestNN::test_swap_module_params_poisons_acc_grad, test/test_nn.py::TestNN::test_to, test/test_nn.py::TestNN::test_transformer_args_check, test/test_nn.py::TestNN::test_transformerdecoderlayer_gelu, test/test_nn.py::TestNN::test_triplet_margin_loss_no_reduce, test/test_nn.py::TestNN::test_triplet_margin_loss_swap, test/test_nn.py::TestNN::test_type, test/test_nn.py::TestNN::test_unflatten, test/test_nn.py::TestNN::test_unfold_invalid_arg, test/test_nn.py::TestNN::test_upsamplingBilinear2d_spatial_invariance, test/test_nn.py::TestNN::test_upsamplingLinear1d_spatial_invariance, test/test_nn.py::TestNN::test_upsampling_bfloat16, test/test_nn.py::TestNN::test_upsampling_not_recompute_scale_factor, test/test_nn.py::TestNN::test_upsampling_small_scale, test/test_nn.py::TestFusionEval::test_fuse_module_eval_numerics, test/test_nn.py::TestConstantPadNd::test_constant_pad_nd, test/test_nn.py::TestAddRelu::test_add_relu, test/test_nn.py::TestFunctionalPickle::test_pickle_softsign, test/test_nn.py::TestFusionUtils::test_fuse_linear_bn_requires_grad, test/test_nn.py::TestUtils::test_consume_prefix_in_state_dict_if_present, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_mean_use_module_form_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_none_use_module_form_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_sum_use_module_form_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_sum_use_module_form_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_memory_format_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_numeric_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_InstanceNorm1d_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_InstanceNorm2d_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_InstanceNorm3d_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_LSTM_differentiable_backward_using_oneDNN_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_LSTM_grad_and_gradgrad_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_LayerNorm_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_MarginLoss_empty_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_MarginLoss_warnings_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad2d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad_empty_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad2d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad_empty_cpu_complex128, test/test_nn.py::TestNNDeviceTypeCPU::test_TransformerEncoder_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_Unfold_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_activations_bfloat16_half_cpu_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_activations_bfloat16_half_cpu_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_adaptiveavg_pool1d_shmem_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_affine_2d_rotate0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_affine_3d_rotateRandom_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_avg_pool_large_tensor_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_affine_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_affine_mixed_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_affine_mixed_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_eval_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_eval_mixed_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_eval_mixed_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_grad_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_simple_average_mixed_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_0_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_1_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_2_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_inf_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_True_norm_type_0_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_True_norm_type_2_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_multi_device_foreach_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_value_foreach_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_value_foreach_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_conv_empty_input_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_conv_empty_input_cpu_complex128, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_64bit_reduction_none_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_label_smoothing_errors_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_label_smoothing_weight_ignore_indices_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_large_tensor_reduction_none_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_large_tensor_reduction_sum_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_one_hot_target_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_mean_weighted_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_none_weighted_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_sum_weighted_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_sum_weighted_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ctc_loss_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ctc_loss_cudnn_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_elu_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_elu_inplace_with_neg_alpha_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_glu_bfloat16_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_nan_inf_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_groupnorm_nhwc_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_groupnorm_nhwc_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_groupnorm_nhwc_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_gumbel_softmax_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_gumbel_softmax_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_hardswish_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_for_single_spatial_element_during_training_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm2d_no_batch_dim_False_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_False_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_False_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_True_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_less_than_one_value_per_channel_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_invalid_reduction_strings_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_leaky_relu_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_leaky_relu_inplace_with_neg_slope_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_leaky_relu_inplace_with_zero_slope_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_linear_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_log_softmax_big_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_log_softmax_cpu_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_forward_with_nans_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_transformer_layout_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_module_to_empty_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_module_to_empty_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_byte_target_matches_long_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_invalid_weights_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_large_tensor_reduction_mean_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_large_tensor_reduction_none_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_out_of_bounds_ignore_index_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_total_weight_is_zero_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nn_scalars_reductions_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nonlinearity_propagate_nan_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_one_hot_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_overwrite_module_params_on_conversion_cpu_device_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_pad_cpu_complex128, test/test_nn.py::TestNNDeviceTypeCPU::test_prelu_backward_32bit_indexing_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_rnn_fused_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_skip_init_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_smooth_l1_loss_vs_huber_loss_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_cpu_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_results_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_threshold_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_to_complex_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_transformerencoderlayer_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_transformerencoderlayer_fast_path_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_triplet_margin_with_distance_loss_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_triplet_margin_with_distance_loss_default_parity_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format0_align_corners_False_input_size_403_output_size_377_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format1_align_corners_False_input_size_399_output_size_437_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format1_align_corners_True_input_size_399_output_size_437_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format1_align_corners_True_input_size_403_output_size_377_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_False_mode_bicubic_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_True_mode_bicubic_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_True_mode_bilinear_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_False_mode_bicubic_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_False_mode_bilinear_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_True_mode_bicubic_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_True_mode_bilinear_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBicubic2d_correctness_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBilinear2d_aa_correctness_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_correctness_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_correctness_memory_format0_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_correctness_memory_format1_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_launch_fail_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_launch_rocm_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_memory_format0_mode_nearest_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_memory_format1_mode_nearest-exact_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_memory_format1_mode_nearest_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_correctness_memory_format0_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_correctness_memory_format0_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_correctness_memory_format1_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_launch_config_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact1d_correctness_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact1d_correctness_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact2d_correctness_memory_format0_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact2d_correctness_memory_format1_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact3d_correctness_memory_format0_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact3d_correctness_memory_format0_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact3d_correctness_memory_format1_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact3d_correctness_memory_format1_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsampling_64bit_indexing_channels_last_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingnearest2d_backward_64bit_indexing_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_variable_sequence_cpu_float32 2024-06-26T05:07:10.1312176Z 2024-06-26T05:07:10.1312610Z Running test_cpp_api_parity 1/1 ... [2024-06-26 05:07:09.991260] 2024-06-26T05:07:10.1314385Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cpp_api_parity.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:07:09.991542] 2024-06-26T05:09:21.1978298Z 2024-06-26T05:09:21.1980208Z test_cpp_api_parity 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_api_parity_1.1_edf47a19d969b92c_.log 2024-06-26T05:09:21.2302401Z Running 488 items in this shard: test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_mean, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_mean_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_none, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_none_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_sum, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_sum_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_mean, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_mean_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_none, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_none_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_sum, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_sum_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_circular_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_circular_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_dilated, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_dilated_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_groups, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_groups_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad1, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad1_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad1size1, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad1size1_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad2size1, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad2size1_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_same, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_same2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_same2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_same_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_same_dilated, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_same_dilated_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_valid, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_valid_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_reflect_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_reflect_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_replicate_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_replicate_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_stride, 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test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_dilated, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_dilated_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_padded, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_padded_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_strided, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_strided_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_with_multiplier, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_with_multiplier_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_dilated, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_dilated_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_groups, 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test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_3d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_3d_zero_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_3d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_scale_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_scale_3d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_scale_3d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_scale_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_tuple_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_tuple_3d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_tuple_3d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_tuple_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_dim0, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_dim0_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_dim3, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_dim3_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_lastdim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_lastdim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_scalar, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_scalar_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_spatial, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_spatial_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_spatial_special, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_spatial_special_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_multimarginloss_1d_input_0d_target_no_reduce, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_multimarginloss_1d_input_0d_target_no_reduce_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_has_parity, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_has_parity_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_no_parity, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_no_parity_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim0, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim0_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim3, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim3_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_scalar, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_scalar_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim_dtype, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim_dtype_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_dtype, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_dtype_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_special, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_special_cuda 2024-06-26T05:09:21.2531497Z 2024-06-26T05:09:21.2531903Z Running test_torch 1/1 ... [2024-06-26 05:09:21.198705] 2024-06-26T05:09:21.2533663Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_torch.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:09:21.198974] 2024-06-26T05:13:34.1235174Z 2024-06-26T05:13:34.1236702Z test_torch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_torch_1.1_d80c4c5a7a827d14_.log 2024-06-26T05:13:34.1786985Z Running 1020 items in this shard: test/test_torch.py::TestBasicVitalSigns::test_basic_vitals, test/test_torch.py::TestBasicVitalSigns::test_basic_vitals_read_write, test/test_torch.py::TestBasicVitalSigns::test_dataloader_vitals, test/test_torch.py::TestTorch::test_RNGState, test/test_torch.py::TestTorch::test_RNGStateAliasing, test/test_torch.py::TestTorch::test_RNG_after_pickle, test/test_torch.py::TestTorch::test_Size, test/test_torch.py::TestTorch::test_Size_iter, test/test_torch.py::TestTorch::test_Size_scalar, test/test_torch.py::TestTorch::test_add_meta_scalar, test/test_torch.py::TestTorch::test_allow_tensor_metadata_change, test/test_torch.py::TestTorch::test_apply, test/test_torch.py::TestTorch::test_as_subclass, test/test_torch.py::TestTorch::test_assert_async, test/test_torch.py::TestTorch::test_backward_hooks_traverse, test/test_torch.py::TestTorch::test_batch_norm_cpu_inference, test/test_torch.py::TestTorch::test_bmm_multithreaded, test/test_torch.py::TestTorch::test_boxMullerState, test/test_torch.py::TestTorch::test_cat_neg_dim, test/test_torch.py::TestTorch::test_check, test/test_torch.py::TestTorch::test_chunk_neg_dim, test/test_torch.py::TestTorch::test_conj_neg_tolist, test/test_torch.py::TestTorch::test_contains, test/test_torch.py::TestTorch::test_copy_broadcast, test/test_torch.py::TestTorch::test_copy_dtypes, test/test_torch.py::TestTorch::test_copy_float16, test/test_torch.py::TestTorch::test_copy_many_to_one, test/test_torch.py::TestTorch::test_copy_transpose, test/test_torch.py::TestTorch::test_cuda_not_built, test/test_torch.py::TestTorch::test_cummax_neg_dim, test/test_torch.py::TestTorch::test_cummin_neg_dim, test/test_torch.py::TestTorch::test_cumprod_neg_dim, test/test_torch.py::TestTorch::test_cumsum_neg_dim, test/test_torch.py::TestTorch::test_cxx_flags, test/test_torch.py::TestTorch::test_data_ptr_of_empty_tensor_with_storage, test/test_torch.py::TestTorch::test_data_ptr_of_empty_view_with_storage, test/test_torch.py::TestTorch::test_deepcopy_gradient, test/test_torch.py::TestTorch::test_deepcopy_parameter, test/test_torch.py::TestTorch::test_deterministic_fill_uninitialized_memory, test/test_torch.py::TestTorch::test_deterministic_flag, test/test_torch.py::TestTorch::test_device, test/test_torch.py::TestTorch::test_dim_order, test/test_torch.py::TestTorch::test_dir, test/test_torch.py::TestTorch::test_doc, test/test_torch.py::TestTorch::test_doc_template, test/test_torch.py::TestTorch::test_dot_data_use, test/test_torch.py::TestTorch::test_dtype_is_signed, test/test_torch.py::TestTorch::test_element_size, test/test_torch.py::TestTorch::test_empty_meta, test/test_torch.py::TestTorch::test_empty_storage_view, test/test_torch.py::TestTorch::test_equal, test/test_torch.py::TestTorch::test_error_msg_type_translation, test/test_torch.py::TestTorch::test_fill_diagonal, test/test_torch.py::TestTorch::test_format_scalar_meta, test/test_torch.py::TestTorch::test_from_buffer, test/test_torch.py::TestTorch::test_from_file, test/test_torch.py::TestTorch::test_gather_neg_dim, test/test_torch.py::TestTorch::test_generator_cpu, test/test_torch.py::TestTorch::test_get_cpu_capability, test/test_torch.py::TestTorch::test_has_internal_overlap, test/test_torch.py::TestTorch::test_has_storage, test/test_torch.py::TestTorch::test_index_add, test/test_torch.py::TestTorch::test_index_add_all_dtypes, test/test_torch.py::TestTorch::test_index_add_cornercase, test/test_torch.py::TestTorch::test_index_add_correctness, test/test_torch.py::TestTorch::test_index_add_neg_dim, test/test_torch.py::TestTorch::test_index_copy_neg_dim, test/test_torch.py::TestTorch::test_index_fill_neg_dim, test/test_torch.py::TestTorch::test_index_select_neg_dim, test/test_torch.py::TestTorch::test_invalid_arg_error_handling, test/test_torch.py::TestTorch::test_invalid_generator_raises, test/test_torch.py::TestTorch::test_is_nonzero, test/test_torch.py::TestTorch::test_is_same_size, test/test_torch.py::TestTorch::test_iter, test/test_torch.py::TestTorch::test_kthvalue_neg_dim, test/test_torch.py::TestTorch::test_linspace_logspace, test/test_torch.py::TestTorch::test_logcumsumexp_neg_dim, test/test_torch.py::TestTorch::test_manual_seed, test/test_torch.py::TestTorch::test_map, test/test_torch.py::TestTorch::test_map2, test/test_torch.py::TestTorch::test_max_neg_dim, test/test_torch.py::TestTorch::test_mean_neg_dim, test/test_torch.py::TestTorch::test_median_neg_dim, test/test_torch.py::TestTorch::test_memory_format, test/test_torch.py::TestTorch::test_memory_format_contiguous_returns_same_tensor_if_already_satisfies, test/test_torch.py::TestTorch::test_memory_format_empty, test/test_torch.py::TestTorch::test_min_neg_dim, test/test_torch.py::TestTorch::test_mode_neg_dim, test/test_torch.py::TestTorch::test_multinomial_invalid_probs, test/test_torch.py::TestTorch::test_nanmedian_neg_dim, test/test_torch.py::TestTorch::test_narrow_neg_dim, test/test_torch.py::TestTorch::test_nbytes, test/test_torch.py::TestTorch::test_ndim, test/test_torch.py::TestTorch::test_new, test/test_torch.py::TestTorch::test_newaxis_numpy_comparison, test/test_torch.py::TestTorch::test_newindex, test/test_torch.py::TestTorch::test_no_cuda_monkeypatch, test/test_torch.py::TestTorch::test_norm_neg_dim, test/test_torch.py::TestTorch::test_normal_shape, test/test_torch.py::TestTorch::test_numel, test/test_torch.py::TestTorch::test_parallel_info, test/test_torch.py::TestTorch::test_parsing_double, test/test_torch.py::TestTorch::test_parsing_int64, test/test_torch.py::TestTorch::test_parsing_intlist, test/test_torch.py::TestTorch::test_permute, test/test_torch.py::TestTorch::test_pickle, test/test_torch.py::TestTorch::test_pickle_dtype, test/test_torch.py::TestTorch::test_pickle_function, test/test_torch.py::TestTorch::test_pickle_generator, test/test_torch.py::TestTorch::test_pickle_parameter, test/test_torch.py::TestTorch::test_pickle_parameter_no_requires_grad, test/test_torch.py::TestTorch::test_pickle_size, test/test_torch.py::TestTorch::test_pin_memory, test/test_torch.py::TestTorch::test_print, test/test_torch.py::TestTorch::test_prod_neg_dim, test/test_torch.py::TestTorch::test_pyobj_preserved, test/test_torch.py::TestTorch::test_qengine, test/test_torch.py::TestTorch::test_renorm_neg_dim, test/test_torch.py::TestTorch::test_resizable, test/test_torch.py::TestTorch::test_reversed, test/test_torch.py::TestTorch::test_scatter_neg_dim, test/test_torch.py::TestTorch::test_select_neg_dim, test/test_torch.py::TestTorch::test_set_flush_denormal, test/test_torch.py::TestTorch::test_setting_real_imag_to_a_number, test/test_torch.py::TestTorch::test_show_config, test/test_torch.py::TestTorch::test_size_neg_dim, test/test_torch.py::TestTorch::test_size_stride, test/test_torch.py::TestTorch::test_sizeof, test/test_torch.py::TestTorch::test_slice, test/test_torch.py::TestTorch::test_slow_test, test/test_torch.py::TestTorch::test_sobolengine_bounds, test/test_torch.py::TestTorch::test_sobolengine_bounds_scrambled, test/test_torch.py::TestTorch::test_sobolengine_continuing, test/test_torch.py::TestTorch::test_sobolengine_continuing_scrambled, test/test_torch.py::TestTorch::test_sobolengine_default_dtype, test/test_torch.py::TestTorch::test_sobolengine_distribution, test/test_torch.py::TestTorch::test_sobolengine_distribution_scrambled, test/test_torch.py::TestTorch::test_sobolengine_draw, test/test_torch.py::TestTorch::test_sobolengine_draw_base2, test/test_torch.py::TestTorch::test_sobolengine_draw_base2_scrambled, test/test_torch.py::TestTorch::test_sobolengine_draw_scrambled, test/test_torch.py::TestTorch::test_sobolengine_fast_forward, test/test_torch.py::TestTorch::test_sobolengine_fast_forward_scrambled, test/test_torch.py::TestTorch::test_sobolengine_first_point, test/test_torch.py::TestTorch::test_sobolengine_high_dim, test/test_torch.py::TestTorch::test_sobolengine_raise, test/test_torch.py::TestTorch::test_sobolengine_reset, test/test_torch.py::TestTorch::test_sobolengine_reset_scrambled, test/test_torch.py::TestTorch::test_sort_neg_dim, test/test_torch.py::TestTorch::test_split_neg_dim, test/test_torch.py::TestTorch::test_split_with_sizes_copy_out, test/test_torch.py::TestTorch::test_squeeze_neg_dim, test/test_torch.py::TestTorch::test_std_neg_dim, test/test_torch.py::TestTorch::test_storage_base_init, test/test_torch.py::TestTorch::test_storage_base_new, test/test_torch.py::TestTorch::test_storage_byteswap, test/test_torch.py::TestTorch::test_storage_casts, test/test_torch.py::TestTorch::test_storage_cycle_via_dict, test/test_torch.py::TestTorch::test_storage_cycle_via_slots, test/test_torch.py::TestTorch::test_storage_dead_weak_ref, test/test_torch.py::TestTorch::test_storage_dealloc, test/test_torch.py::TestTorch::test_storage_dealloc_resurrected, test/test_torch.py::TestTorch::test_storage_dealloc_subclass_resurrected, test/test_torch.py::TestTorch::test_storage_dealloc_subclass_zombie, test/test_torch.py::TestTorch::test_storage_dict_dealloc, test/test_torch.py::TestTorch::test_storage_error, test/test_torch.py::TestTorch::test_storage_error_no_attribute, test/test_torch.py::TestTorch::test_storage_finalizer_dealloc, test/test_torch.py::TestTorch::test_storage_fix_weakref_no_leak, test/test_torch.py::TestTorch::test_storage_from_tensor_dealloc, test/test_torch.py::TestTorch::test_storage_from_tensor_dealloc_resurrected, test/test_torch.py::TestTorch::test_storage_from_tensor_dealloc_zombie, test/test_torch.py::TestTorch::test_storage_preserve_nonhermetic_in_hermetic_context, test/test_torch.py::TestTorch::test_storage_resurrected_weak_ref, test/test_torch.py::TestTorch::test_storage_slot_dealloc, test/test_torch.py::TestTorch::test_storage_weakref_dealloc, test/test_torch.py::TestTorch::test_structseq_repr, test/test_torch.py::TestTorch::test_subclass_preserved, test/test_torch.py::TestTorch::test_subclass_tensors, test/test_torch.py::TestTorch::test_sum_neg_dim, test/test_torch.py::TestTorch::test_swap_basic, test/test_torch.py::TestTorch::test_swap_fail_slots, test/test_torch.py::TestTorch::test_t_not_2d_error, test/test_torch.py::TestTorch::test_tensor_base_init, test/test_torch.py::TestTorch::test_tensor_base_new, test/test_torch.py::TestTorch::test_tensor_ctor_scalar, test/test_torch.py::TestTorch::test_tensor_cycle_via_dict, test/test_torch.py::TestTorch::test_tensor_cycle_via_slots, test/test_torch.py::TestTorch::test_tensor_dead_weak_ref, test/test_torch.py::TestTorch::test_tensor_dict_dealloc, test/test_torch.py::TestTorch::test_tensor_finalizer_dealloc, test/test_torch.py::TestTorch::test_tensor_fix_weakref_no_leak, test/test_torch.py::TestTorch::test_tensor_resurrected_weak_ref, test/test_torch.py::TestTorch::test_tensor_set, test/test_torch.py::TestTorch::test_tensor_set_errors, test/test_torch.py::TestTorch::test_tensor_slot_dealloc, test/test_torch.py::TestTorch::test_tensor_weakref_dealloc, test/test_torch.py::TestTorch::test_tensor_where_scalar, test/test_torch.py::TestTorch::test_tensoriterator_output_setup, test/test_torch.py::TestTorch::test_terminate_handler_on_crash, test/test_torch.py::TestTorch::test_to, test/test_torch.py::TestTorch::test_to_with_tensor, test/test_torch.py::TestTorch::test_topk_neg_dim, test/test_torch.py::TestTorch::test_torch_from_file, test/test_torch.py::TestTorch::test_transpose_neg_dim, test/test_torch.py::TestTorch::test_type, test/test_torch.py::TestTorch::test_type_alias, test/test_torch.py::TestTorch::test_type_conversion_via_dtype_name, test/test_torch.py::TestTorch::test_typed_storage_deprecation_warning, test/test_torch.py::TestTorch::test_typed_storage_internal_no_warning, test/test_torch.py::TestTorch::test_unbind_neg_dim, test/test_torch.py::TestTorch::test_unflatten, test/test_torch.py::TestTorch::test_unfold_neg_dim, test/test_torch.py::TestTorch::test_unsqueeze_neg_dim, test/test_torch.py::TestTorch::test_upsample_nearest1d_meta, test/test_torch.py::TestTorch::test_upsample_nearest2d_meta, test/test_torch.py::TestTorch::test_var_neg_dim, test/test_torch.py::TestTorch::test_warn_types, test/test_torch.py::TestTorch::test_wildcard_import, test/test_torch.py::TestVitalSignsCudaCPU::test_cuda_vitals_gpu_only_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_assertRaisesRegex_ignore_msg_non_native_device_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_edge_cases_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_edge_cases_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_edge_cases_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_p_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_p_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_p_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_p_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_bfloat16_neg_abs_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_bool_tensor_value_change_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_add_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_addcdiv_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_addcmul_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_atan2_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_copy_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_dist_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_div_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_eq_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_fmod_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_ge_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_gt_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_le_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_lerp_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_lt_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_map2_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_map_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_masked_fill_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_masked_scatter_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_masked_select_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_max_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_min_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_mul_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_ne_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_pow_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_remainder_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_sub_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_kstest_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_no_inf_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_no_inf_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_cuda_backward_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_euclidean_large_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_grad_p_lt_1_no_nan_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_large_batch_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_large_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_non_contiguous_batch_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_non_contiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_norm_batch_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_norm_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_same_inputs_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_check_tensor_all_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_check_tensor_internal_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_clone_all_dtypes_and_devices_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_clone_not_memory_dense_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_clone_zero_stride_dim_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_complex_half_experimental_warning_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_constants_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_conv_transposed_backward_agnostic_to_memory_format_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_conv_transposed_large_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_complex32, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_all_dtypes_and_devices_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_math_view_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_mem_overlap_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_transpose_math_view_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_transpose_math_view_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_transpose_math_view_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_corrcoef_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_corrcoef_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_corrcoef_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_cov_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cpp_warnings_have_python_context_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cublas_config_nondeterministic_alert_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cummax_cummin_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cummax_discontiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cummin_discontiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cumprod_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cumsum_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_scalar_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_scalar_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_int16, 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, 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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, 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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, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_uint8, 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test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_qint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_qint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_quint4x2, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_quint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_strides_propagation_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_sync_warning_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_set_errors_multigpu_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_shape_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_type_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_ternary_op_mem_overlap_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_unfold_all_devices_and_dtypes_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_unfold_scalars_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_untyped_storage_meta_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_warn_always_caught_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_where_scalar_handcrafted_values_cpu 2024-06-26T05:13:34.2169859Z 2024-06-26T05:13:34.2170329Z Running test_ci_sanity_check_fail 1/1 ... [2024-06-26 05:13:34.126025] 2024-06-26T05:13:34.2172153Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_ci_sanity_check_fail.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:13:34.126311] 2024-06-26T05:13:42.0852608Z Running test_show_pickle 1/1 ... [2024-06-26 05:13:42.084854] 2024-06-26T05:13:42.0855160Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_show_pickle.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:13:42.085140] 2024-06-26T05:13:44.6531122Z 2024-06-26T05:13:44.6532979Z test_show_pickle 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_show_pickle_1.1_623e327c8534c6fd_.log 2024-06-26T05:13:44.6534997Z Running 1 items in this shard: test/test_show_pickle.py::TestShowPickle::test_scripted_model 2024-06-26T05:13:44.6535594Z 2024-06-26T05:13:44.6535878Z Running test_autocast 1/1 ... [2024-06-26 05:13:44.653291] 2024-06-26T05:13:44.6539385Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_autocast.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:13:44.653622] 2024-06-26T05:13:58.4366826Z 2024-06-26T05:13:58.4368360Z test_autocast 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_autocast_1.1_3f8ae17057f98dfa_.log 2024-06-26T05:13:58.4376021Z Running 16 items in this shard: test/test_autocast.py::TestAutocastCPU::test_autocast_disabled_with_fp32_dtype, test/test_autocast.py::TestAutocastCPU::test_autocast_methods_expect_builtin_promote, test/test_autocast.py::TestAutocastCPU::test_autocast_nn_16, test/test_autocast.py::TestAutocastCPU::test_autocast_nn_fp32, test/test_autocast.py::TestAutocastCPU::test_autocast_rnn, test/test_autocast.py::TestAutocastCPU::test_autocast_torch_16, test/test_autocast.py::TestAutocastCPU::test_autocast_torch_expect_builtin_promote, test/test_autocast.py::TestAutocastCPU::test_autocast_torch_fp32, test/test_autocast.py::TestAutocastCPU::test_autocast_torch_need_autocast_promote, test/test_autocast.py::TestAutocastCPU::test_cpu_autocast_deprecated_warning, test/test_autocast.py::TestAutocastCPU::test_generic_autocast, test/test_autocast.py::TestAutocastGPU::test_cache_disabled, test/test_autocast.py::TestAutocastGPU::test_cast_cache_is_global, test/test_autocast.py::TestTorchAutocast::test_autocast_fast_dtype, test/test_autocast.py::TestTorchAutocast::test_invalid_device, test/test_autocast.py::TestTorchAutocast::test_non_string_device 2024-06-26T05:13:58.4382647Z 2024-06-26T05:13:58.4382925Z Running test_utils 1/1 ... [2024-06-26 05:13:58.436833] 2024-06-26T05:13:58.4385058Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_utils.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:13:58.438132] 2024-06-26T05:15:20.3266626Z 2024-06-26T05:15:20.3269664Z test_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_utils_1.1_15c0c29195f92643_.log 2024-06-26T05:15:20.6679374Z Running 5895 items in this shard: test/test_utils.py::TestCheckpoint::test_checkpoint, test/test_utils.py::TestCheckpoint::test_checkpoint_module_list, test/test_utils.py::TestCheckpoint::test_checkpoint_no_tensors, test/test_utils.py::TestCheckpoint::test_checkpoint_non_tensor, test/test_utils.py::TestCheckpoint::test_checkpoint_non_tensor_inputs_outputs, test/test_utils.py::TestCheckpoint::test_checkpoint_not_preserve_rng_state_and_without_reentrant, test/test_utils.py::TestCheckpoint::test_checkpoint_partial_grad, test/test_utils.py::TestCheckpoint::test_checkpoint_rng_cpu, test/test_utils.py::TestCheckpoint::test_checkpoint_rng_cuda, test/test_utils.py::TestCheckpoint::test_checkpoint_sequential_deprecated_multiple_args, test/test_utils.py::TestCheckpoint::test_checkpoint_sequential_deprecated_no_args, test/test_utils.py::TestCheckpoint::test_checkpoint_trigger, test/test_utils.py::TestCheckpoint::test_checkpoint_valid, test/test_utils.py::TestCheckpoint::test_checkpointing_without_reentrant_early_free, test/test_utils.py::TestCheckpoint::test_get_device_states_recursive, test/test_utils.py::TestCheckpoint::test_infer_device_state_recursive_meta, test/test_utils.py::TestCheckpoint::test_infer_device_state_recursive_multi_cuda, test/test_utils.py::TestDataLoaderUtils::test_multi_drop, test/test_utils.py::TestDataLoaderUtils::test_multi_keep, test/test_utils.py::TestDataLoaderUtils::test_random_seed, test/test_utils.py::TestDataLoaderUtils::test_single_drop, test/test_utils.py::TestDataLoaderUtils::test_single_keep, test/test_utils.py::TestBottleneck::test_bottleneck_cpu_only, test/test_utils.py::TestBottleneck::test_bottleneck_cuda, test/test_utils.py::TestCollectEnv::test_smoke, test/test_utils.py::TestONNXUtils::test_check_onnx_broadcast, test/test_utils.py::TestONNXUtils::test_prepare_onnx_paddings, test/test_utils.py::TestHipify::test_import_hipify, test/test_utils.py::TestHipifyTrie::test_add_and_search_trie, test/test_utils.py::TestHipifyTrie::test_add_multiple_and_search_trie, test/test_utils.py::TestHipifyTrie::test_char_export_trie_to_regex, test/test_utils.py::TestHipifyTrie::test_export_trie_to_regex, test/test_utils.py::TestHipifyTrie::test_prefix_words_export_trie_to_regex, test/test_utils.py::TestHipifyTrie::test_quote_escape, test/test_utils.py::TestHipifyTrie::test_single_export_trie_to_regex, test/test_utils.py::TestHipifyTrie::test_special_char_export_trie_to_regex, test/test_utils.py::TestAssert::test_assert_scriptable, test/test_utils.py::TestAssert::test_assert_true, test/test_utils.py::TestStandaloneCPPJIT::test_load_standalone, test/test_utils.py::TestExtensionUtils::test_external_module_register, test/test_utils.py::TestExtensionUtils::test_external_module_register_with_renamed_backend, test/test_utils.py::TestRenderUtils::test_basic, test/test_utils.py::TestDeviceUtilsCPU::test_basic_cpu, test/test_utils.py::TestDeviceUtilsCPU::test_decorator_cpu, test/test_utils.py::TestDeviceUtilsCPU::test_decorator_generator_cpu, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rand___cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rand___cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rand___cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rand___cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rand___cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rand___cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmatmul___cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmatmul___cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmatmul___cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmatmul___cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmatmul___cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmatmul___cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmatmul___cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmatmul___cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmatmul___cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmatmul___cpu_int8, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_baddbmm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_baddbmm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_baddbmm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_baddbmm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_baddbmm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_baddbmm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_baddbmm_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_baddbmm_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_baddbmm_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_baddbmm_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_baddbmm_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bernoulli_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bernoulli_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bernoulli_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bernoulli_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bincount_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bincount_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bincount_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bincount_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bincount_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_and_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_and_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_and_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_and_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_and_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_and_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_left_shift_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_left_shift_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_left_shift_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_left_shift_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_left_shift_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_not_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_not_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_not_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_not_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_not_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_not_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_or_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_or_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_or_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_or_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_or_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_or_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_right_shift_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_right_shift_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_right_shift_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_right_shift_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_right_shift_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_xor_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_xor_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_xor_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_xor_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_xor_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_xor_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_shapes_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cauchy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cauchy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cauchy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cauchy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdist_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdist_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_inverse_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_inverse_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_inverse_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_inverse_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_complex_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_complex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_complex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exponential_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exponential_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exponential_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exponential_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frac_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frac_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frac_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frac_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geqrf_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geqrf_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geqrf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geqrf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histc_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histc_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histc_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histc_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histogram_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histogram_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histogramdd_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histogramdd_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hypot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hypot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hypot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hypot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igamma_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igamma_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igamma_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igamma_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igammac_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igammac_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igammac_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igammac_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_imag_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_imag_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_imag_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_istft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_istft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lcm_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lcm_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lcm_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lcm_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lcm_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_ex_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_ex_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_ex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_ex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cond_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cond_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cond_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cond_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_singular_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_singular_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_singular_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_singular_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eig_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eig_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eig_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eig_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvals_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvals_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvals_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvals_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvalsh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvalsh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvalsh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvalsh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_householder_product_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_householder_product_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_householder_product_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_householder_product_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_ex_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_ex_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_ex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_ex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_ex_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_ex_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_ex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_ex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_grad_oriented_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_grad_oriented_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_grad_oriented_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_grad_oriented_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_ex_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_ex_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_ex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_ex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_power_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_power_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_power_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_power_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_hermitian_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_hermitian_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_hermitian_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_hermitian_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_hermitian_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_hermitian_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_hermitian_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_hermitian_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_singular_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_singular_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_singular_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_singular_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_qr_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_qr_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_qr_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_qr_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_slogdet_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_slogdet_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_slogdet_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_slogdet_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_ex_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_ex_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_ex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_ex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_triangular_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_triangular_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_triangular_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_triangular_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svd_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svd_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svd_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svd_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svdvals_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svdvals_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svdvals_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svdvals_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorinv_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorinv_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorinv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorinv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorsolve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorsolve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorsolve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorsolve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_normal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_normal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_normal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_normal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp2_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp2_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logdet_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logdet_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logdet_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logdet_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_unpack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_unpack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_unpack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_unpack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_log_softmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_log_softmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_log_softmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_log_softmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logaddexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logaddexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logaddexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logaddexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_median_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_median_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_median_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_median_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_normalize_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_normalize_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_normalize_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_normalize_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_normalize_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_normalize_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_pool2d_with_indices_backward_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_pool2d_with_indices_backward_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_pool2d_with_indices_backward_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_pool2d_with_indices_backward_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_multinomial_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_multinomial_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_multinomial_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_multinomial_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanquantile_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanquantile_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_batch_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_batch_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_batch_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_batch_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_layer_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_layer_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_layer_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_layer_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nextafter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nextafter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nextafter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nextafter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_alpha_dropout_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_alpha_dropout_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_alpha_dropout_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_alpha_dropout_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool1d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool2d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool3d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_batch_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_batch_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_batch_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_batch_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_with_logits_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_with_logits_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_with_logits_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_celu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_celu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_celu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_celu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_similarity_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_similarity_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_similarity_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_similarity_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cross_entropy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cross_entropy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cross_entropy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cross_entropy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_ctc_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_ctc_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_elu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_elu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_elu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_elu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_bag_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_bag_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_bag_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_bag_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_with_train_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_with_train_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_with_train_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gaussian_nll_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gaussian_nll_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gaussian_nll_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gaussian_nll_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gelu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gelu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gelu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gelu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_glu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_glu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_glu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_glu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_grid_sample_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_grid_sample_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_group_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_group_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_group_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_group_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardshrink_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardshrink_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardshrink_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardshrink_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardsigmoid_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardsigmoid_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardsigmoid_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardsigmoid_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardswish_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardswish_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardswish_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardswish_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hinge_embedding_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hinge_embedding_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hinge_embedding_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hinge_embedding_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_huber_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_huber_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_huber_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_huber_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_instance_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_instance_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_instance_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_instance_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_area_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_area_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_area_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_area_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bicubic_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bicubic_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bicubic_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bicubic_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bicubic_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bilinear_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bilinear_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bilinear_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bilinear_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bilinear_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_linear_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_linear_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_linear_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_linear_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest-exact_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest-exact_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest-exact_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest-exact_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest-exact_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_trilinear_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_trilinear_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_trilinear_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_trilinear_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_kl_div_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_kl_div_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_kl_div_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_kl_div_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_l1_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_l1_loss_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_l1_loss_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_l1_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_l1_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_l1_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_layer_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_layer_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_layer_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_layer_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_leaky_relu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_leaky_relu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_leaky_relu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_leaky_relu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_local_response_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_local_response_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_local_response_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_local_response_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_local_response_norm_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_logsigmoid_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_logsigmoid_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_logsigmoid_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_logsigmoid_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_grad_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_grad_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_grad_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_grad_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_grad_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_grad_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mish_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mish_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mish_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mish_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mse_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mse_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mse_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multi_head_attention_forward_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multi_head_attention_forward_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multi_head_attention_forward_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multi_head_attention_forward_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multi_margin_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multi_margin_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multilabel_margin_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multilabel_margin_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multilabel_soft_margin_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multilabel_soft_margin_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multilabel_soft_margin_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_nll_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_nll_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_nll_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_nll_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_normalize_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_normalize_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_normalize_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_normalize_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_normalize_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_normalize_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_one_hot_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pdist_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pdist_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_prelu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_prelu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_prelu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_prelu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rms_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rms_norm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rms_norm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rms_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rms_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rms_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rrelu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rrelu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rrelu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_scaled_dot_product_attention_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_scaled_dot_product_attention_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_scaled_dot_product_attention_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_scaled_dot_product_attention_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_selu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_selu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_selu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_selu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_silu_complex_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_silu_complex_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_silu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_silu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_silu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_silu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_smooth_l1_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_smooth_l1_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_smooth_l1_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_smooth_l1_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_soft_margin_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_soft_margin_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_soft_margin_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_soft_margin_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softplus_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softplus_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softplus_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softplus_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softshrink_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softshrink_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softshrink_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softshrink_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_bilinear_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_bilinear_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_bilinear_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_bilinear_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_bilinear_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_nearest_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_nearest_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_nearest_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_nearest_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_nearest_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_fro_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_fro_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_fro_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_fro_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_fro_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_fro_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_nuc_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_nuc_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_nuc_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_nuc_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_in_place_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_in_place_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_in_place_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_in_place_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_in_place_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_in_place_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_number_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_number_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_number_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_number_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ormqr_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ormqr_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ormqr_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ormqr_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pca_lowrank_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pca_lowrank_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pca_lowrank_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pca_lowrank_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pinverse_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pinverse_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pinverse_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pinverse_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polar_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polar_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_qr_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_qr_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_qr_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_qr_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_quantile_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_quantile_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_renorm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_renorm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_renorm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_renorm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_renorm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_renorm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_0_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_0_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_0_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_0_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_3_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_3_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_3_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_neg_3_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_neg_3_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_neg_3_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_bartlett_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_bartlett_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_blackman_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_blackman_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_cosine_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_cosine_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_exponential_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_exponential_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_gaussian_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_gaussian_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_general_cosine_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_general_cosine_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_general_hamming_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_general_hamming_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_hamming_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_hamming_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_hann_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_hann_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_kaiser_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_kaiser_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_nuttall_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_nuttall_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_mm_reduce_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_mm_reduce_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_mm_reduce_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_sampled_addmm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_sampled_addmm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_sampled_addmm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_sampled_addmm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y0_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y0_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y0_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y0_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y0_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y0_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y0_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y0_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_t_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_t_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_t_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_t_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_t_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_t_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_t_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_t_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_u_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_u_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_u_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_u_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_u_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_u_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_u_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_u_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_v_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_v_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_v_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_v_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_v_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_v_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_v_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_v_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_w_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_w_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_w_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_w_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_w_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_w_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_w_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_w_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_entr_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_entr_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_entr_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_entr_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_entr_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_entr_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_entr_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_entr_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_entr_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_entr_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_erfcx_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_erfcx_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_erfcx_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_erfcx_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_erfcx_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_erfcx_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_erfcx_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_erfcx_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_hermite_polynomial_h_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_hermite_polynomial_h_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_hermite_polynomial_h_cpu_float64, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_lowrank_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_lowrank_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_lowrank_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_lowrank_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triangular_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triangular_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triangular_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triangular_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_indices_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_indices_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_indices_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_indices_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_uint16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_uint32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_uint64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_complex_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_complex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_complex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_real_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_real_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_get_default_device_cpu, test/test_utils.py::TestDeviceUtilsCPU::test_get_default_device_more_cpu, test/test_utils.py::TestDeviceUtilsCPU::test_nn_module_cpu, test/test_utils.py::TestDeviceUtilsCPU::test_set_default_device_cpu, test/test_utils.py::TestCppExtensionUtils::test_cc_compiler_is_ok, test/test_utils.py::TestCppExtensionUtils::test_cpp_compiler_is_ok, test/test_utils.py::TestTraceback::test_basic, test/test_utils.py::TestTraceback::test_captured_traceback, test/test_utils.py::TestTraceback::test_captured_traceback_format_all, test/test_utils.py::TestTraceback::test_captured_traceback_format_all_cached, test/test_utils.py::TestTraceback::test_format_traceback_short 2024-06-26T05:15:20.9169480Z 2024-06-26T05:15:20.9169938Z Running test_tensorexpr 1/1 ... [2024-06-26 05:15:20.334419] 2024-06-26T05:15:20.9171689Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_tensorexpr.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:15:20.334741] 2024-06-26T05:15:23.1035734Z 2024-06-26T05:15:23.1037724Z test_tensorexpr 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_tensorexpr_1.1_195d9a7972722958_.log 2024-06-26T05:15:23.1081548Z Running 74 items in this shard: test/test_tensorexpr.py::TestTensorExprFuser::test_add_const_rhs, test/test_tensorexpr.py::TestTensorExprFuser::test_add_sub, test/test_tensorexpr.py::TestTensorExprFuser::test_alias_analysis_input_and_module, test/test_tensorexpr.py::TestTensorExprFuser::test_alias_analysis_inputs, test/test_tensorexpr.py::TestTensorExprFuser::test_alias_analysis_module, test/test_tensorexpr.py::TestTensorExprFuser::test_all_combos, test/test_tensorexpr.py::TestTensorExprFuser::test_alpha, test/test_tensorexpr.py::TestTensorExprFuser::test_binary_ops, test/test_tensorexpr.py::TestTensorExprFuser::test_bitwise_ops, test/test_tensorexpr.py::TestTensorExprFuser::test_broadcast, test/test_tensorexpr.py::TestTensorExprFuser::test_broadcast3, test/test_tensorexpr.py::TestTensorExprFuser::test_broadcast_2, test/test_tensorexpr.py::TestTensorExprFuser::test_broadcast_big2, test/test_tensorexpr.py::TestTensorExprFuser::test_cat, test/test_tensorexpr.py::TestTensorExprFuser::test_cat_empty_tensors, test/test_tensorexpr.py::TestTensorExprFuser::test_cat_negative_dim, test/test_tensorexpr.py::TestTensorExprFuser::test_cat_only, test/test_tensorexpr.py::TestTensorExprFuser::test_cat_promote_inputs, test/test_tensorexpr.py::TestTensorExprFuser::test_cat_with_constant_dim, test/test_tensorexpr.py::TestTensorExprFuser::test_char, test/test_tensorexpr.py::TestTensorExprFuser::test_chunk, test/test_tensorexpr.py::TestTensorExprFuser::test_clamp, test/test_tensorexpr.py::TestTensorExprFuser::test_constant, test/test_tensorexpr.py::TestTensorExprFuser::test_double, test/test_tensorexpr.py::TestTensorExprFuser::test_double_intrinsics, test/test_tensorexpr.py::TestTensorExprFuser::test_dynamic_shape, test/test_tensorexpr.py::TestTensorExprFuser::test_easy, test/test_tensorexpr.py::TestTensorExprFuser::test_eq, test/test_tensorexpr.py::TestTensorExprFuser::test_exp_pow, test/test_tensorexpr.py::TestTensorExprFuser::test_four_arg, test/test_tensorexpr.py::TestTensorExprFuser::test_ge, test/test_tensorexpr.py::TestTensorExprFuser::test_gt, test/test_tensorexpr.py::TestTensorExprFuser::test_guard_fails, test/test_tensorexpr.py::TestTensorExprFuser::test_half_bn_relu, test/test_tensorexpr.py::TestTensorExprFuser::test_half_gelu, test/test_tensorexpr.py::TestTensorExprFuser::test_int64_promotion, test/test_tensorexpr.py::TestTensorExprFuser::test_int_output, test/test_tensorexpr.py::TestTensorExprFuser::test_le, test/test_tensorexpr.py::TestTensorExprFuser::test_loop, test/test_tensorexpr.py::TestTensorExprFuser::test_lt, test/test_tensorexpr.py::TestTensorExprFuser::test_mask, test/test_tensorexpr.py::TestTensorExprFuser::test_min_max, test/test_tensorexpr.py::TestTensorExprFuser::test_min_max_reduction, test/test_tensorexpr.py::TestTensorExprFuser::test_min_max_reduction2, test/test_tensorexpr.py::TestTensorExprFuser::test_min_max_reduction_dim1, test/test_tensorexpr.py::TestTensorExprFuser::test_min_max_reduction_dim1_2, test/test_tensorexpr.py::TestTensorExprFuser::test_multi_rand, test/test_tensorexpr.py::TestTensorExprFuser::test_multioutput, test/test_tensorexpr.py::TestTensorExprFuser::test_multiple_outputs, test/test_tensorexpr.py::TestTensorExprFuser::test_nans, test/test_tensorexpr.py::TestTensorExprFuser::test_ne, test/test_tensorexpr.py::TestTensorExprFuser::test_promotion, test/test_tensorexpr.py::TestTensorExprFuser::test_propagated_mem_layout, test/test_tensorexpr.py::TestTensorExprFuser::test_rand_like, test/test_tensorexpr.py::TestTensorExprFuser::test_rank_two, test/test_tensorexpr.py::TestTensorExprFuser::test_relu, test/test_tensorexpr.py::TestTensorExprFuser::test_remainder, test/test_tensorexpr.py::TestTensorExprFuser::test_reps, test/test_tensorexpr.py::TestTensorExprFuser::test_round_2, test/test_tensorexpr.py::TestTensorExprFuser::test_scalar, test/test_tensorexpr.py::TestTensorExprFuser::test_short, test/test_tensorexpr.py::TestTensorExprFuser::test_simple_add, test/test_tensorexpr.py::TestTensorExprFuser::test_sin_pow, test/test_tensorexpr.py::TestTensorExprFuser::test_slice, test/test_tensorexpr.py::TestTensorExprFuser::test_sliced_stride, test/test_tensorexpr.py::TestTensorExprFuser::test_softmax_cpu, test/test_tensorexpr.py::TestTensorExprFuser::test_softmax_cuda, test/test_tensorexpr.py::TestTensorExprFuser::test_strided_output_preserved, test/test_tensorexpr.py::TestTensorExprFuser::test_three_arg, test/test_tensorexpr.py::TestTensorExprFuser::test_three_arg2, test/test_tensorexpr.py::TestTensorExprFuser::test_transpose, test/test_tensorexpr.py::TestTensorExprFuser::test_unary_ops, test/test_tensorexpr.py::TestTensorExprFuser::test_unsqueeze, test/test_tensorexpr.py::TestTensorExprFuser::test_where 2024-06-26T05:15:23.1125940Z 2024-06-26T05:15:23.1126754Z Running test_autograd_fallback 1/1 ... [2024-06-26 05:15:23.103790] 2024-06-26T05:15:23.1129936Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_autograd_fallback.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:15:23.104107] 2024-06-26T05:15:28.0262061Z 2024-06-26T05:15:28.0264213Z test_autograd_fallback 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_autograd_fallback_1.1_d899e1efd8365d8a_.log 2024-06-26T05:15:28.0280597Z Running 28 items in this shard: test/test_autograd_fallback.py::TestAutogradFallback::test_autograd_function_registered_to_cpu_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_autograd_function_registered_to_cpu_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_base_does_not_require_grad_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_base_does_not_require_grad_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_composite_registered_to_cpu_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_composite_registered_to_cpu_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_cpu_return_self_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_cpu_return_self_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_inplace_autograd_function_registered_to_cpu_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_inplace_autograd_function_registered_to_cpu_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_inplace_on_tensor_that_does_not_require_grad_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_inplace_on_tensor_that_does_not_require_grad_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_no_autograd_kernel_inplace_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_no_autograd_kernel_inplace_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_no_autograd_kernel_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_no_autograd_kernel_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_no_grad_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_no_grad_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_post_autograd_returns_leaf_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_post_autograd_returns_leaf_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_post_autograd_returns_mix_of_requires_grad_tensors_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_post_autograd_returns_mix_of_requires_grad_tensors_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_supports_tensor_lists_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_supports_tensor_lists_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_undefined_grads_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_undefined_grads_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_undefined_inputs_outputs_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_undefined_inputs_outputs_mode_warn 2024-06-26T05:15:28.0295228Z 2024-06-26T05:15:28.0295592Z Running test_python_dispatch 1/1 ... [2024-06-26 05:15:28.026294] 2024-06-26T05:15:28.0297374Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_python_dispatch.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:15:28.026578] 2024-06-26T05:15:40.7101507Z 2024-06-26T05:15:40.7102897Z test_python_dispatch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_python_dispatch_1.1_db8a52f89b3c45b8_.log 2024-06-26T05:15:40.7154026Z Running 111 items in this shard: test/test_python_dispatch.py::TestDispatcherPythonBindings::test_call_boxed, test/test_python_dispatch.py::TestPythonRegistration::test_alias_analysis, test/test_python_dispatch.py::TestPythonRegistration::test_create_new_library, test/test_python_dispatch.py::TestPythonRegistration::test_create_new_library_fragment_no_existing, test/test_python_dispatch.py::TestPythonRegistration::test_create_new_library_fragment_with_existing, test/test_python_dispatch.py::TestPythonRegistration::test_error_for_unsupported_ns_or_kind, test/test_python_dispatch.py::TestPythonRegistration::test_error_if_fn_not_callable, test/test_python_dispatch.py::TestPythonRegistration::test_extend_library_with_dispatch_key_arg, test/test_python_dispatch.py::TestPythonRegistration::test_finalizer, test/test_python_dispatch.py::TestPythonRegistration::test_override_aten_ops_with_multiple_libraries, test/test_python_dispatch.py::TestPythonRegistration::test_override_cpu_sum, test/test_python_dispatch.py::TestPythonRegistration::test_override_cuda_with_jiterator, test/test_python_dispatch.py::TestPythonRegistration::test_register_fallthrough, test/test_python_dispatch.py::TestPythonRegistration::test_register_functional_op_error_cases, test/test_python_dispatch.py::TestPythonRegistration::test_register_functional_op_multiple_returns, test/test_python_dispatch.py::TestPythonRegistration::test_register_functional_op_no_returns, test/test_python_dispatch.py::TestPythonRegistration::test_register_functional_op_one_return, test/test_python_dispatch.py::TestPythonRegistration::test_register_functional_op_with_optional, test/test_python_dispatch.py::TestPythonRegistration::test_returning_symint, test/test_python_dispatch.py::TestPythonDispatch::test_all_same_mode, test/test_python_dispatch.py::TestPythonDispatch::test_autograd_in_attr, test/test_python_dispatch.py::TestPythonDispatch::test_basic, test/test_python_dispatch.py::TestPythonDispatch::test_capture_logs_with_torch_dispatch_mode, test/test_python_dispatch.py::TestPythonDispatch::test_construct_int_tensor, test/test_python_dispatch.py::TestPythonDispatch::test_custom_autograd, test/test_python_dispatch.py::TestPythonDispatch::test_custom_size_policy_dynamic_shapes, test/test_python_dispatch.py::TestPythonDispatch::test_data_ptr_respects_numel_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_deepcopy_non_wrapper_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_deepcopy_wrapper_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_deepcopy_wrapper_subclass_with_clone_returning_different_type, test/test_python_dispatch.py::TestPythonDispatch::test_detach_appears_twice_when_called_once, test/test_python_dispatch.py::TestPythonDispatch::test_device_slowpath, test/test_python_dispatch.py::TestPythonDispatch::test_dim_slowpath, test/test_python_dispatch.py::TestPythonDispatch::test_dispatch_super_call, test/test_python_dispatch.py::TestPythonDispatch::test_dispatch_super_call_list_arg, test/test_python_dispatch.py::TestPythonDispatch::test_dispatch_super_dont_autograd, test/test_python_dispatch.py::TestPythonDispatch::test_error_using_class_method_on_mode, test/test_python_dispatch.py::TestPythonDispatch::test_exception_handling, test/test_python_dispatch.py::TestPythonDispatch::test_fancy_strides, test/test_python_dispatch.py::TestPythonDispatch::test_format, test/test_python_dispatch.py::TestPythonDispatch::test_get_cur_mode, test/test_python_dispatch.py::TestPythonDispatch::test_get_mode_stack, test/test_python_dispatch.py::TestPythonDispatch::test_index_put_where_only_index_is_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_invalid_ret, test/test_python_dispatch.py::TestPythonDispatch::test_is_contiguous_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_kwarg_only, test/test_python_dispatch.py::TestPythonDispatch::test_kwarg_only_and_positional_default, test/test_python_dispatch.py::TestPythonDispatch::test_layout_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_like, test/test_python_dispatch.py::TestPythonDispatch::test_list_ret, test/test_python_dispatch.py::TestPythonDispatch::test_make_fx_with_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_make_subclass_with_modes, test/test_python_dispatch.py::TestPythonDispatch::test_make_wrapper_subclass_noalloc, test/test_python_dispatch.py::TestPythonDispatch::test_make_wrapper_subclass_propagates_metadata, test/test_python_dispatch.py::TestPythonDispatch::test_maybe_tuple_bug, test/test_python_dispatch.py::TestPythonDispatch::test_mode_with_make_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_multiple_ops_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_nested_push_logging_tensor_mode, test/test_python_dispatch.py::TestPythonDispatch::test_nesting_same_mode, test/test_python_dispatch.py::TestPythonDispatch::test_new_ones, test/test_python_dispatch.py::TestPythonDispatch::test_none_wrapping, test/test_python_dispatch.py::TestPythonDispatch::test_notimplemented_mode, test/test_python_dispatch.py::TestPythonDispatch::test_optional_tensor_list, test/test_python_dispatch.py::TestPythonDispatch::test_out, test/test_python_dispatch.py::TestPythonDispatch::test_produce_real_type, test/test_python_dispatch.py::TestPythonDispatch::test_record_stream, test/test_python_dispatch.py::TestPythonDispatch::test_return_and_correct_aliasing_gives_correct_stride, test/test_python_dispatch.py::TestPythonDispatch::test_return_stream, test/test_python_dispatch.py::TestPythonDispatch::test_set_data, test/test_python_dispatch.py::TestPythonDispatch::test_shallow_copy_and_detach, test/test_python_dispatch.py::TestPythonDispatch::test_sizes_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_standard_is_not_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_storage, test/test_python_dispatch.py::TestPythonDispatch::test_storage_can_be_converted_to_python_object, test/test_python_dispatch.py::TestPythonDispatch::test_strides_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_subclass_autograd_device_check, test/test_python_dispatch.py::TestPythonDispatch::test_subclass_creation, test/test_python_dispatch.py::TestPythonDispatch::test_subclass_priority, test/test_python_dispatch.py::TestPythonDispatch::test_sym_sizes_strides_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_tolist_numpy_with_torch_dispatch_mode, test/test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_basic, test/test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_respects_no_dispatch, test/test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_subclass_priority, test/test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_unrelated_tensors, test/test_python_dispatch.py::TestPythonDispatch::test_version, test/test_python_dispatch.py::TestPythonDispatch::test_with_mode_created_separately, test/test_python_dispatch.py::TestPythonDispatch::test_with_nested_modes, test/test_python_dispatch.py::TestPythonDispatch::test_wrapper_subclass_extra_dispatch_keys, test/test_python_dispatch.py::TestPythonDispatch::test_wrapper_subclass_multiprocessing_preserves_dtype, test/test_python_dispatch.py::TestPythonDispatch::test_wrapper_subclass_serializes, test/test_python_dispatch.py::TestPythonDispatcher::test_basic, test/test_python_dispatch.py::TestPythonDispatcher::test_lstsq, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_cat_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_conv2d_cpu, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyCatCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyCubeCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyMulCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyMulScalarCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyNMSCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyNonzeroCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpySortCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpySplitCopyCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpySplitCopyWithIntCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyTakeCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyViewCopyCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_mul_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_native_batch_norm_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_out_op_cpu, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_split_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_split_list_args_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_view_cpu_float32 2024-06-26T05:15:40.7204017Z 2024-06-26T05:15:40.7204465Z Running test_cpp_extensions_stream_and_event 1/1 ... [2024-06-26 05:15:40.710441] 2024-06-26T05:15:40.7206384Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cpp_extensions_stream_and_event.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:15:40.710705] 2024-06-26T05:15:44.1296757Z 2024-06-26T05:15:44.1298583Z test_cpp_extensions_stream_and_event 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_extensions_stream_and_event_1.1_4ecf64357947ba68_.log 2024-06-26T05:15:44.1300606Z Running 1 items in this shard: test/test_cpp_extensions_stream_and_event.py::TestCppExtensionStreamAndEvent::test_stream_event 2024-06-26T05:15:44.1301392Z 2024-06-26T05:15:44.1301780Z Running test_cpp_extensions_mtia_backend 1/1 ... [2024-06-26 05:15:44.129848] 2024-06-26T05:15:44.1304076Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cpp_extensions_mtia_backend.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:15:44.130132] 2024-06-26T05:15:47.4489741Z 2024-06-26T05:15:47.4491655Z test_cpp_extensions_mtia_backend 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_extensions_mtia_backend_1.1_f88e2ba32bba69ca_.log 2024-06-26T05:15:47.4496547Z Running 5 items in this shard: test/test_cpp_extensions_mtia_backend.py::TestCppExtensionMTIABackend::test_device_context, test/test_cpp_extensions_mtia_backend.py::TestCppExtensionMTIABackend::test_get_device_module, test/test_cpp_extensions_mtia_backend.py::TestCppExtensionMTIABackend::test_stream_basic, test/test_cpp_extensions_mtia_backend.py::TestCppExtensionMTIABackend::test_stream_context, test/test_cpp_extensions_mtia_backend.py::TestCppExtensionMTIABackend::test_stream_context_different_device 2024-06-26T05:15:47.4499268Z 2024-06-26T05:15:47.4499564Z Running test_overrides 1/1 ... [2024-06-26 05:15:47.449099] 2024-06-26T05:15:47.4501244Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_overrides.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:15:47.449421] 2024-06-26T05:19:53.2143761Z 2024-06-26T05:19:53.2146613Z test_overrides 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_overrides_1.1_65e6ab0c054a373e_.log 2024-06-26T05:19:53.2818835Z Running 1456 items in this shard: test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_H___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_T___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__backward_hooks___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__base___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__cdata___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__grad___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__grad_fn___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__post_accumulate_grad_hooks___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__version___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_data___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_device___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_dtype___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_grad___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_grad_fn___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_imag___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_cpu___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_cuda___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_ipu___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_leaf___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_maia___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_meta___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_mkldnn___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_mps___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_mtia___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_nested___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_quantized___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_sparse___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_sparse_csr___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_vulkan___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_xla___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_xpu___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_itemsize___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_layout___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_mH___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_mT___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_name___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_names___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_nbytes___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_ndim___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_output_nr___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_real___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_requires_grad___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_retains_grad___get__, 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test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_igammac_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_add, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_add_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_copy, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_copy_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_fill, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_fill_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_put, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_put_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_reduce_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_select, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_indices, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_inner, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_int, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_int_repr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ipu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_coalesced, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_complex, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_conj, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_contiguous, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_distributed, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_floating_point, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_inference, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_neg, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_pinned, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_same_size, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_set_to, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_shared, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_signed, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isclose, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isfinite, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isinf, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isnan, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isneginf, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isposinf, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isreal, 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test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_less_equal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_less_equal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lgamma, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lgamma_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log10, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log10_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log1p, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log1p_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log2, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log2_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log_normal_, 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test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logit, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logit_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logsumexp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_long, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lt, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lt_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lu_solve, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_map2_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_map_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_fill, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_fill_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_scatter, 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test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_module_load, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_moveaxis, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_movedim, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_msort, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mul, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mul_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_multinomial, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_multiply, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_multiply_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mv, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mvlgamma, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mvlgamma_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nan_to_num, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nan_to_num_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nanmean, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nanmedian, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nanquantile, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nansum, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_narrow, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_narrow_copy, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ndimension, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ne, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ne_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_neg, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_neg_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_negative, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_negative_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nelement, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nextafter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nextafter_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nonzero_static, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_norm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_normal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_not_equal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_not_equal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_numel, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_numpy, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_orgqr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ormqr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_outer, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_permute, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_pin_memory, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_pinverse, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_polygamma, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_polygamma_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_positive, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_pow, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_pow_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_prelu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_prod, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_put, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_put_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_q_per_channel_axis, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_q_per_channel_scales, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_q_per_channel_zero_points, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_q_scale, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_q_zero_point, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_qr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_qscheme, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_quantile, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rad2deg, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rad2deg_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_random_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ravel, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_reciprocal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_reciprocal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_record_stream, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_refine_names, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_register_hook, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_register_post_accumulate_grad_hook, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_relu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_relu_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_remainder, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_remainder_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rename, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rename_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_renorm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_renorm_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_repeat, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_repeat_interleave, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_requires_grad_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_reshape, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_reshape_as, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resize, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resize_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resize_as, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resize_as_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resize_as_sparse_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resolve_conj, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resolve_neg, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_retain_grad, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_roll, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rot90, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_round, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_round_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_row_indices, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rsqrt, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rsqrt_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_scatter_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_scatter_add, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_scatter_add_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_scatter_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_scatter_reduce_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_select, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_select_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_set_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sgn, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sgn_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_share_memory_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_short, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sigmoid, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sigmoid_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sign, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sign_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_signbit, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sin, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sin_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sinc, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sinc_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sinh, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sinh_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_size, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_slice_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_slice_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_slogdet, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_smm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sort, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sparse_dim, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sparse_mask, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sparse_resize_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sparse_resize_and_clear_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_split, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_split_with_sizes, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sqrt, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sqrt_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_square, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_square_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_squeeze, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_squeeze_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sspaddmm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_std, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_stft, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_storage, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_storage_offset, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_storage_type, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sub, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sub_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_subtract, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_subtract_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sum, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sum_to_size, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_svd, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_swapaxes, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_swapaxes_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_swapdims, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_swapdims_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_t, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_t_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_take, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_take_along_dim, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tan, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tan_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tanh, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tanh_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tensor_split, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tile, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_to, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_to_dense, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_to_mkldnn, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_to_sparse, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tolist, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_topk, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_trace, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_transpose, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_transpose_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_triangular_solve, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tril, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tril_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_triu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_triu_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_true_divide, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_true_divide_, 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test/test_overrides.py::TestTorchFunctionOverride::test_torch_add, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addbmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addcdiv, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addcmul, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addmv, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_adjoint, test/test_overrides.py::TestTorchFunctionOverride::test_torch_affine_grid_generator, test/test_overrides.py::TestTorchFunctionOverride::test_torch_alias_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_all, test/test_overrides.py::TestTorchFunctionOverride::test_torch_allclose, test/test_overrides.py::TestTorchFunctionOverride::test_torch_alpha_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_amax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_amin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_aminmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_angle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_any, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arccos, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arccosh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arcsin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arcsinh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arctan, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arctan2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arctanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_argmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_argmin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_argsort, test/test_overrides.py::TestTorchFunctionOverride::test_torch_argwhere, test/test_overrides.py::TestTorchFunctionOverride::test_torch_as_strided_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_as_strided_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_asin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_asinh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_atan, test/test_overrides.py::TestTorchFunctionOverride::test_torch_atan2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_atanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_avg_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_baddbmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_backward_elemt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_backward_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_elemt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_gather_stats, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_gather_stats_with_counts, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_stats, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_update_stats, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bernoulli, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bilinear, test/test_overrides.py::TestTorchFunctionOverride::test_torch_binary_cross_entropy_with_logits, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bincount, test/test_overrides.py::TestTorchFunctionOverride::test_torch_binomial, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_and, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_left_shift, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_not, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_or, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_right_shift, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_xor, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_broadcast_to, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bucketize, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cat, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ccol_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ceil, test/test_overrides.py::TestTorchFunctionOverride::test_torch_celu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_channel_shuffle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cholesky, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cholesky_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cholesky_solve, test/test_overrides.py::TestTorchFunctionOverride::test_torch_choose_qparams_optimized, test/test_overrides.py::TestTorchFunctionOverride::test_torch_chunk, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clamp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clamp_max, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clamp_min, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clip, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clone, test/test_overrides.py::TestTorchFunctionOverride::test_torch_col_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_column_stack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_combinations, test/test_overrides.py::TestTorchFunctionOverride::test_torch_complex, test/test_overrides.py::TestTorchFunctionOverride::test_torch_concat, test/test_overrides.py::TestTorchFunctionOverride::test_torch_concatenate, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conj, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conj_physical, test/test_overrides.py::TestTorchFunctionOverride::test_torch_constant_pad_nd, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv_tbc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv_transpose1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv_transpose2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv_transpose3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_convolution, test/test_overrides.py::TestTorchFunctionOverride::test_torch_copysign, test/test_overrides.py::TestTorchFunctionOverride::test_torch_corrcoef, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cos, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cosh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cosine_embedding_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cosine_similarity, test/test_overrides.py::TestTorchFunctionOverride::test_torch_count_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cov, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cross, test/test_overrides.py::TestTorchFunctionOverride::test_torch_crow_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ctc_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cummax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cummin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cumprod, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cumsum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cumulative_trapezoid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_deg2rad, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dequantize, test/test_overrides.py::TestTorchFunctionOverride::test_torch_det, test/test_overrides.py::TestTorchFunctionOverride::test_torch_detach, test/test_overrides.py::TestTorchFunctionOverride::test_torch_detach_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diag_embed, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diagflat, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diagonal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diagonal_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diagonal_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diff, test/test_overrides.py::TestTorchFunctionOverride::test_torch_digamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dist, test/test_overrides.py::TestTorchFunctionOverride::test_torch_div, test/test_overrides.py::TestTorchFunctionOverride::test_torch_divide, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dot, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dsmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dsplit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dstack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_embedding, test/test_overrides.py::TestTorchFunctionOverride::test_torch_embedding_bag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_empty_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_eq, test/test_overrides.py::TestTorchFunctionOverride::test_torch_equal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_erf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_erfc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_erfinv, test/test_overrides.py::TestTorchFunctionOverride::test_torch_exp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_exp2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_expand_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_expm1, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fake_quantize_per_channel_affine, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fake_quantize_per_tensor_affine, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_fp16_weight, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_fp16_weight_fp32_activation, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_int8_weight, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_int8_weight_fp32_activation, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_quantize_weight, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_pack_gemm_matrix_fp16, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_pack_quantized_matrix, test/test_overrides.py::TestTorchFunctionOverride::test_torch_feature_alpha_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_feature_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fix, test/test_overrides.py::TestTorchFunctionOverride::test_torch_flatten, test/test_overrides.py::TestTorchFunctionOverride::test_torch_flip, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fliplr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_flipud, test/test_overrides.py::TestTorchFunctionOverride::test_torch_float_power, test/test_overrides.py::TestTorchFunctionOverride::test_torch_floor, test/test_overrides.py::TestTorchFunctionOverride::test_torch_floor_divide, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fmin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fmod, test/test_overrides.py::TestTorchFunctionOverride::test_torch_frac, test/test_overrides.py::TestTorchFunctionOverride::test_torch_frexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_frobenius_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_full_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_atleast_1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_atleast_2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_atleast_3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_block_diag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_broadcast_tensors, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_cartesian_prod, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_cdist, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_chain_matmul, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_einsum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_lu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_meshgrid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_split, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_stft, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_tensordot, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_unique, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_unique_consecutive, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_unravel_index, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fused_moving_avg_obs_fake_quant, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gather, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gcd, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ge, test/test_overrides.py::TestTorchFunctionOverride::test_torch_geqrf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ger, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gradient, test/test_overrides.py::TestTorchFunctionOverride::test_torch_greater, test/test_overrides.py::TestTorchFunctionOverride::test_torch_greater_equal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_grid_sampler, test/test_overrides.py::TestTorchFunctionOverride::test_torch_grid_sampler_2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_grid_sampler_3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_group_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gru, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gru_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hardshrink, test/test_overrides.py::TestTorchFunctionOverride::test_torch_heaviside, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hinge_embedding_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_histc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_histogram, test/test_overrides.py::TestTorchFunctionOverride::test_torch_histogramdd, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hsmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hsplit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hstack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hypot, test/test_overrides.py::TestTorchFunctionOverride::test_torch_i0, test/test_overrides.py::TestTorchFunctionOverride::test_torch_igamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_igammac, test/test_overrides.py::TestTorchFunctionOverride::test_torch_imag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_add, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_fill, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_put, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_select, test/test_overrides.py::TestTorchFunctionOverride::test_torch_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_inner, test/test_overrides.py::TestTorchFunctionOverride::test_torch_instance_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_int_repr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_complex, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_conj, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_distributed, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_floating_point, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_inference, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_neg, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_same_size, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_signed, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isclose, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isfinite, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isinf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isnan, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isneginf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isposinf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isreal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_istft, test/test_overrides.py::TestTorchFunctionOverride::test_torch_kl_div, test/test_overrides.py::TestTorchFunctionOverride::test_torch_kron, test/test_overrides.py::TestTorchFunctionOverride::test_torch_kthvalue, test/test_overrides.py::TestTorchFunctionOverride::test_torch_layer_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lcm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ldexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_le, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lerp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_less, test/test_overrides.py::TestTorchFunctionOverride::test_torch_less_equal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lgamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_log, test/test_overrides.py::TestTorchFunctionOverride::test_torch_log10, test/test_overrides.py::TestTorchFunctionOverride::test_torch_log1p, test/test_overrides.py::TestTorchFunctionOverride::test_torch_log2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_log_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logaddexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logaddexp2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logcumsumexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logdet, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logical_and, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logical_not, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logical_or, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logical_xor, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logsumexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lstm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lstm_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lu_solve, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lu_unpack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_margin_ranking_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_masked_fill, test/test_overrides.py::TestTorchFunctionOverride::test_torch_masked_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_masked_select, test/test_overrides.py::TestTorchFunctionOverride::test_torch_matmul, test/test_overrides.py::TestTorchFunctionOverride::test_torch_matrix_exp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_matrix_power, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max_pool1d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_maximum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mean, test/test_overrides.py::TestTorchFunctionOverride::test_torch_median, test/test_overrides.py::TestTorchFunctionOverride::test_torch_min, test/test_overrides.py::TestTorchFunctionOverride::test_torch_minimum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_convolution, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_convolution_add_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_convolution_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_convolution_transpose, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_depthwise_convolution, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_rnn, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mode, test/test_overrides.py::TestTorchFunctionOverride::test_torch_moveaxis, test/test_overrides.py::TestTorchFunctionOverride::test_torch_movedim, test/test_overrides.py::TestTorchFunctionOverride::test_torch_msort, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mul, test/test_overrides.py::TestTorchFunctionOverride::test_torch_multinomial, test/test_overrides.py::TestTorchFunctionOverride::test_torch_multiply, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mv, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mvlgamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nan_to_num, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nanmean, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nanmedian, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nanquantile, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nansum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_narrow, test/test_overrides.py::TestTorchFunctionOverride::test_torch_narrow_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_channel_shuffle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_group_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_layer_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ne, test/test_overrides.py::TestTorchFunctionOverride::test_torch_neg, test/test_overrides.py::TestTorchFunctionOverride::test_torch_negative, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nextafter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional__threshold, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_avg_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_avg_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool1d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool2d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool3d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_affine_grid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_alpha_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_binary_cross_entropy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_binary_cross_entropy_with_logits, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_celu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_cosine_embedding_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_cross_entropy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_ctc_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_dropout1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_dropout2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_dropout3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_elu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_embedding, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_embedding_bag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_feature_alpha_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fold, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fractional_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fractional_max_pool2d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fractional_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fractional_max_pool3d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_gaussian_nll_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_glu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_grid_sample, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_group_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_gumbel_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_hardtanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_hinge_embedding_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_huber_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_instance_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_interpolate, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_kl_div, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_l1_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_layer_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_leaky_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_local_response_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_log_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_lp_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_lp_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_lp_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_margin_ranking_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool1d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool2d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool3d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_unpool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_unpool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_unpool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_mish, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_mse_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_multi_head_attention_forward, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_multi_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_multilabel_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_multilabel_soft_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_nll_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_normalize, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_pad, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_poisson_nll_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_relu6, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_rms_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_rrelu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_selu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_silu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_smooth_l1_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_soft_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_softmin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_softsign, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_tanhshrink, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_triplet_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_triplet_margin_with_distance_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_unfold, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_init_constant_, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_init_kaiming_uniform_, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_init_normal_, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_init_uniform_, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nonzero_static, test/test_overrides.py::TestTorchFunctionOverride::test_torch_norm_except_dim, test/test_overrides.py::TestTorchFunctionOverride::test_torch_not_equal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nuclear_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_numel, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ones_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_orgqr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ormqr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_outer, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pairwise_distance, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pdist, test/test_overrides.py::TestTorchFunctionOverride::test_torch_permute, test/test_overrides.py::TestTorchFunctionOverride::test_torch_permute_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pinverse, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pixel_shuffle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pixel_unshuffle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_poisson, test/test_overrides.py::TestTorchFunctionOverride::test_torch_poisson_nll_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_polar, test/test_overrides.py::TestTorchFunctionOverride::test_torch_polygamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_positive, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pow, test/test_overrides.py::TestTorchFunctionOverride::test_torch_prelu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_prod, test/test_overrides.py::TestTorchFunctionOverride::test_torch_put, test/test_overrides.py::TestTorchFunctionOverride::test_torch_q_per_channel_axis, test/test_overrides.py::TestTorchFunctionOverride::test_torch_q_per_channel_scales, test/test_overrides.py::TestTorchFunctionOverride::test_torch_q_per_channel_zero_points, test/test_overrides.py::TestTorchFunctionOverride::test_torch_q_scale, test/test_overrides.py::TestTorchFunctionOverride::test_torch_q_zero_point, test/test_overrides.py::TestTorchFunctionOverride::test_torch_qr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantile, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantize_per_channel, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantize_per_tensor, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantize_per_tensor_dynamic, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_gru_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_lstm_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_rnn_relu_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_rnn_tanh_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rad2deg, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rand_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_randint_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_randn_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ravel, test/test_overrides.py::TestTorchFunctionOverride::test_torch_real, test/test_overrides.py::TestTorchFunctionOverride::test_torch_reciprocal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_remainder, test/test_overrides.py::TestTorchFunctionOverride::test_torch_renorm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_repeat_interleave, test/test_overrides.py::TestTorchFunctionOverride::test_torch_reshape, test/test_overrides.py::TestTorchFunctionOverride::test_torch_resolve_conj, test/test_overrides.py::TestTorchFunctionOverride::test_torch_resolve_neg, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rms_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rnn_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rnn_relu_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rnn_tanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rnn_tanh_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_roll, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rot90, test/test_overrides.py::TestTorchFunctionOverride::test_torch_round, test/test_overrides.py::TestTorchFunctionOverride::test_torch_row_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_row_stack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rrelu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rsqrt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rsub, test/test_overrides.py::TestTorchFunctionOverride::test_torch_saddmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_scatter_add, test/test_overrides.py::TestTorchFunctionOverride::test_torch_scatter_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_torch_searchsorted, test/test_overrides.py::TestTorchFunctionOverride::test_torch_segment_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_torch_select, test/test_overrides.py::TestTorchFunctionOverride::test_torch_select_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_select_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_selu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sgn, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sigmoid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sign, test/test_overrides.py::TestTorchFunctionOverride::test_torch_signbit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sinc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sinh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_slice_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_slice_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_torch_slice_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_slogdet, test/test_overrides.py::TestTorchFunctionOverride::test_torch_smm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sort, test/test_overrides.py::TestTorchFunctionOverride::test_torch_split_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_split_with_sizes, test/test_overrides.py::TestTorchFunctionOverride::test_torch_split_with_sizes_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sqrt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_square, test/test_overrides.py::TestTorchFunctionOverride::test_torch_squeeze, test/test_overrides.py::TestTorchFunctionOverride::test_torch_squeeze_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_stack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_std, test/test_overrides.py::TestTorchFunctionOverride::test_torch_std_mean, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sub, test/test_overrides.py::TestTorchFunctionOverride::test_torch_subtract, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_svd, test/test_overrides.py::TestTorchFunctionOverride::test_torch_swapaxes, test/test_overrides.py::TestTorchFunctionOverride::test_torch_swapdims, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_float, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_int, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_ite, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_max, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_min, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_not, test/test_overrides.py::TestTorchFunctionOverride::test_torch_t, test/test_overrides.py::TestTorchFunctionOverride::test_torch_t_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_take, test/test_overrides.py::TestTorchFunctionOverride::test_torch_take_along_dim, test/test_overrides.py::TestTorchFunctionOverride::test_torch_tan, test/test_overrides.py::TestTorchFunctionOverride::test_torch_tanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_tensor_split, test/test_overrides.py::TestTorchFunctionOverride::test_torch_threshold, test/test_overrides.py::TestTorchFunctionOverride::test_torch_tile, test/test_overrides.py::TestTorchFunctionOverride::test_torch_topk, test/test_overrides.py::TestTorchFunctionOverride::test_torch_trace, test/test_overrides.py::TestTorchFunctionOverride::test_torch_transpose, test/test_overrides.py::TestTorchFunctionOverride::test_torch_transpose_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_trapezoid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_trapz, test/test_overrides.py::TestTorchFunctionOverride::test_torch_triangular_solve, test/test_overrides.py::TestTorchFunctionOverride::test_torch_tril, test/test_overrides.py::TestTorchFunctionOverride::test_torch_triplet_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_triu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_true_divide, test/test_overrides.py::TestTorchFunctionOverride::test_torch_trunc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unbind, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unbind_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unflatten, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unfold_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unsafe_chunk, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unsafe_split, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unsafe_split_with_sizes, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unsqueeze, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unsqueeze_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_values_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_var, test/test_overrides.py::TestTorchFunctionOverride::test_torch_var_mean, test/test_overrides.py::TestTorchFunctionOverride::test_torch_vdot, test/test_overrides.py::TestTorchFunctionOverride::test_torch_view_as_complex, test/test_overrides.py::TestTorchFunctionOverride::test_torch_view_as_complex_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_view_as_real, test/test_overrides.py::TestTorchFunctionOverride::test_torch_view_as_real_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_view_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_vsplit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_vstack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_where, test/test_overrides.py::TestTorchFunctionOverride::test_torch_xlogy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_zeros_like, test/test_overrides.py::TestTorchFunctionOverride::test_user_implementation_raises, test/test_overrides.py::TestEinsumOverride::test_wrapper, test/test_overrides.py::TestGradCheckOverride::test_gradcheck, test/test_overrides.py::TestNamedTuple::test_max, test/test_overrides.py::TestGradNewOnesOverride::test_newones, test/test_overrides.py::TestPickle::test_pickle, test/test_overrides.py::TestBroadcastAllOverride::test_broadcast_all, test/test_overrides.py::TestWrapTorchFunction::test_wrap_torch_function, test/test_overrides.py::TestIndexing::test_getitem, test/test_overrides.py::TestIndexing::test_getitem_subclass, test/test_overrides.py::TestIndexing::test_setitem, test/test_overrides.py::TestIndexing::test_setitem_subclass, test/test_overrides.py::TestIndexing::test_setitem_val, test/test_overrides.py::TestIterator::test_iterator, test/test_overrides.py::TestRNN::test_rnn, test/test_overrides.py::TestDisabledTorchFunction::test_parameter_does_not_prevent_dispatch, test/test_overrides.py::TestResolveName::test_resolve_name, test/test_overrides.py::TestTorchFunctionWarning::test_warn_on_invalid_torch_function, test/test_overrides.py::TestDisabledUserWarnings::test_no_implicit_user_warning_for_deprecated_functions, test/test_overrides.py::TestTorchFunctionMode::test_all_same_mode, test/test_overrides.py::TestTorchFunctionMode::test_basic, test/test_overrides.py::TestTorchFunctionMode::test_custom_device_type, test/test_overrides.py::TestTorchFunctionMode::test_disable_enable_subclass, test/test_overrides.py::TestTorchFunctionMode::test_disable_subclass_mode, test/test_overrides.py::TestTorchFunctionMode::test_disable_subclass_not_mode, test/test_overrides.py::TestTorchFunctionMode::test_distributions_bernoulli, test/test_overrides.py::TestTorchFunctionMode::test_error_using_class_method_on_mode, test/test_overrides.py::TestTorchFunctionMode::test_factory_override, test/test_overrides.py::TestTorchFunctionMode::test_get_cur_mode, test/test_overrides.py::TestTorchFunctionMode::test_get_mode_stack, test/test_overrides.py::TestTorchFunctionMode::test_getitem_call, test/test_overrides.py::TestTorchFunctionMode::test_mode_notimplemented_loop, test/test_overrides.py::TestTorchFunctionMode::test_modes_handle_first, test/test_overrides.py::TestTorchFunctionMode::test_modes_return_notimplemented, test/test_overrides.py::TestTorchFunctionMode::test_nested_modes_with_python_has_torch_function, test/test_overrides.py::TestTorchFunctionMode::test_nested_same_mode, test/test_overrides.py::TestTorchFunctionMode::test_nn_parse_to, test/test_overrides.py::TestTorchFunctionMode::test_reentrant_mode_idiom, test/test_overrides.py::TestTorchFunctionMode::test_restacking_with_ancestor, test/test_overrides.py::TestTorchFunctionMode::test_subclass_hash, test/test_overrides.py::TestTorchFunctionMode::test_with_mode, test/test_overrides.py::TestTorchFunctionMode::test_with_mode_created_separately, test/test_overrides.py::TestTorchFunctionMode::test_with_nested_modes 2024-06-26T05:19:53.3388070Z 2024-06-26T05:19:53.3388495Z Running test_jit_disabled 1/1 ... [2024-06-26 05:19:53.216188] 2024-06-26T05:19:53.3390251Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_jit_disabled.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:19:53.216479] 2024-06-26T05:19:55.8849166Z 2024-06-26T05:19:55.8850906Z test_jit_disabled 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jit_disabled_1.1_831aa40423aaa373_.log 2024-06-26T05:19:55.8854116Z Running 3 items in this shard: test/test_jit_disabled.py::TestJitDisabled::test_attribute, test/test_jit_disabled.py::TestJitDisabled::test_recursive_script, test/test_jit_disabled.py::TestJitDisabled::test_script_module_construction 2024-06-26T05:19:55.8855825Z 2024-06-26T05:19:55.8856144Z Running test_native_mha 1/1 ... [2024-06-26 05:19:55.884973] 2024-06-26T05:19:55.8857843Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_native_mha.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:19:55.885248] 2024-06-26T05:20:20.8939982Z 2024-06-26T05:20:20.8941726Z test_native_mha 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_native_mha_1.1_f4a2c49879fbefc6_.log 2024-06-26T05:20:20.8969499Z Running 28 items in this shard: test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_attention_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_encoder_decoder_attention_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_False_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_False_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_True_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_True_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_False_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_False_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_True_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_True_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_False_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_False_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_True_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_True_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_False_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_False_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_True_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_True_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_False_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_False_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_True_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_True_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_False_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_False_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_True_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_True_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_transform_bias_rescale_qkv_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_transform_bias_rescale_qkv_nested_cpu_float32 2024-06-26T05:20:20.8994543Z 2024-06-26T05:20:20.8994911Z Running test_cpp_extensions_jit 1/1 ... [2024-06-26 05:20:20.894135] 2024-06-26T05:20:20.8996707Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cpp_extensions_jit.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:20:20.894424] 2024-06-26T05:20:53.2034041Z 2024-06-26T05:20:53.2037487Z 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_419177f170a39f5b_.log 2024-06-26T05:20:53.2051009Z Running 26 items in this shard: test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_autograd_from_cpp, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_compilation_error_formatting, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_has_same_output_as_python, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_has_up_to_date_attributes, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_python_inter_op, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_python_inter_op_with_cuda, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_custom_compound_op_autograd, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_custom_functorch_error, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_gen_extension_h_pch, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_half_support, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_custom_op_cuda, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_cuda, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_multiple_sources_and_no_functions, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_throws_when_functions_is_bad, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_with_functions_as_dict, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_with_functions_as_list, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_compile_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_cuda_archflags, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_cuda_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_cudnn_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_lenient_flag_handling_in_jit_extensions, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_mps_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_reload_jit_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_returns_shared_library_path_when_is_python_module_is_true, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_set_default_type_also_changes_aten_default_type, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_warning 2024-06-26T05:20:53.2063666Z 2024-06-26T05:20:53.2064126Z Running test_cpp_extensions_open_device_registration 1/1 ... [2024-06-26 05:20:53.203555] 2024-06-26T05:20:53.2066163Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cpp_extensions_open_device_registration.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:20:53.203849] 2024-06-26T05:21:00.2277633Z 2024-06-26T05:21:00.2279641Z test_cpp_extensions_open_device_registration 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_extensions_open_device_registration_1.1_80ed65e8dd7a1b3d_.log 2024-06-26T05:21:00.2302401Z Running 21 items in this shard: test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_base_device_registration, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_common_registration, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_compile_autograd_function_aliasing, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_compile_autograd_function_returns_self, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_dispatchstub, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_faketensor, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_generator_registration_and_hooks, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_named_tensor, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_numpy_serialization_map_location, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_packed_sequence, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_quantized, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_random, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_scalar_type_fallback, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_serialization, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_storage, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_storage_pin_memory, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_storage_resize, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_storage_type, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_tensor, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_tensor_type_fallback, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_tensorlist_type_fallback 2024-06-26T05:21:00.2327783Z 2024-06-26T05:21:00.2328433Z Running test_sort_and_select 1/1 ... [2024-06-26 05:21:00.228338] 2024-06-26T05:21:00.2331627Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_sort_and_select.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:21:00.228736] 2024-06-26T05:21:43.3501942Z 2024-06-26T05:21:43.3503656Z test_sort_and_select 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_sort_and_select_1.1_96dd676c8c62e8fb_.log 2024-06-26T05:21:43.3551367Z Running 110 items in this shard: test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_dtypes_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_kthvalue_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_kthvalue_scalar_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_output_discontiguous_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_parallel_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_parallel_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_parallel_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_parallel_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_parallel_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_discontiguous_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_discontiguous_slow_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_expanded_tensor_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_large_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_large_slice_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_restride_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_stable_none_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_1d_output_discontiguous_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_4d_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_arguments_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_integral_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_integral_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_integral_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_integral_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_integral_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_lower_precision_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_lower_precision_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_noncontiguous_gpu_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_nonfinite_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_nonfinite_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_nonfinite_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_nonfinite_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_quantized_scalar_input_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_bool, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_bool, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_dim_cpu 2024-06-26T05:21:43.3596625Z 2024-06-26T05:21:43.3597015Z Running test_multiprocessing 1/1 ... [2024-06-26 05:21:43.350873] 2024-06-26T05:21:43.3598784Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_multiprocessing.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:21:43.351160] 2024-06-26T05:22:11.2566662Z 2024-06-26T05:22:11.2568324Z test_multiprocessing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_multiprocessing_1.1_7f09a51f72543da4_.log 2024-06-26T05:22:11.2585681Z Running 38 items in this shard: test/test_multiprocessing.py::TestMultiprocessing::test_autograd_errors, test/test_multiprocessing.py::TestMultiprocessing::test_autograd_fine_with_spawn, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_bad_call, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_ipc_deadlock, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_memory_allocation, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_parameter_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_send_many, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_simple, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_small_tensors, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_variable_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_empty_shared, test/test_multiprocessing.py::TestMultiprocessing::test_empty_tensor_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_empty_tensor_sharing_cuda, test/test_multiprocessing.py::TestMultiprocessing::test_event, test/test_multiprocessing.py::TestMultiprocessing::test_event_handle_exporter, test/test_multiprocessing.py::TestMultiprocessing::test_event_handle_importer, test/test_multiprocessing.py::TestMultiprocessing::test_event_handle_multi_gpu, test/test_multiprocessing.py::TestMultiprocessing::test_event_multiprocess, test/test_multiprocessing.py::TestMultiprocessing::test_fd_pool, test/test_multiprocessing.py::TestMultiprocessing::test_fd_preserve_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_fd_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_fs, test/test_multiprocessing.py::TestMultiprocessing::test_fs_is_shared, test/test_multiprocessing.py::TestMultiprocessing::test_fs_pool, test/test_multiprocessing.py::TestMultiprocessing::test_fs_preserve_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_fs_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_inherit_tensor, test/test_multiprocessing.py::TestMultiprocessing::test_integer_parameter_serialization_cpu, test/test_multiprocessing.py::TestMultiprocessing::test_integer_parameter_serialization_cuda, test/test_multiprocessing.py::TestMultiprocessing::test_is_shared, test/test_multiprocessing.py::TestMultiprocessing::test_is_shared_cuda, test/test_multiprocessing.py::TestMultiprocessing::test_leaf_variable_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_mixed_types_cuda_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_non_leaf_variable_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_parameter_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_set_thread_name, test/test_multiprocessing.py::TestMultiprocessing::test_variable_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_wrong_cuda_fork 2024-06-26T05:22:11.2600905Z 2024-06-26T05:22:11.2601285Z Running test_mobile_optimizer 1/1 ... [2024-06-26 05:22:11.256951] 2024-06-26T05:22:11.2603113Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_mobile_optimizer.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:22:11.257310] 2024-06-26T05:22:18.2310049Z 2024-06-26T05:22:18.2311802Z test_mobile_optimizer 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mobile_optimizer_1.1_a3a99c51670ce42e_.log 2024-06-26T05:22:18.2316119Z Running 7 items in this shard: test/test_mobile_optimizer.py::TestOptimizer::test_clone_module_with_class, test/test_mobile_optimizer.py::TestOptimizer::test_generate_mobile_module_lints, test/test_mobile_optimizer.py::TestOptimizer::test_hoist_conv_packed_params, test/test_mobile_optimizer.py::TestOptimizer::test_mobilenet_optimize_for_mobile, test/test_mobile_optimizer.py::TestOptimizer::test_optimize_for_mobile, test/test_mobile_optimizer.py::TestOptimizer::test_preserve_bundled_inputs_methods, test/test_mobile_optimizer.py::TestOptimizer::test_quantized_conv_no_asan_failures 2024-06-26T05:22:18.2320432Z 2024-06-26T05:22:18.2320958Z Running nn/test_pooling 1/1 ... [2024-06-26 05:22:18.231222] 2024-06-26T05:22:18.2324199Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'nn/test_pooling.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:22:18.231537] 2024-06-26T05:23:15.1333193Z 2024-06-26T05:23:15.1335289Z nn/test_pooling 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_pooling_1.1_11697b8c2cd9d5c3_.log 2024-06-26T05:23:15.1387253Z Running 100 items in this shard: test/nn/test_pooling.py::TestAvgPool::test_avg_pool1d_ceil_mode, test/nn/test_pooling.py::TestAvgPool::test_avg_pool2d_ceil_mode, test/nn/test_pooling.py::TestAvgPool::test_avg_pool3d_ceil_mode, test/nn/test_pooling.py::TestAvgPool::test_doubletensor_avg_pool2d, test/nn/test_pooling.py::TestAvgPool::test_doubletensor_avg_pool2d_with_divisor, test/nn/test_pooling.py::TestAvgPool::test_doubletensor_avg_pool3d, test/nn/test_pooling.py::TestAvgPool::test_doubletensor_avg_pool3d_with_divisor, test/nn/test_pooling.py::TestPoolingNN::test_MaxUnpool2d_output_size, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_avg_pooling_nhwc_overflow, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_avg_pooling_overflow, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_avg_nhwc, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_avg_nhwc_launch_config_backward, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_avg_nhwc_launch_config_forward, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_avg_nhwc_non_contiguous, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_lower_precision, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_size_none, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_size_overflow, test/nn/test_pooling.py::TestPoolingNN::test_max_unpool, test/nn/test_pooling.py::TestPoolingNN::test_max_unpool2d_nhwc_cpu, test/nn/test_pooling.py::TestPoolingNN::test_max_unpool3d_input_check, test/nn/test_pooling.py::TestPoolingNN::test_quantized_max_pool1d_empty_kernel, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AdaptiveMaxPool1d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AdaptiveMaxPool2d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AdaptiveMaxPool3d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AdaptiveMaxPool_zero_batch_dim_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AvgPool2d_empty_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AvgPool3d_backward_after_cat_dim1_device_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool2d_zero_batch_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool2d_zero_out_size_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool2d_zero_samples_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool3d_zero_batch_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool3d_zero_out_size_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool3d_zero_samples_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxPool1d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxPool2d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxPool3d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxPool_zero_batch_dim_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case10_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case1_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case2_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case3_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case4_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case5_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case6_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case7_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case8_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case9_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_zero_batch_dim_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_avg_pool2d_output_size_one_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_avg_pool3d_output_size_one_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pool_odd_size_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_empty_output_size_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_empty_output_size_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_max_nhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_max_nhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_no_suppot_input_cpu_int16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_no_suppot_input_cpu_int32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_no_suppot_input_cpu_int64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_no_suppot_input_cpu_int8, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_no_suppot_input_cpu_uint8, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_zero_batch_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_zero_batch_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_avg_pool2d_nhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_avg_pool2d_nhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_avg_pool2d_reduced_floating_cpu_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_avg_pool2d_reduced_floating_cpu_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_fractional_max_pool2d_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_fractional_max_pool3d_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_fractional_max_pool_nan_inf_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool1d_corner_cases_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool1d_corner_cases_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool1d_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool1d_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_corner_cases_cpu_int32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_corner_cases_cpu_int64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_indices_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_nhwc_cpu_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_nhwc_cpu_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_nhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_nhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool3d_ndhwc_cpu_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool3d_ndhwc_cpu_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool3d_ndhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool3d_ndhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool_bfloat16_half_cpu_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool_bfloat16_half_cpu_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool_nan_inf_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_maxpool3d_non_square_backward_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_maxpool_indices_no_batch_dim_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pool3d_large_size_int64_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pool3d_size_one_feature_dim_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pool_invalid_size_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pool_large_size_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_bfloat16_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_large_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_max_nhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_max_nhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_shape_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_zero_stride_cpu 2024-06-26T05:23:15.1433087Z 2024-06-26T05:23:15.1433515Z Running test_tensor_creation_ops 1/1 ... [2024-06-26 05:23:15.134009] 2024-06-26T05:23:15.1435317Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_tensor_creation_ops.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:23:15.134363] 2024-06-26T05:26:37.0045799Z 2024-06-26T05:26:37.0047189Z test_tensor_creation_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_tensor_creation_ops_1.1_5dcedde224d19b76_.log 2024-06-26T05:26:37.0453732Z Running 625 items in this shard: test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_device_vs_cpu_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_device_vs_cpu_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_device_vs_cpu_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_device_vs_cpu_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_inference_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_lowp_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_lowp_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_as_strided_neg_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_as_tensor_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_block_diag_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_block_diag_scipy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cartesian_prod_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat2_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat2_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat2_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_all_dtypes_and_devices_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_big_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_empty_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_empty_legacy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_in_channels_last_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_mem_overlap_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_channels_last_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_uint16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_uint32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_uint64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_memory_format_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_preserve_channels_last_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_stack_cross_devices_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_combinations_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_complex_type_conversions_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_constructor_device_legacy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_constructor_dtypes_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_ctor_with_numpy_array_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_device_rounding_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_device_rounding_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_diag_embed_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_diagflat_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dsplit_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dsplit_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dsplit_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_empty_full_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_empty_overflow_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_empty_strided_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_empty_tensor_props_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_eye_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_fill_all_dtypes_and_devices_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_from_file_shared_False_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_from_file_shared_True_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_full_inference_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_full_inference_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_full_inference_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_full_out_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hsplit_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hsplit_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hsplit_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_large_linspace_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_large_linspace_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_like_fn_stride_proparation_vs_tensoriterator_unary_op_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linlogspace_mem_overlap_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_deduction_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_complex_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_base2_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_base2_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_deduction_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_device_vs_cpu_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_device_vs_cpu_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_special_steps_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_special_steps_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_vs_numpy_complex_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_vs_numpy_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_vs_numpy_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_default_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_empty_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_ij_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_ij_indexing_is_default_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_inconsistent_device_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_inconsistent_dtype_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_non_1d_tensor_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_unsupported_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_vs_numpy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_warns_if_no_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_xy_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_new_empty_strided_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_new_methods_requires_grad_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_new_tensor_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_offset_scalar_cast_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_ones_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_bool_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_bool_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_uint16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_uint32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_uint16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_uint32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_uint16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_uint32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_range_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_range_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_range_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_range_factories_64bit_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_range_warning_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_repeat_interleave_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_roll_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_bartlett_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_bartlett_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_bartlett_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_blackman_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_blackman_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_blackman_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hamming_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hamming_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hamming_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hann_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hann_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hann_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_bartlett_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_bartlett_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_blackman_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_blackman_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_cosine_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_cosine_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_hamming_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_hamming_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_hann_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_hann_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_nuttall_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_nuttall_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_simple_scalar_cast_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_stack_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_stack_out_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_storage_filename_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_strided_mismatched_stride_shape_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_ctor_device_inference_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_device_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factories_empty_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factory_copy_var_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factory_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factory_gpu_type_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factory_gpu_type_inference_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factory_type_inference_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_from_non_writable_numpy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_from_sequence_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_out_dtype_error_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_out_dtype_error_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_same_dtype_error_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_same_dtype_error_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_polar_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_polar_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_unpack_double_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_unpack_double_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vsplit_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vsplit_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vsplit_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_out_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_normal_cpu_float32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_normal_cpu_float64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_normal_std_error_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_cpu_complex128, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_cpu_complex32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_cpu_complex64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_cpu_float32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_cpu_float64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randint_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randint_inference_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_bfloat16, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_complex128, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_complex32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_complex64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_float16, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_float32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_float64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_random_neg_values_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randperm_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randperm_device_compatibility_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_uniform_from_to_cpu_float16, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_uniform_from_to_cpu_float32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_uniform_from_to_cpu_float64, test/test_tensor_creation_ops.py::TestLikeTensorCreationCPU::test_empty_like_cpu, test/test_tensor_creation_ops.py::TestLikeTensorCreationCPU::test_full_like_inference_cpu, test/test_tensor_creation_ops.py::TestLikeTensorCreationCPU::test_ones_like_cpu, test/test_tensor_creation_ops.py::TestLikeTensorCreationCPU::test_ones_like_multiple_device_cpu, test/test_tensor_creation_ops.py::TestLikeTensorCreationCPU::test_zeros_like_cpu, test/test_tensor_creation_ops.py::TestLikeTensorCreationCPU::test_zeros_like_multiple_device_cpu, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_byte_to_int_cpu, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_uint16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_uint32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_uint64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_uint16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_uint32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_uint64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_astensor_consistency_cpu, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_uint16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_uint32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_uint64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_uint16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_uint32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_uint64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_default_device_cpu, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_device_without_index_cpu, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_numpy_scalars_cpu, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_retain_autograd_history_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_retain_autograd_history_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_unsupported_alias_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_unsupported_alias_mult_devices_cpu_float32 2024-06-26T05:26:37.0731785Z 2024-06-26T05:26:37.0732210Z Running test_reductions 1/1 ... [2024-06-26 05:26:37.006332] 2024-06-26T05:26:37.0734007Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_reductions.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:26:37.006603] 2024-06-26T05:40:37.5632609Z 2024-06-26T05:40:37.5633968Z test_reductions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_reductions_1.1_228fe0100af3df0c_.log 2024-06-26T05:40:37.7787276Z Running 4585 items in this shard: test/test_reductions.py::TestReductionsCPU::test_accreal_type_cpu, test/test_reductions.py::TestReductionsCPU::test_all_any_cpu, test/test_reductions.py::TestReductionsCPU::test_all_any_empty_cpu, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_all_any_with_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_all_issue117215_cpu, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_amin_amax_some_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_aminmax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_aminmax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_aminmax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_aminmax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_argminmax_axis_with_dim_one_cpu, test/test_reductions.py::TestReductionsCPU::test_argminmax_large_axis_cpu, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_bincount_cpu, test/test_reductions.py::TestReductionsCPU::test_bucketization_cpu, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_cumprod_integer_upcast_cpu, test/test_reductions.py::TestReductionsCPU::test_cumsum_integer_upcast_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_dim_default__refs_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default__refs_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default__refs_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default__refs_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default__refs_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default__refs_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default__refs_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default__refs_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default__refs_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default__refs_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default__refs_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_argmax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_argmin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_amin_cpu, 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test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_std_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_std_mean_all_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_std_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_std_mean_some_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_std_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_std_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_std_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_std_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_all_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_sum_all_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_cpu_device_mismatch_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_dim_reduction_uint8_overflow_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_integer_upcast_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_noncontig_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_noncontig_lowp_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_sum_noncontig_lowp_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_sum_out_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_parallel_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_tensor_compare_ops_argmax_argmix_kthvalue_dim_empty_cpu, test/test_reductions.py::TestReductionsCPU::test_tensor_compare_ops_empty_cpu, test/test_reductions.py::TestReductionsCPU::test_tensor_reduce_ops_empty_cpu, test/test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_var_cpu, test/test_reductions.py::TestReductionsCPU::test_var_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_var_large_input_cpu, test/test_reductions.py::TestReductionsCPU::test_var_mean_all_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_var_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_var_mean_some_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_var_stability2_cpu, test/test_reductions.py::TestReductionsCPU::test_var_stability_cpu, test/test_reductions.py::TestReductionsCPU::test_var_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_float64 2024-06-26T05:40:38.0001438Z 2024-06-26T05:40:38.0002178Z Running test_cuda_primary_ctx 1/1 ... [2024-06-26 05:40:37.569973] 2024-06-26T05:40:38.0005907Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cuda_primary_ctx.py', '--shard-id=1', '--num-shards=1', '-v', '--subprocess', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:40:37.570266] 2024-06-26T05:40:39.8029096Z 2024-06-26T05:40:39.8030553Z test_cuda_primary_ctx 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_primary_ctx_1.1_bbeff79f01172392_.log 2024-06-26T05:40:39.8031754Z Running 0 items in this shard: 2024-06-26T05:40:39.8032148Z 2024-06-26T05:40:39.8032756Z Running test_dispatch 1/1 ... [2024-06-26 05:40:39.803086] 2024-06-26T05:40:39.8036488Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_dispatch.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:40:39.803372] 2024-06-26T05:41:19.6226764Z 2024-06-26T05:41:19.6228272Z test_dispatch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_dispatch_1.1_9f5e750ea3091e29_.log 2024-06-26T05:41:19.6241920Z Running 32 items in this shard: test/test_dispatch.py::TestDispatch::test_all_invariants, test/test_dispatch.py::TestDispatch::test_computed_table, test/test_dispatch.py::TestDispatch::test_computed_table_with_ambiguous_autogradother, test/test_dispatch.py::TestDispatch::test_computed_table_with_autograd, test/test_dispatch.py::TestDispatch::test_computed_table_with_cpu_autograd_defaultbackend, test/test_dispatch.py::TestDispatch::test_computed_table_with_cpu_autograd_math, test/test_dispatch.py::TestDispatch::test_computed_table_with_cpu_autograd_math_defaultbackend, test/test_dispatch.py::TestDispatch::test_computed_table_with_cpu_defaultbackend, test/test_dispatch.py::TestDispatch::test_computed_table_with_cpu_math, test/test_dispatch.py::TestDispatch::test_computed_table_with_cpu_math_autogradcpu_fallthrough, test/test_dispatch.py::TestDispatch::test_computed_table_with_math, test/test_dispatch.py::TestDispatch::test_def, test/test_dispatch.py::TestDispatch::test_def_impl_schema_mismatch, test/test_dispatch.py::TestDispatch::test_def_only, test/test_dispatch.py::TestDispatch::test_def_with_explicit_alias, test/test_dispatch.py::TestDispatch::test_def_with_inference, test/test_dispatch.py::TestDispatch::test_dispatch_print_registrations_for_dispatch_key_invalid, test/test_dispatch.py::TestDispatch::test_find_dangling_impls, test/test_dispatch.py::TestDispatch::test_find_dangling_impls_ext, test/test_dispatch.py::TestDispatch::test_impl_only, test/test_dispatch.py::TestDispatch::test_multiple_def_alias_defaulting, test/test_dispatch.py::TestDispatch::test_multiple_def_alias_mismatch, test/test_dispatch.py::TestDispatch::test_multiple_def_error, test/test_dispatch.py::TestDispatch::test_multiple_fallback, test/test_dispatch.py::TestDispatch::test_overwrite_math, test/test_dispatch.py::TestPythonDispatcher::test_autogradother, test/test_dispatch.py::TestPythonDispatcher::test_basic, test/test_dispatch.py::TestPythonDispatcher::test_defaultbackend_autogradcpu, test/test_dispatch.py::TestPythonDispatcher::test_defaultbackend_math, test/test_dispatch.py::TestPythonDispatcher::test_duplicate_registrations, test/test_dispatch.py::TestPythonDispatcher::test_math_autogradcpu, test/test_dispatch.py::TestPythonDispatcher::test_quantized_structured_not_implemented 2024-06-26T05:41:19.6254929Z 2024-06-26T05:41:19.6255322Z Running test_cuda_trace 1/1 ... [2024-06-26 05:41:19.622858] 2024-06-26T05:41:19.6257227Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cuda_trace.py', '--shard-id=1', '--num-shards=1', '-v', '--subprocess', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:41:19.623197] 2024-06-26T05:41:21.8855265Z 2024-06-26T05:41:21.8857085Z test_cuda_trace 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_trace_1.1_e87426a20fbf75c8_.log 2024-06-26T05:41:21.8858479Z Running 0 items in this shard: 2024-06-26T05:41:21.8858746Z 2024-06-26T05:41:21.8859125Z Running test_multiprocessing_spawn 1/1 ... [2024-06-26 05:41:21.885634] 2024-06-26T05:41:21.8862723Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_multiprocessing_spawn.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:41:21.885996] 2024-06-26T05:41:48.3893913Z 2024-06-26T05:41:48.3895378Z test_multiprocessing_spawn 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_multiprocessing_spawn_1.1_b0163e812f7e05de_.log 2024-06-26T05:41:48.3906626Z Running 19 items in this shard: test/test_multiprocessing_spawn.py::SpawnTest::test_exception_all, test/test_multiprocessing_spawn.py::SpawnTest::test_exception_raises, test/test_multiprocessing_spawn.py::SpawnTest::test_exception_single, test/test_multiprocessing_spawn.py::SpawnTest::test_first_argument_index, test/test_multiprocessing_spawn.py::SpawnTest::test_signal_raises, test/test_multiprocessing_spawn.py::SpawnTest::test_success, test/test_multiprocessing_spawn.py::SpawnTest::test_success_first_then_exception, test/test_multiprocessing_spawn.py::SpawnTest::test_success_non_blocking, test/test_multiprocessing_spawn.py::SpawnTest::test_terminate_exit, test/test_multiprocessing_spawn.py::SpawnTest::test_terminate_signal, test/test_multiprocessing_spawn.py::ForkTest::test_exception_all, test/test_multiprocessing_spawn.py::ForkTest::test_exception_single, test/test_multiprocessing_spawn.py::ForkTest::test_first_argument_index, test/test_multiprocessing_spawn.py::ForkTest::test_success, test/test_multiprocessing_spawn.py::ForkTest::test_success_first_then_exception, test/test_multiprocessing_spawn.py::ForkTest::test_success_non_blocking, test/test_multiprocessing_spawn.py::ForkTest::test_terminate_exit, test/test_multiprocessing_spawn.py::ForkTest::test_terminate_signal, test/test_multiprocessing_spawn.py::ErrorTest::test_errors_pickleable 2024-06-26T05:41:48.3913682Z 2024-06-26T05:41:48.3914099Z Running test_cuda_nvml_based_avail 1/1 ... [2024-06-26 05:41:48.389613] 2024-06-26T05:41:48.3916005Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cuda_nvml_based_avail.py', '--shard-id=1', '--num-shards=1', '-v', '--subprocess', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:41:48.389968] 2024-06-26T05:41:50.7719240Z 2024-06-26T05:41:50.7721617Z test_cuda_nvml_based_avail 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_nvml_based_avail_1.1_94b27d91ee9ac421_.log 2024-06-26T05:41:50.7723544Z Running 0 items in this shard: 2024-06-26T05:41:50.7723966Z 2024-06-26T05:41:50.7724442Z Running test_spectral_ops 1/1 ... [2024-06-26 05:41:50.772110] 2024-06-26T05:41:50.7728408Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_spectral_ops.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:41:50.772478] 2024-06-26T05:42:24.3354786Z 2024-06-26T05:42:24.3356553Z test_spectral_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_spectral_ops_1.1_c5836ebcfc22dba8_.log 2024-06-26T05:42:24.3560687Z Running 280 items in this shard: test/test_spectral_ops.py::TestFFTCPU::test_batch_istft_cpu, test/test_spectral_ops.py::TestFFTCPU::test_complex_istft_real_equiv_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_definition_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_onesided_cpu, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_real_equiv_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_roundtrip_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_roundtrip_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_cufft_context_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_cufft_context_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_cufft_plan_cache_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfftn_cpu_complex64, 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test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_ifftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_ihfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_irfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_rfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_noop_transform_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fftn_noop_transform_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_noop_transform_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fftn_noop_transform_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_noop_transform_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_complex32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_frequencies_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_frequencies_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_numpy_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_numpy_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_numpy_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_numpy_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_hfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_hfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_hfftn_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_ihfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_ihfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_ihfftn_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_against_librosa_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_linearity_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_of_sine_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_requires_window_cpu, test/test_spectral_ops.py::TestFFTCPU::test_istft_round_trip_simple_cases_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_round_trip_various_params_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_round_trip_with_padding_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_throws_cpu, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_fft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_fft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_hfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_hfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_ifft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_ifft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_ihfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_irfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_irfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_rfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_fft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_fft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_hfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_hfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_ifft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_ifft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_ihfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_irfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_irfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_rfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_fftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_hfftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_ifftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_irfftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_fftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_hfftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_ifftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_irfftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_stft_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_stft_requires_complex_cpu, test/test_spectral_ops.py::TestFFTCPU::test_stft_requires_window_cpu, test/test_spectral_ops.py::TestFFTCPU::test_stft_roundtrip_complex_window_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_stft_roundtrip_complex_window_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_stft_window_device_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_fft2_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_fft_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_fftfreq_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_fftn_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_fftshift_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_hfft_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_ifft2_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_ifft_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_ifftn_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_ifftshift_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_ihfft_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_irfft2_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_irfft_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_irfftn_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_rfft2_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_rfft_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_rfftfreq_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_rfftn_cpu 2024-06-26T05:42:24.3672173Z 2024-06-26T05:42:24.3672699Z Running distributions/test_distributions 1/2 ... [2024-06-26 05:42:24.336181] 2024-06-26T05:42:24.3674644Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'distributions/test_distributions.py', '--shard-id=1', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:42:24.336475] 2024-06-26T05:47:53.1504779Z 2024-06-26T05:47:53.1506496Z distributions/test_distributions 1/2 was successful, full logs can be found in artifacts with path test/test-reports/distributions.test_distributions_1.2_07424bd06af0058f_.log 2024-06-26T05:47:53.1584128Z Running 130 items in this shard: test/distributions/test_distributions.py::TestDistributions::test_argmax_relaxed_categorical, test/distributions/test_distributions.py::TestDistributions::test_bernoulli, test/distributions/test_distributions.py::TestDistributions::test_bernoulli_3d, test/distributions/test_distributions.py::TestDistributions::test_bernoulli_enumerate_support, test/distributions/test_distributions.py::TestDistributions::test_beta_log_prob, test/distributions/test_distributions.py::TestDistributions::test_beta_underflow, test/distributions/test_distributions.py::TestDistributions::test_binomial, test/distributions/test_distributions.py::TestDistributions::test_binomial_half, test/distributions/test_distributions.py::TestDistributions::test_binomial_log_prob_vectorized_count, test/distributions/test_distributions.py::TestDistributions::test_binomial_vectorized_count, test/distributions/test_distributions.py::TestDistributions::test_categorical_1d, test/distributions/test_distributions.py::TestDistributions::test_categorical_2d, test/distributions/test_distributions.py::TestDistributions::test_categorical_enumerate_support, test/distributions/test_distributions.py::TestDistributions::test_cauchy, test/distributions/test_distributions.py::TestDistributions::test_cdf_icdf_inverse, test/distributions/test_distributions.py::TestDistributions::test_cdf_log_prob, test/distributions/test_distributions.py::TestDistributions::test_chi2_shape, test/distributions/test_distributions.py::TestDistributions::test_continuous_bernoulli, test/distributions/test_distributions.py::TestDistributions::test_continuous_bernoulli_3d, test/distributions/test_distributions.py::TestDistributions::test_distribution_expand, test/distributions/test_distributions.py::TestDistributions::test_enumerate_support_type, test/distributions/test_distributions.py::TestDistributions::test_exponential, test/distributions/test_distributions.py::TestDistributions::test_exponential_sample, test/distributions/test_distributions.py::TestDistributions::test_fishersnedecor, test/distributions/test_distributions.py::TestDistributions::test_gamma_sample, test/distributions/test_distributions.py::TestDistributions::test_gamma_shape, test/distributions/test_distributions.py::TestDistributions::test_geometric, test/distributions/test_distributions.py::TestDistributions::test_geometric_log_prob_and_entropy, test/distributions/test_distributions.py::TestDistributions::test_geometric_sample, test/distributions/test_distributions.py::TestDistributions::test_gumbel_sample, test/distributions/test_distributions.py::TestDistributions::test_halfcauchy, test/distributions/test_distributions.py::TestDistributions::test_halfnormal, test/distributions/test_distributions.py::TestDistributions::test_halfnormal_logprob, test/distributions/test_distributions.py::TestDistributions::test_has_examples, test/distributions/test_distributions.py::TestDistributions::test_independent_expand, test/distributions/test_distributions.py::TestDistributions::test_independent_shape, test/distributions/test_distributions.py::TestDistributions::test_invalid_parameter_broadcasting, test/distributions/test_distributions.py::TestDistributions::test_inversegamma, test/distributions/test_distributions.py::TestDistributions::test_inversegamma_sample, test/distributions/test_distributions.py::TestDistributions::test_kumaraswamy_mean_variance, test/distributions/test_distributions.py::TestDistributions::test_kumaraswamy_shape, test/distributions/test_distributions.py::TestDistributions::test_lkj_cholesky_log_prob, test/distributions/test_distributions.py::TestDistributions::test_logisticnormal_logprob, test/distributions/test_distributions.py::TestDistributions::test_logisticnormal_sample, test/distributions/test_distributions.py::TestDistributions::test_lognormal_logprob, test/distributions/test_distributions.py::TestDistributions::test_lognormal_sample, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_log_prob, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_moments, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_shape, test/distributions/test_distributions.py::TestDistributions::test_mixture_same_family_log_prob, test/distributions/test_distributions.py::TestDistributions::test_multinomial_1d_log_prob_and_entropy, test/distributions/test_distributions.py::TestDistributions::test_multinomial_2d, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_log_prob, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_moments, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_properties, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_shape, test/distributions/test_distributions.py::TestDistributions::test_negative_binomial, test/distributions/test_distributions.py::TestDistributions::test_normal, test/distributions/test_distributions.py::TestDistributions::test_normal_sample, test/distributions/test_distributions.py::TestDistributions::test_one_hot_categorical_2d, test/distributions/test_distributions.py::TestDistributions::test_pareto, test/distributions/test_distributions.py::TestDistributions::test_pareto_sample, test/distributions/test_distributions.py::TestDistributions::test_poisson_forward_ad, test/distributions/test_distributions.py::TestDistributions::test_poisson_log_prob, test/distributions/test_distributions.py::TestDistributions::test_repr, test/distributions/test_distributions.py::TestDistributions::test_rounded_relaxed_bernoulli, test/distributions/test_distributions.py::TestDistributions::test_studentT, test/distributions/test_distributions.py::TestDistributions::test_studentT_log_prob, test/distributions/test_distributions.py::TestDistributions::test_studentT_sample, test/distributions/test_distributions.py::TestDistributions::test_support_attributes, test/distributions/test_distributions.py::TestDistributions::test_vonmises_logprob, test/distributions/test_distributions.py::TestDistributions::test_vonmises_sample, test/distributions/test_distributions.py::TestDistributions::test_wishart_log_prob, test/distributions/test_distributions.py::TestDistributions::test_wishart_moments, test/distributions/test_distributions.py::TestDistributions::test_wishart_properties, test/distributions/test_distributions.py::TestDistributions::test_wishart_sample, test/distributions/test_distributions.py::TestDistributions::test_wishart_stable_with_precision_matrix, test/distributions/test_distributions.py::TestDistributions::test_zero_excluded_binomial, test/distributions/test_distributions.py::TestRsample::test_beta_wrt_alpha, test/distributions/test_distributions.py::TestRsample::test_beta_wrt_beta, test/distributions/test_distributions.py::TestRsample::test_dirichlet_on_diagonal, test/distributions/test_distributions.py::TestRsample::test_dirichlet_tangent_field, test/distributions/test_distributions.py::TestDistributionShapes::test_bernoulli_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_beta_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_binomial_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_binomial_shape_vectorized_n, test/distributions/test_distributions.py::TestDistributionShapes::test_categorical_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_cauchy_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_cauchy_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_chi2_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_exponential_shape_scalar_param, test/distributions/test_distributions.py::TestDistributionShapes::test_exponential_shape_tensor_param, test/distributions/test_distributions.py::TestDistributionShapes::test_gamma_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_gamma_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_gumbel_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_laplace_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_mixture_same_family_mean_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_multinomial_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_normal_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_one_hot_categorical_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_studentT_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_studentT_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_uniform_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_uniform_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_vonmises_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_wishart_shape_scalar_params, test/distributions/test_distributions.py::TestKL::test_entropy_monte_carlo, test/distributions/test_distributions.py::TestKL::test_kl_exponential_family, test/distributions/test_distributions.py::TestKL::test_kl_lowrank_multivariate_normal, test/distributions/test_distributions.py::TestKL::test_kl_lowrank_multivariate_normal_batched, test/distributions/test_distributions.py::TestKL::test_kl_multivariate_normal_batched, test/distributions/test_distributions.py::TestKL::test_kl_multivariate_normal_batched_broadcasted, test/distributions/test_distributions.py::TestKL::test_kl_transformed, test/distributions/test_distributions.py::TestConstraints::test_support_constraints, test/distributions/test_distributions.py::TestNumericalStability::test_bernoulli_gradient, test/distributions/test_distributions.py::TestNumericalStability::test_categorical_log_prob_with_logits, test/distributions/test_distributions.py::TestNumericalStability::test_continuous_bernoulli_gradient, test/distributions/test_distributions.py::TestNumericalStability::test_continuous_bernoulli_with_logits_overflow, test/distributions/test_distributions.py::TestNumericalStability::test_multinomial_log_prob, test/distributions/test_distributions.py::TestLazyLogitsInitialization::test_lazy_logits_initialization, test/distributions/test_distributions.py::TestAgainstScipy::test_icdf, test/distributions/test_distributions.py::TestAgainstScipy::test_mean, test/distributions/test_distributions.py::TestFunctors::test_cat_event_dim, test/distributions/test_distributions.py::TestFunctors::test_stack_transform, test/distributions/test_distributions.py::TestValidation::test_invalid_log_probs_arg, test/distributions/test_distributions.py::TestValidation::test_valid, test/distributions/test_distributions.py::TestValidation::test_warning_unimplemented_constraints, test/distributions/test_distributions.py::TestJit::test_cdf, test/distributions/test_distributions.py::TestJit::test_mean, test/distributions/test_distributions.py::TestJit::test_sample 2024-06-26T05:47:53.1644386Z 2024-06-26T05:47:53.1644893Z Running distributions/test_distributions 2/2 ... [2024-06-26 05:47:53.150872] 2024-06-26T05:47:53.1646820Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'distributions/test_distributions.py', '--shard-id=2', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:47:53.151170] 2024-06-26T05:54:15.9862761Z 2024-06-26T05:54:15.9864760Z distributions/test_distributions 2/2 was successful, full logs can be found in artifacts with path test/test-reports/distributions.test_distributions_2.2_5f59df39fcc789a4_.log 2024-06-26T05:54:15.9913353Z Running 95 items in this shard: test/distributions/test_distributions.py::TestDistributions::test_beta_sample, test/distributions/test_distributions.py::TestDistributions::test_beta_shape, test/distributions/test_distributions.py::TestDistributions::test_beta_underflow_gpu, test/distributions/test_distributions.py::TestDistributions::test_binomial_bfloat16, test/distributions/test_distributions.py::TestDistributions::test_binomial_enumerate_support, test/distributions/test_distributions.py::TestDistributions::test_binomial_extreme_vals, test/distributions/test_distributions.py::TestDistributions::test_binomial_log_prob_and_entropy, test/distributions/test_distributions.py::TestDistributions::test_binomial_sample, test/distributions/test_distributions.py::TestDistributions::test_binomial_stable, test/distributions/test_distributions.py::TestDistributions::test_chi2_sample, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_log_prob, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_log_prob_zero, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_mode, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_sample, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_shape, test/distributions/test_distributions.py::TestDistributions::test_distribution_subclass_expand, test/distributions/test_distributions.py::TestDistributions::test_fishersnedecor_sample, test/distributions/test_distributions.py::TestDistributions::test_gamma_gpu_sample, test/distributions/test_distributions.py::TestDistributions::test_gamma_gpu_shape, test/distributions/test_distributions.py::TestDistributions::test_gamma_log_prob_at_boundary, test/distributions/test_distributions.py::TestDistributions::test_gumbel, test/distributions/test_distributions.py::TestDistributions::test_halfnormal_sample, test/distributions/test_distributions.py::TestDistributions::test_laplace, test/distributions/test_distributions.py::TestDistributions::test_laplace_sample, test/distributions/test_distributions.py::TestDistributions::test_lazy_property_grad, test/distributions/test_distributions.py::TestDistributions::test_logisticnormal, test/distributions/test_distributions.py::TestDistributions::test_lognormal, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_properties, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_sample, test/distributions/test_distributions.py::TestDistributions::test_mixture_same_family_sample, test/distributions/test_distributions.py::TestDistributions::test_mixture_same_family_shape, test/distributions/test_distributions.py::TestDistributions::test_mode, test/distributions/test_distributions.py::TestDistributions::test_multinomial_1d, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_sample, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_stable_with_precision_matrix, test/distributions/test_distributions.py::TestDistributions::test_negative_binomial_log_prob, test/distributions/test_distributions.py::TestDistributions::test_negative_binomial_log_prob_vectorized_count, test/distributions/test_distributions.py::TestDistributions::test_one_hot_categorical_1d, test/distributions/test_distributions.py::TestDistributions::test_one_hot_categorical_enumerate_support, test/distributions/test_distributions.py::TestDistributions::test_poisson_gpu_sample, test/distributions/test_distributions.py::TestDistributions::test_poisson_sample, test/distributions/test_distributions.py::TestDistributions::test_poisson_shape, test/distributions/test_distributions.py::TestDistributions::test_relaxed_bernoulli, test/distributions/test_distributions.py::TestDistributions::test_relaxed_one_hot_categorical_1d, test/distributions/test_distributions.py::TestDistributions::test_relaxed_one_hot_categorical_2d, test/distributions/test_distributions.py::TestDistributions::test_rsample_requires_grad, test/distributions/test_distributions.py::TestDistributions::test_sample_detached, test/distributions/test_distributions.py::TestDistributions::test_uniform, test/distributions/test_distributions.py::TestDistributions::test_valid_parameter_broadcasting, test/distributions/test_distributions.py::TestDistributions::test_wishart_shape, test/distributions/test_distributions.py::TestRsample::test_chi2, test/distributions/test_distributions.py::TestRsample::test_dirichlet_multivariate, test/distributions/test_distributions.py::TestRsample::test_gamma, test/distributions/test_distributions.py::TestDistributionShapes::test_bernoulli_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_beta_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_chi2_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_continuous_bernoulli_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_continuous_bernoulli_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_dirichlet_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_entropy_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_geometric_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_geometric_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_halfcauchy_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_halfcauchy_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_kumaraswamy_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_laplace_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_mixture_same_family_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_normal_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_pareto_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_vonmises_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_weibull_scale_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_wishart_shape_tensor_params, test/distributions/test_distributions.py::TestKL::test_entropy_exponential_family, test/distributions/test_distributions.py::TestKL::test_kl_edgecases, test/distributions/test_distributions.py::TestKL::test_kl_infinite, test/distributions/test_distributions.py::TestKL::test_kl_monte_carlo, test/distributions/test_distributions.py::TestKL::test_kl_multivariate_normal, test/distributions/test_distributions.py::TestKL::test_kl_shape, test/distributions/test_distributions.py::TestConstraints::test_params_constraints, test/distributions/test_distributions.py::TestNumericalStability::test_bernoulli_with_logits_overflow, test/distributions/test_distributions.py::TestNumericalStability::test_bernoulli_with_logits_underflow, test/distributions/test_distributions.py::TestNumericalStability::test_categorical_log_prob, test/distributions/test_distributions.py::TestNumericalStability::test_continuous_bernoulli_with_logits_underflow, test/distributions/test_distributions.py::TestNumericalStability::test_multinomial_log_prob_with_logits, test/distributions/test_distributions.py::TestLazyLogitsInitialization::test_lazy_probs_initialization, test/distributions/test_distributions.py::TestAgainstScipy::test_cdf, test/distributions/test_distributions.py::TestAgainstScipy::test_variance_stddev, test/distributions/test_distributions.py::TestFunctors::test_cat_transform, test/distributions/test_distributions.py::TestFunctors::test_cat_transform_non_uniform, test/distributions/test_distributions.py::TestValidation::test_invalid, test/distributions/test_distributions.py::TestJit::test_entropy, test/distributions/test_distributions.py::TestJit::test_enumerate_support, test/distributions/test_distributions.py::TestJit::test_log_prob, test/distributions/test_distributions.py::TestJit::test_rsample, test/distributions/test_distributions.py::TestJit::test_variance 2024-06-26T05:54:15.9957732Z 2024-06-26T05:54:15.9958038Z Running doctests 1/1 ... [2024-06-26 05:54:15.986651] 2024-06-26T05:54:15.9958831Z Start doctest_module('/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch') 2024-06-26T05:54:15.9959462Z Listing tests 2024-06-26T05:54:16.2260903Z msg = Cannot scrape callname=meshgrid in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=426. 2024-06-26T05:54:16.2262136Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:16.2263581Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors. 2024-06-26T05:54:16.2264374Z 2024-06-26T05:54:16.2264631Z This is helpful when you want to visualize data over some 2024-06-26T05:54:16.2265224Z range of inputs. See below for a plotting example. 2024-06-26T05:54:16.2265601Z 2024-06-26T05:54:16.2265890Z Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as 2024-06-26T05:54:16.2266694Z inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`, 2024-06-26T05:54:16.2267417Z this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots 2024-06-26T05:54:16.2268105Z G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where 2024-06-26T05:54:16.2268749Z the output :math:`G_i` is constructed by expanding :math:`T_i` 2024-06-26T05:54:16.2269281Z to the result shape. 2024-06-26T05:54:16.2269608Z 2024-06-26T05:54:16.2269731Z .. note:: 2024-06-26T05:54:16.2270126Z 0D inputs are treated equivalently to 1D inputs of a 2024-06-26T05:54:16.2270629Z single element. 2024-06-26T05:54:16.2270849Z 2024-06-26T05:54:16.2270953Z .. warning:: 2024-06-26T05:54:16.2271391Z `torch.meshgrid(*tensors)` currently has the same behavior 2024-06-26T05:54:16.2272064Z as calling `numpy.meshgrid(*arrays, indexing='ij')`. 2024-06-26T05:54:16.2272429Z 2024-06-26T05:54:16.2272636Z In the future `torch.meshgrid` will transition to 2024-06-26T05:54:16.2273185Z `indexing='xy'` as the default. 2024-06-26T05:54:16.2273493Z 2024-06-26T05:54:16.2273728Z https://github.com/pytorch/pytorch/issues/50276 tracks 2024-06-26T05:54:16.2274418Z this issue with the goal of migrating to NumPy's behavior. 2024-06-26T05:54:16.2274816Z 2024-06-26T05:54:16.2274923Z .. seealso:: 2024-06-26T05:54:16.2275122Z 2024-06-26T05:54:16.2275343Z :func:`torch.cartesian_prod` has the same effect but it 2024-06-26T05:54:16.2275908Z collects the data in a tensor of vectors. 2024-06-26T05:54:16.2276239Z 2024-06-26T05:54:16.2276335Z Args: 2024-06-26T05:54:16.2276861Z tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be 2024-06-26T05:54:16.2277610Z treated as tensors of size :math:`(1,)` automatically 2024-06-26T05:54:16.2277989Z 2024-06-26T05:54:16.2278235Z indexing: (str, optional): the indexing mode, either "xy" 2024-06-26T05:54:16.2279158Z or "ij", defaults to "ij". See warning for future changes. 2024-06-26T05:54:16.2279832Z 2024-06-26T05:54:16.2280167Z If "xy" is selected, the first dimension corresponds 2024-06-26T05:54:16.2281123Z to the cardinality of the second input and the second 2024-06-26T05:54:16.2282182Z dimension corresponds to the cardinality of the first 2024-06-26T05:54:16.2282816Z input. 2024-06-26T05:54:16.2283009Z 2024-06-26T05:54:16.2283230Z If "ij" is selected, the dimensions are in the same 2024-06-26T05:54:16.2283770Z order as the cardinality of the inputs. 2024-06-26T05:54:16.2284109Z 2024-06-26T05:54:16.2284207Z Returns: 2024-06-26T05:54:16.2284599Z seq (sequence of Tensors): If the input has :math:`N` 2024-06-26T05:54:16.2285252Z tensors of size :math:`S_0 \ldots S_{N-1}``, then the 2024-06-26T05:54:16.2285874Z output will also have :math:`N` tensors, where each tensor 2024-06-26T05:54:16.2286494Z is of shape :math:`(S_0, ..., S_{N-1})`. 2024-06-26T05:54:16.2286811Z 2024-06-26T05:54:16.2286929Z Example:: 2024-06-26T05:54:16.2287101Z 2024-06-26T05:54:16.2287237Z >>> x = torch.tensor([1, 2, 3]) 2024-06-26T05:54:16.2287670Z >>> y = torch.tensor([4, 5, 6]) 2024-06-26T05:54:16.2287957Z 2024-06-26T05:54:16.2288276Z Observe the element-wise pairings across the grid, (1, 4), 2024-06-26T05:54:16.2288858Z (1, 5), ..., (3, 6). This is the same thing as the 2024-06-26T05:54:16.2289463Z cartesian product. 2024-06-26T05:54:16.2289975Z >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij') 2024-06-26T05:54:16.2290447Z >>> grid_x 2024-06-26T05:54:16.2290758Z tensor([[1, 1, 1], 2024-06-26T05:54:16.2291093Z [2, 2, 2], 2024-06-26T05:54:16.2291413Z [3, 3, 3]]) 2024-06-26T05:54:16.2291823Z >>> grid_y 2024-06-26T05:54:16.2292128Z tensor([[4, 5, 6], 2024-06-26T05:54:16.2292449Z [4, 5, 6], 2024-06-26T05:54:16.2292783Z [4, 5, 6]]) 2024-06-26T05:54:16.2293011Z 2024-06-26T05:54:16.2293244Z This correspondence can be seen when these grids are 2024-06-26T05:54:16.2293974Z stacked properly. 2024-06-26T05:54:16.2294479Z >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))), 2024-06-26T05:54:16.2295076Z ... torch.cartesian_prod(x, y)) 2024-06-26T05:54:16.2295508Z True 2024-06-26T05:54:16.2295687Z 2024-06-26T05:54:16.2295917Z `torch.meshgrid` is commonly used to produce a grid for 2024-06-26T05:54:16.2296415Z plotting. 2024-06-26T05:54:16.2296788Z >>> # xdoctest: +REQUIRES(module:matplotlib) 2024-06-26T05:54:16.2297285Z >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW) 2024-06-26T05:54:16.2297783Z >>> import matplotlib.pyplot as plt 2024-06-26T05:54:16.2298324Z >>> xs = torch.linspace(-5, 5, steps=100) 2024-06-26T05:54:16.2298852Z >>> ys = torch.linspace(-5, 5, steps=100) 2024-06-26T05:54:16.2299412Z >>> x, y = torch.meshgrid(xs, ys, indexing='xy') 2024-06-26T05:54:16.2299928Z >>> z = torch.sin(torch.sqrt(x * x + y * y)) 2024-06-26T05:54:16.2300444Z >>> ax = plt.axes(projection='3d') 2024-06-26T05:54:16.2300958Z >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy()) 2024-06-26T05:54:16.2301434Z >>> plt.show() 2024-06-26T05:54:16.2301651Z 2024-06-26T05:54:16.2301809Z .. image:: ../_static/img/meshgrid.png 2024-06-26T05:54:16.2302226Z :width: 512 2024-06-26T05:54:16.2302423Z 2024-06-26T05:54:16.2302532Z 2024-06-26T05:54:16.2303049Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:16.2303538Z 2024-06-26T05:54:16.2304363Z msg = Cannot scrape callname=_unique_impl in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=815. 2024-06-26T05:54:16.2305549Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:16.2306625Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> Tuple[Tensor, Tensor, Tensor] 2024-06-26T05:54:16.2307286Z 2024-06-26T05:54:16.2307474Z Returns the unique elements of the input tensor. 2024-06-26T05:54:16.2307819Z 2024-06-26T05:54:16.2308231Z .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that 2024-06-26T05:54:16.2309109Z this function also eliminates non-consecutive duplicate values. 2024-06-26T05:54:16.2309552Z 2024-06-26T05:54:16.2309868Z .. note:: Currently in the CUDA implementation and the CPU implementation, 2024-06-26T05:54:16.2310737Z `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument. 2024-06-26T05:54:16.2311705Z Sorting could be slow, so if your input tensor is already sorted, it is recommended to use 2024-06-26T05:54:16.2312485Z :func:`torch.unique_consecutive` which avoids the sorting. 2024-06-26T05:54:16.2312891Z 2024-06-26T05:54:16.2312987Z Args: 2024-06-26T05:54:16.2313281Z input (Tensor): the input tensor 2024-06-26T05:54:16.2313845Z sorted (bool): Whether to sort the unique elements in ascending order 2024-06-26T05:54:16.2314437Z before returning as output. 2024-06-26T05:54:16.2314997Z return_inverse (bool): Whether to also return the indices for where 2024-06-26T05:54:16.2315843Z elements in the original input ended up in the returned unique list. 2024-06-26T05:54:16.2316605Z return_counts (bool): Whether to also return the counts for each unique 2024-06-26T05:54:16.2317166Z element. 2024-06-26T05:54:16.2317629Z dim (int, optional): the dimension to operate upon. If ``None``, the 2024-06-26T05:54:16.2318448Z unique of the flattened input is returned. Otherwise, each of the 2024-06-26T05:54:16.2319165Z tensors indexed by the given dimension is treated as one of the 2024-06-26T05:54:16.2319883Z elements to apply the unique operation upon. See examples for more 2024-06-26T05:54:16.2320447Z details. Default: ``None`` 2024-06-26T05:54:16.2320830Z 2024-06-26T05:54:16.2320930Z Returns: 2024-06-26T05:54:16.2321472Z (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing 2024-06-26T05:54:16.2322007Z 2024-06-26T05:54:16.2322333Z - **output** (*Tensor*): the output list of unique scalar elements. 2024-06-26T05:54:16.2323000Z - **inverse_indices** (*Tensor*): (optional) if 2024-06-26T05:54:16.2323581Z :attr:`return_inverse` is True, there will be an additional 2024-06-26T05:54:16.2324241Z returned tensor (same shape as input) representing the indices 2024-06-26T05:54:16.2324945Z for where elements in the original input map to in the output; 2024-06-26T05:54:16.2325617Z otherwise, this function will only return a single tensor. 2024-06-26T05:54:16.2326225Z - **counts** (*Tensor*): (optional) if 2024-06-26T05:54:16.2326770Z :attr:`return_counts` is True, there will be an additional 2024-06-26T05:54:16.2327412Z returned tensor (same shape as output or output.size(dim), 2024-06-26T05:54:16.2328072Z if dim was specified) representing the number of occurrences 2024-06-26T05:54:16.2328628Z for each unique value or tensor. 2024-06-26T05:54:16.2328944Z 2024-06-26T05:54:16.2329047Z Example:: 2024-06-26T05:54:16.2329207Z 2024-06-26T05:54:16.2329499Z >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long)) 2024-06-26T05:54:16.2330033Z >>> output 2024-06-26T05:54:16.2330321Z tensor([1, 2, 3]) 2024-06-26T05:54:16.2330527Z 2024-06-26T05:54:16.2330731Z >>> output, inverse_indices = torch.unique( 2024-06-26T05:54:16.2331462Z ... torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True) 2024-06-26T05:54:16.2332062Z >>> output 2024-06-26T05:54:16.2332351Z tensor([1, 2, 3]) 2024-06-26T05:54:16.2332656Z >>> inverse_indices 2024-06-26T05:54:16.2332990Z tensor([0, 2, 1, 2]) 2024-06-26T05:54:16.2333225Z 2024-06-26T05:54:16.2333390Z >>> output, inverse_indices = torch.unique( 2024-06-26T05:54:16.2334154Z ... torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True) 2024-06-26T05:54:16.2334745Z >>> output 2024-06-26T05:54:16.2335033Z tensor([1, 2, 3]) 2024-06-26T05:54:16.2335355Z >>> inverse_indices 2024-06-26T05:54:16.2335670Z tensor([[0, 2], 2024-06-26T05:54:16.2335977Z [1, 2]]) 2024-06-26T05:54:16.2336174Z 2024-06-26T05:54:16.2336304Z >>> a = torch.tensor([ 2024-06-26T05:54:16.2336633Z ... [ 2024-06-26T05:54:16.2336920Z ... [1, 1, 0, 0], 2024-06-26T05:54:16.2337267Z ... [1, 1, 0, 0], 2024-06-26T05:54:16.2337597Z ... [0, 0, 1, 1], 2024-06-26T05:54:16.2337936Z ... ], 2024-06-26T05:54:16.2338209Z ... [ 2024-06-26T05:54:16.2338476Z ... [0, 0, 1, 1], 2024-06-26T05:54:16.2338826Z ... [0, 0, 1, 1], 2024-06-26T05:54:16.2339172Z ... [1, 1, 1, 1], 2024-06-26T05:54:16.2339494Z ... ], 2024-06-26T05:54:16.2339768Z ... [ 2024-06-26T05:54:16.2340053Z ... [1, 1, 0, 0], 2024-06-26T05:54:16.2340538Z ... [1, 1, 0, 0], 2024-06-26T05:54:16.2340890Z ... [0, 0, 1, 1], 2024-06-26T05:54:16.2341227Z ... ], 2024-06-26T05:54:16.2341487Z ... ]) 2024-06-26T05:54:16.2341661Z 2024-06-26T05:54:16.2341965Z >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]` 2024-06-26T05:54:16.2342808Z >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match 2024-06-26T05:54:16.2343432Z >>> # each other, so one of them will be removed. 2024-06-26T05:54:16.2343913Z >>> (a[0, :, :] == a[2, :, :]).all() 2024-06-26T05:54:16.2344314Z tensor(True) 2024-06-26T05:54:16.2344654Z >>> a_unique_dim0 = torch.unique(a, dim=0) 2024-06-26T05:54:16.2345091Z >>> a_unique_dim0 2024-06-26T05:54:16.2345416Z tensor([[[0, 0, 1, 1], 2024-06-26T05:54:16.2345743Z [0, 0, 1, 1], 2024-06-26T05:54:16.2346081Z [1, 1, 1, 1]], 2024-06-26T05:54:16.2346429Z [[1, 1, 0, 0], 2024-06-26T05:54:16.2346747Z [1, 1, 0, 0], 2024-06-26T05:54:16.2347082Z [0, 0, 1, 1]]]) 2024-06-26T05:54:16.2347314Z 2024-06-26T05:54:16.2347690Z >>> # Notice which sub-tensors from `a` match with the sub-tensors from 2024-06-26T05:54:16.2348242Z >>> # `a_unique_dim0`: 2024-06-26T05:54:16.2348650Z >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all() 2024-06-26T05:54:16.2349080Z tensor(True) 2024-06-26T05:54:16.2349422Z >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all() 2024-06-26T05:54:16.2349855Z tensor(True) 2024-06-26T05:54:16.2350039Z 2024-06-26T05:54:16.2350338Z >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are 2024-06-26T05:54:16.2351031Z >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of 2024-06-26T05:54:16.2351581Z >>> # them will be removed. 2024-06-26T05:54:16.2351989Z >>> (a[:, 0, :] == a[:, 1, :]).all() 2024-06-26T05:54:16.2352389Z tensor(True) 2024-06-26T05:54:16.2352691Z >>> torch.unique(a, dim=1) 2024-06-26T05:54:16.2353066Z tensor([[[0, 0, 1, 1], 2024-06-26T05:54:16.2353407Z [1, 1, 0, 0]], 2024-06-26T05:54:16.2353742Z [[1, 1, 1, 1], 2024-06-26T05:54:16.2354077Z [0, 0, 1, 1]], 2024-06-26T05:54:16.2354424Z [[0, 0, 1, 1], 2024-06-26T05:54:16.2354748Z [1, 1, 0, 0]]]) 2024-06-26T05:54:16.2354997Z 2024-06-26T05:54:16.2355293Z >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared. 2024-06-26T05:54:16.2356001Z >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and 2024-06-26T05:54:16.2356646Z >>> # `a[:, :, 3]` match each other as well. So in this case, two of the 2024-06-26T05:54:16.2357251Z >>> # sub-tensors will be removed. 2024-06-26T05:54:16.2357688Z >>> (a[:, :, 0] == a[:, :, 1]).all() 2024-06-26T05:54:16.2358072Z tensor(True) 2024-06-26T05:54:16.2358395Z >>> (a[:, :, 2] == a[:, :, 3]).all() 2024-06-26T05:54:16.2358787Z tensor(True) 2024-06-26T05:54:16.2359087Z >>> torch.unique(a, dim=2) 2024-06-26T05:54:16.2359454Z tensor([[[0, 1], 2024-06-26T05:54:16.2359763Z [0, 1], 2024-06-26T05:54:16.2360053Z [1, 0]], 2024-06-26T05:54:16.2360364Z [[1, 0], 2024-06-26T05:54:16.2360741Z [1, 0], 2024-06-26T05:54:16.2361029Z [1, 1]], 2024-06-26T05:54:16.2361337Z [[0, 1], 2024-06-26T05:54:16.2361635Z [0, 1], 2024-06-26T05:54:16.2361919Z [1, 0]]]) 2024-06-26T05:54:16.2362221Z 2024-06-26T05:54:16.2362750Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:16.2363225Z 2024-06-26T05:54:16.2585069Z msg = Cannot scrape callname=opcheck in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=931. 2024-06-26T05:54:16.2586468Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:16.2587310Z Given an operator and some sample arguments, tests if the operator is 2024-06-26T05:54:16.2587888Z registered correctly. 2024-06-26T05:54:16.2588107Z 2024-06-26T05:54:16.2588493Z That is, when you use the torch.library/TORCH_LIBRARY APIs to create a 2024-06-26T05:54:16.2589294Z custom op, you specified metadata (e.g. mutability info) about the custom op 2024-06-26T05:54:16.2590097Z and these APIs require that the functions you pass them satisfy certain 2024-06-26T05:54:16.2590874Z properties (e.g. no data pointer access in the fake/meta/abstract kernel) 2024-06-26T05:54:16.2591523Z ``opcheck`` tests these metadata and properties. 2024-06-26T05:54:16.2591882Z 2024-06-26T05:54:16.2592021Z Concretely, we test the following: 2024-06-26T05:54:16.2592559Z - test_schema: if the operator's schema is correct. 2024-06-26T05:54:16.2593231Z - test_autograd_registration: if autograd was registered correctly. 2024-06-26T05:54:16.2593958Z - test_faketensor: If the operator has a FakeTensor kernel 2024-06-26T05:54:16.2594595Z (and if it is correct). The FakeTensor kernel is necessary ( 2024-06-26T05:54:16.2595279Z but not sufficient) for the operator to work with PyTorch compilation 2024-06-26T05:54:16.2595882Z APIs (torch.compile/export/FX). 2024-06-26T05:54:16.2596476Z - test_aot_dispatch_dynamic: If the operator has correct behavior 2024-06-26T05:54:16.2597116Z with PyTorch compilation APIs (torch.compile/export/FX). 2024-06-26T05:54:16.2597794Z This checks that the outputs (and gradients, if applicable) are the 2024-06-26T05:54:16.2598481Z same under eager-mode PyTorch and torch.compile. 2024-06-26T05:54:16.2599008Z This test is a superset of ``test_faketensor``. 2024-06-26T05:54:16.2599346Z 2024-06-26T05:54:16.2599602Z For best results, please call ``opcheck`` multiple times with a 2024-06-26T05:54:16.2600262Z representative set of inputs. If your operator supports 2024-06-26T05:54:16.2601053Z autograd, please use ``opcheck`` with inputs with ``requires_grad = True``; 2024-06-26T05:54:16.2601819Z if your operator supports multiple devices (e.g. CPU and CUDA), please 2024-06-26T05:54:16.2602486Z use ``opcheck`` with inputs on all supported devices. 2024-06-26T05:54:16.2602849Z 2024-06-26T05:54:16.2602960Z Args: 2024-06-26T05:54:16.2603341Z op: The operator. Must either be a function decorated with 2024-06-26T05:54:16.2604034Z :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket 2024-06-26T05:54:16.2604784Z found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo) 2024-06-26T05:54:16.2605374Z args: The args to the operator 2024-06-26T05:54:16.2605804Z kwargs: The kwargs to the operator 2024-06-26T05:54:16.2606346Z test_utils: Tests that we should run. Default: all of them. 2024-06-26T05:54:16.2606927Z Example: ("test_schema", "test_faketensor") 2024-06-26T05:54:16.2607506Z raise_exception: If we should raise an exception on the first 2024-06-26T05:54:16.2608141Z error. If False, we will return a dict with information 2024-06-26T05:54:16.2608666Z on if each test passed or not. 2024-06-26T05:54:16.2608958Z 2024-06-26T05:54:16.2609076Z .. warning:: 2024-06-26T05:54:16.2609266Z 2024-06-26T05:54:16.2609563Z opcheck and :func:`torch.autograd.gradcheck` test different things; 2024-06-26T05:54:16.2610300Z opcheck tests if your usage of torch.library APIs is correct while 2024-06-26T05:54:16.2611017Z :func:`torch.autograd.gradcheck` tests if your autograd formula is 2024-06-26T05:54:16.2611742Z mathematically correct. Use both to test custom ops that support 2024-06-26T05:54:16.2612308Z gradient computation. 2024-06-26T05:54:16.2612546Z 2024-06-26T05:54:16.2612656Z Example: 2024-06-26T05:54:16.2612811Z 2024-06-26T05:54:16.2612993Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-06-26T05:54:16.2613876Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2024-06-26T05:54:16.2614551Z >>> def numpy_add(x: Tensor, y: float) -> Tensor: 2024-06-26T05:54:16.2615021Z >>> x_np = x.numpy(force=True) 2024-06-26T05:54:16.2615436Z >>> z_np = x_np + y 2024-06-26T05:54:16.2615953Z >>> return torch.from_numpy(z_np).to(x.device) 2024-06-26T05:54:16.2616378Z >>> 2024-06-26T05:54:16.2616656Z >>> @numpy_sin.register_fake 2024-06-26T05:54:16.2617045Z >>> def _(x, y): 2024-06-26T05:54:16.2617380Z >>> return torch.empty_like(x) 2024-06-26T05:54:16.2617775Z >>> 2024-06-26T05:54:16.2618094Z >>> def setup_context(ctx, inputs, output): 2024-06-26T05:54:16.2618514Z >>> y, = inputs 2024-06-26T05:54:16.2618841Z >>> ctx.y = y 2024-06-26T05:54:16.2619152Z >>> 2024-06-26T05:54:16.2619419Z >>> def backward(ctx, grad): 2024-06-26T05:54:16.2619836Z >>> return grad * ctx.y, None 2024-06-26T05:54:16.2620220Z >>> 2024-06-26T05:54:16.2620644Z >>> numpy_sin.register_autograd(backward, setup_context=setup_context) 2024-06-26T05:54:16.2621189Z >>> 2024-06-26T05:54:16.2621462Z >>> sample_inputs = [ 2024-06-26T05:54:16.2621816Z >>> (torch.randn(3), 3.14), 2024-06-26T05:54:16.2622340Z >>> (torch.randn(2, 3, device='cuda'), 2.718), 2024-06-26T05:54:16.2622870Z >>> (torch.randn(1, 10, requires_grad=True), 1.234), 2024-06-26T05:54:16.2623547Z >>> (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18), 2024-06-26T05:54:16.2624080Z >>> ] 2024-06-26T05:54:16.2624339Z >>> 2024-06-26T05:54:16.2624610Z >>> for args in sample_inputs: 2024-06-26T05:54:16.2625055Z >>> torch.library.opcheck(foo, args) 2024-06-26T05:54:16.2625370Z 2024-06-26T05:54:16.2625475Z 2024-06-26T05:54:16.2625981Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:16.2626477Z 2024-06-26T05:54:16.4261367Z msg = Cannot scrape callname=cudart in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/__init__.py line=340. 2024-06-26T05:54:16.4262615Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-06-26T05:54:16.4263242Z Retrieves the CUDA runtime API module. 2024-06-26T05:54:16.4263545Z 2024-06-26T05:54:16.4263550Z 2024-06-26T05:54:16.4263873Z This function initializes the CUDA runtime environment if it is not already 2024-06-26T05:54:16.4264666Z initialized and returns the CUDA runtime API module (_cudart). The CUDA 2024-06-26T05:54:16.4265426Z runtime API module provides access to various CUDA runtime functions. 2024-06-26T05:54:16.4265877Z 2024-06-26T05:54:16.4265971Z Args: 2024-06-26T05:54:16.4266222Z ``None`` 2024-06-26T05:54:16.4266391Z 2024-06-26T05:54:16.4266504Z Returns: 2024-06-26T05:54:16.4266848Z module: The CUDA runtime API module (_cudart). 2024-06-26T05:54:16.4267200Z 2024-06-26T05:54:16.4267294Z Raises: 2024-06-26T05:54:16.4267813Z RuntimeError: If CUDA cannot be re-initialized in a forked subprocess. 2024-06-26T05:54:16.4268748Z AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable. 2024-06-26T05:54:16.4269399Z 2024-06-26T05:54:16.4269568Z Example of CUDA operations with profiling: 2024-06-26T05:54:16.4270004Z >>> import torch 2024-06-26T05:54:16.4270395Z >>> from torch.cuda import cudart, check_error 2024-06-26T05:54:16.4270936Z >>> import os 2024-06-26T05:54:16.4271226Z >>> 2024-06-26T05:54:16.4271570Z >>> os.environ['CUDA_PROFILE'] = '1' 2024-06-26T05:54:16.4271958Z >>> 2024-06-26T05:54:16.4272293Z >>> def perform_cuda_operations_with_streams(): 2024-06-26T05:54:16.4272781Z >>> stream = torch.cuda.Stream() 2024-06-26T05:54:16.4273226Z >>> with torch.cuda.stream(stream): 2024-06-26T05:54:16.4273994Z >>> x = torch.randn(100, 100, device='cuda') 2024-06-26T05:54:16.4274558Z >>> y = torch.randn(100, 100, device='cuda') 2024-06-26T05:54:16.4275003Z >>> z = torch.mul(x, y) 2024-06-26T05:54:16.4275387Z >>> return z 2024-06-26T05:54:16.4275786Z >>> 2024-06-26T05:54:16.4276058Z >>> torch.cuda.synchronize() 2024-06-26T05:54:16.4276511Z >>> print("====== Start nsys profiling ======") 2024-06-26T05:54:16.4277028Z >>> check_error(cudart().cudaProfilerStart()) 2024-06-26T05:54:16.4277538Z >>> with torch.autograd.profiler.emit_nvtx(): 2024-06-26T05:54:16.4278081Z >>> result = perform_cuda_operations_with_streams() 2024-06-26T05:54:16.4278604Z >>> print("CUDA operations completed.") 2024-06-26T05:54:16.4279129Z >>> check_error(torch.cuda.cudart().cudaProfilerStop()) 2024-06-26T05:54:16.4279668Z >>> print("====== End nsys profiling ======") 2024-06-26T05:54:16.4279994Z 2024-06-26T05:54:16.4280275Z To run this example and save the profiling information, execute: 2024-06-26T05:54:16.4281306Z >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2024-06-26T05:54:16.4281946Z 2024-06-26T05:54:16.4282274Z This command profiles the CUDA operations in the provided script and saves 2024-06-26T05:54:16.4283006Z the profiling information to a file named `trace_name.prof`. 2024-06-26T05:54:16.4283784Z The `--profile-from-start off` option ensures that profiling starts only 2024-06-26T05:54:16.4284439Z after the `cudaProfilerStart` call in the script. 2024-06-26T05:54:16.4285147Z The `--csv` and `--print-summary` options format the profiling output as a 2024-06-26T05:54:16.4285786Z CSV file and print a summary, respectively. 2024-06-26T05:54:16.4286491Z The `-o` option specifies the output file name, and the `-f` option forces the 2024-06-26T05:54:16.4287179Z overwrite of the output file if it already exists. 2024-06-26T05:54:16.4287625Z 2024-06-26T05:54:16.4288720Z Original Error: SyntaxError('invalid syntax', ('', 1, 1, '$ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py\n', 1, 2)) 2024-06-26T05:54:16.4289716Z 2024-06-26T05:54:16.4290246Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2024-06-26T05:54:16.4290954Z ^ 2024-06-26T05:54:16.4365961Z msg = Cannot scrape callname=Future.then in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py line=101. 2024-06-26T05:54:16.4367222Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:16.4367766Z 2024-06-26T05:54:16.4368068Z Append the given callback function to this ``Future``, which will be run 2024-06-26T05:54:16.4368814Z when the ``Future`` is completed. Multiple callbacks can be added to 2024-06-26T05:54:16.4369556Z the same ``Future``, but the order in which they will be executed cannot 2024-06-26T05:54:16.4370239Z be guaranteed (to enforce a certain order consider chaining: 2024-06-26T05:54:16.4370922Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which 2024-06-26T05:54:16.4371759Z is the reference to this ``Future``. The callback function can use the 2024-06-26T05:54:16.4372472Z :meth:`value` method to get the value. Note that if this ``Future`` is 2024-06-26T05:54:16.4373203Z already completed, the given callback will be run immediately inline. 2024-06-26T05:54:16.4373786Z 2024-06-26T05:54:16.4374127Z If the ``Future``'s value contains tensors that reside on GPUs, the 2024-06-26T05:54:16.4374925Z callback might be invoked while the async kernels that are populating 2024-06-26T05:54:16.4375948Z those tensors haven't yet finished executing on the device. However, the 2024-06-26T05:54:16.4377111Z callback will be invoked with some dedicated streams set as current 2024-06-26T05:54:16.4378028Z (fetched from a global pool) which will be synchronized with those 2024-06-26T05:54:16.4378749Z kernels. Hence any operation performed by the callback on these tensors 2024-06-26T05:54:16.4379494Z will be scheduled on the device after the kernels complete. In other 2024-06-26T05:54:16.4380400Z words, as long as the callback doesn't switch streams, it can safely 2024-06-26T05:54:16.4381123Z manipulate the result without any additional synchronization. This is 2024-06-26T05:54:16.4381829Z similar to the non-blocking behavior of :meth:`wait`. 2024-06-26T05:54:16.4382183Z 2024-06-26T05:54:16.4382482Z Similarly, if the callback returns a value that contains tensors that 2024-06-26T05:54:16.4383193Z reside on a GPU, it can do so even if the kernels that are producing 2024-06-26T05:54:16.4383925Z these tensors are still running on the device, as long as the callback 2024-06-26T05:54:16.4384695Z didn't change streams during its execution. If one wants to change 2024-06-26T05:54:16.4385471Z streams, one must be careful to re-synchronize them with the original 2024-06-26T05:54:16.4386200Z streams, that is, those that were current when the callback was invoked. 2024-06-26T05:54:16.4386663Z 2024-06-26T05:54:16.4386752Z Args: 2024-06-26T05:54:16.4387161Z callback(``Callable``): a ``Callable`` that takes this ``Future`` as 2024-06-26T05:54:16.4387729Z the only argument. 2024-06-26T05:54:16.4388036Z 2024-06-26T05:54:16.4388133Z Returns: 2024-06-26T05:54:16.4388510Z A new ``Future`` object that holds the return value of the 2024-06-26T05:54:16.4389114Z ``callback`` and will be marked as completed when the given 2024-06-26T05:54:16.4389619Z ``callback`` finishes. 2024-06-26T05:54:16.4389835Z 2024-06-26T05:54:16.4390088Z .. note:: Note that if the callback function throws, either 2024-06-26T05:54:16.4390734Z through the original future being completed with an exception and 2024-06-26T05:54:16.4391435Z calling ``fut.wait()``, or through other code in the callback, the 2024-06-26T05:54:16.4392131Z future returned by ``then`` will be marked appropriately with the 2024-06-26T05:54:16.4392808Z encountered error. However, if this callback later completes 2024-06-26T05:54:16.4393481Z additional futures, those futures are not marked as completed with 2024-06-26T05:54:16.4394216Z an error and the user is responsible for handling completion/waiting 2024-06-26T05:54:16.4394804Z on those futures independently. 2024-06-26T05:54:16.4395073Z 2024-06-26T05:54:16.4395173Z Example:: 2024-06-26T05:54:16.4395519Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2024-06-26T05:54:16.4395981Z >>> def callback(fut): 2024-06-26T05:54:16.4396366Z ... print(f"RPC return value is {fut.wait()}.") 2024-06-26T05:54:16.4396845Z >>> fut = torch.futures.Future() 2024-06-26T05:54:16.4397349Z >>> # The inserted callback will print the return value when 2024-06-26T05:54:16.4397880Z >>> # receiving the response from "worker1" 2024-06-26T05:54:16.4398325Z >>> cb_fut = fut.then(callback) 2024-06-26T05:54:16.4398713Z >>> chain_cb_fut = cb_fut.then( 2024-06-26T05:54:16.4399166Z ... lambda x : print(f"Chained cb done. {x.wait()}") 2024-06-26T05:54:16.4399625Z ... ) 2024-06-26T05:54:16.4399889Z >>> fut.set_result(5) 2024-06-26T05:54:16.4400207Z RPC return value is 5. 2024-06-26T05:54:16.4400554Z Chained cb done. None 2024-06-26T05:54:16.4400851Z 2024-06-26T05:54:16.4401263Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:16.4401742Z 2024-06-26T05:54:16.4402598Z msg = Cannot scrape callname=Future.set_result in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py line=209. 2024-06-26T05:54:16.4403843Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:16.4404344Z 2024-06-26T05:54:16.4404628Z Set the result for this ``Future``, which will mark this ``Future`` as 2024-06-26T05:54:16.4405461Z completed and trigger all attached callbacks. Note that a ``Future`` 2024-06-26T05:54:16.4406042Z cannot be marked completed twice. 2024-06-26T05:54:16.4406302Z 2024-06-26T05:54:16.4406589Z If the result contains tensors that reside on GPUs, this method can be 2024-06-26T05:54:16.4407316Z called even if the asynchronous kernels that are populating those 2024-06-26T05:54:16.4408168Z tensors haven't yet completed running on the device, provided that the 2024-06-26T05:54:16.4408906Z streams on which those kernels were enqueued are set as the current ones 2024-06-26T05:54:16.4409715Z when this method is called. Put simply, it's safe to call this method 2024-06-26T05:54:16.4410432Z immediately after launching those kernels, without any additional 2024-06-26T05:54:16.4411204Z synchronization, as long as one doesn't change streams in between. This 2024-06-26T05:54:16.4411965Z method will record events on all the relevant current streams and will 2024-06-26T05:54:16.4412687Z use them to ensure proper scheduling for all the consumers of this 2024-06-26T05:54:16.4413209Z ``Future``. 2024-06-26T05:54:16.4413373Z 2024-06-26T05:54:16.4413462Z Args: 2024-06-26T05:54:16.4413926Z result (object): the result object of this ``Future``. 2024-06-26T05:54:16.4414288Z 2024-06-26T05:54:16.4414403Z Example:: 2024-06-26T05:54:16.4414738Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2024-06-26T05:54:16.4415205Z >>> import threading 2024-06-26T05:54:16.4415523Z >>> import time 2024-06-26T05:54:16.4415830Z >>> def slow_set_future(fut, value): 2024-06-26T05:54:16.4416227Z ... time.sleep(0.5) 2024-06-26T05:54:16.4416576Z ... fut.set_result(value) 2024-06-26T05:54:16.4416949Z >>> fut = torch.futures.Future() 2024-06-26T05:54:16.4417351Z >>> t = threading.Thread( 2024-06-26T05:54:16.4417715Z ... target=slow_set_future, 2024-06-26T05:54:16.4418097Z ... args=(fut, torch.ones(2) * 3) 2024-06-26T05:54:16.4418478Z ... ) 2024-06-26T05:54:16.4418710Z >>> t.start() 2024-06-26T05:54:16.4419003Z >>> print(fut.wait()) 2024-06-26T05:54:16.4419323Z tensor([3., 3.]) 2024-06-26T05:54:16.4419602Z >>> t.join() 2024-06-26T05:54:16.4419788Z 2024-06-26T05:54:16.4420176Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:16.4420666Z 2024-06-26T05:54:16.4600226Z msg = Cannot scrape callname=sum in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py line=191. 2024-06-26T05:54:16.4601526Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:16.4602266Z Return the sum of each row of the given sparse tensor. 2024-06-26T05:54:16.4602633Z 2024-06-26T05:54:16.4602952Z Returns the sum of each row of the sparse tensor :attr:`input` in the given 2024-06-26T05:54:16.4603695Z dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions, 2024-06-26T05:54:16.4604406Z reduce over all of them. When sum over all ``sparse_dim``, this method 2024-06-26T05:54:16.4605050Z returns a dense tensor instead of a sparse tensor. 2024-06-26T05:54:16.4605413Z 2024-06-26T05:54:16.4605764Z All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output 2024-06-26T05:54:16.4606542Z tensor having :attr:`dim` fewer dimensions than :attr:`input`. 2024-06-26T05:54:16.4606947Z 2024-06-26T05:54:16.4607250Z During backward, only gradients at ``nnz`` locations of :attr:`input` 2024-06-26T05:54:16.4608011Z will propagate back. Note that the gradients of :attr:`input` is coalesced. 2024-06-26T05:54:16.4608504Z 2024-06-26T05:54:16.4608598Z Args: 2024-06-26T05:54:16.4608914Z input (Tensor): the input sparse tensor 2024-06-26T05:54:16.4609612Z dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce 2024-06-26T05:54:16.4610276Z over all dims. 2024-06-26T05:54:16.4610838Z dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. 2024-06-26T05:54:16.4611656Z Default: dtype of :attr:`input`. 2024-06-26T05:54:16.4611972Z 2024-06-26T05:54:16.4612080Z Example:: 2024-06-26T05:54:16.4612239Z 2024-06-26T05:54:16.4612349Z >>> nnz = 3 2024-06-26T05:54:16.4612633Z >>> dims = [5, 5, 2, 3] 2024-06-26T05:54:16.4613091Z >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), 2024-06-26T05:54:16.4613958Z torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) 2024-06-26T05:54:16.4614548Z >>> V = torch.randn(nnz, dims[2], dims[3]) 2024-06-26T05:54:16.4614982Z >>> size = torch.Size(dims) 2024-06-26T05:54:16.4615499Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-06-26T05:54:16.4616016Z >>> S = torch.sparse_coo_tensor(I, V, size) 2024-06-26T05:54:16.4616427Z >>> S 2024-06-26T05:54:16.4616724Z tensor(indices=tensor([[2, 0, 3], 2024-06-26T05:54:16.4617136Z [2, 4, 1]]), 2024-06-26T05:54:16.4617641Z values=tensor([[[-0.6438, -1.6467, 1.4004], 2024-06-26T05:54:16.4618186Z [ 0.3411, 0.0918, -0.2312]], 2024-06-26T05:54:16.4618495Z 2024-06-26T05:54:16.4618712Z [[ 0.5348, 0.0634, -2.0494], 2024-06-26T05:54:16.4619221Z [-0.7125, -1.0646, 2.1844]], 2024-06-26T05:54:16.4619542Z 2024-06-26T05:54:16.4619750Z [[ 0.1276, 0.1874, -0.6334], 2024-06-26T05:54:16.4620272Z [-1.9682, -0.5340, 0.7483]]]), 2024-06-26T05:54:16.4620770Z size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo) 2024-06-26T05:54:16.4621133Z 2024-06-26T05:54:16.4621394Z # when sum over only part of sparse_dims, return a sparse tensor 2024-06-26T05:54:16.4621961Z >>> torch.sparse.sum(S, [1, 3]) 2024-06-26T05:54:16.4622394Z tensor(indices=tensor([[0, 2, 3]]), 2024-06-26T05:54:16.4622873Z values=tensor([[-1.4512, 0.4073], 2024-06-26T05:54:16.4623362Z [-0.8901, 0.2017], 2024-06-26T05:54:16.4623856Z [-0.3183, -1.7539]]), 2024-06-26T05:54:16.4624322Z size=(5, 2), nnz=3, layout=torch.sparse_coo) 2024-06-26T05:54:16.4624669Z 2024-06-26T05:54:16.4624873Z # when sum over all sparse dim, return a dense tensor 2024-06-26T05:54:16.4625376Z # with summed dims squeezed 2024-06-26T05:54:16.4625772Z >>> torch.sparse.sum(S, [0, 1, 3]) 2024-06-26T05:54:16.4626224Z tensor([-2.6596, -1.1450]) 2024-06-26T05:54:16.4626569Z 2024-06-26T05:54:16.4627072Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:16.4627557Z 2024-06-26T05:54:16.9048563Z msg = Cannot scrape callname=vmap in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/apis.py line=38. 2024-06-26T05:54:16.9050158Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:16.9051016Z 2024-06-26T05:54:16.9051327Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that 2024-06-26T05:54:16.9052055Z maps ``func`` over some dimension of the inputs. Semantically, vmap 2024-06-26T05:54:16.9052773Z pushes the map into PyTorch operations called by ``func``, effectively 2024-06-26T05:54:16.9053360Z vectorizing those operations. 2024-06-26T05:54:16.9053778Z 2024-06-26T05:54:16.9054096Z vmap is useful for handling batch dimensions: one can write a function 2024-06-26T05:54:16.9054816Z ``func`` that runs on examples and then lift it to a function that can 2024-06-26T05:54:16.9055540Z take batches of examples with ``vmap(func)``. vmap can also be used to 2024-06-26T05:54:16.9056200Z compute batched gradients when composed with autograd. 2024-06-26T05:54:16.9056566Z 2024-06-26T05:54:16.9056680Z .. note:: 2024-06-26T05:54:16.9057074Z :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for 2024-06-26T05:54:16.9057702Z convenience. Use whichever one you'd like. 2024-06-26T05:54:16.9058252Z 2024-06-26T05:54:16.9058357Z Args: 2024-06-26T05:54:16.9058765Z func (function): A Python function that takes one or more arguments. 2024-06-26T05:54:16.9059349Z Must return one or more Tensors. 2024-06-26T05:54:16.9059920Z in_dims (int or nested structure): Specifies which dimension of the 2024-06-26T05:54:16.9060658Z inputs should be mapped over. ``in_dims`` should have a 2024-06-26T05:54:16.9061304Z structure like the inputs. If the ``in_dim`` for a particular 2024-06-26T05:54:16.9061975Z input is None, then that indicates there is no map dimension. 2024-06-26T05:54:16.9062485Z Default: 0. 2024-06-26T05:54:16.9062942Z out_dims (int or Tuple[int]): Specifies where the mapped dimension 2024-06-26T05:54:16.9063634Z should appear in the outputs. If ``out_dims`` is a Tuple, then 2024-06-26T05:54:16.9064241Z it should have one element per output. Default: 0. 2024-06-26T05:54:16.9064833Z randomness (str): Specifies whether the randomness in this 2024-06-26T05:54:16.9065591Z vmap should be the same or different across batches. If 'different', 2024-06-26T05:54:16.9066361Z the randomness for each batch will be different. If 'same', the 2024-06-26T05:54:16.9067140Z randomness will be the same across batches. If 'error', any calls to 2024-06-26T05:54:16.9067932Z random functions will error. Default: 'error'. WARNING: this flag 2024-06-26T05:54:16.9068639Z only applies to random PyTorch operations and does not apply to 2024-06-26T05:54:16.9069279Z Python's random module or numpy randomness. 2024-06-26T05:54:16.9069935Z chunk_size (None or int): If None (default), apply a single vmap over inputs. 2024-06-26T05:54:16.9070723Z If not None, then compute the vmap :attr:`chunk_size` samples at a time. 2024-06-26T05:54:16.9071610Z Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop. 2024-06-26T05:54:16.9072571Z If you run into memory issues computing the vmap, please try a non-None chunk_size. 2024-06-26T05:54:16.9073106Z 2024-06-26T05:54:16.9073217Z Returns: 2024-06-26T05:54:16.9073604Z Returns a new "batched" function. It takes the same inputs as 2024-06-26T05:54:16.9074261Z ``func``, except each input has an extra dimension at the index 2024-06-26T05:54:16.9074923Z specified by ``in_dims``. It takes returns the same outputs as 2024-06-26T05:54:16.9075565Z ``func``, except each output has an extra dimension at the index 2024-06-26T05:54:16.9076099Z specified by ``out_dims``. 2024-06-26T05:54:16.9076350Z 2024-06-26T05:54:16.9076448Z .. warning: 2024-06-26T05:54:16.9076935Z :func:`vmap` works best with functional-style code. Please do not 2024-06-26T05:54:16.9077649Z perform any side-effects in ``func``, with the exception of 2024-06-26T05:54:16.9078417Z in-place PyTorch operations. Examples of side-effects include mutating 2024-06-26T05:54:16.9079181Z Python data structures and assigning values to variables not captured 2024-06-26T05:54:16.9079736Z in ``func``. 2024-06-26T05:54:16.9079921Z 2024-06-26T05:54:16.9080245Z One example of using :func:`vmap` is to compute batched dot products. PyTorch 2024-06-26T05:54:16.9081159Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully 2024-06-26T05:54:16.9081908Z rummaging through docs, use :func:`vmap` to construct a new function. 2024-06-26T05:54:16.9082349Z 2024-06-26T05:54:16.9082597Z >>> torch.dot # [D], [D] -> [] 2024-06-26T05:54:16.9083277Z >>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N] 2024-06-26T05:54:16.9083875Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2024-06-26T05:54:16.9084318Z >>> batched_dot(x, y) 2024-06-26T05:54:16.9084528Z 2024-06-26T05:54:16.9084843Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler 2024-06-26T05:54:16.9085445Z model authoring experience. 2024-06-26T05:54:16.9085674Z 2024-06-26T05:54:16.9085819Z >>> batch_size, feature_size = 3, 5 2024-06-26T05:54:16.9086408Z >>> weights = torch.randn(feature_size, requires_grad=True) 2024-06-26T05:54:16.9086885Z >>> 2024-06-26T05:54:16.9087150Z >>> def model(feature_vec): 2024-06-26T05:54:16.9087559Z >>> # Very simple linear model with activation 2024-06-26T05:54:16.9088056Z >>> return feature_vec.dot(weights).relu() 2024-06-26T05:54:16.9088542Z >>> 2024-06-26T05:54:16.9088870Z >>> examples = torch.randn(batch_size, feature_size) 2024-06-26T05:54:16.9089375Z >>> result = torch.vmap(model)(examples) 2024-06-26T05:54:16.9089678Z 2024-06-26T05:54:16.9090032Z :func:`vmap` can also help vectorize computations that were previously difficult 2024-06-26T05:54:16.9090905Z or impossible to batch. One example is higher-order gradient computation. 2024-06-26T05:54:16.9091740Z The PyTorch autograd engine computes vjps (vector-Jacobian products). 2024-06-26T05:54:16.9092554Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually 2024-06-26T05:54:16.9093361Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`, 2024-06-26T05:54:16.9094285Z we can vectorize the whole computation, computing the Jacobian in a single 2024-06-26T05:54:16.9094885Z call to ``autograd.grad``. 2024-06-26T05:54:16.9095106Z 2024-06-26T05:54:16.9095219Z >>> # Setup 2024-06-26T05:54:16.9095481Z >>> N = 5 2024-06-26T05:54:16.9095765Z >>> f = lambda x: x ** 2 2024-06-26T05:54:16.9096152Z >>> x = torch.randn(N, requires_grad=True) 2024-06-26T05:54:16.9096551Z >>> y = f(x) 2024-06-26T05:54:16.9096834Z >>> I_N = torch.eye(N) 2024-06-26T05:54:16.9097145Z >>> 2024-06-26T05:54:16.9097393Z >>> # Sequential approach 2024-06-26T05:54:16.9097910Z >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0] 2024-06-26T05:54:16.9098495Z >>> for v in I_N.unbind()] 2024-06-26T05:54:16.9098935Z >>> jacobian = torch.stack(jacobian_rows) 2024-06-26T05:54:16.9099340Z >>> 2024-06-26T05:54:16.9099626Z >>> # vectorized gradient computation 2024-06-26T05:54:16.9100014Z >>> def get_vjp(v): 2024-06-26T05:54:16.9100377Z >>> return torch.autograd.grad(y, x, v) 2024-06-26T05:54:16.9100836Z >>> jacobian = torch.vmap(get_vjp)(I_N) 2024-06-26T05:54:16.9101132Z 2024-06-26T05:54:16.9101494Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions 2024-06-26T05:54:16.9102029Z 2024-06-26T05:54:16.9102274Z >>> torch.dot # [D], [D] -> [] 2024-06-26T05:54:16.9103082Z >>> batched_dot = torch.vmap(torch.vmap(torch.dot)) # [N1, N0, D], [N1, N0, D] -> [N1, N0] 2024-06-26T05:54:16.9103791Z >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5) 2024-06-26T05:54:16.9104297Z >>> batched_dot(x, y) # tensor of size [2, 3] 2024-06-26T05:54:16.9104625Z 2024-06-26T05:54:16.9104950Z If the inputs are not batched along the first dimension, ``in_dims`` specifies 2024-06-26T05:54:16.9105631Z the dimension that each inputs are batched along as 2024-06-26T05:54:16.9105985Z 2024-06-26T05:54:16.9106227Z >>> torch.dot # [N], [N] -> [] 2024-06-26T05:54:16.9106935Z >>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D] 2024-06-26T05:54:16.9107554Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2024-06-26T05:54:16.9108214Z >>> batched_dot(x, y) # output is [5] instead of [2] if batched along the 0th dimension 2024-06-26T05:54:16.9108718Z 2024-06-26T05:54:16.9109064Z If there are multiple inputs each of which is batched along different dimensions, 2024-06-26T05:54:16.9109853Z ``in_dims`` must be a tuple with the batch dimension for each input as 2024-06-26T05:54:16.9110282Z 2024-06-26T05:54:16.9110535Z >>> torch.dot # [D], [D] -> [] 2024-06-26T05:54:16.9111257Z >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N] 2024-06-26T05:54:16.9111899Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2024-06-26T05:54:16.9112627Z >>> batched_dot(x, y) # second arg doesn't have a batch dim because in_dim[1] was None 2024-06-26T05:54:16.9113255Z 2024-06-26T05:54:16.9113590Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct 2024-06-26T05:54:16.9114222Z matching the shape of the input: 2024-06-26T05:54:16.9114476Z 2024-06-26T05:54:16.9114800Z >>> f = lambda dict: torch.dot(dict['x'], dict['y']) 2024-06-26T05:54:16.9115286Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2024-06-26T05:54:16.9115753Z >>> input = {'x': x, 'y': y} 2024-06-26T05:54:16.9116293Z >>> batched_dot = torch.vmap(f, in_dims=({'x': 0, 'y': None},)) 2024-06-26T05:54:16.9116793Z >>> batched_dot(input) 2024-06-26T05:54:16.9117021Z 2024-06-26T05:54:16.9117397Z By default, the output is batched along the first dimension. However, it can be batched 2024-06-26T05:54:16.9118085Z along any dimension by using ``out_dims`` 2024-06-26T05:54:16.9118379Z 2024-06-26T05:54:16.9118505Z >>> f = lambda x: x ** 2 2024-06-26T05:54:16.9118841Z >>> x = torch.randn(2, 5) 2024-06-26T05:54:16.9119232Z >>> batched_pow = torch.vmap(f, out_dims=1) 2024-06-26T05:54:16.9119663Z >>> batched_pow(x) # [5, 2] 2024-06-26T05:54:16.9119902Z 2024-06-26T05:54:16.9120295Z For any function that uses kwargs, the returned function will not batch the kwargs but will 2024-06-26T05:54:16.9121053Z accept kwargs 2024-06-26T05:54:16.9121222Z 2024-06-26T05:54:16.9121355Z >>> x = torch.randn([2, 5]) 2024-06-26T05:54:16.9121704Z >>> def fn(x, scale=4.): 2024-06-26T05:54:16.9122048Z >>> return x * scale 2024-06-26T05:54:16.9122366Z >>> 2024-06-26T05:54:16.9122623Z >>> batched_pow = torch.vmap(fn) 2024-06-26T05:54:16.9123083Z >>> assert torch.allclose(batched_pow(x), x * 4) 2024-06-26T05:54:16.9123730Z >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5] 2024-06-26T05:54:16.9124197Z 2024-06-26T05:54:16.9124298Z .. note:: 2024-06-26T05:54:16.9124804Z vmap does not provide general autobatching or handle variable-length 2024-06-26T05:54:16.9125385Z sequences out of the box. 2024-06-26T05:54:16.9125621Z 2024-06-26T05:54:16.9126015Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:16.9126493Z 2024-06-26T05:54:17.9991564Z msg = Cannot scrape callname=assert_almost_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=329. 2024-06-26T05:54:17.9993918Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:17.9994440Z 2024-06-26T05:54:17.9994775Z Raises an AssertionError if two items are not equal up to desired 2024-06-26T05:54:17.9995312Z precision. 2024-06-26T05:54:17.9995463Z 2024-06-26T05:54:17.9995726Z .. note:: It is recommended to use one of `assert_allclose`, 2024-06-26T05:54:17.9996332Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-06-26T05:54:17.9996979Z instead of this function for more consistent floating point 2024-06-26T05:54:17.9997654Z comparisons. 2024-06-26T05:54:17.9997977Z 2024-06-26T05:54:17.9998374Z The test verifies that the elements of `actual` and `desired` satisfy. 2024-06-26T05:54:17.9999060Z 2024-06-26T05:54:17.9999548Z ``abs(desired-actual) < float64(1.5 * 10**(-decimal))`` 2024-06-26T05:54:18.0000131Z 2024-06-26T05:54:18.0000468Z That is a looser test than originally documented, but agrees with what the 2024-06-26T05:54:18.0001300Z actual implementation in `assert_array_almost_equal` did up to rounding 2024-06-26T05:54:18.0002068Z vagaries. An exception is raised at conflicting values. For ndarrays this 2024-06-26T05:54:18.0002793Z delegates to assert_array_almost_equal 2024-06-26T05:54:18.0003236Z 2024-06-26T05:54:18.0003350Z Parameters 2024-06-26T05:54:18.0003742Z ---------- 2024-06-26T05:54:18.0004187Z actual : array_like 2024-06-26T05:54:18.0004739Z The object to check. 2024-06-26T05:54:18.0005073Z desired : array_like 2024-06-26T05:54:18.0005384Z The expected object. 2024-06-26T05:54:18.0005957Z decimal : int, optional 2024-06-26T05:54:18.0006289Z Desired precision, default is 7. 2024-06-26T05:54:18.0006682Z err_msg : str, optional 2024-06-26T05:54:18.0007087Z The error message to be printed in case of failure. 2024-06-26T05:54:18.0007548Z verbose : bool, optional 2024-06-26T05:54:18.0008145Z If True, the conflicting values are appended to the error message. 2024-06-26T05:54:18.0008578Z 2024-06-26T05:54:18.0008682Z Raises 2024-06-26T05:54:18.0008933Z ------ 2024-06-26T05:54:18.0009179Z AssertionError 2024-06-26T05:54:18.0009611Z If actual and desired are not equal up to specified precision. 2024-06-26T05:54:18.0010024Z 2024-06-26T05:54:18.0010121Z See Also 2024-06-26T05:54:18.0010381Z -------- 2024-06-26T05:54:18.0010823Z assert_allclose: Compare two array_like objects for equality with desired 2024-06-26T05:54:18.0011445Z relative and/or absolute precision. 2024-06-26T05:54:18.0012028Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-06-26T05:54:18.0012448Z 2024-06-26T05:54:18.0012557Z Examples 2024-06-26T05:54:18.0012805Z -------- 2024-06-26T05:54:18.0013159Z >>> from torch._numpy.testing import assert_almost_equal 2024-06-26T05:54:18.0013870Z >>> assert_almost_equal(2.3333333333333, 2.33333334) 2024-06-26T05:54:18.0014399Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) 2024-06-26T05:54:18.0014914Z Traceback (most recent call last): 2024-06-26T05:54:18.0015280Z ... 2024-06-26T05:54:18.0015519Z AssertionError: 2024-06-26T05:54:18.0015851Z Arrays are not almost equal to 10 decimals 2024-06-26T05:54:18.0016268Z ACTUAL: 2.3333333333333 2024-06-26T05:54:18.0016572Z DESIRED: 2.33333334 2024-06-26T05:54:18.0016773Z 2024-06-26T05:54:18.0016959Z >>> assert_almost_equal(np.array([1.0,2.3333333333333]), 2024-06-26T05:54:18.0017475Z ... np.array([1.0,2.33333334]), decimal=9) 2024-06-26T05:54:18.0017928Z Traceback (most recent call last): 2024-06-26T05:54:18.0018304Z ... 2024-06-26T05:54:18.0018556Z AssertionError: 2024-06-26T05:54:18.0018878Z Arrays are not almost equal to 9 decimals 2024-06-26T05:54:18.0019282Z 2024-06-26T05:54:18.0019566Z Mismatched elements: 1 / 2 (50%) 2024-06-26T05:54:18.0020027Z Max absolute difference: 6.666699636781459e-09 2024-06-26T05:54:18.0020556Z Max relative difference: 2.8571569790287484e-09 2024-06-26T05:54:18.0021056Z x: torch.ndarray([1.0000, 2.3333], dtype=float64) 2024-06-26T05:54:18.0021546Z y: torch.ndarray([1.0000, 2.3333], dtype=float64) 2024-06-26T05:54:18.0021888Z 2024-06-26T05:54:18.0021893Z 2024-06-26T05:54:18.0022277Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:18.0022771Z 2024-06-26T05:54:18.0023622Z msg = Cannot scrape callname=assert_approx_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=454. 2024-06-26T05:54:18.0024908Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:18.0025402Z 2024-06-26T05:54:18.0025687Z Raises an AssertionError if two items are not equal up to significant 2024-06-26T05:54:18.0026240Z digits. 2024-06-26T05:54:18.0026377Z 2024-06-26T05:54:18.0026623Z .. note:: It is recommended to use one of `assert_allclose`, 2024-06-26T05:54:18.0027228Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-06-26T05:54:18.0027878Z instead of this function for more consistent floating point 2024-06-26T05:54:18.0028397Z comparisons. 2024-06-26T05:54:18.0028591Z 2024-06-26T05:54:18.0028838Z Given two numbers, check that they are approximately equal. 2024-06-26T05:54:18.0029494Z Approximately equal is defined as the number of significant digits 2024-06-26T05:54:18.0030028Z that agree. 2024-06-26T05:54:18.0030177Z 2024-06-26T05:54:18.0030291Z Parameters 2024-06-26T05:54:18.0030553Z ---------- 2024-06-26T05:54:18.0030810Z actual : scalar 2024-06-26T05:54:18.0031103Z The object to check. 2024-06-26T05:54:18.0031408Z desired : scalar 2024-06-26T05:54:18.0031818Z The expected object. 2024-06-26T05:54:18.0032156Z significant : int, optional 2024-06-26T05:54:18.0032508Z Desired precision, default is 7. 2024-06-26T05:54:18.0032898Z err_msg : str, optional 2024-06-26T05:54:18.0033306Z The error message to be printed in case of failure. 2024-06-26T05:54:18.0033766Z verbose : bool, optional 2024-06-26T05:54:18.0034356Z If True, the conflicting values are appended to the error message. 2024-06-26T05:54:18.0034792Z 2024-06-26T05:54:18.0034900Z Raises 2024-06-26T05:54:18.0035143Z ------ 2024-06-26T05:54:18.0035391Z AssertionError 2024-06-26T05:54:18.0035823Z If actual and desired are not equal up to specified precision. 2024-06-26T05:54:18.0036237Z 2024-06-26T05:54:18.0036332Z See Also 2024-06-26T05:54:18.0036595Z -------- 2024-06-26T05:54:18.0037041Z assert_allclose: Compare two array_like objects for equality with desired 2024-06-26T05:54:18.0037662Z relative and/or absolute precision. 2024-06-26T05:54:18.0049934Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-06-26T05:54:18.0050503Z 2024-06-26T05:54:18.0050610Z Examples 2024-06-26T05:54:18.0050920Z -------- 2024-06-26T05:54:18.0051524Z >>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20) # doctest: +SKIP 2024-06-26T05:54:18.0052454Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20, # doctest: +SKIP 2024-06-26T05:54:18.0053108Z ... significant=8) 2024-06-26T05:54:18.0053943Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20, # doctest: +SKIP 2024-06-26T05:54:18.0054849Z ... significant=8) 2024-06-26T05:54:18.0055527Z Traceback (most recent call last): 2024-06-26T05:54:18.0056160Z ... 2024-06-26T05:54:18.0056387Z AssertionError: 2024-06-26T05:54:18.0056716Z Items are not equal to 8 significant digits: 2024-06-26T05:54:18.0057186Z ACTUAL: 1.234567e-21 2024-06-26T05:54:18.0057505Z DESIRED: 1.2345672e-21 2024-06-26T05:54:18.0057729Z 2024-06-26T05:54:18.0057927Z the evaluated condition that raises the exception is 2024-06-26T05:54:18.0058293Z 2024-06-26T05:54:18.0058585Z >>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1) 2024-06-26T05:54:18.0059059Z True 2024-06-26T05:54:18.0059192Z 2024-06-26T05:54:18.0059196Z 2024-06-26T05:54:18.0059600Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:18.0060080Z 2024-06-26T05:54:18.0061013Z msg = Cannot scrape callname=assert_array_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=733. 2024-06-26T05:54:18.0062293Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:18.0062784Z 2024-06-26T05:54:18.0063049Z Raises an AssertionError if two array_like objects are not equal. 2024-06-26T05:54:18.0063488Z 2024-06-26T05:54:18.0063756Z Given two array_like objects, check that the shape is equal and all 2024-06-26T05:54:18.0064477Z elements of these objects are equal (but see the Notes for the special 2024-06-26T05:54:18.0065209Z handling of a scalar). An exception is raised at shape mismatch or 2024-06-26T05:54:18.0065914Z conflicting values. In contrast to the standard usage in numpy, NaNs 2024-06-26T05:54:18.0066650Z are compared like numbers, no assertion is raised if both objects have 2024-06-26T05:54:18.0067230Z NaNs in the same positions. 2024-06-26T05:54:18.0067459Z 2024-06-26T05:54:18.0067755Z The usual caution for verifying equality with floating point numbers is 2024-06-26T05:54:18.0068310Z advised. 2024-06-26T05:54:18.0068454Z 2024-06-26T05:54:18.0068562Z Parameters 2024-06-26T05:54:18.0068820Z ---------- 2024-06-26T05:54:18.0069068Z x : array_like 2024-06-26T05:54:18.0069356Z The actual object to check. 2024-06-26T05:54:18.0069698Z y : array_like 2024-06-26T05:54:18.0069994Z The desired, expected object. 2024-06-26T05:54:18.0070361Z err_msg : str, optional 2024-06-26T05:54:18.0070754Z The error message to be printed in case of failure. 2024-06-26T05:54:18.0071390Z verbose : bool, optional 2024-06-26T05:54:18.0071885Z If True, the conflicting values are appended to the error message. 2024-06-26T05:54:18.0072420Z strict : bool, optional 2024-06-26T05:54:18.0072904Z If True, raise an AssertionError when either the shape or the data 2024-06-26T05:54:18.0073658Z type of the array_like objects does not match. The special 2024-06-26T05:54:18.0074306Z handling for scalars mentioned in the Notes section is disabled. 2024-06-26T05:54:18.0074746Z 2024-06-26T05:54:18.0074835Z Raises 2024-06-26T05:54:18.0075088Z ------ 2024-06-26T05:54:18.0075321Z AssertionError 2024-06-26T05:54:18.0075661Z If actual and desired objects are not equal. 2024-06-26T05:54:18.0075983Z 2024-06-26T05:54:18.0076088Z See Also 2024-06-26T05:54:18.0076335Z -------- 2024-06-26T05:54:18.0076775Z assert_allclose: Compare two array_like objects for equality with desired 2024-06-26T05:54:18.0077405Z relative and/or absolute precision. 2024-06-26T05:54:18.0077980Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-06-26T05:54:18.0078404Z 2024-06-26T05:54:18.0078491Z Notes 2024-06-26T05:54:18.0078739Z ----- 2024-06-26T05:54:18.0079128Z When one of `x` and `y` is a scalar and the other is array_like, the 2024-06-26T05:54:18.0079846Z function checks that each element of the array_like object is equal to 2024-06-26T05:54:18.0080592Z the scalar. This behaviour can be disabled with the `strict` parameter. 2024-06-26T05:54:18.0081123Z 2024-06-26T05:54:18.0081231Z Examples 2024-06-26T05:54:18.0081481Z -------- 2024-06-26T05:54:18.0081793Z The first assert does not raise an exception: 2024-06-26T05:54:18.0082114Z 2024-06-26T05:54:18.0082319Z >>> np.testing.assert_array_equal([1.0,2.33333,np.nan], 2024-06-26T05:54:18.0082824Z ... [np.exp(0),2.33333, np.nan]) 2024-06-26T05:54:18.0083160Z 2024-06-26T05:54:18.0083458Z Use `assert_allclose` or one of the nulp (number of floating point values) 2024-06-26T05:54:18.0084057Z functions for these cases instead: 2024-06-26T05:54:18.0084316Z 2024-06-26T05:54:18.0084494Z >>> np.testing.assert_allclose([1.0,np.pi,np.nan], 2024-06-26T05:54:18.0085000Z ... [1, np.sqrt(np.pi)**2, np.nan], 2024-06-26T05:54:18.0085517Z ... rtol=1e-10, atol=0) 2024-06-26T05:54:18.0085813Z 2024-06-26T05:54:18.0086092Z As mentioned in the Notes section, `assert_array_equal` has special 2024-06-26T05:54:18.0086804Z handling for scalars. Here the test checks that each value in `x` is 3: 2024-06-26T05:54:18.0087265Z 2024-06-26T05:54:18.0087393Z >>> x = np.full((2, 5), fill_value=3) 2024-06-26T05:54:18.0087802Z >>> np.testing.assert_array_equal(x, 3) 2024-06-26T05:54:18.0088087Z 2024-06-26T05:54:18.0088375Z Use `strict` to raise an AssertionError when comparing a scalar with an 2024-06-26T05:54:18.0088919Z array: 2024-06-26T05:54:18.0089053Z 2024-06-26T05:54:18.0089252Z >>> np.testing.assert_array_equal(x, 3, strict=True) 2024-06-26T05:54:18.0089723Z Traceback (most recent call last): 2024-06-26T05:54:18.0090082Z ... 2024-06-26T05:54:18.0090330Z AssertionError: 2024-06-26T05:54:18.0090601Z Arrays are not equal 2024-06-26T05:54:18.0090894Z 2024-06-26T05:54:18.0091163Z (shapes (2, 5), () mismatch) 2024-06-26T05:54:18.0091501Z x: torch.ndarray([[3, 3, 3, 3, 3], 2024-06-26T05:54:18.0091869Z [3, 3, 3, 3, 3]]) 2024-06-26T05:54:18.0092176Z y: torch.ndarray(3) 2024-06-26T05:54:18.0092366Z 2024-06-26T05:54:18.0092640Z The `strict` parameter also ensures that the array data types match: 2024-06-26T05:54:18.0093086Z 2024-06-26T05:54:18.0093198Z >>> x = np.array([2, 2, 2]) 2024-06-26T05:54:18.0093707Z >>> y = np.array([2., 2., 2.], dtype=np.float32) 2024-06-26T05:54:18.0094205Z >>> np.testing.assert_array_equal(x, y, strict=True) 2024-06-26T05:54:18.0094683Z Traceback (most recent call last): 2024-06-26T05:54:18.0095039Z ... 2024-06-26T05:54:18.0095272Z AssertionError: 2024-06-26T05:54:18.0095559Z Arrays are not equal 2024-06-26T05:54:18.0095971Z 2024-06-26T05:54:18.0096290Z (dtypes dtype("int64"), dtype("float32") mismatch) 2024-06-26T05:54:18.0096751Z x: torch.ndarray([2, 2, 2]) 2024-06-26T05:54:18.0097104Z y: torch.ndarray([2., 2., 2.]) 2024-06-26T05:54:18.0097347Z 2024-06-26T05:54:18.0097841Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:18.0098337Z 2024-06-26T05:54:18.0099214Z msg = Cannot scrape callname=assert_array_almost_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=839. 2024-06-26T05:54:18.0100526Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:18.0101014Z 2024-06-26T05:54:18.0101302Z Raises an AssertionError if two objects are not equal up to desired 2024-06-26T05:54:18.0101830Z precision. 2024-06-26T05:54:18.0101993Z 2024-06-26T05:54:18.0102228Z .. note:: It is recommended to use one of `assert_allclose`, 2024-06-26T05:54:18.0102845Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-06-26T05:54:18.0103472Z instead of this function for more consistent floating point 2024-06-26T05:54:18.0103995Z comparisons. 2024-06-26T05:54:18.0104201Z 2024-06-26T05:54:18.0104509Z The test verifies identical shapes and that the elements of ``actual`` and 2024-06-26T05:54:18.0105092Z ``desired`` satisfy. 2024-06-26T05:54:18.0105280Z 2024-06-26T05:54:18.0105490Z ``abs(desired-actual) < 1.5 * 10**(-decimal)`` 2024-06-26T05:54:18.0105819Z 2024-06-26T05:54:18.0106128Z That is a looser test than originally documented, but agrees with what the 2024-06-26T05:54:18.0106910Z actual implementation did up to rounding vagaries. An exception is raised 2024-06-26T05:54:18.0107682Z at shape mismatch or conflicting values. In contrast to the standard usage 2024-06-26T05:54:18.0108448Z in numpy, NaNs are compared like numbers, no assertion is raised if both 2024-06-26T05:54:18.0109056Z objects have NaNs in the same positions. 2024-06-26T05:54:18.0109353Z 2024-06-26T05:54:18.0109451Z Parameters 2024-06-26T05:54:18.0109725Z ---------- 2024-06-26T05:54:18.0109976Z x : array_like 2024-06-26T05:54:18.0110255Z The actual object to check. 2024-06-26T05:54:18.0110606Z y : array_like 2024-06-26T05:54:18.0110905Z The desired, expected object. 2024-06-26T05:54:18.0111263Z decimal : int, optional 2024-06-26T05:54:18.0111607Z Desired precision, default is 6. 2024-06-26T05:54:18.0111997Z err_msg : str, optional 2024-06-26T05:54:18.0112386Z The error message to be printed in case of failure. 2024-06-26T05:54:18.0112853Z verbose : bool, optional 2024-06-26T05:54:18.0113342Z If True, the conflicting values are appended to the error message. 2024-06-26T05:54:18.0113768Z 2024-06-26T05:54:18.0113857Z Raises 2024-06-26T05:54:18.0114108Z ------ 2024-06-26T05:54:18.0114350Z AssertionError 2024-06-26T05:54:18.0114766Z If actual and desired are not equal up to specified precision. 2024-06-26T05:54:18.0115194Z 2024-06-26T05:54:18.0115295Z See Also 2024-06-26T05:54:18.0115552Z -------- 2024-06-26T05:54:18.0115984Z assert_allclose: Compare two array_like objects for equality with desired 2024-06-26T05:54:18.0116613Z relative and/or absolute precision. 2024-06-26T05:54:18.0117193Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-06-26T05:54:18.0117607Z 2024-06-26T05:54:18.0117716Z Examples 2024-06-26T05:54:18.0117962Z -------- 2024-06-26T05:54:18.0118266Z the first assert does not raise an exception 2024-06-26T05:54:18.0118578Z 2024-06-26T05:54:18.0118807Z >>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan], 2024-06-26T05:54:18.0119331Z ... [1.0,2.333,np.nan]) 2024-06-26T05:54:18.0119655Z 2024-06-26T05:54:18.0119877Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2024-06-26T05:54:18.0120436Z ... [1.0,2.33339,np.nan], decimal=5) 2024-06-26T05:54:18.0121005Z Traceback (most recent call last): 2024-06-26T05:54:18.0121482Z ... 2024-06-26T05:54:18.0121730Z AssertionError: 2024-06-26T05:54:18.0122044Z Arrays are not almost equal to 5 decimals 2024-06-26T05:54:18.0122443Z 2024-06-26T05:54:18.0122726Z Mismatched elements: 1 / 3 (33.3%) 2024-06-26T05:54:18.0123189Z Max absolute difference: 5.999999999994898e-05 2024-06-26T05:54:18.0123776Z Max relative difference: 2.5713661239633743e-05 2024-06-26T05:54:18.0124307Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2024-06-26T05:54:18.0124869Z y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64) 2024-06-26T05:54:18.0125238Z 2024-06-26T05:54:18.0125463Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2024-06-26T05:54:18.0126019Z ... [1.0,2.33333, 5], decimal=5) 2024-06-26T05:54:18.0126468Z Traceback (most recent call last): 2024-06-26T05:54:18.0126828Z ... 2024-06-26T05:54:18.0127074Z AssertionError: 2024-06-26T05:54:18.0127387Z Arrays are not almost equal to 5 decimals 2024-06-26T05:54:18.0127788Z 2024-06-26T05:54:18.0128067Z x and y nan location mismatch: 2024-06-26T05:54:18.0128506Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2024-06-26T05:54:18.0129069Z y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64) 2024-06-26T05:54:18.0129426Z 2024-06-26T05:54:18.0129444Z 2024-06-26T05:54:18.0129825Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:18.0130300Z 2024-06-26T05:54:18.0131192Z msg = Cannot scrape callname=clear_and_catch_warnings in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=1789. 2024-06-26T05:54:18.0132487Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:18.0133247Z Context manager that resets warning registry for catching warnings 2024-06-26T05:54:18.0133793Z 2024-06-26T05:54:18.0134130Z Warnings can be slippery, because, whenever a warning is triggered, Python 2024-06-26T05:54:18.0134912Z adds a ``__warningregistry__`` member to the *calling* module. This makes 2024-06-26T05:54:18.0135669Z it impossible to retrigger the warning in this module, whatever you put in 2024-06-26T05:54:18.0136467Z the warnings filters. This context manager accepts a sequence of `modules` 2024-06-26T05:54:18.0137124Z as a keyword argument to its constructor and: 2024-06-26T05:54:18.0137455Z 2024-06-26T05:54:18.0137755Z * stores and removes any ``__warningregistry__`` entries in given `modules` 2024-06-26T05:54:18.0138317Z on entry; 2024-06-26T05:54:18.0138725Z * resets ``__warningregistry__`` to its previous state on exit. 2024-06-26T05:54:18.0139121Z 2024-06-26T05:54:18.0139421Z This makes it possible to trigger any warning afresh inside the context 2024-06-26T05:54:18.0140111Z manager without disturbing the state of warnings outside. 2024-06-26T05:54:18.0140504Z 2024-06-26T05:54:18.0140806Z For compatibility with Python 3.0, please consider all arguments to be 2024-06-26T05:54:18.0141406Z keyword-only. 2024-06-26T05:54:18.0141581Z 2024-06-26T05:54:18.0141679Z Parameters 2024-06-26T05:54:18.0141969Z ---------- 2024-06-26T05:54:18.0142248Z record : bool, optional 2024-06-26T05:54:18.0142703Z Specifies whether warnings should be captured by a custom 2024-06-26T05:54:18.0143410Z implementation of ``warnings.showwarning()`` and be appended to a list 2024-06-26T05:54:18.0144152Z returned by the context manager. Otherwise None is returned by the 2024-06-26T05:54:18.0144875Z context manager. The objects appended to the list are arguments whose 2024-06-26T05:54:18.0145549Z attributes mirror the arguments to ``showwarning()``. 2024-06-26T05:54:18.0146051Z modules : sequence, optional 2024-06-26T05:54:18.0146587Z Sequence of modules for which to reset warnings registry on entry and 2024-06-26T05:54:18.0147365Z restore on exit. To work correctly, all 'ignore' filters should 2024-06-26T05:54:18.0148038Z filter by one of these modules. 2024-06-26T05:54:18.0148319Z 2024-06-26T05:54:18.0148417Z Examples 2024-06-26T05:54:18.0148700Z -------- 2024-06-26T05:54:18.0148969Z >>> import warnings 2024-06-26T05:54:18.0149414Z >>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP 2024-06-26T05:54:18.0149987Z ... modules=[np.core.fromnumeric]): 2024-06-26T05:54:18.0150570Z ... warnings.simplefilter('always') 2024-06-26T05:54:18.0151208Z ... warnings.filterwarnings('ignore', module='np.core.fromnumeric') 2024-06-26T05:54:18.0151891Z ... # do something that raises a warning but ignore those in 2024-06-26T05:54:18.0152412Z ... # np.core.fromnumeric 2024-06-26T05:54:18.0152757Z 2024-06-26T05:54:18.0153264Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:18.0153751Z 2024-06-26T05:54:18.1495979Z msg = Cannot scrape callname=Conv1d in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py line=273. 2024-06-26T05:54:18.1497387Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:18.1498227Z Applies a 1D convolution over a quantized input signal composed of 2024-06-26T05:54:18.1498852Z several quantized input planes. 2024-06-26T05:54:18.1499196Z 2024-06-26T05:54:18.1499497Z For details on input arguments, parameters, and implementation see 2024-06-26T05:54:18.1500129Z :class:`~torch.nn.Conv1d`. 2024-06-26T05:54:18.1500366Z 2024-06-26T05:54:18.1500481Z .. note:: 2024-06-26T05:54:18.1500973Z Only `zeros` is supported for the :attr:`padding_mode` argument. 2024-06-26T05:54:18.1501436Z 2024-06-26T05:54:18.1501561Z .. note:: 2024-06-26T05:54:18.1501943Z Only `torch.quint8` is supported for the input data type. 2024-06-26T05:54:18.1502407Z 2024-06-26T05:54:18.1502413Z 2024-06-26T05:54:18.1502516Z Attributes: 2024-06-26T05:54:18.1502989Z weight (Tensor): packed tensor derived from the learnable weight 2024-06-26T05:54:18.1503590Z parameter. 2024-06-26T05:54:18.1504101Z scale (Tensor): scalar for the output scale 2024-06-26T05:54:18.1504720Z zero_point (Tensor): scalar for the output zero point 2024-06-26T05:54:18.1505084Z 2024-06-26T05:54:18.1505300Z See :class:`~torch.nn.Conv1d` for other attributes. 2024-06-26T05:54:18.1505712Z 2024-06-26T05:54:18.1505816Z Examples:: 2024-06-26T05:54:18.1505995Z 2024-06-26T05:54:18.1506241Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) 2024-06-26T05:54:18.1506783Z >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2) 2024-06-26T05:54:18.1507315Z >>> input = torch.randn(20, 16, 100) 2024-06-26T05:54:18.1507774Z >>> # quantize input to quint8 2024-06-26T05:54:18.1508197Z >>> # xdoctest: +SKIP 2024-06-26T05:54:18.1508755Z >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, 2024-06-26T05:54:18.1509434Z ... dtype=torch.quint8) 2024-06-26T05:54:18.1509903Z >>> output = m(q_input) 2024-06-26T05:54:18.1510178Z 2024-06-26T05:54:18.1510303Z 2024-06-26T05:54:18.1510822Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:18.1511373Z 2024-06-26T05:54:18.1681921Z msg = Cannot scrape callname=LSTM in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/rnn.py line=9. 2024-06-26T05:54:18.1683190Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:18.1683902Z A quantized long short-term memory (LSTM). 2024-06-26T05:54:18.1684223Z 2024-06-26T05:54:18.1684593Z For the description and the argument types, please, refer to :class:`~torch.nn.LSTM` 2024-06-26T05:54:18.1685136Z 2024-06-26T05:54:18.1685238Z Attributes: 2024-06-26T05:54:18.1685574Z layers : instances of the `_LSTMLayer` 2024-06-26T05:54:18.1685881Z 2024-06-26T05:54:18.1686231Z .. note:: 2024-06-26T05:54:18.1686695Z To access the weights and biases, you need to access them per layer. 2024-06-26T05:54:18.1687374Z See examples in :class:`~torch.ao.nn.quantizable.LSTM` 2024-06-26T05:54:18.1687757Z 2024-06-26T05:54:18.1687858Z Examples:: 2024-06-26T05:54:18.1688145Z >>> # xdoctest: +SKIP 2024-06-26T05:54:18.1688608Z >>> custom_module_config = { 2024-06-26T05:54:18.1689102Z ... 'float_to_observed_custom_module_class': { 2024-06-26T05:54:18.1689605Z ... nn.LSTM: nn.quantizable.LSTM, 2024-06-26T05:54:18.1690025Z ... }, 2024-06-26T05:54:18.1690441Z ... 'observed_to_quantized_custom_module_class': { 2024-06-26T05:54:18.1691102Z ... nn.quantizable.LSTM: nn.quantized.LSTM, 2024-06-26T05:54:18.1691553Z ... } 2024-06-26T05:54:18.1691809Z ... } 2024-06-26T05:54:18.1692256Z >>> tq.prepare(model, prepare_custom_module_class=custom_module_config) 2024-06-26T05:54:18.1692989Z >>> tq.convert(model, convert_custom_module_class=custom_module_config) 2024-06-26T05:54:18.1693755Z 2024-06-26T05:54:18.1694281Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:18.1694768Z 2024-06-26T05:54:18.2501217Z msg = Cannot scrape callname=BaseSparsifier.squash_mask in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py line=230. 2024-06-26T05:54:18.2502834Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:18.2503594Z Squashes the sparse masks into the appropriate tensors. 2024-06-26T05:54:18.2504006Z 2024-06-26T05:54:18.2504352Z If either the `params_to_keep` or `params_to_keep_per_layer` is set, 2024-06-26T05:54:18.2505109Z the module will have a `sparse_params` dict attached to it. 2024-06-26T05:54:18.2505511Z 2024-06-26T05:54:18.2505620Z Args: 2024-06-26T05:54:18.2506081Z params_to_keep: List of keys to save in the module or a dict 2024-06-26T05:54:18.2506774Z representing the modules and keys that will have 2024-06-26T05:54:18.2507355Z sparsity parameters saved 2024-06-26T05:54:18.2507969Z params_to_keep_per_layer: Dict to specify the params that should be 2024-06-26T05:54:18.2508696Z saved for specific layers. The keys in the dict 2024-06-26T05:54:18.2509358Z should be the module fqn, while the values should 2024-06-26T05:54:18.2510024Z be a list of strings with the names of the variables 2024-06-26T05:54:18.2510631Z to save in the `sparse_params` 2024-06-26T05:54:18.2510967Z 2024-06-26T05:54:18.2511067Z Examples: 2024-06-26T05:54:18.2511478Z >>> # xdoctest: +SKIP("locals are undefined") 2024-06-26T05:54:18.2512003Z >>> # Don't save any sparse params 2024-06-26T05:54:18.2512519Z >>> sparsifier.squash_mask() 2024-06-26T05:54:18.2513106Z >>> hasattr(model.submodule1, 'sparse_params') 2024-06-26T05:54:18.2513535Z False 2024-06-26T05:54:18.2513777Z 2024-06-26T05:54:18.2513930Z >>> # Keep sparse params per layer 2024-06-26T05:54:18.2514370Z >>> sparsifier.squash_mask( 2024-06-26T05:54:18.2514856Z ... params_to_keep_per_layer={ 2024-06-26T05:54:18.2515449Z ... 'submodule1.linear1': ('foo', 'bar'), 2024-06-26T05:54:18.2516019Z ... 'submodule2.linear42': ('baz',) 2024-06-26T05:54:18.2516490Z ... }) 2024-06-26T05:54:18.2516948Z >>> print(model.submodule1.linear1.sparse_params) 2024-06-26T05:54:18.2517476Z {'foo': 42, 'bar': 24} 2024-06-26T05:54:18.2517985Z >>> print(model.submodule2.linear42.sparse_params) 2024-06-26T05:54:18.2518557Z {'baz': 0.1} 2024-06-26T05:54:18.2518776Z 2024-06-26T05:54:18.2518937Z >>> # Keep sparse params for all layers 2024-06-26T05:54:18.2519867Z >>> sparsifier.squash_mask(params_to_keep=('foo', 'bar')) 2024-06-26T05:54:18.2520514Z >>> print(model.submodule1.linear1.sparse_params) 2024-06-26T05:54:18.2521169Z {'foo': 42, 'bar': 24} 2024-06-26T05:54:18.2521791Z >>> print(model.submodule2.linear42.sparse_params) 2024-06-26T05:54:18.2522311Z {'foo': 42, 'bar': 24} 2024-06-26T05:54:18.2522634Z 2024-06-26T05:54:18.2522901Z >>> # Keep some sparse params for all layers, and specific ones for 2024-06-26T05:54:18.2523513Z >>> # some other layers 2024-06-26T05:54:18.2523894Z >>> sparsifier.squash_mask( 2024-06-26T05:54:18.2524445Z ... params_to_keep=('foo', 'bar'), 2024-06-26T05:54:18.2524965Z ... params_to_keep_per_layer={ 2024-06-26T05:54:18.2525473Z ... 'submodule2.linear42': ('baz',) 2024-06-26T05:54:18.2525970Z ... }) 2024-06-26T05:54:18.2526376Z >>> print(model.submodule1.linear1.sparse_params) 2024-06-26T05:54:18.2526951Z {'foo': 42, 'bar': 24} 2024-06-26T05:54:18.2527471Z >>> print(model.submodule2.linear42.sparse_params) 2024-06-26T05:54:18.2528059Z {'foo': 42, 'bar': 24, 'baz': 0.1} 2024-06-26T05:54:18.2528464Z 2024-06-26T05:54:18.2529067Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:18.2529560Z 2024-06-26T05:54:18.3298373Z msg = Cannot scrape callname=DTypeConfig in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/backend_config/backend_config.py line=178. 2024-06-26T05:54:18.3299952Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:18.3300497Z 2024-06-26T05:54:18.3300877Z Config object that specifies the supported data types passed as arguments to 2024-06-26T05:54:18.3301746Z quantize ops in the reference model spec, for input and output activations, 2024-06-26T05:54:18.3302419Z weights, and biases. 2024-06-26T05:54:18.3302615Z 2024-06-26T05:54:18.3302885Z For example, consider the following reference model: 2024-06-26T05:54:18.3303238Z 2024-06-26T05:54:18.3303503Z quant1 - [dequant1 - fp32_linear - quant2] - dequant2 2024-06-26T05:54:18.3303915Z 2024-06-26T05:54:18.3304212Z The pattern in the square brackets refers to the reference pattern of 2024-06-26T05:54:18.3305020Z statically quantized linear. Setting the input dtype as `torch.quint8` 2024-06-26T05:54:18.3305850Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument 2024-06-26T05:54:18.3306672Z to the first quantize op (quant1). Similarly, setting the output dtype as 2024-06-26T05:54:18.3307492Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to 2024-06-26T05:54:18.3308139Z the second quantize op (quant2). 2024-06-26T05:54:18.3308450Z 2024-06-26T05:54:18.3308742Z Note that the dtype here does not refer to the interface dtypes of the 2024-06-26T05:54:18.3309548Z op. For example, the "input dtype" here is not the dtype of the input 2024-06-26T05:54:18.3310332Z tensor passed to the quantized linear op. Though it can still be the 2024-06-26T05:54:18.3311106Z same as the interface dtype, this is not always the case, e.g. the 2024-06-26T05:54:18.3311869Z interface dtype is fp32 in dynamic quantization but the "input dtype" 2024-06-26T05:54:18.3312660Z specified in the DTypeConfig would still be quint8. The semantics of 2024-06-26T05:54:18.3313448Z dtypes here are the same as the semantics of the dtypes specified in 2024-06-26T05:54:18.3314040Z the observers. 2024-06-26T05:54:18.3314221Z 2024-06-26T05:54:18.3314521Z These dtypes are matched against the ones specified in the user's 2024-06-26T05:54:18.3315313Z QConfig. If there is a match, and the QConfig satisfies the constraints 2024-06-26T05:54:18.3316112Z specified in the DTypeConfig (if any), then we will quantize the given 2024-06-26T05:54:18.3316917Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and 2024-06-26T05:54:18.3317751Z the pattern will not be quantized. 2024-06-26T05:54:18.3318078Z 2024-06-26T05:54:18.3318200Z Example usage:: 2024-06-26T05:54:18.3318383Z 2024-06-26T05:54:18.3318507Z >>> # xdoctest: +SKIP(failing) 2024-06-26T05:54:18.3318959Z >>> dtype_config1 = DTypeConfig( 2024-06-26T05:54:18.3319444Z ... input_dtype=torch.quint8, 2024-06-26T05:54:18.3319914Z ... output_dtype=torch.quint8, 2024-06-26T05:54:18.3320364Z ... weight_dtype=torch.qint8, 2024-06-26T05:54:18.3320863Z ... bias_dtype=torch.float) 2024-06-26T05:54:18.3321166Z 2024-06-26T05:54:18.3321309Z >>> dtype_config2 = DTypeConfig( 2024-06-26T05:54:18.3321750Z ... input_dtype=DTypeWithConstraints( 2024-06-26T05:54:18.3322256Z ... dtype=torch.quint8, 2024-06-26T05:54:18.3322632Z ... quant_min_lower_bound=0, 2024-06-26T05:54:18.3323112Z ... quant_max_upper_bound=255, 2024-06-26T05:54:18.3323537Z ... ), 2024-06-26T05:54:18.3323882Z ... output_dtype=DTypeWithConstraints( 2024-06-26T05:54:18.3324371Z ... dtype=torch.quint8, 2024-06-26T05:54:18.3324769Z ... quant_min_lower_bound=0, 2024-06-26T05:54:18.3325228Z ... quant_max_upper_bound=255, 2024-06-26T05:54:18.3325618Z ... ), 2024-06-26T05:54:18.3326009Z ... weight_dtype=DTypeWithConstraints( 2024-06-26T05:54:18.3326438Z ... dtype=torch.qint8, 2024-06-26T05:54:18.3326950Z ... quant_min_lower_bound=-128, 2024-06-26T05:54:18.3327381Z ... quant_max_upper_bound=127, 2024-06-26T05:54:18.3327821Z ... ), 2024-06-26T05:54:18.3328112Z ... bias_dtype=torch.float) 2024-06-26T05:54:18.3328433Z 2024-06-26T05:54:18.3328576Z >>> dtype_config1.input_dtype 2024-06-26T05:54:18.3328924Z torch.quint8 2024-06-26T05:54:18.3329169Z 2024-06-26T05:54:18.3329293Z >>> dtype_config2.input_dtype 2024-06-26T05:54:18.3329655Z torch.quint8 2024-06-26T05:54:18.3329840Z 2024-06-26T05:54:18.3330055Z >>> dtype_config2.input_dtype_with_constraints 2024-06-26T05:54:18.3331137Z DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None) 2024-06-26T05:54:18.3332036Z 2024-06-26T05:54:18.3332503Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:18.3332978Z 2024-06-26T05:54:18.4317828Z msg = Cannot scrape callname=ModelReportVisualizer.generate_filtered_tables in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=287. 2024-06-26T05:54:18.4319974Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:18.4320541Z 2024-06-26T05:54:18.4321011Z Takes in optional filter values and generates two tables with desired information. 2024-06-26T05:54:18.4321596Z 2024-06-26T05:54:18.4321946Z The generated tables are presented in both a list-of-lists format 2024-06-26T05:54:18.4322480Z 2024-06-26T05:54:18.4322868Z The reason for the two tables are that they handle different things: 2024-06-26T05:54:18.4323727Z 1.) the first table handles all tensor level information 2024-06-26T05:54:18.4324876Z 2.) the second table handles and displays all channel based information 2024-06-26T05:54:18.4325327Z 2024-06-26T05:54:18.4325807Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2024-06-26T05:54:18.4327294Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2024-06-26T05:54:18.4328447Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2024-06-26T05:54:18.4329152Z 2024-06-26T05:54:18.4329282Z Tensor table columns: 2024-06-26T05:54:18.4329796Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2024-06-26T05:54:18.4330534Z ---- --------- --------- --------- --------- --------- 2024-06-26T05:54:18.4331177Z 2024-06-26T05:54:18.4331341Z Per-Channel table columns: 2024-06-26T05:54:18.4331937Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2024-06-26T05:54:18.4332719Z ---- --------- ------- --------- --------- --------- --------- 2024-06-26T05:54:18.4333110Z 2024-06-26T05:54:18.4333305Z Args: 2024-06-26T05:54:18.4334096Z feature_filter (str, optional): Filters the features presented to only those that 2024-06-26T05:54:18.4334796Z contain this filter substring 2024-06-26T05:54:18.4335359Z Default = "", results in all the features being printed 2024-06-26T05:54:18.4336138Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-06-26T05:54:18.4337040Z Default = "", results in all the modules in the reports to be visible in the table 2024-06-26T05:54:18.4337616Z 2024-06-26T05:54:18.4337748Z Returns a dictionary with two keys: 2024-06-26T05:54:18.4338322Z (Dict[str, Tuple[List, List]]) A dict containing two keys: 2024-06-26T05:54:18.4338861Z "tensor_level_info", "channel_level_info" 2024-06-26T05:54:18.4339352Z Each key maps to a tuple with: 2024-06-26T05:54:18.4339850Z A list of the headers of each table 2024-06-26T05:54:18.4340417Z A list of lists containing the table information row by row 2024-06-26T05:54:18.4341119Z The 0th index row will contain the headers of the columns 2024-06-26T05:54:18.4341746Z The rest of the rows will contain data 2024-06-26T05:54:18.4342081Z 2024-06-26T05:54:18.4342238Z Example Use: 2024-06-26T05:54:18.4342556Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:18.4343130Z >>> mod_report_visualizer.generate_filtered_tables( 2024-06-26T05:54:18.4343651Z ... feature_filter = "per_channel_min", 2024-06-26T05:54:18.4344137Z ... module_fqn_filter = "block1" 2024-06-26T05:54:18.4344845Z ... ) # generates table with per_channel_min info for all modules in block 1 of the model 2024-06-26T05:54:18.4345430Z 2024-06-26T05:54:18.4345840Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:18.4346380Z 2024-06-26T05:54:18.4347818Z msg = Cannot scrape callname=ModelReportVisualizer.generate_table_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=380. 2024-06-26T05:54:18.4349618Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:18.4350184Z 2024-06-26T05:54:18.4350545Z Takes in optional filter values and prints out formatted tables of the information. 2024-06-26T05:54:18.4351127Z 2024-06-26T05:54:18.4351639Z The reason for the two tables printed out instead of one large one are that they handle different things: 2024-06-26T05:54:18.4352582Z 1.) the first table handles all tensor level information 2024-06-26T05:54:18.4353269Z 2.) the second table handles and displays all channel based information 2024-06-26T05:54:18.4353757Z 2024-06-26T05:54:18.4354274Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2024-06-26T05:54:18.4355471Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2024-06-26T05:54:18.4356615Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2024-06-26T05:54:18.4357322Z 2024-06-26T05:54:18.4357434Z Tensor table columns: 2024-06-26T05:54:18.4357892Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2024-06-26T05:54:18.4358549Z ---- --------- --------- --------- --------- --------- 2024-06-26T05:54:18.4358895Z 2024-06-26T05:54:18.4359050Z Per-Channel table columns: 2024-06-26T05:54:18.4359286Z 2024-06-26T05:54:18.4359645Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2024-06-26T05:54:18.4360369Z ---- --------- ------- --------- --------- --------- --------- 2024-06-26T05:54:18.4360932Z 2024-06-26T05:54:18.4361025Z Args: 2024-06-26T05:54:18.4361513Z feature_filter (str, optional): Filters the features presented to only those that 2024-06-26T05:54:18.4362161Z contain this filter substring 2024-06-26T05:54:18.4362742Z Default = "", results in all the features being printed 2024-06-26T05:54:18.4363536Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-06-26T05:54:18.4364387Z Default = "", results in all the modules in the reports to be visible in the table 2024-06-26T05:54:18.4364883Z 2024-06-26T05:54:18.4364999Z Example Use: 2024-06-26T05:54:18.4365317Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:18.4365850Z >>> mod_report_visualizer.generate_table_visualization( 2024-06-26T05:54:18.4366381Z ... feature_filter = "per_channel_min", 2024-06-26T05:54:18.4366816Z ... module_fqn_filter = "block1" 2024-06-26T05:54:18.4367199Z ... ) 2024-06-26T05:54:18.4367594Z >>> # prints out neatly formatted table with per_channel_min info 2024-06-26T05:54:18.4368156Z >>> # for all modules in block 1 of the model 2024-06-26T05:54:18.4368484Z 2024-06-26T05:54:18.4368877Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:18.4369351Z 2024-06-26T05:54:18.4370766Z msg = Cannot scrape callname=ModelReportVisualizer.generate_plot_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=533. 2024-06-26T05:54:18.4372451Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:18.4372942Z 2024-06-26T05:54:18.4373276Z Takes in a feature and optional module_filter and plots of the desired data. 2024-06-26T05:54:18.4373852Z 2024-06-26T05:54:18.4374216Z For per channel features, it averages the value across the channels and plots a point 2024-06-26T05:54:18.4375112Z per module. The reason for this is that for models with hundreds of channels, it can 2024-06-26T05:54:18.4376012Z be hard to differentiate one channel line from another, and so the point of generating 2024-06-26T05:54:18.4376906Z a single average point per module is to give a sense of general trends that encourage 2024-06-26T05:54:18.4377550Z further deep dives. 2024-06-26T05:54:18.4377741Z 2024-06-26T05:54:18.4377844Z Note: 2024-06-26T05:54:18.4378336Z Only features in the report that have tensor value data are plottable by this class 2024-06-26T05:54:18.4379085Z When the tensor information is plotted, it will plot: 2024-06-26T05:54:18.4379638Z idx as the x val, feature value as the y_val 2024-06-26T05:54:18.4380190Z When the channel information is plotted, it will plot: 2024-06-26T05:54:18.4380943Z the first idx of each module as the x val, feature value as the y_val [for each channel] 2024-06-26T05:54:18.4381813Z The reason for this is that we want to be able to compare values across the 2024-06-26T05:54:18.4382625Z channels for same layer, and it will be hard if values are staggered by idx 2024-06-26T05:54:18.4383328Z This means each module is represented by only 1 x value 2024-06-26T05:54:18.4383878Z Args: 2024-06-26T05:54:18.4384319Z feature_filter (str): Filters the features presented to only those that 2024-06-26T05:54:18.4384903Z contain this filter substring 2024-06-26T05:54:18.4385522Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-06-26T05:54:18.4386373Z Default = "", results in all the modules in the reports to be visible in the table 2024-06-26T05:54:18.4386874Z 2024-06-26T05:54:18.4386975Z Example Use: 2024-06-26T05:54:18.4387312Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:18.4387843Z >>> mod_report_visualizer.generate_plot_visualization( 2024-06-26T05:54:18.4388369Z ... feature_filter = "per_channel_min", 2024-06-26T05:54:18.4388915Z ... module_fqn_filter = "block1" 2024-06-26T05:54:18.4389304Z ... ) 2024-06-26T05:54:18.4389688Z >>> # outputs line plot of per_channel_min information for all 2024-06-26T05:54:18.4390392Z >>> # modules in block1 of model each channel gets it's own line, 2024-06-26T05:54:18.4391098Z >>> # and it's plotted across the in-order modules on the x-axis 2024-06-26T05:54:18.4391564Z 2024-06-26T05:54:18.4391958Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:18.4392436Z 2024-06-26T05:54:18.4393751Z msg = Cannot scrape callname=ModelReportVisualizer.generate_histogram_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=601. 2024-06-26T05:54:18.4395462Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:18.4395966Z 2024-06-26T05:54:18.4396336Z Takes in a feature and optional module_filter and plots the histogram of desired data. 2024-06-26T05:54:18.4396868Z 2024-06-26T05:54:18.4396975Z Note: 2024-06-26T05:54:18.4397471Z Only features in the report that have tensor value data can be viewed as a histogram 2024-06-26T05:54:18.4398382Z If you want to plot a histogram from all the channel values of a specific feature for 2024-06-26T05:54:18.4399262Z a specific model, make sure to specify both the model and the feature properly 2024-06-26T05:54:18.4400099Z in the filters and you should be able to see a distribution of the channel data 2024-06-26T05:54:18.4400684Z 2024-06-26T05:54:18.4400777Z Args: 2024-06-26T05:54:18.4401263Z feature_filter (str, optional): Filters the features presented to only those that 2024-06-26T05:54:18.4401905Z contain this filter substring 2024-06-26T05:54:18.4402386Z Default = "", results in all the features being printed 2024-06-26T05:54:18.4403103Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-06-26T05:54:18.4403957Z Default = "", results in all the modules in the reports to be visible in the table 2024-06-26T05:54:18.4404755Z num_bins (int, optional): The number of bins to create the histogram with 2024-06-26T05:54:18.4405476Z Default = 10, the values will be split into 10 equal sized bins 2024-06-26T05:54:18.4405885Z 2024-06-26T05:54:18.4406002Z Example Use: 2024-06-26T05:54:18.4406271Z >>> # xdoctest: +SKIP 2024-06-26T05:54:18.4406871Z >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization( 2024-06-26T05:54:18.4407577Z ... feature_filter = "per_channel_min", 2024-06-26T05:54:18.4408014Z ... module_fqn_filter = "block1" 2024-06-26T05:54:18.4408392Z ... ) 2024-06-26T05:54:18.4408906Z # outputs histogram of per_channel_min information for all modules in block1 of model 2024-06-26T05:54:18.4409766Z information is gathered across all channels for all modules in block 1 for the 2024-06-26T05:54:18.4410650Z per_channel_min and is displayed in a histogram of equally sized bins 2024-06-26T05:54:18.4411120Z 2024-06-26T05:54:18.4411506Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:18.4411980Z 2024-06-26T05:54:18.6778123Z msg = Cannot scrape callname=gather_object in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py line=2736. 2024-06-26T05:54:18.6779466Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:18.6779978Z 2024-06-26T05:54:18.6780291Z Gathers picklable objects from the whole group in a single process. 2024-06-26T05:54:18.6780744Z 2024-06-26T05:54:18.6781063Z Similar to :func:`gather`, but Python objects can be passed in. Note that the 2024-06-26T05:54:18.6781729Z object must be picklable in order to be gathered. 2024-06-26T05:54:18.6782069Z 2024-06-26T05:54:18.6782173Z Args: 2024-06-26T05:54:18.6782473Z obj (Any): Input object. Must be picklable. 2024-06-26T05:54:18.6783351Z object_gather_list (list[Any]): Output list. On the ``dst`` rank, it 2024-06-26T05:54:18.6784036Z should be correctly sized as the size of the group for this 2024-06-26T05:54:18.6784785Z collective and will contain the output. Must be ``None`` on non-dst 2024-06-26T05:54:18.6785363Z ranks. (default is ``None``) 2024-06-26T05:54:18.6786248Z dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). (default is 0) 2024-06-26T05:54:18.6787202Z group: (ProcessGroup, optional): The process group to work on. If None, 2024-06-26T05:54:18.6787918Z the default process group will be used. Default is ``None``. 2024-06-26T05:54:18.6788323Z 2024-06-26T05:54:18.6788432Z Returns: 2024-06-26T05:54:18.6788832Z None. On the ``dst`` rank, ``object_gather_list`` will contain the 2024-06-26T05:54:18.6789376Z output of the collective. 2024-06-26T05:54:18.6789613Z 2024-06-26T05:54:18.6789935Z .. note:: Note that this API differs slightly from the gather collective 2024-06-26T05:54:18.6790699Z since it does not provide an async_op handle and thus will be a blocking 2024-06-26T05:54:18.6791253Z call. 2024-06-26T05:54:18.6791410Z 2024-06-26T05:54:18.6791780Z .. note:: For NCCL-based processed groups, internal tensor representations 2024-06-26T05:54:18.6792544Z of objects must be moved to the GPU device before communication takes 2024-06-26T05:54:18.6793174Z place. In this case, the device used is given by 2024-06-26T05:54:18.6793866Z ``torch.cuda.current_device()`` and it is the user's responsiblity to 2024-06-26T05:54:18.6794601Z ensure that this is set so that each rank has an individual GPU, via 2024-06-26T05:54:18.6795161Z ``torch.cuda.set_device()``. 2024-06-26T05:54:18.6795424Z 2024-06-26T05:54:18.6795526Z .. warning:: 2024-06-26T05:54:18.6795958Z :func:`gather_object` uses ``pickle`` module implicitly, which is 2024-06-26T05:54:18.6796671Z known to be insecure. It is possible to construct malicious pickle data 2024-06-26T05:54:18.6797428Z which will execute arbitrary code during unpickling. Only call this 2024-06-26T05:54:18.6798006Z function with data you trust. 2024-06-26T05:54:18.6798262Z 2024-06-26T05:54:18.6798374Z .. warning:: 2024-06-26T05:54:18.6798809Z Calling :func:`gather_object` with GPU tensors is not well supported 2024-06-26T05:54:18.6799623Z and inefficient as it incurs GPU -> CPU transfer since tensors would be 2024-06-26T05:54:18.6800306Z pickled. Please consider using :func:`gather` instead. 2024-06-26T05:54:18.6800747Z 2024-06-26T05:54:18.6800848Z Example:: 2024-06-26T05:54:18.6801181Z >>> # xdoctest: +SKIP("need process group init") 2024-06-26T05:54:18.6801755Z >>> # Note: Process group initialization omitted on each rank. 2024-06-26T05:54:18.6802285Z >>> import torch.distributed as dist 2024-06-26T05:54:18.6802708Z >>> # Assumes world_size of 3. 2024-06-26T05:54:18.6803204Z >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object 2024-06-26T05:54:18.6803750Z >>> output = [None for _ in gather_objects] 2024-06-26T05:54:18.6804181Z >>> dist.gather_object( 2024-06-26T05:54:18.6804561Z ... gather_objects[dist.get_rank()], 2024-06-26T05:54:18.6805029Z ... output if dist.get_rank() == 0 else None, 2024-06-26T05:54:18.6805463Z ... dst=0 2024-06-26T05:54:18.6805739Z ... ) 2024-06-26T05:54:18.6805974Z >>> # On rank 0 2024-06-26T05:54:18.6806255Z >>> output 2024-06-26T05:54:18.6806565Z ['foo', 12, {1: 2}] 2024-06-26T05:54:18.6806762Z 2024-06-26T05:54:18.6807144Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:18.6807634Z 2024-06-26T05:54:18.6942130Z msg = Cannot scrape callname=__doc__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/launch.py line=2. 2024-06-26T05:54:18.6943345Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:18.6943916Z 2024-06-26T05:54:18.6944238Z Module ``torch.distributed.launch``. 2024-06-26T05:54:18.6944543Z 2024-06-26T05:54:18.6944870Z ``torch.distributed.launch`` is a module that spawns up multiple distributed 2024-06-26T05:54:18.6945549Z training processes on each of the training nodes. 2024-06-26T05:54:18.6945890Z 2024-06-26T05:54:18.6946000Z .. warning:: 2024-06-26T05:54:18.6946173Z 2024-06-26T05:54:18.6946696Z This module is going to be deprecated in favor of :ref:`torchrun `. 2024-06-26T05:54:18.6947213Z 2024-06-26T05:54:18.6947623Z The utility can be used for single-node distributed training, in which one or 2024-06-26T05:54:18.6948431Z more processes per node will be spawned. The utility can be used for either 2024-06-26T05:54:18.6949214Z CPU training or GPU training. If the utility is used for GPU training, 2024-06-26T05:54:18.6950179Z each distributed process will be operating on a single GPU. This can achieve 2024-06-26T05:54:18.6951038Z well-improved single-node training performance. It can also be used in 2024-06-26T05:54:18.6951903Z multi-node distributed training, by spawning up multiple processes on each node 2024-06-26T05:54:18.6952772Z for well-improved multi-node distributed training performance as well. 2024-06-26T05:54:18.6953536Z This will especially be beneficial for systems with multiple Infiniband 2024-06-26T05:54:18.6954389Z interfaces that have direct-GPU support, since all of them can be utilized for 2024-06-26T05:54:18.6955029Z aggregated communication bandwidth. 2024-06-26T05:54:18.6955301Z 2024-06-26T05:54:18.6955694Z In both cases of single-node distributed training or multi-node distributed 2024-06-26T05:54:18.6956471Z training, this utility will launch the given number of processes per node 2024-06-26T05:54:18.6957309Z (``--nproc-per-node``). If used for GPU training, this number needs to be less 2024-06-26T05:54:18.6958095Z or equal to the number of GPUs on the current system (``nproc_per_node``), 2024-06-26T05:54:18.6958835Z and each process will be operating on a single GPU from *GPU 0 to 2024-06-26T05:54:18.6959409Z GPU (nproc_per_node - 1)*. 2024-06-26T05:54:18.6959643Z 2024-06-26T05:54:18.6959759Z **How to use this module:** 2024-06-26T05:54:18.6959984Z 2024-06-26T05:54:18.6960229Z 1. Single-Node multi-process distributed training 2024-06-26T05:54:18.6960586Z 2024-06-26T05:54:18.6960815Z :: 2024-06-26T05:54:18.6960988Z 2024-06-26T05:54:18.6961416Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-06-26T05:54:18.6962194Z YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other 2024-06-26T05:54:18.6962749Z arguments of your training script) 2024-06-26T05:54:18.6963079Z 2024-06-26T05:54:18.6963410Z 2. Multi-Node multi-process distributed training: (e.g. two nodes) 2024-06-26T05:54:18.6963851Z 2024-06-26T05:54:18.6963857Z 2024-06-26T05:54:18.6964046Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)* 2024-06-26T05:54:18.6964390Z 2024-06-26T05:54:18.6964496Z :: 2024-06-26T05:54:18.6964624Z 2024-06-26T05:54:18.6964991Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-06-26T05:54:18.6965725Z --nnodes=2 --node-rank=0 --master-addr="192.168.1.1" 2024-06-26T05:54:18.6966426Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2024-06-26T05:54:18.6967046Z and all other arguments of your training script) 2024-06-26T05:54:18.6967424Z 2024-06-26T05:54:18.6967520Z Node 2: 2024-06-26T05:54:18.6967656Z 2024-06-26T05:54:18.6967759Z :: 2024-06-26T05:54:18.6967889Z 2024-06-26T05:54:18.6968262Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-06-26T05:54:18.6968975Z --nnodes=2 --node-rank=1 --master-addr="192.168.1.1" 2024-06-26T05:54:18.6969668Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2024-06-26T05:54:18.6970301Z and all other arguments of your training script) 2024-06-26T05:54:18.6970658Z 2024-06-26T05:54:18.6970877Z 3. To look up what optional arguments this module offers: 2024-06-26T05:54:18.6971416Z 2024-06-26T05:54:18.6971508Z :: 2024-06-26T05:54:18.6971635Z 2024-06-26T05:54:18.6971866Z python -m torch.distributed.launch --help 2024-06-26T05:54:18.6972188Z 2024-06-26T05:54:18.6972193Z 2024-06-26T05:54:18.6972319Z **Important Notices:** 2024-06-26T05:54:18.6972522Z 2024-06-26T05:54:18.6972887Z 1. This utility and multi-process distributed (single-node or 2024-06-26T05:54:18.6973824Z multi-node) GPU training currently only achieves the best performance using 2024-06-26T05:54:18.6974642Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to 2024-06-26T05:54:18.6975237Z use for GPU training. 2024-06-26T05:54:18.6975451Z 2024-06-26T05:54:18.6975801Z 2. In your training program, you must parse the command-line argument: 2024-06-26T05:54:18.6976613Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module. 2024-06-26T05:54:18.6977375Z If your training program uses GPUs, you should ensure that your code only 2024-06-26T05:54:18.6978120Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by: 2024-06-26T05:54:18.6978545Z 2024-06-26T05:54:18.6978684Z Parsing the local_rank argument 2024-06-26T05:54:18.6978935Z 2024-06-26T05:54:18.6979039Z :: 2024-06-26T05:54:18.6979165Z 2024-06-26T05:54:18.6979277Z >>> # xdoctest: +SKIP 2024-06-26T05:54:18.6979610Z >>> import argparse 2024-06-26T05:54:18.6979974Z >>> parser = argparse.ArgumentParser() 2024-06-26T05:54:18.6980582Z >>> parser.add_argument("--local-rank", "--local_rank", type=int) 2024-06-26T05:54:18.6981120Z >>> args = parser.parse_args() 2024-06-26T05:54:18.6981378Z 2024-06-26T05:54:18.6981542Z Set your device to local rank using either 2024-06-26T05:54:18.6981847Z 2024-06-26T05:54:18.6981940Z :: 2024-06-26T05:54:18.6982078Z 2024-06-26T05:54:18.6982344Z >>> torch.cuda.set_device(args.local_rank) # before your code runs 2024-06-26T05:54:18.6982766Z 2024-06-26T05:54:18.6982867Z or 2024-06-26T05:54:18.6982991Z 2024-06-26T05:54:18.6983085Z :: 2024-06-26T05:54:18.6983223Z 2024-06-26T05:54:18.6983389Z >>> with torch.cuda.device(args.local_rank): 2024-06-26T05:54:18.6983830Z >>> # your code to run 2024-06-26T05:54:18.6984150Z >>> ... 2024-06-26T05:54:18.6984323Z 2024-06-26T05:54:18.6984444Z .. versionchanged:: 2.0.0 2024-06-26T05:54:18.6984668Z 2024-06-26T05:54:18.6985060Z The launcher will passes the ``--local-rank=`` argument to your script. 2024-06-26T05:54:18.6985944Z From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the 2024-06-26T05:54:18.6986639Z previously used underscored ``--local_rank``. 2024-06-26T05:54:18.6986978Z 2024-06-26T05:54:18.6987288Z For backward compatibility, it may be necessary for users to handle both 2024-06-26T05:54:18.6988189Z cases in their argument parsing code. This means including both ``"--local-rank"`` 2024-06-26T05:54:18.6989050Z and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is 2024-06-26T05:54:18.6989847Z provided, the launcher will trigger an error: "error: unrecognized arguments: 2024-06-26T05:54:18.6990710Z --local-rank=". For training code that only supports PyTorch 2.0.0+, 2024-06-26T05:54:18.6991419Z including ``"--local-rank"`` should be sufficient. 2024-06-26T05:54:18.6991764Z 2024-06-26T05:54:18.6992084Z 3. In your training program, you are supposed to call the following function 2024-06-26T05:54:18.6992895Z at the beginning to start the distributed backend. It is strongly recommended 2024-06-26T05:54:18.6993685Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work, 2024-06-26T05:54:18.6994395Z but ``env://`` is the one that is officially supported by this module. 2024-06-26T05:54:18.6994838Z 2024-06-26T05:54:18.6994930Z :: 2024-06-26T05:54:18.6995058Z 2024-06-26T05:54:18.6995390Z >>> torch.distributed.init_process_group(backend='YOUR BACKEND', 2024-06-26T05:54:18.6996041Z >>> init_method='env://') 2024-06-26T05:54:18.6996497Z 2024-06-26T05:54:18.6996820Z 4. In your training program, you can either use regular distributed functions 2024-06-26T05:54:18.6997635Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your 2024-06-26T05:54:18.6998392Z training program uses GPUs for training and you would like to use 2024-06-26T05:54:18.6999130Z :func:`torch.nn.parallel.DistributedDataParallel` module, 2024-06-26T05:54:18.6999668Z here is how to configure it. 2024-06-26T05:54:18.6999900Z 2024-06-26T05:54:18.7000004Z :: 2024-06-26T05:54:18.7000132Z 2024-06-26T05:54:18.7000391Z >>> model = torch.nn.parallel.DistributedDataParallel(model, 2024-06-26T05:54:18.7001084Z >>> device_ids=[args.local_rank], 2024-06-26T05:54:18.7001651Z >>> output_device=args.local_rank) 2024-06-26T05:54:18.7002014Z 2024-06-26T05:54:18.7002335Z Please ensure that ``device_ids`` argument is set to be the only GPU device id 2024-06-26T05:54:18.7003152Z that your code will be operating on. This is generally the local rank of the 2024-06-26T05:54:18.7003957Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``, 2024-06-26T05:54:18.7004732Z and ``output_device`` needs to be ``args.local_rank`` in order to use this 2024-06-26T05:54:18.7005269Z utility 2024-06-26T05:54:18.7005425Z 2024-06-26T05:54:18.7005763Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable 2024-06-26T05:54:18.7006553Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with 2024-06-26T05:54:18.7007349Z ``--use-env=True``. You must adjust the subprocess example above to replace 2024-06-26T05:54:18.7008132Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher 2024-06-26T05:54:18.7008825Z will not pass ``--local-rank`` when you specify this flag. 2024-06-26T05:54:18.7009202Z 2024-06-26T05:54:18.7009303Z .. warning:: 2024-06-26T05:54:18.7009474Z 2024-06-26T05:54:18.7009751Z ``local_rank`` is NOT globally unique: it is only unique per process 2024-06-26T05:54:18.7010506Z on a machine. Thus, don't use it to decide if you should, e.g., 2024-06-26T05:54:18.7011080Z write to a networked filesystem. See 2024-06-26T05:54:18.7011648Z https://github.com/pytorch/pytorch/issues/12042 for an example of 2024-06-26T05:54:18.7012368Z how things can go wrong if you don't do this correctly. 2024-06-26T05:54:18.7012740Z 2024-06-26T05:54:18.7012744Z 2024-06-26T05:54:18.7012749Z 2024-06-26T05:54:18.7012753Z 2024-06-26T05:54:18.7013145Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:18.7013748Z 2024-06-26T05:54:18.7523401Z msg = Cannot scrape callname=init_from_local_shards in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py line=361. 2024-06-26T05:54:18.7524958Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:18.7525480Z 2024-06-26T05:54:18.7525895Z Creates an :class:`ShardedTensor` from local shards and the global metadata. 2024-06-26T05:54:18.7526623Z Needs to be called on all ranks in an SPMD fashion. 2024-06-26T05:54:18.7526971Z 2024-06-26T05:54:18.7527074Z Args: 2024-06-26T05:54:18.7527634Z local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list 2024-06-26T05:54:18.7528451Z of shards that represent the local shards on this rank. 2024-06-26T05:54:18.7529209Z global_size (int...): a list, tuple, or `torch.Size` of integers defining the 2024-06-26T05:54:18.7529888Z shape of the overall sharded tensor. 2024-06-26T05:54:18.7530205Z 2024-06-26T05:54:18.7530307Z Keyword args: 2024-06-26T05:54:18.7530870Z process_group (ProcessGroup, optional): The process group to work on. If None, 2024-06-26T05:54:18.7531584Z the default process group will be used. 2024-06-26T05:54:18.7532190Z init_rrefs (bool, optional): Whether or not to initialize 2024-06-26T05:54:18.7533101Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2024-06-26T05:54:18.7533996Z Need to initialize the RPC Framework if specified as ``True``. 2024-06-26T05:54:18.7534587Z Default: ``False``. 2024-06-26T05:54:18.7534821Z 2024-06-26T05:54:18.7534917Z Returns: 2024-06-26T05:54:18.7535430Z A :class:`ShardedTensor` object handle on this rank 2024-06-26T05:54:18.7535783Z 2024-06-26T05:54:18.7535788Z 2024-06-26T05:54:18.7535885Z Examples: 2024-06-26T05:54:18.7536449Z Suppose we want construct a sharded tensor on two ranks, global size = (10, 5), 2024-06-26T05:54:18.7537274Z each shard have a (5, 5) local tensor, we can do it like below: 2024-06-26T05:54:18.7537743Z 2024-06-26T05:54:18.7537843Z on rank 0: 2024-06-26T05:54:18.7538172Z >>> # xdoctest: +SKIP("not distributed") 2024-06-26T05:54:18.7538704Z >>> local_shard_metadata = ShardMetadata( 2024-06-26T05:54:18.7539165Z >>> shard_offsets=[0, 0], 2024-06-26T05:54:18.7539562Z >>> shard_lengths=[5, 5], 2024-06-26T05:54:18.7540001Z >>> placement="rank:0/cuda:0" 2024-06-26T05:54:18.7540355Z >>> ) 2024-06-26T05:54:18.7540812Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2024-06-26T05:54:18.7541487Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2024-06-26T05:54:18.7541934Z 2024-06-26T05:54:18.7542052Z on rank 1: 2024-06-26T05:54:18.7542423Z >>> # xdoctest: +SKIP("not distributed") 2024-06-26T05:54:18.7542890Z >>> local_shard_metadata = ShardMetadata( 2024-06-26T05:54:18.7543385Z >>> shard_offsets=[5, 0], 2024-06-26T05:54:18.7543742Z >>> shard_lengths=[5, 5], 2024-06-26T05:54:18.7544182Z >>> placement="rank:1/cuda:1" 2024-06-26T05:54:18.7544549Z >>> ) 2024-06-26T05:54:18.7544992Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2024-06-26T05:54:18.7545711Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2024-06-26T05:54:18.7546118Z 2024-06-26T05:54:18.7546595Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:18.7547105Z 2024-06-26T05:54:18.7623695Z msg = Cannot scrape callname=ShardedTensor._init_from_local_tensor in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=784. 2024-06-26T05:54:18.7625200Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:18.7625718Z 2024-06-26T05:54:18.7626062Z Initialize a ShardedTensor given only one local tensor, global sharded tensor 2024-06-26T05:54:18.7626681Z size and sharding spec on each rank. 2024-06-26T05:54:18.7626973Z 2024-06-26T05:54:18.7627065Z Args: 2024-06-26T05:54:18.7627509Z local_tensor (Tensor): Single tensor of local shard stored in each rank. 2024-06-26T05:54:18.7628293Z sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): 2024-06-26T05:54:18.7629013Z The specification describing how to shard the Tensor. 2024-06-26T05:54:18.7629675Z global_size (Sequence[int]): Size of the sharded tensor. 2024-06-26T05:54:18.7630652Z process_group (ProcessGroup, optional): The process group to aggregate on. 2024-06-26T05:54:18.7631362Z Default: None 2024-06-26T05:54:18.7631839Z init_rrefs (bool, optional): Whether or not to initialize 2024-06-26T05:54:18.7632506Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2024-06-26T05:54:18.7633195Z Need to initialize the RPC Framework if specified as ``True``. 2024-06-26T05:54:18.7633739Z Default: ``False``. 2024-06-26T05:54:18.7633959Z 2024-06-26T05:54:18.7634068Z Returns: 2024-06-26T05:54:18.7634533Z A :class:`ShardedTensor` sharded based on the given sharding_spec with local 2024-06-26T05:54:18.7635164Z tensor stored in the current rank. 2024-06-26T05:54:18.7635458Z 2024-06-26T05:54:18.7635568Z Examples: 2024-06-26T05:54:18.7635820Z >>> # xdoctest: +SKIP 2024-06-26T05:54:18.7636419Z >>> # All tensors below are of torch.int64 type. 2024-06-26T05:54:18.7636913Z >>> # We have 2 process groups, 2 ranks. 2024-06-26T05:54:18.7637446Z >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank 2024-06-26T05:54:18.7638105Z >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2])) 2024-06-26T05:54:18.7638636Z >>> local_tensor 2024-06-26T05:54:18.7639039Z tensor([[1, 2, 3, 4]]) # Rank 0 2024-06-26T05:54:18.7639421Z tensor([[3, 4, 5, 6]]) # Rank 1 2024-06-26T05:54:18.7639789Z >>> sharding_dim = 0 2024-06-26T05:54:18.7640140Z >>> sharding_spec = ChunkShardingSpec( 2024-06-26T05:54:18.7640565Z dim=sharding_dim, 2024-06-26T05:54:18.7640992Z placements=[ 2024-06-26T05:54:18.7641305Z "rank:0/cuda:0", 2024-06-26T05:54:18.7641661Z "rank:1/cuda:1", 2024-06-26T05:54:18.7641998Z ], 2024-06-26T05:54:18.7642245Z ) 2024-06-26T05:54:18.7642735Z >>> st = ShardedTensor._init_from_local_tensor(local_tensor, sharding_spec, [2, 4]) 2024-06-26T05:54:18.7643334Z >>> st 2024-06-26T05:54:18.7643577Z ShardedTensor( 2024-06-26T05:54:18.7643890Z ShardedTensorMetadata( 2024-06-26T05:54:18.7644250Z shards_metadata=[ 2024-06-26T05:54:18.7644822Z ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0), 2024-06-26T05:54:18.7645667Z ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1), 2024-06-26T05:54:18.7646267Z ], 2024-06-26T05:54:18.7646555Z size=torch.Size([2, 4]) 2024-06-26T05:54:18.7646899Z ) 2024-06-26T05:54:18.7647149Z >>> st.local_tensor() 2024-06-26T05:54:18.7647484Z tensor([1, 2, 3, 4]) # Rank 0 2024-06-26T05:54:18.7647840Z tensor([3, 4, 5, 6]) # Rank 1 2024-06-26T05:54:18.7648095Z 2024-06-26T05:54:18.7648454Z Warning: This API is experimental and subject to change. It lacks of a fully across 2024-06-26T05:54:18.7649305Z rank validations, and we only validate the local shard on the current rank. 2024-06-26T05:54:18.7650099Z We fully rely on the user to ensure local tensor is sharded based on the 2024-06-26T05:54:18.7650693Z sharding spec. 2024-06-26T05:54:18.7650893Z 2024-06-26T05:54:18.7651312Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:18.7651792Z 2024-06-26T05:54:18.7652847Z msg = Cannot scrape callname=ShardedTensor.reshard in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=1023. 2024-06-26T05:54:18.7654442Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:18.7654936Z 2024-06-26T05:54:18.7655274Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support 2024-06-26T05:54:18.7655889Z single local shard. 2024-06-26T05:54:18.7656077Z 2024-06-26T05:54:18.7656443Z If ``resharding_spec`` is same as the original one, this becomes a no-op. 2024-06-26T05:54:18.7657215Z If only ``resharding_spec`` shares the same sharding dim with the original one, 2024-06-26T05:54:18.7657831Z we swap local shards directly. 2024-06-26T05:54:18.7658426Z For more generic cases, we merge different shards across different ranks and split 2024-06-26T05:54:18.7659278Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API. 2024-06-26T05:54:18.7659795Z 2024-06-26T05:54:18.7659883Z Args: 2024-06-26T05:54:18.7660391Z resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The 2024-06-26T05:54:18.7661135Z specification describing how the tensor is sharded. 2024-06-26T05:54:18.7661499Z 2024-06-26T05:54:18.7661592Z Returns: 2024-06-26T05:54:18.7662008Z A :class:`ShardedTensor` object whose local shards are resharded. 2024-06-26T05:54:18.7662431Z 2024-06-26T05:54:18.7662538Z Examples: 2024-06-26T05:54:18.7662791Z >>> # xdoctest: +SKIP 2024-06-26T05:54:18.7663159Z >>> # We have 2 process groups, 2 ranks. 2024-06-26T05:54:18.7663825Z >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank 2024-06-26T05:54:18.7664363Z >>> tensor = torch.stack([tensor, tensor]) 2024-06-26T05:54:18.7664776Z >>> tensor 2024-06-26T05:54:18.7665096Z tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0 2024-06-26T05:54:18.7665634Z tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1 2024-06-26T05:54:18.7666169Z tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2 2024-06-26T05:54:18.7666635Z tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3 2024-06-26T05:54:18.7667054Z >>> sharding_dim = 0 2024-06-26T05:54:18.7667406Z >>> spec = ChunkShardingSpec( 2024-06-26T05:54:18.7667787Z dim=sharding_dim, 2024-06-26T05:54:18.7668117Z placements=[ 2024-06-26T05:54:18.7668442Z "rank:0/cuda:0", 2024-06-26T05:54:18.7668789Z "rank:1/cuda:1", 2024-06-26T05:54:18.7669124Z "rank:2/cuda:2", 2024-06-26T05:54:18.7669472Z "rank:3/cuda:3", 2024-06-26T05:54:18.7669809Z ], 2024-06-26T05:54:18.7670052Z ) 2024-06-26T05:54:18.7670319Z >>> current_offsets = [0] * 2 2024-06-26T05:54:18.7670698Z >>> current_offsets[0] = rank * 2 2024-06-26T05:54:18.7671096Z >>> shard_metadata = ShardMetadata( 2024-06-26T05:54:18.7671567Z shard_offsets=copy.deepcopy(current_offsets), 2024-06-26T05:54:18.7672047Z shard_sizes=tensor.size(), 2024-06-26T05:54:18.7672460Z placement=spec.placements[rank], 2024-06-26T05:54:18.7672863Z ) 2024-06-26T05:54:18.7673120Z >>> local_shards = [ 2024-06-26T05:54:18.7673414Z Shard( 2024-06-26T05:54:18.7673699Z tensor=tensor, 2024-06-26T05:54:18.7674058Z metadata=shard_metadata, 2024-06-26T05:54:18.7674418Z ) 2024-06-26T05:54:18.7674668Z ] 2024-06-26T05:54:18.7675107Z >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size()) 2024-06-26T05:54:18.7675667Z >>> sharding_dim = 1 2024-06-26T05:54:18.7676043Z >>> resharding_spec = ChunkShardingSpec( 2024-06-26T05:54:18.7676470Z dim=sharding_dim, 2024-06-26T05:54:18.7676796Z placements=[ 2024-06-26T05:54:18.7677111Z "rank:0/cuda:0", 2024-06-26T05:54:18.7677462Z "rank:1/cuda:1", 2024-06-26T05:54:18.7677794Z "rank:2/cuda:2", 2024-06-26T05:54:18.7678146Z "rank:3/cuda:3", 2024-06-26T05:54:18.7678474Z ], 2024-06-26T05:54:18.7678720Z ) 2024-06-26T05:54:18.7678993Z >>> st.reshard(resharding_spec) 2024-06-26T05:54:18.7679407Z >>> tensor = st.local_shards()[0].tensor 2024-06-26T05:54:18.7679790Z >>> tensor 2024-06-26T05:54:18.7680145Z tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0 2024-06-26T05:54:18.7680826Z tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1 2024-06-26T05:54:18.7681347Z tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2 2024-06-26T05:54:18.7681886Z tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3 2024-06-26T05:54:18.7682240Z 2024-06-26T05:54:18.7682654Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:18.7683127Z 2024-06-26T05:54:18.7759705Z msg = Cannot scrape callname=ShardingPlan in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharding_plan/api.py line=12. 2024-06-26T05:54:18.7761126Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:18.7761659Z 2024-06-26T05:54:18.7761935Z Representation of a sharding plan, describes how to shard a module 2024-06-26T05:54:18.7762749Z across hosts. `plan` is used to shard module parameters according to the spec provided, 2024-06-26T05:54:18.7763657Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output 2024-06-26T05:54:18.7764530Z layout of a module with a spec, and when to convert back to data parallel fashion. 2024-06-26T05:54:18.7765050Z 2024-06-26T05:54:18.7765277Z Args: 2024-06-26T05:54:18.7765784Z plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`, 2024-06-26T05:54:18.7766603Z :class:`torch.distributed._shard.sharder.Sharder`]): 2024-06-26T05:54:18.7767511Z a dict describes how to shard a module, there're currently two ways to shard a module: 2024-06-26T05:54:18.7768494Z 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of 2024-06-26T05:54:18.7769163Z a parameter to a `ShardingSpec`. 2024-06-26T05:54:18.7769841Z 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module 2024-06-26T05:54:18.7770486Z to a `Sharder` object. 2024-06-26T05:54:18.7771194Z output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional): 2024-06-26T05:54:18.7772207Z a dict specifies the layout of a module's output which produces a ShardedTensor, 2024-06-26T05:54:18.7773057Z keyed by the name of module to ShardingSpec("" in key means the root module). 2024-06-26T05:54:18.7773934Z Default: `None` 2024-06-26T05:54:18.7774470Z return_local_tensor (List[str], optional): a list of string, each element enables 2024-06-26T05:54:18.7775388Z a module's sharded output to be returned as a Tensor from its local shards to 2024-06-26T05:54:18.7776226Z ensure further processing in a data parallel fashion. ("" in list means the 2024-06-26T05:54:18.7776812Z root module). 2024-06-26T05:54:18.7777119Z Default: None 2024-06-26T05:54:18.7777413Z Example: 2024-06-26T05:54:18.7777950Z Suppose we want to shard a module with two linear layers and then run it with DDP, we also 2024-06-26T05:54:18.7778909Z want to convert the output of the second linear layer back to DDP, we can do it as follows: 2024-06-26T05:54:18.7779490Z 2024-06-26T05:54:18.7779709Z >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d) 2024-06-26T05:54:18.7789055Z >>> class MyModule(nn.Module): 2024-06-26T05:54:18.7789463Z >>> def __init__(self): 2024-06-26T05:54:18.7789816Z >>> super().__init__() 2024-06-26T05:54:18.7790194Z >>> self.fc1 = nn.Linear() 2024-06-26T05:54:18.7790588Z >>> self.gelu = nn.GELU() 2024-06-26T05:54:18.7790970Z >>> self.fc2 = nn.Linear() 2024-06-26T05:54:18.7791373Z >>> self.relu = nn.Linear() 2024-06-26T05:54:18.7791742Z >>> 2024-06-26T05:54:18.7792001Z >>> def forward(self, input): 2024-06-26T05:54:18.7792499Z >>> return self.relu(self.fc2(self.gelu(self.fc1(input)))) 2024-06-26T05:54:18.7792887Z 2024-06-26T05:54:18.7792892Z 2024-06-26T05:54:18.7793072Z >>> # xdoctest: +SKIP("Undefined spec1, spec2) 2024-06-26T05:54:18.7793540Z >>> sharding_plan = ShardingPlan( 2024-06-26T05:54:18.7793906Z >>> plan={ 2024-06-26T05:54:18.7794201Z >>> "fc1.weight": spec1, 2024-06-26T05:54:18.7794580Z >>> "fc2.weight": spec2 2024-06-26T05:54:18.7794922Z >>> }, 2024-06-26T05:54:18.7795191Z >>> output_plan={ 2024-06-26T05:54:18.7795515Z >>> "fc2": output_spec 2024-06-26T05:54:18.7795845Z >>> }, 2024-06-26T05:54:18.7796131Z >>> return_local_tensor=["fc2"] 2024-06-26T05:54:18.7796498Z >>> ) 2024-06-26T05:54:18.7796639Z 2024-06-26T05:54:18.7797063Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:18.7797556Z 2024-06-26T05:54:18.8730757Z msg = Cannot scrape callname=local_map in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_tensor/experimental/local_map.py line=30. 2024-06-26T05:54:18.8732730Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:18.8733227Z 2024-06-26T05:54:18.8733817Z ``local_map`` is an experimental API that allows users to apply on :class:`DTensors` 2024-06-26T05:54:18.8734610Z a function that is written to be applied on :class:`~torch.Tensors`. 2024-06-26T05:54:18.8735316Z 2024-06-26T05:54:18.8735406Z Args: 2024-06-26T05:54:18.8735832Z func (Callable): the function to be applied on each local shard of 2024-06-26T05:54:18.8736375Z :class:`DTensor`s. 2024-06-26T05:54:18.8736892Z out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]): 2024-06-26T05:54:18.8737875Z the desired placements of the :class:`DTensor`s in `func`'s flattened output. 2024-06-26T05:54:18.8738696Z If the flattened `output` is a single value, the `out_placements` should be 2024-06-26T05:54:18.8739481Z of type `PlacementType`. Otherwise if the flattened `output` has multiple 2024-06-26T05:54:18.8740287Z values, the `out_placements` should be a tuple of `PlacementType` values 1:1 2024-06-26T05:54:18.8740923Z mapping to the flattened `output`. 2024-06-26T05:54:18.8741480Z Besides, for :class:`Tensor` output, we use `PlacementType` as its 2024-06-26T05:54:18.8742292Z placements (a `Tuple[Placement]` value). For non-:class:`Tensor` output, 2024-06-26T05:54:18.8742921Z the `PlacementType` should be `None`. 2024-06-26T05:54:18.8743548Z Note that the only exception is when no :class:`DTensor` argument is passed 2024-06-26T05:54:18.8744360Z in. In this case, even if `out_placements` is not `None`, the result function 2024-06-26T05:54:18.8745166Z should ignore the desired placements because the application is not on 2024-06-26T05:54:18.8745743Z :class:`DTensors`. 2024-06-26T05:54:18.8746177Z in_placements (Tuple[`PlacementType`, ...], optional): 2024-06-26T05:54:18.8746951Z the required placements of the :class:`DTensor`s in `func`'s flattened input. 2024-06-26T05:54:18.8747742Z If `in_placements` is specified, `local_map` would examine whether the 2024-06-26T05:54:18.8748490Z placements of each :class:`DTensor` argument is the same as the required 2024-06-26T05:54:18.8749205Z placements or not. If the placements are not the same and 2024-06-26T05:54:18.8749927Z `redistribute_inputs` is `False`, an exception will be raised. Otherwise if 2024-06-26T05:54:18.8750718Z `redistribute_inputs` is `True`, the argument will be first redistributed to 2024-06-26T05:54:18.8751539Z the required sharding placements before passing its local tensor to `func`. 2024-06-26T05:54:18.8752331Z The only exception is when required placements are not `None` and the 2024-06-26T05:54:18.8753112Z argument is a :class:`torch.Tensor`. In this case, the placements examination 2024-06-26T05:54:18.8753904Z will be skipped and the argument will be directly passed to `func`. 2024-06-26T05:54:18.8754667Z If `in_placements` is `None`, no placements examination will be performed. 2024-06-26T05:54:18.8755252Z Default: `None` 2024-06-26T05:54:18.8755615Z device_mesh (:class:`DeviceMesh`, optional): 2024-06-26T05:54:18.8756235Z the device mesh that all the :class:`DTensor`s are placed on. If not 2024-06-26T05:54:18.8757072Z specified, this will be inferred from the input :class:`DTensor`s' device 2024-06-26T05:54:18.8757852Z mesh. `local_map` requires every :class:`DTensor`s to be placed on the same 2024-06-26T05:54:18.8758468Z device mesh. Default: `None`. 2024-06-26T05:54:18.8758898Z redistribute_inputs (bool, optional): 2024-06-26T05:54:18.8759527Z the bool value indicating whether to reshard the input :class:`DTensor`s when 2024-06-26T05:54:18.8760360Z their placements are different from the required input placements. If this 2024-06-26T05:54:18.8761249Z value is `False` and some :class:`DTensor` input has a different placement, 2024-06-26T05:54:18.8761904Z an exception will be raised. Default: `False`. 2024-06-26T05:54:18.8762262Z 2024-06-26T05:54:18.8762357Z Returns: 2024-06-26T05:54:18.8762857Z A `Callable` that applies `func` to each local shard of the input :class:`DTensor` 2024-06-26T05:54:18.8763679Z and returns a :class:`DTensor` constructed from the return value of `func`. 2024-06-26T05:54:18.8764243Z 2024-06-26T05:54:18.8764337Z Raises: 2024-06-26T05:54:18.8764829Z AssertionError: If the input :class:`DTensor`s are not placed on the same device 2024-06-26T05:54:18.8765668Z mesh, or if they are placed on a different device mesh than the `device_mesh` 2024-06-26T05:54:18.8766264Z argument passed in. 2024-06-26T05:54:18.8766552Z 2024-06-26T05:54:18.8766978Z AssertionError: For any non-:class:`DTensor` output, we require its corresponding 2024-06-26T05:54:18.8767840Z output placement in `out_placements` be `None`. An AssertionError will be raised 2024-06-26T05:54:18.8768462Z if this is not the case. 2024-06-26T05:54:18.8768706Z 2024-06-26T05:54:18.8769042Z ValueError: If `redistribute_inputs=False` but the input :class:`DTensor` needs 2024-06-26T05:54:18.8769735Z a redistribution according to `in_placements`. 2024-06-26T05:54:18.8770080Z 2024-06-26T05:54:18.8770190Z Example: 2024-06-26T05:54:18.8770470Z >>> # xdoctest: +SKIP("distributed") 2024-06-26T05:54:18.8770948Z >>> def mm_allreduce_forward(device_mesh, W, X): 2024-06-26T05:54:18.8771445Z >>> partial_sum_tensor = torch.mm(W, X) 2024-06-26T05:54:18.8772062Z >>> reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh) 2024-06-26T05:54:18.8772669Z >>> return reduced_tensor 2024-06-26T05:54:18.8773027Z >>> 2024-06-26T05:54:18.8773324Z >>> W = torch.randn(12, 8, requires_grad=False) 2024-06-26T05:54:18.8773969Z >>> X = torch.randn(8, 16, requires_grad=False) 2024-06-26T05:54:18.8774415Z >>> Y = torch.mm(W, X) 2024-06-26T05:54:18.8774963Z >>> row_wise = [Shard(0)] # row-wise sharding placements on 1-d mesh 2024-06-26T05:54:18.8775706Z >>> col_wise = [Shard(1)] # col-wise sharding placements on 1-d mesh 2024-06-26T05:54:18.8776225Z >>> 2024-06-26T05:54:18.8776714Z >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor convertion 2024-06-26T05:54:18.8777407Z >>> local_mm_allreduce_forward = local_map( 2024-06-26T05:54:18.8777865Z >>> mm_allreduce_forward, 2024-06-26T05:54:18.8778246Z >>> out_placements=[Replicate()], 2024-06-26T05:54:18.8778695Z >>> in_placements=[col_wise, row_wise], 2024-06-26T05:54:18.8779135Z >>> device_mesh=device_mesh, 2024-06-26T05:54:18.8779490Z >>> ) 2024-06-26T05:54:18.8779738Z >>> 2024-06-26T05:54:18.8780310Z >>> W_dt = distribute_tensor(W, device_mesh, (col_wise)) # col-wisely sharded W tensor 2024-06-26T05:54:18.8781243Z >>> X_dt = distribute_tensor(X, device_mesh, (row_wise)) # row-wisely sharded X tensor 2024-06-26T05:54:18.8782219Z >>> Y_dt = local_mm_allreduce_forward(device_mesh, W_dt, X_dt) # apply local_mm_allreduce_forward to DTensors 2024-06-26T05:54:18.8782832Z 2024-06-26T05:54:18.8783098Z NOTE: This API is currently experimental and subject to change 2024-06-26T05:54:18.8783505Z 2024-06-26T05:54:18.8783902Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:18.8784384Z 2024-06-26T05:54:18.9489175Z msg = Cannot scrape callname=post_localSGD_hook in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/post_localSGD_hook.py line=72. 2024-06-26T05:54:18.9490713Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:18.9491261Z 2024-06-26T05:54:18.9491422Z Run post-localSGD algorithm. 2024-06-26T05:54:18.9491658Z 2024-06-26T05:54:18.9492037Z This DDP communication hook is used for running post-localSGD algorithm, 2024-06-26T05:54:18.9492696Z by combining with a model averaging component (e.g., 2024-06-26T05:54:18.9493675Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`) 2024-06-26T05:54:18.9494421Z that runs after the optimizer step. 2024-06-26T05:54:18.9494695Z 2024-06-26T05:54:18.9494785Z Args: 2024-06-26T05:54:18.9495279Z state (PostLocalSGDState): State information to run post-localSGD. 2024-06-26T05:54:18.9496335Z Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD. 2024-06-26T05:54:18.9497525Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2024-06-26T05:54:18.9498572Z Note that since DDP comm hook only supports single process single device mode, 2024-06-26T05:54:18.9499417Z only exactly one tensor is stored in this bucket. 2024-06-26T05:54:18.9499770Z 2024-06-26T05:54:18.9499881Z Returns: 2024-06-26T05:54:18.9500346Z Future handler of the communication, which updates the gradients in place. 2024-06-26T05:54:18.9500842Z 2024-06-26T05:54:18.9500959Z Example:: 2024-06-26T05:54:18.9501233Z >>> # xdoctest: +SKIP 2024-06-26T05:54:18.9501759Z >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup, 2024-06-26T05:54:18.9502400Z start_localSGD_iter=10) 2024-06-26T05:54:18.9502949Z >>> ddp_model.register_comm_hook(state, post_localSGD_hook) 2024-06-26T05:54:18.9503788Z >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``. 2024-06-26T05:54:18.9504896Z >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module. 2024-06-26T05:54:18.9505541Z 2024-06-26T05:54:18.9505926Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:18.9506403Z 2024-06-26T05:54:18.9536814Z msg = Cannot scrape callname=powerSGD_hook in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py line=342. 2024-06-26T05:54:18.9538315Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:18.9538811Z 2024-06-26T05:54:18.9538944Z Implement PowerSGD algorithm. 2024-06-26T05:54:18.9539204Z 2024-06-26T05:54:18.9539491Z This DDP communication hook implements PowerSGD gradient compression 2024-06-26T05:54:18.9540265Z algorithm described in the `paper `_. 2024-06-26T05:54:18.9541046Z Once gradient tensors are aggregated across all workers, this hook applies 2024-06-26T05:54:18.9541631Z compression as follows: 2024-06-26T05:54:18.9541855Z 2024-06-26T05:54:18.9542563Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups: 2024-06-26T05:54:18.9543317Z 2024-06-26T05:54:18.9543904Z 1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth. 2024-06-26T05:54:18.9544621Z 2024-06-26T05:54:18.9545179Z 1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases). 2024-06-26T05:54:18.9545903Z 2024-06-26T05:54:18.9546036Z 2. Handles uncompressed tensors: 2024-06-26T05:54:18.9546307Z 2024-06-26T05:54:18.9546974Z 2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression; 2024-06-26T05:54:18.9547798Z 2024-06-26T05:54:18.9548266Z 2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor. 2024-06-26T05:54:18.9548875Z 2024-06-26T05:54:18.9549184Z 3. Handles the tensors that should be compressed by PowerSGD compression: 2024-06-26T05:54:18.9549668Z 2024-06-26T05:54:18.9550077Z 3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M, 2024-06-26T05:54:18.9551031Z such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized; 2024-06-26T05:54:18.9551628Z 2024-06-26T05:54:18.9551837Z 3.2. Computes each P in Ps, which is equal to MQ; 2024-06-26T05:54:18.9552182Z 2024-06-26T05:54:18.9552311Z 3.3. Allreduces Ps as a batch; 2024-06-26T05:54:18.9552586Z 2024-06-26T05:54:18.9552722Z 3.4. Orthogonalizes each P in Ps; 2024-06-26T05:54:18.9553001Z 2024-06-26T05:54:18.9553286Z 3.5. Computes each Q in Qs, which is approximately equal to M^TP; 2024-06-26T05:54:18.9553867Z 2024-06-26T05:54:18.9554007Z 3.6. Allreduces Qs as a batch; 2024-06-26T05:54:18.9554262Z 2024-06-26T05:54:18.9554676Z 3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T. 2024-06-26T05:54:18.9555261Z 2024-06-26T05:54:18.9555887Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations. 2024-06-26T05:54:18.9556981Z This not only gives the user more control over the tradeoff between speedup and accuracy, 2024-06-26T05:54:18.9558100Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers. 2024-06-26T05:54:18.9558849Z 2024-06-26T05:54:18.9558941Z Args: 2024-06-26T05:54:18.9559645Z state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc. 2024-06-26T05:54:18.9560907Z To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter`` 2024-06-26T05:54:18.9561677Z and ``min_compression_rate``. 2024-06-26T05:54:18.9562614Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2024-06-26T05:54:18.9563679Z Note that since DDP comm hook only supports single process single device mode, 2024-06-26T05:54:18.9564381Z only exactly one tensor is stored in this bucket. 2024-06-26T05:54:18.9564748Z 2024-06-26T05:54:18.9564845Z Returns: 2024-06-26T05:54:18.9565324Z Future handler of the communication, which updates the gradients in place. 2024-06-26T05:54:18.9565807Z 2024-06-26T05:54:18.9565931Z Example:: 2024-06-26T05:54:18.9566191Z >>> # xdoctest: +SKIP 2024-06-26T05:54:18.9566758Z >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, 2024-06-26T05:54:18.9567495Z start_powerSGD_iter=10, min_compression_rate=0.5) 2024-06-26T05:54:18.9568079Z >>> ddp_model.register_comm_hook(state, powerSGD_hook) 2024-06-26T05:54:18.9568453Z 2024-06-26T05:54:18.9568837Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:18.9569311Z 2024-06-26T05:54:18.9575193Z msg = Cannot scrape callname=PeriodicModelAverager in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/model_averaging/averagers.py line=36. 2024-06-26T05:54:18.9576734Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:18.9577258Z 2024-06-26T05:54:18.9577540Z Averages parameters periodically after the warm-up stage. 2024-06-26T05:54:18.9577946Z 2024-06-26T05:54:18.9578372Z This can be used for running `post-local SGD `_, 2024-06-26T05:54:18.9579135Z by running :class:`~torch.nn.DistributedDataParallel` (DDP) 2024-06-26T05:54:18.9579853Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`. 2024-06-26T05:54:18.9580324Z 2024-06-26T05:54:18.9580418Z Args: 2024-06-26T05:54:18.9580781Z period (int): The number of steps per model averaging. 2024-06-26T05:54:18.9581522Z Usually the period should be greater than ``1`` to reduce the communication cost. 2024-06-26T05:54:18.9582209Z Otherwise, only DDP needs to be used. 2024-06-26T05:54:18.9582883Z warmup_steps (int): The number of warm-up steps. During this stage, 2024-06-26T05:54:18.9583477Z model averaging is skipped. 2024-06-26T05:54:18.9584075Z process_group: The process group to be used for all-reduce. 2024-06-26T05:54:18.9584673Z If ``None``, the default process group, which 2024-06-26T05:54:18.9585291Z is created by :func:`torch.distributed.init_process_group`, 2024-06-26T05:54:18.9585877Z will be used. (default: ``None``) 2024-06-26T05:54:18.9586196Z 2024-06-26T05:54:18.9586305Z Example:: 2024-06-26T05:54:18.9586610Z 2024-06-26T05:54:18.9586773Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:18.9587206Z >>> import torch 2024-06-26T05:54:18.9587531Z >>> import torch.distributed as dist 2024-06-26T05:54:18.9588234Z >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD 2024-06-26T05:54:18.9589629Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2024-06-26T05:54:18.9590629Z >>> import torch.nn as nn 2024-06-26T05:54:18.9591151Z >>> 2024-06-26T05:54:18.9591573Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2024-06-26T05:54:18.9592081Z >>> torch.cuda.set_device(rank) 2024-06-26T05:54:18.9592521Z >>> module = nn.Linear(1, 1, bias=False).cuda() 2024-06-26T05:54:18.9593056Z >>> model = nn.parallel.DistributedDataParallel( 2024-06-26T05:54:18.9593592Z >>> module, device_ids=[rank], output_device=rank 2024-06-26T05:54:18.9594033Z >>> ) 2024-06-26T05:54:18.9594442Z >>> # Register a post-localSGD communication hook. 2024-06-26T05:54:18.9595163Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2024-06-26T05:54:18.9595918Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-06-26T05:54:18.9596380Z >>> 2024-06-26T05:54:18.9596888Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2024-06-26T05:54:18.9597638Z >>> # After 100 steps, run model averaging every 4 steps. 2024-06-26T05:54:18.9598450Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-06-26T05:54:18.9599376Z >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2024-06-26T05:54:18.9599974Z >>> for step in range(0, 200): 2024-06-26T05:54:18.9600363Z >>> optimizer.zero_grad() 2024-06-26T05:54:18.9600831Z >>> loss = loss_fn(output, labels) 2024-06-26T05:54:18.9601224Z >>> loss.backward() 2024-06-26T05:54:18.9601571Z >>> optimizer.step() 2024-06-26T05:54:18.9602061Z >>> # Will average model parameters globally every 4 steps. Thus, 2024-06-26T05:54:18.9602799Z >>> # inter-node communication only occurs every 4 iterations after 2024-06-26T05:54:18.9603394Z >>> # the initial ``warmup_steps`` period. 2024-06-26T05:54:18.9603929Z >>> averager.average_parameters(model.parameters()) 2024-06-26T05:54:18.9604290Z 2024-06-26T05:54:18.9604674Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:18.9605170Z 2024-06-26T05:54:18.9606429Z msg = Cannot scrape callname=HierarchicalModelAverager in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/model_averaging/hierarchical_model_averager.py line=18. 2024-06-26T05:54:18.9608061Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:18.9608576Z 2024-06-26T05:54:18.9609001Z Runs hierarchical model averaging (`hierarchical SGD `_). 2024-06-26T05:54:18.9609590Z 2024-06-26T05:54:18.9610011Z Process groups of different sizes are organized in a hierarchy, and they average parameters 2024-06-26T05:54:18.9610892Z by using different periods concurrently after the warm-up stage. 2024-06-26T05:54:18.9611882Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager` 2024-06-26T05:54:18.9613129Z that supports `post-local SGD `_, which essentially only supports 2024-06-26T05:54:18.9614334Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine 2024-06-26T05:54:18.9615369Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`. 2024-06-26T05:54:18.9616464Z Similarly, the process groups within this class do not have such an intra-machine process 2024-06-26T05:54:18.9617456Z subgroup, which should be embedded by the post-local SGD communication hook instead. 2024-06-26T05:54:18.9618134Z 2024-06-26T05:54:18.9618225Z Args: 2024-06-26T05:54:18.9618716Z period_group_size_dict: An ordered dict mapping keys of model averaging period to 2024-06-26T05:54:18.9619499Z process group size, used for initializing process groups of 2024-06-26T05:54:18.9620304Z different sizes in a hierarchy to average parameters concurrently. 2024-06-26T05:54:18.9621059Z Particularly, at each iteration, there will be at most a single 2024-06-26T05:54:18.9621899Z process group that runs averaging -- the period of such group should 2024-06-26T05:54:18.9622675Z have the largest period which the current step can be divided by. 2024-06-26T05:54:18.9623356Z For example, if the dict has three keys: 2, 4, and 8, 2024-06-26T05:54:18.9624037Z then this means totally three process groups will be created to 2024-06-26T05:54:18.9624775Z average parameters every 2, 4, and 8 iterations, respectively. 2024-06-26T05:54:18.9625480Z At the 4th iteration, only the second process group will run 2024-06-26T05:54:18.9626157Z averaging, because the first process group should be a 2024-06-26T05:54:18.9626882Z subset of the second process group, and no need to execute the first 2024-06-26T05:54:18.9627522Z process group redundantly. 2024-06-26T05:54:18.9628124Z On the other hand, the third process group can only be triggered 2024-06-26T05:54:18.9628892Z every 8 iterations, so it will not be triggered at the 4th iteration. 2024-06-26T05:54:18.9629861Z warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped. 2024-06-26T05:54:18.9630996Z process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging. 2024-06-26T05:54:18.9631990Z If ``None``, the default process group, which is created 2024-06-26T05:54:18.9632710Z by :func:`torch.distributed.init_process_group`, will be used. 2024-06-26T05:54:18.9633335Z (default: ``None``) 2024-06-26T05:54:18.9633664Z 2024-06-26T05:54:18.9633770Z Example:: 2024-06-26T05:54:18.9634123Z >>> # xdoctest: +SKIP('undefined rank') 2024-06-26T05:54:18.9634582Z >>> from collections import OrderedDict 2024-06-26T05:54:18.9634985Z >>> import torch 2024-06-26T05:54:18.9635332Z >>> import torch.distributed as dist 2024-06-26T05:54:18.9635987Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2024-06-26T05:54:18.9636634Z >>> PostLocalSGDState, 2024-06-26T05:54:18.9637003Z >>> post_localSGD_hook, 2024-06-26T05:54:18.9637351Z >>> ) 2024-06-26T05:54:18.9637979Z >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD 2024-06-26T05:54:18.9638746Z >>> import torch.nn as nn 2024-06-26T05:54:18.9639082Z >>> 2024-06-26T05:54:18.9639444Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2024-06-26T05:54:18.9639969Z >>> torch.cuda.set_device(rank) 2024-06-26T05:54:18.9640418Z >>> module = nn.Linear(1, 1, bias=False).to(rank) 2024-06-26T05:54:18.9641020Z >>> model = nn.parallel.DistributedDataParallel( 2024-06-26T05:54:18.9641570Z >>> module, device_ids=[rank], output_device=rank 2024-06-26T05:54:18.9642013Z >>> ) 2024-06-26T05:54:18.9642395Z >>> # Register a post-localSGD communication hook. 2024-06-26T05:54:18.9643212Z >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4. 2024-06-26T05:54:18.9643916Z >>> subgroup, _ = dist.new_subgroups() 2024-06-26T05:54:18.9644715Z >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100) 2024-06-26T05:54:18.9645488Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-06-26T05:54:18.9645983Z >>> 2024-06-26T05:54:18.9646516Z >>> # Average parameters among each group of 8 processes every 4 iterations, and among all 2024-06-26T05:54:18.9647275Z >>> # the 16 processes every 16 iterations. 2024-06-26T05:54:18.9647853Z >>> averager = hierarchicalSGD.HierarchicalModelAverager( 2024-06-26T05:54:18.9648566Z >>> period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100) 2024-06-26T05:54:18.9649464Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-06-26T05:54:18.9650443Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2024-06-26T05:54:18.9651193Z >>> # After 100 steps, run model averaging at two levels. 2024-06-26T05:54:18.9651677Z >>> for step in range(0, 200): 2024-06-26T05:54:18.9652066Z >>> optimizer.zero_grad() 2024-06-26T05:54:18.9652461Z >>> loss = loss_fn(output, labels) 2024-06-26T05:54:18.9652853Z >>> loss.backward() 2024-06-26T05:54:18.9653201Z >>> optimizer.step() 2024-06-26T05:54:18.9653801Z >>> # Average parameters after ``optimizer.step()``. 2024-06-26T05:54:18.9654636Z >>> # Thus, the inter-node communication only occurs periodically after ``warmup_steps``. 2024-06-26T05:54:18.9655394Z >>> averager.average_parameters(model.parameters()) 2024-06-26T05:54:18.9655772Z 2024-06-26T05:54:18.9655881Z .. warning :: 2024-06-26T05:54:18.9656418Z The last group size in the dict must be the size of the provided ``process_group``, 2024-06-26T05:54:18.9657243Z which indicates model averaging at the highest level of the hierarchy. 2024-06-26T05:54:18.9658128Z If ``process_group`` is not provided, then the last group size should be equal to the world size. 2024-06-26T05:54:18.9658706Z 2024-06-26T05:54:18.9658824Z .. warning :: 2024-06-26T05:54:18.9659273Z `HierarchicalModelAverager` is experimental and subject to change. 2024-06-26T05:54:18.9659738Z 2024-06-26T05:54:18.9660120Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:18.9660594Z 2024-06-26T05:54:18.9787446Z msg = Cannot scrape callname=BroadcastingTorchSaveReader in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/format_utils.py line=40. 2024-06-26T05:54:18.9789005Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:18.9789516Z 2024-06-26T05:54:18.9789922Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint 2024-06-26T05:54:18.9791343Z on the coordinator rank, and then broadcast and shard each tensor to all ranks. 2024-06-26T05:54:18.9791857Z 2024-06-26T05:54:18.9792073Z . N.B. Intended to be used with DynamicMetaLoadPlanner 2024-06-26T05:54:18.9792445Z 2024-06-26T05:54:18.9792569Z .. warning:: 2024-06-26T05:54:18.9792944Z Current implementation only supports loading Tensors. 2024-06-26T05:54:18.9793329Z 2024-06-26T05:54:18.9793469Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:18.9793875Z >>> sd = {"mode": model} 2024-06-26T05:54:18.9794181Z >>> dcp.load( 2024-06-26T05:54:18.9794446Z >>> sd, 2024-06-26T05:54:18.9794801Z >>> storage_reader=BroadcastingTorchSaveReader(), 2024-06-26T05:54:18.9795292Z >>> planner=DynamicMetaLoadPlanner(), 2024-06-26T05:54:18.9795734Z >>> checkpoint_id="path_to_model.pt" 2024-06-26T05:54:18.9796112Z >>> ) 2024-06-26T05:54:18.9796251Z 2024-06-26T05:54:18.9796642Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:18.9797134Z 2024-06-26T05:54:18.9798140Z msg = Cannot scrape callname=DynamicMetaLoadPlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/format_utils.py line=148. 2024-06-26T05:54:18.9799731Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:18.9800220Z 2024-06-26T05:54:18.9800781Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, 2024-06-26T05:54:18.9801937Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a 2024-06-26T05:54:18.9802779Z metadata file, like Torch Save files. 2024-06-26T05:54:18.9803060Z 2024-06-26T05:54:18.9803318Z . N.B. Intended to be used with BroadcastingTorchSaveReader 2024-06-26T05:54:18.9803714Z 2024-06-26T05:54:18.9803833Z .. warning:: 2024-06-26T05:54:18.9804203Z Current implementation only supports loading Tensors. 2024-06-26T05:54:18.9804588Z 2024-06-26T05:54:18.9804726Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:18.9805127Z >>> sd = {"mode": model} 2024-06-26T05:54:18.9805426Z >>> dcp.load( 2024-06-26T05:54:18.9805695Z >>> sd, 2024-06-26T05:54:18.9806043Z >>> storage_reader=BroadcastingTorchSaveReader(), 2024-06-26T05:54:18.9806540Z >>> planner=DynamicMetaLoadPlanner(), 2024-06-26T05:54:18.9806980Z >>> checkpoint_id="path_to_model.pt" 2024-06-26T05:54:18.9807365Z >>> ) 2024-06-26T05:54:18.9807500Z 2024-06-26T05:54:18.9807883Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:18.9808377Z 2024-06-26T05:54:18.9844047Z msg = Cannot scrape callname=load_sharded_optimizer_state_dict in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/optimizer.py line=213. 2024-06-26T05:54:18.9845500Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:18.9846018Z 2024-06-26T05:54:18.9846312Z Load a state_dict in conjunction with FSDP sharded optimizer state. 2024-06-26T05:54:18.9846749Z 2024-06-26T05:54:18.9846963Z This is the current recommended way to checkpoint FSDP. 2024-06-26T05:54:18.9847452Z >>> # xdoctest: +SKIP 2024-06-26T05:54:18.9847847Z >>> import torch.distributed.checkpoint as dist_cp 2024-06-26T05:54:18.9848295Z >>> # Save 2024-06-26T05:54:18.9848571Z >>> model: torch.nn.Model 2024-06-26T05:54:18.9848938Z >>> optim_params = model.parameters() 2024-06-26T05:54:18.9849390Z >>> optim = torch.optim.SGD(optim_params, lr=0.01) 2024-06-26T05:54:18.9849827Z >>> # Save 2024-06-26T05:54:18.9850280Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT): 2024-06-26T05:54:18.9850830Z >>> state_dict = { 2024-06-26T05:54:18.9851236Z >>> "optimizer": FSDP.optim_state_dict(model, optim), 2024-06-26T05:54:18.9851731Z >>> "model": model.state_dict() 2024-06-26T05:54:18.9852114Z >>> } 2024-06-26T05:54:18.9852373Z >>> dist_cp.save_state_dict( 2024-06-26T05:54:18.9852749Z >>> state_dict=optim_state, 2024-06-26T05:54:18.9853240Z >>> storage_writer=dist_cp.FileSystemWriter("checkpoint"), 2024-06-26T05:54:18.9853938Z >>> planner=dist_cp.DefaultSavePlanner(), 2024-06-26T05:54:18.9854365Z >>> ) 2024-06-26T05:54:18.9854612Z >>> 2024-06-26T05:54:18.9854833Z >>> # Load 2024-06-26T05:54:18.9855290Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT): 2024-06-26T05:54:18.9855917Z >>> model_state_dict = model_tp.state_dict() 2024-06-26T05:54:18.9856339Z >>> checkpoint = { 2024-06-26T05:54:18.9856672Z >>> "model": model_state_dict 2024-06-26T05:54:18.9857043Z >>> } 2024-06-26T05:54:18.9857299Z >>> dist_cp.load_state_dict( 2024-06-26T05:54:18.9857671Z >>> state_dict=checkpoint, 2024-06-26T05:54:18.9858171Z >>> storage_reader=dist_cp.FileSystemReader(checkpoint_file), 2024-06-26T05:54:18.9858733Z >>> planner=dist_cp.DefaultLoadPlanner(), 2024-06-26T05:54:18.9859160Z >>> ) 2024-06-26T05:54:18.9859507Z >>> model.load_state_dict(checkpoint["model_state"]) 2024-06-26T05:54:18.9859937Z >>> 2024-06-26T05:54:18.9860300Z >>> optim_state = dist_cp.load_sharded_optimizer_state_dict( 2024-06-26T05:54:18.9860799Z >>> model_state_dict, 2024-06-26T05:54:18.9861301Z >>> optimizer_key="optimizer", 2024-06-26T05:54:18.9861808Z >>> storage_reader=dist_cp.FileSystemReader("checkpoint"), 2024-06-26T05:54:18.9862294Z >>> ) 2024-06-26T05:54:18.9862519Z >>> 2024-06-26T05:54:18.9862838Z >>> flattened_osd = FSDP.optim_state_dict_to_load( 2024-06-26T05:54:18.9863343Z >>> model, optim, optim_state["optimizer"] 2024-06-26T05:54:18.9863832Z >>> ) 2024-06-26T05:54:18.9864076Z >>> 2024-06-26T05:54:18.9864364Z >>> optim.load_state_dict(flattened_osd) 2024-06-26T05:54:18.9864668Z 2024-06-26T05:54:18.9865062Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:18.9865555Z 2024-06-26T05:54:18.9869099Z msg = Cannot scrape callname=SavePlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/planner.py line=110. 2024-06-26T05:54:18.9870666Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:18.9871198Z 2024-06-26T05:54:18.9871598Z Abstract class defining the protocol used by save_state_dict to plan the save process. 2024-06-26T05:54:18.9872140Z 2024-06-26T05:54:18.9872535Z SavePlanners are stateful objects that can be used to customize the whole save process. 2024-06-26T05:54:18.9873079Z 2024-06-26T05:54:18.9873466Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it 2024-06-26T05:54:18.9874159Z will be visible to the whole process. 2024-06-26T05:54:18.9874439Z 2024-06-26T05:54:18.9874887Z A planner subclass can expect the following sequence of calls during save_state_dict: 2024-06-26T05:54:18.9875606Z 2024-06-26T05:54:18.9875802Z 1) set_up_planner - called on all ranks. 2024-06-26T05:54:18.9876492Z Signals the start of a checkpoint save. 2024-06-26T05:54:18.9877084Z 2024-06-26T05:54:18.9877456Z 2) create_local_plan - called on all ranks. 2024-06-26T05:54:18.9878759Z Process the state_dict and produces a `SavePlan` that will be sent for global planning. 2024-06-26T05:54:18.9879313Z 2024-06-26T05:54:18.9879599Z 3) create_global_plan - called on the coordinator rank only. 2024-06-26T05:54:18.9880254Z Takes the SavePlan from all ranks and make any global decision. 2024-06-26T05:54:18.9880740Z 2024-06-26T05:54:18.9880932Z 4) finish_plan - called on all ranks. 2024-06-26T05:54:18.9881502Z This gives each rank a chance to adjust to global planning decisions. 2024-06-26T05:54:18.9881968Z 2024-06-26T05:54:18.9882207Z 5) resolve_data - called multiple times on each rank 2024-06-26T05:54:18.9882842Z Lookups a value on the `state_dict` for the storage layer to write. 2024-06-26T05:54:18.9883278Z 2024-06-26T05:54:18.9883690Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as 2024-06-26T05:54:18.9884488Z most changes can be expressed by changes in a single method. 2024-06-26T05:54:18.9884898Z 2024-06-26T05:54:18.9885048Z There are 3 usual patterns of extension: 2024-06-26T05:54:18.9885342Z 2024-06-26T05:54:18.9885689Z Rewriting state_dict. This is the simplest way to extend the save process as it 2024-06-26T05:54:18.9886529Z doesn't requite understanding the intrincacies of how SavePlan works: 2024-06-26T05:54:18.9886992Z 2024-06-26T05:54:18.9887129Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:18.9887592Z >>> class RenamePlanner(DefaultSavePlanner): 2024-06-26T05:54:18.9888029Z >>> def set_up_planner( 2024-06-26T05:54:18.9888360Z >>> self, 2024-06-26T05:54:18.9888672Z >>> state_dict: STATE_DICT_TYPE, 2024-06-26T05:54:18.9889106Z >>> storage_meta: Optional[StorageMeta], 2024-06-26T05:54:18.9889551Z >>> is_coordinator: bool, 2024-06-26T05:54:18.9889939Z >>> ) -> None: 2024-06-26T05:54:18.9890252Z >>> # prefix all keys with `foo_`` 2024-06-26T05:54:18.9890970Z >>> super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator) 2024-06-26T05:54:18.9891554Z 2024-06-26T05:54:18.9892005Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted 2024-06-26T05:54:18.9892739Z 2024-06-26T05:54:18.9892891Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:18.9893326Z >>> class FP16Planner(DefaultSavePlanner): 2024-06-26T05:54:18.9893978Z >>> def create_local_plan(self): 2024-06-26T05:54:18.9894409Z >>> plan = super().create_local_plan() 2024-06-26T05:54:18.9894949Z >>> for p in plan: 2024-06-26T05:54:18.9895321Z >>> if p.tensor_data is not None: 2024-06-26T05:54:18.9895840Z >>> p.tensor_data.properties.dtype = torch.float16 2024-06-26T05:54:18.9896312Z >>> return plan 2024-06-26T05:54:18.9896607Z >>> 2024-06-26T05:54:18.9896887Z >>> def resolve_data(self, write_item): 2024-06-26T05:54:18.9897339Z >>> item = super().resolve_data(write_item) 2024-06-26T05:54:18.9898035Z >>> return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16) 2024-06-26T05:54:18.9898569Z 2024-06-26T05:54:18.9899129Z Using the global planning step to make central decisions that can't be made individually by each rank 2024-06-26T05:54:18.9899752Z 2024-06-26T05:54:18.9899903Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:18.9900311Z >>> from itertools import islice 2024-06-26T05:54:18.9900704Z >>> from dataclasses import replace 2024-06-26T05:54:18.9901202Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner): 2024-06-26T05:54:18.9902023Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0 2024-06-26T05:54:18.9902793Z >>> # This sample doesn't handle ShardedTensors 2024-06-26T05:54:18.9903293Z >>> def create_global_plan(self, all_plans): 2024-06-26T05:54:18.9903720Z >>> def chunk(it, size): 2024-06-26T05:54:18.9904083Z >>> it = iter(it) 2024-06-26T05:54:18.9904537Z >>> return list(iter(lambda: tuple(islice(it, size)), ())) 2024-06-26T05:54:18.9905014Z >>> all_plans = [ 2024-06-26T05:54:18.9905422Z >>> replace(plan, items=items) for plan, items in 2024-06-26T05:54:18.9906022Z >>> zip(all_plans, chunk(all_plans[0].items, len(all_plans))) 2024-06-26T05:54:18.9906519Z >>> ] 2024-06-26T05:54:18.9906868Z >>> return super().create_global_plan(all_plans) 2024-06-26T05:54:18.9907201Z 2024-06-26T05:54:18.9907576Z Finally, some planners need to save additional metadata in the checkpoint, this is 2024-06-26T05:54:18.9908444Z accomplished by having each rank contribute their data items in the local plan and 2024-06-26T05:54:18.9909109Z the global planner aggregate them: 2024-06-26T05:54:18.9909389Z 2024-06-26T05:54:18.9909529Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:18.9910016Z >>> class SaveExtraDataPlanner(DefaultSavePlanner): 2024-06-26T05:54:18.9910574Z >>> def create_local_plan(self) -> SavePlan: 2024-06-26T05:54:18.9911050Z >>> plan = super().create_local_plan() 2024-06-26T05:54:18.9911621Z >>> return replace(plan, planner_data="per-rank-data") 2024-06-26T05:54:18.9912078Z >>> 2024-06-26T05:54:18.9912690Z >>> def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]: 2024-06-26T05:54:18.9913503Z >>> global_plan, metadata = super().create_global_plan(all_plans) 2024-06-26T05:54:18.9914119Z >>> merged_data = [p.planner_data for p in global_plan] 2024-06-26T05:54:18.9914722Z >>> metadata = replace(metadata, planner_data=merged_data) 2024-06-26T05:54:18.9915246Z >>> return global_plan, metadata 2024-06-26T05:54:18.9915529Z 2024-06-26T05:54:18.9915911Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:18.9916402Z 2024-06-26T05:54:18.9917331Z msg = Cannot scrape callname=LoadPlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/planner.py line=275. 2024-06-26T05:54:18.9918657Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:18.9919163Z 2024-06-26T05:54:18.9919535Z Abstract class defining the protocol used by load_state_dict to plan the load process. 2024-06-26T05:54:18.9920157Z 2024-06-26T05:54:18.9920546Z LoadPlanner are stateful objects that can be used to customize the whole load process. 2024-06-26T05:54:18.9921203Z 2024-06-26T05:54:18.9921584Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it 2024-06-26T05:54:18.9922344Z will be visible to the whole process. 2024-06-26T05:54:18.9922623Z 2024-06-26T05:54:18.9923002Z A planner subclass can expect the following sequence of calls during load_state_dict: 2024-06-26T05:54:18.9923535Z 2024-06-26T05:54:18.9923738Z 1) set_up_planner - called on all ranks. 2024-06-26T05:54:18.9924196Z Signals the start of loading a checkpoint. 2024-06-26T05:54:18.9924526Z 2024-06-26T05:54:18.9924720Z 2) create_local_plan - called on all ranks. 2024-06-26T05:54:18.9925420Z Process the state_dict and produces a `LoadPlan` that will be sent for global planning. 2024-06-26T05:54:18.9925963Z 2024-06-26T05:54:18.9926254Z 3) create_global_plan - called on the coordinator rank only. 2024-06-26T05:54:18.9926915Z Takes the LoadPlan from all ranks and make any global decision. 2024-06-26T05:54:18.9927332Z 2024-06-26T05:54:18.9927573Z 4) load_bytes - called multiple times on each rank 2024-06-26T05:54:18.9928174Z This is called once per non-tensor value in state_dict. 2024-06-26T05:54:18.9928563Z 2024-06-26T05:54:18.9928911Z 5) resolve_tensor and commit_tensor - called multiple times on each rank 2024-06-26T05:54:18.9929616Z They are called in pair for each Tensor value in state_dict. 2024-06-26T05:54:18.9930016Z 2024-06-26T05:54:18.9930423Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as 2024-06-26T05:54:18.9931215Z most changes can be expressed by changes in a single method. 2024-06-26T05:54:18.9931623Z 2024-06-26T05:54:18.9931779Z There are two usual patterns of extension: 2024-06-26T05:54:18.9932086Z 2024-06-26T05:54:18.9932433Z Rewriting state_dict. This is the simplest way to extend the load process as it 2024-06-26T05:54:18.9933331Z doesn't requite understanding the intrincacies of how LoadPlan works. We need 2024-06-26T05:54:18.9934216Z to keep a reference to the original state_dict as load happens in place so 2024-06-26T05:54:18.9934840Z we need to be able to perform it in place 2024-06-26T05:54:18.9935141Z 2024-06-26T05:54:18.9935296Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:18.9935746Z >>> class RenamePlanner(DefaultLoadPlanner): 2024-06-26T05:54:18.9936195Z >>> def set_up_planner( 2024-06-26T05:54:18.9936526Z >>> self, 2024-06-26T05:54:18.9936825Z >>> state_dict: STATE_DICT_TYPE, 2024-06-26T05:54:18.9937231Z >>> metadata: Metadata, 2024-06-26T05:54:18.9937601Z >>> is_coordinator: bool, 2024-06-26T05:54:18.9937974Z >>> ) -> None: 2024-06-26T05:54:18.9938314Z >>> self.original_state_dict = state_dict 2024-06-26T05:54:18.9938865Z >>> state_dict = {"foo_" + k: v for k, v in state_dict.items()} 2024-06-26T05:54:18.9939342Z >>> 2024-06-26T05:54:18.9939627Z >>> if self.flatten_sharded_tensors: 2024-06-26T05:54:18.9940139Z >>> state_dict = _flatten_sharded_tensors(state_dict) 2024-06-26T05:54:18.9940582Z >>> 2024-06-26T05:54:18.9940851Z >>> if self.flatten_state_dict: 2024-06-26T05:54:18.9941382Z >>> state_dict, self.mappings = flatten_state_dict(state_dict) 2024-06-26T05:54:18.9941868Z >>> 2024-06-26T05:54:18.9942137Z >>> self.state_dict = state_dict 2024-06-26T05:54:18.9942547Z >>> self.metadata = metadata 2024-06-26T05:54:18.9942963Z >>> self.is_coordinator = is_coordinator 2024-06-26T05:54:18.9943368Z >>> 2024-06-26T05:54:18.9943662Z >>> def load_bytes(self, read_item, value): 2024-06-26T05:54:18.9944093Z >>> # Remove the "foo_" prefix 2024-06-26T05:54:18.9944686Z >>> self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value) 2024-06-26T05:54:18.9945166Z 2024-06-26T05:54:18.9945264Z 2024-06-26T05:54:18.9945608Z Modifying resolve_tensor and commit_tensor to handle load time transformation. 2024-06-26T05:54:18.9946094Z 2024-06-26T05:54:18.9946244Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:18.9946721Z >>> class MetaModelMaterialize(DefaultSavePlanner): 2024-06-26T05:54:18.9947234Z >>> def resolve_tensor(self, read_item): 2024-06-26T05:54:18.9947793Z >>> tensor = super().resolve_tensor(read_item) 2024-06-26T05:54:18.9948300Z >>> return torch.empty_like(tensor, device="cpu") 2024-06-26T05:54:18.9948743Z >>> 2024-06-26T05:54:18.9949046Z >>> def commit_tensor(self, read_item, tensor): 2024-06-26T05:54:18.9949566Z >>> self.state_dict[read_item.dest_index.fqn] = tensor 2024-06-26T05:54:18.9949938Z 2024-06-26T05:54:18.9950396Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:18.9950871Z 2024-06-26T05:54:19.0048495Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_loader.py line=60. 2024-06-26T05:54:19.0049867Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:19.0050401Z 2024-06-26T05:54:19.0050595Z Load a distributed ``state_dict`` in SPMD style. 2024-06-26T05:54:19.0050931Z 2024-06-26T05:54:19.0051182Z Each rank will try to read the least amount of data necessary 2024-06-26T05:54:19.0051901Z to fullfill the requested `state_dict`. When loading :class:`ShardedTensor` 2024-06-26T05:54:19.0052713Z or :class:`DTensor` instances, each rank only reads data for their local shards. 2024-06-26T05:54:19.0053202Z 2024-06-26T05:54:19.0053731Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2024-06-26T05:54:19.0054594Z load will first call ``state_dict`` before attempting deserialization, followed by 2024-06-26T05:54:19.0055317Z ``load_state_dict`` once the deserialization is complete. 2024-06-26T05:54:19.0055685Z 2024-06-26T05:54:19.0055824Z .. warning:: 2024-06-26T05:54:19.0056198Z All tensors in ``state_dict`` must be allocated on their 2024-06-26T05:54:19.0056786Z destination device *prior to* calling this function. 2024-06-26T05:54:19.0057148Z 2024-06-26T05:54:19.0057538Z All non-tensor data is loaded using `torch.load()` and modified in place 2024-06-26T05:54:19.0058109Z on state_dict. 2024-06-26T05:54:19.0058305Z 2024-06-26T05:54:19.0058405Z .. warning:: 2024-06-26T05:54:19.0058851Z Users must call `load_state_dict` on the root module to ensure load 2024-06-26T05:54:19.0059561Z pos-processing and non-tensor data properly propagates. 2024-06-26T05:54:19.0059964Z 2024-06-26T05:54:19.0060063Z .. note: 2024-06-26T05:54:19.0060526Z If no process group is initialized, this function will assume the intent 2024-06-26T05:54:19.0061296Z is to load a checkpoint into the local process. This can be useful in the 2024-06-26T05:54:19.0062106Z case of local inference, and when using regular Tensors (as opposed to DTensor 2024-06-26T05:54:19.0062732Z or ShardedTensor) 2024-06-26T05:54:19.0062936Z 2024-06-26T05:54:19.0063031Z .. note: 2024-06-26T05:54:19.0063363Z Rank 0 is assumed to be the coordinator rank. 2024-06-26T05:54:19.0063705Z 2024-06-26T05:54:19.0063795Z Args: 2024-06-26T05:54:19.0064136Z state_dict (Dict[str, Any]): The state_dict to save. 2024-06-26T05:54:19.0064662Z checkpoint_id (Union[str, os.PathLike, None]): 2024-06-26T05:54:19.0065300Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-06-26T05:54:19.0066042Z depends on the storage. It can be a path to a folder or to a file. 2024-06-26T05:54:19.0066825Z It can also be a key if the storage is a key-value store. 2024-06-26T05:54:19.0067332Z (Default: ``None``) 2024-06-26T05:54:19.0067719Z storage_reader (Optional[StorageReader]): 2024-06-26T05:54:19.0068296Z Instance of StorageWriter used to perform reads. If this is not 2024-06-26T05:54:19.0068998Z specified, DCP will automatically infer the reader based on the 2024-06-26T05:54:19.0069837Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-06-26T05:54:19.0070377Z be raised. (Default: ``None``) 2024-06-26T05:54:19.0070799Z planner (Optional[LoadPlanner]): 2024-06-26T05:54:19.0071622Z Instance of LoadPlanner. If this is not specificed, the default 2024-06-26T05:54:19.0072286Z planner will be used. (Default: ``None``) 2024-06-26T05:54:19.0072850Z process_group (Optional[ProcessGroup]): 2024-06-26T05:54:19.0073816Z ProcessGroup to be used for cross-rank synchronization. 2024-06-26T05:54:19.0074446Z (Default: ``None``) 2024-06-26T05:54:19.0074679Z 2024-06-26T05:54:19.0074776Z Returns: 2024-06-26T05:54:19.0075027Z None. 2024-06-26T05:54:19.0075169Z 2024-06-26T05:54:19.0075273Z Examples 2024-06-26T05:54:19.0075521Z >>> # xdoctest: +SKIP 2024-06-26T05:54:19.0075853Z >>> my_model = MyModule() 2024-06-26T05:54:19.0076263Z >>> optimizer = Adagrad(my_model.parameters()) 2024-06-26T05:54:19.0076756Z >>> model_state_dict = my_model.state_dict() 2024-06-26T05:54:19.0077472Z >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader("/checkpoint/1") 2024-06-26T05:54:19.0078054Z 2024-06-26T05:54:19.0078371Z >>> torch.distributed.checkpoint.load_state_dict( 2024-06-26T05:54:19.0078971Z >>> state_dict=model_state_dict, 2024-06-26T05:54:19.0079519Z >>> storage_reader=fs_storage_reader, 2024-06-26T05:54:19.0080010Z >>> ) 2024-06-26T05:54:19.0080153Z 2024-06-26T05:54:19.0080411Z >>> # module.load_state_dict() function might have customized steps 2024-06-26T05:54:19.0081081Z >>> # to flush the state_dict, must call it to 2024-06-26T05:54:19.0081534Z >>> # ensure correct behavior. 2024-06-26T05:54:19.0081948Z >>> my_model.load_state_dict(model_state_dict) 2024-06-26T05:54:19.0082281Z 2024-06-26T05:54:19.0082381Z .. note:: 2024-06-26T05:54:19.0082808Z load_state_dict uses collectives to coordinate reads across ranks. 2024-06-26T05:54:19.0083589Z For NCCL-based process groups, internal tensor representations of 2024-06-26T05:54:19.0084346Z objects must be moved to the GPU device before communication takes place. 2024-06-26T05:54:19.0085130Z In this case, the device used is given by ``torch.cuda.current_device()`` 2024-06-26T05:54:19.0086001Z and it is the user's responsibility to ensure that this is set so that each 2024-06-26T05:54:19.0086729Z rank has an individual GPU, via ``torch.cuda.set_device()``. 2024-06-26T05:54:19.0087142Z 2024-06-26T05:54:19.0087524Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:19.0087999Z 2024-06-26T05:54:19.0088962Z msg = Cannot scrape callname=save in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=66. 2024-06-26T05:54:19.0090301Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:19.0090793Z 2024-06-26T05:54:19.0090938Z Save a distributed model in SPMD style. 2024-06-26T05:54:19.0091245Z 2024-06-26T05:54:19.0091490Z This function is different from ``torch.save()`` as it handles 2024-06-26T05:54:19.0092242Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards. 2024-06-26T05:54:19.0092742Z 2024-06-26T05:54:19.0093101Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2024-06-26T05:54:19.0093888Z save will call ``state_dict`` before serialization. 2024-06-26T05:54:19.0094246Z 2024-06-26T05:54:19.0094349Z .. warning:: 2024-06-26T05:54:19.0094838Z There is no guarantees of Backwards Compatibility across PyTorch versions 2024-06-26T05:54:19.0095421Z for saved state_dicts. 2024-06-26T05:54:19.0095657Z 2024-06-26T05:54:19.0095757Z .. warning:: 2024-06-26T05:54:19.0096209Z If using the `process_group` argument, make sure that only its ranks 2024-06-26T05:54:19.0097029Z call `save_state_dict` and that all data in state_dict belong to it. 2024-06-26T05:54:19.0097637Z 2024-06-26T05:54:19.0097735Z .. note:: 2024-06-26T05:54:19.0098317Z When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of 2024-06-26T05:54:19.0099183Z the shard_group should be calling `save_state_dict` and the corresponding process 2024-06-26T05:54:19.0099893Z group needs to be passed in. 2024-06-26T05:54:19.0100158Z 2024-06-26T05:54:19.0100254Z .. note:: 2024-06-26T05:54:19.0100776Z If no process group is available, this function assumes the intention is to save the 2024-06-26T05:54:19.0101433Z state_dict in the local process. 2024-06-26T05:54:19.0101723Z 2024-06-26T05:54:19.0101819Z .. note: 2024-06-26T05:54:19.0102144Z Rank 0 is assumed to be the coordinator rank. 2024-06-26T05:54:19.0102468Z 2024-06-26T05:54:19.0102472Z 2024-06-26T05:54:19.0102563Z Args: 2024-06-26T05:54:19.0102901Z state_dict (Dict[str, Any]): The state_dict to save. 2024-06-26T05:54:19.0103434Z checkpoint_id (Union[str, os.PathLike, None]): 2024-06-26T05:54:19.0104058Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-06-26T05:54:19.0104796Z depends on the storage. It can be a path to a folder or to a file. 2024-06-26T05:54:19.0105533Z It can also be a key if the storage is a key-value store. 2024-06-26T05:54:19.0106040Z (Default: ``None``) 2024-06-26T05:54:19.0106418Z storage_writer (Optional[StorageWriter]): 2024-06-26T05:54:19.0107012Z Instance of StorageWriter used to perform writes. If this is not 2024-06-26T05:54:19.0107854Z specified, DCP will automatically infer the writer based on the 2024-06-26T05:54:19.0108554Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-06-26T05:54:19.0109114Z be raised. (Default: ``None``) 2024-06-26T05:54:19.0109530Z planner (Optional[SavePlanner]): 2024-06-26T05:54:19.0110067Z Instance of SavePlanner. If this is not specificed, the default 2024-06-26T05:54:19.0110666Z planner will be used. (Default: ``None``) 2024-06-26T05:54:19.0111146Z process_group (Optional[ProcessGroup]): 2024-06-26T05:54:19.0111743Z ProcessGroup to be used for cross-rank synchronization. 2024-06-26T05:54:19.0112250Z (Default: ``None``) 2024-06-26T05:54:19.0112468Z 2024-06-26T05:54:19.0112575Z Returns: 2024-06-26T05:54:19.0112918Z Metadata: Metadata object for the saved checkpoint. 2024-06-26T05:54:19.0113288Z 2024-06-26T05:54:19.0113383Z Example: 2024-06-26T05:54:19.0113647Z >>> # xdoctest: +SKIP 2024-06-26T05:54:19.0113968Z >>> my_model = MyModule() 2024-06-26T05:54:19.0114212Z 2024-06-26T05:54:19.0114344Z >>> state_dict = {"model": my_model} 2024-06-26T05:54:19.0114637Z 2024-06-26T05:54:19.0115033Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2024-06-26T05:54:19.0115761Z >>> torch.distributed.checkpoint.save( 2024-06-26T05:54:19.0116189Z >>> state_dict=state_dict, 2024-06-26T05:54:19.0116599Z >>> storage_writer=fs_storage_writer, 2024-06-26T05:54:19.0117000Z >>> ) 2024-06-26T05:54:19.0117141Z 2024-06-26T05:54:19.0117238Z .. note:: 2024-06-26T05:54:19.0117670Z save_state_dict uses collectives to coordinate writes across ranks. 2024-06-26T05:54:19.0118455Z For NCCL-based process groups, internal tensor representations of 2024-06-26T05:54:19.0119206Z objects must be moved to the GPU device before communication takes place. 2024-06-26T05:54:19.0119987Z In this case, the device used is given by ``torch.cuda.current_device()`` 2024-06-26T05:54:19.0120895Z and it is the user's responsibility to ensure that this is set so that 2024-06-26T05:54:19.0121613Z each rank has an individual GPU, via ``torch.cuda.set_device()``. 2024-06-26T05:54:19.0122056Z 2024-06-26T05:54:19.0122440Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:19.0122932Z 2024-06-26T05:54:19.0123874Z msg = Cannot scrape callname=async_save in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=169. 2024-06-26T05:54:19.0125336Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:19.0126233Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the 2024-06-26T05:54:19.0127195Z staging storage (defaults to CPU memory), and then calls the `save` in a separate thread. 2024-06-26T05:54:19.0127747Z 2024-06-26T05:54:19.0127865Z .. warning:: 2024-06-26T05:54:19.0128236Z This feature is experimental and subject to change. 2024-06-26T05:54:19.0128606Z 2024-06-26T05:54:19.0128701Z Args: 2024-06-26T05:54:19.0129055Z state_dict (Dict[str, Any]): The state_dict to save. 2024-06-26T05:54:19.0129604Z checkpoint_id (Union[str, os.PathLike, None]): 2024-06-26T05:54:19.0130236Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-06-26T05:54:19.0130979Z depends on the storage. It can be a path to a folder or to a file. 2024-06-26T05:54:19.0131729Z It can also be a key if the storage is a key-value store. 2024-06-26T05:54:19.0132231Z (Default: ``None``) 2024-06-26T05:54:19.0132646Z storage_writer (Optional[StorageWriter]): 2024-06-26T05:54:19.0133319Z Instance of StorageWriter used to perform 'stage' and 'save'. If 2024-06-26T05:54:19.0134263Z this is not specified, DCP will automatically infer the writer based on the 2024-06-26T05:54:19.0135024Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-06-26T05:54:19.0135594Z be raised. (Default: ``None``) 2024-06-26T05:54:19.0136021Z planner (Optional[SavePlanner]): 2024-06-26T05:54:19.0136590Z Instance of SavePlanner. If this is not specificed, the default 2024-06-26T05:54:19.0137201Z planner will be used. (Default: ``None``) 2024-06-26T05:54:19.0137683Z process_group (Optional[ProcessGroup]): 2024-06-26T05:54:19.0138308Z ProcessGroup to be used for cross-rank synchronization. 2024-06-26T05:54:19.0138819Z (Default: ``None``) 2024-06-26T05:54:19.0139056Z 2024-06-26T05:54:19.0139168Z Returns: 2024-06-26T05:54:19.0139600Z Future: A future holding the resultant Metadata object from `save`. 2024-06-26T05:54:19.0140049Z 2024-06-26T05:54:19.0140152Z Example: 2024-06-26T05:54:19.0140434Z >>> # xdoctest: +SKIP 2024-06-26T05:54:19.0140779Z >>> my_model = MyModule() 2024-06-26T05:54:19.0141038Z 2024-06-26T05:54:19.0141182Z >>> state_dict = {"model": my_model} 2024-06-26T05:54:19.0141474Z 2024-06-26T05:54:19.0141890Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2024-06-26T05:54:19.0142715Z >>> checkpoint_future = torch.distributed.checkpoint.async_save( 2024-06-26T05:54:19.0143278Z >>> state_dict=state_dict, 2024-06-26T05:54:19.0143705Z >>> storage_writer=fs_storage_writer, 2024-06-26T05:54:19.0144105Z >>> ) 2024-06-26T05:54:19.0144364Z >>> 2024-06-26T05:54:19.0144636Z >>> # ... do some work ... 2024-06-26T05:54:19.0144977Z >>> 2024-06-26T05:54:19.0145263Z >>> checkpoint_future.result() 2024-06-26T05:54:19.0145539Z 2024-06-26T05:54:19.0145640Z 2024-06-26T05:54:19.0146153Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:19.0146642Z 2024-06-26T05:54:19.0171646Z msg = Cannot scrape callname=construct_and_record_rdzv_event in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/elastic/events/__init__.py line=91. 2024-06-26T05:54:19.0173086Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:19.0173764Z 2024-06-26T05:54:19.0174011Z Initialize rendezvous event object and record its operations. 2024-06-26T05:54:19.0174414Z 2024-06-26T05:54:19.0174521Z Args: 2024-06-26T05:54:19.0174815Z run_id (str): The run id of the rendezvous. 2024-06-26T05:54:19.0175461Z message (str): The message describing the event. 2024-06-26T05:54:19.0176146Z node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED). 2024-06-26T05:54:19.0176894Z name (str): Event name. (E.g. Current action being performed). 2024-06-26T05:54:19.0177547Z hostname (str): Hostname of the node. 2024-06-26T05:54:19.0178032Z pid (Optional[int]): The process id of the node. 2024-06-26T05:54:19.0178691Z master_endpoint (str): The master endpoint for the rendezvous store, if known. 2024-06-26T05:54:19.0179553Z local_id (Optional[int]): The local_id of the node, if defined in dynamic_rendezvous.py 2024-06-26T05:54:19.0180292Z rank (Optional[int]): The rank of the node, if known. 2024-06-26T05:54:19.0180740Z Returns: 2024-06-26T05:54:19.0180985Z None 2024-06-26T05:54:19.0181227Z Example: 2024-06-26T05:54:19.0181528Z >>> # See DynamicRendezvousHandler class 2024-06-26T05:54:19.0181954Z >>> def _record( 2024-06-26T05:54:19.0182246Z ... self, 2024-06-26T05:54:19.0182517Z ... message: str, 2024-06-26T05:54:19.0182917Z ... node_state: NodeState = NodeState.RUNNING, 2024-06-26T05:54:19.0183393Z ... rank: Optional[int] = None, 2024-06-26T05:54:19.0183799Z ... ) -> None: 2024-06-26T05:54:19.0184137Z ... construct_and_record_rdzv_event( 2024-06-26T05:54:19.0184660Z ... name=f"{self.__class__.__name__}.{get_method_name()}", 2024-06-26T05:54:19.0185175Z ... run_id=self._settings.run_id, 2024-06-26T05:54:19.0185601Z ... message=message, 2024-06-26T05:54:19.0185977Z ... node_state=node_state, 2024-06-26T05:54:19.0186383Z ... hostname=self._this_node.addr, 2024-06-26T05:54:19.0186821Z ... pid=self._this_node.pid, 2024-06-26T05:54:19.0187261Z ... local_id=self._this_node.local_id, 2024-06-26T05:54:19.0187675Z ... rank=rank, 2024-06-26T05:54:19.0187989Z ... ) 2024-06-26T05:54:19.0188150Z 2024-06-26T05:54:19.0188547Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:19.0189018Z 2024-06-26T05:54:19.1705018Z msg = Cannot scrape callname=MixedPrecision in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/api.py line=113. 2024-06-26T05:54:19.1706402Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:19.1706915Z 2024-06-26T05:54:19.1707235Z This configures FSDP-native mixed precision training. 2024-06-26T05:54:19.1707595Z 2024-06-26T05:54:19.1707696Z Attributes: 2024-06-26T05:54:19.1708163Z param_dtype (Optional[torch.dtype]): This specifies the dtype for model 2024-06-26T05:54:19.1708894Z parameters during forward and backward and thus the dtype for 2024-06-26T05:54:19.1709589Z forward and backward computation. Outside forward and backward, the 2024-06-26T05:54:19.1710296Z *sharded* parameters are kept in full precision (e.g. for the 2024-06-26T05:54:19.1710992Z optimizer step), and for model checkpointing, the parameters are 2024-06-26T05:54:19.1711616Z always saved in full precision. (Default: ``None``) 2024-06-26T05:54:19.1712261Z reduce_dtype (Optional[torch.dtype]): This specifies the dtype for 2024-06-26T05:54:19.1713053Z gradient reduction (i.e. reduce-scatter or all-reduce). If this is 2024-06-26T05:54:19.1713732Z ``None`` but ``param_dtype`` is not ``None``, then this takes on 2024-06-26T05:54:19.1714414Z the ``param_dtype`` value, still running gradient reduction in low 2024-06-26T05:54:19.1715122Z precision. This is permitted to differ from ``param_dtype``, e.g. 2024-06-26T05:54:19.1715829Z to force gradient reduction to run in full precision. (Default: 2024-06-26T05:54:19.1716346Z ``None``) 2024-06-26T05:54:19.1716802Z buffer_dtype (Optional[torch.dtype]): This specifies the dtype for 2024-06-26T05:54:19.1717513Z buffers. FSDP does not shard buffers. Rather, FSDP casts them to 2024-06-26T05:54:19.1718411Z ``buffer_dtype`` in the first forward pass and keeps them in that 2024-06-26T05:54:19.1719116Z dtype thereafter. For model checkpointing, the buffers are saved 2024-06-26T05:54:19.1719805Z in full precision except for ``LOCAL_STATE_DICT``. (Default: 2024-06-26T05:54:19.1720424Z ``None``) 2024-06-26T05:54:19.1720941Z keep_low_precision_grads (bool): If ``False``, then FSDP upcasts 2024-06-26T05:54:19.1721647Z gradients to full precision after the backward pass in preparation 2024-06-26T05:54:19.1722360Z for the optimizer step. If ``True``, then FSDP keeps the gradients 2024-06-26T05:54:19.1723086Z in the dtype used for gradient reduction, which can save memory if 2024-06-26T05:54:19.1723802Z using a custom optimizer that supports running in low precision. 2024-06-26T05:54:19.1724352Z (Default: ``False``) 2024-06-26T05:54:19.1724838Z cast_forward_inputs (bool): If ``True``, then this FSDP module casts 2024-06-26T05:54:19.1725542Z its forward args and kwargs to ``param_dtype``. This is to ensure 2024-06-26T05:54:19.1726255Z that parameter and input dtypes match for forward computation, as 2024-06-26T05:54:19.1726962Z required by many ops. This may need to be set to ``True`` when only 2024-06-26T05:54:19.1727700Z applying mixed precision to some but not all FSDP modules, in which 2024-06-26T05:54:19.1728495Z case a mixed-precision FSDP submodule needs to recast its inputs. 2024-06-26T05:54:19.1729037Z (Default: ``False``) 2024-06-26T05:54:19.1729546Z cast_root_forward_inputs (bool): If ``True``, then the root FSDP module 2024-06-26T05:54:19.1730260Z casts its forward args and kwargs to ``param_dtype``, overriding 2024-06-26T05:54:19.1730992Z the value of ``cast_forward_inputs``. For non-root FSDP modules, 2024-06-26T05:54:19.1731590Z this does not do anything. (Default: ``True``) 2024-06-26T05:54:19.1732219Z _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies 2024-06-26T05:54:19.1732900Z module classes to ignore for mixed precision when using an 2024-06-26T05:54:19.1733718Z ``auto_wrap_policy``: Modules of these classes will have FSDP 2024-06-26T05:54:19.1734410Z applied to them separately with mixed precision disabled (meaning 2024-06-26T05:54:19.1735131Z that the final FSDP construction would deviate from the specified 2024-06-26T05:54:19.1735820Z policy). If ``auto_wrap_policy`` is not specified, then this does 2024-06-26T05:54:19.1736517Z not do anything. This API is experimental and subject to change. 2024-06-26T05:54:19.1737080Z (Default: ``(_BatchNorm,)``) 2024-06-26T05:54:19.1737352Z 2024-06-26T05:54:19.1737589Z .. note:: This API is experimental and subject to change. 2024-06-26T05:54:19.1737967Z 2024-06-26T05:54:19.1738257Z .. note:: Only floating point tensors are cast to their specified dtypes. 2024-06-26T05:54:19.1738721Z 2024-06-26T05:54:19.1738964Z .. note:: In ``summon_full_params``, parameters are forced to full 2024-06-26T05:54:19.1739501Z precision, but buffers are not. 2024-06-26T05:54:19.1739768Z 2024-06-26T05:54:19.1740041Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when 2024-06-26T05:54:19.1740771Z their inputs are in a low precision like ``float16`` or ``bfloat16``. 2024-06-26T05:54:19.1741592Z Disabling FSDP's mixed precision for those norm modules only means that 2024-06-26T05:54:19.1742325Z the affine parameters are kept in ``float32``. However, this incurs 2024-06-26T05:54:19.1743133Z separate all-gathers and reduce-scatters for those norm modules, which 2024-06-26T05:54:19.1743903Z may be inefficient, so if the workload permits, the user should prefer 2024-06-26T05:54:19.1744532Z to still apply mixed precision to those modules. 2024-06-26T05:54:19.1744887Z 2024-06-26T05:54:19.1745172Z .. note:: By default, if the user passes a model with any ``_BatchNorm`` 2024-06-26T05:54:19.1746027Z modules and specifies an ``auto_wrap_policy``, then the batch norm 2024-06-26T05:54:19.1746763Z modules will have FSDP applied to them separately with mixed precision 2024-06-26T05:54:19.1747437Z disabled. See the ``_module_classes_to_ignore`` argument. 2024-06-26T05:54:19.1747832Z 2024-06-26T05:54:19.1748178Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and 2024-06-26T05:54:19.1748890Z ``cast_forward_inputs=False`` by default. For the root FSDP instance, 2024-06-26T05:54:19.1749538Z its ``cast_root_forward_inputs`` takes precedence over its 2024-06-26T05:54:19.1750202Z ``cast_forward_inputs``. For non-root FSDP instances, their 2024-06-26T05:54:19.1750876Z ``cast_root_forward_inputs`` values are ignored. The default setting is 2024-06-26T05:54:19.1751601Z sufficient for the typical case where each FSDP instance has the same 2024-06-26T05:54:19.1752345Z ``MixedPrecision`` configuration and only needs to cast inputs to the 2024-06-26T05:54:19.1753096Z ``param_dtype`` at the beginning of the model's forward pass. 2024-06-26T05:54:19.1753490Z 2024-06-26T05:54:19.1753778Z .. note:: For nested FSDP instances with different ``MixedPrecision`` 2024-06-26T05:54:19.1754503Z configurations, we recommend setting individual ``cast_forward_inputs`` 2024-06-26T05:54:19.1755269Z values to configure casting inputs or not before each instance's 2024-06-26T05:54:19.1755961Z forward. In such a case, since the casts happen before each FSDP 2024-06-26T05:54:19.1756715Z instance's forward, a parent FSDP instance should have its non-FSDP 2024-06-26T05:54:19.1757463Z submodules run before its FSDP submodules to avoid the activation dtype 2024-06-26T05:54:19.1758216Z being changed due to a different ``MixedPrecision`` configuration. 2024-06-26T05:54:19.1758658Z 2024-06-26T05:54:19.1758759Z Example:: 2024-06-26T05:54:19.1758935Z 2024-06-26T05:54:19.1759108Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:19.1759662Z >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3)) 2024-06-26T05:54:19.1760159Z >>> model[1] = FSDP( 2024-06-26T05:54:19.1760480Z >>> model[1], 2024-06-26T05:54:19.1761144Z >>> mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True), 2024-06-26T05:54:19.1761788Z >>> ) 2024-06-26T05:54:19.1762051Z >>> model = FSDP( 2024-06-26T05:54:19.1762368Z >>> model, 2024-06-26T05:54:19.1762946Z >>> mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), 2024-06-26T05:54:19.1763575Z >>> ) 2024-06-26T05:54:19.1763742Z 2024-06-26T05:54:19.1764031Z The above shows a working example. On the other hand, if ``model[1]`` 2024-06-26T05:54:19.1764747Z were replaced with ``model[0]``, meaning that the submodule using 2024-06-26T05:54:19.1765449Z different ``MixedPrecision`` ran its forward first, then ``model[1]`` 2024-06-26T05:54:19.1766189Z would incorrectly see ``float16`` activations instead of ``bfloat16`` 2024-06-26T05:54:19.1766730Z ones. 2024-06-26T05:54:19.1766873Z 2024-06-26T05:54:19.1766879Z 2024-06-26T05:54:19.1767266Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:19.1767755Z 2024-06-26T05:54:19.1842301Z msg = Cannot scrape callname=FullyShardedDataParallel.set_state_dict_type in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=648. 2024-06-26T05:54:19.1844506Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:19.1845378Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2024-06-26T05:54:19.1845887Z 2024-06-26T05:54:19.1846410Z Also takes (optional) configuration for the model's and optimizer's state dict. 2024-06-26T05:54:19.1847572Z The target module does not have to be a FSDP module. If the target 2024-06-26T05:54:19.1849041Z module is a FSDP module, its ``state_dict_type`` will also be changed. 2024-06-26T05:54:19.1849493Z 2024-06-26T05:54:19.1849859Z .. note:: This API should be called for only the top-level (root) 2024-06-26T05:54:19.1850391Z module. 2024-06-26T05:54:19.1850568Z 2024-06-26T05:54:19.1850955Z .. note:: This API enables users to transparently use the conventional 2024-06-26T05:54:19.1851668Z ``state_dict`` API to take model checkpoints in cases where the 2024-06-26T05:54:19.1852373Z root FSDP module is wrapped by another ``nn.Module``. For example, 2024-06-26T05:54:19.1853145Z the following will ensure ``state_dict`` is called on all non-FSDP 2024-06-26T05:54:19.1854086Z instances, while dispatching into `sharded_state_dict` implementation 2024-06-26T05:54:19.1854661Z for FSDP: 2024-06-26T05:54:19.1854850Z 2024-06-26T05:54:19.1854954Z Example:: 2024-06-26T05:54:19.1855148Z 2024-06-26T05:54:19.1855324Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:19.1855788Z >>> model = DDP(FSDP(...)) 2024-06-26T05:54:19.1856186Z >>> FSDP.set_state_dict_type( 2024-06-26T05:54:19.1856582Z >>> model, 2024-06-26T05:54:19.1856971Z >>> StateDictType.SHARDED_STATE_DICT, 2024-06-26T05:54:19.1857593Z >>> state_dict_config = ShardedStateDictConfig(offload_to_cpu=True), 2024-06-26T05:54:19.1858331Z >>> optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True), 2024-06-26T05:54:19.1858896Z >>> ) 2024-06-26T05:54:19.1859240Z >>> param_state_dict = model.state_dict() 2024-06-26T05:54:19.1871382Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2024-06-26T05:54:19.1871863Z 2024-06-26T05:54:19.1871978Z Args: 2024-06-26T05:54:19.1872302Z module (torch.nn.Module): Root module. 2024-06-26T05:54:19.1872943Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2024-06-26T05:54:19.1873733Z state_dict_config (Optional[StateDictConfig]): the configuration for the 2024-06-26T05:54:19.1874336Z target ``state_dict_type``. 2024-06-26T05:54:19.1874958Z optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration 2024-06-26T05:54:19.1875612Z for the optimizer state dict. 2024-06-26T05:54:19.1875913Z 2024-06-26T05:54:19.1876011Z Returns: 2024-06-26T05:54:19.1876472Z A StateDictSettings that include the previous state_dict type and 2024-06-26T05:54:19.1877055Z configuration for the module. 2024-06-26T05:54:19.1877430Z 2024-06-26T05:54:19.1878001Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:19.1878474Z 2024-06-26T05:54:19.1879673Z msg = Cannot scrape callname=FullyShardedDataParallel.state_dict_type in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=804. 2024-06-26T05:54:19.1881336Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:19.1882160Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2024-06-26T05:54:19.1882681Z 2024-06-26T05:54:19.1883114Z This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of 2024-06-26T05:54:19.1883872Z :meth:`set_state_dict_type` for the detail. 2024-06-26T05:54:19.1884194Z 2024-06-26T05:54:19.1884322Z Example:: 2024-06-26T05:54:19.1884494Z 2024-06-26T05:54:19.1884672Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:19.1885140Z >>> model = DDP(FSDP(...)) 2024-06-26T05:54:19.1885557Z >>> with FSDP.state_dict_type( 2024-06-26T05:54:19.1885943Z >>> model, 2024-06-26T05:54:19.1886332Z >>> StateDictType.SHARDED_STATE_DICT, 2024-06-26T05:54:19.1886925Z >>> ): 2024-06-26T05:54:19.1887239Z >>> checkpoint = model.state_dict() 2024-06-26T05:54:19.1887559Z 2024-06-26T05:54:19.1887654Z Args: 2024-06-26T05:54:19.1887977Z module (torch.nn.Module): Root module. 2024-06-26T05:54:19.1888706Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2024-06-26T05:54:19.1889485Z state_dict_config (Optional[StateDictConfig]): the model ``state_dict`` 2024-06-26T05:54:19.1890160Z configuration for the target ``state_dict_type``. 2024-06-26T05:54:19.1890824Z optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer 2024-06-26T05:54:19.1891557Z ``state_dict`` configuration for the target ``state_dict_type``. 2024-06-26T05:54:19.1892078Z 2024-06-26T05:54:19.1892594Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:19.1893079Z 2024-06-26T05:54:19.1905893Z msg = Cannot scrape callname=FullyShardedDataParallel.optim_state_dict in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1801. 2024-06-26T05:54:19.1907482Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:19.1908017Z 2024-06-26T05:54:19.1908406Z Transform the state-dict of an optimizer corresponding to a sharded model. 2024-06-26T05:54:19.1909091Z 2024-06-26T05:54:19.1909409Z The given state-dict can be transformed to one of three types: 2024-06-26T05:54:19.1910862Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict. 2024-06-26T05:54:19.1911938Z 2024-06-26T05:54:19.1912334Z For full optimizer state_dict, all states are unflattened and not sharded. 2024-06-26T05:54:19.1913105Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to 2024-06-26T05:54:19.1913660Z avoid OOM. 2024-06-26T05:54:19.1913811Z 2024-06-26T05:54:19.1914115Z For sharded optimizer state_dict, all states are unflattened but sharded. 2024-06-26T05:54:19.1914872Z CPU only can be specified via :meth:`state_dict_type` to further save 2024-06-26T05:54:19.1915407Z memory. 2024-06-26T05:54:19.1915544Z 2024-06-26T05:54:19.1915831Z For local state_dict, no transformation will be performed. But a state 2024-06-26T05:54:19.1916601Z will be converted from nn.Tensor to ShardedTensor to represent its sharding 2024-06-26T05:54:19.1917217Z nature (this is not supported yet). 2024-06-26T05:54:19.1917486Z 2024-06-26T05:54:19.1917601Z Example:: 2024-06-26T05:54:19.1917746Z 2024-06-26T05:54:19.1917910Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:19.1918544Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2024-06-26T05:54:19.1919225Z >>> from torch.distributed.fsdp import StateDictType 2024-06-26T05:54:19.1919810Z >>> from torch.distributed.fsdp import FullStateDictConfig 2024-06-26T05:54:19.1920466Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2024-06-26T05:54:19.1921088Z >>> # Save a checkpoint 2024-06-26T05:54:19.1921417Z >>> model, optim = ... 2024-06-26T05:54:19.1921764Z >>> FSDP.set_state_dict_type( 2024-06-26T05:54:19.1922123Z >>> model, 2024-06-26T05:54:19.1922430Z >>> StateDictType.FULL_STATE_DICT, 2024-06-26T05:54:19.1922907Z >>> FullStateDictConfig(rank0_only=False), 2024-06-26T05:54:19.1923424Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-06-26T05:54:19.1923857Z >>> ) 2024-06-26T05:54:19.1924141Z >>> state_dict = model.state_dict() 2024-06-26T05:54:19.1924642Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2024-06-26T05:54:19.1925186Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2024-06-26T05:54:19.1925643Z >>> # Load a checkpoint 2024-06-26T05:54:19.1925980Z >>> model, optim = ... 2024-06-26T05:54:19.1926381Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2024-06-26T05:54:19.1926859Z >>> FSDP.set_state_dict_type( 2024-06-26T05:54:19.1927362Z >>> model, 2024-06-26T05:54:19.1927670Z >>> StateDictType.FULL_STATE_DICT, 2024-06-26T05:54:19.1928135Z >>> FullStateDictConfig(rank0_only=False), 2024-06-26T05:54:19.1928655Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-06-26T05:54:19.1929084Z >>> ) 2024-06-26T05:54:19.1929447Z >>> model.load_state_dict(state_dict) 2024-06-26T05:54:19.1929932Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2024-06-26T05:54:19.1930408Z >>> model, optim, optim_state_dict 2024-06-26T05:54:19.1930799Z >>> ) 2024-06-26T05:54:19.1931102Z >>> optim.load_state_dict(optim_state_dict) 2024-06-26T05:54:19.1931412Z 2024-06-26T05:54:19.1931501Z Args: 2024-06-26T05:54:19.1931899Z model (torch.nn.Module): Root module (which may or may not be a 2024-06-26T05:54:19.1932582Z :class:`FullyShardedDataParallel` instance) whose parameters 2024-06-26T05:54:19.1933160Z were passed into the optimizer ``optim``. 2024-06-26T05:54:19.1934081Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2024-06-26T05:54:19.1934585Z parameters. 2024-06-26T05:54:19.1935057Z optim_state_dict (Dict[str, Any]): the target optimizer state_dict to 2024-06-26T05:54:19.1935770Z transform. If the value is None, optim.state_dict() will be used. ( 2024-06-26T05:54:19.1936329Z Default: ``None``) 2024-06-26T05:54:19.1936927Z group (dist.ProcessGroup): Model's process group across which parameters 2024-06-26T05:54:19.1937638Z are sharded or ``None`` if using the default process group. ( 2024-06-26T05:54:19.1938156Z Default: ``None``) 2024-06-26T05:54:19.1938369Z 2024-06-26T05:54:19.1938475Z Returns: 2024-06-26T05:54:19.1938878Z Dict[str, Any]: A :class:`dict` containing the optimizer state for 2024-06-26T05:54:19.1939769Z ``model``. The sharding of the optimizer state is based on 2024-06-26T05:54:19.1940262Z ``state_dict_type``. 2024-06-26T05:54:19.1940468Z 2024-06-26T05:54:19.1940858Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:19.1941345Z 2024-06-26T05:54:19.1942600Z msg = Cannot scrape callname=FullyShardedDataParallel.optim_state_dict_to_load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1899. 2024-06-26T05:54:19.1944218Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:19.1944722Z 2024-06-26T05:54:19.1945294Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model. 2024-06-26T05:54:19.1945938Z 2024-06-26T05:54:19.1946165Z Given a ``optim_state_dict`` that is transformed through 2024-06-26T05:54:19.1946797Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer 2024-06-26T05:54:19.1947522Z state_dict that can be loaded to ``optim`` which is the optimizer for 2024-06-26T05:54:19.1948218Z ``model``. ``model`` must be sharded by FullyShardedDataParallel. 2024-06-26T05:54:19.1948624Z 2024-06-26T05:54:19.1948798Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:19.1949417Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2024-06-26T05:54:19.1950100Z >>> from torch.distributed.fsdp import StateDictType 2024-06-26T05:54:19.1950704Z >>> from torch.distributed.fsdp import FullStateDictConfig 2024-06-26T05:54:19.1951350Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2024-06-26T05:54:19.1951891Z >>> # Save a checkpoint 2024-06-26T05:54:19.1952274Z >>> model, optim = ... 2024-06-26T05:54:19.1952653Z >>> FSDP.set_state_dict_type( 2024-06-26T05:54:19.1953005Z >>> model, 2024-06-26T05:54:19.1953329Z >>> StateDictType.FULL_STATE_DICT, 2024-06-26T05:54:19.1953782Z >>> FullStateDictConfig(rank0_only=False), 2024-06-26T05:54:19.1954304Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-06-26T05:54:19.1954751Z >>> ) 2024-06-26T05:54:19.1955157Z >>> state_dict = model.state_dict() 2024-06-26T05:54:19.1955587Z >>> original_osd = optim.state_dict() 2024-06-26T05:54:19.1956045Z >>> optim_state_dict = FSDP.optim_state_dict( 2024-06-26T05:54:19.1956456Z >>> model, 2024-06-26T05:54:19.1956735Z >>> optim, 2024-06-26T05:54:19.1957049Z >>> optim_state_dict=original_osd 2024-06-26T05:54:19.1957491Z >>> ) 2024-06-26T05:54:19.1957825Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2024-06-26T05:54:19.1958286Z >>> # Load a checkpoint 2024-06-26T05:54:19.1958612Z >>> model, optim = ... 2024-06-26T05:54:19.1959030Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2024-06-26T05:54:19.1959507Z >>> FSDP.set_state_dict_type( 2024-06-26T05:54:19.1959851Z >>> model, 2024-06-26T05:54:19.1960174Z >>> StateDictType.FULL_STATE_DICT, 2024-06-26T05:54:19.1960721Z >>> FullStateDictConfig(rank0_only=False), 2024-06-26T05:54:19.1961229Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-06-26T05:54:19.1961676Z >>> ) 2024-06-26T05:54:19.1961962Z >>> model.load_state_dict(state_dict) 2024-06-26T05:54:19.1962430Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2024-06-26T05:54:19.1962922Z >>> model, optim, optim_state_dict 2024-06-26T05:54:19.1963432Z >>> ) 2024-06-26T05:54:19.1963732Z >>> optim.load_state_dict(optim_state_dict) 2024-06-26T05:54:19.1964054Z 2024-06-26T05:54:19.1964144Z Args: 2024-06-26T05:54:19.1964541Z model (torch.nn.Module): Root module (which may or may not be a 2024-06-26T05:54:19.1965214Z :class:`FullyShardedDataParallel` instance) whose parameters 2024-06-26T05:54:19.1965806Z were passed into the optimizer ``optim``. 2024-06-26T05:54:19.1966418Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2024-06-26T05:54:19.1966903Z parameters. 2024-06-26T05:54:19.1967365Z optim_state_dict (Dict[str, Any]): The optimizer states to be loaded. 2024-06-26T05:54:19.1968074Z is_named_optimizer (bool): Is this optimizer a NamedOptimizer or 2024-06-26T05:54:19.1968761Z KeyedOptimizer. Only set to True if ``optim`` is TorchRec's 2024-06-26T05:54:19.1969453Z KeyedOptimizer or torch.distributed's NamedOptimizer. 2024-06-26T05:54:19.1970096Z load_directly (bool): If this is set to True, this API will also 2024-06-26T05:54:19.1970788Z call optim.load_state_dict(result) before returning the result. 2024-06-26T05:54:19.1971491Z Otherwise, users are responsible to call ``optim.load_state_dict()`` 2024-06-26T05:54:19.1972052Z (Default: ``False``) 2024-06-26T05:54:19.1972655Z group (dist.ProcessGroup): Model's process group across which parameters 2024-06-26T05:54:19.1973365Z are sharded or ``None`` if using the default process group. ( 2024-06-26T05:54:19.1974071Z Default: ``None``) 2024-06-26T05:54:19.1974286Z 2024-06-26T05:54:19.1974684Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:19.1975162Z 2024-06-26T05:54:19.2100899Z msg = Cannot scrape callname=_RemoteModule.__init__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=137. 2024-06-26T05:54:19.2102306Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:19.2102851Z 2024-06-26T05:54:19.2103146Z RemoteModule instance can only be created after RPC initialization. 2024-06-26T05:54:19.2103600Z 2024-06-26T05:54:19.2103908Z It creates a user-specified module on a specified remote node. 2024-06-26T05:54:19.2104646Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2024-06-26T05:54:19.2105240Z executed on the remote node. 2024-06-26T05:54:19.2105795Z It takes care of autograd recording to ensure the backward pass propagates 2024-06-26T05:54:19.2106459Z gradients back to the corresponding remote module. 2024-06-26T05:54:19.2107273Z It can be shared across processors using `RPC framework `__, 2024-06-26T05:54:19.2108294Z without incurring any overheads of copying the actual module, 2024-06-26T05:54:19.2109124Z which is equivalent to an :class:`~torch.distributed.rpc.RRef` 2024-06-26T05:54:19.2109850Z pointing to the remote module. 2024-06-26T05:54:19.2110094Z 2024-06-26T05:54:19.2110559Z The arguments of ``forward_async`` and ``forward`` are the same as 2024-06-26T05:54:19.2111659Z the ``forward`` method of the module returned by the ``module_cls``. 2024-06-26T05:54:19.2112299Z 2024-06-26T05:54:19.2113017Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now. 2024-06-26T05:54:19.2113761Z 2024-06-26T05:54:19.2114151Z Particularly, to create a hybrid model, typically the local modules should be 2024-06-26T05:54:19.2115204Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``). 2024-06-26T05:54:19.2116028Z Hybrid Example: 2024-06-26T05:54:19.2116408Z >>> class HybridModel(nn.Module): 2024-06-26T05:54:19.2116812Z >>> def __init__(self): 2024-06-26T05:54:19.2117269Z >>> nn.Module.__init__(self) 2024-06-26T05:54:19.2117767Z >>> self.remote_embedding = RemoteModule(...) 2024-06-26T05:54:19.2118304Z >>> self.local_linear = nn.Linear(...) 2024-06-26T05:54:19.2118695Z 2024-06-26T05:54:19.2119053Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2024-06-26T05:54:19.2120308Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``, 2024-06-26T05:54:19.2121637Z the generated ``RemoteModule`` will have 2 methods in signature of 2024-06-26T05:54:19.2122443Z ``def forward(input: Tensor) -> Tensor:`` and 2024-06-26T05:54:19.2123018Z ``def forward_async(input: Tensor) -> Future[Tensor]:``. 2024-06-26T05:54:19.2123375Z 2024-06-26T05:54:19.2123489Z .. note:: 2024-06-26T05:54:19.2123828Z If the remote module is placed on a cuda device, 2024-06-26T05:54:19.2124492Z any input CPU tensors will be automatically moved to the same cuda device, 2024-06-26T05:54:19.2125529Z and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend. 2024-06-26T05:54:19.2126253Z 2024-06-26T05:54:19.2126345Z Args: 2024-06-26T05:54:19.2126965Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-06-26T05:54:19.2127935Z The device can be a local device or a remote device specified by one of the following remote 2024-06-26T05:54:19.2128596Z formats: 2024-06-26T05:54:19.2128779Z 2024-06-26T05:54:19.2128965Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2024-06-26T05:54:19.2129506Z 2. "/" (ex: "trainer0/cuda:0"). 2024-06-26T05:54:19.2129859Z 2024-06-26T05:54:19.2130195Z In addition, the device field can be optional and the default value is "cpu". 2024-06-26T05:54:19.2130841Z module_cls (nn.Module): For example, 2024-06-26T05:54:19.2131268Z >>> class MyModule(nn.Module): 2024-06-26T05:54:19.2131657Z >>> def forward(input): 2024-06-26T05:54:19.2132037Z >>> return input + 1 2024-06-26T05:54:19.2132398Z >>> 2024-06-26T05:54:19.2132656Z >>> module_cls = MyModule 2024-06-26T05:54:19.2133168Z args (Sequence, optional): args to be passed to ``module_cls``. 2024-06-26T05:54:19.2133979Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2024-06-26T05:54:19.2134749Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2024-06-26T05:54:19.2135597Z to be created. The type object should be decorated by @torch.jit.interface. 2024-06-26T05:54:19.2136451Z If not provided, the generated RemoteModule is not torchscript-able. 2024-06-26T05:54:19.2137233Z Warning, this is an experimental API and susceptible to frequent changes. 2024-06-26T05:54:19.2137706Z 2024-06-26T05:54:19.2137800Z Returns: 2024-06-26T05:54:19.2138428Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-06-26T05:54:19.2139274Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2024-06-26T05:54:19.2140072Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-06-26T05:54:19.2140904Z on the user-provided module on the remote side. 2024-06-26T05:54:19.2141245Z 2024-06-26T05:54:19.2141360Z Example:: 2024-06-26T05:54:19.2141699Z Run the following code in two different processes: 2024-06-26T05:54:19.2142061Z 2024-06-26T05:54:19.2142200Z >>> # xdoctest: +SKIP("distributed") 2024-06-26T05:54:19.2142601Z >>> # On worker 0: 2024-06-26T05:54:19.2142907Z >>> import torch 2024-06-26T05:54:19.2143240Z >>> import torch.distributed.rpc as rpc 2024-06-26T05:54:19.2143685Z >>> from torch import nn, Tensor 2024-06-26T05:54:19.2144250Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-06-26T05:54:19.2144787Z >>> 2024-06-26T05:54:19.2144980Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-06-26T05:54:19.2145131Z >>> remote_linear_module = RemoteModule( 2024-06-26T05:54:19.2145295Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-06-26T05:54:19.2145401Z >>> ) 2024-06-26T05:54:19.2145527Z >>> input = torch.randn(128, 20) 2024-06-26T05:54:19.2145731Z >>> ret_fut = remote_linear_module.forward_async(input) 2024-06-26T05:54:19.2145857Z >>> ret = ret_fut.wait() 2024-06-26T05:54:19.2145963Z >>> rpc.shutdown() 2024-06-26T05:54:19.2145970Z 2024-06-26T05:54:19.2146075Z >>> # On worker 1: 2024-06-26T05:54:19.2146191Z >>> import torch 2024-06-26T05:54:19.2146343Z >>> import torch.distributed.rpc as rpc 2024-06-26T05:54:19.2146435Z >>> 2024-06-26T05:54:19.2146623Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-06-26T05:54:19.2146728Z >>> rpc.shutdown() 2024-06-26T05:54:19.2146733Z 2024-06-26T05:54:19.2147130Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:19.2147141Z 2024-06-26T05:54:19.2148248Z msg = Cannot scrape callname=_RemoteModule.init_from_module_rref in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=514. 2024-06-26T05:54:19.2148645Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:19.2148651Z 2024-06-26T05:54:19.2149063Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef. 2024-06-26T05:54:19.2149069Z 2024-06-26T05:54:19.2149492Z This alternate initialization method can be particularly useful if we want to create multiple 2024-06-26T05:54:19.2149892Z RemoteModule instances that share the same underlying module and reduce memory consumption. 2024-06-26T05:54:19.2149898Z 2024-06-26T05:54:19.2150267Z Moreover, this also provides a workaround for passing script RemoteModule over RPC, 2024-06-26T05:54:19.2150495Z which is not supported. The recommended way is as follows: 2024-06-26T05:54:19.2150505Z 2024-06-26T05:54:19.2150645Z 1. the sender creates a RemoteModule; 2024-06-26T05:54:19.2150844Z 2. the sender sends its ``module_rref`` over RPC; 2024-06-26T05:54:19.2151292Z 3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``. 2024-06-26T05:54:19.2151298Z 2024-06-26T05:54:19.2151402Z Example:: 2024-06-26T05:54:19.2151610Z Run the following code in two different processes: 2024-06-26T05:54:19.2151615Z 2024-06-26T05:54:19.2151754Z >>> # xdoctest: +SKIP("distributed") 2024-06-26T05:54:19.2151874Z >>> # On worker 0: 2024-06-26T05:54:19.2151978Z >>> import torch 2024-06-26T05:54:19.2152131Z >>> import torch.distributed.rpc as rpc 2024-06-26T05:54:19.2152269Z >>> from torch import nn, Tensor 2024-06-26T05:54:19.2152558Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-06-26T05:54:19.2152649Z >>> 2024-06-26T05:54:19.2152833Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-06-26T05:54:19.2153086Z >>> remote_module = RemoteModule( 2024-06-26T05:54:19.2153251Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-06-26T05:54:19.2153355Z >>> ) 2024-06-26T05:54:19.2153446Z >>> 2024-06-26T05:54:19.2153576Z >>> remote_module1 = rpc.rpc_sync( 2024-06-26T05:54:19.2153699Z >>> "worker1/cpu", 2024-06-26T05:54:19.2153916Z >>> RemoteModule.init_from_module_rref, 2024-06-26T05:54:19.2154117Z >>> ("worker1/cpu", remote_module1.get_module_rref()), 2024-06-26T05:54:19.2154222Z >>> ) 2024-06-26T05:54:19.2154330Z >>> rpc.shutdown() 2024-06-26T05:54:19.2154336Z 2024-06-26T05:54:19.2154439Z >>> # On worker 1: 2024-06-26T05:54:19.2154553Z >>> import torch 2024-06-26T05:54:19.2154705Z >>> import torch.distributed.rpc as rpc 2024-06-26T05:54:19.2154809Z >>> 2024-06-26T05:54:19.2154981Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-06-26T05:54:19.2155087Z >>> rpc.shutdown() 2024-06-26T05:54:19.2155097Z 2024-06-26T05:54:19.2155195Z Args: 2024-06-26T05:54:19.2155667Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-06-26T05:54:19.2156084Z The device can be a local device or a remote device specified by one of the following remote 2024-06-26T05:54:19.2156196Z formats: 2024-06-26T05:54:19.2156205Z 2024-06-26T05:54:19.2156388Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2024-06-26T05:54:19.2156581Z 2. "/" (ex: "trainer0/cuda:0"). 2024-06-26T05:54:19.2156587Z 2024-06-26T05:54:19.2156932Z In addition, the device field can be optional and the default value is "cpu". 2024-06-26T05:54:19.2157264Z module_rref (RRef[nn.Module]): The module reference shared by both the caller and 2024-06-26T05:54:19.2157398Z the created remote module. 2024-06-26T05:54:19.2157752Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2024-06-26T05:54:19.2158081Z to be created. The type object should be decorated by @torch.jit.interface. 2024-06-26T05:54:19.2158449Z If not provided, the generated RemoteModule is not torchscript-able. 2024-06-26T05:54:19.2158764Z Warning, this is an experimental API and susceptible to frequent changes. 2024-06-26T05:54:19.2158770Z 2024-06-26T05:54:19.2158869Z Returns: 2024-06-26T05:54:19.2159197Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-06-26T05:54:19.2159560Z user-provided ``module_rref``, it has a blocking ``forward`` method and an 2024-06-26T05:54:19.2159915Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-06-26T05:54:19.2160138Z on the user-provided module on the remote side. 2024-06-26T05:54:19.2160144Z 2024-06-26T05:54:19.2160519Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:19.2160525Z 2024-06-26T05:54:19.2161539Z msg = Cannot scrape callname=RemoteModule in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=606. 2024-06-26T05:54:19.2161940Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:19.2161945Z 2024-06-26T05:54:19.2162251Z A RemoteModule instance can only be created after RPC initialization. 2024-06-26T05:54:19.2162260Z 2024-06-26T05:54:19.2162570Z It creates a user-specified module on a specified remote node. 2024-06-26T05:54:19.2162890Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2024-06-26T05:54:19.2163028Z executed on the remote node. 2024-06-26T05:54:19.2163340Z It takes care of autograd recording to ensure the backward pass propagates 2024-06-26T05:54:19.2163535Z gradients back to the corresponding remote module. 2024-06-26T05:54:19.2163540Z 2024-06-26T05:54:19.2163839Z It generates two methods ``forward_async`` and ``forward`` based on the 2024-06-26T05:54:19.2164200Z signature of the ``forward`` method of ``module_cls``. ``forward_async`` 2024-06-26T05:54:19.2164537Z runs asynchronously and returns a Future. The arguments of ``forward_async`` 2024-06-26T05:54:19.2164805Z and ``forward`` are the same as the ``forward`` method of the module 2024-06-26T05:54:19.2164933Z returned by the ``module_cls``. 2024-06-26T05:54:19.2164996Z 2024-06-26T05:54:19.2165279Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2024-06-26T05:54:19.2165685Z that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``, 2024-06-26T05:54:19.2165978Z the generated ``RemoteModule`` will have 2 methods with the signatures: 2024-06-26T05:54:19.2165983Z 2024-06-26T05:54:19.2166197Z | ``def forward(input: Tensor) -> Tensor:`` 2024-06-26T05:54:19.2166454Z | ``def forward_async(input: Tensor) -> Future[Tensor]:`` 2024-06-26T05:54:19.2166460Z 2024-06-26T05:54:19.2166563Z Args: 2024-06-26T05:54:19.2167032Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-06-26T05:54:19.2167490Z The format should be "/", where the device field can be parsed as torch.device type. 2024-06-26T05:54:19.2167686Z E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0". 2024-06-26T05:54:19.2168018Z In addition, the device field can be optional and the default value is "cpu". 2024-06-26T05:54:19.2168359Z module_cls (nn.Module): Class for the module to be created remotely. For example, 2024-06-26T05:54:19.2168365Z 2024-06-26T05:54:19.2168511Z >>> class MyModule(nn.Module): 2024-06-26T05:54:19.2168631Z >>> def forward(input): 2024-06-26T05:54:19.2168763Z >>> return input + 1 2024-06-26T05:54:19.2168855Z >>> 2024-06-26T05:54:19.2168971Z >>> module_cls = MyModule 2024-06-26T05:54:19.2168976Z 2024-06-26T05:54:19.2169242Z args (Sequence, optional): args to be passed to ``module_cls``. 2024-06-26T05:54:19.2169495Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2024-06-26T05:54:19.2169501Z 2024-06-26T05:54:19.2169593Z Returns: 2024-06-26T05:54:19.2169923Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-06-26T05:54:19.2170285Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2024-06-26T05:54:19.2170633Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-06-26T05:54:19.2170871Z on the user-provided module on the remote side. 2024-06-26T05:54:19.2170877Z 2024-06-26T05:54:19.2170977Z Example:: 2024-06-26T05:54:19.2171187Z Run the following code in two different processes: 2024-06-26T05:54:19.2171192Z 2024-06-26T05:54:19.2171331Z >>> # xdoctest: +SKIP("distributed") 2024-06-26T05:54:19.2171437Z >>> # On worker 0: 2024-06-26T05:54:19.2171555Z >>> import torch 2024-06-26T05:54:19.2171710Z >>> import torch.distributed.rpc as rpc 2024-06-26T05:54:19.2171838Z >>> from torch import nn, Tensor 2024-06-26T05:54:19.2172140Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-06-26T05:54:19.2172232Z >>> 2024-06-26T05:54:19.2172405Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-06-26T05:54:19.2172567Z >>> remote_linear_module = RemoteModule( 2024-06-26T05:54:19.2172734Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-06-26T05:54:19.2172828Z >>> ) 2024-06-26T05:54:19.2172966Z >>> input = torch.randn(128, 20) 2024-06-26T05:54:19.2173166Z >>> ret_fut = remote_linear_module.forward_async(input) 2024-06-26T05:54:19.2173291Z >>> ret = ret_fut.wait() 2024-06-26T05:54:19.2173398Z >>> rpc.shutdown() 2024-06-26T05:54:19.2173403Z 2024-06-26T05:54:19.2173679Z >>> # On worker 1: 2024-06-26T05:54:19.2173803Z >>> import torch 2024-06-26T05:54:19.2173956Z >>> import torch.distributed.rpc as rpc 2024-06-26T05:54:19.2174048Z >>> 2024-06-26T05:54:19.2174236Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-06-26T05:54:19.2174432Z >>> rpc.shutdown() 2024-06-26T05:54:19.2174438Z 2024-06-26T05:54:19.2174679Z Furthermore, a more practical example that is combined with 2024-06-26T05:54:19.2175309Z `DistributedDataParallel `__ (DDP) 2024-06-26T05:54:19.2175832Z can be found in this `tutorial `__. 2024-06-26T05:54:19.2175839Z 2024-06-26T05:54:19.2176239Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:19.2176244Z 2024-06-26T05:54:19.2267243Z msg = Cannot scrape callname=DistributedOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/optimizer.py line=129. 2024-06-26T05:54:19.2267661Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:19.2267713Z 2024-06-26T05:54:19.2268004Z DistributedOptimizer takes remote references to parameters scattered 2024-06-26T05:54:19.2268329Z across workers and applies the given optimizer locally for each parameter. 2024-06-26T05:54:19.2268334Z 2024-06-26T05:54:19.2268664Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order 2024-06-26T05:54:19.2268852Z to retrieve the gradients for specific parameters. 2024-06-26T05:54:19.2268858Z 2024-06-26T05:54:19.2268987Z Concurrent calls to 2024-06-26T05:54:19.2269262Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`, 2024-06-26T05:54:19.2269437Z either from the same or different clients, will 2024-06-26T05:54:19.2269811Z be serialized on each worker -- as each worker's optimizer can only work 2024-06-26T05:54:19.2270101Z on one set of gradients at a time. However, there is no guarantee that 2024-06-26T05:54:19.2270475Z the full forward-backward-optimizer sequence will execute for one client 2024-06-26T05:54:19.2270792Z at a time. This means that the gradients being applied may not correspond 2024-06-26T05:54:19.2271091Z to the latest forward pass executed on a given worker. Also, there is no 2024-06-26T05:54:19.2271222Z guaranteed ordering across workers. 2024-06-26T05:54:19.2271228Z 2024-06-26T05:54:19.2271561Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled 2024-06-26T05:54:19.2271868Z by default, so that optimizer updates are not blocked by the Python Global 2024-06-26T05:54:19.2272210Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed 2024-06-26T05:54:19.2272522Z Model Parallel). This feature is currently enabled for most optimizers. You 2024-06-26T05:54:19.2272858Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support 2024-06-26T05:54:19.2272994Z for your own custom optimizers. 2024-06-26T05:54:19.2273000Z 2024-06-26T05:54:19.2273089Z Args: 2024-06-26T05:54:19.2273338Z optimizer_class (optim.Optimizer): the class of optimizer to 2024-06-26T05:54:19.2273479Z instantiate on each worker. 2024-06-26T05:54:19.2273760Z params_rref (list[RRef]): list of RRefs to local or remote parameters 2024-06-26T05:54:19.2273870Z to optimize. 2024-06-26T05:54:19.2274163Z args: arguments to pass to the optimizer constructor on each worker. 2024-06-26T05:54:19.2274453Z kwargs: arguments to pass to the optimizer constructor on each worker. 2024-06-26T05:54:19.2274459Z 2024-06-26T05:54:19.2274578Z Example:: 2024-06-26T05:54:19.2274717Z >>> # xdoctest: +SKIP("distributed") 2024-06-26T05:54:19.2274933Z >>> import torch.distributed.autograd as dist_autograd 2024-06-26T05:54:19.2275100Z >>> import torch.distributed.rpc as rpc 2024-06-26T05:54:19.2275220Z >>> from torch import optim 2024-06-26T05:54:19.2275463Z >>> from torch.distributed.optim import DistributedOptimizer 2024-06-26T05:54:19.2275569Z >>> 2024-06-26T05:54:19.2275738Z >>> with dist_autograd.context() as context_id: 2024-06-26T05:54:19.2275849Z >>> # Forward pass. 2024-06-26T05:54:19.2276126Z >>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3)) 2024-06-26T05:54:19.2276508Z >>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1)) 2024-06-26T05:54:19.2276666Z >>> loss = rref1.to_here() + rref2.to_here() 2024-06-26T05:54:19.2276770Z >>> 2024-06-26T05:54:19.2276881Z >>> # Backward pass. 2024-06-26T05:54:19.2277159Z >>> dist_autograd.backward(context_id, [loss.sum()]) 2024-06-26T05:54:19.2277268Z >>> 2024-06-26T05:54:19.2277371Z >>> # Optimizer. 2024-06-26T05:54:19.2277548Z >>> dist_optim = DistributedOptimizer( 2024-06-26T05:54:19.2277710Z >>> optim.SGD, 2024-06-26T05:54:19.2277867Z >>> [rref1, rref2], 2024-06-26T05:54:19.2278015Z >>> lr=0.05, 2024-06-26T05:54:19.2278156Z >>> ) 2024-06-26T05:54:19.2278350Z >>> dist_optim.step(context_id) 2024-06-26T05:54:19.2278358Z 2024-06-26T05:54:19.2278612Z __ https://github.com/pytorch/tutorials/pull/1465 2024-06-26T05:54:19.2278617Z 2024-06-26T05:54:19.2279012Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:19.2279023Z 2024-06-26T05:54:19.2280684Z msg = Cannot scrape callname=PostLocalSGDOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/post_localSGD_optimizer.py line=9. 2024-06-26T05:54:19.2281251Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:19.2281257Z 2024-06-26T05:54:19.2281860Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD `_, 2024-06-26T05:54:19.2282062Z This optimizer runs local optimizer at every step. 2024-06-26T05:54:19.2282574Z After the warm-up stage, it averages parameters periodically afer the local optimizer is applied. 2024-06-26T05:54:19.2282580Z 2024-06-26T05:54:19.2282684Z Args: 2024-06-26T05:54:19.2282807Z optim: The local optimizer. 2024-06-26T05:54:19.2283147Z averager: A model averager instance to run post-localSGD algorithm. 2024-06-26T05:54:19.2283157Z 2024-06-26T05:54:19.2283270Z Example:: 2024-06-26T05:54:19.2283275Z 2024-06-26T05:54:19.2283435Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:19.2283539Z >>> import torch 2024-06-26T05:54:19.2283695Z >>> import torch.distributed as dist 2024-06-26T05:54:19.2284047Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2024-06-26T05:54:19.2284180Z >>> import torch.nn as nn 2024-06-26T05:54:19.2284432Z >>> from torch.distributed.optim import PostLocalSGDOptimizer 2024-06-26T05:54:19.2284785Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2024-06-26T05:54:19.2284920Z >>> PostLocalSGDState, 2024-06-26T05:54:19.2285033Z >>> post_localSGD_hook, 2024-06-26T05:54:19.2285124Z >>> ) 2024-06-26T05:54:19.2285228Z >>> 2024-06-26T05:54:19.2285431Z >>> model = nn.parallel.DistributedDataParallel( 2024-06-26T05:54:19.2285611Z >>> module, device_ids=[rank], output_device=rank 2024-06-26T05:54:19.2285721Z >>> ) 2024-06-26T05:54:19.2285810Z >>> 2024-06-26T05:54:19.2286044Z >>> # Register a post-localSGD communication hook. 2024-06-26T05:54:19.2286428Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2024-06-26T05:54:19.2286632Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-06-26T05:54:19.2286727Z >>> 2024-06-26T05:54:19.2287063Z >>> # Create a post-localSGD optimizer that wraps a local optimizer. 2024-06-26T05:54:19.2287405Z >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as 2024-06-26T05:54:19.2287634Z >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-06-26T05:54:19.2287907Z >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01) 2024-06-26T05:54:19.2288044Z >>> opt = PostLocalSGDOptimizer( 2024-06-26T05:54:19.2288175Z >>> optim=local_optim, 2024-06-26T05:54:19.2288490Z >>> averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2024-06-26T05:54:19.2288680Z >>> ) 2024-06-26T05:54:19.2288785Z >>> 2024-06-26T05:54:19.2289099Z >>> # In the first 100 steps, DDP runs global gradient averaging at every step. 2024-06-26T05:54:19.2289585Z >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default), 2024-06-26T05:54:19.2290243Z >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer. 2024-06-26T05:54:19.2290370Z >>> for step in range(0, 200): 2024-06-26T05:54:19.2290481Z >>> opt.zero_grad() 2024-06-26T05:54:19.2290628Z >>> loss = loss_fn(output, labels) 2024-06-26T05:54:19.2290738Z >>> loss.backward() 2024-06-26T05:54:19.2290857Z >>> opt.step() 2024-06-26T05:54:19.2290862Z 2024-06-26T05:54:19.2291239Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:19.2291245Z 2024-06-26T05:54:19.2385558Z msg = Cannot scrape callname=ZeroRedundancyOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/zero_redundancy_optimizer.py line=282. 2024-06-26T05:54:19.2386009Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:19.2386016Z 2024-06-26T05:54:19.2386620Z Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group. 2024-06-26T05:54:19.2386628Z 2024-06-26T05:54:19.2386789Z The sharing is done as described by ZeRO_. 2024-06-26T05:54:19.2386795Z 2024-06-26T05:54:19.2386980Z The local optimizer instance in each rank is only 2024-06-26T05:54:19.2387300Z responsible for updating approximately ``1 / world_size`` parameters and 2024-06-26T05:54:19.2387570Z hence only needs to keep ``1 / world_size`` optimizer states. After 2024-06-26T05:54:19.2387890Z parameters are updated locally, each rank will broadcast its parameters to 2024-06-26T05:54:19.2388149Z all other peers to keep all model replicas in the same state. 2024-06-26T05:54:19.2388392Z ``ZeroRedundancyOptimizer`` can be used in conjunction with 2024-06-26T05:54:19.2388815Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak 2024-06-26T05:54:19.2388924Z memory consumption. 2024-06-26T05:54:19.2388930Z 2024-06-26T05:54:19.2389312Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number 2024-06-26T05:54:19.2389641Z of parameters at each rank. Each parameter belongs to a single rank and is 2024-06-26T05:54:19.2389960Z not divided among ranks. The partition is arbitrary and might not match the 2024-06-26T05:54:19.2390117Z the parameter registration or usage order. 2024-06-26T05:54:19.2390123Z 2024-06-26T05:54:19.2390232Z Arguments: 2024-06-26T05:54:19.2390485Z params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s 2024-06-26T05:54:19.2390740Z or :class:`dict` s giving all parameters, which will be sharded 2024-06-26T05:54:19.2390859Z across ranks. 2024-06-26T05:54:19.2390865Z 2024-06-26T05:54:19.2390969Z Keyword Args: 2024-06-26T05:54:19.2391265Z optimizer_class (:class:`torch.nn.Optimizer`): the class of the local 2024-06-26T05:54:19.2391367Z optimizer. 2024-06-26T05:54:19.2391636Z process_group (``ProcessGroup``, optional): ``torch.distributed`` 2024-06-26T05:54:19.2391898Z ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by 2024-06-26T05:54:19.2392084Z :meth:`torch.distributed.init_process_group`). 2024-06-26T05:54:19.2392371Z parameters_as_bucket_view (bool, optional): if ``True``, parameters are 2024-06-26T05:54:19.2392665Z packed into buckets to speed up communication, and ``param.data`` 2024-06-26T05:54:19.2392930Z fields point to bucket views at different offsets; if ``False``, 2024-06-26T05:54:19.2393196Z each individual parameter is communicated separately, and each 2024-06-26T05:54:19.2393403Z ``params.data`` stays intact (default: ``False``). 2024-06-26T05:54:19.2393649Z overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is 2024-06-26T05:54:19.2394150Z overlapped with :class:`DistributedDataParallel` 's gradient 2024-06-26T05:54:19.2394418Z synchronization; this requires (1) either a functional optimizer 2024-06-26T05:54:19.2394661Z for the ``optimizer_class`` argument or one with a functional 2024-06-26T05:54:19.2394980Z equivalent and (2) registering a DDP communication hook 2024-06-26T05:54:19.2395239Z constructed from one of the functions in ``ddp_zero_hook.py``; 2024-06-26T05:54:19.2395450Z parameters are packed into buckets matching those in 2024-06-26T05:54:19.2395677Z :class:`DistributedDataParallel`, meaning that the 2024-06-26T05:54:19.2395867Z ``parameters_as_bucket_view`` argument is ignored. 2024-06-26T05:54:19.2396124Z If ``False``, :meth:`step` runs disjointly after the backward pass 2024-06-26T05:54:19.2396242Z (per normal). 2024-06-26T05:54:19.2396354Z (default: ``False``) 2024-06-26T05:54:19.2396645Z **defaults: any trailing arguments, which are forwarded to the local 2024-06-26T05:54:19.2396744Z optimizer. 2024-06-26T05:54:19.2396750Z 2024-06-26T05:54:19.2396860Z Example:: 2024-06-26T05:54:19.2396866Z 2024-06-26T05:54:19.2396989Z >>> # xdoctest: +SKIP 2024-06-26T05:54:19.2397106Z >>> import torch.nn as nn 2024-06-26T05:54:19.2397376Z >>> from torch.distributed.optim import ZeroRedundancyOptimizer 2024-06-26T05:54:19.2397656Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2024-06-26T05:54:19.2397959Z >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)]) 2024-06-26T05:54:19.2398104Z >>> ddp = DDP(model, device_ids=[rank]) 2024-06-26T05:54:19.2398261Z >>> opt = ZeroRedundancyOptimizer( 2024-06-26T05:54:19.2398377Z >>> ddp.parameters(), 2024-06-26T05:54:19.2398531Z >>> optimizer_class=torch.optim.Adam, 2024-06-26T05:54:19.2398643Z >>> lr=0.01 2024-06-26T05:54:19.2398734Z >>> ) 2024-06-26T05:54:19.2398864Z >>> ddp(inputs).sum().backward() 2024-06-26T05:54:19.2398978Z >>> opt.step() 2024-06-26T05:54:19.2398984Z 2024-06-26T05:54:19.2399082Z .. warning:: 2024-06-26T05:54:19.2399366Z Currently, ``ZeroRedundancyOptimizer`` requires that all of the 2024-06-26T05:54:19.2399594Z passed-in parameters are the same dense type. 2024-06-26T05:54:19.2399604Z 2024-06-26T05:54:19.2399703Z .. warning:: 2024-06-26T05:54:19.2400001Z If you pass ``overlap_with_ddp=True``, be wary of the following: Given 2024-06-26T05:54:19.2400267Z the way that overlapping :class:`DistributedDataParallel` with 2024-06-26T05:54:19.2400559Z :class:`ZeroRedundancyOptimizer` is currently implemented, the first 2024-06-26T05:54:19.2400940Z two or three training iterations do not perform parameter updates in 2024-06-26T05:54:19.2401187Z the optimizer step, depending on if ``static_graph=False`` or 2024-06-26T05:54:19.2401432Z ``static_graph=True``, respectively. This is because it needs 2024-06-26T05:54:19.2401662Z information about the gradient bucketing strategy used by 2024-06-26T05:54:19.2401939Z :class:`DistributedDataParallel`, which is not finalized until the 2024-06-26T05:54:19.2402212Z second forward pass if ``static_graph=False`` or until the third 2024-06-26T05:54:19.2402495Z forward pass if ``static_graph=True``. To adjust for this, one option 2024-06-26T05:54:19.2402620Z is to prepend dummy inputs. 2024-06-26T05:54:19.2402625Z 2024-06-26T05:54:19.2402955Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change. 2024-06-26T05:54:19.2402961Z 2024-06-26T05:54:19.2403120Z .. _ZeRO: https://arxiv.org/abs/1910.02054 2024-06-26T05:54:19.2403126Z 2024-06-26T05:54:19.2403130Z 2024-06-26T05:54:19.2403531Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:19.2403537Z 2024-06-26T05:54:19.2545959Z msg = Cannot scrape callname=_CustomReducer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/pipelining/microbatch.py line=28. 2024-06-26T05:54:19.2546585Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:19.2546626Z 2024-06-26T05:54:19.2546931Z Custom reducer class that can be used to specify a custom operation that 2024-06-26T05:54:19.2547147Z reduces losses of multiple microbatches into one value. 2024-06-26T05:54:19.2547246Z 2024-06-26T05:54:19.2547356Z Example: 2024-06-26T05:54:19.2547465Z >>> # xdoctest: +SKIP 2024-06-26T05:54:19.2547589Z >>> sum_reducer = _CustomReducer( 2024-06-26T05:54:19.2547715Z >>> torch.tensor(0.0), 2024-06-26T05:54:19.2547823Z >>> lambda a, b: a + b 2024-06-26T05:54:19.2547913Z >>> ) 2024-06-26T05:54:19.2547918Z 2024-06-26T05:54:19.2548315Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:19.2548321Z 2024-06-26T05:54:19.3722526Z msg = Cannot scrape callname=async_execution in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/functions.py line=6. 2024-06-26T05:54:19.3724086Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:19.3724649Z 2024-06-26T05:54:19.3724984Z A decorator for a function indicating that the return value of the function 2024-06-26T05:54:19.3725884Z is guaranteed to be a :class:`~torch.futures.Future` object and this 2024-06-26T05:54:19.3726710Z function can run asynchronously on the RPC callee. More specifically, the 2024-06-26T05:54:19.3727605Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped 2024-06-26T05:54:19.3728469Z function and installs subsequent processing steps as a callback to that 2024-06-26T05:54:19.3729304Z :class:`~torch.futures.Future`. The installed callback will read the value 2024-06-26T05:54:19.3730110Z from the :class:`~torch.futures.Future` when completed and send the 2024-06-26T05:54:19.3730863Z value back as the RPC response. That also means the returned 2024-06-26T05:54:19.3731620Z :class:`~torch.futures.Future` only exists on the callee side and is never 2024-06-26T05:54:19.3732492Z sent through RPC. This decorator is useful when the wrapped function's 2024-06-26T05:54:19.3733275Z (``fn``) execution needs to pause and resume due to, e.g., containing 2024-06-26T05:54:19.3734249Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals. 2024-06-26T05:54:19.3734707Z 2024-06-26T05:54:19.3735064Z .. note:: To enable asynchronous execution, applications must pass the 2024-06-26T05:54:19.3735877Z function object returned by this decorator to RPC APIs. If RPC detected 2024-06-26T05:54:19.3736701Z attributes installed by this decorator, it knows that this function 2024-06-26T05:54:19.3737460Z returns a ``Future`` object and will handle that accordingly. 2024-06-26T05:54:19.3738205Z However, this does not mean this decorator has to be outmost one when 2024-06-26T05:54:19.3739022Z defining a function. For example, when combined with ``@staticmethod`` 2024-06-26T05:54:19.3739823Z or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the 2024-06-26T05:54:19.3740624Z inner decorator to allow the target function be recognized as a static 2024-06-26T05:54:19.3741448Z or class function. This target function can still execute asynchronously 2024-06-26T05:54:19.3742282Z because, when accessed, the static or class method preserves attributes 2024-06-26T05:54:19.3742996Z installed by ``@rpc.functions.async_execution``. 2024-06-26T05:54:19.3743359Z 2024-06-26T05:54:19.3743364Z 2024-06-26T05:54:19.3743488Z Example:: 2024-06-26T05:54:19.3743955Z The returned :class:`~torch.futures.Future` object can come from 2024-06-26T05:54:19.3744613Z :meth:`~torch.distributed.rpc.rpc_async`, 2024-06-26T05:54:19.3745277Z :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future` 2024-06-26T05:54:19.3746024Z constructor. The example below shows directly using the 2024-06-26T05:54:19.3746585Z :class:`~torch.futures.Future` returned by 2024-06-26T05:54:19.3747309Z :meth:`~torch.futures.Future.then`. 2024-06-26T05:54:19.3747679Z 2024-06-26T05:54:19.3747828Z >>> from torch.distributed import rpc 2024-06-26T05:54:19.3748250Z >>> 2024-06-26T05:54:19.3748560Z >>> # omitting setup and shutdown RPC 2024-06-26T05:54:19.3748948Z >>> 2024-06-26T05:54:19.3749261Z >>> # On all workers 2024-06-26T05:54:19.3749702Z >>> @rpc.functions.async_execution 2024-06-26T05:54:19.3750202Z >>> def async_add_chained(to, x, y, z): 2024-06-26T05:54:19.3750828Z >>> # This function runs on "worker1" and returns immediately when 2024-06-26T05:54:19.3751557Z >>> # the callback is installed through the `then(cb)` API. In the 2024-06-26T05:54:19.3752285Z >>> # mean time, the `rpc_async` to "worker2" can run concurrently. 2024-06-26T05:54:19.3752927Z >>> # When the return value of that `rpc_async` arrives at 2024-06-26T05:54:19.3753600Z >>> # "worker1", "worker1" will run the lambda function accordingly 2024-06-26T05:54:19.3754348Z >>> # and set the value for the previously returned `Future`, which 2024-06-26T05:54:19.3755086Z >>> # will then trigger RPC to send the result back to "worker0". 2024-06-26T05:54:19.3755774Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-06-26T05:54:19.3756354Z >>> lambda fut: fut.wait() + z 2024-06-26T05:54:19.3756749Z >>> ) 2024-06-26T05:54:19.3757037Z >>> 2024-06-26T05:54:19.3757303Z >>> # On worker0 2024-06-26T05:54:19.3757610Z >>> # xdoctest: +SKIP 2024-06-26T05:54:19.3757990Z >>> ret = rpc.rpc_sync( 2024-06-26T05:54:19.3758323Z >>> "worker1", 2024-06-26T05:54:19.3758728Z >>> async_add_chained, 2024-06-26T05:54:19.3759104Z >>> args=("worker2", torch.ones(2), 1, 1) 2024-06-26T05:54:19.3759516Z >>> ) 2024-06-26T05:54:19.3759886Z >>> print(ret) # prints tensor([3., 3.]) 2024-06-26T05:54:19.3760190Z 2024-06-26T05:54:19.3760602Z When combined with TorchScript decorators, this decorator must be the 2024-06-26T05:54:19.3761370Z outmost one. 2024-06-26T05:54:19.3761558Z 2024-06-26T05:54:19.3761680Z >>> from torch import Tensor 2024-06-26T05:54:19.3762342Z >>> from torch.futures import Future 2024-06-26T05:54:19.3763150Z >>> from torch.distributed import rpc 2024-06-26T05:54:19.3763878Z >>> 2024-06-26T05:54:19.3764389Z >>> # omitting setup and shutdown RPC 2024-06-26T05:54:19.3764767Z >>> 2024-06-26T05:54:19.3765014Z >>> # On all workers 2024-06-26T05:54:19.3765339Z >>> @torch.jit.script 2024-06-26T05:54:19.3765822Z >>> def script_add(x: Tensor, y: Tensor) -> Tensor: 2024-06-26T05:54:19.3766279Z >>> return x + y 2024-06-26T05:54:19.3766583Z >>> 2024-06-26T05:54:19.3766852Z >>> @rpc.functions.async_execution 2024-06-26T05:54:19.3767253Z >>> @torch.jit.script 2024-06-26T05:54:19.3767788Z >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]: 2024-06-26T05:54:19.3768378Z >>> return rpc.rpc_async(to, script_add, (x, y)) 2024-06-26T05:54:19.3768822Z >>> 2024-06-26T05:54:19.3769067Z >>> # On worker0 2024-06-26T05:54:19.3769360Z >>> ret = rpc.rpc_sync( 2024-06-26T05:54:19.3769690Z >>> "worker1", 2024-06-26T05:54:19.3769993Z >>> async_add, 2024-06-26T05:54:19.3770327Z >>> args=("worker2", torch.ones(2), 1) 2024-06-26T05:54:19.3770727Z >>> ) 2024-06-26T05:54:19.3771020Z >>> print(ret) # prints tensor([2., 2.]) 2024-06-26T05:54:19.3771335Z 2024-06-26T05:54:19.3771628Z When combined with static or class method, this decorator must be the 2024-06-26T05:54:19.3772191Z inner one. 2024-06-26T05:54:19.3772358Z 2024-06-26T05:54:19.3772518Z >>> from torch.distributed import rpc 2024-06-26T05:54:19.3772898Z >>> 2024-06-26T05:54:19.3773183Z >>> # omitting setup and shutdown RPC 2024-06-26T05:54:19.3773691Z >>> 2024-06-26T05:54:19.3773936Z >>> # On all workers 2024-06-26T05:54:19.3774281Z >>> class AsyncExecutionClass: 2024-06-26T05:54:19.3774646Z >>> 2024-06-26T05:54:19.3775042Z >>> @staticmethod 2024-06-26T05:54:19.3775399Z >>> @rpc.functions.async_execution 2024-06-26T05:54:19.3775851Z >>> def static_async_add(to, x, y, z): 2024-06-26T05:54:19.3776374Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-06-26T05:54:19.3776910Z >>> lambda fut: fut.wait() + z 2024-06-26T05:54:19.3777402Z >>> ) 2024-06-26T05:54:19.3777658Z >>> 2024-06-26T05:54:19.3777907Z >>> @classmethod 2024-06-26T05:54:19.3778260Z >>> @rpc.functions.async_execution 2024-06-26T05:54:19.3778708Z >>> def class_async_add(cls, to, x, y, z): 2024-06-26T05:54:19.3779187Z >>> ret_fut = torch.futures.Future() 2024-06-26T05:54:19.3779702Z >>> rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-06-26T05:54:19.3780250Z >>> lambda fut: ret_fut.set_result(fut.wait() + z) 2024-06-26T05:54:19.3780710Z >>> ) 2024-06-26T05:54:19.3780996Z >>> return ret_fut 2024-06-26T05:54:19.3781320Z >>> 2024-06-26T05:54:19.3781602Z >>> @rpc.functions.async_execution 2024-06-26T05:54:19.3782067Z >>> def bound_async_add(self, to, x, y, z): 2024-06-26T05:54:19.3782605Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-06-26T05:54:19.3783140Z >>> lambda fut: fut.wait() + z 2024-06-26T05:54:19.3783544Z >>> ) 2024-06-26T05:54:19.3783796Z >>> 2024-06-26T05:54:19.3784044Z >>> # On worker0 2024-06-26T05:54:19.3784350Z >>> ret = rpc.rpc_sync( 2024-06-26T05:54:19.3784669Z >>> "worker1", 2024-06-26T05:54:19.3785037Z >>> AsyncExecutionClass.static_async_add, 2024-06-26T05:54:19.3785524Z >>> args=("worker2", torch.ones(2), 1, 2) 2024-06-26T05:54:19.3785914Z >>> ) 2024-06-26T05:54:19.3786214Z >>> print(ret) # prints tensor([4., 4.]) 2024-06-26T05:54:19.3786617Z >>> 2024-06-26T05:54:19.3786861Z >>> ret = rpc.rpc_sync( 2024-06-26T05:54:19.3787191Z >>> "worker1", 2024-06-26T05:54:19.3787564Z >>> AsyncExecutionClass.class_async_add, 2024-06-26T05:54:19.3788035Z >>> args=("worker2", torch.ones(2), 1, 2) 2024-06-26T05:54:19.3788439Z >>> ) 2024-06-26T05:54:19.3788738Z >>> print(ret) # prints tensor([4., 4.]) 2024-06-26T05:54:19.3789039Z 2024-06-26T05:54:19.3789249Z This decorator also works with RRef helpers, i.e., . 2024-06-26T05:54:19.3789797Z :meth:`torch.distributed.rpc.RRef.rpc_sync`, 2024-06-26T05:54:19.3790343Z :meth:`torch.distributed.rpc.RRef.rpc_async`, and 2024-06-26T05:54:19.3790874Z :meth:`torch.distributed.rpc.RRef.remote`. 2024-06-26T05:54:19.3791225Z 2024-06-26T05:54:19.3791372Z >>> from torch.distributed import rpc 2024-06-26T05:54:19.3791767Z >>> 2024-06-26T05:54:19.3792073Z >>> # reuse the AsyncExecutionClass class above 2024-06-26T05:54:19.3792618Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-06-26T05:54:19.3793264Z >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2) 2024-06-26T05:54:19.3793849Z >>> print(ret) # prints tensor([4., 4.]) 2024-06-26T05:54:19.3794253Z >>> 2024-06-26T05:54:19.3794596Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-06-26T05:54:19.3795261Z >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait() 2024-06-26T05:54:19.3795896Z >>> print(ret) # prints tensor([4., 4.]) 2024-06-26T05:54:19.3796299Z >>> 2024-06-26T05:54:19.3796643Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-06-26T05:54:19.3797300Z >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here() 2024-06-26T05:54:19.3797934Z >>> print(ret) # prints tensor([4., 4.]) 2024-06-26T05:54:19.3798234Z 2024-06-26T05:54:19.3798647Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:19.3799127Z 2024-06-26T05:54:19.3800223Z msg = Cannot scrape callname=TensorPipeRpcBackendOptions.set_device_map in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/options.py line=108. 2024-06-26T05:54:19.3801848Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:19.3802356Z 2024-06-26T05:54:19.3802620Z Set device mapping between each RPC caller and callee pair. This 2024-06-26T05:54:19.3803357Z function can be called multiple times to incrementally add 2024-06-26T05:54:19.3803870Z device placement configurations. 2024-06-26T05:54:19.3804143Z 2024-06-26T05:54:19.3804233Z Args: 2024-06-26T05:54:19.3804490Z to (str): Callee name. 2024-06-26T05:54:19.3804962Z device_map (Dict of int, str, or torch.device): Device placement 2024-06-26T05:54:19.3805623Z mappings from this worker to the callee. This map must be 2024-06-26T05:54:19.3806130Z invertible. 2024-06-26T05:54:19.3806310Z 2024-06-26T05:54:19.3806406Z Example: 2024-06-26T05:54:19.3806694Z >>> # xdoctest: +SKIP("distributed") 2024-06-26T05:54:19.3807091Z >>> # both workers 2024-06-26T05:54:19.3807388Z >>> def add(x, y): 2024-06-26T05:54:19.3807818Z >>> print(x) # tensor([1., 1.], device='cuda:1') 2024-06-26T05:54:19.3808288Z >>> return x + y, (x + y).to(2) 2024-06-26T05:54:19.3808652Z >>> 2024-06-26T05:54:19.3808905Z >>> # on worker 0 2024-06-26T05:54:19.3809275Z >>> options = TensorPipeRpcBackendOptions( 2024-06-26T05:54:19.3809722Z >>> num_worker_threads=8, 2024-06-26T05:54:19.3810111Z >>> device_maps={"worker1": {0: 1}} 2024-06-26T05:54:19.3810637Z >>> # maps worker0's cuda:0 to worker1's cuda:1 2024-06-26T05:54:19.3811052Z >>> ) 2024-06-26T05:54:19.3811361Z >>> options.set_device_map("worker1", {1: 2}) 2024-06-26T05:54:19.3811897Z >>> # maps worker0's cuda:1 to worker1's cuda:2 2024-06-26T05:54:19.3812317Z >>> 2024-06-26T05:54:19.3812552Z >>> rpc.init_rpc( 2024-06-26T05:54:19.3812852Z >>> "worker0", 2024-06-26T05:54:19.3813135Z >>> rank=0, 2024-06-26T05:54:19.3813421Z >>> world_size=2, 2024-06-26T05:54:19.3813929Z >>> backend=rpc.BackendType.TENSORPIPE, 2024-06-26T05:54:19.3814375Z >>> rpc_backend_options=options 2024-06-26T05:54:19.3814753Z >>> ) 2024-06-26T05:54:19.3814999Z >>> 2024-06-26T05:54:19.3815237Z >>> x = torch.ones(2) 2024-06-26T05:54:19.3815665Z >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1)) 2024-06-26T05:54:19.3816292Z >>> # The first argument will be moved to cuda:1 on worker1. When 2024-06-26T05:54:19.3816939Z >>> # sending the return value back, it will follow the invert of 2024-06-26T05:54:19.3817599Z >>> # the device map, and hence will be moved back to cuda:0 and 2024-06-26T05:54:19.3818115Z >>> # cuda:1 on worker0 2024-06-26T05:54:19.3818592Z >>> print(rets[0]) # tensor([2., 2.], device='cuda:0') 2024-06-26T05:54:19.3819180Z >>> print(rets[1]) # tensor([2., 2.], device='cuda:1') 2024-06-26T05:54:19.3819535Z 2024-06-26T05:54:19.3819917Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:19.3820403Z 2024-06-26T05:54:19.3969340Z msg = Cannot scrape callname=PrepareModuleInput in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py line=366. 2024-06-26T05:54:19.3970767Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:19.3971282Z 2024-06-26T05:54:19.3971933Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to 2024-06-26T05:54:19.3973246Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``. 2024-06-26T05:54:19.3974280Z 2024-06-26T05:54:19.3974473Z Keyword Args: 2024-06-26T05:54:19.3975263Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2024-06-26T05:54:19.3976309Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2024-06-26T05:54:19.3977442Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2024-06-26T05:54:19.3978470Z as a placeholder. default: None. 2024-06-26T05:54:19.3979037Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2024-06-26T05:54:19.3980113Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2024-06-26T05:54:19.3981339Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2024-06-26T05:54:19.3982188Z input_kwarg_layouts (Dict[str, Placement]): 2024-06-26T05:54:19.3983051Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2024-06-26T05:54:19.3983851Z default: None 2024-06-26T05:54:19.3984255Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2024-06-26T05:54:19.3985123Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2024-06-26T05:54:19.3986002Z have the desired DTensor layouts. default: None. 2024-06-26T05:54:19.3986492Z use_local_output (bool, optional): 2024-06-26T05:54:19.3987259Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2024-06-26T05:54:19.3988012Z Returns: 2024-06-26T05:54:19.3988670Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs. 2024-06-26T05:54:19.3989241Z 2024-06-26T05:54:19.3989358Z Example:: 2024-06-26T05:54:19.3989635Z >>> # xdoctest: +SKIP(failing) 2024-06-26T05:54:19.3990302Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput 2024-06-26T05:54:19.3991110Z >>> from torch.distributed.device_mesh import init_device_mesh 2024-06-26T05:54:19.3991614Z >>> ... 2024-06-26T05:54:19.3992183Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2024-06-26T05:54:19.3992924Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2024-06-26T05:54:19.3993322Z >>> 2024-06-26T05:54:19.3993921Z >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor 2024-06-26T05:54:19.3994728Z >>> # and then redistributed to Replicated DTensor. 2024-06-26T05:54:19.3995189Z >>> parallelize_module( 2024-06-26T05:54:19.3995600Z >>> block, # this can be a submodule or module 2024-06-26T05:54:19.3996038Z >>> tp_mesh, 2024-06-26T05:54:19.3996338Z >>> parallelize_plan={ 2024-06-26T05:54:19.3996725Z >>> "attn": PrepareModuleInput( 2024-06-26T05:54:19.3997214Z >>> input_layouts=(Shard(0), None, None, ...), 2024-06-26T05:54:19.3997765Z >>> desired_input_layouts=(Replicate(), None, None, ...) 2024-06-26T05:54:19.3998248Z >>> ), 2024-06-26T05:54:19.3998524Z >>> } 2024-06-26T05:54:19.3998766Z >>> ) 2024-06-26T05:54:19.3998928Z 2024-06-26T05:54:19.3999312Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:19.3999786Z 2024-06-26T05:54:19.4000889Z msg = Cannot scrape callname=PrepareModuleOutput in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py line=521. 2024-06-26T05:54:19.4002300Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:19.4002792Z 2024-06-26T05:54:19.4003413Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to 2024-06-26T05:54:19.4004536Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``. 2024-06-26T05:54:19.4005127Z 2024-06-26T05:54:19.4005226Z Keyword Args: 2024-06-26T05:54:19.4005603Z output_layouts (Union[Placement, Tuple[Placement]]): 2024-06-26T05:54:19.4006422Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2024-06-26T05:54:19.4007692Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2024-06-26T05:54:19.4008568Z ``None`` need to be specified as a placeholder. 2024-06-26T05:54:19.4009146Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2024-06-26T05:54:19.4010168Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2024-06-26T05:54:19.4011007Z have the desired DTensor layouts. 2024-06-26T05:54:19.4011426Z use_local_output (bool, optional): 2024-06-26T05:54:19.4012259Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2024-06-26T05:54:19.4013006Z Returns: 2024-06-26T05:54:19.4013803Z A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs. 2024-06-26T05:54:19.4014391Z 2024-06-26T05:54:19.4014504Z Example:: 2024-06-26T05:54:19.4014791Z >>> # xdoctest: +SKIP(failing) 2024-06-26T05:54:19.4015473Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput 2024-06-26T05:54:19.4016281Z >>> from torch.distributed.device_mesh import init_device_mesh 2024-06-26T05:54:19.4016789Z >>> ... 2024-06-26T05:54:19.4017366Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2024-06-26T05:54:19.4018083Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2024-06-26T05:54:19.4018493Z >>> 2024-06-26T05:54:19.4019178Z >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor 2024-06-26T05:54:19.4020048Z >>> # and then redistributed to Sharded DTensor. 2024-06-26T05:54:19.4020501Z >>> parallelize_module( 2024-06-26T05:54:19.4020909Z >>> block, # this can be a submodule or module 2024-06-26T05:54:19.4021335Z >>> tp_mesh, 2024-06-26T05:54:19.4021704Z >>> parallelize_plan = PrepareModuleOutput( 2024-06-26T05:54:19.4022187Z >>> output_layouts=Replicate(), 2024-06-26T05:54:19.4022624Z >>> desired_output_layouts=Shard(0) 2024-06-26T05:54:19.4023028Z >>> ) 2024-06-26T05:54:19.4023285Z >>> ) 2024-06-26T05:54:19.4023427Z 2024-06-26T05:54:19.4023818Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:19.4024305Z 2024-06-26T05:54:19.4532073Z msg = Cannot scrape callname=MixtureSameFamily in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/mixture_same_family.py line=12. 2024-06-26T05:54:19.4534692Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:19.4535206Z 2024-06-26T05:54:19.4535487Z The `MixtureSameFamily` distribution implements a (batch of) mixture 2024-06-26T05:54:19.4536243Z distribution where all component are from different parameterizations of 2024-06-26T05:54:19.4536997Z the same distribution type. It is parameterized by a `Categorical` 2024-06-26T05:54:19.4537677Z "selecting distribution" (over `k` component) and a component 2024-06-26T05:54:19.4538348Z distribution, i.e., a `Distribution` with a rightmost batch shape 2024-06-26T05:54:19.4538972Z (equal to `[k]`) which indexes each (batch of) component. 2024-06-26T05:54:19.4539351Z 2024-06-26T05:54:19.4539475Z Examples:: 2024-06-26T05:54:19.4539625Z 2024-06-26T05:54:19.4539783Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:19.4540338Z >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally 2024-06-26T05:54:19.4540909Z >>> # weighted normal distributions 2024-06-26T05:54:19.4541343Z >>> mix = D.Categorical(torch.ones(5,)) 2024-06-26T05:54:19.4541842Z >>> comp = D.Normal(torch.randn(5,), torch.rand(5,)) 2024-06-26T05:54:19.4542329Z >>> gmm = MixtureSameFamily(mix, comp) 2024-06-26T05:54:19.4542639Z 2024-06-26T05:54:19.4542903Z >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally 2024-06-26T05:54:19.4543735Z >>> # weighted bivariate normal distributions 2024-06-26T05:54:19.4544195Z >>> mix = D.Categorical(torch.ones(5,)) 2024-06-26T05:54:19.4544634Z >>> comp = D.Independent(D.Normal( 2024-06-26T05:54:19.4545093Z ... torch.randn(5,2), torch.rand(5,2)), 1) 2024-06-26T05:54:19.4545658Z >>> gmm = MixtureSameFamily(mix, comp) 2024-06-26T05:54:19.4545971Z 2024-06-26T05:54:19.4546208Z >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each 2024-06-26T05:54:19.4546878Z >>> # consisting of 5 random weighted bivariate normal distributions 2024-06-26T05:54:19.4547447Z >>> mix = D.Categorical(torch.rand(3,5)) 2024-06-26T05:54:19.4547894Z >>> comp = D.Independent(D.Normal( 2024-06-26T05:54:19.4548355Z ... torch.randn(3,5,2), torch.rand(3,5,2)), 1) 2024-06-26T05:54:19.4548828Z >>> gmm = MixtureSameFamily(mix, comp) 2024-06-26T05:54:19.4549136Z 2024-06-26T05:54:19.4549225Z Args: 2024-06-26T05:54:19.4549691Z mixture_distribution: `torch.distributions.Categorical`-like 2024-06-26T05:54:19.4550335Z instance. Manages the probability of selecting component. 2024-06-26T05:54:19.4550962Z The number of categories must match the rightmost batch 2024-06-26T05:54:19.4551588Z dimension of the `component_distribution`. Must have either 2024-06-26T05:54:19.4552176Z scalar `batch_shape` or `batch_shape` matching 2024-06-26T05:54:19.4552726Z `component_distribution.batch_shape[:-1]` 2024-06-26T05:54:19.4553391Z component_distribution: `torch.distributions.Distribution`-like 2024-06-26T05:54:19.4554112Z instance. Right-most batch dimension indexes component. 2024-06-26T05:54:19.4554496Z 2024-06-26T05:54:19.4554875Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:19.4555365Z 2024-06-26T05:54:19.4640979Z msg = Cannot scrape callname=RelaxedBernoulli in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/relaxed_bernoulli.py line=109. 2024-06-26T05:54:19.4642548Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:19.4643040Z 2024-06-26T05:54:19.4643284Z Creates a RelaxedBernoulli distribution, parametrized by 2024-06-26T05:54:19.4643900Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits` 2024-06-26T05:54:19.4644609Z (but not both). This is a relaxed version of the `Bernoulli` distribution, 2024-06-26T05:54:19.4645317Z so the values are in (0, 1), and has reparametrizable samples. 2024-06-26T05:54:19.4645718Z 2024-06-26T05:54:19.4645835Z Example:: 2024-06-26T05:54:19.4645984Z 2024-06-26T05:54:19.4646218Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-06-26T05:54:19.4646728Z >>> m = RelaxedBernoulli(torch.tensor([2.2]), 2024-06-26T05:54:19.4647231Z ... torch.tensor([0.1, 0.2, 0.3, 0.99])) 2024-06-26T05:54:19.4647659Z >>> m.sample() 2024-06-26T05:54:19.4647987Z tensor([ 0.2951, 0.3442, 0.8918, 0.9021]) 2024-06-26T05:54:19.4648296Z 2024-06-26T05:54:19.4648402Z Args: 2024-06-26T05:54:19.4648715Z temperature (Tensor): relaxation temperature 2024-06-26T05:54:19.4649255Z probs (Number, Tensor): the probability of sampling `1` 2024-06-26T05:54:19.4649892Z logits (Number, Tensor): the log-odds of sampling `1` 2024-06-26T05:54:19.4650260Z 2024-06-26T05:54:19.4650641Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:19.4651110Z 2024-06-26T05:54:19.4655918Z msg = Cannot scrape callname=RelaxedOneHotCategorical in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/relaxed_categorical.py line=97. 2024-06-26T05:54:19.4657486Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:19.4657981Z 2024-06-26T05:54:19.4658254Z Creates a RelaxedOneHotCategorical distribution parametrized by 2024-06-26T05:54:19.4658937Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`. 2024-06-26T05:54:19.4659834Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so 2024-06-26T05:54:19.4660512Z its samples are on simplex, and are reparametrizable. 2024-06-26T05:54:19.4660889Z 2024-06-26T05:54:19.4661002Z Example:: 2024-06-26T05:54:19.4661148Z 2024-06-26T05:54:19.4661395Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-06-26T05:54:19.4662057Z >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]), 2024-06-26T05:54:19.4662612Z ... torch.tensor([0.1, 0.2, 0.3, 0.4])) 2024-06-26T05:54:19.4663069Z >>> m.sample() 2024-06-26T05:54:19.4663384Z tensor([ 0.1294, 0.2324, 0.3859, 0.2523]) 2024-06-26T05:54:19.4663701Z 2024-06-26T05:54:19.4663790Z Args: 2024-06-26T05:54:19.4664102Z temperature (Tensor): relaxation temperature 2024-06-26T05:54:19.4664576Z probs (Tensor): event probabilities 2024-06-26T05:54:19.4665102Z logits (Tensor): unnormalized log probability for each event 2024-06-26T05:54:19.4665519Z 2024-06-26T05:54:19.4665905Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:19.4666379Z 2024-06-26T05:54:19.7391363Z msg = Cannot scrape callname=assoc_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=230. 2024-06-26T05:54:19.7393207Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:19.7394421Z Return a new dict with new, potentially nested, key value pair 2024-06-26T05:54:19.7394832Z 2024-06-26T05:54:19.7395003Z >>> purchase = {'name': 'Alice', 2024-06-26T05:54:19.7395505Z ... 'order': {'items': ['Apple', 'Orange'], 2024-06-26T05:54:19.7396049Z ... 'costs': [0.50, 1.25]}, 2024-06-26T05:54:19.7396710Z ... 'credit card': '5555-1234-1234-1234'} 2024-06-26T05:54:19.7397647Z >>> assoc_in(purchase, ['order', 'costs'], [0.25, 1.00]) # doctest: +SKIP 2024-06-26T05:54:19.7398267Z {'credit card': '5555-1234-1234-1234', 2024-06-26T05:54:19.7398678Z 'name': 'Alice', 2024-06-26T05:54:19.7399208Z 'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}} 2024-06-26T05:54:19.7399747Z 2024-06-26T05:54:19.7400416Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:19.7401366Z 2024-06-26T05:54:19.7402371Z msg = Cannot scrape callname=update_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=245. 2024-06-26T05:54:19.7403795Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:19.7404480Z Update value in a (potentially) nested dictionary 2024-06-26T05:54:19.7404817Z 2024-06-26T05:54:19.7404913Z inputs: 2024-06-26T05:54:19.7405240Z d - dictionary on which to operate 2024-06-26T05:54:19.7405882Z keys - list or tuple giving the location of the value to be changed in d 2024-06-26T05:54:19.7406524Z func - function to operate on that value 2024-06-26T05:54:19.7406845Z 2024-06-26T05:54:19.7407131Z If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the 2024-06-26T05:54:19.7407876Z original dictionary with v replaced by func(v), but does not mutate the 2024-06-26T05:54:19.7408441Z original dictionary. 2024-06-26T05:54:19.7408659Z 2024-06-26T05:54:19.7408985Z If k0 is not a key in d, update_in creates nested dictionaries to the depth 2024-06-26T05:54:19.7409755Z specified by the keys, with the innermost value set to func(default). 2024-06-26T05:54:19.7410203Z 2024-06-26T05:54:19.7410334Z >>> inc = lambda x: x + 1 2024-06-26T05:54:19.7410725Z >>> update_in({'a': 0}, ['a'], inc) 2024-06-26T05:54:19.7411119Z {'a': 1} 2024-06-26T05:54:19.7411267Z 2024-06-26T05:54:19.7411448Z >>> transaction = {'name': 'Alice', 2024-06-26T05:54:19.7411954Z ... 'purchase': {'items': ['Apple', 'Orange'], 2024-06-26T05:54:19.7412516Z ... 'costs': [0.50, 1.25]}, 2024-06-26T05:54:19.7413284Z ... 'credit card': '5555-1234-1234-1234'} 2024-06-26T05:54:19.7414118Z >>> update_in(transaction, ['purchase', 'costs'], sum) # doctest: +SKIP 2024-06-26T05:54:19.7414737Z {'credit card': '5555-1234-1234-1234', 2024-06-26T05:54:19.7415154Z 'name': 'Alice', 2024-06-26T05:54:19.7415700Z 'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}} 2024-06-26T05:54:19.7416087Z 2024-06-26T05:54:19.7416247Z >>> # updating a value when k0 is not in d 2024-06-26T05:54:19.7416725Z >>> update_in({}, [1, 2, 3], str, default="bar") 2024-06-26T05:54:19.7417180Z {1: {2: {3: 'bar'}}} 2024-06-26T05:54:19.7417584Z >>> update_in({1: 'foo'}, [2, 3, 4], inc, 0) 2024-06-26T05:54:19.7418036Z {1: 'foo', 2: {3: {4: 1}}} 2024-06-26T05:54:19.7418348Z 2024-06-26T05:54:19.7418863Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:19.7419340Z 2024-06-26T05:54:19.7420358Z msg = Cannot scrape callname=get_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=303. 2024-06-26T05:54:19.7421763Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:19.7422451Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys. 2024-06-26T05:54:19.7422827Z 2024-06-26T05:54:19.7423089Z If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless 2024-06-26T05:54:19.7423776Z ``no_default`` is specified, then it raises KeyError or IndexError. 2024-06-26T05:54:19.7424193Z 2024-06-26T05:54:19.7424483Z ``get_in`` is a generalization of ``operator.getitem`` for nested data 2024-06-26T05:54:19.7425080Z structures such as dictionaries and lists. 2024-06-26T05:54:19.7425406Z 2024-06-26T05:54:19.7425577Z >>> transaction = {'name': 'Alice', 2024-06-26T05:54:19.7426096Z ... 'purchase': {'items': ['Apple', 'Orange'], 2024-06-26T05:54:19.7426647Z ... 'costs': [0.50, 1.25]}, 2024-06-26T05:54:19.7427194Z ... 'credit card': '5555-1234-1234-1234'} 2024-06-26T05:54:19.7427742Z >>> get_in(['purchase', 'items', 0], transaction) 2024-06-26T05:54:19.7428182Z 'Apple' 2024-06-26T05:54:19.7428500Z >>> get_in(['name'], transaction) 2024-06-26T05:54:19.7428886Z 'Alice' 2024-06-26T05:54:19.7429231Z >>> get_in(['purchase', 'total'], transaction) 2024-06-26T05:54:19.7429796Z >>> get_in(['purchase', 'items', 'apple'], transaction) 2024-06-26T05:54:19.7430372Z >>> get_in(['purchase', 'items', 10], transaction) 2024-06-26T05:54:19.7430914Z >>> get_in(['purchase', 'total'], transaction, 0) 2024-06-26T05:54:19.7431340Z 0 2024-06-26T05:54:19.7431653Z >>> get_in(['y'], {}, no_default=True) 2024-06-26T05:54:19.7432061Z Traceback (most recent call last): 2024-06-26T05:54:19.7432434Z ... 2024-06-26T05:54:19.7432713Z KeyError: 'y' 2024-06-26T05:54:19.7432883Z 2024-06-26T05:54:19.7432979Z See Also: 2024-06-26T05:54:19.7433253Z itertoolz.get 2024-06-26T05:54:19.7433559Z operator.getitem 2024-06-26T05:54:19.7433846Z 2024-06-26T05:54:19.7434368Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:19.7434844Z 2024-06-26T05:54:19.7435839Z msg = Cannot scrape callname=groupby in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=355. 2024-06-26T05:54:19.7437237Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:19.7437861Z Group a collection by a key function 2024-06-26T05:54:19.7438151Z 2024-06-26T05:54:19.7438440Z >>> names = ['Alice', 'Bob', 'Charlie', 'Dan', 'Edith', 'Frank'] 2024-06-26T05:54:19.7438987Z >>> groupby(len, names) # doctest: +SKIP 2024-06-26T05:54:19.7439578Z {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} 2024-06-26T05:54:19.7439981Z 2024-06-26T05:54:19.7440109Z >>> iseven = lambda x: x % 2 == 0 2024-06-26T05:54:19.7440828Z >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP 2024-06-26T05:54:19.7441353Z {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} 2024-06-26T05:54:19.7441662Z 2024-06-26T05:54:19.7441886Z Non-callable keys imply grouping on a member. 2024-06-26T05:54:19.7442228Z 2024-06-26T05:54:19.7442482Z >>> groupby('gender', [{'name': 'Alice', 'gender': 'F'}, 2024-06-26T05:54:19.7443136Z ... {'name': 'Bob', 'gender': 'M'}, 2024-06-26T05:54:19.7443731Z ... {'name': 'Charlie', 'gender': 'M'}]) # doctest:+SKIP 2024-06-26T05:54:19.7444305Z {'F': [{'gender': 'F', 'name': 'Alice'}], 2024-06-26T05:54:19.7444789Z 'M': [{'gender': 'M', 'name': 'Bob'}, 2024-06-26T05:54:19.7445254Z {'gender': 'M', 'name': 'Charlie'}]} 2024-06-26T05:54:19.7445569Z 2024-06-26T05:54:19.7445747Z Not to be confused with ``itertools.groupby`` 2024-06-26T05:54:19.7446079Z 2024-06-26T05:54:19.7446194Z See Also: 2024-06-26T05:54:19.7446445Z countby 2024-06-26T05:54:19.7446706Z 2024-06-26T05:54:19.7447226Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:19.7447702Z 2024-06-26T05:54:20.0307561Z msg = Cannot scrape callname=SyncBatchNorm in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py line=601. 2024-06-26T05:54:20.0309099Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:20.0309852Z Applies Batch Normalization over a N-Dimensional input. 2024-06-26T05:54:20.0310297Z 2024-06-26T05:54:20.0310896Z The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper 2024-06-26T05:54:20.0311831Z `Batch Normalization: Accelerating Deep Network Training by Reducing 2024-06-26T05:54:20.0312556Z Internal Covariate Shift `__ . 2024-06-26T05:54:20.0312975Z 2024-06-26T05:54:20.0313104Z .. math:: 2024-06-26T05:54:20.0313264Z 2024-06-26T05:54:20.0313673Z y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta 2024-06-26T05:54:20.0314168Z 2024-06-26T05:54:20.0314530Z The mean and standard-deviation are calculated per-dimension over all 2024-06-26T05:54:20.0315370Z mini-batches of the same process groups. :math:`\gamma` and :math:`\beta` 2024-06-26T05:54:20.0316165Z are learnable parameter vectors of size `C` (where `C` is the input size). 2024-06-26T05:54:20.0316865Z By default, the elements of :math:`\gamma` are sampled from 2024-06-26T05:54:20.0317547Z :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. 2024-06-26T05:54:20.0318399Z The standard-deviation is calculated via the biased estimator, equivalent to 2024-06-26T05:54:20.0319021Z `torch.var(input, unbiased=False)`. 2024-06-26T05:54:20.0319316Z 2024-06-26T05:54:20.0319628Z Also by default, during training this layer keeps running estimates of its 2024-06-26T05:54:20.0320412Z computed mean and variance, which are then used for normalization during 2024-06-26T05:54:20.0321273Z evaluation. The running estimates are kept with a default :attr:`momentum` 2024-06-26T05:54:20.0321851Z of 0.1. 2024-06-26T05:54:20.0322013Z 2024-06-26T05:54:20.0322316Z If :attr:`track_running_stats` is set to ``False``, this layer then does not 2024-06-26T05:54:20.0323072Z keep running estimates, and batch statistics are instead used during 2024-06-26T05:54:20.0323626Z evaluation time as well. 2024-06-26T05:54:20.0323868Z 2024-06-26T05:54:20.0323967Z .. note:: 2024-06-26T05:54:20.0324422Z This :attr:`momentum` argument is different from one used in optimizer 2024-06-26T05:54:20.0325162Z classes and the conventional notion of momentum. Mathematically, the 2024-06-26T05:54:20.0325787Z update rule for running statistics here is 2024-06-26T05:54:20.0326597Z :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, 2024-06-26T05:54:20.0327666Z where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the 2024-06-26T05:54:20.0328228Z new observed value. 2024-06-26T05:54:20.0328449Z 2024-06-26T05:54:20.0328865Z Because the Batch Normalization is done for each channel in the ``C`` dimension, computing 2024-06-26T05:54:20.0329952Z statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch 2024-06-26T05:54:20.0330720Z Normalization or Spatio-temporal Batch Normalization. 2024-06-26T05:54:20.0331101Z 2024-06-26T05:54:20.0331273Z Currently :class:`SyncBatchNorm` only supports 2024-06-26T05:54:20.0331984Z :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use 2024-06-26T05:54:20.0332788Z :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert 2024-06-26T05:54:20.0333680Z :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping 2024-06-26T05:54:20.0334227Z Network with DDP. 2024-06-26T05:54:20.0334422Z 2024-06-26T05:54:20.0334521Z Args: 2024-06-26T05:54:20.0334884Z num_features: :math:`C` from an expected input of size 2024-06-26T05:54:20.0335371Z :math:`(N, C, +)` 2024-06-26T05:54:20.0335850Z eps: a value added to the denominator for numerical stability. 2024-06-26T05:54:20.0336421Z Default: ``1e-5`` 2024-06-26T05:54:20.0336906Z momentum: the value used for the running_mean and running_var 2024-06-26T05:54:20.0337593Z computation. Can be set to ``None`` for cumulative moving average 2024-06-26T05:54:20.0338171Z (i.e. simple average). Default: 0.1 2024-06-26T05:54:20.0338751Z affine: a boolean value that when set to ``True``, this module has 2024-06-26T05:54:20.0339376Z learnable affine parameters. Default: ``True`` 2024-06-26T05:54:20.0339996Z track_running_stats: a boolean value that when set to ``True``, this 2024-06-26T05:54:20.0340746Z module tracks the running mean and variance, and when set to ``False``, 2024-06-26T05:54:20.0341520Z this module does not track such statistics, and initializes statistics 2024-06-26T05:54:20.0342241Z buffers :attr:`running_mean` and :attr:`running_var` as ``None``. 2024-06-26T05:54:20.0342990Z When these buffers are ``None``, this module always uses batch statistics. 2024-06-26T05:54:20.0343682Z in both training and eval modes. Default: ``True`` 2024-06-26T05:54:20.0344342Z process_group: synchronization of stats happen within each process group 2024-06-26T05:54:20.0345107Z individually. Default behavior is synchronization across the whole 2024-06-26T05:54:20.0345659Z world 2024-06-26T05:54:20.0345830Z 2024-06-26T05:54:20.0345934Z Shape: 2024-06-26T05:54:20.0346231Z - Input: :math:`(N, C, +)` 2024-06-26T05:54:20.0346726Z - Output: :math:`(N, C, +)` (same shape as input) 2024-06-26T05:54:20.0347063Z 2024-06-26T05:54:20.0347179Z .. note:: 2024-06-26T05:54:20.0347756Z Synchronization of batchnorm statistics occurs only while training, i.e. 2024-06-26T05:54:20.0348494Z synchronization is disabled when ``model.eval()`` is set or if 2024-06-26T05:54:20.0349076Z ``self.training`` is otherwise ``False``. 2024-06-26T05:54:20.0349390Z 2024-06-26T05:54:20.0349495Z Examples:: 2024-06-26T05:54:20.0349673Z 2024-06-26T05:54:20.0349828Z >>> # xdoctest: +SKIP 2024-06-26T05:54:20.0350216Z >>> # With Learnable Parameters 2024-06-26T05:54:20.0350626Z >>> m = nn.SyncBatchNorm(100) 2024-06-26T05:54:20.0351055Z >>> # creating process group (optional) 2024-06-26T05:54:20.0351555Z >>> # ranks is a list of int identifying rank ids. 2024-06-26T05:54:20.0352009Z >>> ranks = list(range(8)) 2024-06-26T05:54:20.0352392Z >>> r1, r2 = ranks[:4], ranks[4:] 2024-06-26T05:54:20.0352866Z >>> # Note: every rank calls into new_group for every 2024-06-26T05:54:20.0353407Z >>> # process group created, even if that rank is not 2024-06-26T05:54:20.0354025Z >>> # part of the group. 2024-06-26T05:54:20.0354602Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2024-06-26T05:54:20.0355353Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2024-06-26T05:54:20.0355910Z >>> # Without Learnable Parameters 2024-06-26T05:54:20.0356573Z >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group) 2024-06-26T05:54:20.0357176Z >>> input = torch.randn(20, 100, 35, 45, 10) 2024-06-26T05:54:20.0357600Z >>> output = m(input) 2024-06-26T05:54:20.0357839Z 2024-06-26T05:54:20.0357982Z >>> # network is nn.BatchNorm layer 2024-06-26T05:54:20.0358819Z >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group) 2024-06-26T05:54:20.0359544Z >>> # only single gpu per process is currently supported 2024-06-26T05:54:20.0360220Z >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel( 2024-06-26T05:54:20.0360908Z >>> sync_bn_network, 2024-06-26T05:54:20.0361380Z >>> device_ids=[args.local_rank], 2024-06-26T05:54:20.0361894Z >>> output_device=args.local_rank) 2024-06-26T05:54:20.0362322Z 2024-06-26T05:54:20.0362857Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:20.0363345Z 2024-06-26T05:54:20.0364314Z msg = Cannot scrape callname=SyncBatchNorm.convert_sync_batchnorm in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py line=824. 2024-06-26T05:54:20.0365696Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:20.0366604Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers. 2024-06-26T05:54:20.0367178Z 2024-06-26T05:54:20.0367273Z Args: 2024-06-26T05:54:20.0367757Z module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers 2024-06-26T05:54:20.0368534Z process_group (optional): process group to scope synchronization, 2024-06-26T05:54:20.0369105Z default is the whole world 2024-06-26T05:54:20.0369403Z 2024-06-26T05:54:20.0369500Z Returns: 2024-06-26T05:54:20.0370019Z The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm` 2024-06-26T05:54:20.0370823Z layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer, 2024-06-26T05:54:20.0371554Z a new :class:`torch.nn.SyncBatchNorm` layer object will be returned 2024-06-26T05:54:20.0372101Z instead. 2024-06-26T05:54:20.0372282Z 2024-06-26T05:54:20.0372396Z Example:: 2024-06-26T05:54:20.0372567Z 2024-06-26T05:54:20.0372721Z >>> # Network with nn.BatchNorm layer 2024-06-26T05:54:20.0373222Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-06-26T05:54:20.0373827Z >>> module = torch.nn.Sequential( 2024-06-26T05:54:20.0374286Z >>> torch.nn.Linear(20, 100), 2024-06-26T05:54:20.0374765Z >>> torch.nn.BatchNorm1d(100), 2024-06-26T05:54:20.0375199Z >>> ).cuda() 2024-06-26T05:54:20.0375589Z >>> # creating process group (optional) 2024-06-26T05:54:20.0376111Z >>> # ranks is a list of int identifying rank ids. 2024-06-26T05:54:20.0376583Z >>> ranks = list(range(8)) 2024-06-26T05:54:20.0376981Z >>> r1, r2 = ranks[:4], ranks[4:] 2024-06-26T05:54:20.0377470Z >>> # Note: every rank calls into new_group for every 2024-06-26T05:54:20.0378030Z >>> # process group created, even if that rank is not 2024-06-26T05:54:20.0378514Z >>> # part of the group. 2024-06-26T05:54:20.0378924Z >>> # xdoctest: +SKIP("distributed") 2024-06-26T05:54:20.0379556Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2024-06-26T05:54:20.0380453Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2024-06-26T05:54:20.0381265Z >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group) 2024-06-26T05:54:20.0381832Z 2024-06-26T05:54:20.0381925Z 2024-06-26T05:54:20.0382539Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:20.0383013Z 2024-06-26T05:54:20.0534683Z msg = Cannot scrape callname=Unflatten in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py line=60. 2024-06-26T05:54:20.0536600Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:20.0537106Z 2024-06-26T05:54:20.0537608Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`. 2024-06-26T05:54:20.0538199Z 2024-06-26T05:54:20.0538560Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can 2024-06-26T05:54:20.0539419Z be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively. 2024-06-26T05:54:20.0539978Z 2024-06-26T05:54:20.0540405Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be 2024-06-26T05:54:20.0541379Z a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input; a `NamedShape` 2024-06-26T05:54:20.0542137Z (tuple of `(name, size)` tuples) for `NamedTensor` input. 2024-06-26T05:54:20.0542504Z 2024-06-26T05:54:20.0542595Z Shape: 2024-06-26T05:54:20.0543132Z - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at 2024-06-26T05:54:20.0543947Z dimension :attr:`dim` and :math:`*` means any number of dimensions including none. 2024-06-26T05:54:20.0544828Z - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and 2024-06-26T05:54:20.0545443Z :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`. 2024-06-26T05:54:20.0545770Z 2024-06-26T05:54:20.0545864Z Args: 2024-06-26T05:54:20.0546196Z dim (Union[int, str]): Dimension to be unflattened 2024-06-26T05:54:20.0546981Z unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension 2024-06-26T05:54:20.0547597Z 2024-06-26T05:54:20.0547692Z Examples: 2024-06-26T05:54:20.0547973Z >>> input = torch.randn(2, 50) 2024-06-26T05:54:20.0548338Z >>> # With tuple of ints 2024-06-26T05:54:20.0548683Z >>> m = nn.Sequential( 2024-06-26T05:54:20.0549011Z >>> nn.Linear(50, 50), 2024-06-26T05:54:20.0549360Z >>> nn.Unflatten(1, (2, 5, 5)) 2024-06-26T05:54:20.0549725Z >>> ) 2024-06-26T05:54:20.0549981Z >>> output = m(input) 2024-06-26T05:54:20.0550288Z >>> output.size() 2024-06-26T05:54:20.0550594Z torch.Size([2, 2, 5, 5]) 2024-06-26T05:54:20.0550928Z >>> # With torch.Size 2024-06-26T05:54:20.0551238Z >>> m = nn.Sequential( 2024-06-26T05:54:20.0551567Z >>> nn.Linear(50, 50), 2024-06-26T05:54:20.0551956Z >>> nn.Unflatten(1, torch.Size([2, 5, 5])) 2024-06-26T05:54:20.0552354Z >>> ) 2024-06-26T05:54:20.0552610Z >>> output = m(input) 2024-06-26T05:54:20.0552928Z >>> output.size() 2024-06-26T05:54:20.0553224Z torch.Size([2, 2, 5, 5]) 2024-06-26T05:54:20.0553595Z >>> # With namedshape (tuple of tuples) 2024-06-26T05:54:20.0554156Z >>> input = torch.randn(2, 50, names=('N', 'features')) 2024-06-26T05:54:20.0554838Z >>> unflatten = nn.Unflatten('features', (('C', 2), ('H', 5), ('W', 5))) 2024-06-26T05:54:20.0555400Z >>> output = unflatten(input) 2024-06-26T05:54:20.0555761Z >>> output.size() 2024-06-26T05:54:20.0556056Z torch.Size([2, 2, 5, 5]) 2024-06-26T05:54:20.0556290Z 2024-06-26T05:54:20.0556668Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:20.0557140Z 2024-06-26T05:54:20.0872088Z msg = Cannot scrape callname=TripletMarginWithDistanceLoss in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py line=1696. 2024-06-26T05:54:20.0874162Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:20.0875025Z Creates a criterion that measures the triplet loss given input 2024-06-26T05:54:20.0875744Z tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor, 2024-06-26T05:54:20.0876615Z positive, and negative examples, respectively), and a nonnegative, 2024-06-26T05:54:20.0877557Z real-valued function ("distance function") used to compute the relationship 2024-06-26T05:54:20.0878390Z between the anchor and positive example ("positive distance") and the 2024-06-26T05:54:20.0879101Z anchor and negative example ("negative distance"). 2024-06-26T05:54:20.0879454Z 2024-06-26T05:54:20.0879870Z The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``) 2024-06-26T05:54:20.0880412Z can be described as: 2024-06-26T05:54:20.0880755Z 2024-06-26T05:54:20.0880868Z .. math:: 2024-06-26T05:54:20.0881226Z \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad 2024-06-26T05:54:20.0881933Z l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} 2024-06-26T05:54:20.0882381Z 2024-06-26T05:54:20.0882798Z where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function 2024-06-26T05:54:20.0883820Z quantifying the closeness of two tensors, referred to as the :attr:`distance_function`; 2024-06-26T05:54:20.0884757Z and :math:`margin` is a nonnegative margin representing the minimum difference 2024-06-26T05:54:20.0885627Z between the positive and negative distances that is required for the loss to 2024-06-26T05:54:20.0886501Z be 0. The input tensors have :math:`N` elements each and can be of any shape 2024-06-26T05:54:20.0887194Z that the distance function can handle. 2024-06-26T05:54:20.0887508Z 2024-06-26T05:54:20.0887738Z If :attr:`reduction` is not ``'none'`` 2024-06-26T05:54:20.0888185Z (default ``'mean'``), then: 2024-06-26T05:54:20.0888502Z 2024-06-26T05:54:20.0888606Z .. math:: 2024-06-26T05:54:20.0888867Z \ell(x, y) = 2024-06-26T05:54:20.0889213Z \begin{cases} 2024-06-26T05:54:20.0889748Z \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ 2024-06-26T05:54:20.0890563Z \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} 2024-06-26T05:54:20.0891140Z \end{cases} 2024-06-26T05:54:20.0891330Z 2024-06-26T05:54:20.0891711Z See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet 2024-06-26T05:54:20.0892587Z loss for input tensors using the :math:`l_p` distance as the distance function. 2024-06-26T05:54:20.0893147Z 2024-06-26T05:54:20.0893242Z Args: 2024-06-26T05:54:20.0894042Z distance_function (Callable, optional): A nonnegative, real-valued function that 2024-06-26T05:54:20.0894877Z quantifies the closeness of two tensors. If not specified, 2024-06-26T05:54:20.0895561Z `nn.PairwiseDistance` will be used. Default: ``None`` 2024-06-26T05:54:20.0896370Z margin (float, optional): A nonnegative margin representing the minimum difference 2024-06-26T05:54:20.0897314Z between the positive and negative distances required for the loss to be 0. Larger 2024-06-26T05:54:20.0898276Z margins penalize cases where the negative examples are not distant enough from the 2024-06-26T05:54:20.0899087Z anchors, relative to the positives. Default: :math:`1`. 2024-06-26T05:54:20.0899870Z swap (bool, optional): Whether to use the distance swap described in the paper 2024-06-26T05:54:20.0900826Z `Learning shallow convolutional feature descriptors with triplet losses` by 2024-06-26T05:54:20.0901719Z V. Balntas, E. Riba et al. If True, and if the positive example is closer to the 2024-06-26T05:54:20.0902648Z negative example than the anchor is, swaps the positive example and the anchor in 2024-06-26T05:54:20.0903412Z the loss computation. Default: ``False``. 2024-06-26T05:54:20.0904288Z reduction (str, optional): Specifies the (optional) reduction to apply to the output: 2024-06-26T05:54:20.0905229Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2024-06-26T05:54:20.0906048Z ``'mean'``: the sum of the output will be divided by the number of 2024-06-26T05:54:20.0907076Z elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'`` 2024-06-26T05:54:20.0907571Z 2024-06-26T05:54:20.0907577Z 2024-06-26T05:54:20.0907692Z Shape: 2024-06-26T05:54:20.0908294Z - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions 2024-06-26T05:54:20.0909020Z as supported by the distance function. 2024-06-26T05:54:20.0909765Z - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar 2024-06-26T05:54:20.0910457Z otherwise. 2024-06-26T05:54:20.0910643Z 2024-06-26T05:54:20.0910762Z Examples:: 2024-06-26T05:54:20.0910930Z 2024-06-26T05:54:20.0911061Z >>> # Initialize embeddings 2024-06-26T05:54:20.0911441Z >>> embedding = nn.Embedding(1000, 128) 2024-06-26T05:54:20.0911902Z >>> anchor_ids = torch.randint(0, 1000, (1,)) 2024-06-26T05:54:20.0912449Z >>> positive_ids = torch.randint(0, 1000, (1,)) 2024-06-26T05:54:20.0912935Z >>> negative_ids = torch.randint(0, 1000, (1,)) 2024-06-26T05:54:20.0913395Z >>> anchor = embedding(anchor_ids) 2024-06-26T05:54:20.0913822Z >>> positive = embedding(positive_ids) 2024-06-26T05:54:20.0914246Z >>> negative = embedding(negative_ids) 2024-06-26T05:54:20.0914630Z >>> 2024-06-26T05:54:20.0914938Z >>> # Built-in Distance Function 2024-06-26T05:54:20.0915314Z >>> triplet_loss = \ 2024-06-26T05:54:20.0915869Z >>> nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance()) 2024-06-26T05:54:20.0916653Z >>> output = triplet_loss(anchor, positive, negative) 2024-06-26T05:54:20.0917114Z >>> output.backward() 2024-06-26T05:54:20.0917430Z >>> 2024-06-26T05:54:20.0917693Z >>> # Custom Distance Function 2024-06-26T05:54:20.0918059Z >>> def l_infinity(x1, x2): 2024-06-26T05:54:20.0918566Z >>> return torch.max(torch.abs(x1 - x2), dim=1).values 2024-06-26T05:54:20.0919020Z >>> 2024-06-26T05:54:20.0919406Z >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time") 2024-06-26T05:54:20.0919933Z >>> triplet_loss = ( 2024-06-26T05:54:20.0920492Z >>> nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5)) 2024-06-26T05:54:20.0921274Z >>> output = triplet_loss(anchor, positive, negative) 2024-06-26T05:54:20.0921750Z >>> output.backward() 2024-06-26T05:54:20.0922066Z >>> 2024-06-26T05:54:20.0922337Z >>> # Custom Distance Function (Lambda) 2024-06-26T05:54:20.0922749Z >>> triplet_loss = ( 2024-06-26T05:54:20.0923198Z >>> nn.TripletMarginWithDistanceLoss( 2024-06-26T05:54:20.0923857Z >>> distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y))) 2024-06-26T05:54:20.0924496Z >>> output = triplet_loss(anchor, positive, negative) 2024-06-26T05:54:20.0924963Z >>> output.backward() 2024-06-26T05:54:20.0925174Z 2024-06-26T05:54:20.0925273Z Reference: 2024-06-26T05:54:20.0925847Z V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses: 2024-06-26T05:54:20.0926659Z http://www.bmva.org/bmvc/2016/papers/paper119/index.html 2024-06-26T05:54:20.0927131Z 2024-06-26T05:54:20.0927653Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17)) 2024-06-26T05:54:20.0928139Z 2024-06-26T05:54:20.1423708Z msg = Cannot scrape callname=MaxUnpool2d in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py line=395. 2024-06-26T05:54:20.1425176Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:20.1425845Z Computes a partial inverse of :class:`MaxPool2d`. 2024-06-26T05:54:20.1426452Z 2024-06-26T05:54:20.1426871Z :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost. 2024-06-26T05:54:20.1427396Z 2024-06-26T05:54:20.1427686Z :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d` 2024-06-26T05:54:20.1428466Z including the indices of the maximal values and computes a partial inverse 2024-06-26T05:54:20.1429312Z in which all non-maximal values are set to zero. 2024-06-26T05:54:20.1429657Z 2024-06-26T05:54:20.1429754Z Note: 2024-06-26T05:54:20.1430315Z This operation may behave nondeterministically when the input indices has repeat values. 2024-06-26T05:54:20.1431382Z See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. 2024-06-26T05:54:20.1432030Z 2024-06-26T05:54:20.1432345Z .. note:: :class:`MaxPool2d` can map several input sizes to the same output 2024-06-26T05:54:20.1433033Z sizes. Hence, the inversion process can get ambiguous. 2024-06-26T05:54:20.1433681Z To accommodate this, you can provide the needed output size 2024-06-26T05:54:20.1434361Z as an additional argument :attr:`output_size` in the forward call. 2024-06-26T05:54:20.1434960Z See the Inputs and Example below. 2024-06-26T05:54:20.1435280Z 2024-06-26T05:54:20.1435376Z Args: 2024-06-26T05:54:20.1435766Z kernel_size (int or tuple): Size of the max pooling window. 2024-06-26T05:54:20.1436375Z stride (int or tuple): Stride of the max pooling window. 2024-06-26T05:54:20.1436931Z It is set to :attr:`kernel_size` by default. 2024-06-26T05:54:20.1437514Z padding (int or tuple): Padding that was added to the input 2024-06-26T05:54:20.1437913Z 2024-06-26T05:54:20.1438008Z Inputs: 2024-06-26T05:54:20.1438362Z - `input`: the input Tensor to invert 2024-06-26T05:54:20.1438997Z - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d` 2024-06-26T05:54:20.1439670Z - `output_size` (optional): the targeted output size 2024-06-26T05:54:20.1440041Z 2024-06-26T05:54:20.1440137Z Shape: 2024-06-26T05:54:20.1440689Z - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. 2024-06-26T05:54:20.1441478Z - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where 2024-06-26T05:54:20.1441956Z 2024-06-26T05:54:20.1442066Z .. math:: 2024-06-26T05:54:20.1442749Z H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} 2024-06-26T05:54:20.1443313Z 2024-06-26T05:54:20.1443426Z .. math:: 2024-06-26T05:54:20.1444080Z W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} 2024-06-26T05:54:20.1444643Z 2024-06-26T05:54:20.1444868Z or as given by :attr:`output_size` in the call operator 2024-06-26T05:54:20.1445245Z 2024-06-26T05:54:20.1445358Z Example:: 2024-06-26T05:54:20.1445517Z 2024-06-26T05:54:20.1445739Z >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True) 2024-06-26T05:54:20.1446268Z >>> unpool = nn.MaxUnpool2d(2, stride=2) 2024-06-26T05:54:20.1446758Z >>> input = torch.tensor([[[[ 1., 2., 3., 4.], 2024-06-26T05:54:20.1447244Z [ 5., 6., 7., 8.], 2024-06-26T05:54:20.1447697Z [ 9., 10., 11., 12.], 2024-06-26T05:54:20.1448157Z [13., 14., 15., 16.]]]]) 2024-06-26T05:54:20.1448610Z >>> output, indices = pool(input) 2024-06-26T05:54:20.1449018Z >>> unpool(output, indices) 2024-06-26T05:54:20.1449415Z tensor([[[[ 0., 0., 0., 0.], 2024-06-26T05:54:20.1449822Z [ 0., 6., 0., 8.], 2024-06-26T05:54:20.1450209Z [ 0., 0., 0., 0.], 2024-06-26T05:54:20.1450614Z [ 0., 14., 0., 16.]]]]) 2024-06-26T05:54:20.1451189Z >>> # Now using output_size to resolve an ambiguous size for the inverse 2024-06-26T05:54:20.1451894Z >>> input = torch.tensor([[[[ 1., 2., 3., 4., 5.], 2024-06-26T05:54:20.1452394Z [ 6., 7., 8., 9., 10.], 2024-06-26T05:54:20.1452866Z [11., 12., 13., 14., 15.], 2024-06-26T05:54:20.1453404Z [16., 17., 18., 19., 20.]]]]) 2024-06-26T05:54:20.1454030Z >>> output, indices = pool(input) 2024-06-26T05:54:20.1454553Z >>> # This call will not work without specifying output_size 2024-06-26T05:54:20.1455132Z >>> unpool(output, indices, output_size=input.size()) 2024-06-26T05:54:20.1455630Z tensor([[[[ 0., 0., 0., 0., 0.], 2024-06-26T05:54:20.1456053Z [ 0., 7., 0., 9., 0.], 2024-06-26T05:54:20.1456456Z [ 0., 0., 0., 0., 0.], 2024-06-26T05:54:20.1456870Z [ 0., 17., 0., 19., 0.]]]]) 2024-06-26T05:54:20.1457173Z 2024-06-26T05:54:20.1457178Z 2024-06-26T05:54:20.1457271Z 2024-06-26T05:54:20.1457797Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:20.1458269Z 2024-06-26T05:54:20.1700580Z msg = Cannot scrape callname=EmbeddingBag in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py line=270. 2024-06-26T05:54:20.1702692Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:20.1703696Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings. 2024-06-26T05:54:20.1704288Z 2024-06-26T05:54:20.1704744Z For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`, 2024-06-26T05:54:20.1705472Z and with 2D inputs, this class 2024-06-26T05:54:20.1705731Z 2024-06-26T05:54:20.1706158Z * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``, 2024-06-26T05:54:20.1707176Z * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``, 2024-06-26T05:54:20.1708202Z * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``. 2024-06-26T05:54:20.1708781Z 2024-06-26T05:54:20.1709267Z However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these 2024-06-26T05:54:20.1710019Z operations. 2024-06-26T05:54:20.1710200Z 2024-06-26T05:54:20.1710596Z EmbeddingBag also supports per-sample weights as an argument to the forward 2024-06-26T05:54:20.1711407Z pass. This scales the output of the Embedding before performing a weighted 2024-06-26T05:54:20.1712216Z reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the 2024-06-26T05:54:20.1713039Z only supported ``mode`` is ``"sum"``, which computes a weighted sum according to 2024-06-26T05:54:20.1713653Z :attr:`per_sample_weights`. 2024-06-26T05:54:20.1713899Z 2024-06-26T05:54:20.1713998Z Args: 2024-06-26T05:54:20.1714386Z num_embeddings (int): size of the dictionary of embeddings 2024-06-26T05:54:20.1714997Z embedding_dim (int): the size of each embedding vector 2024-06-26T05:54:20.1715787Z max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm` 2024-06-26T05:54:20.1716594Z is renormalized to have norm :attr:`max_norm`. 2024-06-26T05:54:20.1717549Z norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``. 2024-06-26T05:54:20.1718641Z scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of 2024-06-26T05:54:20.1719548Z the words in the mini-batch. Default ``False``. 2024-06-26T05:54:20.1720201Z Note: this option is not supported when ``mode="max"``. 2024-06-26T05:54:20.1721238Z mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag. 2024-06-26T05:54:20.1722047Z ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights` 2024-06-26T05:54:20.1722806Z into consideration. ``"mean"`` computes the average of the values 2024-06-26T05:54:20.1723634Z in the bag, ``"max"`` computes the max value over each bag. 2024-06-26T05:54:20.1724207Z Default: ``"mean"`` 2024-06-26T05:54:20.1724954Z sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See 2024-06-26T05:54:20.1725916Z Notes for more details regarding sparse gradients. Note: this option is not 2024-06-26T05:54:20.1726597Z supported when ``mode="max"``. 2024-06-26T05:54:20.1727416Z include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element 2024-06-26T05:54:20.1728402Z is equivalent to the size of `indices`. This matches the CSR format. 2024-06-26T05:54:20.1729324Z padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the 2024-06-26T05:54:20.1730302Z gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated 2024-06-26T05:54:20.1731152Z during training, i.e. it remains as a fixed "pad". For a newly constructed 2024-06-26T05:54:20.1732011Z EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all 2024-06-26T05:54:20.1732879Z zeros, but can be updated to another value to be used as the padding vector. 2024-06-26T05:54:20.1733897Z Note that the embedding vector at :attr:`padding_idx` is excluded from the 2024-06-26T05:54:20.1734533Z reduction. 2024-06-26T05:54:20.1734836Z 2024-06-26T05:54:20.1734939Z Attributes: 2024-06-26T05:54:20.1735537Z weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)` 2024-06-26T05:54:20.1736302Z initialized from :math:`\mathcal{N}(0, 1)`. 2024-06-26T05:54:20.1736669Z 2024-06-26T05:54:20.1736782Z Examples:: 2024-06-26T05:54:20.1736947Z 2024-06-26T05:54:20.1737193Z >>> # an EmbeddingBag module containing 10 tensors of size 3 2024-06-26T05:54:20.1737853Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum') 2024-06-26T05:54:20.1738387Z >>> # a batch of 2 samples of 4 indices each 2024-06-26T05:54:20.1738970Z >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long) 2024-06-26T05:54:20.1739586Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2024-06-26T05:54:20.1740177Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-06-26T05:54:20.1740665Z >>> embedding_sum(input, offsets) 2024-06-26T05:54:20.1741138Z tensor([[-0.8861, -5.4350, -0.0523], 2024-06-26T05:54:20.1741591Z [ 1.1306, -2.5798, -1.0044]]) 2024-06-26T05:54:20.1741886Z 2024-06-26T05:54:20.1742014Z >>> # Example with padding_idx 2024-06-26T05:54:20.1742637Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2) 2024-06-26T05:54:20.1743320Z >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long) 2024-06-26T05:54:20.1743930Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2024-06-26T05:54:20.1744419Z >>> embedding_sum(input, offsets) 2024-06-26T05:54:20.1744825Z tensor([[ 0.0000, 0.0000, 0.0000], 2024-06-26T05:54:20.1745329Z [-0.7082, 3.2145, -2.6251]]) 2024-06-26T05:54:20.1745607Z 2024-06-26T05:54:20.1745854Z >>> # An EmbeddingBag can be loaded from an Embedding like so 2024-06-26T05:54:20.1746539Z >>> embedding = nn.Embedding(10, 3, padding_idx=2) 2024-06-26T05:54:20.1747095Z >>> embedding_sum = nn.EmbeddingBag.from_pretrained( 2024-06-26T05:54:20.1747587Z embedding.weight, 2024-06-26T05:54:20.1747990Z padding_idx=embedding.padding_idx, 2024-06-26T05:54:20.1748540Z mode='sum') 2024-06-26T05:54:20.1748848Z 2024-06-26T05:54:20.1749356Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:20.1749847Z 2024-06-26T05:54:20.2045409Z msg = Cannot scrape callname=DistributedDataParallel.join in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py line=1743. 2024-06-26T05:54:20.2046828Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:20.2047323Z 2024-06-26T05:54:20.2047692Z Context manager for training with uneven inputs across processes in DDP. 2024-06-26T05:54:20.2048169Z 2024-06-26T05:54:20.2048531Z This context manager will keep track of already-joined DDP processes, 2024-06-26T05:54:20.2049259Z and "shadow" the forward and backward passes by inserting collective 2024-06-26T05:54:20.2050050Z communication operations to match with the ones created by non-joined 2024-06-26T05:54:20.2050813Z DDP processes. This will ensure each collective call has a corresponding 2024-06-26T05:54:20.2051619Z call by already-joined DDP processes, preventing hangs or errors that 2024-06-26T05:54:20.2052326Z would otherwise happen when training with uneven inputs across 2024-06-26T05:54:20.2053032Z processes. Alternatively, if the flag ``throw_on_early_termination`` is 2024-06-26T05:54:20.2054009Z specified to be ``True``, all trainers will throw an error once one rank 2024-06-26T05:54:20.2054732Z runs out of inputs, allowing these errors to be caught and handled 2024-06-26T05:54:20.2055290Z according to application logic. 2024-06-26T05:54:20.2055547Z 2024-06-26T05:54:20.2055839Z Once all DDP processes have joined, the context manager will broadcast 2024-06-26T05:54:20.2056595Z the model corresponding to the last joined process to all processes to 2024-06-26T05:54:20.2057233Z ensure the model is the same across all processes 2024-06-26T05:54:20.2057702Z (which is guaranteed by DDP). 2024-06-26T05:54:20.2057939Z 2024-06-26T05:54:20.2058218Z To use this to enable training with uneven inputs across processes, 2024-06-26T05:54:20.2058941Z simply wrap this context manager around your training loop. No further 2024-06-26T05:54:20.2059608Z modifications to the model or data loading is required. 2024-06-26T05:54:20.2059977Z 2024-06-26T05:54:20.2060090Z .. warning:: 2024-06-26T05:54:20.2060546Z If the model or training loop this context manager is wrapped around 2024-06-26T05:54:20.2061229Z has additional distributed collective operations, such as 2024-06-26T05:54:20.2061906Z ``SyncBatchNorm`` in the model's forward pass, then the flag 2024-06-26T05:54:20.2062574Z ``throw_on_early_termination`` must be enabled. This is because this 2024-06-26T05:54:20.2063340Z context manager is not aware of non-DDP collective communication. 2024-06-26T05:54:20.2063985Z This flag will cause all ranks to throw when any one rank 2024-06-26T05:54:20.2064639Z exhausts inputs, allowing these errors to be caught and recovered 2024-06-26T05:54:20.2065191Z from across all ranks. 2024-06-26T05:54:20.2065408Z 2024-06-26T05:54:20.2065508Z Args: 2024-06-26T05:54:20.2065870Z divide_by_initial_world_size (bool): If ``True``, will divide 2024-06-26T05:54:20.2066536Z gradients by the initial ``world_size`` DDP training was launched 2024-06-26T05:54:20.2067198Z with. If ``False``, will compute the effective world size 2024-06-26T05:54:20.2067821Z (number of ranks that have not depleted their inputs yet) and 2024-06-26T05:54:20.2068417Z divide gradients by that during allreduce. Set 2024-06-26T05:54:20.2068990Z ``divide_by_initial_world_size=True`` to ensure every input 2024-06-26T05:54:20.2069955Z sample including the uneven inputs have equal weight in terms of 2024-06-26T05:54:20.2070956Z how much they contribute to the global gradient. This is 2024-06-26T05:54:20.2072005Z achieved by always dividing the gradient by the initial 2024-06-26T05:54:20.2073013Z ``world_size`` even when we encounter uneven inputs. If you set 2024-06-26T05:54:20.2073668Z this to ``False``, we divide the gradient by the remaining 2024-06-26T05:54:20.2074327Z number of nodes. This ensures parity with training on a smaller 2024-06-26T05:54:20.2074979Z ``world_size`` although it also means the uneven inputs would 2024-06-26T05:54:20.2075628Z contribute more towards the global gradient. Typically, you 2024-06-26T05:54:20.2076300Z would want to set this to ``True`` for cases where the last few 2024-06-26T05:54:20.2076982Z inputs of your training job are uneven. In extreme cases, where 2024-06-26T05:54:20.2077648Z there is a large discrepancy in the number of inputs, setting 2024-06-26T05:54:20.2078260Z this to ``False`` might provide better results. 2024-06-26T05:54:20.2078885Z enable (bool): Whether to enable uneven input detection or not. Pass 2024-06-26T05:54:20.2079546Z in ``enable=False`` to disable in cases where you know that 2024-06-26T05:54:20.2080192Z inputs are even across participating processes. Default is 2024-06-26T05:54:20.2080781Z ``True``. 2024-06-26T05:54:20.2081185Z throw_on_early_termination (bool): Whether to throw an error 2024-06-26T05:54:20.2081816Z or continue training when at least one rank has exhausted 2024-06-26T05:54:20.2082464Z inputs. If ``True``, will throw upon the first rank reaching end 2024-06-26T05:54:20.2083109Z of data. If ``False``, will continue training with a smaller 2024-06-26T05:54:20.2083763Z effective world size until all ranks are joined. Note that if 2024-06-26T05:54:20.2084333Z this flag is specified, then the flag 2024-06-26T05:54:20.2084854Z ``divide_by_initial_world_size`` would be ignored. Default 2024-06-26T05:54:20.2085341Z is ``False``. 2024-06-26T05:54:20.2085542Z 2024-06-26T05:54:20.2085548Z 2024-06-26T05:54:20.2085651Z Example:: 2024-06-26T05:54:20.2085796Z 2024-06-26T05:54:20.2085947Z >>> # xdoctest: +SKIP("Distributed") 2024-06-26T05:54:20.2086336Z >>> import torch 2024-06-26T05:54:20.2086676Z >>> import torch.distributed as dist 2024-06-26T05:54:20.2087072Z >>> import os 2024-06-26T05:54:20.2087388Z >>> import torch.multiprocessing as mp 2024-06-26T05:54:20.2087812Z >>> import torch.nn as nn 2024-06-26T05:54:20.2088172Z >>> # On each spawned worker 2024-06-26T05:54:20.2088522Z >>> def worker(rank): 2024-06-26T05:54:20.2088958Z >>> dist.init_process_group("nccl", rank=rank, world_size=2) 2024-06-26T05:54:20.2089478Z >>> torch.cuda.set_device(rank) 2024-06-26T05:54:20.2089921Z >>> model = nn.Linear(1, 1, bias=False).to(rank) 2024-06-26T05:54:20.2090499Z >>> model = torch.nn.parallel.DistributedDataParallel( 2024-06-26T05:54:20.2091080Z >>> model, device_ids=[rank], output_device=rank 2024-06-26T05:54:20.2091509Z >>> ) 2024-06-26T05:54:20.2091839Z >>> # Rank 1 gets one more input than rank 0. 2024-06-26T05:54:20.2092421Z >>> inputs = [torch.tensor([1]).float() for _ in range(10 + rank)] 2024-06-26T05:54:20.2092943Z >>> with model.join(): 2024-06-26T05:54:20.2093295Z >>> for _ in range(5): 2024-06-26T05:54:20.2093798Z >>> for inp in inputs: 2024-06-26T05:54:20.2094208Z >>> loss = model(inp).sum() 2024-06-26T05:54:20.2094638Z >>> loss.backward() 2024-06-26T05:54:20.2095174Z >>> # Without the join() API, the below synchronization will hang 2024-06-26T05:54:20.2095858Z >>> # blocking for rank 1's allreduce to complete. 2024-06-26T05:54:20.2096365Z >>> torch.cuda.synchronize(device=rank) 2024-06-26T05:54:20.2096799Z 2024-06-26T05:54:20.2097196Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:20.2097670Z 2024-06-26T05:54:20.2098843Z msg = Cannot scrape callname=DistributedDataParallel._register_fused_optim in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py line=2034. 2024-06-26T05:54:20.2100300Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:20.2100789Z 2024-06-26T05:54:20.2101197Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction. 2024-06-26T05:54:20.2101779Z 2024-06-26T05:54:20.2102043Z Registers an optimizer with DDP such that the optimization for a 2024-06-26T05:54:20.2102795Z parameter will run immediately when that parameter's gradient is 2024-06-26T05:54:20.2103465Z finished with reduction, instead of waiting for all parameters' 2024-06-26T05:54:20.2104165Z gradients to finish reduction. This can result in a training speedup 2024-06-26T05:54:20.2104903Z depending on your workload since the optimizer can run while gradient 2024-06-26T05:54:20.2105644Z reduction for other parameters are still ongoing. In addition, this has 2024-06-26T05:54:20.2106406Z the potential to reduce peak memory consumption during training, as it 2024-06-26T05:54:20.2107191Z only needs to load the per-parameter optimizer states of a single 2024-06-26T05:54:20.2107953Z parameter at a time, instead of loading all per-parameter optimizer 2024-06-26T05:54:20.2108483Z states at once. 2024-06-26T05:54:20.2108665Z 2024-06-26T05:54:20.2108757Z Args: 2024-06-26T05:54:20.2109161Z optim (Type): a ``torch.optim.Optimizer`` class to be registered 2024-06-26T05:54:20.2109691Z as a fused optimizer. 2024-06-26T05:54:20.2110122Z *args (Sequence[Any]): Arguments to forward to `optim`. 2024-06-26T05:54:20.2110768Z optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters 2024-06-26T05:54:20.2111486Z to optimize, similar to `params` argument of traditional `torch.optim` 2024-06-26T05:54:20.2112213Z Optimizers. If this is omitted, all DDP model parameters will be 2024-06-26T05:54:20.2112745Z optimized. 2024-06-26T05:54:20.2113169Z **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`. 2024-06-26T05:54:20.2113604Z 2024-06-26T05:54:20.2113709Z .. warning :: 2024-06-26T05:54:20.2114154Z _register_fused_optim should only be called once on a DDP instance, 2024-06-26T05:54:20.2114844Z and registering multiple fused optimizers for the same DDP model 2024-06-26T05:54:20.2115428Z is not currently supported. Please ping 2024-06-26T05:54:20.2116043Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-06-26T05:54:20.2116597Z for your use case. 2024-06-26T05:54:20.2116807Z 2024-06-26T05:54:20.2116907Z .. warning :: 2024-06-26T05:54:20.2117312Z _register_fused_optim and register_comm_hook currently do not 2024-06-26T05:54:20.2117986Z compose together, meaning that custom DDP communication hooks are 2024-06-26T05:54:20.2118629Z not supported with overlapped optimizers. Please ping 2024-06-26T05:54:20.2119292Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-06-26T05:54:20.2119863Z for your use case. 2024-06-26T05:54:20.2120060Z 2024-06-26T05:54:20.2120160Z .. warning :: 2024-06-26T05:54:20.2120707Z Gradient accumulation and DDP `no_sync` are currently not supported 2024-06-26T05:54:20.2121313Z with overlapped optimizer. Please ping 2024-06-26T05:54:20.2121900Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-06-26T05:54:20.2122465Z for your use case. 2024-06-26T05:54:20.2122661Z 2024-06-26T05:54:20.2122774Z Example:: 2024-06-26T05:54:20.2122923Z 2024-06-26T05:54:20.2123090Z >>> # xdoctest: +SKIP("No rendezvous handler") 2024-06-26T05:54:20.2123878Z >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...') 2024-06-26T05:54:20.2124689Z >>> net = torch.nn.parallel.DistributedDataParallel(model, pg) 2024-06-26T05:54:20.2125312Z >>> lr = 1e-2 2024-06-26T05:54:20.2125606Z >>> betas = (0.9, 0.99) 2024-06-26T05:54:20.2125950Z >>> eps = 1e-6 2024-06-26T05:54:20.2126407Z >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps) 2024-06-26T05:54:20.2127118Z >>> # Example with subset of parameters 2024-06-26T05:54:20.2127599Z >>> params_to_opt = [list(net.parameters())[0]] 2024-06-26T05:54:20.2128057Z >>> net._register_fused_optim( 2024-06-26T05:54:20.2128619Z ... torch.optim.Adam, lr, optim_params=params_to_opt, betas=betas, eps=eps 2024-06-26T05:54:20.2129189Z ... ) 2024-06-26T05:54:20.2129331Z 2024-06-26T05:54:20.2129727Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:20.2130199Z 2024-06-26T05:54:20.2316324Z msg = Cannot scrape callname=convert_conv2d_weight_memory_format in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/memory_format.py line=6. 2024-06-26T05:54:20.2317710Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:20.2318523Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``. 2024-06-26T05:54:20.2318959Z 2024-06-26T05:54:20.2319332Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2024-06-26T05:54:20.2320221Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2024-06-26T05:54:20.2321181Z This function is used to facilitate the computation to adopt NHWC kernels, which 2024-06-26T05:54:20.2322239Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2024-06-26T05:54:20.2322907Z 2024-06-26T05:54:20.2323113Z .. note:: 2024-06-26T05:54:20.2323978Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2024-06-26T05:54:20.2325442Z than the utility function ``convert_conv2d_weight_memory_format``. Any 2024-06-26T05:54:20.2326588Z layer with 4d weight will be affected by ``model.to``, which does not 2024-06-26T05:54:20.2327323Z necessarily benefit from conversion to specified ``memory_format``. 2024-06-26T05:54:20.2328080Z One place we are confident in is that NHWC(channels_last) conversion for 2024-06-26T05:54:20.2328854Z convolution in cuDNN, As it is beneficial to run convolution in NHWC, 2024-06-26T05:54:20.2329576Z even in cases where we have to apply permutation to input tensors. 2024-06-26T05:54:20.2330027Z 2024-06-26T05:54:20.2330330Z Hence our strategy here is to convert only the weight of convolution to 2024-06-26T05:54:20.2330936Z channels_last. This ensures that; 2024-06-26T05:54:20.2331517Z 1. Fast convolution kernels will be used, the benefit of which could 2024-06-26T05:54:20.2332249Z outweigh overhead of permutation (if input is not in the same format) 2024-06-26T05:54:20.2333017Z 2. No unnecessary permutations are applied on layers that do not benefit 2024-06-26T05:54:20.2333779Z from memory_format conversion. 2024-06-26T05:54:20.2334058Z 2024-06-26T05:54:20.2334367Z The optimal case is that, layers between convolution layers are channels 2024-06-26T05:54:20.2335150Z last compatible. Input tensor would be permuted to channels last when it 2024-06-26T05:54:20.2335932Z encounters the first convolution layer and stay in that memory format. 2024-06-26T05:54:20.2336692Z Hence following convolutions will not need to permute its input tensor. 2024-06-26T05:54:20.2337170Z 2024-06-26T05:54:20.2337473Z In case where a channels last incompatible layer is between convolution 2024-06-26T05:54:20.2338232Z layers, we need to permute the input tensor back to contiguous format 2024-06-26T05:54:20.2338995Z for that layer. The input tensor will go through the remaining layers in 2024-06-26T05:54:20.2339752Z contiguous format and be permuted to channels last when it encounters 2024-06-26T05:54:20.2340748Z another convolution layer. There's no point in propagating that 2024-06-26T05:54:20.2341481Z permutation to an earlier layer, as most layers are quite agnostic to 2024-06-26T05:54:20.2342039Z ``memory_format``. 2024-06-26T05:54:20.2342266Z 2024-06-26T05:54:20.2342655Z This claim might change when PyTorch supports fusion of permutation, as 2024-06-26T05:54:20.2343436Z there might have been a better spot to fuse the permutation other than 2024-06-26T05:54:20.2344048Z immediately before a convolution. 2024-06-26T05:54:20.2344338Z 2024-06-26T05:54:20.2344438Z Args: 2024-06-26T05:54:20.2344883Z module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container 2024-06-26T05:54:20.2345461Z ``nn.Module`` 2024-06-26T05:54:20.2345912Z memory_format: user specified ``memory_format``, 2024-06-26T05:54:20.2346509Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2024-06-26T05:54:20.2346911Z 2024-06-26T05:54:20.2347019Z Returns: 2024-06-26T05:54:20.2347353Z The original module with updated ``nn.Conv2d`` 2024-06-26T05:54:20.2347704Z 2024-06-26T05:54:20.2347801Z Example: 2024-06-26T05:54:20.2348148Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-06-26T05:54:20.2348677Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2024-06-26T05:54:20.2349357Z >>> input = torch.randint(1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda") 2024-06-26T05:54:20.2349964Z >>> model = nn.Sequential( 2024-06-26T05:54:20.2350358Z >>> nn.Conv2d(8, 4, 3)).cuda().half() 2024-06-26T05:54:20.2350787Z >>> # This is identical to: 2024-06-26T05:54:20.2351366Z >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2024-06-26T05:54:20.2352191Z >>> model = nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2024-06-26T05:54:20.2352805Z >>> out = model(input) 2024-06-26T05:54:20.2353135Z 2024-06-26T05:54:20.2353660Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:20.2353666Z 2024-06-26T05:54:20.2354639Z msg = Cannot scrape callname=convert_conv3d_weight_memory_format in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/memory_format.py line=81. 2024-06-26T05:54:20.2355040Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:20.2355307Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format`` 2024-06-26T05:54:20.2355656Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2024-06-26T05:54:20.2356043Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2024-06-26T05:54:20.2356387Z This function is used to facilitate the computation to adopt NHWC kernels, which 2024-06-26T05:54:20.2356797Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2024-06-26T05:54:20.2356807Z 2024-06-26T05:54:20.2356925Z .. note:: 2024-06-26T05:54:20.2357233Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2024-06-26T05:54:20.2357539Z than the utility function ``convert_conv3d_weight_memory_format``. Any 2024-06-26T05:54:20.2357833Z layer with 4d weight will be affected by ``model.to``, which does not 2024-06-26T05:54:20.2358114Z necessarily benefit from conversion to specified ``memory_format``. 2024-06-26T05:54:20.2358435Z One place we are confident in is that NHWC(channels_last) conversion for 2024-06-26T05:54:20.2358725Z convolution in cuDNN, As it is beneficial to run convolution in NHWC, 2024-06-26T05:54:20.2359001Z even in cases where we have to apply permutation to input tensors. 2024-06-26T05:54:20.2359007Z 2024-06-26T05:54:20.2359320Z Hence our strategy here is to convert only the weight of convolution to 2024-06-26T05:54:20.2359523Z channels_last. This ensures that; 2024-06-26T05:54:20.2359807Z 1. Fast convolution kernels will be used, the benefit of which could 2024-06-26T05:54:20.2360110Z outweigh overhead of permutation (if input is not in the same format) 2024-06-26T05:54:20.2360474Z 2. No unnecessary permutations are applied on layers that do not benefit 2024-06-26T05:54:20.2360686Z from memory_format conversion. 2024-06-26T05:54:20.2360692Z 2024-06-26T05:54:20.2361000Z The optimal case is that, layers between convolution layers are channels 2024-06-26T05:54:20.2361306Z last compatible. Input tensor would be permuted to channels last when it 2024-06-26T05:54:20.2361615Z encounters the first convolution layer and stay in that memory format. 2024-06-26T05:54:20.2361920Z Hence following convolutions will not need to permute its input tensor. 2024-06-26T05:54:20.2361925Z 2024-06-26T05:54:20.2362236Z In case where a channels last incompatible layer is between convolution 2024-06-26T05:54:20.2362529Z layers, we need to permute the input tensor back to contiguous format 2024-06-26T05:54:20.2362835Z for that layer. The input tensor will go through the remaining layers in 2024-06-26T05:54:20.2363147Z contiguous format and be permuted to channels last when it encounters 2024-06-26T05:54:20.2363484Z another convolution layer. There's no point in propagating that 2024-06-26T05:54:20.2363776Z permutation to an earlier layer, as most layers are quite agnostic to 2024-06-26T05:54:20.2363900Z ``memory_format``. 2024-06-26T05:54:20.2363905Z 2024-06-26T05:54:20.2364216Z This claim might change when PyTorch supports fusion of permutation, as 2024-06-26T05:54:20.2364514Z there might have been a better spot to fuse the permutation other than 2024-06-26T05:54:20.2364667Z immediately before a convolution. 2024-06-26T05:54:20.2364673Z 2024-06-26T05:54:20.2364769Z Args: 2024-06-26T05:54:20.2365075Z module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container 2024-06-26T05:54:20.2365196Z ``nn.Module`` 2024-06-26T05:54:20.2365384Z memory_format: user specified ``memory_format``, 2024-06-26T05:54:20.2365635Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2024-06-26T05:54:20.2365645Z 2024-06-26T05:54:20.2365740Z Returns: 2024-06-26T05:54:20.2365924Z The original module with updated ``nn.Conv3d`` 2024-06-26T05:54:20.2365929Z 2024-06-26T05:54:20.2366035Z Example: 2024-06-26T05:54:20.2366213Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-06-26T05:54:20.2366420Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2024-06-26T05:54:20.2366747Z >>> input = torch.randint(1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda") 2024-06-26T05:54:20.2366868Z >>> model = nn.Sequential( 2024-06-26T05:54:20.2367028Z >>> nn.Conv3d(8, 4, 3)).cuda().half() 2024-06-26T05:54:20.2367155Z >>> # This is identical to: 2024-06-26T05:54:20.2367470Z >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last) 2024-06-26T05:54:20.2367827Z >>> model = nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last) 2024-06-26T05:54:20.2367941Z >>> out = model(input) 2024-06-26T05:54:20.2368032Z 2024-06-26T05:54:20.2368426Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:20.2368432Z 2024-06-26T05:54:20.2533964Z msg = Cannot scrape callname=random_structured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=937. 2024-06-26T05:54:20.2534468Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:20.2534798Z Prune tensor by removing random channels along the specified dimension. 2024-06-26T05:54:20.2534805Z 2024-06-26T05:54:20.2535120Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2024-06-26T05:54:20.2535618Z by removing the specified ``amount`` of (currently unpruned) channels 2024-06-26T05:54:20.2535796Z along the specified ``dim`` selected at random. 2024-06-26T05:54:20.2536127Z Modifies module in place (and also return the modified module) 2024-06-26T05:54:20.2536224Z by: 2024-06-26T05:54:20.2536231Z 2024-06-26T05:54:20.2536722Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-06-26T05:54:20.2537159Z binary mask applied to the parameter ``name`` by the pruning method. 2024-06-26T05:54:20.2537593Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-06-26T05:54:20.2537863Z original (unpruned) parameter is stored in a new parameter named 2024-06-26T05:54:20.2538026Z ``name+'_orig'``. 2024-06-26T05:54:20.2538032Z 2024-06-26T05:54:20.2538169Z Args: 2024-06-26T05:54:20.2538570Z module (nn.Module): module containing the tensor to prune 2024-06-26T05:54:20.2539040Z name (str): parameter name within ``module`` on which pruning 2024-06-26T05:54:20.2539235Z will act. 2024-06-26T05:54:20.2539669Z amount (int or float): quantity of parameters to prune. 2024-06-26T05:54:20.2540114Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-06-26T05:54:20.2540623Z fraction of parameters to prune. If ``int``, it represents the 2024-06-26T05:54:20.2540863Z absolute number of parameters to prune. 2024-06-26T05:54:20.2541145Z dim (int): index of the dim along which we define channels to prune. 2024-06-26T05:54:20.2541151Z 2024-06-26T05:54:20.2541250Z Returns: 2024-06-26T05:54:20.2541558Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-06-26T05:54:20.2541563Z 2024-06-26T05:54:20.2541663Z Examples: 2024-06-26T05:54:20.2541792Z >>> # xdoctest: +SKIP 2024-06-26T05:54:20.2541928Z >>> m = prune.random_structured( 2024-06-26T05:54:20.2542176Z ... nn.Linear(5, 3), 'weight', amount=3, dim=1 2024-06-26T05:54:20.2542280Z ... ) 2024-06-26T05:54:20.2542513Z >>> columns_pruned = int(sum(torch.sum(m.weight, dim=0) == 0)) 2024-06-26T05:54:20.2542631Z >>> print(columns_pruned) 2024-06-26T05:54:20.2542735Z 3 2024-06-26T05:54:20.2542823Z 2024-06-26T05:54:20.2543213Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:20.2543219Z 2024-06-26T05:54:20.2544325Z msg = Cannot scrape callname=ln_structured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=978. 2024-06-26T05:54:20.2544848Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:20.2545731Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension. 2024-06-26T05:54:20.2545741Z 2024-06-26T05:54:20.2546319Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2024-06-26T05:54:20.2546885Z by removing the specified ``amount`` of (currently unpruned) channels 2024-06-26T05:54:20.2547364Z along the specified ``dim`` with the lowest L\ ``n``-norm. 2024-06-26T05:54:20.2547619Z Modifies module in place (and also return the modified module) 2024-06-26T05:54:20.2547729Z by: 2024-06-26T05:54:20.2547735Z 2024-06-26T05:54:20.2548086Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-06-26T05:54:20.2548377Z binary mask applied to the parameter ``name`` by the pruning method. 2024-06-26T05:54:20.2548756Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-06-26T05:54:20.2549021Z original (unpruned) parameter is stored in a new parameter named 2024-06-26T05:54:20.2549162Z ``name+'_orig'``. 2024-06-26T05:54:20.2549167Z 2024-06-26T05:54:20.2549272Z Args: 2024-06-26T05:54:20.2549503Z module (nn.Module): module containing the tensor to prune 2024-06-26T05:54:20.2549865Z name (str): parameter name within ``module`` on which pruning 2024-06-26T05:54:20.2550189Z will act. 2024-06-26T05:54:20.2550561Z amount (int or float): quantity of parameters to prune. 2024-06-26T05:54:20.2551030Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-06-26T05:54:20.2551600Z fraction of parameters to prune. If ``int``, it represents the 2024-06-26T05:54:20.2551911Z absolute number of parameters to prune. 2024-06-26T05:54:20.2552303Z n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid 2024-06-26T05:54:20.2552500Z entries for argument ``p`` in :func:`torch.norm`. 2024-06-26T05:54:20.2552779Z dim (int): index of the dim along which we define channels to prune. 2024-06-26T05:54:20.2553091Z importance_scores (torch.Tensor): tensor of importance scores (of same 2024-06-26T05:54:20.2553347Z shape as module parameter) used to compute mask for pruning. 2024-06-26T05:54:20.2553670Z The values in this tensor indicate the importance of the corresponding 2024-06-26T05:54:20.2553834Z elements in the parameter being pruned. 2024-06-26T05:54:20.2554142Z If unspecified or None, the module parameter will be used in its place. 2024-06-26T05:54:20.2554148Z 2024-06-26T05:54:20.2554258Z Returns: 2024-06-26T05:54:20.2554555Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-06-26T05:54:20.2554561Z 2024-06-26T05:54:20.2554658Z Examples: 2024-06-26T05:54:20.2554821Z >>> from torch.nn.utils import prune 2024-06-26T05:54:20.2554946Z >>> m = prune.ln_structured( 2024-06-26T05:54:20.2555272Z ... nn.Conv2d(5, 3, 2), 'weight', amount=0.3, dim=1, n=float('-inf') 2024-06-26T05:54:20.2555379Z ... ) 2024-06-26T05:54:20.2555467Z 2024-06-26T05:54:20.2555856Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:20.2555862Z 2024-06-26T05:54:20.2556726Z msg = Cannot scrape callname=global_unstructured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=1025. 2024-06-26T05:54:20.2557130Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:20.2557135Z 2024-06-26T05:54:20.2557684Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``. 2024-06-26T05:54:20.2557690Z 2024-06-26T05:54:20.2557812Z Modifies modules in place by: 2024-06-26T05:54:20.2557830Z 2024-06-26T05:54:20.2558165Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-06-26T05:54:20.2558449Z binary mask applied to the parameter ``name`` by the pruning method. 2024-06-26T05:54:20.2558742Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-06-26T05:54:20.2559005Z original (unpruned) parameter is stored in a new parameter named 2024-06-26T05:54:20.2559136Z ``name+'_orig'``. 2024-06-26T05:54:20.2559145Z 2024-06-26T05:54:20.2559245Z Args: 2024-06-26T05:54:20.2559496Z parameters (Iterable of (module, name) tuples): parameters of 2024-06-26T05:54:20.2559759Z the model to prune in a global fashion, i.e. by aggregating all 2024-06-26T05:54:20.2560039Z weights prior to deciding which ones to prune. module must be of 2024-06-26T05:54:20.2560237Z type :class:`nn.Module`, and name must be a string. 2024-06-26T05:54:20.2560533Z pruning_method (function): a valid pruning function from this module, 2024-06-26T05:54:20.2560851Z or a custom one implemented by the user that satisfies the 2024-06-26T05:54:20.2561202Z implementation guidelines and has ``PRUNING_TYPE='unstructured'``. 2024-06-26T05:54:20.2561509Z importance_scores (dict): a dictionary mapping (module, name) tuples to 2024-06-26T05:54:20.2561849Z the corresponding parameter's importance scores tensor. The tensor 2024-06-26T05:54:20.2562141Z should be the same shape as the parameter, and is used for computing 2024-06-26T05:54:20.2562384Z mask for pruning. 2024-06-26T05:54:20.2562665Z If unspecified or None, the parameter will be used in place of its 2024-06-26T05:54:20.2562778Z importance scores. 2024-06-26T05:54:20.2562947Z kwargs: other keyword arguments such as: 2024-06-26T05:54:20.2563274Z amount (int or float): quantity of parameters to prune across the 2024-06-26T05:54:20.2563395Z specified parameters. 2024-06-26T05:54:20.2563642Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-06-26T05:54:20.2563900Z fraction of parameters to prune. If ``int``, it represents the 2024-06-26T05:54:20.2564075Z absolute number of parameters to prune. 2024-06-26T05:54:20.2564080Z 2024-06-26T05:54:20.2564172Z Raises: 2024-06-26T05:54:20.2564411Z TypeError: if ``PRUNING_TYPE != 'unstructured'`` 2024-06-26T05:54:20.2564417Z 2024-06-26T05:54:20.2564519Z Note: 2024-06-26T05:54:20.2564853Z Since global structured pruning doesn't make much sense unless the 2024-06-26T05:54:20.2565129Z norm is normalized by the size of the parameter, we now limit the 2024-06-26T05:54:20.2565329Z scope of global pruning to unstructured methods. 2024-06-26T05:54:20.2565335Z 2024-06-26T05:54:20.2565431Z Examples: 2024-06-26T05:54:20.2565572Z >>> from torch.nn.utils import prune 2024-06-26T05:54:20.2565734Z >>> from collections import OrderedDict 2024-06-26T05:54:20.2565875Z >>> net = nn.Sequential(OrderedDict([ 2024-06-26T05:54:20.2566061Z ... ('first', nn.Linear(10, 4)), 2024-06-26T05:54:20.2566227Z ... ('second', nn.Linear(4, 1)), 2024-06-26T05:54:20.2566320Z ... ])) 2024-06-26T05:54:20.2566454Z >>> parameters_to_prune = ( 2024-06-26T05:54:20.2566606Z ... (net.first, 'weight'), 2024-06-26T05:54:20.2566762Z ... (net.second, 'weight'), 2024-06-26T05:54:20.2566865Z ... ) 2024-06-26T05:54:20.2566988Z >>> prune.global_unstructured( 2024-06-26T05:54:20.2567108Z ... parameters_to_prune, 2024-06-26T05:54:20.2567290Z ... pruning_method=prune.L1Unstructured, 2024-06-26T05:54:20.2567392Z ... amount=10, 2024-06-26T05:54:20.2567482Z ... ) 2024-06-26T05:54:20.2567787Z >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0)) 2024-06-26T05:54:20.2567884Z tensor(10) 2024-06-26T05:54:20.2567889Z 2024-06-26T05:54:20.2567898Z 2024-06-26T05:54:20.2568275Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:20.2568294Z 2024-06-26T05:54:20.2569088Z msg = Cannot scrape callname=custom_from_mask in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=1144. 2024-06-26T05:54:20.2569483Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:20.2570099Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``. 2024-06-26T05:54:20.2570105Z 2024-06-26T05:54:20.2570383Z Modifies module in place (and also return the modified module) by: 2024-06-26T05:54:20.2570389Z 2024-06-26T05:54:20.2570743Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-06-26T05:54:20.2571028Z binary mask applied to the parameter ``name`` by the pruning method. 2024-06-26T05:54:20.2571310Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-06-26T05:54:20.2571586Z original (unpruned) parameter is stored in a new parameter named 2024-06-26T05:54:20.2571722Z ``name+'_orig'``. 2024-06-26T05:54:20.2571727Z 2024-06-26T05:54:20.2571821Z Args: 2024-06-26T05:54:20.2572063Z module (nn.Module): module containing the tensor to prune 2024-06-26T05:54:20.2572304Z name (str): parameter name within ``module`` on which pruning 2024-06-26T05:54:20.2572405Z will act. 2024-06-26T05:54:20.2572649Z mask (Tensor): binary mask to be applied to the parameter. 2024-06-26T05:54:20.2572655Z 2024-06-26T05:54:20.2572815Z Returns: 2024-06-26T05:54:20.2573121Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-06-26T05:54:20.2573127Z 2024-06-26T05:54:20.2573226Z Examples: 2024-06-26T05:54:20.2575859Z >>> from torch.nn.utils import prune 2024-06-26T05:54:20.2576003Z >>> m = prune.custom_from_mask( 2024-06-26T05:54:20.2576407Z ... nn.Linear(5, 3), name='bias', mask=torch.tensor([0, 1, 0]) 2024-06-26T05:54:20.2576504Z ... ) 2024-06-26T05:54:20.2576633Z >>> print(m.bias_mask) 2024-06-26T05:54:20.2576739Z tensor([0., 1., 0.]) 2024-06-26T05:54:20.2576746Z 2024-06-26T05:54:20.2576832Z 2024-06-26T05:54:20.2577232Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:20.2577237Z 2024-06-26T05:54:20.3603754Z msg = Cannot scrape callname=AveragedModel in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py line=103. 2024-06-26T05:54:20.3605034Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:20.3606061Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA). 2024-06-26T05:54:20.3606691Z 2024-06-26T05:54:20.3607037Z Stochastic Weight Averaging was proposed in `Averaging Weights Leads to 2024-06-26T05:54:20.3607816Z Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii 2024-06-26T05:54:20.3608539Z Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson 2024-06-26T05:54:20.3609064Z (UAI 2018). 2024-06-26T05:54:20.3609246Z 2024-06-26T05:54:20.3609523Z Exponential Moving Average is a variation of `Polyak averaging`_, 2024-06-26T05:54:20.3610273Z but using exponential weights instead of equal weights across iterations. 2024-06-26T05:54:20.3610748Z 2024-06-26T05:54:20.3611054Z AveragedModel class creates a copy of the provided module :attr:`model` 2024-06-26T05:54:20.3611928Z on the device :attr:`device` and allows to compute running averages of the 2024-06-26T05:54:20.3612652Z parameters of the :attr:`model`. 2024-06-26T05:54:20.3612959Z 2024-06-26T05:54:20.3613071Z Args: 2024-06-26T05:54:20.3613425Z model (torch.nn.Module): model to use with SWA/EMA 2024-06-26T05:54:20.3614751Z device (torch.device, optional): if provided, the averaged model will be 2024-06-26T05:54:20.3615433Z stored on the :attr:`device` 2024-06-26T05:54:20.3615977Z avg_fn (function, optional): the averaging function used to update 2024-06-26T05:54:20.3616674Z parameters; the function must take in the current value of the 2024-06-26T05:54:20.3617390Z :class:`AveragedModel` parameter, the current value of :attr:`model` 2024-06-26T05:54:20.3618095Z parameter, and the number of models already averaged; if None, 2024-06-26T05:54:20.3618733Z an equally weighted average is used (default: None) 2024-06-26T05:54:20.3619404Z multi_avg_fn (function, optional): the averaging function used to update 2024-06-26T05:54:20.3620172Z parameters inplace; the function must take in the current values of the 2024-06-26T05:54:20.3620998Z :class:`AveragedModel` parameters as a list, the current values of :attr:`model` 2024-06-26T05:54:20.3621848Z parameters as a list, and the number of models already averaged; if None, 2024-06-26T05:54:20.3622545Z an equally weighted average is used (default: None) 2024-06-26T05:54:20.3623183Z use_buffers (bool): if ``True``, it will compute running averages for 2024-06-26T05:54:20.3623933Z both the parameters and the buffers of the model. (default: ``False``) 2024-06-26T05:54:20.3624394Z 2024-06-26T05:54:20.3624506Z Example: 2024-06-26T05:54:20.3624828Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:20.3625327Z >>> loader, optimizer, model, loss_fn = ... 2024-06-26T05:54:20.3625889Z >>> swa_model = torch.optim.swa_utils.AveragedModel(model) 2024-06-26T05:54:20.3626715Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 2024-06-26T05:54:20.3627331Z >>> T_max=300) 2024-06-26T05:54:20.3627765Z >>> swa_start = 160 2024-06-26T05:54:20.3628238Z >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05) 2024-06-26T05:54:20.3628807Z >>> for i in range(300): 2024-06-26T05:54:20.3629211Z >>> for input, target in loader: 2024-06-26T05:54:20.3629641Z >>> optimizer.zero_grad() 2024-06-26T05:54:20.3630115Z >>> loss_fn(model(input), target).backward() 2024-06-26T05:54:20.3630576Z >>> optimizer.step() 2024-06-26T05:54:20.3645014Z >>> if i > swa_start: 2024-06-26T05:54:20.3645532Z >>> swa_model.update_parameters(model) 2024-06-26T05:54:20.3645999Z >>> swa_scheduler.step() 2024-06-26T05:54:20.3646391Z >>> else: 2024-06-26T05:54:20.3646716Z >>> scheduler.step() 2024-06-26T05:54:20.3647078Z >>> 2024-06-26T05:54:20.3647437Z >>> # Update bn statistics for the swa_model at the end 2024-06-26T05:54:20.3648022Z >>> torch.optim.swa_utils.update_bn(loader, swa_model) 2024-06-26T05:54:20.3648394Z 2024-06-26T05:54:20.3648810Z You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters. 2024-06-26T05:54:20.3649650Z If no averaging function is provided, the default is to compute 2024-06-26T05:54:20.3650351Z equally-weighted average of the weights (SWA). 2024-06-26T05:54:20.3650693Z 2024-06-26T05:54:20.3650805Z Example: 2024-06-26T05:54:20.3651129Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:20.3651732Z >>> # Compute exponential moving averages of the weights and buffers 2024-06-26T05:54:20.3652403Z >>> ema_model = torch.optim.swa_utils.AveragedModel(model, 2024-06-26T05:54:20.3653069Z >>> torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True) 2024-06-26T05:54:20.3653639Z 2024-06-26T05:54:20.3653755Z .. note:: 2024-06-26T05:54:20.3654225Z When using SWA/EMA with models containing Batch Normalization you may 2024-06-26T05:54:20.3654960Z need to update the activation statistics for Batch Normalization. 2024-06-26T05:54:20.3655737Z This can be done either by using the :meth:`torch.optim.swa_utils.update_bn` 2024-06-26T05:54:20.3656549Z or by setting :attr:`use_buffers` to `True`. The first approach updates the 2024-06-26T05:54:20.3657413Z statistics in a post-training step by passing data through the model. The 2024-06-26T05:54:20.3658225Z second does it during the parameter update phase by averaging all buffers. 2024-06-26T05:54:20.3659041Z Empirical evidence has shown that updating the statistics in normalization 2024-06-26T05:54:20.3659831Z layers increases accuracy, but you may wish to empirically test which 2024-06-26T05:54:20.3660485Z approach yields the best results in your problem. 2024-06-26T05:54:20.3660854Z 2024-06-26T05:54:20.3660955Z .. note:: 2024-06-26T05:54:20.3661480Z :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model. 2024-06-26T05:54:20.3661995Z 2024-06-26T05:54:20.3662094Z .. note:: 2024-06-26T05:54:20.3662526Z When :meth:`update_parameters` is called for the first time (i.e. 2024-06-26T05:54:20.3663202Z :attr:`n_averaged` is `0`) the parameters of `model` are copied 2024-06-26T05:54:20.3663877Z to the parameters of :class:`AveragedModel`. For every subsequent 2024-06-26T05:54:20.3664572Z call of :meth:`update_parameters` the function `avg_fn` is used 2024-06-26T05:54:20.3665116Z to update the parameters. 2024-06-26T05:54:20.3665371Z 2024-06-26T05:54:20.3665675Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2024-06-26T05:54:20.3666256Z https://arxiv.org/abs/1803.05407 2024-06-26T05:54:20.3666999Z .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should 2024-06-26T05:54:20.3667588Z Average: 2024-06-26T05:54:20.3667891Z https://arxiv.org/abs/1806.05594 2024-06-26T05:54:20.3668511Z .. _SWALP: Stochastic Weight Averaging in Low-Precision Training: 2024-06-26T05:54:20.3669152Z https://arxiv.org/abs/1904.11943 2024-06-26T05:54:20.3669872Z .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That 2024-06-26T05:54:20.3670441Z Generalizes Well: 2024-06-26T05:54:20.3670804Z https://arxiv.org/abs/2001.02312 2024-06-26T05:54:20.3671199Z .. _Polyak averaging: 2024-06-26T05:54:20.3671694Z https://paperswithcode.com/method/polyak-averaging 2024-06-26T05:54:20.3672170Z 2024-06-26T05:54:20.3672676Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:20.3673161Z 2024-06-26T05:54:20.3673935Z msg = Cannot scrape callname=SWALR in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py line=354. 2024-06-26T05:54:20.3675121Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:20.3675897Z Anneals the learning rate in each parameter group to a fixed value. 2024-06-26T05:54:20.3676338Z 2024-06-26T05:54:20.3676645Z This learning rate scheduler is meant to be used with Stochastic Weight 2024-06-26T05:54:20.3677397Z Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`). 2024-06-26T05:54:20.3677849Z 2024-06-26T05:54:20.3677945Z Args: 2024-06-26T05:54:20.3678300Z optimizer (torch.optim.Optimizer): wrapped optimizer 2024-06-26T05:54:20.3678969Z swa_lrs (float or list): the learning rate value for all param groups 2024-06-26T05:54:20.3679583Z together or separately for each group. 2024-06-26T05:54:20.3680175Z annealing_epochs (int): number of epochs in the annealing phase 2024-06-26T05:54:20.3680814Z (default: 10) 2024-06-26T05:54:20.3681349Z annealing_strategy (str): "cos" or "linear"; specifies the annealing 2024-06-26T05:54:20.3682128Z strategy: "cos" for cosine annealing, "linear" for linear annealing 2024-06-26T05:54:20.3682679Z (default: "cos") 2024-06-26T05:54:20.3683220Z last_epoch (int): the index of the last epoch (default: -1) 2024-06-26T05:54:20.3683615Z 2024-06-26T05:54:20.3683876Z The :class:`SWALR` scheduler can be used together with other 2024-06-26T05:54:20.3684561Z schedulers to switch to a constant learning rate late in the training 2024-06-26T05:54:20.3685142Z as in the example below. 2024-06-26T05:54:20.3685375Z 2024-06-26T05:54:20.3685488Z Example: 2024-06-26T05:54:20.3685817Z >>> # xdoctest: +SKIP("Undefined variables") 2024-06-26T05:54:20.3686294Z >>> loader, optimizer, model = ... 2024-06-26T05:54:20.3686733Z >>> lr_lambda = lambda epoch: 0.9 2024-06-26T05:54:20.3687302Z >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, 2024-06-26T05:54:20.3687885Z >>> lr_lambda=lr_lambda) 2024-06-26T05:54:20.3688387Z >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, 2024-06-26T05:54:20.3688998Z >>> anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05) 2024-06-26T05:54:20.3689492Z >>> swa_start = 160 2024-06-26T05:54:20.3689837Z >>> for i in range(300): 2024-06-26T05:54:20.3690235Z >>> for input, target in loader: 2024-06-26T05:54:20.3690663Z >>> optimizer.zero_grad() 2024-06-26T05:54:20.3691134Z >>> loss_fn(model(input), target).backward() 2024-06-26T05:54:20.3691601Z >>> optimizer.step() 2024-06-26T05:54:20.3691979Z >>> if i > swa_start: 2024-06-26T05:54:20.3692366Z >>> swa_scheduler.step() 2024-06-26T05:54:20.3692753Z >>> else: 2024-06-26T05:54:20.3693056Z >>> scheduler.step() 2024-06-26T05:54:20.3693329Z 2024-06-26T05:54:20.3693704Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2024-06-26T05:54:20.3694389Z https://arxiv.org/abs/1803.05407 2024-06-26T05:54:20.3694759Z 2024-06-26T05:54:20.3695287Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:20.3695813Z 2024-06-26T05:54:20.7890665Z msg = Cannot scrape callname=assert_close in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_comparison.py line=1268. 2024-06-26T05:54:20.7892010Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:20.7892684Z Asserts that ``actual`` and ``expected`` are close. 2024-06-26T05:54:20.7893138Z 2024-06-26T05:54:20.7893991Z If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if 2024-06-26T05:54:20.7894644Z 2024-06-26T05:54:20.7894763Z .. math:: 2024-06-26T05:54:20.7894941Z 2024-06-26T05:54:20.7895574Z \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert 2024-06-26T05:54:20.7896277Z 2024-06-26T05:54:20.7896842Z Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are 2024-06-26T05:54:20.7897753Z only considered equal to each other if ``equal_nan`` is ``True``. 2024-06-26T05:54:20.7898179Z 2024-06-26T05:54:20.7898456Z In addition, they are only considered close if they have the same 2024-06-26T05:54:20.7898895Z 2024-06-26T05:54:20.7899209Z - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``), 2024-06-26T05:54:20.7899836Z - ``dtype`` (if ``check_dtype`` is ``True``), 2024-06-26T05:54:20.7900371Z - ``layout`` (if ``check_layout`` is ``True``), and 2024-06-26T05:54:20.7900912Z - stride (if ``check_stride`` is ``True``). 2024-06-26T05:54:20.7901220Z 2024-06-26T05:54:20.7901654Z If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed. 2024-06-26T05:54:20.7902236Z 2024-06-26T05:54:20.7902760Z If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are 2024-06-26T05:54:20.7903913Z checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR, 2024-06-26T05:54:20.7904881Z or ``ccol_indices`` and ``row_indices`` for CSC and BSC layouts, respectively, 2024-06-26T05:54:20.7905880Z are always checked for equality whereas the values are checked for closeness according to the definition above. 2024-06-26T05:54:20.7906553Z 2024-06-26T05:54:20.7906955Z If ``actual`` and ``expected`` are quantized, they are considered close if they have the same 2024-06-26T05:54:20.7907974Z :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the 2024-06-26T05:54:20.7908731Z definition above. 2024-06-26T05:54:20.7908927Z 2024-06-26T05:54:20.7909443Z ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which 2024-06-26T05:54:20.7910626Z :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types 2024-06-26T05:54:20.7911855Z have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s 2024-06-26T05:54:20.7913132Z or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all 2024-06-26T05:54:20.7914122Z their elements are considered close according to the above definition. 2024-06-26T05:54:20.7914581Z 2024-06-26T05:54:20.7914681Z .. note:: 2024-06-26T05:54:20.7914854Z 2024-06-26T05:54:20.7915299Z Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e. 2024-06-26T05:54:20.7916440Z :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus, 2024-06-26T05:54:20.7917492Z Python scalars of different types can be checked, but require ``check_dtype=False``. 2024-06-26T05:54:20.7918037Z 2024-06-26T05:54:20.7918133Z Args: 2024-06-26T05:54:20.7918416Z actual (Any): Actual input. 2024-06-26T05:54:20.7918888Z expected (Any): Expected input. 2024-06-26T05:54:20.7919733Z allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types 2024-06-26T05:54:20.7920583Z are allowed. Otherwise type equality is required. 2024-06-26T05:54:20.7921524Z rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default 2024-06-26T05:54:20.7922526Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2024-06-26T05:54:20.7923541Z atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default 2024-06-26T05:54:20.7924547Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2024-06-26T05:54:20.7925438Z equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal. 2024-06-26T05:54:20.7926334Z check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same 2024-06-26T05:54:20.7927228Z :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different 2024-06-26T05:54:20.7928120Z :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared. 2024-06-26T05:54:20.7929061Z check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this 2024-06-26T05:54:20.7930245Z check is disabled, tensors with different ``dtype``'s are promoted to a common ``dtype`` (according to 2024-06-26T05:54:20.7931084Z :func:`torch.promote_types`) before being compared. 2024-06-26T05:54:20.7931930Z check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this 2024-06-26T05:54:20.7933109Z check is disabled, tensors with different ``layout``'s are converted to strided tensors before being 2024-06-26T05:54:20.7934011Z compared. 2024-06-26T05:54:20.7934715Z check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride. 2024-06-26T05:54:20.7935829Z msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during 2024-06-26T05:54:20.7936966Z the comparison. Can also passed as callable in which case it will be called with the generated message and 2024-06-26T05:54:20.7937765Z should return the new message. 2024-06-26T05:54:20.7938059Z 2024-06-26T05:54:20.7938157Z Raises: 2024-06-26T05:54:20.7938622Z ValueError: If no :class:`torch.Tensor` can be constructed from an input. 2024-06-26T05:54:20.7939313Z ValueError: If only ``rtol`` or ``atol`` is specified. 2024-06-26T05:54:20.7940114Z AssertionError: If corresponding inputs are not Python scalars and are not directly related. 2024-06-26T05:54:20.7941278Z AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have 2024-06-26T05:54:20.7942035Z different types. 2024-06-26T05:54:20.7942849Z AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match. 2024-06-26T05:54:20.7944056Z AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match. 2024-06-26T05:54:20.7945121Z AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`. 2024-06-26T05:54:20.7946089Z AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same 2024-06-26T05:54:20.7946793Z :attr:`~torch.Tensor.layout`. 2024-06-26T05:54:20.7947360Z AssertionError: If only one of corresponding tensors is quantized. 2024-06-26T05:54:20.7948481Z AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s. 2024-06-26T05:54:20.7949539Z AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same 2024-06-26T05:54:20.7950364Z :attr:`~torch.Tensor.device`. 2024-06-26T05:54:20.7951175Z AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``. 2024-06-26T05:54:20.7952396Z AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride. 2024-06-26T05:54:20.7953500Z AssertionError: If the values of corresponding tensors are not close according to the definition above. 2024-06-26T05:54:20.7954143Z 2024-06-26T05:54:20.7954733Z The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching 2024-06-26T05:54:20.7955640Z ``dtype``'s, the maximum of both tolerances is used. 2024-06-26T05:54:20.7955993Z 2024-06-26T05:54:20.7956214Z +---------------------------+------------+----------+ 2024-06-26T05:54:20.7956701Z | ``dtype`` | ``rtol`` | ``atol`` | 2024-06-26T05:54:20.7957185Z +===========================+============+==========+ 2024-06-26T05:54:20.7957736Z | :attr:`~torch.float16` | ``1e-3`` | ``1e-5`` | 2024-06-26T05:54:20.7958266Z +---------------------------+------------+----------+ 2024-06-26T05:54:20.7958820Z | :attr:`~torch.bfloat16` | ``1.6e-2`` | ``1e-5`` | 2024-06-26T05:54:20.7959367Z +---------------------------+------------+----------+ 2024-06-26T05:54:20.7959903Z | :attr:`~torch.float32` | ``1.3e-6`` | ``1e-5`` | 2024-06-26T05:54:20.7960444Z +---------------------------+------------+----------+ 2024-06-26T05:54:20.7961073Z | :attr:`~torch.float64` | ``1e-7`` | ``1e-7`` | 2024-06-26T05:54:20.7961618Z +---------------------------+------------+----------+ 2024-06-26T05:54:20.7962168Z | :attr:`~torch.complex32` | ``1e-3`` | ``1e-5`` | 2024-06-26T05:54:20.7962831Z +---------------------------+------------+----------+ 2024-06-26T05:54:20.7963380Z | :attr:`~torch.complex64` | ``1.3e-6`` | ``1e-5`` | 2024-06-26T05:54:20.7963913Z +---------------------------+------------+----------+ 2024-06-26T05:54:20.7964472Z | :attr:`~torch.complex128` | ``1e-7`` | ``1e-7`` | 2024-06-26T05:54:20.7965013Z +---------------------------+------------+----------+ 2024-06-26T05:54:20.7965542Z | :attr:`~torch.quint8` | ``1.3e-6`` | ``1e-5`` | 2024-06-26T05:54:20.7966083Z +---------------------------+------------+----------+ 2024-06-26T05:54:20.7966627Z | :attr:`~torch.quint2x4` | ``1.3e-6`` | ``1e-5`` | 2024-06-26T05:54:20.7967152Z +---------------------------+------------+----------+ 2024-06-26T05:54:20.7967696Z | :attr:`~torch.quint4x2` | ``1.3e-6`` | ``1e-5`` | 2024-06-26T05:54:20.7968234Z +---------------------------+------------+----------+ 2024-06-26T05:54:20.7968761Z | :attr:`~torch.qint8` | ``1.3e-6`` | ``1e-5`` | 2024-06-26T05:54:20.7969300Z +---------------------------+------------+----------+ 2024-06-26T05:54:20.7969844Z | :attr:`~torch.qint32` | ``1.3e-6`` | ``1e-5`` | 2024-06-26T05:54:20.7970371Z +---------------------------+------------+----------+ 2024-06-26T05:54:20.7970858Z | other | ``0.0`` | ``0.0`` | 2024-06-26T05:54:20.7971379Z +---------------------------+------------+----------+ 2024-06-26T05:54:20.7971685Z 2024-06-26T05:54:20.7971805Z .. note:: 2024-06-26T05:54:20.7971967Z 2024-06-26T05:54:20.7972471Z :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged 2024-06-26T05:54:20.7973830Z to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might 2024-06-26T05:54:20.7974863Z define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default: 2024-06-26T05:54:20.7975465Z 2024-06-26T05:54:20.7975580Z >>> import functools 2024-06-26T05:54:20.7976157Z >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0) 2024-06-26T05:54:20.7976889Z >>> assert_equal(1e-9, 1e-10) 2024-06-26T05:54:20.7977408Z Traceback (most recent call last): 2024-06-26T05:54:20.7977805Z ... 2024-06-26T05:54:20.7978123Z AssertionError: Scalars are not equal! 2024-06-26T05:54:20.7978544Z 2024-06-26T05:54:20.7978888Z Expected 1e-10 but got 1e-09. 2024-06-26T05:54:20.7979377Z Absolute difference: 9.000000000000001e-10 2024-06-26T05:54:20.7979827Z Relative difference: 9.0 2024-06-26T05:54:20.7980075Z 2024-06-26T05:54:20.7980174Z Examples: 2024-06-26T05:54:20.7980476Z >>> # tensor to tensor comparison 2024-06-26T05:54:20.7980993Z >>> expected = torch.tensor([1e0, 1e-1, 1e-2]) 2024-06-26T05:54:20.7981490Z >>> actual = torch.acos(torch.cos(expected)) 2024-06-26T05:54:20.7982009Z >>> torch.testing.assert_close(actual, expected) 2024-06-26T05:54:20.7982353Z 2024-06-26T05:54:20.7982504Z >>> # scalar to scalar comparison 2024-06-26T05:54:20.7982892Z >>> import math 2024-06-26T05:54:20.7983227Z >>> expected = math.sqrt(2.0) 2024-06-26T05:54:20.7983646Z >>> actual = 2.0 / math.sqrt(2.0) 2024-06-26T05:54:20.7984107Z >>> torch.testing.assert_close(actual, expected) 2024-06-26T05:54:20.7984458Z 2024-06-26T05:54:20.7984621Z >>> # numpy array to numpy array comparison 2024-06-26T05:54:20.7985075Z >>> import numpy as np 2024-06-26T05:54:20.7985513Z >>> expected = np.array([1e0, 1e-1, 1e-2]) 2024-06-26T05:54:20.7985988Z >>> actual = np.arccos(np.cos(expected)) 2024-06-26T05:54:20.7986496Z >>> torch.testing.assert_close(actual, expected) 2024-06-26T05:54:20.7986837Z 2024-06-26T05:54:20.7986990Z >>> # sequence to sequence comparison 2024-06-26T05:54:20.7987420Z >>> import numpy as np 2024-06-26T05:54:20.7988020Z >>> # The types of the sequences do not have to match. They only have to have the same 2024-06-26T05:54:20.7988722Z >>> # length and their elements have to match. 2024-06-26T05:54:20.7989256Z >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)] 2024-06-26T05:54:20.7989755Z >>> actual = tuple(expected) 2024-06-26T05:54:20.7990212Z >>> torch.testing.assert_close(actual, expected) 2024-06-26T05:54:20.7990551Z 2024-06-26T05:54:20.7990696Z >>> # mapping to mapping comparison 2024-06-26T05:54:20.7991162Z >>> from collections import OrderedDict 2024-06-26T05:54:20.7991601Z >>> import numpy as np 2024-06-26T05:54:20.7991954Z >>> foo = torch.tensor(1.0) 2024-06-26T05:54:20.7992325Z >>> bar = 2.0 2024-06-26T05:54:20.7992635Z >>> baz = np.array(3.0) 2024-06-26T05:54:20.7993215Z >>> # The types and a possible ordering of mappings do not have to match. They only 2024-06-26T05:54:20.7994023Z >>> # have to have the same set of keys and their elements have to match. 2024-06-26T05:54:20.7994751Z >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)]) 2024-06-26T05:54:20.7995356Z >>> actual = {"baz": baz, "bar": bar, "foo": foo} 2024-06-26T05:54:20.7995891Z >>> torch.testing.assert_close(actual, expected) 2024-06-26T05:54:20.7996235Z 2024-06-26T05:54:20.7996413Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2024-06-26T05:54:20.7996870Z >>> actual = expected.clone() 2024-06-26T05:54:20.7997366Z >>> # By default, directly related instances can be compared 2024-06-26T05:54:20.7998040Z >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected) 2024-06-26T05:54:20.7998747Z >>> # This check can be made more strict with allow_subclasses=False 2024-06-26T05:54:20.7999305Z >>> torch.testing.assert_close( 2024-06-26T05:54:20.7999913Z ... torch.nn.Parameter(actual), expected, allow_subclasses=False 2024-06-26T05:54:20.8000442Z ... ) 2024-06-26T05:54:20.8000833Z Traceback (most recent call last): 2024-06-26T05:54:20.8001288Z ... 2024-06-26T05:54:20.8001709Z TypeError: No comparison pair was able to handle inputs of type 2024-06-26T05:54:20.8002559Z and . 2024-06-26T05:54:20.8003331Z >>> # If the inputs are not directly related, they are never considered close 2024-06-26T05:54:20.8004036Z >>> torch.testing.assert_close(actual.numpy(), expected) 2024-06-26T05:54:20.8004555Z Traceback (most recent call last): 2024-06-26T05:54:20.8004956Z ... 2024-06-26T05:54:20.8005581Z TypeError: No comparison pair was able to handle inputs of type 2024-06-26T05:54:20.8006291Z and . 2024-06-26T05:54:20.8006919Z >>> # Exceptions to these rules are Python scalars. They can be checked regardless of 2024-06-26T05:54:20.8007599Z >>> # their type if check_dtype=False. 2024-06-26T05:54:20.8008116Z >>> torch.testing.assert_close(1.0, 1, check_dtype=False) 2024-06-26T05:54:20.8008508Z 2024-06-26T05:54:20.8008634Z >>> # NaN != NaN by default. 2024-06-26T05:54:20.8009074Z >>> expected = torch.tensor(float("Nan")) 2024-06-26T05:54:20.8009528Z >>> actual = expected.clone() 2024-06-26T05:54:20.8009984Z >>> torch.testing.assert_close(actual, expected) 2024-06-26T05:54:20.8010478Z Traceback (most recent call last): 2024-06-26T05:54:20.8010879Z ... 2024-06-26T05:54:20.8011183Z AssertionError: Scalars are not close! 2024-06-26T05:54:20.8011604Z 2024-06-26T05:54:20.8011915Z Expected nan but got nan. 2024-06-26T05:54:20.8012400Z Absolute difference: nan (up to 1e-05 allowed) 2024-06-26T05:54:20.8012995Z Relative difference: nan (up to 1.3e-06 allowed) 2024-06-26T05:54:20.8013785Z >>> torch.testing.assert_close(actual, expected, equal_nan=True) 2024-06-26T05:54:20.8014220Z 2024-06-26T05:54:20.8014389Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2024-06-26T05:54:20.8014877Z >>> actual = torch.tensor([1.0, 4.0, 5.0]) 2024-06-26T05:54:20.8015394Z >>> # The default error message can be overwritten. 2024-06-26T05:54:20.8016119Z >>> torch.testing.assert_close(actual, expected, msg="Argh, the tensors are not close!") 2024-06-26T05:54:20.8016810Z Traceback (most recent call last): 2024-06-26T05:54:20.8017209Z ... 2024-06-26T05:54:20.8017547Z AssertionError: Argh, the tensors are not close! 2024-06-26T05:54:20.8018233Z >>> # If msg is a callable, it can be used to augment the generated message with 2024-06-26T05:54:20.8018844Z >>> # extra information 2024-06-26T05:54:20.8019216Z >>> torch.testing.assert_close( 2024-06-26T05:54:20.8019770Z ... actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter" 2024-06-26T05:54:20.8020299Z ... ) 2024-06-26T05:54:20.8020605Z Traceback (most recent call last): 2024-06-26T05:54:20.8020988Z ... 2024-06-26T05:54:20.8021269Z AssertionError: Header 2024-06-26T05:54:20.8021602Z 2024-06-26T05:54:20.8021955Z Tensor-likes are not close! 2024-06-26T05:54:20.8022332Z 2024-06-26T05:54:20.8022645Z Mismatched elements: 2 / 3 (66.7%) 2024-06-26T05:54:20.8023305Z Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed) 2024-06-26T05:54:20.8024120Z Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed) 2024-06-26T05:54:20.8024682Z 2024-06-26T05:54:20.8024956Z Footer 2024-06-26T05:54:20.8025209Z 2024-06-26T05:54:20.8025726Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:20.8026200Z 2024-06-26T05:54:21.8597901Z msg = Cannot scrape callname=register_pytree_node in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py line=115. 2024-06-26T05:54:21.8599436Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:21.8600243Z Register a container-like type as pytree node. 2024-06-26T05:54:21.8600597Z 2024-06-26T05:54:21.8600907Z Args: 2024-06-26T05:54:21.8601311Z cls (type): A Python type to treat as an internal pytree node. 2024-06-26T05:54:21.8602102Z flatten_fn (callable): A function to be used during flattening, taking an instance of 2024-06-26T05:54:21.8602988Z ``cls`` and returning a pair, with (1) an iterable for the children to be flattened 2024-06-26T05:54:21.8603908Z recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be 2024-06-26T05:54:21.8604602Z passed to the ``unflatten_fn``. 2024-06-26T05:54:21.8605278Z unflatten_fn (callable): A function taking two arguments: the auxiliary data that was 2024-06-26T05:54:21.8606175Z returned by ``flatten_fn`` and stored in the treespec, and the unflattened children. 2024-06-26T05:54:21.8606898Z The function should return an instance of ``cls``. 2024-06-26T05:54:21.8607617Z serialized_type_name (str, optional): A keyword argument used to specify the fully 2024-06-26T05:54:21.8608353Z qualified name used when serializing the tree spec. 2024-06-26T05:54:21.8609131Z to_dumpable_context (callable, optional): An optional keyword argument to custom specify how 2024-06-26T05:54:21.8610076Z to convert the context of the pytree to a custom json dumpable representation. This is 2024-06-26T05:54:21.8611002Z used for json serialization, which is being used in :mod:`torch.export` right now. 2024-06-26T05:54:21.8611920Z from_dumpable_context (callable, optional): An optional keyword argument to custom specify 2024-06-26T05:54:21.8612827Z how to convert the custom json dumpable representation of the context back to the 2024-06-26T05:54:21.8613951Z original context. This is used for json deserialization, which is being used in 2024-06-26T05:54:21.8614620Z :mod:`torch.export` right now. 2024-06-26T05:54:21.8614917Z 2024-06-26T05:54:21.8615045Z Example:: 2024-06-26T05:54:21.8615221Z 2024-06-26T05:54:21.8615345Z >>> # xdoctest: +SKIP 2024-06-26T05:54:21.8615771Z >>> # Registry a Python type with lambda functions 2024-06-26T05:54:21.8616249Z >>> register_pytree_node( 2024-06-26T05:54:21.8616595Z ... set, 2024-06-26T05:54:21.8616940Z ... lambda s: (sorted(s), None, None), 2024-06-26T05:54:21.8617418Z ... lambda children, _: set(children), 2024-06-26T05:54:21.8617822Z ... ) 2024-06-26T05:54:21.8618073Z 2024-06-26T05:54:21.8618618Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:21.8619094Z 2024-06-26T05:54:21.9054953Z msg = Cannot scrape callname=SelectiveCheckpointContext in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py line=1186. 2024-06-26T05:54:21.9057604Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:21.9058432Z 2024-06-26T05:54:21.9058725Z Context passed to policy function during selective checkpointing. 2024-06-26T05:54:21.9059155Z 2024-06-26T05:54:21.9059482Z This class is used to pass relevant metadata to the policy function during 2024-06-26T05:54:21.9060548Z selective checkpointing. The metadata includes whether the current invocation 2024-06-26T05:54:21.9061611Z of the policy function is during recomputation or not. 2024-06-26T05:54:21.9062245Z 2024-06-26T05:54:21.9062451Z Example: 2024-06-26T05:54:21.9062932Z >>> # xdoctest: +SKIP(stub) 2024-06-26T05:54:21.9063597Z >>> 2024-06-26T05:54:21.9063921Z >>> def policy_fn(ctx, op, *args, **kwargs): 2024-06-26T05:54:21.9064357Z >>> print(ctx.is_recompute) 2024-06-26T05:54:21.9064894Z >>> 2024-06-26T05:54:21.9065381Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2024-06-26T05:54:21.9065980Z >>> 2024-06-26T05:54:21.9066308Z >>> out = torch.utils.checkpoint.checkpoint( 2024-06-26T05:54:21.9066835Z >>> fn, x, y, 2024-06-26T05:54:21.9067238Z >>> use_reentrant=False, 2024-06-26T05:54:21.9067613Z >>> context_fn=context_fn, 2024-06-26T05:54:21.9067965Z >>> ) 2024-06-26T05:54:21.9068108Z 2024-06-26T05:54:21.9068515Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:21.9069003Z 2024-06-26T05:54:21.9069960Z msg = Cannot scrape callname=create_selective_checkpoint_contexts in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py line=1319. 2024-06-26T05:54:21.9071314Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:21.9071802Z 2024-06-26T05:54:21.9072131Z Helper to avoid recomputing certain ops during activation checkpointing. 2024-06-26T05:54:21.9072598Z 2024-06-26T05:54:21.9072900Z Use this with `torch.utils.checkpoint.checkpoint` to control which 2024-06-26T05:54:21.9073531Z operations are recomputed during the backward pass. 2024-06-26T05:54:21.9073898Z 2024-06-26T05:54:21.9073990Z Args: 2024-06-26T05:54:21.9074277Z policy_fn_or_list (Callable or List): 2024-06-26T05:54:21.9074830Z - If a policy function is provided, it should accept a 2024-06-26T05:54:21.9075514Z :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and 2024-06-26T05:54:21.9076277Z kwargs to the op, and return a :class:`CheckpointPolicy` enum value 2024-06-26T05:54:21.9077027Z indicating whether the execution of the op should be recomputed or not. 2024-06-26T05:54:21.9077842Z - If a list of operations is provided, it is equivalent to a policy 2024-06-26T05:54:21.9078522Z returning `CheckpointPolicy.MUST_SAVE` for the specified 2024-06-26T05:54:21.9079193Z operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other 2024-06-26T05:54:21.9079747Z operations. 2024-06-26T05:54:21.9080209Z allow_cache_entry_mutation (bool, optional): By default, an error is 2024-06-26T05:54:21.9081026Z raised if any tensors cached by selective activation checkpoint are 2024-06-26T05:54:21.9081760Z mutated in order to ensure correctness. If set to `True`, this check 2024-06-26T05:54:21.9082319Z is disabled. 2024-06-26T05:54:21.9082606Z Returns: 2024-06-26T05:54:21.9082880Z A tuple of two context managers. 2024-06-26T05:54:21.9083169Z 2024-06-26T05:54:21.9083261Z Example: 2024-06-26T05:54:21.9083535Z >>> # xdoctest: +REQUIRES(LINUX) 2024-06-26T05:54:21.9083907Z >>> import functools 2024-06-26T05:54:21.9084210Z >>> 2024-06-26T05:54:21.9084510Z >>> x = torch.rand(10, 10, requires_grad=True) 2024-06-26T05:54:21.9084979Z >>> y = torch.rand(10, 10, requires_grad=True) 2024-06-26T05:54:21.9085394Z >>> 2024-06-26T05:54:21.9085641Z >>> ops_to_save = [ 2024-06-26T05:54:21.9085973Z >>> torch.ops.aten.mm.default, 2024-06-26T05:54:21.9086351Z >>> ] 2024-06-26T05:54:21.9086591Z >>> 2024-06-26T05:54:21.9086886Z >>> def policy_fn(ctx, op, *args, **kwargs): 2024-06-26T05:54:21.9087327Z >>> if op in ops_to_save: 2024-06-26T05:54:21.9087747Z >>> return CheckpointPolicy.MUST_SAVE 2024-06-26T05:54:21.9088160Z >>> else: 2024-06-26T05:54:21.9088520Z >>> return CheckpointPolicy.PREFER_RECOMPUTE 2024-06-26T05:54:21.9088960Z >>> 2024-06-26T05:54:21.9089439Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2024-06-26T05:54:21.9090052Z >>> 2024-06-26T05:54:21.9090307Z >>> # or equivalently 2024-06-26T05:54:21.9090865Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save) 2024-06-26T05:54:21.9091479Z >>> 2024-06-26T05:54:21.9091722Z >>> def fn(x, y): 2024-06-26T05:54:21.9092231Z >>> return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y 2024-06-26T05:54:21.9092751Z >>> 2024-06-26T05:54:21.9093078Z >>> out = torch.utils.checkpoint.checkpoint( 2024-06-26T05:54:21.9093742Z >>> fn, x, y, 2024-06-26T05:54:21.9094146Z >>> use_reentrant=False, 2024-06-26T05:54:21.9094631Z >>> context_fn=context_fn, 2024-06-26T05:54:21.9094973Z >>> ) 2024-06-26T05:54:21.9095130Z 2024-06-26T05:54:21.9095530Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:21.9096005Z 2024-06-26T05:54:21.9255926Z msg = Cannot scrape callname=CppExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=923. 2024-06-26T05:54:21.9257319Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:21.9257851Z 2024-06-26T05:54:21.9258030Z Create a :class:`setuptools.Extension` for C++. 2024-06-26T05:54:21.9258380Z 2024-06-26T05:54:21.9258700Z Convenience method that creates a :class:`setuptools.Extension` with the 2024-06-26T05:54:21.9259472Z bare minimum (but often sufficient) arguments to build a C++ extension. 2024-06-26T05:54:21.9259926Z 2024-06-26T05:54:21.9260197Z All arguments are forwarded to the :class:`setuptools.Extension` 2024-06-26T05:54:21.9260906Z constructor. Full list arguments can be found at 2024-06-26T05:54:21.9262083Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2024-06-26T05:54:21.9262978Z 2024-06-26T05:54:21.9263156Z Example: 2024-06-26T05:54:21.9263636Z >>> # xdoctest: +SKIP 2024-06-26T05:54:21.9264386Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-06-26T05:54:21.9265295Z >>> from setuptools import setup 2024-06-26T05:54:21.9265962Z >>> from torch.utils.cpp_extension import BuildExtension, CppExtension 2024-06-26T05:54:21.9266520Z >>> setup( 2024-06-26T05:54:21.9266847Z ... name='extension', 2024-06-26T05:54:21.9267183Z ... ext_modules=[ 2024-06-26T05:54:21.9267512Z ... CppExtension( 2024-06-26T05:54:21.9267909Z ... name='extension', 2024-06-26T05:54:21.9268361Z ... sources=['extension.cpp'], 2024-06-26T05:54:21.9268866Z ... extra_compile_args=['-g'], 2024-06-26T05:54:21.9269433Z ... extra_link_flags=['-Wl,--no-as-needed', '-lm']) 2024-06-26T05:54:21.9269885Z ... ], 2024-06-26T05:54:21.9270155Z ... cmdclass={ 2024-06-26T05:54:21.9270531Z ... 'build_ext': BuildExtension 2024-06-26T05:54:21.9270917Z ... }) 2024-06-26T05:54:21.9271089Z 2024-06-26T05:54:21.9271473Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:21.9272115Z 2024-06-26T05:54:21.9273330Z msg = Cannot scrape callname=CUDAExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=974. 2024-06-26T05:54:21.9275236Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:21.9275735Z 2024-06-26T05:54:21.9275941Z Create a :class:`setuptools.Extension` for CUDA/C++. 2024-06-26T05:54:21.9276315Z 2024-06-26T05:54:21.9276621Z Convenience method that creates a :class:`setuptools.Extension` with the 2024-06-26T05:54:21.9277359Z bare minimum (but often sufficient) arguments to build a CUDA/C++ 2024-06-26T05:54:21.9278091Z extension. This includes the CUDA include path, library path and runtime 2024-06-26T05:54:21.9278644Z library. 2024-06-26T05:54:21.9278806Z 2024-06-26T05:54:21.9279071Z All arguments are forwarded to the :class:`setuptools.Extension` 2024-06-26T05:54:21.9279678Z constructor. Full list arguments can be found at 2024-06-26T05:54:21.9280493Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2024-06-26T05:54:21.9281159Z 2024-06-26T05:54:21.9281255Z Example: 2024-06-26T05:54:21.9281523Z >>> # xdoctest: +SKIP 2024-06-26T05:54:21.9281923Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-06-26T05:54:21.9282645Z >>> from setuptools import setup 2024-06-26T05:54:21.9283305Z >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension 2024-06-26T05:54:21.9284140Z >>> setup( 2024-06-26T05:54:21.9284865Z ... name='cuda_extension', 2024-06-26T05:54:21.9285238Z ... ext_modules=[ 2024-06-26T05:54:21.9285642Z ... CUDAExtension( 2024-06-26T05:54:21.9286080Z ... name='cuda_extension', 2024-06-26T05:54:21.9286663Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-06-26T05:54:21.9287255Z ... extra_compile_args={'cxx': ['-g'], 2024-06-26T05:54:21.9287814Z ... 'nvcc': ['-O2']}, 2024-06-26T05:54:21.9288416Z ... extra_link_flags=['-Wl,--no-as-needed', '-lcuda']) 2024-06-26T05:54:21.9288895Z ... ], 2024-06-26T05:54:21.9289155Z ... cmdclass={ 2024-06-26T05:54:21.9289538Z ... 'build_ext': BuildExtension 2024-06-26T05:54:21.9289937Z ... }) 2024-06-26T05:54:21.9290096Z 2024-06-26T05:54:21.9290211Z Compute capabilities: 2024-06-26T05:54:21.9290425Z 2024-06-26T05:54:21.9290835Z By default the extension will be compiled to run on all archs of the cards visible during the 2024-06-26T05:54:21.9291821Z building process of the extension, plus PTX. If down the road a new card is installed the 2024-06-26T05:54:21.9292815Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's 2024-06-26T05:54:21.9294106Z newer than the newest version for which your nvcc can build fully-compiled binaries, Pytorch 2024-06-26T05:54:21.9295087Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does 2024-06-26T05:54:21.9295783Z support (see below for details on PTX). 2024-06-26T05:54:21.9296090Z 2024-06-26T05:54:21.9296508Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which 2024-06-26T05:54:21.9297234Z CCs you want the extension to support: 2024-06-26T05:54:21.9297517Z 2024-06-26T05:54:21.9297767Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py`` 2024-06-26T05:54:21.9298493Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py`` 2024-06-26T05:54:21.9299010Z 2024-06-26T05:54:21.9299437Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified 2024-06-26T05:54:21.9300537Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >= 2024-06-26T05:54:21.9301608Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with 2024-06-26T05:54:21.9302678Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to 2024-06-26T05:54:21.9303739Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on 2024-06-26T05:54:21.9304792Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better 2024-06-26T05:54:21.9305777Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6, 2024-06-26T05:54:21.9306871Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but 2024-06-26T05:54:21.9307580Z "8.0 8.6" would be better. 2024-06-26T05:54:21.9307797Z 2024-06-26T05:54:21.9308280Z Note that while it's possible to include all supported archs, the more archs get included the 2024-06-26T05:54:21.9309260Z slower the building process will be, as it will build a separate kernel image for each arch. 2024-06-26T05:54:21.9309837Z 2024-06-26T05:54:21.9310369Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows. 2024-06-26T05:54:21.9311277Z To workaround the issue, move python binding logic to pure C++ file. 2024-06-26T05:54:21.9311717Z 2024-06-26T05:54:21.9311817Z Example use: 2024-06-26T05:54:21.9312101Z #include 2024-06-26T05:54:21.9312627Z at::Tensor SigmoidAlphaBlendForwardCuda(....) 2024-06-26T05:54:21.9312972Z 2024-06-26T05:54:21.9313074Z Instead of: 2024-06-26T05:54:21.9313363Z #include 2024-06-26T05:54:21.9313819Z torch::Tensor SigmoidAlphaBlendForwardCuda(...) 2024-06-26T05:54:21.9314226Z 2024-06-26T05:54:21.9314692Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460 2024-06-26T05:54:21.9315867Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48 2024-06-26T05:54:21.9316678Z 2024-06-26T05:54:21.9316807Z Relocatable device code linking: 2024-06-26T05:54:21.9317060Z 2024-06-26T05:54:21.9317448Z If you want to reference device symbols across compilation units (across object files), 2024-06-26T05:54:21.9318410Z the object files need to be built with `relocatable device code` (-rdc=true or -dc). 2024-06-26T05:54:21.9319434Z An exception to this rule is "dynamic parallelism" (nested kernel launches) which is not used a lot anymore. 2024-06-26T05:54:21.9320549Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it. 2024-06-26T05:54:21.9321741Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step 2024-06-26T05:54:21.9322623Z help reduce the protentional perf degradation of `-rdc`. 2024-06-26T05:54:21.9323234Z Note that it needs to be used at both steps to be useful. 2024-06-26T05:54:21.9323610Z 2024-06-26T05:54:21.9324240Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step. 2024-06-26T05:54:21.9325202Z There is also a case where `-dlink` is used without `-rdc`: 2024-06-26T05:54:21.9326008Z when an extension is linked against a static lib containing rdc-compiled objects 2024-06-26T05:54:21.9326791Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem). 2024-06-26T05:54:21.9327224Z 2024-06-26T05:54:21.9327496Z Note: Ninja is required to build a CUDA Extension with RDC linking. 2024-06-26T05:54:21.9327946Z 2024-06-26T05:54:21.9328043Z Example: 2024-06-26T05:54:21.9328308Z >>> # xdoctest: +SKIP 2024-06-26T05:54:21.9328704Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-06-26T05:54:21.9329171Z >>> CUDAExtension( 2024-06-26T05:54:21.9329541Z ... name='cuda_extension', 2024-06-26T05:54:21.9330079Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-06-26T05:54:21.9330547Z ... dlink=True, 2024-06-26T05:54:21.9330913Z ... dlink_libraries=["dlink_lib"], 2024-06-26T05:54:21.9331420Z ... extra_compile_args={'cxx': ['-g'], 2024-06-26T05:54:21.9331955Z ... 'nvcc': ['-O2', '-rdc=true']}) 2024-06-26T05:54:21.9332300Z 2024-06-26T05:54:21.9332684Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:21.9333161Z 2024-06-26T05:54:21.9334132Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1232. 2024-06-26T05:54:21.9335357Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:21.9335848Z 2024-06-26T05:54:21.9336096Z Load a PyTorch C++ extension just-in-time (JIT). 2024-06-26T05:54:21.9336433Z 2024-06-26T05:54:21.9336721Z To load an extension, a Ninja build file is emitted, which is used to 2024-06-26T05:54:21.9337437Z compile the given sources into a dynamic library. This library is 2024-06-26T05:54:21.9338143Z subsequently loaded into the current Python process as a module and 2024-06-26T05:54:21.9338746Z returned from this function, ready for use. 2024-06-26T05:54:21.9339059Z 2024-06-26T05:54:21.9339341Z By default, the directory to which the build file is emitted and the 2024-06-26T05:54:21.9340089Z resulting library compiled to is ``/torch_extensions/``, where 2024-06-26T05:54:21.9340837Z ```` is the temporary folder on the current platform and ```` 2024-06-26T05:54:21.9341636Z the name of the extension. This location can be overridden in two ways. 2024-06-26T05:54:21.9342385Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it 2024-06-26T05:54:21.9343119Z replaces ``/torch_extensions`` and all extensions will be compiled 2024-06-26T05:54:21.9343964Z into subfolders of this directory. Second, if the ``build_directory`` 2024-06-26T05:54:21.9344723Z argument to this function is supplied, it overrides the entire path, i.e. 2024-06-26T05:54:21.9345413Z the library will be compiled into that folder directly. 2024-06-26T05:54:21.9345780Z 2024-06-26T05:54:21.9346075Z To compile the sources, the default system compiler (``c++``) is used, 2024-06-26T05:54:21.9346837Z which can be overridden by setting the ``CXX`` environment variable. To pass 2024-06-26T05:54:21.9347611Z additional arguments to the compilation process, ``extra_cflags`` or 2024-06-26T05:54:21.9348350Z ``extra_ldflags`` can be provided. For example, to compile your extension 2024-06-26T05:54:21.9349128Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use 2024-06-26T05:54:21.9349753Z ``extra_cflags`` to pass further include directories. 2024-06-26T05:54:21.9350101Z 2024-06-26T05:54:21.9350423Z CUDA support with mixed compilation is provided. Simply pass CUDA source 2024-06-26T05:54:21.9351142Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be 2024-06-26T05:54:21.9351888Z detected and compiled with nvcc rather than the C++ compiler. This includes 2024-06-26T05:54:21.9352653Z passing the CUDA lib64 directory as a library directory, and linking 2024-06-26T05:54:21.9353286Z ``cudart``. You can pass additional flags to nvcc via 2024-06-26T05:54:21.9353918Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various 2024-06-26T05:54:21.9354661Z heuristics for finding the CUDA install directory are used, which usually 2024-06-26T05:54:21.9355419Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the 2024-06-26T05:54:21.9355977Z safest option. 2024-06-26T05:54:21.9356155Z 2024-06-26T05:54:21.9356245Z Args: 2024-06-26T05:54:21.9356684Z name: The name of the extension to build. This MUST be the same as the 2024-06-26T05:54:21.9357261Z name of the pybind11 module! 2024-06-26T05:54:21.9357819Z sources: A list of relative or absolute paths to C++ source files. 2024-06-26T05:54:21.9358550Z extra_cflags: optional list of compiler flags to forward to the build. 2024-06-26T05:54:21.9359278Z extra_cuda_cflags: optional list of compiler flags to forward to nvcc 2024-06-26T05:54:21.9359861Z when building CUDA sources. 2024-06-26T05:54:21.9360420Z extra_ldflags: optional list of linker flags to forward to the build. 2024-06-26T05:54:21.9361217Z extra_include_paths: optional list of include directories to forward 2024-06-26T05:54:21.9361769Z to the build. 2024-06-26T05:54:21.9362203Z build_directory: optional path to use as build workspace. 2024-06-26T05:54:21.9362823Z verbose: If ``True``, turns on verbose logging of load steps. 2024-06-26T05:54:21.9363517Z with_cuda: Determines whether CUDA headers and libraries are added to 2024-06-26T05:54:21.9364186Z the build. If set to ``None`` (default), this value is 2024-06-26T05:54:21.9364825Z automatically determined based on the existence of ``.cu`` or 2024-06-26T05:54:21.9365487Z ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers 2024-06-26T05:54:21.9366026Z and libraries to be included. 2024-06-26T05:54:21.9366582Z is_python_module: If ``True`` (default), imports the produced shared 2024-06-26T05:54:21.9367255Z library as a Python module. If ``False``, behavior depends on 2024-06-26T05:54:21.9367779Z ``is_standalone``. 2024-06-26T05:54:21.9368282Z is_standalone: If ``False`` (default) loads the constructed extension 2024-06-26T05:54:21.9368985Z into the process as a plain dynamic library. If ``True``, build a 2024-06-26T05:54:21.9369539Z standalone executable. 2024-06-26T05:54:21.9369828Z 2024-06-26T05:54:21.9369938Z Returns: 2024-06-26T05:54:21.9370218Z If ``is_python_module`` is ``True``: 2024-06-26T05:54:21.9370738Z Returns the loaded PyTorch extension as a Python module. 2024-06-26T05:54:21.9371162Z 2024-06-26T05:54:21.9371522Z If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``: 2024-06-26T05:54:21.9372233Z Returns nothing. (The shared library is loaded into the process as 2024-06-26T05:54:21.9372781Z a side effect.) 2024-06-26T05:54:21.9372994Z 2024-06-26T05:54:21.9373126Z If ``is_standalone`` is ``True``. 2024-06-26T05:54:21.9373858Z Return the path to the executable. (On Windows, TORCH_LIB_PATH is 2024-06-26T05:54:21.9374523Z added to the PATH environment variable as a side effect.) 2024-06-26T05:54:21.9374933Z 2024-06-26T05:54:21.9375031Z Example: 2024-06-26T05:54:21.9375297Z >>> # xdoctest: +SKIP 2024-06-26T05:54:21.9375677Z >>> from torch.utils.cpp_extension import load 2024-06-26T05:54:21.9376135Z >>> module = load( 2024-06-26T05:54:21.9376505Z ... name='extension', 2024-06-26T05:54:21.9376981Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-06-26T05:54:21.9377509Z ... extra_cflags=['-O2'], 2024-06-26T05:54:21.9377873Z ... verbose=True) 2024-06-26T05:54:21.9378081Z 2024-06-26T05:54:21.9378462Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:21.9378955Z 2024-06-26T05:54:21.9379765Z msg = Cannot scrape callname=load_inline in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1524. 2024-06-26T05:54:21.9381017Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:21.9381513Z 2024-06-26T05:54:21.9381865Z Load a PyTorch C++ extension just-in-time (JIT) from string sources. 2024-06-26T05:54:21.9382301Z 2024-06-26T05:54:21.9382603Z This function behaves exactly like :func:`load`, but takes its sources as 2024-06-26T05:54:21.9383381Z strings rather than filenames. These strings are stored to files in the 2024-06-26T05:54:21.9384118Z build directory, after which the behavior of :func:`load_inline` is 2024-06-26T05:54:21.9384667Z identical to :func:`load`. 2024-06-26T05:54:21.9384901Z 2024-06-26T05:54:21.9385001Z See `the 2024-06-26T05:54:21.9385569Z tests `_ 2024-06-26T05:54:21.9386298Z for good examples of using this function. 2024-06-26T05:54:21.9386609Z 2024-06-26T05:54:21.9386985Z Sources may omit two required parts of a typical non-inline C++ extension: 2024-06-26T05:54:21.9387780Z the necessary header includes, as well as the (pybind11) binding code. More 2024-06-26T05:54:21.9388574Z precisely, strings passed to ``cpp_sources`` are first concatenated into a 2024-06-26T05:54:21.9389289Z single ``.cpp`` file. This file is then prepended with ``#include 2024-06-26T05:54:21.9389819Z ``. 2024-06-26T05:54:21.9390025Z 2024-06-26T05:54:21.9390331Z Furthermore, if the ``functions`` argument is supplied, bindings will be 2024-06-26T05:54:21.9391073Z automatically generated for each function specified. ``functions`` can 2024-06-26T05:54:21.9391845Z either be a list of function names, or a dictionary mapping from function 2024-06-26T05:54:21.9392627Z names to docstrings. If a list is given, the name of each function is used 2024-06-26T05:54:21.9393196Z as its docstring. 2024-06-26T05:54:21.9393387Z 2024-06-26T05:54:21.9393678Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu`` 2024-06-26T05:54:21.9394359Z file and prepended with ``torch/types.h``, ``cuda.h`` and 2024-06-26T05:54:21.9395021Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled 2024-06-26T05:54:21.9395739Z separately, but ultimately linked into a single library. Note that no 2024-06-26T05:54:21.9396492Z bindings are generated for functions in ``cuda_sources`` per se. To bind 2024-06-26T05:54:21.9397350Z to a CUDA kernel, you must create a C++ function that calls it, and either 2024-06-26T05:54:21.9398097Z declare or define this C++ function in one of the ``cpp_sources`` (and 2024-06-26T05:54:21.9398687Z include its name in ``functions``). 2024-06-26T05:54:21.9399005Z 2024-06-26T05:54:21.9399259Z See :func:`load` for a description of arguments omitted below. 2024-06-26T05:54:21.9399733Z 2024-06-26T05:54:21.9399825Z Args: 2024-06-26T05:54:21.9400265Z cpp_sources: A string, or list of strings, containing C++ source code. 2024-06-26T05:54:21.9401094Z cuda_sources: A string, or list of strings, containing CUDA source code. 2024-06-26T05:54:21.9401818Z functions: A list of function names for which to generate function 2024-06-26T05:54:21.9402543Z bindings. If a dictionary is given, it should map function names to 2024-06-26T05:54:21.9403227Z docstrings (which are otherwise just the function names). 2024-06-26T05:54:21.9403907Z with_cuda: Determines whether CUDA headers and libraries are added to 2024-06-26T05:54:21.9404560Z the build. If set to ``None`` (default), this value is 2024-06-26T05:54:21.9405195Z automatically determined based on whether ``cuda_sources`` is 2024-06-26T05:54:21.9405825Z provided. Set it to ``True`` to force CUDA headers 2024-06-26T05:54:21.9406303Z and libraries to be included. 2024-06-26T05:54:21.9406850Z with_pytorch_error_handling: Determines whether pytorch error and 2024-06-26T05:54:21.9407545Z warning macros are handled by pytorch instead of pybind. To do 2024-06-26T05:54:21.9408253Z this, each function ``foo`` is called via an intermediary ``_safe_foo`` 2024-06-26T05:54:21.9408976Z function. This redirection might cause issues in obscure cases 2024-06-26T05:54:21.9409664Z of cpp. This flag should be set to ``False`` when this redirect 2024-06-26T05:54:21.9410183Z causes issues. 2024-06-26T05:54:21.9410394Z 2024-06-26T05:54:21.9410490Z Example: 2024-06-26T05:54:21.9410838Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-06-26T05:54:21.9411380Z >>> from torch.utils.cpp_extension import load_inline 2024-06-26T05:54:21.9411853Z >>> source = """ 2024-06-26T05:54:21.9412244Z at::Tensor sin_add(at::Tensor x, at::Tensor y) { 2024-06-26T05:54:21.9412709Z return x.sin() + y.sin(); 2024-06-26T05:54:21.9413041Z } 2024-06-26T05:54:21.9413276Z """ 2024-06-26T05:54:21.9413766Z >>> module = load_inline(name='inline_extension', 2024-06-26T05:54:21.9414263Z ... cpp_sources=[source], 2024-06-26T05:54:21.9414779Z ... functions=['sin_add']) 2024-06-26T05:54:21.9415090Z 2024-06-26T05:54:21.9415202Z .. note:: 2024-06-26T05:54:21.9415609Z By default, the Ninja backend uses #CPUS + 2 workers to build the 2024-06-26T05:54:21.9416317Z extension. This may use up too many resources on some systems. One 2024-06-26T05:54:21.9417054Z can control the number of workers by setting the `MAX_JOBS` environment 2024-06-26T05:54:21.9417686Z variable to a non-negative number. 2024-06-26T05:54:21.9417977Z 2024-06-26T05:54:21.9418358Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:21.9418837Z 2024-06-26T05:54:21.9456762Z msg = Cannot scrape callname=ThroughputBenchmark in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/throughput_benchmark.py line=61. 2024-06-26T05:54:21.9458139Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:21.9458638Z 2024-06-26T05:54:21.9459041Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark. 2024-06-26T05:54:21.9459609Z 2024-06-26T05:54:21.9459990Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible 2024-06-26T05:54:21.9460864Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference 2024-06-26T05:54:21.9461677Z server like load. It can emulate multiple calling threads to a single module 2024-06-26T05:54:21.9462608Z provided. In the future we plan to enhance this component to support inter and 2024-06-26T05:54:21.9463506Z intra-op parallelism as well as multiple models running in a single process. 2024-06-26T05:54:21.9464051Z 2024-06-26T05:54:21.9464404Z Please note that even though nn.Module is supported, it might incur an overhead 2024-06-26T05:54:21.9465308Z from the need to hold GIL every time we execute Python code or pass around 2024-06-26T05:54:21.9466115Z inputs as Python objects. As soon as you have a ScriptModule version of your 2024-06-26T05:54:21.9466913Z model for inference deployment it is better to switch to using it in this 2024-06-26T05:54:21.9467485Z benchmark. 2024-06-26T05:54:21.9467636Z 2024-06-26T05:54:21.9467734Z Example:: 2024-06-26T05:54:21.9467896Z 2024-06-26T05:54:21.9468043Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:21.9468535Z >>> from torch.utils import ThroughputBenchmark 2024-06-26T05:54:21.9469033Z >>> bench = ThroughputBenchmark(my_module) 2024-06-26T05:54:21.9469627Z >>> # Pre-populate benchmark's data set with the inputs 2024-06-26T05:54:21.9470120Z >>> for input in inputs: 2024-06-26T05:54:21.9470645Z ... # Both args and kwargs work, same as any PyTorch Module / ScriptModule 2024-06-26T05:54:21.9471282Z ... bench.add_input(input[0], x2=input[1]) 2024-06-26T05:54:21.9471866Z >>> # Inputs supplied above are randomly used during the execution 2024-06-26T05:54:21.9472400Z >>> stats = bench.benchmark( 2024-06-26T05:54:21.9472776Z ... num_calling_threads=4, 2024-06-26T05:54:21.9473155Z ... num_warmup_iters = 100, 2024-06-26T05:54:21.9473524Z ... num_iters = 1000, 2024-06-26T05:54:21.9473850Z ... ) 2024-06-26T05:54:21.9474233Z >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms)) 2024-06-26T05:54:21.9474848Z >>> print("Number of iterations: {}".format(stats.num_iters)) 2024-06-26T05:54:21.9475243Z 2024-06-26T05:54:21.9475624Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:21.9476112Z 2024-06-26T05:54:22.0266664Z msg = Cannot scrape callname=DistributedSampler in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/distributed.py line=14. 2024-06-26T05:54:22.0268025Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:22.0268767Z Sampler that restricts data loading to a subset of the dataset. 2024-06-26T05:54:22.0269226Z 2024-06-26T05:54:22.0269397Z It is especially useful in conjunction with 2024-06-26T05:54:22.0270070Z :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each 2024-06-26T05:54:22.0270938Z process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a 2024-06-26T05:54:22.0271763Z :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the 2024-06-26T05:54:22.0272402Z original dataset that is exclusive to it. 2024-06-26T05:54:22.0272714Z 2024-06-26T05:54:22.0272852Z .. note:: 2024-06-26T05:54:22.0273334Z Dataset is assumed to be of constant size and that any instance of it always 2024-06-26T05:54:22.0274017Z returns the same elements in the same order. 2024-06-26T05:54:22.0274366Z 2024-06-26T05:54:22.0274461Z Args: 2024-06-26T05:54:22.0274761Z dataset: Dataset used for sampling. 2024-06-26T05:54:22.0275341Z num_replicas (int, optional): Number of processes participating in 2024-06-26T05:54:22.0276098Z distributed training. By default, :attr:`world_size` is retrieved from the 2024-06-26T05:54:22.0276722Z current distributed group. 2024-06-26T05:54:22.0277317Z rank (int, optional): Rank of the current process within :attr:`num_replicas`. 2024-06-26T05:54:22.0278083Z By default, :attr:`rank` is retrieved from the current distributed 2024-06-26T05:54:22.0278617Z group. 2024-06-26T05:54:22.0279084Z shuffle (bool, optional): If ``True`` (default), sampler will shuffle the 2024-06-26T05:54:22.0279809Z indices. 2024-06-26T05:54:22.0280261Z seed (int, optional): random seed used to shuffle the sampler if 2024-06-26T05:54:22.0281018Z :attr:`shuffle=True`. This number should be identical across all 2024-06-26T05:54:22.0281752Z processes in the distributed group. Default: ``0``. 2024-06-26T05:54:22.0282517Z drop_last (bool, optional): if ``True``, then the sampler will drop the 2024-06-26T05:54:22.0283242Z tail of the data to make it evenly divisible across the number of 2024-06-26T05:54:22.0283960Z replicas. If ``False``, the sampler will add extra indices to make 2024-06-26T05:54:22.0284679Z the data evenly divisible across the replicas. Default: ``False``. 2024-06-26T05:54:22.0285125Z 2024-06-26T05:54:22.0285247Z .. warning:: 2024-06-26T05:54:22.0285660Z In distributed mode, calling the :meth:`set_epoch` method at 2024-06-26T05:54:22.0286417Z the beginning of each epoch **before** creating the :class:`DataLoader` iterator 2024-06-26T05:54:22.0287281Z is necessary to make shuffling work properly across multiple epochs. Otherwise, 2024-06-26T05:54:22.0287938Z the same ordering will be always used. 2024-06-26T05:54:22.0288258Z 2024-06-26T05:54:22.0288359Z Example:: 2024-06-26T05:54:22.0288520Z 2024-06-26T05:54:22.0288651Z >>> # xdoctest: +SKIP 2024-06-26T05:54:22.0289159Z >>> sampler = DistributedSampler(dataset) if is_distributed else None 2024-06-26T05:54:22.0289824Z >>> loader = DataLoader(dataset, shuffle=(sampler is None), 2024-06-26T05:54:22.0290359Z ... sampler=sampler) 2024-06-26T05:54:22.0290839Z >>> for epoch in range(start_epoch, n_epochs): 2024-06-26T05:54:22.0291284Z ... if is_distributed: 2024-06-26T05:54:22.0291686Z ... sampler.set_epoch(epoch) 2024-06-26T05:54:22.0292099Z ... train(loader) 2024-06-26T05:54:22.0292407Z 2024-06-26T05:54:22.0292946Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:22.0293422Z 2024-06-26T05:54:22.2077353Z gathering tests 2024-06-26T05:54:22.2090672Z running 696 test(s) 2024-06-26T05:54:22.2116910Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::typename:0, line 893 <- wrt source file 2024-06-26T05:54:22.2125225Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::typename:0 2024-06-26T05:54:22.2126718Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::is_tensor:0, line 929 <- wrt source file 2024-06-26T05:54:22.2129950Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::is_tensor:0 2024-06-26T05:54:22.2131861Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_device:0, line 998 <- wrt source file 2024-06-26T05:54:22.2133444Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_device:0 2024-06-26T05:54:22.2135678Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_tensor_type:0, line 1047 <- wrt source file 2024-06-26T05:54:22.2137635Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_tensor_type:0 2024-06-26T05:54:22.2139559Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_dtype:0, line 1084 <- wrt source file 2024-06-26T05:54:22.2141515Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_dtype:0 2024-06-26T05:54:22.2143128Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::use_deterministic_algorithms:0, line 1239 <- wrt source file 2024-06-26T05:54:22.2144962Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::use_deterministic_algorithms:0 2024-06-26T05:54:22.2146499Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::compile:0, line 2281 <- wrt source file 2024-06-26T05:54:22.2148088Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::compile:0 2024-06-26T05:54:22.2149706Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_C.cpython-312-x86_64-linux-gnu.so::Generator:0, line 15 <- wrt source file 2024-06-26T05:54:22.2151442Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_C.cpython-312-x86_64-linux-gnu.so::Generator:0 2024-06-26T05:54:22.2153180Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_C.cpython-312-x86_64-linux-gnu.so::_LinAlgError:0, line 5 <- wrt source file 2024-06-26T05:54:22.2154934Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_C.cpython-312-x86_64-linux-gnu.so::_LinAlgError:0 2024-06-26T05:54:22.2156554Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::custom_op:0, line 54 <- wrt source file 2024-06-26T05:54:22.2158020Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::custom_op:0 2024-06-26T05:54:22.2159469Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl:0, line 136 <- wrt source file 2024-06-26T05:54:22.2160949Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl:0 2024-06-26T05:54:22.2162444Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl_abstract:0, line 205 <- wrt source file 2024-06-26T05:54:22.2367914Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl_abstract:0 2024-06-26T05:54:22.2369588Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_namedtensor_internals.py::update_names:0, line 117 <- wrt source file 2024-06-26T05:54:22.2371250Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_namedtensor_internals.py::update_names:0 2024-06-26T05:54:22.2372865Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_hook:0, line 546 <- wrt source file 2024-06-26T05:54:22.2539478Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_hook:0 2024-06-26T05:54:22.2541165Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0, line 603 <- wrt source file 2024-06-26T05:54:22.2560150Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0 2024-06-26T05:54:22.2562079Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.refine_names:0, line 1223 <- wrt source file 2024-06-26T05:54:22.2675196Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.refine_names:0 2024-06-26T05:54:22.2678835Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.align_to:0, line 1268 <- wrt source file 2024-06-26T05:54:22.2683142Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.align_to:0 2024-06-26T05:54:22.2684680Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.rename:0, line 1341 <- wrt source file 2024-06-26T05:54:22.2690241Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.rename:0 2024-06-26T05:54:22.2691972Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0, line 1371 <- wrt source file 2024-06-26T05:54:22.2695718Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0 2024-06-26T05:54:22.2697429Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.dim_order:0, line 1394 <- wrt source file 2024-06-26T05:54:22.2700132Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.dim_order:0 2024-06-26T05:54:22.2701679Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor_str.py::set_printoptions:0, line 53 <- wrt source file 2024-06-26T05:54:22.2718899Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor_str.py::set_printoptions:0 2024-06-26T05:54:22.2720500Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_tensors:0, line 63 <- wrt source file 2024-06-26T05:54:22.2724864Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_tensors:0 2024-06-26T05:54:22.2726694Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_shapes:0, line 91 <- wrt source file 2024-06-26T05:54:22.2728507Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_shapes:0 2024-06-26T05:54:22.2730160Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::split:0, line 178 <- wrt source file 2024-06-26T05:54:22.2739576Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::split:0 2024-06-26T05:54:22.2741182Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::einsum:0, line 287 <- wrt source file 2024-06-26T05:54:22.2788249Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::einsum:0 2024-06-26T05:54:22.2789847Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_unique_consecutive_impl:0, line 1014 <- wrt source file 2024-06-26T05:54:22.2799581Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_unique_consecutive_impl:0 2024-06-26T05:54:22.2801250Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::tensordot:0, line 1289 <- wrt source file 2024-06-26T05:54:22.2809692Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::tensordot:0 2024-06-26T05:54:22.2811233Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cartesian_prod:0, line 1373 <- wrt source file 2024-06-26T05:54:22.2816982Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cartesian_prod:0 2024-06-26T05:54:22.2818684Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::block_diag:0, line 1407 <- wrt source file 2024-06-26T05:54:22.2825296Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::block_diag:0 2024-06-26T05:54:22.2826767Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cdist:0, line 1458 <- wrt source file 2024-06-26T05:54:22.2838005Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cdist:0 2024-06-26T05:54:22.2839498Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_1d:0, line 1499 <- wrt source file 2024-06-26T05:54:22.2853270Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_1d:0 2024-06-26T05:54:22.2855068Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_2d:0, line 1535 <- wrt source file 2024-06-26T05:54:22.2869387Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_2d:0 2024-06-26T05:54:22.2870914Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_3d:0, line 1573 <- wrt source file 2024-06-26T05:54:22.2889050Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_3d:0 2024-06-26T05:54:22.2890524Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::norm:0, line 1746 <- wrt source file 2024-06-26T05:54:22.2919168Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::norm:0 2024-06-26T05:54:22.2920762Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::unravel_index:0, line 1913 <- wrt source file 2024-06-26T05:54:22.2945461Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::unravel_index:0 2024-06-26T05:54:22.2947319Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::chain_matmul:0, line 2013 <- wrt source file 2024-06-26T05:54:22.2949554Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::chain_matmul:0 2024-06-26T05:54:22.2952140Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_lu_impl:0, line 2113 <- wrt source file 2024-06-26T05:54:22.2953902Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_lu_impl:0 2024-06-26T05:54:22.2955425Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::list:0, line 468 <- wrt source file 2024-06-26T05:54:22.2957258Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::list:0 2024-06-26T05:54:22.2958634Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::help:0, line 528 <- wrt source file 2024-06-26T05:54:22.2959956Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::help:0 2024-06-26T05:54:22.2961358Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::load:0, line 619 <- wrt source file 2024-06-26T05:54:22.2962692Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::load:0 2024-06-26T05:54:22.2964077Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::_load_local:0, line 665 <- wrt source file 2024-06-26T05:54:22.2965531Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::_load_local:0 2024-06-26T05:54:22.2966995Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::download_url_to_file:0, line 696 <- wrt source file 2024-06-26T05:54:22.2968496Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::download_url_to_file:0 2024-06-26T05:54:22.2970042Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::load_state_dict_from_url:0, line 833 <- wrt source file 2024-06-26T05:54:22.2971570Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::load_state_dict_from_url:0 2024-06-26T05:54:22.2973111Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.define:0, line 125 <- wrt source file 2024-06-26T05:54:22.2974748Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.define:0 2024-06-26T05:54:22.2976475Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library._impl_with_aoti_compile:0, line 192 <- wrt source file 2024-06-26T05:54:22.3004495Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library._impl_with_aoti_compile:0 2024-06-26T05:54:22.3006239Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.impl:0, line 241 <- wrt source file 2024-06-26T05:54:22.3008298Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.impl:0 2024-06-26T05:54:22.3009756Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::define:0, line 392 <- wrt source file 2024-06-26T05:54:22.3019249Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::define:0 2024-06-26T05:54:22.3020663Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::impl:0, line 459 <- wrt source file 2024-06-26T05:54:22.3028054Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::impl:0 2024-06-26T05:54:22.3029969Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_kernel:0, line 567 <- wrt source file 2024-06-26T05:54:22.3031479Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_kernel:0 2024-06-26T05:54:22.3032974Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_fake:0, line 641 <- wrt source file 2024-06-26T05:54:22.3763002Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_fake:0 2024-06-26T05:54:22.3764561Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_autograd:0, line 767 <- wrt source file 2024-06-26T05:54:22.3898947Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_autograd:0 2024-06-26T05:54:22.3900591Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_ignored_functions:0, line 111 <- wrt source file 2024-06-26T05:54:22.3904844Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_ignored_functions:0 2024-06-26T05:54:22.3906467Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_testing_overrides:0, line 418 <- wrt source file 2024-06-26T05:54:22.3941768Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_testing_overrides:0 2024-06-26T05:54:22.3943508Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::wrap_torch_function:0, line 1565 <- wrt source file 2024-06-26T05:54:22.3945273Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::wrap_torch_function:0 2024-06-26T05:54:22.3947098Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::handle_torch_function:0, line 1700 <- wrt source file 2024-06-26T05:54:22.3948840Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::handle_torch_function:0 2024-06-26T05:54:22.3950510Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_method_or_property:0, line 1948 <- wrt source file 2024-06-26T05:54:22.3975900Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_method_or_property:0 2024-06-26T05:54:22.3977526Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_like:0, line 1967 <- wrt source file 2024-06-26T05:54:22.3982288Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_like:0 2024-06-26T05:54:22.3984426Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/quasirandom.py::SobolEngine:0, line 39 <- wrt source file 2024-06-26T05:54:22.3986188Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/quasirandom.py::SobolEngine:0 2024-06-26T05:54:22.3988414Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::add_safe_globals:0, line 214 <- wrt source file 2024-06-26T05:54:22.3990286Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::add_safe_globals:0 2024-06-26T05:54:22.3992540Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::register_package:0, line 277 <- wrt source file 2024-06-26T05:54:22.3994505Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::register_package:0 2024-06-26T05:54:22.3996635Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::save:0, line 738 <- wrt source file 2024-06-26T05:54:22.3998533Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::save:0 2024-06-26T05:54:22.4000001Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::load:0, line 1089 <- wrt source file 2024-06-26T05:54:22.4002228Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::load:0 2024-06-26T05:54:22.4004687Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/torch_version.py::TorchVersion:0, line 20 <- wrt source file 2024-06-26T05:54:22.4006231Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/torch_version.py::TorchVersion:0 2024-06-26T05:54:22.4008093Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_mode_options:0, line 124 <- wrt source file 2024-06-26T05:54:22.4009840Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_mode_options:0 2024-06-26T05:54:22.4012061Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_options:0, line 154 <- wrt source file 2024-06-26T05:54:22.4014188Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_options:0 2024-06-26T05:54:22.4016188Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims_common/__init__.py::compute_required_storage_length:0, line 1746 <- wrt source file 2024-06-26T05:54:22.4018402Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims_common/__init__.py::compute_required_storage_length:0 2024-06-26T05:54:22.4020775Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::allow_in_graph:0, line 94 <- wrt source file 2024-06-26T05:54:22.4022383Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::allow_in_graph:0 2024-06-26T05:54:22.4023962Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::wrap_numpy:0, line 194 <- wrt source file 2024-06-26T05:54:22.4025500Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::wrap_numpy:0 2024-06-26T05:54:22.4027079Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_compiling:0, line 222 <- wrt source file 2024-06-26T05:54:22.4028636Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_compiling:0 2024-06-26T05:54:22.4030371Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0, line 242 <- wrt source file 2024-06-26T05:54:22.4032187Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0 2024-06-26T05:54:22.4033773Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::save:0, line 215 <- wrt source file 2024-06-26T05:54:22.4035229Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::save:0 2024-06-26T05:54:22.4036688Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::load:0, line 279 <- wrt source file 2024-06-26T05:54:22.4038136Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::load:0 2024-06-26T05:54:22.4039705Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::register_dataclass:0, line 320 <- wrt source file 2024-06-26T05:54:22.4041407Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::register_dataclass:0 2024-06-26T05:54:22.4043365Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::Future.add_done_callback:0, line 196 <- wrt source file 2024-06-26T05:54:22.4045125Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::Future.add_done_callback:0 2024-06-26T05:54:22.4046829Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::Future.set_exception:0, line 258 <- wrt source file 2024-06-26T05:54:22.4049001Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::Future.set_exception:0 2024-06-26T05:54:22.4051984Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::collect_all:0, line 289 <- wrt source file 2024-06-26T05:54:22.4054915Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::collect_all:0 2024-06-26T05:54:22.4057713Z * DOCTEST : 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor:0, line 210 <- wrt source file 2024-06-26T05:54:22.4078051Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor:0 2024-06-26T05:54:22.4080935Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::narrow:0, line 272 <- wrt source file 2024-06-26T05:54:22.4109561Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::narrow:0 2024-06-26T05:54:22.4112617Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor_from_jagged:0, line 348 <- wrt source file 2024-06-26T05:54:22.4128437Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor_from_jagged:0 2024-06-26T05:54:22.4131770Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::check_sparse_tensor_invariants:0, line 437 <- wrt source file 2024-06-26T05:54:22.4137301Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::check_sparse_tensor_invariants:0 2024-06-26T05:54:22.4140427Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::as_sparse_gradcheck:0, line 522 <- wrt source file 2024-06-26T05:54:22.4181220Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::as_sparse_gradcheck:0 2024-06-26T05:54:22.4184151Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_op/impl.py::custom_op:0, line 83 <- wrt source file 2024-06-26T05:54:22.4186927Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_op/impl.py::custom_op:0 2024-06-26T05:54:22.4189779Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_op/impl.py::CustomOp.impl:0, line 281 <- wrt source file 2024-06-26T05:54:22.4192641Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_op/impl.py::CustomOp.impl:0 2024-06-26T05:54:22.4195638Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_op/impl.py::CustomOp.impl_abstract:0, line 365 <- wrt source file 2024-06-26T05:54:22.4304606Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_op/impl.py::CustomOp.impl_abstract:0 2024-06-26T05:54:22.4307702Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/variables/base.py::VariableTracker.python_type:0, line 199 <- wrt source file 2024-06-26T05:54:22.4310795Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/variables/base.py::VariableTracker.python_type:0 2024-06-26T05:54:22.4313895Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/aot_autograd.py::aot_function:0, line 774 <- wrt source file 2024-06-26T05:54:22.4939259Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/aot_autograd.py::aot_function:0 2024-06-26T05:54:22.4942192Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/apis.py::grad:0, line 322 <- wrt source file 2024-06-26T05:54:22.4944829Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/apis.py::grad:0 2024-06-26T05:54:22.4947845Z * 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 2024-06-26T05:54:22.4951119Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/benchmark_utils.py::benchmark_utilization:0 2024-06-26T05:54:22.4954213Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::vjp:0, line 272 <- wrt source file 2024-06-26T05:54:22.4978683Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::vjp:0 2024-06-26T05:54:22.4981723Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacrev:0, line 511 <- wrt source file 2024-06-26T05:54:22.5038284Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacrev:0 2024-06-26T05:54:22.5041573Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jvp:0, line 1065 <- wrt source file 2024-06-26T05:54:22.5902281Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jvp:0 2024-06-26T05:54:22.5905401Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0, line 1221 <- wrt source file 2024-06-26T05:54:22.5961361Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0 2024-06-26T05:54:22.5964458Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::hessian:0, line 1386 <- wrt source file 2024-06-26T05:54:22.5978557Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::hessian:0 2024-06-26T05:54:22.5981713Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::functionalize:0, line 1550 <- wrt source file 2024-06-26T05:54:22.5984888Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::functionalize:0 2024-06-26T05:54:22.5988030Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::linearize:0, line 1751 <- wrt source file 2024-06-26T05:54:22.6258104Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::linearize:0 2024-06-26T05:54:22.6259901Z * 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 2024-06-26T05:54:22.6262429Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/functional_call.py::functional_call:0 2024-06-26T05:54:22.6265582Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/fx_minifier.py::minifier:0, line 192 <- wrt source file 2024-06-26T05:54:22.6267570Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/fx_minifier.py::minifier:0 2024-06-26T05:54:22.6269417Z * 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 2024-06-26T05:54:22.6271466Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py::CompilerWrapper.post_compile:0 2024-06-26T05:54:22.6273419Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/associative_scan.py::associative_scan:0, line 67 <- wrt source file 2024-06-26T05:54:22.6275248Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/associative_scan.py::associative_scan:0 2024-06-26T05:54:22.6276955Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/cond.py::cond:0, line 73 <- wrt source file 2024-06-26T05:54:22.6278476Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/cond.py::cond:0 2024-06-26T05:54:22.6280097Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/while_loop.py::while_loop:0, line 93 <- wrt source file 2024-06-26T05:54:22.6281845Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/while_loop.py::while_loop:0 2024-06-26T05:54:22.6283469Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::custom_op:0, line 74 <- wrt source file 2024-06-26T05:54:22.6451084Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::custom_op:0 2024-06-26T05:54:22.6452984Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0, line 193 <- wrt source file 2024-06-26T05:54:22.6455010Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0 2024-06-26T05:54:22.6456806Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_fake:0, line 293 <- wrt source file 2024-06-26T05:54:22.6508056Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_fake:0 2024-06-26T05:54:22.6509886Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0, line 385 <- wrt source file 2024-06-26T05:54:22.6620360Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0 2024-06-26T05:54:22.6622380Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_class_registry.py::register_fake_class:0, line 158 <- wrt source file 2024-06-26T05:54:22.6624144Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_class_registry.py::register_fake_class:0 2024-06-26T05:54:22.6625939Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0, line 159 <- wrt source file 2024-06-26T05:54:22.6669787Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0 2024-06-26T05:54:22.6671520Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_logging/_internal.py::set_logs:0, line 406 <- wrt source file 2024-06-26T05:54:22.6673077Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_logging/_internal.py::set_logs:0 2024-06-26T05:54:22.6674693Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_equal:0, line 169 <- wrt source file 2024-06-26T05:54:22.6706271Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_equal:0 2024-06-26T05:54:22.6707983Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::print_assert_equal:0, line 304 <- wrt source file 2024-06-26T05:54:22.6709678Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::print_assert_equal:0 2024-06-26T05:54:22.6711382Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0, line 995 <- wrt source file 2024-06-26T05:54:22.6755119Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0 2024-06-26T05:54:22.6756892Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0, line 1060 <- wrt source file 2024-06-26T05:54:22.6758598Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0 2024-06-26T05:54:22.6760281Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0, line 1281 <- wrt source file 2024-06-26T05:54:22.6772820Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0 2024-06-26T05:54:22.6774962Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0, line 1347 <- wrt source file 2024-06-26T05:54:22.6777203Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0 2024-06-26T05:54:22.6778997Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0, line 1410 <- wrt source file 2024-06-26T05:54:22.6781516Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0 2024-06-26T05:54:22.6783272Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0, line 1455 <- wrt source file 2024-06-26T05:54:22.6784863Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0 2024-06-26T05:54:22.6786731Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_warns:0, line 1565 <- wrt source file 2024-06-26T05:54:22.6789002Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_warns:0 2024-06-26T05:54:22.6791263Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims/context.py::TorchRefsMode:0, line 88 <- wrt source file 2024-06-26T05:54:22.6793321Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims/context.py::TorchRefsMode:0 2024-06-26T05:54:22.6795042Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/amp/grad_scaler.py::GradScaler:0, line 60 <- wrt source file 2024-06-26T05:54:22.6796571Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/amp/grad_scaler.py::GradScaler:0 2024-06-26T05:54:22.6798338Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0, line 22 <- wrt source file 2024-06-26T05:54:22.6800271Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0 2024-06-26T05:54:22.6802395Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0, line 22 <- wrt source file 2024-06-26T05:54:22.6804524Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0 2024-06-26T05:54:22.6806604Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0, line 23 <- wrt source file 2024-06-26T05:54:22.6808622Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0 2024-06-26T05:54:22.6810683Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0, line 60 <- wrt source file 2024-06-26T05:54:22.6812957Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0 2024-06-26T05:54:22.6815247Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0, line 127 <- wrt source file 2024-06-26T05:54:22.6817265Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0 2024-06-26T05:54:22.6819162Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0, line 25 <- wrt source file 2024-06-26T05:54:22.6821004Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0 2024-06-26T05:54:22.6822846Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0, line 277 <- wrt source file 2024-06-26T05:54:22.6843577Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0 2024-06-26T05:54:22.6846026Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0, line 167 <- wrt source file 2024-06-26T05:54:22.6848294Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0 2024-06-26T05:54:22.6849969Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0, line 227 <- wrt source file 2024-06-26T05:54:22.6851639Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0 2024-06-26T05:54:22.6853822Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0, line 288 <- wrt source file 2024-06-26T05:54:22.6855498Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0 2024-06-26T05:54:22.6857393Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/__init__.py::Quantize:0, line 75 <- wrt source file 2024-06-26T05:54:22.6860634Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/__init__.py::Quantize:0 2024-06-26T05:54:22.6862955Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/__init__.py::DeQuantize:0, line 115 <- wrt source file 2024-06-26T05:54:22.6865956Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/__init__.py::DeQuantize:0 2024-06-26T05:54:22.6868141Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0, line 34 <- wrt source file 2024-06-26T05:54:22.6870237Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0 2024-06-26T05:54:22.6873130Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0, line 103 <- wrt source file 2024-06-26T05:54:22.6875650Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0 2024-06-26T05:54:22.6877515Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0, line 167 <- wrt source file 2024-06-26T05:54:22.6879335Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0 2024-06-26T05:54:22.6881361Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0, line 231 <- wrt source file 2024-06-26T05:54:22.6883331Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0 2024-06-26T05:54:22.6885753Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0, line 290 <- wrt source file 2024-06-26T05:54:22.6887770Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0 2024-06-26T05:54:22.6889869Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0, line 349 <- wrt source file 2024-06-26T05:54:22.6891966Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0 2024-06-26T05:54:22.6894104Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0, line 29 <- wrt source file 2024-06-26T05:54:22.6896850Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0 2024-06-26T05:54:22.6898709Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0, line 392 <- wrt source file 2024-06-26T05:54:22.6901076Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0 2024-06-26T05:54:22.6904048Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRU:0, line 639 <- wrt source file 2024-06-26T05:54:22.6906859Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRU:0 2024-06-26T05:54:22.6909387Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0, line 975 <- wrt source file 2024-06-26T05:54:22.6911217Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0 2024-06-26T05:54:22.6913074Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0, line 1028 <- wrt source file 2024-06-26T05:54:22.6914921Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0 2024-06-26T05:54:22.6916770Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0, line 1071 <- wrt source file 2024-06-26T05:54:22.6918600Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0 2024-06-26T05:54:22.6920407Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/activation.py::ReLU6:0, line 32 <- wrt source file 2024-06-26T05:54:22.6922264Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/activation.py::ReLU6:0 2024-06-26T05:54:22.6924000Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0, line 405 <- wrt source file 2024-06-26T05:54:22.6925686Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0 2024-06-26T05:54:22.6927397Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0, line 506 <- wrt source file 2024-06-26T05:54:22.6929083Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0 2024-06-26T05:54:22.6930845Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0, line 691 <- wrt source file 2024-06-26T05:54:22.6932675Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0 2024-06-26T05:54:22.6934718Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0, line 781 <- wrt source file 2024-06-26T05:54:22.6937195Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0 2024-06-26T05:54:22.6939212Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0, line 875 <- wrt source file 2024-06-26T05:54:22.6941881Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0 2024-06-26T05:54:22.6944087Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0, line 85 <- wrt source file 2024-06-26T05:54:22.6946742Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0 2024-06-26T05:54:22.6950073Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0, line 209 <- wrt source file 2024-06-26T05:54:22.6951994Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0 2024-06-26T05:54:22.6953992Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0, line 22 <- wrt source file 2024-06-26T05:54:22.6956889Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0 2024-06-26T05:54:22.6958901Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0, line 153 <- wrt source file 2024-06-26T05:54:22.6961374Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0 2024-06-26T05:54:22.6963380Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/linear.py::Linear:0, line 118 <- wrt source file 2024-06-26T05:54:22.6965128Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/linear.py::Linear:0 2024-06-26T05:54:22.6967922Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py::ActivationSparsifier:0, line 60 <- wrt source file 2024-06-26T05:54:22.6970264Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py::ActivationSparsifier:0 2024-06-26T05:54:22.6973269Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py::BaseDataScheduler.get_schedule_param:0, line 92 <- wrt source file 2024-06-26T05:54:22.6977081Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py::BaseDataScheduler.get_schedule_param:0 2024-06-26T05:54:22.6979774Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0, line 54 <- wrt source file 2024-06-26T05:54:22.6982770Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0 2024-06-26T05:54:22.6985450Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0, line 20 <- wrt source file 2024-06-26T05:54:22.6988400Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0 2024-06-26T05:54:22.6991628Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0, line 48 <- wrt source file 2024-06-26T05:54:22.6993982Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0 2024-06-26T05:54:22.6997450Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0, line 145 <- wrt source file 2024-06-26T05:54:22.7000331Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0 2024-06-26T05:54:22.7003094Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0, line 29 <- wrt source file 2024-06-26T05:54:22.7006214Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0 2024-06-26T05:54:22.7009669Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0, line 68 <- wrt source file 2024-06-26T05:54:22.7013235Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0 2024-06-26T05:54:22.7017001Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0, line 116 <- wrt source file 2024-06-26T05:54:22.7020608Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0 2024-06-26T05:54:22.7024222Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0, line 145 <- wrt source file 2024-06-26T05:54:22.7027318Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0 2024-06-26T05:54:22.7030693Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/observer.py::_with_args:0, line 86 <- wrt source file 2024-06-26T05:54:22.7033344Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/observer.py::_with_args:0 2024-06-26T05:54:22.7036462Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0, line 107 <- wrt source file 2024-06-26T05:54:22.7039226Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0 2024-06-26T05:54:22.7041059Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::fuse_fx:0, line 219 <- wrt source file 2024-06-26T05:54:22.7043744Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::fuse_fx:0 2024-06-26T05:54:22.7045473Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0, line 282 <- wrt source file 2024-06-26T05:54:22.7047195Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0 2024-06-26T05:54:22.7048956Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0, line 420 <- wrt source file 2024-06-26T05:54:22.7050867Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0 2024-06-26T05:54:22.7052754Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0, line 588 <- wrt source file 2024-06-26T05:54:22.7054789Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0 2024-06-26T05:54:22.7057177Z * DOCTEST : 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2024-06-26T05:54:22.7068551Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0, line 129 <- wrt source file 2024-06-26T05:54:22.7071157Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0 2024-06-26T05:54:22.7074391Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0, line 217 <- wrt source file 2024-06-26T05:54:22.7076830Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0 2024-06-26T05:54:22.7078614Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0, line 139 <- wrt source file 2024-06-26T05:54:22.7080326Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0 2024-06-26T05:54:22.7082164Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0, line 472 <- wrt source file 2024-06-26T05:54:22.7084585Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0 2024-06-26T05:54:22.7086399Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0, line 493 <- wrt source file 2024-06-26T05:54:22.7088155Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0 2024-06-26T05:54:22.7089942Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0, line 506 <- wrt source file 2024-06-26T05:54:22.7091682Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0 2024-06-26T05:54:22.7093431Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0, line 527 <- wrt source file 2024-06-26T05:54:22.7095341Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0 2024-06-26T05:54:22.7097085Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0, line 646 <- wrt source file 2024-06-26T05:54:22.7099356Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0 2024-06-26T05:54:22.7102893Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/backend_config/onednn.py::_fuse_linear_bn_leaky_relu:0, line 83 <- wrt source file 2024-06-26T05:54:22.7106667Z * SKIPPED: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/pt2e/utils.py::_replace_literals_with_new_placeholders:0, line 385 <- wrt source file 2024-06-26T05:54:22.7128587Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/pt2e/utils.py::_replace_literals_with_new_placeholders:0 2024-06-26T05:54:22.7132199Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/anomaly_mode.py::detect_anomaly:0, line 26 <- wrt source file 2024-06-26T05:54:22.7135497Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/anomaly_mode.py::detect_anomaly:0 2024-06-26T05:54:22.7138610Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::make_dual:0, line 82 <- wrt source file 2024-06-26T05:54:22.7141617Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::make_dual:0 2024-06-26T05:54:22.7144761Z * DOCTEST : 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DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.set_materialize_grads:0, line 235 <- wrt source file 2024-06-26T05:54:22.7173666Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.set_materialize_grads:0 2024-06-26T05:54:22.7176734Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::Function:0, line 478 <- wrt source file 2024-06-26T05:54:22.7179591Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::Function:0 2024-06-26T05:54:22.7182154Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::vjp:0, line 292 <- wrt source file 2024-06-26T05:54:22.7184195Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::vjp:0 2024-06-26T05:54:22.7186912Z * DOCTEST : 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.name:0 2024-06-26T05:54:22.7237017Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.register_hook:0, line 96 <- wrt source file 2024-06-26T05:54:22.7239224Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.register_hook:0 2024-06-26T05:54:22.7240964Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.register_prehook:0, line 133 <- wrt source file 2024-06-26T05:54:22.7243264Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.register_prehook:0 2024-06-26T05:54:22.7245683Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::saved_tensors_hooks:0, line 240 <- wrt source file 2024-06-26T05:54:22.7247527Z * 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 3191 <- wrt source file 2024-06-26T05:54:22.7352824Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::scatter_object_list:0 2024-06-26T05:54:22.7355531Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather:0, line 3286 <- wrt source file 2024-06-26T05:54:22.7357267Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather:0 2024-06-26T05:54:22.7359083Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_into_tensor:0, line 3371 <- wrt source file 2024-06-26T05:54:22.7361770Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_into_tensor:0 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* DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/autograd/__init__.py::context:0, line 39 <- wrt source file 2024-06-26T05:54:22.7409982Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/autograd/__init__.py::context:0 2024-06-26T05:54:22.7412461Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/checkpoint_activation.py::checkpoint:0, line 47 <- wrt source file 2024-06-26T05:54:22.7414541Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/checkpoint_activation.py::checkpoint:0 2024-06-26T05:54:22.7416387Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/contract.py::contract:0, line 40 <- wrt source file 2024-06-26T05:54:22.7418691Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/contract.py::contract:0 2024-06-26T05:54:22.7421762Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/replicate.py::replicate:0, line 186 <- wrt source file 2024-06-26T05:54:22.7423789Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/replicate.py::replicate:0 2024-06-26T05:54:22.7425875Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_optim/__init__.py::named_params_with_sharded_tensor:0, line 30 <- wrt source file 2024-06-26T05:54:22.7429247Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_optim/__init__.py::named_params_with_sharded_tensor:0 2024-06-26T05:54:22.7431757Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py::custom_sharded_op_impl:0, line 457 <- wrt source file 2024-06-26T05:54:22.7435103Z * SKIPPED: 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SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/beta.py::Beta:0 2024-06-26T05:54:22.7668597Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/binomial.py::Binomial:0, line 27 <- wrt source file 2024-06-26T05:54:22.7670215Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/binomial.py::Binomial:0 2024-06-26T05:54:22.7671996Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/categorical.py::Categorical:0, line 39 <- wrt source file 2024-06-26T05:54:22.7673685Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/categorical.py::Categorical:0 2024-06-26T05:54:22.7675467Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/cauchy.py::Cauchy:0, line 21 <- wrt source file 2024-06-26T05:54:22.7677043Z * SUCCESS: 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SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/one_hot_categorical.py::OneHotCategorical:0 2024-06-26T05:54:22.7763290Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/pareto.py::Pareto:0, line 16 <- wrt source file 2024-06-26T05:54:22.7764849Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/pareto.py::Pareto:0 2024-06-26T05:54:22.7766580Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/poisson.py::Poisson:0, line 22 <- wrt source file 2024-06-26T05:54:22.7768190Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/poisson.py::Poisson:0 2024-06-26T05:54:22.7769821Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/studentT.py::StudentT:0, line 19 <- wrt source file 2024-06-26T05:54:22.7771423Z * SUCCESS: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/utils.py::transitive_get:0, line 13 <- wrt source file 2024-06-26T05:54:22.7900306Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/utils.py::transitive_get:0 2024-06-26T05:54:22.7902181Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/utils.py::_toposort:0, line 40 <- wrt source file 2024-06-26T05:54:22.7903994Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/utils.py::_toposort:0 2024-06-26T05:54:22.7905849Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/utils.py::reverse_dict:0, line 68 <- wrt source file 2024-06-26T05:54:22.7907691Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/utils.py::reverse_dict:0 2024-06-26T05:54:22.7909505Z * 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 2024-06-26T05:54:22.7911429Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/utils.py::freeze:0 2024-06-26T05:54:22.7913280Z * 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 2024-06-26T05:54:22.7915156Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/variable.py::variables:0 2024-06-26T05:54:22.7917129Z * 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 2024-06-26T05:54:22.7919185Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/core.py::dispatch:0 2024-06-26T05:54:22.7921468Z * 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 2024-06-26T05:54:22.7923752Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher:0 2024-06-26T05:54:22.7925993Z * 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 2024-06-26T05:54:22.7928276Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.register:0 2024-06-26T05:54:22.7930557Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.add:0, line 175 <- wrt source file 2024-06-26T05:54:22.7932776Z * SKIPPED: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::expand_tuples:0, line 16 <- wrt source file 2024-06-26T05:54:22.7946189Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::expand_tuples:0 2024-06-26T05:54:22.7948287Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::_toposort:0, line 39 <- wrt source file 2024-06-26T05:54:22.7950336Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::_toposort:0 2024-06-26T05:54:22.7952498Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::reverse_dict:0, line 67 <- wrt source file 2024-06-26T05:54:22.7954628Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::reverse_dict:0 2024-06-26T05:54:22.7956714Z * 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 2024-06-26T05:54:22.7958748Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::groupby:0 2024-06-26T05:54:22.7960876Z * 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 2024-06-26T05:54:22.7962913Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::typename:0 2024-06-26T05:54:22.7965168Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::isvariadic:0, line 46 <- wrt source file 2024-06-26T05:54:22.7967421Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::isvariadic:0 2024-06-26T05:54:22.7969556Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::Variadic:0, line 79 <- wrt source file 2024-06-26T05:54:22.7971651Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::Variadic:0 2024-06-26T05:54:22.7973788Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/graph_drawer.py::FxGraphDrawer.get_dot_graph:0, line 108 <- wrt source file 2024-06-26T05:54:22.7975668Z * SKIPPED: 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DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::nll_loss:0, line 3126 <- wrt source file 2024-06-26T05:54:22.9034537Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::nll_loss:0 2024-06-26T05:54:22.9037831Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::cross_entropy:0, line 3451 <- wrt source file 2024-06-26T05:54:22.9043745Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::cross_entropy:0 2024-06-26T05:54:22.9047335Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::binary_cross_entropy:0, line 3523 <- wrt source file 2024-06-26T05:54:22.9051305Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::binary_cross_entropy:0 2024-06-26T05:54:22.9055517Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0, line 3600 <- wrt source file 2024-06-26T05:54:22.9060280Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0 2024-06-26T05:54:22.9063806Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::pad:0, line 5063 <- wrt source file 2024-06-26T05:54:22.9070777Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::pad:0 2024-06-26T05:54:22.9073972Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_input:0, line 32 <- wrt source file 2024-06-26T05:54:22.9080363Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_input:0 2024-06-26T05:54:22.9084211Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_weight:0, line 79 <- wrt source file 2024-06-26T05:54:22.9087901Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_weight:0 2024-06-26T05:54:22.9091225Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_input:0, line 130 <- wrt source file 2024-06-26T05:54:22.9097003Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_input:0 2024-06-26T05:54:22.9100053Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_weight:0, line 177 <- wrt source file 2024-06-26T05:54:22.9104538Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_weight:0 2024-06-26T05:54:22.9107806Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_input:0, line 228 <- wrt source file 2024-06-26T05:54:22.9138245Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_input:0 2024-06-26T05:54:22.9141648Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_weight:0, line 275 <- wrt source file 2024-06-26T05:54:22.9160076Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_weight:0 2024-06-26T05:54:22.9163982Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::calculate_gain:0, line 102 <- wrt source file 2024-06-26T05:54:22.9166817Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::calculate_gain:0 2024-06-26T05:54:22.9169513Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::uniform_:0, line 159 <- wrt source file 2024-06-26T05:54:22.9172196Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::uniform_:0 2024-06-26T05:54:22.9175062Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::normal_:0, line 186 <- wrt source file 2024-06-26T05:54:22.9178107Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::normal_:0 2024-06-26T05:54:22.9181251Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::trunc_normal_:0, line 221 <- wrt source file 2024-06-26T05:54:22.9183977Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::trunc_normal_:0 2024-06-26T05:54:22.9186732Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::constant_:0, line 235 <- wrt source file 2024-06-26T05:54:22.9189379Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::constant_:0 2024-06-26T05:54:22.9191973Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::ones_:0, line 252 <- wrt source file 2024-06-26T05:54:22.9194542Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::ones_:0 2024-06-26T05:54:22.9197032Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::zeros_:0, line 265 <- wrt source file 2024-06-26T05:54:22.9199610Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::zeros_:0 2024-06-26T05:54:22.9202248Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::eye_:0, line 281 <- wrt source file 2024-06-26T05:54:22.9204962Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::eye_:0 2024-06-26T05:54:22.9207771Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::dirac_:0, line 303 <- wrt source file 2024-06-26T05:54:22.9210602Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::dirac_:0 2024-06-26T05:54:22.9213587Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_uniform_:0, line 389 <- wrt source file 2024-06-26T05:54:22.9216293Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_uniform_:0 2024-06-26T05:54:22.9219106Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_normal_:0, line 421 <- wrt source file 2024-06-26T05:54:22.9221640Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_normal_:0 2024-06-26T05:54:22.9224339Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_uniform_:0, line 472 <- wrt source file 2024-06-26T05:54:22.9227210Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_uniform_:0 2024-06-26T05:54:22.9230162Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_normal_:0, line 529 <- wrt source file 2024-06-26T05:54:22.9232783Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_normal_:0 2024-06-26T05:54:22.9235486Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::orthogonal_:0, line 560 <- wrt source file 2024-06-26T05:54:22.9238074Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::orthogonal_:0 2024-06-26T05:54:22.9240837Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::sparse_:0, line 613 <- wrt source file 2024-06-26T05:54:22.9243330Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::sparse_:0 2024-06-26T05:54:22.9246132Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0, line 81 <- wrt source file 2024-06-26T05:54:22.9248990Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0 2024-06-26T05:54:22.9252044Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/bias.py::CausalBias:0, line 92 <- wrt source file 2024-06-26T05:54:22.9254997Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/bias.py::CausalBias:0 2024-06-26T05:54:22.9258035Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Threshold:0, line 70 <- wrt source file 2024-06-26T05:54:22.9279009Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Threshold:0 2024-06-26T05:54:22.9286734Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU:0, line 112 <- wrt source file 2024-06-26T05:54:22.9294279Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU:0 2024-06-26T05:54:22.9297671Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::RReLU:0, line 171 <- wrt source file 2024-06-26T05:54:22.9301076Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::RReLU:0 2024-06-26T05:54:22.9304712Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardtanh:0, line 227 <- wrt source file 2024-06-26T05:54:22.9308069Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardtanh:0 2024-06-26T05:54:22.9311878Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU6:0, line 292 <- wrt source file 2024-06-26T05:54:22.9315051Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU6:0 2024-06-26T05:54:22.9318439Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Sigmoid:0, line 320 <- wrt source file 2024-06-26T05:54:22.9321849Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Sigmoid:0 2024-06-26T05:54:22.9325114Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0, line 352 <- wrt source file 2024-06-26T05:54:22.9328098Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0 2024-06-26T05:54:22.9331106Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanh:0, line 385 <- wrt source file 2024-06-26T05:54:22.9334661Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanh:0 2024-06-26T05:54:22.9337941Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SiLU:0, line 418 <- wrt source file 2024-06-26T05:54:22.9341372Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SiLU:0 2024-06-26T05:54:22.9344631Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Mish:0, line 457 <- wrt source file 2024-06-26T05:54:22.9347590Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Mish:0 2024-06-26T05:54:22.9350340Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardswish:0, line 502 <- wrt source file 2024-06-26T05:54:22.9353430Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardswish:0 2024-06-26T05:54:22.9356290Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ELU:0, line 545 <- wrt source file 2024-06-26T05:54:22.9359200Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ELU:0 2024-06-26T05:54:22.9362735Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::CELU:0, line 587 <- wrt source file 2024-06-26T05:54:22.9365996Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::CELU:0 2024-06-26T05:54:22.9369483Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SELU:0, line 640 <- wrt source file 2024-06-26T05:54:22.9372776Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SELU:0 2024-06-26T05:54:22.9376394Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GLU:0, line 678 <- wrt source file 2024-06-26T05:54:22.9379831Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GLU:0 2024-06-26T05:54:22.9383108Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GELU:0, line 720 <- wrt source file 2024-06-26T05:54:22.9386722Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GELU:0 2024-06-26T05:54:22.9389924Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardshrink:0, line 763 <- wrt source file 2024-06-26T05:54:22.9393391Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardshrink:0 2024-06-26T05:54:22.9397038Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LeakyReLU:0, line 812 <- wrt source file 2024-06-26T05:54:22.9400364Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LeakyReLU:0 2024-06-26T05:54:22.9403680Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSigmoid:0, line 848 <- wrt source file 2024-06-26T05:54:22.9406934Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSigmoid:0 2024-06-26T05:54:22.9410153Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softplus:0, line 881 <- wrt source file 2024-06-26T05:54:22.9413331Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softplus:0 2024-06-26T05:54:22.9416656Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softshrink:0, line 924 <- wrt source file 2024-06-26T05:54:22.9419885Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softshrink:0 2024-06-26T05:54:22.9423263Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0, line 1026 <- wrt source file 2024-06-26T05:54:22.9426717Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0 2024-06-26T05:54:22.9430053Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::PReLU:0, line 1489 <- wrt source file 2024-06-26T05:54:22.9433137Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::PReLU:0 2024-06-26T05:54:22.9436280Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softsign:0, line 1531 <- wrt source file 2024-06-26T05:54:22.9439454Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softsign:0 2024-06-26T05:54:22.9442682Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanhshrink:0, line 1554 <- wrt source file 2024-06-26T05:54:22.9445901Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanhshrink:0 2024-06-26T05:54:22.9449105Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmin:0, line 1589 <- wrt source file 2024-06-26T05:54:22.9452251Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmin:0 2024-06-26T05:54:22.9455511Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax:0, line 1647 <- wrt source file 2024-06-26T05:54:22.9458803Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax:0 2024-06-26T05:54:22.9462000Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax2d:0, line 1688 <- wrt source file 2024-06-26T05:54:22.9465187Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax2d:0 2024-06-26T05:54:22.9468517Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSoftmax:0, line 1724 <- wrt source file 2024-06-26T05:54:22.9471845Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSoftmax:0 2024-06-26T05:54:22.9475102Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0, line 330 <- wrt source file 2024-06-26T05:54:22.9478331Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0 2024-06-26T05:54:22.9481596Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0, line 441 <- wrt source file 2024-06-26T05:54:22.9674859Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0 2024-06-26T05:54:22.9678285Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0, line 552 <- wrt source file 2024-06-26T05:54:23.2203834Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0 2024-06-26T05:54:23.2310804Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0, line 21 <- wrt source file 2024-06-26T05:54:23.2329496Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0 2024-06-26T05:54:23.2332685Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential:0, line 85 <- wrt source file 2024-06-26T05:54:23.2335870Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential:0 2024-06-26T05:54:23.2338907Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleList:0, line 291 <- wrt source file 2024-06-26T05:54:23.2341928Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleList:0 2024-06-26T05:54:23.2344977Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleDict:0, line 465 <- wrt source file 2024-06-26T05:54:23.2347940Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleDict:0 2024-06-26T05:54:23.2351007Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterList:0, line 597 <- wrt source file 2024-06-26T05:54:23.2354067Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterList:0 2024-06-26T05:54:23.2357175Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterDict:0, line 749 <- wrt source file 2024-06-26T05:54:23.2360218Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterDict:0 2024-06-26T05:54:23.2363376Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0, line 38 <- wrt source file 2024-06-26T05:54:23.2366734Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0 2024-06-26T05:54:23.2369870Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0, line 77 <- wrt source file 2024-06-26T05:54:23.2372972Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0 2024-06-26T05:54:23.2376158Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout:0, line 60 <- wrt source file 2024-06-26T05:54:23.2379136Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout:0 2024-06-26T05:54:23.2382153Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout1d:0, line 105 <- wrt source file 2024-06-26T05:54:23.2385027Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout1d:0 2024-06-26T05:54:23.2388350Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout2d:0, line 157 <- wrt source file 2024-06-26T05:54:23.2391717Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout2d:0 2024-06-26T05:54:23.2394948Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout3d:0, line 202 <- wrt source file 2024-06-26T05:54:23.2465811Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout3d:0 2024-06-26T05:54:23.2469323Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0, line 245 <- wrt source file 2024-06-26T05:54:23.2472313Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0 2024-06-26T05:54:23.2475437Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0, line 294 <- wrt source file 2024-06-26T05:54:23.2547146Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0 2024-06-26T05:54:23.2550691Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py::Flatten:0, line 30 <- wrt source file 2024-06-26T05:54:23.2554658Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py::Flatten:0 2024-06-26T05:54:23.2557604Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Fold:0, line 111 <- wrt source file 2024-06-26T05:54:23.2561057Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Fold:0 2024-06-26T05:54:23.2564238Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Unfold:0, line 261 <- wrt source file 2024-06-26T05:54:23.2578317Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Unfold:0 2024-06-26T05:54:23.2581410Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0, line 187 <- wrt source file 2024-06-26T05:54:23.2591910Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0 2024-06-26T05:54:23.2595269Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0, line 303 <- wrt source file 2024-06-26T05:54:23.2788182Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0 2024-06-26T05:54:23.2791438Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0, line 419 <- wrt source file 2024-06-26T05:54:23.5344879Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0 2024-06-26T05:54:23.5452167Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0, line 87 <- wrt source file 2024-06-26T05:54:23.5455348Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0 2024-06-26T05:54:23.5458713Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Identity:0, line 34 <- wrt source file 2024-06-26T05:54:23.5461606Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Identity:0 2024-06-26T05:54:23.5464484Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Linear:0, line 80 <- wrt source file 2024-06-26T05:54:23.5470645Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Linear:0 2024-06-26T05:54:23.5473540Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Bilinear:0, line 179 <- wrt source file 2024-06-26T05:54:23.5491482Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Bilinear:0 2024-06-26T05:54:23.5494516Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::L1Loss:0, line 115 <- wrt source file 2024-06-26T05:54:23.5499816Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::L1Loss:0 2024-06-26T05:54:23.5502672Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::NLLLoss:0, line 211 <- wrt source file 2024-06-26T05:54:23.5526913Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::NLLLoss:0 2024-06-26T05:54:23.5530135Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0, line 321 <- wrt source file 2024-06-26T05:54:23.5535159Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0 2024-06-26T05:54:23.5538219Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0, line 406 <- wrt source file 2024-06-26T05:54:23.5549370Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0 2024-06-26T05:54:23.5552402Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::KLDivLoss:0, line 517 <- wrt source file 2024-06-26T05:54:23.5558267Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::KLDivLoss:0 2024-06-26T05:54:23.5561713Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MSELoss:0, line 595 <- wrt source file 2024-06-26T05:54:23.5565005Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MSELoss:0 2024-06-26T05:54:23.5568068Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCELoss:0, line 677 <- wrt source file 2024-06-26T05:54:23.5573117Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCELoss:0 2024-06-26T05:54:23.5576540Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0, line 748 <- wrt source file 2024-06-26T05:54:23.5585111Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0 2024-06-26T05:54:23.5588228Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0, line 941 <- wrt source file 2024-06-26T05:54:23.5592152Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0 2024-06-26T05:54:23.5595249Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0, line 1261 <- wrt source file 2024-06-26T05:54:23.5601506Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0 2024-06-26T05:54:23.5605192Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0, line 1401 <- wrt source file 2024-06-26T05:54:23.5610920Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0 2024-06-26T05:54:23.5614150Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0, line 1466 <- wrt source file 2024-06-26T05:54:23.5618724Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0 2024-06-26T05:54:23.5621760Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0, line 1545 <- wrt source file 2024-06-26T05:54:23.5627066Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0 2024-06-26T05:54:23.5630133Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0, line 1645 <- wrt source file 2024-06-26T05:54:23.5639342Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0 2024-06-26T05:54:23.5643015Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CTCLoss:0, line 1886 <- wrt source file 2024-06-26T05:54:23.5670810Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CTCLoss:0 2024-06-26T05:54:23.5673917Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.register_buffer:0, line 544 <- wrt source file 2024-06-26T05:54:23.5676991Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.register_buffer:0 2024-06-26T05:54:23.5679915Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.apply:0, line 945 <- wrt source file 2024-06-26T05:54:23.5688775Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.apply:0 2024-06-26T05:54:23.5691695Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.to:0, line 1180 <- wrt source file 2024-06-26T05:54:23.5697545Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.to:0 2024-06-26T05:54:23.5700643Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.state_dict:0, line 2032 <- wrt source file 2024-06-26T05:54:23.5703831Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.state_dict:0 2024-06-26T05:54:23.5707023Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.parameters:0, line 2461 <- wrt source file 2024-06-26T05:54:23.5710268Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.parameters:0 2024-06-26T05:54:23.5713372Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_parameters:0, line 2489 <- wrt source file 2024-06-26T05:54:23.5716884Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_parameters:0 2024-06-26T05:54:23.5720186Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.buffers:0, line 2516 <- wrt source file 2024-06-26T05:54:23.5723673Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.buffers:0 2024-06-26T05:54:23.5726677Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_buffers:0, line 2543 <- wrt source file 2024-06-26T05:54:23.5730456Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_buffers:0 2024-06-26T05:54:23.5734690Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_children:0, line 2574 <- wrt source file 2024-06-26T05:54:23.5738089Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_children:0 2024-06-26T05:54:23.5741753Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.modules:0, line 2598 <- wrt source file 2024-06-26T05:54:23.5744466Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.modules:0 2024-06-26T05:54:23.5747419Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_modules:0, line 2636 <- wrt source file 2024-06-26T05:54:23.5751349Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_modules:0 2024-06-26T05:54:23.5755474Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0, line 38 <- wrt source file 2024-06-26T05:54:23.5770896Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0 2024-06-26T05:54:23.5775306Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LayerNorm:0, line 151 <- wrt source file 2024-06-26T05:54:23.5784166Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LayerNorm:0 2024-06-26T05:54:23.5789204Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::GroupNorm:0, line 262 <- wrt source file 2024-06-26T05:54:23.5794390Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::GroupNorm:0 2024-06-26T05:54:23.5798541Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::RMSNorm:0, line 355 <- wrt source file 2024-06-26T05:54:23.5802389Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::RMSNorm:0 2024-06-26T05:54:23.5806163Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad1d:0, line 69 <- wrt source file 2024-06-26T05:54:23.5810013Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad1d:0 2024-06-26T05:54:23.5813769Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad2d:0, line 120 <- wrt source file 2024-06-26T05:54:23.5832064Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad2d:0 2024-06-26T05:54:23.5835458Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad3d:0, line 184 <- wrt source file 2024-06-26T05:54:24.2169105Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad3d:0 2024-06-26T05:54:24.2374522Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0, line 238 <- wrt source file 2024-06-26T05:54:24.2383347Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0 2024-06-26T05:54:24.2386337Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0, line 291 <- wrt source file 2024-06-26T05:54:24.2390596Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0 2024-06-26T05:54:24.2393615Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0, line 347 <- wrt source file 2024-06-26T05:54:24.2418293Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0 2024-06-26T05:54:24.2421251Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0, line 391 <- wrt source file 2024-06-26T05:54:24.2426128Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0 2024-06-26T05:54:24.2429279Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0, line 435 <- wrt source file 2024-06-26T05:54:24.2432683Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0 2024-06-26T05:54:24.2435789Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0, line 492 <- wrt source file 2024-06-26T05:54:24.2438843Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0 2024-06-26T05:54:24.2442042Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0, line 550 <- wrt source file 2024-06-26T05:54:24.2445146Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0 2024-06-26T05:54:24.2448283Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0, line 593 <- wrt source file 2024-06-26T05:54:24.2451342Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0 2024-06-26T05:54:24.2454592Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0, line 650 <- wrt source file 2024-06-26T05:54:24.7600470Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0 2024-06-26T05:54:24.7804870Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0, line 684 <- wrt source file 2024-06-26T05:54:24.7814702Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0 2024-06-26T05:54:24.7818616Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0, line 739 <- wrt source file 2024-06-26T05:54:24.7822139Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0 2024-06-26T05:54:24.7825681Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0, line 798 <- wrt source file 2024-06-26T05:54:24.7844225Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0 2024-06-26T05:54:24.7847305Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0, line 40 <- wrt source file 2024-06-26T05:54:24.7850818Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0 2024-06-26T05:54:24.7854184Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0, line 93 <- wrt source file 2024-06-26T05:54:24.7857546Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0 2024-06-26T05:54:24.7860633Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0, line 118 <- wrt source file 2024-06-26T05:54:24.7863572Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0 2024-06-26T05:54:24.7866524Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0, line 195 <- wrt source file 2024-06-26T05:54:24.7915542Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0 2024-06-26T05:54:24.7918545Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0, line 278 <- wrt source file 2024-06-26T05:54:25.0182392Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0 2024-06-26T05:54:25.0225045Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0, line 352 <- wrt source file 2024-06-26T05:54:25.0236744Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0 2024-06-26T05:54:25.0239646Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0, line 534 <- wrt source file 2024-06-26T05:54:25.0979878Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0 2024-06-26T05:54:25.1016633Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0, line 622 <- wrt source file 2024-06-26T05:54:25.1028298Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0 2024-06-26T05:54:25.1031171Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0, line 714 <- wrt source file 2024-06-26T05:54:25.1071303Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0 2024-06-26T05:54:25.1074214Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0, line 827 <- wrt source file 2024-06-26T05:54:25.2795331Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0 2024-06-26T05:54:25.2839461Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0, line 917 <- wrt source file 2024-06-26T05:54:25.2889688Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0 2024-06-26T05:54:25.2892780Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0, line 1003 <- wrt source file 2024-06-26T05:54:25.3755300Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0 2024-06-26T05:54:25.3758682Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool1d:0, line 1117 <- wrt source file 2024-06-26T05:54:25.3764980Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool1d:0 2024-06-26T05:54:25.3768450Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool2d:0, line 1168 <- wrt source file 2024-06-26T05:54:25.3817387Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool2d:0 2024-06-26T05:54:25.3821067Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool3d:0, line 1227 <- wrt source file 2024-06-26T05:54:25.6038827Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool3d:0 2024-06-26T05:54:25.6124139Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0, line 1282 <- wrt source file 2024-06-26T05:54:25.6131329Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0 2024-06-26T05:54:25.6134701Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0, line 1316 <- wrt source file 2024-06-26T05:54:25.6141666Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0 2024-06-26T05:54:25.6144838Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0, line 1359 <- wrt source file 2024-06-26T05:54:25.6174154Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0 2024-06-26T05:54:25.6177356Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0, line 1406 <- wrt source file 2024-06-26T05:54:25.6180502Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0 2024-06-26T05:54:25.6183742Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0, line 1437 <- wrt source file 2024-06-26T05:54:25.6187603Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0 2024-06-26T05:54:25.6190747Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0, line 1476 <- wrt source file 2024-06-26T05:54:25.6211939Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0 2024-06-26T05:54:25.6215005Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNN:0, line 588 <- wrt source file 2024-06-26T05:54:25.6223742Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNN:0 2024-06-26T05:54:25.6226492Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTM:0, line 945 <- wrt source file 2024-06-26T05:54:25.6533277Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTM:0 2024-06-26T05:54:25.6536791Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRU:0, line 1283 <- wrt source file 2024-06-26T05:54:25.6549562Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRU:0 2024-06-26T05:54:25.6552430Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNNCell:0, line 1534 <- wrt source file 2024-06-26T05:54:25.6560992Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNNCell:0 2024-06-26T05:54:25.6563981Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTMCell:0, line 1656 <- wrt source file 2024-06-26T05:54:25.6572441Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTMCell:0 2024-06-26T05:54:25.6576013Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRUCell:0, line 1770 <- wrt source file 2024-06-26T05:54:25.6586667Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRUCell:0 2024-06-26T05:54:25.6589676Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding:0, line 69 <- wrt source file 2024-06-26T05:54:25.6599811Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding:0 2024-06-26T05:54:25.6603130Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0, line 241 <- wrt source file 2024-06-26T05:54:25.6606394Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0 2024-06-26T05:54:25.6609960Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0, line 519 <- wrt source file 2024-06-26T05:54:25.6613320Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0 2024-06-26T05:54:25.6616849Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer:0, line 90 <- wrt source file 2024-06-26T05:54:26.2660527Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer:0 2024-06-26T05:54:26.2675490Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer.forward:0, line 258 <- wrt source file 2024-06-26T05:54:26.2678851Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer.forward:0 2024-06-26T05:54:26.2682274Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0, line 323 <- wrt source file 2024-06-26T05:54:26.3277505Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0 2024-06-26T05:54:26.3308991Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0, line 536 <- wrt source file 2024-06-26T05:54:26.4587441Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0 2024-06-26T05:54:26.4595153Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0, line 657 <- wrt source file 2024-06-26T05:54:26.4810819Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0 2024-06-26T05:54:26.4814453Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0, line 961 <- wrt source file 2024-06-26T05:54:26.5169177Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0 2024-06-26T05:54:26.5172473Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::Upsample:0, line 77 <- wrt source file 2024-06-26T05:54:26.5194249Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::Upsample:0 2024-06-26T05:54:26.5197241Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0, line 223 <- wrt source file 2024-06-26T05:54:26.5207470Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0 2024-06-26T05:54:26.5210629Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0, line 273 <- wrt source file 2024-06-26T05:54:26.5217363Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0 2024-06-26T05:54:26.5220637Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0, line 127 <- wrt source file 2024-06-26T05:54:26.5223767Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0 2024-06-26T05:54:26.5226618Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0, line 619 <- wrt source file 2024-06-26T05:54:26.5229890Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0 2024-06-26T05:54:26.5233204Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0, line 1413 <- wrt source file 2024-06-26T05:54:26.5236699Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0 2024-06-26T05:54:26.5240659Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0, line 1976 <- wrt source file 2024-06-26T05:54:26.5244325Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0 2024-06-26T05:54:26.5248092Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1, line 1986 <- wrt source file 2024-06-26T05:54:26.5252028Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1 2024-06-26T05:54:26.5256368Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0, line 2021 <- wrt source file 2024-06-26T05:54:26.5260423Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0 2024-06-26T05:54:26.5263762Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_per_sample_grad.py::call_for_per_sample_grads:0, line 35 <- wrt source file 2024-06-26T05:54:26.5267116Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_per_sample_grad.py::call_for_per_sample_grads:0 2024-06-26T05:54:26.5270097Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/init.py::skip_init:0, line 33 <- wrt source file 2024-06-26T05:54:26.5272547Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/init.py::skip_init:0 2024-06-26T05:54:26.5275723Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0, line 265 <- wrt source file 2024-06-26T05:54:26.5279059Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0 2024-06-26T05:54:26.5282210Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::weight_norm:0, line 360 <- wrt source file 2024-06-26T05:54:26.5285387Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::weight_norm:0 2024-06-26T05:54:26.5288678Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0, line 591 <- wrt source file 2024-06-26T05:54:26.5291569Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0 2024-06-26T05:54:26.5294914Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrize.py::register_parametrization:0, line 505 <- wrt source file 2024-06-26T05:54:26.5298301Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrize.py::register_parametrization:0 2024-06-26T05:54:26.5301158Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::identity:0, line 846 <- wrt source file 2024-06-26T05:54:26.5303879Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::identity:0 2024-06-26T05:54:26.5306910Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::random_unstructured:0, line 882 <- wrt source file 2024-06-26T05:54:26.5309704Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::random_unstructured:0 2024-06-26T05:54:26.5312763Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::l1_unstructured:0, line 925 <- wrt source file 2024-06-26T05:54:26.5315753Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::l1_unstructured:0 2024-06-26T05:54:26.5318312Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::remove:0, line 1192 <- wrt source file 2024-06-26T05:54:26.5321302Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::remove:0 2024-06-26T05:54:26.5324441Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::is_pruned:0, line 1220 <- wrt source file 2024-06-26T05:54:26.5327608Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::is_pruned:0 2024-06-26T05:54:26.5331096Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pad_packed_sequence:0, line 309 <- wrt source file 2024-06-26T05:54:26.5334466Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pad_packed_sequence:0 2024-06-26T05:54:26.5337279Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pad_sequence:0, line 390 <- wrt source file 2024-06-26T05:54:26.5340342Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pad_sequence:0 2024-06-26T05:54:26.5343154Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0, line 444 <- wrt source file 2024-06-26T05:54:26.5345944Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0 2024-06-26T05:54:26.5348858Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pack_sequence:0, line 501 <- wrt source file 2024-06-26T05:54:26.5351673Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pack_sequence:0 2024-06-26T05:54:26.5354687Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0, line 531 <- wrt source file 2024-06-26T05:54:26.5361271Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0 2024-06-26T05:54:26.5364066Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0, line 313 <- wrt source file 2024-06-26T05:54:26.5370244Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0 2024-06-26T05:54:26.5373716Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0, line 345 <- wrt source file 2024-06-26T05:54:26.5379456Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0 2024-06-26T05:54:26.5383117Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/stateless.py::functional_call:0, line 187 <- wrt source file 2024-06-26T05:54:26.5386151Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/stateless.py::functional_call:0 2024-06-26T05:54:26.5389049Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0, line 133 <- wrt source file 2024-06-26T05:54:26.5392264Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0 2024-06-26T05:54:26.5395229Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0, line 155 <- wrt source file 2024-06-26T05:54:26.5398914Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0 2024-06-26T05:54:26.5402245Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0, line 317 <- wrt source file 2024-06-26T05:54:26.5405427Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0 2024-06-26T05:54:26.5408999Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_expanded_weights/expanded_weights_utils.py::sum_over_all_but_batch_and_last_n:0, line 178 <- wrt source file 2024-06-26T05:54:26.5429386Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_expanded_weights/expanded_weights_utils.py::sum_over_all_but_batch_and_last_n:0 2024-06-26T05:54:26.5432926Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0, line 306 <- wrt source file 2024-06-26T05:54:26.5435832Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0 2024-06-26T05:54:26.5438827Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0, line 408 <- wrt source file 2024-06-26T05:54:26.5442111Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0 2024-06-26T05:54:26.5445221Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::StepLR:0, line 508 <- wrt source file 2024-06-26T05:54:26.5448163Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::StepLR:0 2024-06-26T05:54:26.5451092Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0, line 568 <- wrt source file 2024-06-26T05:54:26.5454501Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0 2024-06-26T05:54:26.5457482Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0, line 633 <- wrt source file 2024-06-26T05:54:26.5460282Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0 2024-06-26T05:54:26.5463173Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LinearLR:0, line 711 <- wrt source file 2024-06-26T05:54:26.5465926Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LinearLR:0 2024-06-26T05:54:26.5468567Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::SequentialLR:0, line 840 <- wrt source file 2024-06-26T05:54:26.5471350Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::SequentialLR:0 2024-06-26T05:54:26.5473902Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0, line 977 <- wrt source file 2024-06-26T05:54:26.5476379Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0 2024-06-26T05:54:26.5478986Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0, line 1133 <- wrt source file 2024-06-26T05:54:26.5481696Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0 2024-06-26T05:54:26.5484473Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ReduceLROnPlateau:0, line 1276 <- wrt source file 2024-06-26T05:54:26.5487295Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ReduceLROnPlateau:0 2024-06-26T05:54:26.5490212Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CyclicLR:0, line 1508 <- wrt source file 2024-06-26T05:54:26.5493098Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CyclicLR:0 2024-06-26T05:54:26.5496391Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0, line 1778 <- wrt source file 2024-06-26T05:54:26.5499906Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0 2024-06-26T05:54:26.5503397Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1, line 1794 <- wrt source file 2024-06-26T05:54:26.5506710Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1 2024-06-26T05:54:26.5509935Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::OneCycleLR:0, line 1939 <- wrt source file 2024-06-26T05:54:26.5512961Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::OneCycleLR:0 2024-06-26T05:54:26.5515729Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py::update_bn:0, line 316 <- wrt source file 2024-06-26T05:54:26.5518622Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py::update_bn:0 2024-06-26T05:54:26.5521601Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/package/glob_group.py::GlobGroup:0, line 20 <- wrt source file 2024-06-26T05:54:26.5524710Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/package/glob_group.py::GlobGroup:0 2024-06-26T05:54:26.5527677Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/profiler/profiler.py::profile:0, line 514 <- wrt source file 2024-06-26T05:54:26.5530562Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/profiler/profiler.py::profile:0 2024-06-26T05:54:26.5533662Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/semi_structured.py::to_sparse_semi_structured:0, line 334 <- wrt source file 2024-06-26T05:54:26.5536958Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/semi_structured.py::to_sparse_semi_structured:0 2024-06-26T05:54:26.5540188Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_creation.py::make_tensor:0, line 112 <- wrt source file 2024-06-26T05:54:26.5543120Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_creation.py::make_tensor:0 2024-06-26T05:54:26.5546248Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::parametrize:0, line 508 <- wrt source file 2024-06-26T05:54:26.5549497Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::parametrize:0 2024-06-26T05:54:26.5552749Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::decorateIf:0, line 663 <- wrt source file 2024-06-26T05:54:26.5555975Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::decorateIf:0 2024-06-26T05:54:26.5559395Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_symmetric_psd_matrix:0, line 4153 <- wrt source file 2024-06-26T05:54:26.5562972Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_symmetric_psd_matrix:0 2024-06-26T05:54:26.5566589Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_psd_matrix:0, line 4167 <- wrt source file 2024-06-26T05:54:26.5570154Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_psd_matrix:0 2024-06-26T05:54:26.5573855Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_pd_matrix:0, line 4197 <- wrt source file 2024-06-26T05:54:26.5577464Z * SKIPPED: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/optests/autograd_registration.py::autograd_registration_check:0, line 29 <- wrt source file 2024-06-26T05:54:26.5599651Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/optests/autograd_registration.py::autograd_registration_check:0 2024-06-26T05:54:26.5603383Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_flatten:0, line 261 <- wrt source file 2024-06-26T05:54:26.5606274Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_flatten:0 2024-06-26T05:54:26.5609262Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0, line 303 <- wrt source file 2024-06-26T05:54:26.5612007Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0 2024-06-26T05:54:26.5615411Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0, line 333 <- wrt source file 2024-06-26T05:54:26.5618777Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0 2024-06-26T05:54:26.5621810Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0, line 368 <- wrt source file 2024-06-26T05:54:26.5624744Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0 2024-06-26T05:54:26.5627773Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0, line 403 <- wrt source file 2024-06-26T05:54:26.5630728Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0 2024-06-26T05:54:26.5633845Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_map:0, line 440 <- wrt source file 2024-06-26T05:54:26.5636746Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_map:0 2024-06-26T05:54:26.5639718Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0, line 816 <- wrt source file 2024-06-26T05:54:26.5642894Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0 2024-06-26T05:54:26.5645651Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::tree_map:0, line 917 <- wrt source file 2024-06-26T05:54:26.5648365Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::tree_map:0 2024-06-26T05:54:26.5651505Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::rename_privateuse1_backend:0, line 69 <- wrt source file 2024-06-26T05:54:26.5655201Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::rename_privateuse1_backend:0 2024-06-26T05:54:26.5658856Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::generate_methods_for_privateuse1_backend:0, line 322 <- wrt source file 2024-06-26T05:54:26.5662854Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::generate_methods_for_privateuse1_backend:0 2024-06-26T05:54:26.5666515Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::_get_custom_mod_func:0, line 354 <- wrt source file 2024-06-26T05:54:26.5670014Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::_get_custom_mod_func:0 2024-06-26T05:54:26.5673389Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::checkpoint_sequential:0, line 534 <- wrt source file 2024-06-26T05:54:26.5676722Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::checkpoint_sequential:0 2024-06-26T05:54:26.5680053Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::set_checkpoint_early_stop:0, line 736 <- wrt source file 2024-06-26T05:54:26.5683384Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::set_checkpoint_early_stop:0 2024-06-26T05:54:26.5686446Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/dlpack.py::from_dlpack:0, line 72 <- wrt source file 2024-06-26T05:54:26.5689394Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/dlpack.py::from_dlpack:0 2024-06-26T05:54:26.5692431Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::IterableDataset:0, line 98 <- wrt source file 2024-06-26T05:54:26.5695682Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::IterableDataset:0 2024-06-26T05:54:26.5698824Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::StackDataset:0, line 223 <- wrt source file 2024-06-26T05:54:26.5701851Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::StackDataset:0 2024-06-26T05:54:26.5704962Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::random_split:0, line 445 <- wrt source file 2024-06-26T05:54:26.5707994Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::random_split:0 2024-06-26T05:54:26.5710964Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::Sampler:0, line 30 <- wrt source file 2024-06-26T05:54:26.5713836Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::Sampler:0 2024-06-26T05:54:26.5716878Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::WeightedRandomSampler:0, line 208 <- wrt source file 2024-06-26T05:54:26.5719762Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::WeightedRandomSampler:0 2024-06-26T05:54:26.5722931Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::BatchSampler:0, line 255 <- wrt source file 2024-06-26T05:54:26.5725582Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::BatchSampler:0 2024-06-26T05:54:26.5728228Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::default_convert:0, line 38 <- wrt source file 2024-06-26T05:54:26.5730940Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::default_convert:0 2024-06-26T05:54:26.5733877Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::collate:0, line 124 <- wrt source file 2024-06-26T05:54:26.5736451Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::collate:0 2024-06-26T05:54:26.5739115Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::default_collate:0, line 283 <- wrt source file 2024-06-26T05:54:26.5741965Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::default_collate:0 2024-06-26T05:54:26.5744984Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/datapipe.py::IterDataPipe:0, line 77 <- wrt source file 2024-06-26T05:54:26.5748517Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/datapipe.py::IterDataPipe:0 2024-06-26T05:54:26.5751822Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/datapipe.py::MapDataPipe:0, line 239 <- wrt source file 2024-06-26T05:54:26.5755230Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/datapipe.py::MapDataPipe:0 2024-06-26T05:54:26.5758828Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/callable.py::MapperIterDataPipe:0, line 48 <- wrt source file 2024-06-26T05:54:26.5762712Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/callable.py::MapperIterDataPipe:0 2024-06-26T05:54:26.5766526Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/callable.py::CollatorIterDataPipe:0, line 191 <- wrt source file 2024-06-26T05:54:26.5770094Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/callable.py::CollatorIterDataPipe:0 2024-06-26T05:54:26.5774109Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combinatorics.py::ShufflerIterDataPipe:0, line 83 <- wrt source file 2024-06-26T05:54:26.5777988Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combinatorics.py::ShufflerIterDataPipe:0 2024-06-26T05:54:26.5781964Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0, line 36 <- wrt source file 2024-06-26T05:54:26.5785841Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0 2024-06-26T05:54:26.5789672Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ForkerIterDataPipe:0, line 86 <- wrt source file 2024-06-26T05:54:26.5793508Z * SKIPPED: 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550 <- wrt source file 2024-06-26T05:54:26.5818826Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::MultiplexerIterDataPipe:0 2024-06-26T05:54:26.5823751Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ZipperIterDataPipe:0, line 616 <- wrt source file 2024-06-26T05:54:26.5828610Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ZipperIterDataPipe:0 2024-06-26T05:54:26.5833783Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/filelister.py::FileListerIterDataPipe:0, line 31 <- wrt source file 2024-06-26T05:54:26.5838831Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/filelister.py::FileListerIterDataPipe:0 2024-06-26T05:54:26.5843961Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/fileopener.py::FileOpenerIterDataPipe:0, line 33 <- wrt source file 2024-06-26T05:54:26.5848939Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/fileopener.py::FileOpenerIterDataPipe:0 2024-06-26T05:54:26.5854029Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::BatcherIterDataPipe:0, line 46 <- wrt source file 2024-06-26T05:54:26.5858846Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::BatcherIterDataPipe:0 2024-06-26T05:54:26.5863658Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::UnBatcherIterDataPipe:0, line 105 <- wrt source file 2024-06-26T05:54:26.5868534Z * SKIPPED: 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<- wrt source file 2024-06-26T05:54:26.5898342Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/streamreader.py::StreamReaderIterDataPipe:0 2024-06-26T05:54:26.5903455Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0, line 24 <- wrt source file 2024-06-26T05:54:26.5908474Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0 2024-06-26T05:54:26.5913356Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/callable.py::MapperMapDataPipe:0, line 32 <- wrt source file 2024-06-26T05:54:26.5918206Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/callable.py::MapperMapDataPipe:0 2024-06-26T05:54:26.5923278Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combinatorics.py::ShufflerIterDataPipe:0, line 32 <- wrt source file 2024-06-26T05:54:26.5928332Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combinatorics.py::ShufflerIterDataPipe:0 2024-06-26T05:54:26.5933420Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0, line 26 <- wrt source file 2024-06-26T05:54:26.5938534Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0 2024-06-26T05:54:26.5943434Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0, line 70 <- wrt source file 2024-06-26T05:54:26.5948037Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0 2024-06-26T05:54:26.5950994Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0, line 25 <- wrt source file 2024-06-26T05:54:26.5953913Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0 2024-06-26T05:54:26.5956938Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0, line 24 <- wrt source file 2024-06-26T05:54:26.5959973Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0 2024-06-26T05:54:26.5963067Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/utils/common.py::validate_input_col:0, line 37 <- wrt source file 2024-06-26T05:54:26.5965998Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/utils/common.py::validate_input_col:0 2024-06-26T05:54:26.5968914Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/utils/decoder.py::basichandlers:0, line 47 <- wrt source file 2024-06-26T05:54:26.5971756Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/utils/decoder.py::basichandlers:0 2024-06-26T05:54:26.5974714Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0, line 433 <- wrt source file 2024-06-26T05:54:26.9404434Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0 2024-06-26T05:54:26.9407871Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0, line 529 <- wrt source file 2024-06-26T05:54:26.9411173Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0 2024-06-26T05:54:26.9414018Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0, line 216 <- wrt source file 2024-06-26T05:54:26.9416978Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0 2024-06-26T05:54:26.9419759Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0, line 314 <- wrt source file 2024-06-26T05:54:26.9422229Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0 2024-06-26T05:54:26.9424590Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0, line 362 <- wrt source file 2024-06-26T05:54:26.9427479Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0 2024-06-26T05:54:26.9430967Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0, line 394 <- wrt source file 2024-06-26T05:54:26.9434494Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0 2024-06-26T05:54:26.9438124Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_tensor:0, line 441 <- wrt source file 2024-06-26T05:54:26.9441595Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_tensor:0 2024-06-26T05:54:26.9444948Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram:0, line 480 <- wrt source file 2024-06-26T05:54:26.9448487Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram:0 2024-06-26T05:54:26.9452065Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram_raw:0, line 533 <- wrt source file 2024-06-26T05:54:26.9456539Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram_raw:0 2024-06-26T05:54:26.9460177Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0, line 599 <- wrt source file 2024-06-26T05:54:26.9463652Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0 2024-06-26T05:54:26.9467208Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_images:0, line 648 <- wrt source file 2024-06-26T05:54:26.9470625Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_images:0 2024-06-26T05:54:26.9474134Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0, line 811 <- wrt source file 2024-06-26T05:54:26.9477620Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0 2024-06-26T05:54:26.9481274Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0, line 878 <- wrt source file 2024-06-26T05:54:26.9484879Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0 2024-06-26T05:54:26.9488419Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0, line 989 <- wrt source file 2024-06-26T05:54:26.9491898Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0 2024-06-26T05:54:26.9495763Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0, line 1063 <- wrt source file 2024-06-26T05:54:26.9499938Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0 2024-06-26T05:54:26.9503855Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_marginchart:0, line 1084 <- wrt source file 2024-06-26T05:54:26.9507832Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_marginchart:0 2024-06-26T05:54:26.9511678Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars:0, line 1108 <- wrt source file 2024-06-26T05:54:26.9515417Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars:0 2024-06-26T05:54:26.9518980Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_mesh:0, line 1154 <- wrt source file 2024-06-26T05:54:26.9522505Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_mesh:0 2024-06-26T05:54:26.9524317Z ============ 2024-06-26T05:54:26.9524918Z Finished doctests 2024-06-26T05:54:26.9525406Z 331 / 696 passed 2024-06-26T05:54:26.9525940Z  2024-06-26T05:54:26.9526558Z === Found 90 parse-time warnings === 2024-06-26T05:54:26.9527484Z --- Parse Warning: 1 / 90 --- 2024-06-26T05:54:26.9530327Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=meshgrid in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=426. 2024-06-26T05:54:26.9533370Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:26.9535052Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors. 2024-06-26T05:54:26.9536134Z 2024-06-26T05:54:26.9536807Z This is helpful when you want to visualize data over some 2024-06-26T05:54:26.9537961Z range of inputs. See below for a plotting example. 2024-06-26T05:54:26.9538796Z 2024-06-26T05:54:26.9539585Z Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as 2024-06-26T05:54:26.9540883Z inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`, 2024-06-26T05:54:26.9542229Z this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots 2024-06-26T05:54:26.9543536Z G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where 2024-06-26T05:54:26.9544771Z the output :math:`G_i` is constructed by expanding :math:`T_i` 2024-06-26T05:54:26.9545803Z to the result shape. 2024-06-26T05:54:26.9546443Z 2024-06-26T05:54:26.9546859Z .. note:: 2024-06-26T05:54:26.9547385Z 0D inputs are treated equivalently to 1D inputs of a 2024-06-26T05:54:26.9547882Z single element. 2024-06-26T05:54:26.9548210Z 2024-06-26T05:54:26.9548440Z .. warning:: 2024-06-26T05:54:26.9548893Z `torch.meshgrid(*tensors)` currently has the same behavior 2024-06-26T05:54:26.9549575Z as calling `numpy.meshgrid(*arrays, indexing='ij')`. 2024-06-26T05:54:26.9550027Z 2024-06-26T05:54:26.9550368Z In the future `torch.meshgrid` will transition to 2024-06-26T05:54:26.9550924Z `indexing='xy'` as the default. 2024-06-26T05:54:26.9551306Z 2024-06-26T05:54:26.9551682Z https://github.com/pytorch/pytorch/issues/50276 tracks 2024-06-26T05:54:26.9552381Z this issue with the goal of migrating to NumPy's behavior. 2024-06-26T05:54:26.9552862Z 2024-06-26T05:54:26.9553104Z .. seealso:: 2024-06-26T05:54:26.9553399Z 2024-06-26T05:54:26.9553837Z :func:`torch.cartesian_prod` has the same effect but it 2024-06-26T05:54:26.9554412Z collects the data in a tensor of vectors. 2024-06-26T05:54:26.9554842Z 2024-06-26T05:54:26.9555065Z Args: 2024-06-26T05:54:26.9555603Z tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be 2024-06-26T05:54:26.9556401Z treated as tensors of size :math:`(1,)` automatically 2024-06-26T05:54:26.9556873Z 2024-06-26T05:54:26.9557229Z indexing: (str, optional): the indexing mode, either "xy" 2024-06-26T05:54:26.9557910Z or "ij", defaults to "ij". See warning for future changes. 2024-06-26T05:54:26.9558405Z 2024-06-26T05:54:26.9558795Z If "xy" is selected, the first dimension corresponds 2024-06-26T05:54:26.9559402Z to the cardinality of the second input and the second 2024-06-26T05:54:26.9560017Z dimension corresponds to the cardinality of the first 2024-06-26T05:54:26.9560500Z input. 2024-06-26T05:54:26.9560873Z 2024-06-26T05:54:26.9561224Z If "ij" is selected, the dimensions are in the same 2024-06-26T05:54:26.9561770Z order as the cardinality of the inputs. 2024-06-26T05:54:26.9562200Z 2024-06-26T05:54:26.9562438Z Returns: 2024-06-26T05:54:26.9562836Z seq (sequence of Tensors): If the input has :math:`N` 2024-06-26T05:54:26.9563497Z tensors of size :math:`S_0 \ldots S_{N-1}``, then the 2024-06-26T05:54:26.9564120Z output will also have :math:`N` tensors, where each tensor 2024-06-26T05:54:26.9564729Z is of shape :math:`(S_0, ..., S_{N-1})`. 2024-06-26T05:54:26.9565144Z 2024-06-26T05:54:26.9565385Z Example:: 2024-06-26T05:54:26.9565658Z 2024-06-26T05:54:26.9565943Z >>> x = torch.tensor([1, 2, 3]) 2024-06-26T05:54:26.9566389Z >>> y = torch.tensor([4, 5, 6]) 2024-06-26T05:54:26.9566766Z 2024-06-26T05:54:26.9567211Z Observe the element-wise pairings across the grid, (1, 4), 2024-06-26T05:54:26.9567809Z (1, 5), ..., (3, 6). This is the same thing as the 2024-06-26T05:54:26.9568266Z cartesian product. 2024-06-26T05:54:26.9568800Z >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij') 2024-06-26T05:54:26.9569286Z >>> grid_x 2024-06-26T05:54:26.9569596Z tensor([[1, 1, 1], 2024-06-26T05:54:26.9569949Z [2, 2, 2], 2024-06-26T05:54:26.9570303Z [3, 3, 3]]) 2024-06-26T05:54:26.9570642Z >>> grid_y 2024-06-26T05:54:26.9570963Z tensor([[4, 5, 6], 2024-06-26T05:54:26.9571312Z [4, 5, 6], 2024-06-26T05:54:26.9571647Z [4, 5, 6]]) 2024-06-26T05:54:26.9571983Z 2024-06-26T05:54:26.9572338Z This correspondence can be seen when these grids are 2024-06-26T05:54:26.9572832Z stacked properly. 2024-06-26T05:54:26.9573347Z >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))), 2024-06-26T05:54:26.9574215Z ... torch.cartesian_prod(x, y)) 2024-06-26T05:54:26.9574641Z True 2024-06-26T05:54:26.9574918Z 2024-06-26T05:54:26.9575295Z `torch.meshgrid` is commonly used to produce a grid for 2024-06-26T05:54:26.9575782Z plotting. 2024-06-26T05:54:26.9576168Z >>> # xdoctest: +REQUIRES(module:matplotlib) 2024-06-26T05:54:26.9576681Z >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW) 2024-06-26T05:54:26.9577179Z >>> import matplotlib.pyplot as plt 2024-06-26T05:54:26.9577719Z >>> xs = torch.linspace(-5, 5, steps=100) 2024-06-26T05:54:26.9578264Z >>> ys = torch.linspace(-5, 5, steps=100) 2024-06-26T05:54:26.9578829Z >>> x, y = torch.meshgrid(xs, ys, indexing='xy') 2024-06-26T05:54:26.9579435Z >>> z = torch.sin(torch.sqrt(x * x + y * y)) 2024-06-26T05:54:26.9579981Z >>> ax = plt.axes(projection='3d') 2024-06-26T05:54:26.9580503Z >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy()) 2024-06-26T05:54:26.9580969Z >>> plt.show() 2024-06-26T05:54:26.9581339Z 2024-06-26T05:54:26.9581639Z .. image:: ../_static/img/meshgrid.png 2024-06-26T05:54:26.9582054Z :width: 512 2024-06-26T05:54:26.9582367Z 2024-06-26T05:54:26.9582599Z 2024-06-26T05:54:26.9583311Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:26.9584253Z 2024-06-26T05:54:26.9584554Z warnings.warn(msg) 2024-06-26T05:54:26.9584845Z 2024-06-26T05:54:26.9585240Z --- Parse Warning: 2 / 90 --- 2024-06-26T05:54:26.9587384Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_unique_impl in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=815. 2024-06-26T05:54:26.9589046Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:26.9590133Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> Tuple[Tensor, Tensor, Tensor] 2024-06-26T05:54:26.9590883Z 2024-06-26T05:54:26.9591204Z Returns the unique elements of the input tensor. 2024-06-26T05:54:26.9591646Z 2024-06-26T05:54:26.9592184Z .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that 2024-06-26T05:54:26.9593080Z this function also eliminates non-consecutive duplicate values. 2024-06-26T05:54:26.9593593Z 2024-06-26T05:54:26.9594040Z .. note:: Currently in the CUDA implementation and the CPU implementation, 2024-06-26T05:54:26.9594908Z `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument. 2024-06-26T05:54:26.9595859Z Sorting could be slow, so if your input tensor is already sorted, it is recommended to use 2024-06-26T05:54:26.9596659Z :func:`torch.unique_consecutive` which avoids the sorting. 2024-06-26T05:54:26.9597145Z 2024-06-26T05:54:26.9597360Z Args: 2024-06-26T05:54:26.9597671Z input (Tensor): the input tensor 2024-06-26T05:54:26.9598264Z sorted (bool): Whether to sort the unique elements in ascending order 2024-06-26T05:54:26.9598845Z before returning as output. 2024-06-26T05:54:26.9599419Z return_inverse (bool): Whether to also return the indices for where 2024-06-26T05:54:26.9600167Z elements in the original input ended up in the returned unique list. 2024-06-26T05:54:26.9601007Z return_counts (bool): Whether to also return the counts for each unique 2024-06-26T05:54:26.9601562Z element. 2024-06-26T05:54:26.9602062Z dim (int, optional): the dimension to operate upon. If ``None``, the 2024-06-26T05:54:26.9602796Z unique of the flattened input is returned. Otherwise, each of the 2024-06-26T05:54:26.9603503Z tensors indexed by the given dimension is treated as one of the 2024-06-26T05:54:26.9604230Z elements to apply the unique operation upon. See examples for more 2024-06-26T05:54:26.9604818Z details. Default: ``None`` 2024-06-26T05:54:26.9605181Z 2024-06-26T05:54:26.9605413Z Returns: 2024-06-26T05:54:26.9605963Z (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing 2024-06-26T05:54:26.9606584Z 2024-06-26T05:54:26.9607049Z - **output** (*Tensor*): the output list of unique scalar elements. 2024-06-26T05:54:26.9607714Z - **inverse_indices** (*Tensor*): (optional) if 2024-06-26T05:54:26.9608291Z :attr:`return_inverse` is True, there will be an additional 2024-06-26T05:54:26.9609036Z returned tensor (same shape as input) representing the indices 2024-06-26T05:54:26.9609739Z for where elements in the original input map to in the output; 2024-06-26T05:54:26.9610408Z otherwise, this function will only return a single tensor. 2024-06-26T05:54:26.9611075Z - **counts** (*Tensor*): (optional) if 2024-06-26T05:54:26.9611637Z :attr:`return_counts` is True, there will be an additional 2024-06-26T05:54:26.9612282Z returned tensor (same shape as output or output.size(dim), 2024-06-26T05:54:26.9612969Z if dim was specified) representing the number of occurrences 2024-06-26T05:54:26.9650350Z for each unique value or tensor. 2024-06-26T05:54:26.9651047Z 2024-06-26T05:54:26.9651297Z Example:: 2024-06-26T05:54:26.9651573Z 2024-06-26T05:54:26.9652007Z >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long)) 2024-06-26T05:54:26.9652559Z >>> output 2024-06-26T05:54:26.9652866Z tensor([1, 2, 3]) 2024-06-26T05:54:26.9653181Z 2024-06-26T05:54:26.9653580Z >>> output, inverse_indices = torch.unique( 2024-06-26T05:54:26.9654256Z ... torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True) 2024-06-26T05:54:26.9654855Z >>> output 2024-06-26T05:54:26.9655147Z tensor([1, 2, 3]) 2024-06-26T05:54:26.9655492Z >>> inverse_indices 2024-06-26T05:54:26.9655849Z tensor([0, 2, 1, 2]) 2024-06-26T05:54:26.9656168Z 2024-06-26T05:54:26.9656474Z >>> output, inverse_indices = torch.unique( 2024-06-26T05:54:26.9657148Z ... torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True) 2024-06-26T05:54:26.9657737Z >>> output 2024-06-26T05:54:26.9658038Z tensor([1, 2, 3]) 2024-06-26T05:54:26.9658374Z >>> inverse_indices 2024-06-26T05:54:26.9658709Z tensor([[0, 2], 2024-06-26T05:54:26.9659031Z [1, 2]]) 2024-06-26T05:54:26.9659336Z 2024-06-26T05:54:26.9659575Z >>> a = torch.tensor([ 2024-06-26T05:54:26.9659930Z ... [ 2024-06-26T05:54:26.9680182Z ... [1, 1, 0, 0], 2024-06-26T05:54:26.9680768Z ... [1, 1, 0, 0], 2024-06-26T05:54:26.9681138Z ... [0, 0, 1, 1], 2024-06-26T05:54:26.9681502Z ... ], 2024-06-26T05:54:26.9681799Z ... [ 2024-06-26T05:54:26.9682097Z ... [0, 0, 1, 1], 2024-06-26T05:54:26.9682460Z ... [0, 0, 1, 1], 2024-06-26T05:54:26.9682828Z ... [1, 1, 1, 1], 2024-06-26T05:54:26.9683165Z ... ], 2024-06-26T05:54:26.9683455Z ... [ 2024-06-26T05:54:26.9683754Z ... [1, 1, 0, 0], 2024-06-26T05:54:26.9684107Z ... [1, 1, 0, 0], 2024-06-26T05:54:26.9684472Z ... [0, 0, 1, 1], 2024-06-26T05:54:26.9684822Z ... ], 2024-06-26T05:54:26.9685098Z ... ]) 2024-06-26T05:54:26.9685365Z 2024-06-26T05:54:26.9685817Z >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]` 2024-06-26T05:54:26.9686576Z >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match 2024-06-26T05:54:26.9687227Z >>> # each other, so one of them will be removed. 2024-06-26T05:54:26.9687825Z >>> (a[0, :, :] == a[2, :, :]).all() 2024-06-26T05:54:26.9688236Z tensor(True) 2024-06-26T05:54:26.9688596Z >>> a_unique_dim0 = torch.unique(a, dim=0) 2024-06-26T05:54:26.9689038Z >>> a_unique_dim0 2024-06-26T05:54:26.9689380Z tensor([[[0, 0, 1, 1], 2024-06-26T05:54:26.9689726Z [0, 0, 1, 1], 2024-06-26T05:54:26.9690081Z [1, 1, 1, 1]], 2024-06-26T05:54:26.9690548Z [[1, 1, 0, 0], 2024-06-26T05:54:26.9690885Z [1, 1, 0, 0], 2024-06-26T05:54:26.9691238Z [0, 0, 1, 1]]]) 2024-06-26T05:54:26.9691584Z 2024-06-26T05:54:26.9692117Z >>> # Notice which sub-tensors from `a` match with the sub-tensors from 2024-06-26T05:54:26.9692690Z >>> # `a_unique_dim0`: 2024-06-26T05:54:26.9693165Z >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all() 2024-06-26T05:54:26.9693778Z tensor(True) 2024-06-26T05:54:26.9694162Z >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all() 2024-06-26T05:54:26.9694655Z tensor(True) 2024-06-26T05:54:26.9694934Z 2024-06-26T05:54:26.9695405Z >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are 2024-06-26T05:54:26.9696125Z >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of 2024-06-26T05:54:26.9696673Z >>> # them will be removed. 2024-06-26T05:54:26.9697096Z >>> (a[:, 0, :] == a[:, 1, :]).all() 2024-06-26T05:54:26.9697501Z tensor(True) 2024-06-26T05:54:26.9697824Z >>> torch.unique(a, dim=1) 2024-06-26T05:54:26.9698216Z tensor([[[0, 0, 1, 1], 2024-06-26T05:54:26.9698574Z [1, 1, 0, 0]], 2024-06-26T05:54:26.9698919Z [[1, 1, 1, 1], 2024-06-26T05:54:26.9699272Z [0, 0, 1, 1]], 2024-06-26T05:54:26.9699627Z [[0, 0, 1, 1], 2024-06-26T05:54:26.9699966Z [1, 1, 0, 0]]]) 2024-06-26T05:54:26.9700313Z 2024-06-26T05:54:26.9700753Z >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared. 2024-06-26T05:54:26.9701452Z >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and 2024-06-26T05:54:26.9702110Z >>> # `a[:, :, 3]` match each other as well. So in this case, two of the 2024-06-26T05:54:26.9702734Z >>> # sub-tensors will be removed. 2024-06-26T05:54:26.9703169Z >>> (a[:, :, 0] == a[:, :, 1]).all() 2024-06-26T05:54:26.9703574Z tensor(True) 2024-06-26T05:54:26.9703918Z >>> (a[:, :, 2] == a[:, :, 3]).all() 2024-06-26T05:54:26.9704307Z tensor(True) 2024-06-26T05:54:26.9704641Z >>> torch.unique(a, dim=2) 2024-06-26T05:54:26.9705028Z tensor([[[0, 1], 2024-06-26T05:54:26.9705340Z [0, 1], 2024-06-26T05:54:26.9705660Z [1, 0]], 2024-06-26T05:54:26.9705989Z [[1, 0], 2024-06-26T05:54:26.9706292Z [1, 0], 2024-06-26T05:54:26.9706608Z [1, 1]], 2024-06-26T05:54:26.9706928Z [[0, 1], 2024-06-26T05:54:26.9707230Z [0, 1], 2024-06-26T05:54:26.9707551Z [1, 0]]]) 2024-06-26T05:54:26.9707871Z 2024-06-26T05:54:26.9708405Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:26.9708985Z 2024-06-26T05:54:26.9709237Z warnings.warn(msg) 2024-06-26T05:54:26.9709527Z 2024-06-26T05:54:26.9709867Z --- Parse Warning: 3 / 90 --- 2024-06-26T05:54:26.9711417Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=opcheck in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=931. 2024-06-26T05:54:26.9713043Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:26.9713840Z Given an operator and some sample arguments, tests if the operator is 2024-06-26T05:54:26.9714419Z registered correctly. 2024-06-26T05:54:26.9714747Z 2024-06-26T05:54:26.9715173Z That is, when you use the torch.library/TORCH_LIBRARY APIs to create a 2024-06-26T05:54:26.9715973Z custom op, you specified metadata (e.g. mutability info) about the custom op 2024-06-26T05:54:26.9716828Z and these APIs require that the functions you pass them satisfy certain 2024-06-26T05:54:26.9717599Z properties (e.g. no data pointer access in the fake/meta/abstract kernel) 2024-06-26T05:54:26.9718267Z ``opcheck`` tests these metadata and properties. 2024-06-26T05:54:26.9718713Z 2024-06-26T05:54:26.9718984Z Concretely, we test the following: 2024-06-26T05:54:26.9719579Z - test_schema: if the operator's schema is correct. 2024-06-26T05:54:26.9720275Z - test_autograd_registration: if autograd was registered correctly. 2024-06-26T05:54:26.9721066Z - test_faketensor: If the operator has a FakeTensor kernel 2024-06-26T05:54:26.9721746Z (and if it is correct). The FakeTensor kernel is necessary ( 2024-06-26T05:54:26.9722481Z but not sufficient) for the operator to work with PyTorch compilation 2024-06-26T05:54:26.9723067Z APIs (torch.compile/export/FX). 2024-06-26T05:54:26.9723681Z - test_aot_dispatch_dynamic: If the operator has correct behavior 2024-06-26T05:54:26.9724343Z with PyTorch compilation APIs (torch.compile/export/FX). 2024-06-26T05:54:26.9725022Z This checks that the outputs (and gradients, if applicable) are the 2024-06-26T05:54:26.9725707Z same under eager-mode PyTorch and torch.compile. 2024-06-26T05:54:26.9726252Z This test is a superset of ``test_faketensor``. 2024-06-26T05:54:26.9726687Z 2024-06-26T05:54:26.9727073Z For best results, please call ``opcheck`` multiple times with a 2024-06-26T05:54:26.9727725Z representative set of inputs. If your operator supports 2024-06-26T05:54:26.9728437Z autograd, please use ``opcheck`` with inputs with ``requires_grad = True``; 2024-06-26T05:54:26.9729201Z if your operator supports multiple devices (e.g. CPU and CUDA), please 2024-06-26T05:54:26.9729867Z use ``opcheck`` with inputs on all supported devices. 2024-06-26T05:54:26.9730314Z 2024-06-26T05:54:26.9730526Z Args: 2024-06-26T05:54:26.9730925Z op: The operator. Must either be a function decorated with 2024-06-26T05:54:26.9731612Z :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket 2024-06-26T05:54:26.9732352Z found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo) 2024-06-26T05:54:26.9732956Z args: The args to the operator 2024-06-26T05:54:26.9733407Z kwargs: The kwargs to the operator 2024-06-26T05:54:26.9734133Z test_utils: Tests that we should run. Default: all of them. 2024-06-26T05:54:26.9734725Z Example: ("test_schema", "test_faketensor") 2024-06-26T05:54:26.9735327Z raise_exception: If we should raise an exception on the first 2024-06-26T05:54:26.9735975Z error. If False, we will return a dict with information 2024-06-26T05:54:26.9736502Z on if each test passed or not. 2024-06-26T05:54:26.9736899Z 2024-06-26T05:54:26.9737148Z .. warning:: 2024-06-26T05:54:26.9737415Z 2024-06-26T05:54:26.9737847Z opcheck and :func:`torch.autograd.gradcheck` test different things; 2024-06-26T05:54:26.9738591Z opcheck tests if your usage of torch.library APIs is correct while 2024-06-26T05:54:26.9739316Z :func:`torch.autograd.gradcheck` tests if your autograd formula is 2024-06-26T05:54:26.9740047Z mathematically correct. Use both to test custom ops that support 2024-06-26T05:54:26.9740612Z gradient computation. 2024-06-26T05:54:26.9740944Z 2024-06-26T05:54:26.9741179Z Example: 2024-06-26T05:54:26.9741468Z 2024-06-26T05:54:26.9741779Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-06-26T05:54:26.9742396Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2024-06-26T05:54:26.9743069Z >>> def numpy_add(x: Tensor, y: float) -> Tensor: 2024-06-26T05:54:26.9743549Z >>> x_np = x.numpy(force=True) 2024-06-26T05:54:26.9743969Z >>> z_np = x_np + y 2024-06-26T05:54:26.9744482Z >>> return torch.from_numpy(z_np).to(x.device) 2024-06-26T05:54:26.9744915Z >>> 2024-06-26T05:54:26.9745222Z >>> @numpy_sin.register_fake 2024-06-26T05:54:26.9745620Z >>> def _(x, y): 2024-06-26T05:54:26.9745984Z >>> return torch.empty_like(x) 2024-06-26T05:54:26.9746423Z >>> 2024-06-26T05:54:26.9746762Z >>> def setup_context(ctx, inputs, output): 2024-06-26T05:54:26.9747189Z >>> y, = inputs 2024-06-26T05:54:26.9747539Z >>> ctx.y = y 2024-06-26T05:54:26.9747899Z >>> 2024-06-26T05:54:26.9748185Z >>> def backward(ctx, grad): 2024-06-26T05:54:26.9748661Z >>> return grad * ctx.y, None 2024-06-26T05:54:26.9749058Z >>> 2024-06-26T05:54:26.9749499Z >>> numpy_sin.register_autograd(backward, setup_context=setup_context) 2024-06-26T05:54:26.9750047Z >>> 2024-06-26T05:54:26.9750333Z >>> sample_inputs = [ 2024-06-26T05:54:26.9750711Z >>> (torch.randn(3), 3.14), 2024-06-26T05:54:26.9751250Z >>> (torch.randn(2, 3, device='cuda'), 2.718), 2024-06-26T05:54:26.9751788Z >>> (torch.randn(1, 10, requires_grad=True), 1.234), 2024-06-26T05:54:26.9752475Z >>> (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18), 2024-06-26T05:54:26.9753008Z >>> ] 2024-06-26T05:54:26.9753279Z >>> 2024-06-26T05:54:26.9753571Z >>> for args in sample_inputs: 2024-06-26T05:54:26.9754038Z >>> torch.library.opcheck(foo, args) 2024-06-26T05:54:26.9754460Z 2024-06-26T05:54:26.9754680Z 2024-06-26T05:54:26.9755227Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:26.9755800Z 2024-06-26T05:54:26.9756035Z warnings.warn(msg) 2024-06-26T05:54:26.9756337Z 2024-06-26T05:54:26.9756671Z --- Parse Warning: 4 / 90 --- 2024-06-26T05:54:26.9758237Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=cudart in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/__init__.py line=340. 2024-06-26T05:54:26.9759879Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-06-26T05:54:26.9760519Z Retrieves the CUDA runtime API module. 2024-06-26T05:54:26.9761012Z 2024-06-26T05:54:26.9761231Z 2024-06-26T05:54:26.9761696Z This function initializes the CUDA runtime environment if it is not already 2024-06-26T05:54:26.9762500Z initialized and returns the CUDA runtime API module (_cudart). The CUDA 2024-06-26T05:54:26.9763258Z runtime API module provides access to various CUDA runtime functions. 2024-06-26T05:54:26.9763811Z 2024-06-26T05:54:26.9764044Z Args: 2024-06-26T05:54:26.9764293Z ``None`` 2024-06-26T05:54:26.9764569Z 2024-06-26T05:54:26.9764801Z Returns: 2024-06-26T05:54:26.9765155Z module: The CUDA runtime API module (_cudart). 2024-06-26T05:54:26.9765594Z 2024-06-26T05:54:26.9765826Z Raises: 2024-06-26T05:54:26.9766356Z RuntimeError: If CUDA cannot be re-initialized in a forked subprocess. 2024-06-26T05:54:26.9767307Z AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable. 2024-06-26T05:54:26.9768045Z 2024-06-26T05:54:26.9768342Z Example of CUDA operations with profiling: 2024-06-26T05:54:26.9768789Z >>> import torch 2024-06-26T05:54:26.9769203Z >>> from torch.cuda import cudart, check_error 2024-06-26T05:54:26.9769640Z >>> import os 2024-06-26T05:54:26.9769949Z >>> 2024-06-26T05:54:26.9770311Z >>> os.environ['CUDA_PROFILE'] = '1' 2024-06-26T05:54:26.9770706Z >>> 2024-06-26T05:54:26.9771061Z >>> def perform_cuda_operations_with_streams(): 2024-06-26T05:54:26.9771611Z >>> stream = torch.cuda.Stream() 2024-06-26T05:54:26.9772070Z >>> with torch.cuda.stream(stream): 2024-06-26T05:54:26.9772631Z >>> x = torch.randn(100, 100, device='cuda') 2024-06-26T05:54:26.9773206Z >>> y = torch.randn(100, 100, device='cuda') 2024-06-26T05:54:26.9773809Z >>> z = torch.mul(x, y) 2024-06-26T05:54:26.9774265Z >>> return z 2024-06-26T05:54:26.9774585Z >>> 2024-06-26T05:54:26.9774885Z >>> torch.cuda.synchronize() 2024-06-26T05:54:26.9775360Z >>> print("====== Start nsys profiling ======") 2024-06-26T05:54:26.9775925Z >>> check_error(cudart().cudaProfilerStart()) 2024-06-26T05:54:26.9776482Z >>> with torch.autograd.profiler.emit_nvtx(): 2024-06-26T05:54:26.9777038Z >>> result = perform_cuda_operations_with_streams() 2024-06-26T05:54:26.9777585Z >>> print("CUDA operations completed.") 2024-06-26T05:54:26.9778129Z >>> check_error(torch.cuda.cudart().cudaProfilerStop()) 2024-06-26T05:54:26.9778689Z >>> print("====== End nsys profiling ======") 2024-06-26T05:54:26.9779116Z 2024-06-26T05:54:26.9779523Z To run this example and save the profiling information, execute: 2024-06-26T05:54:26.9780500Z >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2024-06-26T05:54:26.9781223Z 2024-06-26T05:54:26.9781687Z This command profiles the CUDA operations in the provided script and saves 2024-06-26T05:54:26.9782418Z the profiling information to a file named `trace_name.prof`. 2024-06-26T05:54:26.9783215Z The `--profile-from-start off` option ensures that profiling starts only 2024-06-26T05:54:26.9783894Z after the `cudaProfilerStart` call in the script. 2024-06-26T05:54:26.9784608Z The `--csv` and `--print-summary` options format the profiling output as a 2024-06-26T05:54:26.9785252Z CSV file and print a summary, respectively. 2024-06-26T05:54:26.9785983Z The `-o` option specifies the output file name, and the `-f` option forces the 2024-06-26T05:54:26.9786671Z overwrite of the output file if it already exists. 2024-06-26T05:54:26.9787125Z 2024-06-26T05:54:26.9788248Z Original Error: SyntaxError('invalid syntax', ('', 1, 1, '$ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py\n', 1, 2)) 2024-06-26T05:54:26.9789329Z 2024-06-26T05:54:26.9789989Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2024-06-26T05:54:26.9790704Z ^ 2024-06-26T05:54:26.9790958Z warnings.warn(msg) 2024-06-26T05:54:26.9791250Z 2024-06-26T05:54:26.9791587Z --- Parse Warning: 5 / 90 --- 2024-06-26T05:54:26.9793212Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Future.then in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py line=101. 2024-06-26T05:54:26.9795012Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:26.9795594Z 2024-06-26T05:54:26.9796041Z Append the given callback function to this ``Future``, which will be run 2024-06-26T05:54:26.9796797Z when the ``Future`` is completed. Multiple callbacks can be added to 2024-06-26T05:54:26.9797522Z the same ``Future``, but the order in which they will be executed cannot 2024-06-26T05:54:26.9798232Z be guaranteed (to enforce a certain order consider chaining: 2024-06-26T05:54:26.9798930Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which 2024-06-26T05:54:26.9799658Z is the reference to this ``Future``. The callback function can use the 2024-06-26T05:54:26.9800393Z :meth:`value` method to get the value. Note that if this ``Future`` is 2024-06-26T05:54:26.9801261Z already completed, the given callback will be run immediately inline. 2024-06-26T05:54:26.9801799Z 2024-06-26T05:54:26.9802276Z If the ``Future``'s value contains tensors that reside on GPUs, the 2024-06-26T05:54:26.9803018Z callback might be invoked while the async kernels that are populating 2024-06-26T05:54:26.9803846Z those tensors haven't yet finished executing on the device. However, the 2024-06-26T05:54:26.9804633Z callback will be invoked with some dedicated streams set as current 2024-06-26T05:54:26.9805359Z (fetched from a global pool) which will be synchronized with those 2024-06-26T05:54:26.9806127Z kernels. Hence any operation performed by the callback on these tensors 2024-06-26T05:54:26.9806904Z will be scheduled on the device after the kernels complete. In other 2024-06-26T05:54:26.9807698Z words, as long as the callback doesn't switch streams, it can safely 2024-06-26T05:54:26.9808440Z manipulate the result without any additional synchronization. This is 2024-06-26T05:54:26.9809145Z similar to the non-blocking behavior of :meth:`wait`. 2024-06-26T05:54:26.9809601Z 2024-06-26T05:54:26.9810024Z Similarly, if the callback returns a value that contains tensors that 2024-06-26T05:54:26.9810745Z reside on a GPU, it can do so even if the kernels that are producing 2024-06-26T05:54:26.9811485Z these tensors are still running on the device, as long as the callback 2024-06-26T05:54:26.9812272Z didn't change streams during its execution. If one wants to change 2024-06-26T05:54:26.9813048Z streams, one must be careful to re-synchronize them with the original 2024-06-26T05:54:26.9814002Z streams, that is, those that were current when the callback was invoked. 2024-06-26T05:54:26.9814558Z 2024-06-26T05:54:26.9814790Z Args: 2024-06-26T05:54:26.9815199Z callback(``Callable``): a ``Callable`` that takes this ``Future`` as 2024-06-26T05:54:26.9815784Z the only argument. 2024-06-26T05:54:26.9816182Z 2024-06-26T05:54:26.9816406Z Returns: 2024-06-26T05:54:26.9816801Z A new ``Future`` object that holds the return value of the 2024-06-26T05:54:26.9817427Z ``callback`` and will be marked as completed when the given 2024-06-26T05:54:26.9817931Z ``callback`` finishes. 2024-06-26T05:54:26.9818260Z 2024-06-26T05:54:26.9818633Z .. note:: Note that if the callback function throws, either 2024-06-26T05:54:26.9819286Z through the original future being completed with an exception and 2024-06-26T05:54:26.9819987Z calling ``fut.wait()``, or through other code in the callback, the 2024-06-26T05:54:26.9820689Z future returned by ``then`` will be marked appropriately with the 2024-06-26T05:54:26.9821357Z encountered error. However, if this callback later completes 2024-06-26T05:54:26.9822051Z additional futures, those futures are not marked as completed with 2024-06-26T05:54:26.9822790Z an error and the user is responsible for handling completion/waiting 2024-06-26T05:54:26.9823370Z on those futures independently. 2024-06-26T05:54:26.9823752Z 2024-06-26T05:54:26.9823993Z Example:: 2024-06-26T05:54:26.9824341Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2024-06-26T05:54:26.9824812Z >>> def callback(fut): 2024-06-26T05:54:26.9825236Z ... print(f"RPC return value is {fut.wait()}.") 2024-06-26T05:54:26.9825717Z >>> fut = torch.futures.Future() 2024-06-26T05:54:26.9826240Z >>> # The inserted callback will print the return value when 2024-06-26T05:54:26.9826794Z >>> # receiving the response from "worker1" 2024-06-26T05:54:26.9827239Z >>> cb_fut = fut.then(callback) 2024-06-26T05:54:26.9827647Z >>> chain_cb_fut = cb_fut.then( 2024-06-26T05:54:26.9828131Z ... lambda x : print(f"Chained cb done. {x.wait()}") 2024-06-26T05:54:26.9828579Z ... ) 2024-06-26T05:54:26.9828851Z >>> fut.set_result(5) 2024-06-26T05:54:26.9829276Z RPC return value is 5. 2024-06-26T05:54:26.9829622Z Chained cb done. None 2024-06-26T05:54:26.9829950Z 2024-06-26T05:54:26.9830492Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:26.9831059Z 2024-06-26T05:54:26.9831309Z warnings.warn(msg) 2024-06-26T05:54:26.9831613Z 2024-06-26T05:54:26.9831979Z --- Parse Warning: 6 / 90 --- 2024-06-26T05:54:26.9833636Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Future.set_result in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py line=209. 2024-06-26T05:54:26.9835446Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:26.9836043Z 2024-06-26T05:54:26.9836461Z Set the result for this ``Future``, which will mark this ``Future`` as 2024-06-26T05:54:26.9837214Z completed and trigger all attached callbacks. Note that a ``Future`` 2024-06-26T05:54:26.9837800Z cannot be marked completed twice. 2024-06-26T05:54:26.9838158Z 2024-06-26T05:54:26.9838598Z If the result contains tensors that reside on GPUs, this method can be 2024-06-26T05:54:26.9839331Z called even if the asynchronous kernels that are populating those 2024-06-26T05:54:26.9840103Z tensors haven't yet completed running on the device, provided that the 2024-06-26T05:54:26.9840950Z streams on which those kernels were enqueued are set as the current ones 2024-06-26T05:54:26.9841780Z when this method is called. Put simply, it's safe to call this method 2024-06-26T05:54:26.9842496Z immediately after launching those kernels, without any additional 2024-06-26T05:54:26.9843300Z synchronization, as long as one doesn't change streams in between. This 2024-06-26T05:54:26.9844066Z method will record events on all the relevant current streams and will 2024-06-26T05:54:26.9844808Z use them to ensure proper scheduling for all the consumers of this 2024-06-26T05:54:26.9845334Z ``Future``. 2024-06-26T05:54:26.9845600Z 2024-06-26T05:54:26.9845834Z Args: 2024-06-26T05:54:26.9846181Z result (object): the result object of this ``Future``. 2024-06-26T05:54:26.9846644Z 2024-06-26T05:54:26.9846882Z Example:: 2024-06-26T05:54:26.9847230Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2024-06-26T05:54:26.9847706Z >>> import threading 2024-06-26T05:54:26.9848041Z >>> import time 2024-06-26T05:54:26.9848381Z >>> def slow_set_future(fut, value): 2024-06-26T05:54:26.9848799Z ... time.sleep(0.5) 2024-06-26T05:54:26.9849169Z ... fut.set_result(value) 2024-06-26T05:54:26.9849567Z >>> fut = torch.futures.Future() 2024-06-26T05:54:26.9849983Z >>> t = threading.Thread( 2024-06-26T05:54:26.9850365Z ... target=slow_set_future, 2024-06-26T05:54:26.9850771Z ... args=(fut, torch.ones(2) * 3) 2024-06-26T05:54:26.9851165Z ... ) 2024-06-26T05:54:26.9851428Z >>> t.start() 2024-06-26T05:54:26.9851721Z >>> print(fut.wait()) 2024-06-26T05:54:26.9852061Z tensor([3., 3.]) 2024-06-26T05:54:26.9852392Z >>> t.join() 2024-06-26T05:54:26.9852656Z 2024-06-26T05:54:26.9853178Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:26.9853882Z 2024-06-26T05:54:26.9854137Z warnings.warn(msg) 2024-06-26T05:54:26.9854440Z 2024-06-26T05:54:26.9854760Z --- Parse Warning: 7 / 90 --- 2024-06-26T05:54:26.9856346Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=sum in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py line=191. 2024-06-26T05:54:26.9858004Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:26.9858720Z Return the sum of each row of the given sparse tensor. 2024-06-26T05:54:26.9859240Z 2024-06-26T05:54:26.9859703Z Returns the sum of each row of the sparse tensor :attr:`input` in the given 2024-06-26T05:54:26.9860451Z dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions, 2024-06-26T05:54:26.9861160Z reduce over all of them. When sum over all ``sparse_dim``, this method 2024-06-26T05:54:26.9861870Z returns a dense tensor instead of a sparse tensor. 2024-06-26T05:54:26.9862324Z 2024-06-26T05:54:26.9862805Z All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output 2024-06-26T05:54:26.9863618Z tensor having :attr:`dim` fewer dimensions than :attr:`input`. 2024-06-26T05:54:26.9864123Z 2024-06-26T05:54:26.9864575Z During backward, only gradients at ``nnz`` locations of :attr:`input` 2024-06-26T05:54:26.9865352Z will propagate back. Note that the gradients of :attr:`input` is coalesced. 2024-06-26T05:54:26.9865932Z 2024-06-26T05:54:26.9866151Z Args: 2024-06-26T05:54:26.9866484Z input (Tensor): the input sparse tensor 2024-06-26T05:54:26.9867212Z dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce 2024-06-26T05:54:26.9867888Z over all dims. 2024-06-26T05:54:26.9868456Z dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. 2024-06-26T05:54:26.9869128Z Default: dtype of :attr:`input`. 2024-06-26T05:54:26.9869530Z 2024-06-26T05:54:26.9869758Z Example:: 2024-06-26T05:54:26.9870025Z 2024-06-26T05:54:26.9870261Z >>> nnz = 3 2024-06-26T05:54:26.9870562Z >>> dims = [5, 5, 2, 3] 2024-06-26T05:54:26.9871040Z >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), 2024-06-26T05:54:26.9871673Z torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) 2024-06-26T05:54:26.9872249Z >>> V = torch.randn(nnz, dims[2], dims[3]) 2024-06-26T05:54:26.9872706Z >>> size = torch.Size(dims) 2024-06-26T05:54:26.9873240Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-06-26T05:54:26.9873752Z >>> S = torch.sparse_coo_tensor(I, V, size) 2024-06-26T05:54:26.9874184Z >>> S 2024-06-26T05:54:26.9874501Z tensor(indices=tensor([[2, 0, 3], 2024-06-26T05:54:26.9874926Z [2, 4, 1]]), 2024-06-26T05:54:26.9875462Z values=tensor([[[-0.6438, -1.6467, 1.4004], 2024-06-26T05:54:26.9876007Z [ 0.3411, 0.0918, -0.2312]], 2024-06-26T05:54:26.9876410Z 2024-06-26T05:54:26.9876755Z [[ 0.5348, 0.0634, -2.0494], 2024-06-26T05:54:26.9877288Z [-0.7125, -1.0646, 2.1844]], 2024-06-26T05:54:26.9877683Z 2024-06-26T05:54:26.9878038Z [[ 0.1276, 0.1874, -0.6334], 2024-06-26T05:54:26.9878580Z [-1.9682, -0.5340, 0.7483]]]), 2024-06-26T05:54:26.9879088Z size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo) 2024-06-26T05:54:26.9879541Z 2024-06-26T05:54:26.9879943Z # when sum over only part of sparse_dims, return a sparse tensor 2024-06-26T05:54:26.9880505Z >>> torch.sparse.sum(S, [1, 3]) 2024-06-26T05:54:26.9881036Z tensor(indices=tensor([[0, 2, 3]]), 2024-06-26T05:54:26.9881557Z values=tensor([[-1.4512, 0.4073], 2024-06-26T05:54:26.9882049Z [-0.8901, 0.2017], 2024-06-26T05:54:26.9882553Z [-0.3183, -1.7539]]), 2024-06-26T05:54:26.9883045Z size=(5, 2), nnz=3, layout=torch.sparse_coo) 2024-06-26T05:54:26.9883479Z 2024-06-26T05:54:26.9883814Z # when sum over all sparse dim, return a dense tensor 2024-06-26T05:54:26.9884316Z # with summed dims squeezed 2024-06-26T05:54:26.9884748Z >>> torch.sparse.sum(S, [0, 1, 3]) 2024-06-26T05:54:26.9885247Z tensor([-2.6596, -1.1450]) 2024-06-26T05:54:26.9885613Z 2024-06-26T05:54:26.9886147Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:26.9886714Z 2024-06-26T05:54:26.9886963Z warnings.warn(msg) 2024-06-26T05:54:26.9887269Z 2024-06-26T05:54:26.9887622Z --- Parse Warning: 8 / 90 --- 2024-06-26T05:54:26.9889195Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=vmap in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/apis.py line=38. 2024-06-26T05:54:26.9890938Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:26.9891527Z 2024-06-26T05:54:26.9891966Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that 2024-06-26T05:54:26.9892696Z maps ``func`` over some dimension of the inputs. Semantically, vmap 2024-06-26T05:54:26.9893416Z pushes the map into PyTorch operations called by ``func``, effectively 2024-06-26T05:54:26.9894165Z vectorizing those operations. 2024-06-26T05:54:26.9894522Z 2024-06-26T05:54:26.9894946Z vmap is useful for handling batch dimensions: one can write a function 2024-06-26T05:54:26.9895688Z ``func`` that runs on examples and then lift it to a function that can 2024-06-26T05:54:26.9896420Z take batches of examples with ``vmap(func)``. vmap can also be used to 2024-06-26T05:54:26.9897087Z compute batched gradients when composed with autograd. 2024-06-26T05:54:26.9897540Z 2024-06-26T05:54:26.9897786Z .. note:: 2024-06-26T05:54:26.9898200Z :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for 2024-06-26T05:54:26.9898824Z convenience. Use whichever one you'd like. 2024-06-26T05:54:26.9899252Z 2024-06-26T05:54:26.9899481Z Args: 2024-06-26T05:54:26.9899903Z func (function): A Python function that takes one or more arguments. 2024-06-26T05:54:26.9900504Z Must return one or more Tensors. 2024-06-26T05:54:26.9901082Z in_dims (int or nested structure): Specifies which dimension of the 2024-06-26T05:54:26.9901733Z inputs should be mapped over. ``in_dims`` should have a 2024-06-26T05:54:26.9902379Z structure like the inputs. If the ``in_dim`` for a particular 2024-06-26T05:54:26.9903059Z input is None, then that indicates there is no map dimension. 2024-06-26T05:54:26.9903573Z Default: 0. 2024-06-26T05:54:26.9904051Z out_dims (int or Tuple[int]): Specifies where the mapped dimension 2024-06-26T05:54:26.9904750Z should appear in the outputs. If ``out_dims`` is a Tuple, then 2024-06-26T05:54:26.9905363Z it should have one element per output. Default: 0. 2024-06-26T05:54:26.9905967Z randomness (str): Specifies whether the randomness in this 2024-06-26T05:54:26.9906736Z vmap should be the same or different across batches. If 'different', 2024-06-26T05:54:26.9907508Z the randomness for each batch will be different. If 'same', the 2024-06-26T05:54:26.9908302Z randomness will be the same across batches. If 'error', any calls to 2024-06-26T05:54:26.9909099Z random functions will error. Default: 'error'. WARNING: this flag 2024-06-26T05:54:26.9909817Z only applies to random PyTorch operations and does not apply to 2024-06-26T05:54:26.9910475Z Python's random module or numpy randomness. 2024-06-26T05:54:26.9911142Z chunk_size (None or int): If None (default), apply a single vmap over inputs. 2024-06-26T05:54:26.9911942Z If not None, then compute the vmap :attr:`chunk_size` samples at a time. 2024-06-26T05:54:26.9912948Z Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop. 2024-06-26T05:54:26.9913911Z If you run into memory issues computing the vmap, please try a non-None chunk_size. 2024-06-26T05:54:26.9914609Z 2024-06-26T05:54:26.9914835Z Returns: 2024-06-26T05:54:26.9915252Z Returns a new "batched" function. It takes the same inputs as 2024-06-26T05:54:26.9915923Z ``func``, except each input has an extra dimension at the index 2024-06-26T05:54:26.9916573Z specified by ``in_dims``. It takes returns the same outputs as 2024-06-26T05:54:26.9917278Z ``func``, except each output has an extra dimension at the index 2024-06-26T05:54:26.9917818Z specified by ``out_dims``. 2024-06-26T05:54:26.9918159Z 2024-06-26T05:54:26.9918436Z .. warning: 2024-06-26T05:54:26.9918952Z :func:`vmap` works best with functional-style code. Please do not 2024-06-26T05:54:26.9919722Z perform any side-effects in ``func``, with the exception of 2024-06-26T05:54:26.9920486Z in-place PyTorch operations. Examples of side-effects include mutating 2024-06-26T05:54:26.9921331Z Python data structures and assigning values to variables not captured 2024-06-26T05:54:26.9921893Z in ``func``. 2024-06-26T05:54:26.9922161Z 2024-06-26T05:54:26.9922630Z One example of using :func:`vmap` is to compute batched dot products. PyTorch 2024-06-26T05:54:26.9923487Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully 2024-06-26T05:54:26.9924223Z rummaging through docs, use :func:`vmap` to construct a new function. 2024-06-26T05:54:26.9924771Z 2024-06-26T05:54:26.9925161Z >>> torch.dot # [D], [D] -> [] 2024-06-26T05:54:26.9925844Z >>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N] 2024-06-26T05:54:26.9926458Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2024-06-26T05:54:26.9926907Z >>> batched_dot(x, y) 2024-06-26T05:54:26.9927220Z 2024-06-26T05:54:26.9927672Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler 2024-06-26T05:54:26.9928282Z model authoring experience. 2024-06-26T05:54:26.9928613Z 2024-06-26T05:54:26.9928893Z >>> batch_size, feature_size = 3, 5 2024-06-26T05:54:26.9929414Z >>> weights = torch.randn(feature_size, requires_grad=True) 2024-06-26T05:54:26.9929882Z >>> 2024-06-26T05:54:26.9930157Z >>> def model(feature_vec): 2024-06-26T05:54:26.9930602Z >>> # Very simple linear model with activation 2024-06-26T05:54:26.9931101Z >>> return feature_vec.dot(weights).relu() 2024-06-26T05:54:26.9931525Z >>> 2024-06-26T05:54:26.9931874Z >>> examples = torch.randn(batch_size, feature_size) 2024-06-26T05:54:26.9932380Z >>> result = torch.vmap(model)(examples) 2024-06-26T05:54:26.9932791Z 2024-06-26T05:54:26.9933270Z :func:`vmap` can also help vectorize computations that were previously difficult 2024-06-26T05:54:26.9934265Z or impossible to batch. One example is higher-order gradient computation. 2024-06-26T05:54:26.9935104Z The PyTorch autograd engine computes vjps (vector-Jacobian products). 2024-06-26T05:54:26.9935936Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually 2024-06-26T05:54:26.9936742Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`, 2024-06-26T05:54:26.9937553Z we can vectorize the whole computation, computing the Jacobian in a single 2024-06-26T05:54:26.9938160Z call to ``autograd.grad``. 2024-06-26T05:54:26.9938496Z 2024-06-26T05:54:26.9938717Z >>> # Setup 2024-06-26T05:54:26.9938996Z >>> N = 5 2024-06-26T05:54:26.9939290Z >>> f = lambda x: x ** 2 2024-06-26T05:54:26.9939688Z >>> x = torch.randn(N, requires_grad=True) 2024-06-26T05:54:26.9940114Z >>> y = f(x) 2024-06-26T05:54:26.9940511Z >>> I_N = torch.eye(N) 2024-06-26T05:54:26.9940832Z >>> 2024-06-26T05:54:26.9941111Z >>> # Sequential approach 2024-06-26T05:54:26.9941655Z >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0] 2024-06-26T05:54:26.9942302Z >>> for v in I_N.unbind()] 2024-06-26T05:54:26.9942779Z >>> jacobian = torch.stack(jacobian_rows) 2024-06-26T05:54:26.9943196Z >>> 2024-06-26T05:54:26.9943486Z >>> # vectorized gradient computation 2024-06-26T05:54:26.9943908Z >>> def get_vjp(v): 2024-06-26T05:54:26.9944299Z >>> return torch.autograd.grad(y, x, v) 2024-06-26T05:54:26.9944808Z >>> jacobian = torch.vmap(get_vjp)(I_N) 2024-06-26T05:54:26.9945212Z 2024-06-26T05:54:26.9945715Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions 2024-06-26T05:54:26.9946362Z 2024-06-26T05:54:26.9946758Z >>> torch.dot # [D], [D] -> [] 2024-06-26T05:54:26.9947618Z >>> batched_dot = torch.vmap(torch.vmap(torch.dot)) # [N1, N0, D], [N1, N0, D] -> [N1, N0] 2024-06-26T05:54:26.9948338Z >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5) 2024-06-26T05:54:26.9948862Z >>> batched_dot(x, y) # tensor of size [2, 3] 2024-06-26T05:54:26.9949280Z 2024-06-26T05:54:26.9949736Z If the inputs are not batched along the first dimension, ``in_dims`` specifies 2024-06-26T05:54:26.9950436Z the dimension that each inputs are batched along as 2024-06-26T05:54:26.9950884Z 2024-06-26T05:54:26.9951266Z >>> torch.dot # [N], [N] -> [] 2024-06-26T05:54:26.9951994Z >>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D] 2024-06-26T05:54:26.9952631Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2024-06-26T05:54:26.9953297Z >>> batched_dot(x, y) # output is [5] instead of [2] if batched along the 0th dimension 2024-06-26T05:54:26.9953894Z 2024-06-26T05:54:26.9954392Z If there are multiple inputs each of which is batched along different dimensions, 2024-06-26T05:54:26.9955188Z ``in_dims`` must be a tuple with the batch dimension for each input as 2024-06-26T05:54:26.9955710Z 2024-06-26T05:54:26.9956099Z >>> torch.dot # [D], [D] -> [] 2024-06-26T05:54:26.9956842Z >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N] 2024-06-26T05:54:26.9957484Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2024-06-26T05:54:26.9958230Z >>> batched_dot(x, y) # second arg doesn't have a batch dim because in_dim[1] was None 2024-06-26T05:54:26.9958830Z 2024-06-26T05:54:26.9959291Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct 2024-06-26T05:54:26.9959925Z matching the shape of the input: 2024-06-26T05:54:26.9960295Z 2024-06-26T05:54:26.9960731Z >>> f = lambda dict: torch.dot(dict['x'], dict['y']) 2024-06-26T05:54:26.9961249Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2024-06-26T05:54:26.9961735Z >>> input = {'x': x, 'y': y} 2024-06-26T05:54:26.9962290Z >>> batched_dot = torch.vmap(f, in_dims=({'x': 0, 'y': None},)) 2024-06-26T05:54:26.9962809Z >>> batched_dot(input) 2024-06-26T05:54:26.9963133Z 2024-06-26T05:54:26.9963639Z By default, the output is batched along the first dimension. However, it can be batched 2024-06-26T05:54:26.9964338Z along any dimension by using ``out_dims`` 2024-06-26T05:54:26.9964740Z 2024-06-26T05:54:26.9964980Z >>> f = lambda x: x ** 2 2024-06-26T05:54:26.9965351Z >>> x = torch.randn(2, 5) 2024-06-26T05:54:26.9965770Z >>> batched_pow = torch.vmap(f, out_dims=1) 2024-06-26T05:54:26.9966203Z >>> batched_pow(x) # [5, 2] 2024-06-26T05:54:26.9966558Z 2024-06-26T05:54:26.9967098Z For any function that uses kwargs, the returned function will not batch the kwargs but will 2024-06-26T05:54:26.9967756Z accept kwargs 2024-06-26T05:54:26.9968032Z 2024-06-26T05:54:26.9968294Z >>> x = torch.randn([2, 5]) 2024-06-26T05:54:26.9968657Z >>> def fn(x, scale=4.): 2024-06-26T05:54:26.9969019Z >>> return x * scale 2024-06-26T05:54:26.9969351Z >>> 2024-06-26T05:54:26.9969673Z >>> batched_pow = torch.vmap(fn) 2024-06-26T05:54:26.9970148Z >>> assert torch.allclose(batched_pow(x), x * 4) 2024-06-26T05:54:26.9970804Z >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5] 2024-06-26T05:54:26.9971352Z 2024-06-26T05:54:26.9971596Z .. note:: 2024-06-26T05:54:26.9972160Z vmap does not provide general autobatching or handle variable-length 2024-06-26T05:54:26.9972726Z sequences out of the box. 2024-06-26T05:54:26.9973065Z 2024-06-26T05:54:26.9973697Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:26.9974305Z 2024-06-26T05:54:26.9974541Z warnings.warn(msg) 2024-06-26T05:54:26.9974879Z 2024-06-26T05:54:26.9975201Z --- Parse Warning: 9 / 90 --- 2024-06-26T05:54:26.9976885Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_almost_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=329. 2024-06-26T05:54:26.9978639Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:26.9979225Z 2024-06-26T05:54:26.9979621Z Raises an AssertionError if two items are not equal up to desired 2024-06-26T05:54:26.9980158Z precision. 2024-06-26T05:54:26.9980408Z 2024-06-26T05:54:26.9980764Z .. note:: It is recommended to use one of `assert_allclose`, 2024-06-26T05:54:26.9981379Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-06-26T05:54:26.9982029Z instead of this function for more consistent floating point 2024-06-26T05:54:26.9982540Z comparisons. 2024-06-26T05:54:26.9982846Z 2024-06-26T05:54:26.9983266Z The test verifies that the elements of `actual` and `desired` satisfy. 2024-06-26T05:54:26.9983800Z 2024-06-26T05:54:26.9984200Z ``abs(desired-actual) < float64(1.5 * 10**(-decimal))`` 2024-06-26T05:54:26.9984658Z 2024-06-26T05:54:26.9985097Z That is a looser test than originally documented, but agrees with what the 2024-06-26T05:54:26.9985869Z actual implementation in `assert_array_almost_equal` did up to rounding 2024-06-26T05:54:26.9986634Z vagaries. An exception is raised at conflicting values. For ndarrays this 2024-06-26T05:54:26.9987243Z delegates to assert_array_almost_equal 2024-06-26T05:54:26.9987624Z 2024-06-26T05:54:26.9987859Z Parameters 2024-06-26T05:54:26.9988138Z ---------- 2024-06-26T05:54:26.9988407Z actual : array_like 2024-06-26T05:54:26.9988725Z The object to check. 2024-06-26T05:54:26.9989051Z desired : array_like 2024-06-26T05:54:26.9989376Z The expected object. 2024-06-26T05:54:26.9989718Z decimal : int, optional 2024-06-26T05:54:26.9990064Z Desired precision, default is 7. 2024-06-26T05:54:26.9990470Z err_msg : str, optional 2024-06-26T05:54:26.9990896Z The error message to be printed in case of failure. 2024-06-26T05:54:26.9991363Z verbose : bool, optional 2024-06-26T05:54:26.9991869Z If True, the conflicting values are appended to the error message. 2024-06-26T05:54:26.9992390Z 2024-06-26T05:54:26.9992604Z Raises 2024-06-26T05:54:26.9992872Z ------ 2024-06-26T05:54:26.9993117Z AssertionError 2024-06-26T05:54:26.9993548Z If actual and desired are not equal up to specified precision. 2024-06-26T05:54:26.9994061Z 2024-06-26T05:54:26.9994289Z See Also 2024-06-26T05:54:26.9994551Z -------- 2024-06-26T05:54:26.9995011Z assert_allclose: Compare two array_like objects for equality with desired 2024-06-26T05:54:26.9995654Z relative and/or absolute precision. 2024-06-26T05:54:26.9996235Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-06-26T05:54:26.9996744Z 2024-06-26T05:54:26.9996976Z Examples 2024-06-26T05:54:26.9997237Z -------- 2024-06-26T05:54:26.9997648Z >>> from torch._numpy.testing import assert_almost_equal 2024-06-26T05:54:26.9998197Z >>> assert_almost_equal(2.3333333333333, 2.33333334) 2024-06-26T05:54:26.9998733Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) 2024-06-26T05:54:26.9999254Z Traceback (most recent call last): 2024-06-26T05:54:26.9999638Z ... 2024-06-26T05:54:26.9999921Z AssertionError: 2024-06-26T05:54:27.0000272Z Arrays are not almost equal to 10 decimals 2024-06-26T05:54:27.0000778Z ACTUAL: 2.3333333333333 2024-06-26T05:54:27.0001101Z DESIRED: 2.33333334 2024-06-26T05:54:27.0001427Z 2024-06-26T05:54:27.0001757Z >>> assert_almost_equal(np.array([1.0,2.3333333333333]), 2024-06-26T05:54:27.0002292Z ... np.array([1.0,2.33333334]), decimal=9) 2024-06-26T05:54:27.0002763Z Traceback (most recent call last): 2024-06-26T05:54:27.0003137Z ... 2024-06-26T05:54:27.0003383Z AssertionError: 2024-06-26T05:54:27.0003735Z Arrays are not almost equal to 9 decimals 2024-06-26T05:54:27.0004149Z 2024-06-26T05:54:27.0004434Z Mismatched elements: 1 / 2 (50%) 2024-06-26T05:54:27.0004927Z Max absolute difference: 6.666699636781459e-09 2024-06-26T05:54:27.0005462Z Max relative difference: 2.8571569790287484e-09 2024-06-26T05:54:27.0005954Z x: torch.ndarray([1.0000, 2.3333], dtype=float64) 2024-06-26T05:54:27.0006457Z y: torch.ndarray([1.0000, 2.3333], dtype=float64) 2024-06-26T05:54:27.0006873Z 2024-06-26T05:54:27.0007085Z 2024-06-26T05:54:27.0007601Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0008183Z 2024-06-26T05:54:27.0008415Z warnings.warn(msg) 2024-06-26T05:54:27.0008718Z 2024-06-26T05:54:27.0009052Z --- Parse Warning: 10 / 90 --- 2024-06-26T05:54:27.0010744Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_approx_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=454. 2024-06-26T05:54:27.0012502Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0013091Z 2024-06-26T05:54:27.0013671Z Raises an AssertionError if two items are not equal up to significant 2024-06-26T05:54:27.0014212Z digits. 2024-06-26T05:54:27.0014455Z 2024-06-26T05:54:27.0014830Z .. note:: It is recommended to use one of `assert_allclose`, 2024-06-26T05:54:27.0015442Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-06-26T05:54:27.0016092Z instead of this function for more consistent floating point 2024-06-26T05:54:27.0016612Z comparisons. 2024-06-26T05:54:27.0016906Z 2024-06-26T05:54:27.0017280Z Given two numbers, check that they are approximately equal. 2024-06-26T05:54:27.0017966Z Approximately equal is defined as the number of significant digits 2024-06-26T05:54:27.0018495Z that agree. 2024-06-26T05:54:27.0018751Z 2024-06-26T05:54:27.0018987Z Parameters 2024-06-26T05:54:27.0019268Z ---------- 2024-06-26T05:54:27.0019540Z actual : scalar 2024-06-26T05:54:27.0019841Z The object to check. 2024-06-26T05:54:27.0020163Z desired : scalar 2024-06-26T05:54:27.0020470Z The expected object. 2024-06-26T05:54:27.0020825Z significant : int, optional 2024-06-26T05:54:27.0021209Z Desired precision, default is 7. 2024-06-26T05:54:27.0021616Z err_msg : str, optional 2024-06-26T05:54:27.0022046Z The error message to be printed in case of failure. 2024-06-26T05:54:27.0022515Z verbose : bool, optional 2024-06-26T05:54:27.0023027Z If True, the conflicting values are appended to the error message. 2024-06-26T05:54:27.0023553Z 2024-06-26T05:54:27.0023768Z Raises 2024-06-26T05:54:27.0024030Z ------ 2024-06-26T05:54:27.0024282Z AssertionError 2024-06-26T05:54:27.0024719Z If actual and desired are not equal up to specified precision. 2024-06-26T05:54:27.0025322Z 2024-06-26T05:54:27.0025559Z See Also 2024-06-26T05:54:27.0025828Z -------- 2024-06-26T05:54:27.0026293Z assert_allclose: Compare two array_like objects for equality with desired 2024-06-26T05:54:27.0026933Z relative and/or absolute precision. 2024-06-26T05:54:27.0027518Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-06-26T05:54:27.0028071Z 2024-06-26T05:54:27.0028306Z Examples 2024-06-26T05:54:27.0028572Z -------- 2024-06-26T05:54:27.0029168Z >>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20) # doctest: +SKIP 2024-06-26T05:54:27.0030165Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20, # doctest: +SKIP 2024-06-26T05:54:27.0030810Z ... significant=8) 2024-06-26T05:54:27.0031533Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20, # doctest: +SKIP 2024-06-26T05:54:27.0032183Z ... significant=8) 2024-06-26T05:54:27.0032625Z Traceback (most recent call last): 2024-06-26T05:54:27.0033004Z ... 2024-06-26T05:54:27.0033267Z AssertionError: 2024-06-26T05:54:27.0033613Z Items are not equal to 8 significant digits: 2024-06-26T05:54:27.0034086Z ACTUAL: 1.234567e-21 2024-06-26T05:54:27.0034441Z DESIRED: 1.2345672e-21 2024-06-26T05:54:27.0034738Z 2024-06-26T05:54:27.0035076Z the evaluated condition that raises the exception is 2024-06-26T05:54:27.0035528Z 2024-06-26T05:54:27.0035953Z >>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1) 2024-06-26T05:54:27.0036442Z True 2024-06-26T05:54:27.0036678Z 2024-06-26T05:54:27.0036886Z 2024-06-26T05:54:27.0037407Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0037981Z 2024-06-26T05:54:27.0038211Z warnings.warn(msg) 2024-06-26T05:54:27.0038513Z 2024-06-26T05:54:27.0038845Z --- Parse Warning: 11 / 90 --- 2024-06-26T05:54:27.0040515Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_array_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=733. 2024-06-26T05:54:27.0042386Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0042981Z 2024-06-26T05:54:27.0043381Z Raises an AssertionError if two array_like objects are not equal. 2024-06-26T05:54:27.0043909Z 2024-06-26T05:54:27.0044321Z Given two array_like objects, check that the shape is equal and all 2024-06-26T05:54:27.0045055Z elements of these objects are equal (but see the Notes for the special 2024-06-26T05:54:27.0045768Z handling of a scalar). An exception is raised at shape mismatch or 2024-06-26T05:54:27.0046493Z conflicting values. In contrast to the standard usage in numpy, NaNs 2024-06-26T05:54:27.0047243Z are compared like numbers, no assertion is raised if both objects have 2024-06-26T05:54:27.0047809Z NaNs in the same positions. 2024-06-26T05:54:27.0048151Z 2024-06-26T05:54:27.0048587Z The usual caution for verifying equality with floating point numbers is 2024-06-26T05:54:27.0049139Z advised. 2024-06-26T05:54:27.0049389Z 2024-06-26T05:54:27.0049623Z Parameters 2024-06-26T05:54:27.0049902Z ---------- 2024-06-26T05:54:27.0050166Z x : array_like 2024-06-26T05:54:27.0050473Z The actual object to check. 2024-06-26T05:54:27.0050835Z y : array_like 2024-06-26T05:54:27.0051155Z The desired, expected object. 2024-06-26T05:54:27.0051545Z err_msg : str, optional 2024-06-26T05:54:27.0051960Z The error message to be printed in case of failure. 2024-06-26T05:54:27.0052441Z verbose : bool, optional 2024-06-26T05:54:27.0052949Z If True, the conflicting values are appended to the error message. 2024-06-26T05:54:27.0053607Z strict : bool, optional 2024-06-26T05:54:27.0054184Z If True, raise an AssertionError when either the shape or the data 2024-06-26T05:54:27.0054870Z type of the array_like objects does not match. The special 2024-06-26T05:54:27.0055528Z handling for scalars mentioned in the Notes section is disabled. 2024-06-26T05:54:27.0056052Z 2024-06-26T05:54:27.0056287Z Raises 2024-06-26T05:54:27.0056592Z ------ 2024-06-26T05:54:27.0056845Z AssertionError 2024-06-26T05:54:27.0057205Z If actual and desired objects are not equal. 2024-06-26T05:54:27.0057616Z 2024-06-26T05:54:27.0057853Z See Also 2024-06-26T05:54:27.0058172Z -------- 2024-06-26T05:54:27.0058653Z assert_allclose: Compare two array_like objects for equality with desired 2024-06-26T05:54:27.0059296Z relative and/or absolute precision. 2024-06-26T05:54:27.0059893Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-06-26T05:54:27.0060394Z 2024-06-26T05:54:27.0060624Z Notes 2024-06-26T05:54:27.0060897Z ----- 2024-06-26T05:54:27.0061307Z When one of `x` and `y` is a scalar and the other is array_like, the 2024-06-26T05:54:27.0062048Z function checks that each element of the array_like object is equal to 2024-06-26T05:54:27.0062812Z the scalar. This behaviour can be disabled with the `strict` parameter. 2024-06-26T05:54:27.0063356Z 2024-06-26T05:54:27.0063591Z Examples 2024-06-26T05:54:27.0063874Z -------- 2024-06-26T05:54:27.0064194Z The first assert does not raise an exception: 2024-06-26T05:54:27.0064616Z 2024-06-26T05:54:27.0064955Z >>> np.testing.assert_array_equal([1.0,2.33333,np.nan], 2024-06-26T05:54:27.0065472Z ... [np.exp(0),2.33333, np.nan]) 2024-06-26T05:54:27.0065895Z 2024-06-26T05:54:27.0066340Z Use `assert_allclose` or one of the nulp (number of floating point values) 2024-06-26T05:54:27.0066933Z functions for these cases instead: 2024-06-26T05:54:27.0067304Z 2024-06-26T05:54:27.0067631Z >>> np.testing.assert_allclose([1.0,np.pi,np.nan], 2024-06-26T05:54:27.0068139Z ... [1, np.sqrt(np.pi)**2, np.nan], 2024-06-26T05:54:27.0068682Z ... rtol=1e-10, atol=0) 2024-06-26T05:54:27.0069081Z 2024-06-26T05:54:27.0069480Z As mentioned in the Notes section, `assert_array_equal` has special 2024-06-26T05:54:27.0070220Z handling for scalars. Here the test checks that each value in `x` is 3: 2024-06-26T05:54:27.0070769Z 2024-06-26T05:54:27.0071022Z >>> x = np.full((2, 5), fill_value=3) 2024-06-26T05:54:27.0071456Z >>> np.testing.assert_array_equal(x, 3) 2024-06-26T05:54:27.0071848Z 2024-06-26T05:54:27.0072268Z Use `strict` to raise an AssertionError when comparing a scalar with an 2024-06-26T05:54:27.0072819Z array: 2024-06-26T05:54:27.0073056Z 2024-06-26T05:54:27.0073369Z >>> np.testing.assert_array_equal(x, 3, strict=True) 2024-06-26T05:54:27.0073858Z Traceback (most recent call last): 2024-06-26T05:54:27.0074234Z ... 2024-06-26T05:54:27.0074482Z AssertionError: 2024-06-26T05:54:27.0074779Z Arrays are not equal 2024-06-26T05:54:27.0075095Z 2024-06-26T05:54:27.0075368Z (shapes (2, 5), () mismatch) 2024-06-26T05:54:27.0075741Z x: torch.ndarray([[3, 3, 3, 3, 3], 2024-06-26T05:54:27.0076124Z [3, 3, 3, 3, 3]]) 2024-06-26T05:54:27.0076451Z y: torch.ndarray(3) 2024-06-26T05:54:27.0076749Z 2024-06-26T05:54:27.0077165Z The `strict` parameter also ensures that the array data types match: 2024-06-26T05:54:27.0077686Z 2024-06-26T05:54:27.0077934Z >>> x = np.array([2, 2, 2]) 2024-06-26T05:54:27.0078346Z >>> y = np.array([2., 2., 2.], dtype=np.float32) 2024-06-26T05:54:27.0078857Z >>> np.testing.assert_array_equal(x, y, strict=True) 2024-06-26T05:54:27.0079343Z Traceback (most recent call last): 2024-06-26T05:54:27.0079719Z ... 2024-06-26T05:54:27.0079963Z AssertionError: 2024-06-26T05:54:27.0080262Z Arrays are not equal 2024-06-26T05:54:27.0080709Z 2024-06-26T05:54:27.0081072Z (dtypes dtype("int64"), dtype("float32") mismatch) 2024-06-26T05:54:27.0081543Z x: torch.ndarray([2, 2, 2]) 2024-06-26T05:54:27.0081916Z y: torch.ndarray([2., 2., 2.]) 2024-06-26T05:54:27.0082261Z 2024-06-26T05:54:27.0082798Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0083407Z 2024-06-26T05:54:27.0083639Z warnings.warn(msg) 2024-06-26T05:54:27.0083939Z 2024-06-26T05:54:27.0084272Z --- Parse Warning: 12 / 90 --- 2024-06-26T05:54:27.0086028Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_array_almost_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=839. 2024-06-26T05:54:27.0087827Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0088420Z 2024-06-26T05:54:27.0088830Z Raises an AssertionError if two objects are not equal up to desired 2024-06-26T05:54:27.0089384Z precision. 2024-06-26T05:54:27.0089646Z 2024-06-26T05:54:27.0090004Z .. note:: It is recommended to use one of `assert_allclose`, 2024-06-26T05:54:27.0090631Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-06-26T05:54:27.0091281Z instead of this function for more consistent floating point 2024-06-26T05:54:27.0091807Z comparisons. 2024-06-26T05:54:27.0092100Z 2024-06-26T05:54:27.0092551Z The test verifies identical shapes and that the elements of ``actual`` and 2024-06-26T05:54:27.0093139Z ``desired`` satisfy. 2024-06-26T05:54:27.0093425Z 2024-06-26T05:54:27.0093902Z ``abs(desired-actual) < 1.5 * 10**(-decimal)`` 2024-06-26T05:54:27.0094328Z 2024-06-26T05:54:27.0094769Z That is a looser test than originally documented, but agrees with what the 2024-06-26T05:54:27.0095573Z actual implementation did up to rounding vagaries. An exception is raised 2024-06-26T05:54:27.0096375Z at shape mismatch or conflicting values. In contrast to the standard usage 2024-06-26T05:54:27.0097146Z in numpy, NaNs are compared like numbers, no assertion is raised if both 2024-06-26T05:54:27.0097318Z objects have NaNs in the same positions. 2024-06-26T05:54:27.0097409Z 2024-06-26T05:54:27.0097574Z Parameters 2024-06-26T05:54:27.0097769Z ---------- 2024-06-26T05:54:27.0098007Z x : array_like 2024-06-26T05:54:27.0098162Z The actual object to check. 2024-06-26T05:54:27.0098294Z y : array_like 2024-06-26T05:54:27.0098496Z The desired, expected object. 2024-06-26T05:54:27.0098639Z decimal : int, optional 2024-06-26T05:54:27.0098826Z Desired precision, default is 6. 2024-06-26T05:54:27.0099034Z err_msg : str, optional 2024-06-26T05:54:27.0099264Z The error message to be printed in case of failure. 2024-06-26T05:54:27.0099410Z verbose : bool, optional 2024-06-26T05:54:27.0099755Z If True, the conflicting values are appended to the error message. 2024-06-26T05:54:27.0099872Z 2024-06-26T05:54:27.0100001Z Raises 2024-06-26T05:54:27.0100200Z ------ 2024-06-26T05:54:27.0100332Z AssertionError 2024-06-26T05:54:27.0100620Z If actual and desired are not equal up to specified precision. 2024-06-26T05:54:27.0100773Z 2024-06-26T05:54:27.0100901Z See Also 2024-06-26T05:54:27.0101056Z -------- 2024-06-26T05:54:27.0101445Z assert_allclose: Compare two array_like objects for equality with desired 2024-06-26T05:54:27.0101646Z relative and/or absolute precision. 2024-06-26T05:54:27.0101972Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-06-26T05:54:27.0102096Z 2024-06-26T05:54:27.0102218Z Examples 2024-06-26T05:54:27.0102399Z -------- 2024-06-26T05:54:27.0102613Z the first assert does not raise an exception 2024-06-26T05:54:27.0102729Z 2024-06-26T05:54:27.0103085Z >>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan], 2024-06-26T05:54:27.0103278Z ... [1.0,2.333,np.nan]) 2024-06-26T05:54:27.0103416Z 2024-06-26T05:54:27.0103710Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2024-06-26T05:54:27.0103988Z ... [1.0,2.33339,np.nan], decimal=5) 2024-06-26T05:54:27.0104157Z Traceback (most recent call last): 2024-06-26T05:54:27.0104316Z ... 2024-06-26T05:54:27.0104451Z AssertionError: 2024-06-26T05:54:27.0104670Z Arrays are not almost equal to 5 decimals 2024-06-26T05:54:27.0104839Z 2024-06-26T05:54:27.0105059Z Mismatched elements: 1 / 3 (33.3%) 2024-06-26T05:54:27.0105308Z Max absolute difference: 5.999999999994898e-05 2024-06-26T05:54:27.0105614Z Max relative difference: 2.5713661239633743e-05 2024-06-26T05:54:27.0105854Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2024-06-26T05:54:27.0106138Z y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64) 2024-06-26T05:54:27.0106253Z 2024-06-26T05:54:27.0106531Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2024-06-26T05:54:27.0106766Z ... [1.0,2.33333, 5], decimal=5) 2024-06-26T05:54:27.0106931Z Traceback (most recent call last): 2024-06-26T05:54:27.0107052Z ... 2024-06-26T05:54:27.0107231Z AssertionError: 2024-06-26T05:54:27.0107415Z Arrays are not almost equal to 5 decimals 2024-06-26T05:54:27.0107563Z 2024-06-26T05:54:27.0107779Z x and y nan location mismatch: 2024-06-26T05:54:27.0108019Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2024-06-26T05:54:27.0108257Z y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64) 2024-06-26T05:54:27.0108410Z 2024-06-26T05:54:27.0108525Z 2024-06-26T05:54:27.0108963Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0109120Z 2024-06-26T05:54:27.0109265Z warnings.warn(msg) 2024-06-26T05:54:27.0109381Z 2024-06-26T05:54:27.0109649Z --- Parse Warning: 13 / 90 --- 2024-06-26T05:54:27.0111086Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=clear_and_catch_warnings in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=1789. 2024-06-26T05:54:27.0111572Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0111895Z Context manager that resets warning registry for catching warnings 2024-06-26T05:54:27.0112010Z 2024-06-26T05:54:27.0112400Z Warnings can be slippery, because, whenever a warning is triggered, Python 2024-06-26T05:54:27.0112720Z adds a ``__warningregistry__`` member to the *calling* module. This makes 2024-06-26T05:54:27.0113063Z it impossible to retrigger the warning in this module, whatever you put in 2024-06-26T05:54:27.0113476Z the warnings filters. This context manager accepts a sequence of `modules` 2024-06-26T05:54:27.0113682Z as a keyword argument to its constructor and: 2024-06-26T05:54:27.0113800Z 2024-06-26T05:54:27.0114173Z * stores and removes any ``__warningregistry__`` entries in given `modules` 2024-06-26T05:54:27.0125811Z on entry; 2024-06-26T05:54:27.0126177Z * resets ``__warningregistry__`` to its previous state on exit. 2024-06-26T05:54:27.0126266Z 2024-06-26T05:54:27.0126600Z This makes it possible to trigger any warning afresh inside the context 2024-06-26T05:54:27.0126842Z manager without disturbing the state of warnings outside. 2024-06-26T05:54:27.0126933Z 2024-06-26T05:54:27.0127251Z For compatibility with Python 3.0, please consider all arguments to be 2024-06-26T05:54:27.0127422Z keyword-only. 2024-06-26T05:54:27.0127509Z 2024-06-26T05:54:27.0127723Z Parameters 2024-06-26T05:54:27.0127847Z ---------- 2024-06-26T05:54:27.0127966Z record : bool, optional 2024-06-26T05:54:27.0128220Z Specifies whether warnings should be captured by a custom 2024-06-26T05:54:27.0128527Z implementation of ``warnings.showwarning()`` and be appended to a list 2024-06-26T05:54:27.0128846Z returned by the context manager. Otherwise None is returned by the 2024-06-26T05:54:27.0129155Z context manager. The objects appended to the list are arguments whose 2024-06-26T05:54:27.0129403Z attributes mirror the arguments to ``showwarning()``. 2024-06-26T05:54:27.0129544Z modules : sequence, optional 2024-06-26T05:54:27.0129880Z Sequence of modules for which to reset warnings registry on entry and 2024-06-26T05:54:27.0130210Z restore on exit. To work correctly, all 'ignore' filters should 2024-06-26T05:54:27.0130362Z filter by one of these modules. 2024-06-26T05:54:27.0130453Z 2024-06-26T05:54:27.0130552Z Examples 2024-06-26T05:54:27.0130686Z -------- 2024-06-26T05:54:27.0130796Z >>> import warnings 2024-06-26T05:54:27.0131046Z >>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP 2024-06-26T05:54:27.0131223Z ... modules=[np.core.fromnumeric]): 2024-06-26T05:54:27.0131427Z ... warnings.simplefilter('always') 2024-06-26T05:54:27.0131775Z ... warnings.filterwarnings('ignore', module='np.core.fromnumeric') 2024-06-26T05:54:27.0132024Z ... # do something that raises a warning but ignore those in 2024-06-26T05:54:27.0132152Z ... # np.core.fromnumeric 2024-06-26T05:54:27.0132257Z 2024-06-26T05:54:27.0132641Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0132730Z 2024-06-26T05:54:27.0132851Z warnings.warn(msg) 2024-06-26T05:54:27.0132938Z 2024-06-26T05:54:27.0133141Z --- Parse Warning: 14 / 90 --- 2024-06-26T05:54:27.0134673Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Conv1d in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py line=273. 2024-06-26T05:54:27.0135077Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0135358Z Applies a 1D convolution over a quantized input signal composed of 2024-06-26T05:54:27.0135508Z several quantized input planes. 2024-06-26T05:54:27.0135599Z 2024-06-26T05:54:27.0135884Z For details on input arguments, parameters, and implementation see 2024-06-26T05:54:27.0136020Z :class:`~torch.nn.Conv1d`. 2024-06-26T05:54:27.0136109Z 2024-06-26T05:54:27.0136229Z .. note:: 2024-06-26T05:54:27.0136493Z Only `zeros` is supported for the :attr:`padding_mode` argument. 2024-06-26T05:54:27.0136580Z 2024-06-26T05:54:27.0136690Z .. note:: 2024-06-26T05:54:27.0136926Z Only `torch.quint8` is supported for the input data type. 2024-06-26T05:54:27.0137015Z 2024-06-26T05:54:27.0137115Z 2024-06-26T05:54:27.0137217Z Attributes: 2024-06-26T05:54:27.0137496Z weight (Tensor): packed tensor derived from the learnable weight 2024-06-26T05:54:27.0137639Z parameter. 2024-06-26T05:54:27.0137827Z scale (Tensor): scalar for the output scale 2024-06-26T05:54:27.0138038Z zero_point (Tensor): scalar for the output zero point 2024-06-26T05:54:27.0138139Z 2024-06-26T05:54:27.0138336Z See :class:`~torch.nn.Conv1d` for other attributes. 2024-06-26T05:54:27.0138426Z 2024-06-26T05:54:27.0138537Z Examples:: 2024-06-26T05:54:27.0138626Z 2024-06-26T05:54:27.0138823Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) 2024-06-26T05:54:27.0139016Z >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2) 2024-06-26T05:54:27.0139217Z >>> input = torch.randn(20, 16, 100) 2024-06-26T05:54:27.0139366Z >>> # quantize input to quint8 2024-06-26T05:54:27.0139485Z >>> # xdoctest: +SKIP 2024-06-26T05:54:27.0139762Z >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, 2024-06-26T05:54:27.0140022Z ... dtype=torch.quint8) 2024-06-26T05:54:27.0140141Z >>> output = m(q_input) 2024-06-26T05:54:27.0140228Z 2024-06-26T05:54:27.0140334Z 2024-06-26T05:54:27.0140759Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0140848Z 2024-06-26T05:54:27.0141004Z warnings.warn(msg) 2024-06-26T05:54:27.0141091Z 2024-06-26T05:54:27.0141291Z --- Parse Warning: 15 / 90 --- 2024-06-26T05:54:27.0142630Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=LSTM in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/rnn.py line=9. 2024-06-26T05:54:27.0143031Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0143247Z A quantized long short-term memory (LSTM). 2024-06-26T05:54:27.0143333Z 2024-06-26T05:54:27.0143710Z For the description and the argument types, please, refer to :class:`~torch.nn.LSTM` 2024-06-26T05:54:27.0143810Z 2024-06-26T05:54:27.0143913Z Attributes: 2024-06-26T05:54:27.0144070Z layers : instances of the `_LSTMLayer` 2024-06-26T05:54:27.0144174Z 2024-06-26T05:54:27.0144272Z .. note:: 2024-06-26T05:54:27.0144566Z To access the weights and biases, you need to access them per layer. 2024-06-26T05:54:27.0144805Z See examples in :class:`~torch.ao.nn.quantizable.LSTM` 2024-06-26T05:54:27.0144892Z 2024-06-26T05:54:27.0144995Z Examples:: 2024-06-26T05:54:27.0145125Z >>> # xdoctest: +SKIP 2024-06-26T05:54:27.0145256Z >>> custom_module_config = { 2024-06-26T05:54:27.0145495Z ... 'float_to_observed_custom_module_class': { 2024-06-26T05:54:27.0145677Z ... nn.LSTM: nn.quantizable.LSTM, 2024-06-26T05:54:27.0145774Z ... }, 2024-06-26T05:54:27.0146027Z ... 'observed_to_quantized_custom_module_class': { 2024-06-26T05:54:27.0146230Z ... nn.quantizable.LSTM: nn.quantized.LSTM, 2024-06-26T05:54:27.0146329Z ... } 2024-06-26T05:54:27.0146439Z ... } 2024-06-26T05:54:27.0146722Z >>> tq.prepare(model, prepare_custom_module_class=custom_module_config) 2024-06-26T05:54:27.0146998Z >>> tq.convert(model, convert_custom_module_class=custom_module_config) 2024-06-26T05:54:27.0147101Z 2024-06-26T05:54:27.0147489Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0147577Z 2024-06-26T05:54:27.0147699Z warnings.warn(msg) 2024-06-26T05:54:27.0147785Z 2024-06-26T05:54:27.0147982Z --- Parse Warning: 16 / 90 --- 2024-06-26T05:54:27.0149543Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=BaseSparsifier.squash_mask in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py line=230. 2024-06-26T05:54:27.0149942Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0150176Z Squashes the sparse masks into the appropriate tensors. 2024-06-26T05:54:27.0150265Z 2024-06-26T05:54:27.0150548Z If either the `params_to_keep` or `params_to_keep_per_layer` is set, 2024-06-26T05:54:27.0150807Z the module will have a `sparse_params` dict attached to it. 2024-06-26T05:54:27.0150893Z 2024-06-26T05:54:27.0150989Z Args: 2024-06-26T05:54:27.0151277Z params_to_keep: List of keys to save in the module or a dict 2024-06-26T05:54:27.0151493Z representing the modules and keys that will have 2024-06-26T05:54:27.0151658Z sparsity parameters saved 2024-06-26T05:54:27.0151950Z params_to_keep_per_layer: Dict to specify the params that should be 2024-06-26T05:54:27.0152188Z saved for specific layers. The keys in the dict 2024-06-26T05:54:27.0152405Z should be the module fqn, while the values should 2024-06-26T05:54:27.0152675Z be a list of strings with the names of the variables 2024-06-26T05:54:27.0152869Z to save in the `sparse_params` 2024-06-26T05:54:27.0152966Z 2024-06-26T05:54:27.0153066Z Examples: 2024-06-26T05:54:27.0153243Z >>> # xdoctest: +SKIP("locals are undefined") 2024-06-26T05:54:27.0153456Z >>> # Don't save any sparse params 2024-06-26T05:54:27.0153602Z >>> sparsifier.squash_mask() 2024-06-26T05:54:27.0153843Z >>> hasattr(model.submodule1, 'sparse_params') 2024-06-26T05:54:27.0153951Z False 2024-06-26T05:54:27.0154040Z 2024-06-26T05:54:27.0154196Z >>> # Keep sparse params per layer 2024-06-26T05:54:27.0154350Z >>> sparsifier.squash_mask( 2024-06-26T05:54:27.0154496Z ... params_to_keep_per_layer={ 2024-06-26T05:54:27.0154732Z ... 'submodule1.linear1': ('foo', 'bar'), 2024-06-26T05:54:27.0154970Z ... 'submodule2.linear42': ('baz',) 2024-06-26T05:54:27.0155076Z ... }) 2024-06-26T05:54:27.0155299Z >>> print(model.submodule1.linear1.sparse_params) 2024-06-26T05:54:27.0155459Z {'foo': 42, 'bar': 24} 2024-06-26T05:54:27.0155670Z >>> print(model.submodule2.linear42.sparse_params) 2024-06-26T05:54:27.0155820Z {'baz': 0.1} 2024-06-26T05:54:27.0155909Z 2024-06-26T05:54:27.0156075Z >>> # Keep sparse params for all layers 2024-06-26T05:54:27.0156370Z >>> sparsifier.squash_mask(params_to_keep=('foo', 'bar')) 2024-06-26T05:54:27.0156577Z >>> print(model.submodule1.linear1.sparse_params) 2024-06-26T05:54:27.0156736Z {'foo': 42, 'bar': 24} 2024-06-26T05:54:27.0156960Z >>> print(model.submodule2.linear42.sparse_params) 2024-06-26T05:54:27.0157115Z {'foo': 42, 'bar': 24} 2024-06-26T05:54:27.0157206Z 2024-06-26T05:54:27.0157488Z >>> # Keep some sparse params for all layers, and specific ones for 2024-06-26T05:54:27.0157608Z >>> # some other layers 2024-06-26T05:54:27.0157748Z >>> sparsifier.squash_mask( 2024-06-26T05:54:27.0157964Z ... params_to_keep=('foo', 'bar'), 2024-06-26T05:54:27.0158112Z ... params_to_keep_per_layer={ 2024-06-26T05:54:27.0158349Z ... 'submodule2.linear42': ('baz',) 2024-06-26T05:54:27.0158448Z ... }) 2024-06-26T05:54:27.0158656Z >>> print(model.submodule1.linear1.sparse_params) 2024-06-26T05:54:27.0158825Z {'foo': 42, 'bar': 24} 2024-06-26T05:54:27.0159033Z >>> print(model.submodule2.linear42.sparse_params) 2024-06-26T05:54:27.0159221Z {'foo': 42, 'bar': 24, 'baz': 0.1} 2024-06-26T05:54:27.0159329Z 2024-06-26T05:54:27.0159718Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0159808Z 2024-06-26T05:54:27.0159931Z warnings.warn(msg) 2024-06-26T05:54:27.0160018Z 2024-06-26T05:54:27.0160214Z --- Parse Warning: 17 / 90 --- 2024-06-26T05:54:27.0161876Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DTypeConfig in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/backend_config/backend_config.py line=178. 2024-06-26T05:54:27.0162289Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0162393Z 2024-06-26T05:54:27.0162722Z Config object that specifies the supported data types passed as arguments to 2024-06-26T05:54:27.0163077Z quantize ops in the reference model spec, for input and output activations, 2024-06-26T05:54:27.0163201Z weights, and biases. 2024-06-26T05:54:27.0163288Z 2024-06-26T05:54:27.0163521Z For example, consider the following reference model: 2024-06-26T05:54:27.0163621Z 2024-06-26T05:54:27.0163906Z quant1 - [dequant1 - fp32_linear - quant2] - dequant2 2024-06-26T05:54:27.0163994Z 2024-06-26T05:54:27.0164300Z The pattern in the square brackets refers to the reference pattern of 2024-06-26T05:54:27.0164596Z statically quantized linear. Setting the input dtype as `torch.quint8` 2024-06-26T05:54:27.0164902Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument 2024-06-26T05:54:27.0165218Z to the first quantize op (quant1). Similarly, setting the output dtype as 2024-06-26T05:54:27.0165509Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to 2024-06-26T05:54:27.0165652Z the second quantize op (quant2). 2024-06-26T05:54:27.0165742Z 2024-06-26T05:54:27.0166033Z Note that the dtype here does not refer to the interface dtypes of the 2024-06-26T05:54:27.0166324Z op. For example, the "input dtype" here is not the dtype of the input 2024-06-26T05:54:27.0166611Z tensor passed to the quantized linear op. Though it can still be the 2024-06-26T05:54:27.0166883Z same as the interface dtype, this is not always the case, e.g. the 2024-06-26T05:54:27.0167184Z interface dtype is fp32 in dynamic quantization but the "input dtype" 2024-06-26T05:54:27.0167471Z specified in the DTypeConfig would still be quint8. The semantics of 2024-06-26T05:54:27.0167754Z dtypes here are the same as the semantics of the dtypes specified in 2024-06-26T05:54:27.0167870Z the observers. 2024-06-26T05:54:27.0167957Z 2024-06-26T05:54:27.0168253Z These dtypes are matched against the ones specified in the user's 2024-06-26T05:54:27.0168562Z QConfig. If there is a match, and the QConfig satisfies the constraints 2024-06-26T05:54:27.0168856Z specified in the DTypeConfig (if any), then we will quantize the given 2024-06-26T05:54:27.0169157Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and 2024-06-26T05:54:27.0169295Z the pattern will not be quantized. 2024-06-26T05:54:27.0169383Z 2024-06-26T05:54:27.0169510Z Example usage:: 2024-06-26T05:54:27.0169597Z 2024-06-26T05:54:27.0169722Z >>> # xdoctest: +SKIP(failing) 2024-06-26T05:54:27.0169871Z >>> dtype_config1 = DTypeConfig( 2024-06-26T05:54:27.0170001Z ... input_dtype=torch.quint8, 2024-06-26T05:54:27.0170136Z ... output_dtype=torch.quint8, 2024-06-26T05:54:27.0170277Z ... weight_dtype=torch.qint8, 2024-06-26T05:54:27.0170402Z ... bias_dtype=torch.float) 2024-06-26T05:54:27.0170487Z 2024-06-26T05:54:27.0170631Z >>> dtype_config2 = DTypeConfig( 2024-06-26T05:54:27.0170801Z ... input_dtype=DTypeWithConstraints( 2024-06-26T05:54:27.0170923Z ... dtype=torch.quint8, 2024-06-26T05:54:27.0171069Z ... quant_min_lower_bound=0, 2024-06-26T05:54:27.0171210Z ... quant_max_upper_bound=255, 2024-06-26T05:54:27.0171320Z ... ), 2024-06-26T05:54:27.0171483Z ... output_dtype=DTypeWithConstraints( 2024-06-26T05:54:27.0171605Z ... dtype=torch.quint8, 2024-06-26T05:54:27.0171750Z ... quant_min_lower_bound=0, 2024-06-26T05:54:27.0171888Z ... quant_max_upper_bound=255, 2024-06-26T05:54:27.0171982Z ... ), 2024-06-26T05:54:27.0172183Z ... weight_dtype=DTypeWithConstraints( 2024-06-26T05:54:27.0172304Z ... dtype=torch.qint8, 2024-06-26T05:54:27.0172494Z ... quant_min_lower_bound=-128, 2024-06-26T05:54:27.0172643Z ... quant_max_upper_bound=127, 2024-06-26T05:54:27.0172739Z ... ), 2024-06-26T05:54:27.0172860Z ... bias_dtype=torch.float) 2024-06-26T05:54:27.0172999Z 2024-06-26T05:54:27.0173127Z >>> dtype_config1.input_dtype 2024-06-26T05:54:27.0173228Z torch.quint8 2024-06-26T05:54:27.0173330Z 2024-06-26T05:54:27.0173454Z >>> dtype_config2.input_dtype 2024-06-26T05:54:27.0173699Z torch.quint8 2024-06-26T05:54:27.0173796Z 2024-06-26T05:54:27.0174017Z >>> dtype_config2.input_dtype_with_constraints 2024-06-26T05:54:27.0174717Z DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None) 2024-06-26T05:54:27.0174805Z 2024-06-26T05:54:27.0175195Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0175297Z 2024-06-26T05:54:27.0175406Z warnings.warn(msg) 2024-06-26T05:54:27.0175493Z 2024-06-26T05:54:27.0175706Z --- Parse Warning: 18 / 90 --- 2024-06-26T05:54:27.0177494Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_filtered_tables in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=287. 2024-06-26T05:54:27.0177900Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0178002Z 2024-06-26T05:54:27.0178354Z Takes in optional filter values and generates two tables with desired information. 2024-06-26T05:54:27.0178452Z 2024-06-26T05:54:27.0178785Z The generated tables are presented in both a list-of-lists format 2024-06-26T05:54:27.0178876Z 2024-06-26T05:54:27.0179169Z The reason for the two tables are that they handle different things: 2024-06-26T05:54:27.0179382Z 1.) the first table handles all tensor level information 2024-06-26T05:54:27.0179677Z 2.) the second table handles and displays all channel based information 2024-06-26T05:54:27.0179779Z 2024-06-26T05:54:27.0180234Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2024-06-26T05:54:27.0180758Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2024-06-26T05:54:27.0181253Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2024-06-26T05:54:27.0181342Z 2024-06-26T05:54:27.0181455Z Tensor table columns: 2024-06-26T05:54:27.0181723Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2024-06-26T05:54:27.0181971Z ---- --------- --------- --------- --------- --------- 2024-06-26T05:54:27.0182071Z 2024-06-26T05:54:27.0182227Z Per-Channel table columns: 2024-06-26T05:54:27.0182525Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2024-06-26T05:54:27.0182810Z ---- --------- ------- --------- --------- --------- --------- 2024-06-26T05:54:27.0182900Z 2024-06-26T05:54:27.0182991Z Args: 2024-06-26T05:54:27.0183348Z feature_filter (str, optional): Filters the features presented to only those that 2024-06-26T05:54:27.0183488Z contain this filter substring 2024-06-26T05:54:27.0183701Z Default = "", results in all the features being printed 2024-06-26T05:54:27.0184057Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-06-26T05:54:27.0184396Z Default = "", results in all the modules in the reports to be visible in the table 2024-06-26T05:54:27.0184483Z 2024-06-26T05:54:27.0184670Z Returns a dictionary with two keys: 2024-06-26T05:54:27.0184897Z (Dict[str, Tuple[List, List]]) A dict containing two keys: 2024-06-26T05:54:27.0185066Z "tensor_level_info", "channel_level_info" 2024-06-26T05:54:27.0185207Z Each key maps to a tuple with: 2024-06-26T05:54:27.0185362Z A list of the headers of each table 2024-06-26T05:54:27.0185659Z A list of lists containing the table information row by row 2024-06-26T05:54:27.0185897Z The 0th index row will contain the headers of the columns 2024-06-26T05:54:27.0186097Z The rest of the rows will contain data 2024-06-26T05:54:27.0186196Z 2024-06-26T05:54:27.0186321Z Example Use: 2024-06-26T05:54:27.0186490Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:27.0186699Z >>> mod_report_visualizer.generate_filtered_tables( 2024-06-26T05:54:27.0186857Z ... feature_filter = "per_channel_min", 2024-06-26T05:54:27.0186995Z ... module_fqn_filter = "block1" 2024-06-26T05:54:27.0187370Z ... ) # generates table with per_channel_min info for all modules in block 1 of the model 2024-06-26T05:54:27.0187459Z 2024-06-26T05:54:27.0187857Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0187947Z 2024-06-26T05:54:27.0188057Z warnings.warn(msg) 2024-06-26T05:54:27.0188157Z 2024-06-26T05:54:27.0188359Z --- Parse Warning: 19 / 90 --- 2024-06-26T05:54:27.0190173Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_table_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=380. 2024-06-26T05:54:27.0190587Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0190676Z 2024-06-26T05:54:27.0191041Z Takes in optional filter values and prints out formatted tables of the information. 2024-06-26T05:54:27.0191139Z 2024-06-26T05:54:27.0191616Z The reason for the two tables printed out instead of one large one are that they handle different things: 2024-06-26T05:54:27.0191845Z 1.) the first table handles all tensor level information 2024-06-26T05:54:27.0192139Z 2.) the second table handles and displays all channel based information 2024-06-26T05:54:27.0192225Z 2024-06-26T05:54:27.0192694Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2024-06-26T05:54:27.0193221Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2024-06-26T05:54:27.0193704Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2024-06-26T05:54:27.0193799Z 2024-06-26T05:54:27.0193911Z Tensor table columns: 2024-06-26T05:54:27.0194168Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2024-06-26T05:54:27.0194421Z ---- --------- --------- --------- --------- --------- 2024-06-26T05:54:27.0194508Z 2024-06-26T05:54:27.0194661Z Per-Channel table columns: 2024-06-26T05:54:27.0194761Z 2024-06-26T05:54:27.0195057Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2024-06-26T05:54:27.0195331Z ---- --------- ------- --------- --------- --------- --------- 2024-06-26T05:54:27.0195419Z 2024-06-26T05:54:27.0195512Z Args: 2024-06-26T05:54:27.0195867Z feature_filter (str, optional): Filters the features presented to only those that 2024-06-26T05:54:27.0196005Z contain this filter substring 2024-06-26T05:54:27.0196220Z Default = "", results in all the features being printed 2024-06-26T05:54:27.0196573Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-06-26T05:54:27.0196943Z Default = "", results in all the modules in the reports to be visible in the table 2024-06-26T05:54:27.0197031Z 2024-06-26T05:54:27.0197147Z Example Use: 2024-06-26T05:54:27.0197318Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:27.0197530Z >>> mod_report_visualizer.generate_table_visualization( 2024-06-26T05:54:27.0197740Z ... feature_filter = "per_channel_min", 2024-06-26T05:54:27.0197879Z ... module_fqn_filter = "block1" 2024-06-26T05:54:27.0197983Z ... ) 2024-06-26T05:54:27.0198265Z >>> # prints out neatly formatted table with per_channel_min info 2024-06-26T05:54:27.0198458Z >>> # for all modules in block 1 of the model 2024-06-26T05:54:27.0198560Z 2024-06-26T05:54:27.0198947Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0199035Z 2024-06-26T05:54:27.0199159Z warnings.warn(msg) 2024-06-26T05:54:27.0199247Z 2024-06-26T05:54:27.0199450Z --- Parse Warning: 20 / 90 --- 2024-06-26T05:54:27.0201364Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_plot_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=533. 2024-06-26T05:54:27.0201772Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0201861Z 2024-06-26T05:54:27.0202196Z Takes in a feature and optional module_filter and plots of the desired data. 2024-06-26T05:54:27.0202286Z 2024-06-26T05:54:27.0202675Z For per channel features, it averages the value across the channels and plots a point 2024-06-26T05:54:27.0203035Z per module. The reason for this is that for models with hundreds of channels, it can 2024-06-26T05:54:27.0203413Z be hard to differentiate one channel line from another, and so the point of generating 2024-06-26T05:54:27.0203796Z a single average point per module is to give a sense of general trends that encourage 2024-06-26T05:54:27.0203907Z further deep dives. 2024-06-26T05:54:27.0203996Z 2024-06-26T05:54:27.0204101Z Note: 2024-06-26T05:54:27.0204462Z Only features in the report that have tensor value data are plottable by this class 2024-06-26T05:54:27.0204677Z When the tensor information is plotted, it will plot: 2024-06-26T05:54:27.0204871Z idx as the x val, feature value as the y_val 2024-06-26T05:54:27.0205092Z When the channel information is plotted, it will plot: 2024-06-26T05:54:27.0205493Z the first idx of each module as the x val, feature value as the y_val [for each channel] 2024-06-26T05:54:27.0205815Z The reason for this is that we want to be able to compare values across the 2024-06-26T05:54:27.0206140Z channels for same layer, and it will be hard if values are staggered by idx 2024-06-26T05:54:27.0206377Z This means each module is represented by only 1 x value 2024-06-26T05:54:27.0206474Z Args: 2024-06-26T05:54:27.0206773Z feature_filter (str): Filters the features presented to only those that 2024-06-26T05:54:27.0206923Z contain this filter substring 2024-06-26T05:54:27.0207263Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-06-26T05:54:27.0207604Z Default = "", results in all the modules in the reports to be visible in the table 2024-06-26T05:54:27.0207702Z 2024-06-26T05:54:27.0207805Z Example Use: 2024-06-26T05:54:27.0207975Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:27.0208196Z >>> mod_report_visualizer.generate_plot_visualization( 2024-06-26T05:54:27.0208356Z ... feature_filter = "per_channel_min", 2024-06-26T05:54:27.0208509Z ... module_fqn_filter = "block1" 2024-06-26T05:54:27.0208601Z ... ) 2024-06-26T05:54:27.0208891Z >>> # outputs line plot of per_channel_min information for all 2024-06-26T05:54:27.0209215Z >>> # modules in block1 of model each channel gets it's own line, 2024-06-26T05:54:27.0209516Z >>> # and it's plotted across the in-order modules on the x-axis 2024-06-26T05:54:27.0209604Z 2024-06-26T05:54:27.0210031Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0210118Z 2024-06-26T05:54:27.0210229Z warnings.warn(msg) 2024-06-26T05:54:27.0210327Z 2024-06-26T05:54:27.0210524Z --- Parse Warning: 21 / 90 --- 2024-06-26T05:54:27.0212431Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_histogram_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=601. 2024-06-26T05:54:27.0212846Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0212935Z 2024-06-26T05:54:27.0213322Z Takes in a feature and optional module_filter and plots the histogram of desired data. 2024-06-26T05:54:27.0213409Z 2024-06-26T05:54:27.0213646Z Note: 2024-06-26T05:54:27.0214034Z Only features in the report that have tensor value data can be viewed as a histogram 2024-06-26T05:54:27.0214410Z If you want to plot a histogram from all the channel values of a specific feature for 2024-06-26T05:54:27.0214750Z a specific model, make sure to specify both the model and the feature properly 2024-06-26T05:54:27.0215114Z in the filters and you should be able to see a distribution of the channel data 2024-06-26T05:54:27.0215202Z 2024-06-26T05:54:27.0215299Z Args: 2024-06-26T05:54:27.0215658Z feature_filter (str, optional): Filters the features presented to only those that 2024-06-26T05:54:27.0215795Z contain this filter substring 2024-06-26T05:54:27.0216025Z Default = "", results in all the features being printed 2024-06-26T05:54:27.0216365Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-06-26T05:54:27.0216701Z Default = "", results in all the modules in the reports to be visible in the table 2024-06-26T05:54:27.0217019Z num_bins (int, optional): The number of bins to create the histogram with 2024-06-26T05:54:27.0217273Z Default = 10, the values will be split into 10 equal sized bins 2024-06-26T05:54:27.0217362Z 2024-06-26T05:54:27.0217477Z Example Use: 2024-06-26T05:54:27.0217593Z >>> # xdoctest: +SKIP 2024-06-26T05:54:27.0217971Z >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization( 2024-06-26T05:54:27.0218147Z ... feature_filter = "per_channel_min", 2024-06-26T05:54:27.0218286Z ... module_fqn_filter = "block1" 2024-06-26T05:54:27.0218392Z ... ) 2024-06-26T05:54:27.0218759Z # outputs histogram of per_channel_min information for all modules in block1 of model 2024-06-26T05:54:27.0219101Z information is gathered across all channels for all modules in block 1 for the 2024-06-26T05:54:27.0219405Z per_channel_min and is displayed in a histogram of equally sized bins 2024-06-26T05:54:27.0219494Z 2024-06-26T05:54:27.0219879Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0219979Z 2024-06-26T05:54:27.0220091Z warnings.warn(msg) 2024-06-26T05:54:27.0220179Z 2024-06-26T05:54:27.0220388Z --- Parse Warning: 22 / 90 --- 2024-06-26T05:54:27.0221799Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=gather_object in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py line=2736. 2024-06-26T05:54:27.0222254Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0222344Z 2024-06-26T05:54:27.0222628Z Gathers picklable objects from the whole group in a single process. 2024-06-26T05:54:27.0222727Z 2024-06-26T05:54:27.0223050Z Similar to :func:`gather`, but Python objects can be passed in. Note that the 2024-06-26T05:54:27.0223278Z object must be picklable in order to be gathered. 2024-06-26T05:54:27.0223378Z 2024-06-26T05:54:27.0223471Z Args: 2024-06-26T05:54:27.0223641Z obj (Any): Input object. Must be picklable. 2024-06-26T05:54:27.0223968Z object_gather_list (list[Any]): Output list. On the ``dst`` rank, it 2024-06-26T05:54:27.0224248Z should be correctly sized as the size of the group for this 2024-06-26T05:54:27.0224597Z collective and will contain the output. Must be ``None`` on non-dst 2024-06-26T05:54:27.0224740Z ranks. (default is ``None``) 2024-06-26T05:54:27.0225248Z dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). (default is 0) 2024-06-26T05:54:27.0225558Z group: (ProcessGroup, optional): The process group to work on. If None, 2024-06-26T05:54:27.0225807Z the default process group will be used. Default is ``None``. 2024-06-26T05:54:27.0225894Z 2024-06-26T05:54:27.0226003Z Returns: 2024-06-26T05:54:27.0226260Z None. On the ``dst`` rank, ``object_gather_list`` will contain the 2024-06-26T05:54:27.0226382Z output of the collective. 2024-06-26T05:54:27.0226487Z 2024-06-26T05:54:27.0226789Z .. note:: Note that this API differs slightly from the gather collective 2024-06-26T05:54:27.0227093Z since it does not provide an async_op handle and thus will be a blocking 2024-06-26T05:54:27.0227197Z call. 2024-06-26T05:54:27.0227286Z 2024-06-26T05:54:27.0227656Z .. note:: For NCCL-based processed groups, internal tensor representations 2024-06-26T05:54:27.0227960Z of objects must be moved to the GPU device before communication takes 2024-06-26T05:54:27.0228149Z place. In this case, the device used is given by 2024-06-26T05:54:27.0228511Z ``torch.cuda.current_device()`` and it is the user's responsiblity to 2024-06-26T05:54:27.0228797Z ensure that this is set so that each rank has an individual GPU, via 2024-06-26T05:54:27.0228922Z ``torch.cuda.set_device()``. 2024-06-26T05:54:27.0229019Z 2024-06-26T05:54:27.0229119Z .. warning:: 2024-06-26T05:54:27.0229380Z :func:`gather_object` uses ``pickle`` module implicitly, which is 2024-06-26T05:54:27.0229696Z known to be insecure. It is possible to construct malicious pickle data 2024-06-26T05:54:27.0229977Z which will execute arbitrary code during unpickling. Only call this 2024-06-26T05:54:27.0230107Z function with data you trust. 2024-06-26T05:54:27.0230205Z 2024-06-26T05:54:27.0230306Z .. warning:: 2024-06-26T05:54:27.0230584Z Calling :func:`gather_object` with GPU tensors is not well supported 2024-06-26T05:54:27.0230957Z and inefficient as it incurs GPU -> CPU transfer since tensors would be 2024-06-26T05:54:27.0231175Z pickled. Please consider using :func:`gather` instead. 2024-06-26T05:54:27.0231272Z 2024-06-26T05:54:27.0231369Z Example:: 2024-06-26T05:54:27.0231554Z >>> # xdoctest: +SKIP("need process group init") 2024-06-26T05:54:27.0231804Z >>> # Note: Process group initialization omitted on each rank. 2024-06-26T05:54:27.0231953Z >>> import torch.distributed as dist 2024-06-26T05:54:27.0232082Z >>> # Assumes world_size of 3. 2024-06-26T05:54:27.0232332Z >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object 2024-06-26T05:54:27.0232495Z >>> output = [None for _ in gather_objects] 2024-06-26T05:54:27.0232609Z >>> dist.gather_object( 2024-06-26T05:54:27.0232770Z ... gather_objects[dist.get_rank()], 2024-06-26T05:54:27.0232992Z ... output if dist.get_rank() == 0 else None, 2024-06-26T05:54:27.0233094Z ... dst=0 2024-06-26T05:54:27.0233199Z ... ) 2024-06-26T05:54:27.0233302Z >>> # On rank 0 2024-06-26T05:54:27.0233397Z >>> output 2024-06-26T05:54:27.0233547Z ['foo', 12, {1: 2}] 2024-06-26T05:54:27.0233637Z 2024-06-26T05:54:27.0234053Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0234156Z 2024-06-26T05:54:27.0234265Z warnings.warn(msg) 2024-06-26T05:54:27.0234351Z 2024-06-26T05:54:27.0234596Z --- Parse Warning: 23 / 90 --- 2024-06-26T05:54:27.0235893Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=__doc__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/launch.py line=2. 2024-06-26T05:54:27.0236305Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0236394Z 2024-06-26T05:54:27.0236544Z Module ``torch.distributed.launch``. 2024-06-26T05:54:27.0236643Z 2024-06-26T05:54:27.0236971Z ``torch.distributed.launch`` is a module that spawns up multiple distributed 2024-06-26T05:54:27.0237164Z training processes on each of the training nodes. 2024-06-26T05:54:27.0237261Z 2024-06-26T05:54:27.0237366Z .. warning:: 2024-06-26T05:54:27.0237453Z 2024-06-26T05:54:27.0237887Z This module is going to be deprecated in favor of :ref:`torchrun `. 2024-06-26T05:54:27.0237973Z 2024-06-26T05:54:27.0238369Z The utility can be used for single-node distributed training, in which one or 2024-06-26T05:54:27.0238705Z more processes per node will be spawned. The utility can be used for either 2024-06-26T05:54:27.0239001Z CPU training or GPU training. If the utility is used for GPU training, 2024-06-26T05:54:27.0239340Z each distributed process will be operating on a single GPU. This can achieve 2024-06-26T05:54:27.0239700Z well-improved single-node training performance. It can also be used in 2024-06-26T05:54:27.0240106Z multi-node distributed training, by spawning up multiple processes on each node 2024-06-26T05:54:27.0240477Z for well-improved multi-node distributed training performance as well. 2024-06-26T05:54:27.0240853Z This will especially be beneficial for systems with multiple Infiniband 2024-06-26T05:54:27.0241261Z interfaces that have direct-GPU support, since all of them can be utilized for 2024-06-26T05:54:27.0241414Z aggregated communication bandwidth. 2024-06-26T05:54:27.0241504Z 2024-06-26T05:54:27.0241887Z In both cases of single-node distributed training or multi-node distributed 2024-06-26T05:54:27.0242208Z training, this utility will launch the given number of processes per node 2024-06-26T05:54:27.0242583Z (``--nproc-per-node``). If used for GPU training, this number needs to be less 2024-06-26T05:54:27.0242910Z or equal to the number of GPUs on the current system (``nproc_per_node``), 2024-06-26T05:54:27.0243179Z and each process will be operating on a single GPU from *GPU 0 to 2024-06-26T05:54:27.0243335Z GPU (nproc_per_node - 1)*. 2024-06-26T05:54:27.0243437Z 2024-06-26T05:54:27.0243553Z **How to use this module:** 2024-06-26T05:54:27.0243638Z 2024-06-26T05:54:27.0243899Z 1. Single-Node multi-process distributed training 2024-06-26T05:54:27.0243986Z 2024-06-26T05:54:27.0244077Z :: 2024-06-26T05:54:27.0244176Z 2024-06-26T05:54:27.0244541Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-06-26T05:54:27.0244853Z YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other 2024-06-26T05:54:27.0245030Z arguments of your training script) 2024-06-26T05:54:27.0245119Z 2024-06-26T05:54:27.0245454Z 2. Multi-Node multi-process distributed training: (e.g. two nodes) 2024-06-26T05:54:27.0245555Z 2024-06-26T05:54:27.0245641Z 2024-06-26T05:54:27.0245883Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)* 2024-06-26T05:54:27.0245972Z 2024-06-26T05:54:27.0246063Z :: 2024-06-26T05:54:27.0246163Z 2024-06-26T05:54:27.0246525Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-06-26T05:54:27.0246791Z --nnodes=2 --node-rank=0 --master-addr="192.168.1.1" 2024-06-26T05:54:27.0247168Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2024-06-26T05:54:27.0247371Z and all other arguments of your training script) 2024-06-26T05:54:27.0247485Z 2024-06-26T05:54:27.0247591Z Node 2: 2024-06-26T05:54:27.0247679Z 2024-06-26T05:54:27.0247796Z :: 2024-06-26T05:54:27.0247897Z 2024-06-26T05:54:27.0248260Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-06-26T05:54:27.0248519Z --nnodes=2 --node-rank=1 --master-addr="192.168.1.1" 2024-06-26T05:54:27.0248866Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2024-06-26T05:54:27.0249070Z and all other arguments of your training script) 2024-06-26T05:54:27.0249156Z 2024-06-26T05:54:27.0249394Z 3. To look up what optional arguments this module offers: 2024-06-26T05:54:27.0249483Z 2024-06-26T05:54:27.0249586Z :: 2024-06-26T05:54:27.0249676Z 2024-06-26T05:54:27.0249891Z python -m torch.distributed.launch --help 2024-06-26T05:54:27.0249992Z 2024-06-26T05:54:27.0250077Z 2024-06-26T05:54:27.0250189Z **Important Notices:** 2024-06-26T05:54:27.0250292Z 2024-06-26T05:54:27.0250591Z 1. This utility and multi-process distributed (single-node or 2024-06-26T05:54:27.0250975Z multi-node) GPU training currently only achieves the best performance using 2024-06-26T05:54:27.0251317Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to 2024-06-26T05:54:27.0251432Z use for GPU training. 2024-06-26T05:54:27.0251519Z 2024-06-26T05:54:27.0251887Z 2. In your training program, you must parse the command-line argument: 2024-06-26T05:54:27.0252249Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module. 2024-06-26T05:54:27.0252557Z If your training program uses GPUs, you should ensure that your code only 2024-06-26T05:54:27.0252840Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by: 2024-06-26T05:54:27.0252929Z 2024-06-26T05:54:27.0253056Z Parsing the local_rank argument 2024-06-26T05:54:27.0253156Z 2024-06-26T05:54:27.0253250Z :: 2024-06-26T05:54:27.0253349Z 2024-06-26T05:54:27.0253463Z >>> # xdoctest: +SKIP 2024-06-26T05:54:27.0253693Z >>> import argparse 2024-06-26T05:54:27.0253872Z >>> parser = argparse.ArgumentParser() 2024-06-26T05:54:27.0254182Z >>> parser.add_argument("--local-rank", "--local_rank", type=int) 2024-06-26T05:54:27.0254314Z >>> args = parser.parse_args() 2024-06-26T05:54:27.0254415Z 2024-06-26T05:54:27.0254580Z Set your device to local rank using either 2024-06-26T05:54:27.0254669Z 2024-06-26T05:54:27.0254774Z :: 2024-06-26T05:54:27.0254861Z 2024-06-26T05:54:27.0255127Z >>> torch.cuda.set_device(args.local_rank) # before your code runs 2024-06-26T05:54:27.0255231Z 2024-06-26T05:54:27.0255320Z or 2024-06-26T05:54:27.0255407Z 2024-06-26T05:54:27.0255511Z :: 2024-06-26T05:54:27.0255599Z 2024-06-26T05:54:27.0255770Z >>> with torch.cuda.device(args.local_rank): 2024-06-26T05:54:27.0255898Z >>> # your code to run 2024-06-26T05:54:27.0255995Z >>> ... 2024-06-26T05:54:27.0256082Z 2024-06-26T05:54:27.0256214Z .. versionchanged:: 2.0.0 2024-06-26T05:54:27.0256303Z 2024-06-26T05:54:27.0256699Z The launcher will passes the ``--local-rank=`` argument to your script. 2024-06-26T05:54:27.0257107Z From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the 2024-06-26T05:54:27.0257390Z previously used underscored ``--local_rank``. 2024-06-26T05:54:27.0257497Z 2024-06-26T05:54:27.0257811Z For backward compatibility, it may be necessary for users to handle both 2024-06-26T05:54:27.0258235Z cases in their argument parsing code. This means including both ``"--local-rank"`` 2024-06-26T05:54:27.0258658Z and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is 2024-06-26T05:54:27.0258985Z provided, the launcher will trigger an error: "error: unrecognized arguments: 2024-06-26T05:54:27.0259387Z --local-rank=". For training code that only supports PyTorch 2.0.0+, 2024-06-26T05:54:27.0259675Z including ``"--local-rank"`` should be sufficient. 2024-06-26T05:54:27.0259764Z 2024-06-26T05:54:27.0260087Z 3. In your training program, you are supposed to call the following function 2024-06-26T05:54:27.0260429Z at the beginning to start the distributed backend. It is strongly recommended 2024-06-26T05:54:27.0260725Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work, 2024-06-26T05:54:27.0261007Z but ``env://`` is the one that is officially supported by this module. 2024-06-26T05:54:27.0261093Z 2024-06-26T05:54:27.0261183Z :: 2024-06-26T05:54:27.0261279Z 2024-06-26T05:54:27.0261605Z >>> torch.distributed.init_process_group(backend='YOUR BACKEND', 2024-06-26T05:54:27.0261848Z >>> init_method='env://') 2024-06-26T05:54:27.0261947Z 2024-06-26T05:54:27.0262268Z 4. In your training program, you can either use regular distributed functions 2024-06-26T05:54:27.0262604Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your 2024-06-26T05:54:27.0262885Z training program uses GPUs for training and you would like to use 2024-06-26T05:54:27.0263138Z :func:`torch.nn.parallel.DistributedDataParallel` module, 2024-06-26T05:54:27.0263262Z here is how to configure it. 2024-06-26T05:54:27.0263363Z 2024-06-26T05:54:27.0263452Z :: 2024-06-26T05:54:27.0263538Z 2024-06-26T05:54:27.0263815Z >>> model = torch.nn.parallel.DistributedDataParallel(model, 2024-06-26T05:54:27.0264017Z >>> device_ids=[args.local_rank], 2024-06-26T05:54:27.0264230Z >>> output_device=args.local_rank) 2024-06-26T05:54:27.0264316Z 2024-06-26T05:54:27.0264640Z Please ensure that ``device_ids`` argument is set to be the only GPU device id 2024-06-26T05:54:27.0264976Z that your code will be operating on. This is generally the local rank of the 2024-06-26T05:54:27.0265294Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``, 2024-06-26T05:54:27.0265586Z and ``output_device`` needs to be ``args.local_rank`` in order to use this 2024-06-26T05:54:27.0265689Z utility 2024-06-26T05:54:27.0265775Z 2024-06-26T05:54:27.0266114Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable 2024-06-26T05:54:27.0266415Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with 2024-06-26T05:54:27.0266773Z ``--use-env=True``. You must adjust the subprocess example above to replace 2024-06-26T05:54:27.0267105Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher 2024-06-26T05:54:27.0267384Z will not pass ``--local-rank`` when you specify this flag. 2024-06-26T05:54:27.0267471Z 2024-06-26T05:54:27.0267583Z .. warning:: 2024-06-26T05:54:27.0267673Z 2024-06-26T05:54:27.0267944Z ``local_rank`` is NOT globally unique: it is only unique per process 2024-06-26T05:54:27.0268280Z on a machine. Thus, don't use it to decide if you should, e.g., 2024-06-26T05:54:27.0268434Z write to a networked filesystem. See 2024-06-26T05:54:27.0268715Z https://github.com/pytorch/pytorch/issues/12042 for an example of 2024-06-26T05:54:27.0269030Z how things can go wrong if you don't do this correctly. 2024-06-26T05:54:27.0269118Z 2024-06-26T05:54:27.0269202Z 2024-06-26T05:54:27.0269301Z 2024-06-26T05:54:27.0269385Z 2024-06-26T05:54:27.0269770Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0269870Z 2024-06-26T05:54:27.0270006Z warnings.warn(msg) 2024-06-26T05:54:27.0270091Z 2024-06-26T05:54:27.0270304Z --- Parse Warning: 24 / 90 --- 2024-06-26T05:54:27.0271859Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=init_from_local_shards in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py line=361. 2024-06-26T05:54:27.0272291Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0272377Z 2024-06-26T05:54:27.0272695Z Creates an :class:`ShardedTensor` from local shards and the global metadata. 2024-06-26T05:54:27.0272901Z Needs to be called on all ranks in an SPMD fashion. 2024-06-26T05:54:27.0272989Z 2024-06-26T05:54:27.0273082Z Args: 2024-06-26T05:54:27.0273447Z local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list 2024-06-26T05:54:27.0273671Z of shards that represent the local shards on this rank. 2024-06-26T05:54:27.0273982Z global_size (int...): a list, tuple, or `torch.Size` of integers defining the 2024-06-26T05:54:27.0274150Z shape of the overall sharded tensor. 2024-06-26T05:54:27.0274239Z 2024-06-26T05:54:27.0274339Z Keyword args: 2024-06-26T05:54:27.0274683Z process_group (ProcessGroup, optional): The process group to work on. If None, 2024-06-26T05:54:27.0274844Z the default process group will be used. 2024-06-26T05:54:27.0275078Z init_rrefs (bool, optional): Whether or not to initialize 2024-06-26T05:54:27.0275353Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2024-06-26T05:54:27.0275612Z Need to initialize the RPC Framework if specified as ``True``. 2024-06-26T05:54:27.0275735Z Default: ``False``. 2024-06-26T05:54:27.0275821Z 2024-06-26T05:54:27.0275919Z Returns: 2024-06-26T05:54:27.0276133Z A :class:`ShardedTensor` object handle on this rank 2024-06-26T05:54:27.0276219Z 2024-06-26T05:54:27.0276305Z 2024-06-26T05:54:27.0276417Z Examples: 2024-06-26T05:54:27.0276763Z Suppose we want construct a sharded tensor on two ranks, global size = (10, 5), 2024-06-26T05:54:27.0277018Z each shard have a (5, 5) local tensor, we can do it like below: 2024-06-26T05:54:27.0277116Z 2024-06-26T05:54:27.0277215Z on rank 0: 2024-06-26T05:54:27.0277372Z >>> # xdoctest: +SKIP("not distributed") 2024-06-26T05:54:27.0277539Z >>> local_shard_metadata = ShardMetadata( 2024-06-26T05:54:27.0277662Z >>> shard_offsets=[0, 0], 2024-06-26T05:54:27.0277793Z >>> shard_lengths=[5, 5], 2024-06-26T05:54:27.0277925Z >>> placement="rank:0/cuda:0" 2024-06-26T05:54:27.0278017Z >>> ) 2024-06-26T05:54:27.0278283Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2024-06-26T05:54:27.0278531Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2024-06-26T05:54:27.0278619Z 2024-06-26T05:54:27.0278728Z on rank 1: 2024-06-26T05:54:27.0278883Z >>> # xdoctest: +SKIP("not distributed") 2024-06-26T05:54:27.0279038Z >>> local_shard_metadata = ShardMetadata( 2024-06-26T05:54:27.0279171Z >>> shard_offsets=[5, 0], 2024-06-26T05:54:27.0279288Z >>> shard_lengths=[5, 5], 2024-06-26T05:54:27.0279418Z >>> placement="rank:1/cuda:1" 2024-06-26T05:54:27.0279520Z >>> ) 2024-06-26T05:54:27.0279767Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2024-06-26T05:54:27.0280042Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2024-06-26T05:54:27.0280146Z 2024-06-26T05:54:27.0280532Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0280704Z 2024-06-26T05:54:27.0280817Z warnings.warn(msg) 2024-06-26T05:54:27.0280906Z 2024-06-26T05:54:27.0281157Z --- Parse Warning: 25 / 90 --- 2024-06-26T05:54:27.0282740Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardedTensor._init_from_local_tensor in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=784. 2024-06-26T05:54:27.0283196Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0283300Z 2024-06-26T05:54:27.0283633Z Initialize a ShardedTensor given only one local tensor, global sharded tensor 2024-06-26T05:54:27.0283779Z size and sharding spec on each rank. 2024-06-26T05:54:27.0283881Z 2024-06-26T05:54:27.0283974Z Args: 2024-06-26T05:54:27.0284272Z local_tensor (Tensor): Single tensor of local shard stored in each rank. 2024-06-26T05:54:27.0284620Z sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): 2024-06-26T05:54:27.0284834Z The specification describing how to shard the Tensor. 2024-06-26T05:54:27.0285066Z global_size (Sequence[int]): Size of the sharded tensor. 2024-06-26T05:54:27.0285386Z process_group (ProcessGroup, optional): The process group to aggregate on. 2024-06-26T05:54:27.0285495Z Default: None 2024-06-26T05:54:27.0285733Z init_rrefs (bool, optional): Whether or not to initialize 2024-06-26T05:54:27.0286007Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2024-06-26T05:54:27.0286270Z Need to initialize the RPC Framework if specified as ``True``. 2024-06-26T05:54:27.0286396Z Default: ``False``. 2024-06-26T05:54:27.0286486Z 2024-06-26T05:54:27.0286581Z Returns: 2024-06-26T05:54:27.0286918Z A :class:`ShardedTensor` sharded based on the given sharding_spec with local 2024-06-26T05:54:27.0287071Z tensor stored in the current rank. 2024-06-26T05:54:27.0287156Z 2024-06-26T05:54:27.0287266Z Examples: 2024-06-26T05:54:27.0287383Z >>> # xdoctest: +SKIP 2024-06-26T05:54:27.0287577Z >>> # All tensors below are of torch.int64 type. 2024-06-26T05:54:27.0287731Z >>> # We have 2 process groups, 2 ranks. 2024-06-26T05:54:27.0287970Z >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank 2024-06-26T05:54:27.0288244Z >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2])) 2024-06-26T05:54:27.0288349Z >>> local_tensor 2024-06-26T05:54:27.0288472Z tensor([[1, 2, 3, 4]]) # Rank 0 2024-06-26T05:54:27.0290508Z tensor([[3, 4, 5, 6]]) # Rank 1 2024-06-26T05:54:27.0290622Z >>> sharding_dim = 0 2024-06-26T05:54:27.0290787Z >>> sharding_spec = ChunkShardingSpec( 2024-06-26T05:54:27.0290921Z dim=sharding_dim, 2024-06-26T05:54:27.0291029Z placements=[ 2024-06-26T05:54:27.0291140Z "rank:0/cuda:0", 2024-06-26T05:54:27.0291263Z "rank:1/cuda:1", 2024-06-26T05:54:27.0291361Z ], 2024-06-26T05:54:27.0291451Z ) 2024-06-26T05:54:27.0291797Z >>> st = ShardedTensor._init_from_local_tensor(local_tensor, sharding_spec, [2, 4]) 2024-06-26T05:54:27.0291892Z >>> st 2024-06-26T05:54:27.0292011Z ShardedTensor( 2024-06-26T05:54:27.0292137Z ShardedTensorMetadata( 2024-06-26T05:54:27.0292249Z shards_metadata=[ 2024-06-26T05:54:27.0292605Z ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0), 2024-06-26T05:54:27.0292941Z ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1), 2024-06-26T05:54:27.0293083Z ], 2024-06-26T05:54:27.0293223Z size=torch.Size([2, 4]) 2024-06-26T05:54:27.0293315Z ) 2024-06-26T05:54:27.0293425Z >>> st.local_tensor() 2024-06-26T05:54:27.0293694Z tensor([1, 2, 3, 4]) # Rank 0 2024-06-26T05:54:27.0293813Z tensor([3, 4, 5, 6]) # Rank 1 2024-06-26T05:54:27.0293952Z 2024-06-26T05:54:27.0294331Z Warning: This API is experimental and subject to change. It lacks of a fully across 2024-06-26T05:54:27.0294661Z rank validations, and we only validate the local shard on the current rank. 2024-06-26T05:54:27.0295051Z We fully rely on the user to ensure local tensor is sharded based on the 2024-06-26T05:54:27.0295172Z sharding spec. 2024-06-26T05:54:27.0295258Z 2024-06-26T05:54:27.0295681Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0295765Z 2024-06-26T05:54:27.0295875Z warnings.warn(msg) 2024-06-26T05:54:27.0295976Z 2024-06-26T05:54:27.0296179Z --- Parse Warning: 26 / 90 --- 2024-06-26T05:54:27.0297702Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardedTensor.reshard in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=1023. 2024-06-26T05:54:27.0298124Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0298214Z 2024-06-26T05:54:27.0298559Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support 2024-06-26T05:54:27.0298685Z single local shard. 2024-06-26T05:54:27.0298773Z 2024-06-26T05:54:27.0299129Z If ``resharding_spec`` is same as the original one, this becomes a no-op. 2024-06-26T05:54:27.0299462Z If only ``resharding_spec`` shares the same sharding dim with the original one, 2024-06-26T05:54:27.0299588Z we swap local shards directly. 2024-06-26T05:54:27.0299953Z For more generic cases, we merge different shards across different ranks and split 2024-06-26T05:54:27.0300291Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API. 2024-06-26T05:54:27.0300378Z 2024-06-26T05:54:27.0300482Z Args: 2024-06-26T05:54:27.0300853Z resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The 2024-06-26T05:54:27.0301066Z specification describing how the tensor is sharded. 2024-06-26T05:54:27.0301162Z 2024-06-26T05:54:27.0301261Z Returns: 2024-06-26T05:54:27.0301530Z A :class:`ShardedTensor` object whose local shards are resharded. 2024-06-26T05:54:27.0301629Z 2024-06-26T05:54:27.0301725Z Examples: 2024-06-26T05:54:27.0301838Z >>> # xdoctest: +SKIP 2024-06-26T05:54:27.0302009Z >>> # We have 2 process groups, 2 ranks. 2024-06-26T05:54:27.0302247Z >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank 2024-06-26T05:54:27.0302460Z >>> tensor = torch.stack([tensor, tensor]) 2024-06-26T05:54:27.0302555Z >>> tensor 2024-06-26T05:54:27.0302714Z tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0 2024-06-26T05:54:27.0302879Z tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1 2024-06-26T05:54:27.0303026Z tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2 2024-06-26T05:54:27.0303188Z tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3 2024-06-26T05:54:27.0303311Z >>> sharding_dim = 0 2024-06-26T05:54:27.0303445Z >>> spec = ChunkShardingSpec( 2024-06-26T05:54:27.0303558Z dim=sharding_dim, 2024-06-26T05:54:27.0303678Z placements=[ 2024-06-26T05:54:27.0303793Z "rank:0/cuda:0", 2024-06-26T05:54:27.0303901Z "rank:1/cuda:1", 2024-06-26T05:54:27.0304017Z "rank:2/cuda:2", 2024-06-26T05:54:27.0304123Z "rank:3/cuda:3", 2024-06-26T05:54:27.0304214Z ], 2024-06-26T05:54:27.0304357Z ) 2024-06-26T05:54:27.0304482Z >>> current_offsets = [0] * 2 2024-06-26T05:54:27.0304613Z >>> current_offsets[0] = rank * 2 2024-06-26T05:54:27.0304768Z >>> shard_metadata = ShardMetadata( 2024-06-26T05:54:27.0304958Z shard_offsets=copy.deepcopy(current_offsets), 2024-06-26T05:54:27.0305130Z shard_sizes=tensor.size(), 2024-06-26T05:54:27.0305284Z placement=spec.placements[rank], 2024-06-26T05:54:27.0305378Z ) 2024-06-26T05:54:27.0305500Z >>> local_shards = [ 2024-06-26T05:54:27.0305625Z Shard( 2024-06-26T05:54:27.0305736Z tensor=tensor, 2024-06-26T05:54:27.0305914Z metadata=shard_metadata, 2024-06-26T05:54:27.0306008Z ) 2024-06-26T05:54:27.0306102Z ] 2024-06-26T05:54:27.0306404Z >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size()) 2024-06-26T05:54:27.0306514Z >>> sharding_dim = 1 2024-06-26T05:54:27.0306676Z >>> resharding_spec = ChunkShardingSpec( 2024-06-26T05:54:27.0306800Z dim=sharding_dim, 2024-06-26T05:54:27.0306909Z placements=[ 2024-06-26T05:54:27.0307022Z "rank:0/cuda:0", 2024-06-26T05:54:27.0307142Z "rank:1/cuda:1", 2024-06-26T05:54:27.0307251Z "rank:2/cuda:2", 2024-06-26T05:54:27.0307355Z "rank:3/cuda:3", 2024-06-26T05:54:27.0307462Z ], 2024-06-26T05:54:27.0307556Z ) 2024-06-26T05:54:27.0307685Z >>> st.reshard(resharding_spec) 2024-06-26T05:54:27.0307846Z >>> tensor = st.local_shards()[0].tensor 2024-06-26T05:54:27.0307944Z >>> tensor 2024-06-26T05:54:27.0308150Z tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0 2024-06-26T05:54:27.0308334Z tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1 2024-06-26T05:54:27.0308521Z tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2 2024-06-26T05:54:27.0308724Z tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3 2024-06-26T05:54:27.0308812Z 2024-06-26T05:54:27.0309204Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0309301Z 2024-06-26T05:54:27.0309410Z warnings.warn(msg) 2024-06-26T05:54:27.0309497Z 2024-06-26T05:54:27.0309710Z --- Parse Warning: 27 / 90 --- 2024-06-26T05:54:27.0311158Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardingPlan in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharding_plan/api.py line=12. 2024-06-26T05:54:27.0311571Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0311660Z 2024-06-26T05:54:27.0311938Z Representation of a sharding plan, describes how to shard a module 2024-06-26T05:54:27.0312324Z across hosts. `plan` is used to shard module parameters according to the spec provided, 2024-06-26T05:54:27.0312695Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output 2024-06-26T05:54:27.0313048Z layout of a module with a spec, and when to convert back to data parallel fashion. 2024-06-26T05:54:27.0313148Z 2024-06-26T05:54:27.0313240Z Args: 2024-06-26T05:54:27.0313603Z plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`, 2024-06-26T05:54:27.0313833Z :class:`torch.distributed._shard.sharder.Sharder`]): 2024-06-26T05:54:27.0314295Z a dict describes how to shard a module, there're currently two ways to shard a module: 2024-06-26T05:54:27.0314652Z 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of 2024-06-26T05:54:27.0314807Z a parameter to a `ShardingSpec`. 2024-06-26T05:54:27.0315192Z 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module 2024-06-26T05:54:27.0315331Z to a `Sharder` object. 2024-06-26T05:54:27.0315764Z output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional): 2024-06-26T05:54:27.0316192Z a dict specifies the layout of a module's output which produces a ShardedTensor, 2024-06-26T05:54:27.0316564Z keyed by the name of module to ShardingSpec("" in key means the root module). 2024-06-26T05:54:27.0316675Z Default: `None` 2024-06-26T05:54:27.0317012Z return_local_tensor (List[str], optional): a list of string, each element enables 2024-06-26T05:54:27.0317516Z a module's sharded output to be returned as a Tensor from its local shards to 2024-06-26T05:54:27.0317844Z ensure further processing in a data parallel fashion. ("" in list means the 2024-06-26T05:54:27.0317960Z root module). 2024-06-26T05:54:27.0318067Z Default: None 2024-06-26T05:54:27.0318166Z Example: 2024-06-26T05:54:27.0318572Z Suppose we want to shard a module with two linear layers and then run it with DDP, we also 2024-06-26T05:54:27.0318971Z want to convert the output of the second linear layer back to DDP, we can do it as follows: 2024-06-26T05:54:27.0319058Z 2024-06-26T05:54:27.0319294Z >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d) 2024-06-26T05:54:27.0319425Z >>> class MyModule(nn.Module): 2024-06-26T05:54:27.0319539Z >>> def __init__(self): 2024-06-26T05:54:27.0319670Z >>> super().__init__() 2024-06-26T05:54:27.0319801Z >>> self.fc1 = nn.Linear() 2024-06-26T05:54:27.0319932Z >>> self.gelu = nn.GELU() 2024-06-26T05:54:27.0320068Z >>> self.fc2 = nn.Linear() 2024-06-26T05:54:27.0320198Z >>> self.relu = nn.Linear() 2024-06-26T05:54:27.0320293Z >>> 2024-06-26T05:54:27.0320432Z >>> def forward(self, input): 2024-06-26T05:54:27.0320730Z >>> return self.relu(self.fc2(self.gelu(self.fc1(input)))) 2024-06-26T05:54:27.0320832Z 2024-06-26T05:54:27.0320919Z 2024-06-26T05:54:27.0321095Z >>> # xdoctest: +SKIP("Undefined spec1, spec2) 2024-06-26T05:54:27.0321248Z >>> sharding_plan = ShardingPlan( 2024-06-26T05:54:27.0321348Z >>> plan={ 2024-06-26T05:54:27.0321471Z >>> "fc1.weight": spec1, 2024-06-26T05:54:27.0321604Z >>> "fc2.weight": spec2 2024-06-26T05:54:27.0321699Z >>> }, 2024-06-26T05:54:27.0321808Z >>> output_plan={ 2024-06-26T05:54:27.0321939Z >>> "fc2": output_spec 2024-06-26T05:54:27.0322031Z >>> }, 2024-06-26T05:54:27.0322166Z >>> return_local_tensor=["fc2"] 2024-06-26T05:54:27.0322271Z >>> ) 2024-06-26T05:54:27.0322356Z 2024-06-26T05:54:27.0322743Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0322843Z 2024-06-26T05:54:27.0322955Z warnings.warn(msg) 2024-06-26T05:54:27.0323041Z 2024-06-26T05:54:27.0323255Z --- Parse Warning: 28 / 90 --- 2024-06-26T05:54:27.0324715Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=local_map in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_tensor/experimental/local_map.py line=30. 2024-06-26T05:54:27.0325125Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0325214Z 2024-06-26T05:54:27.0325566Z ``local_map`` is an experimental API that allows users to apply on :class:`DTensors` 2024-06-26T05:54:27.0325865Z a function that is written to be applied on :class:`~torch.Tensors`. 2024-06-26T05:54:27.0325954Z 2024-06-26T05:54:27.0326046Z Args: 2024-06-26T05:54:27.0326335Z func (Callable): the function to be applied on each local shard of 2024-06-26T05:54:27.0326483Z :class:`DTensor`s. 2024-06-26T05:54:27.0326772Z out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]): 2024-06-26T05:54:27.0327185Z the desired placements of the :class:`DTensor`s in `func`'s flattened output. 2024-06-26T05:54:27.0327502Z If the flattened `output` is a single value, the `out_placements` should be 2024-06-26T05:54:27.0327857Z of type `PlacementType`. Otherwise if the flattened `output` has multiple 2024-06-26T05:54:27.0328178Z values, the `out_placements` should be a tuple of `PlacementType` values 1:1 2024-06-26T05:54:27.0328356Z mapping to the flattened `output`. 2024-06-26T05:54:27.0328667Z Besides, for :class:`Tensor` output, we use `PlacementType` as its 2024-06-26T05:54:27.0329036Z placements (a `Tuple[Placement]` value). For non-:class:`Tensor` output, 2024-06-26T05:54:27.0329195Z the `PlacementType` should be `None`. 2024-06-26T05:54:27.0329534Z Note that the only exception is when no :class:`DTensor` argument is passed 2024-06-26T05:54:27.0329853Z in. In this case, even if `out_placements` is not `None`, the result function 2024-06-26T05:54:27.0330157Z should ignore the desired placements because the application is not on 2024-06-26T05:54:27.0330279Z :class:`DTensors`. 2024-06-26T05:54:27.0330490Z in_placements (Tuple[`PlacementType`, ...], optional): 2024-06-26T05:54:27.0330901Z the required placements of the :class:`DTensor`s in `func`'s flattened input. 2024-06-26T05:54:27.0331189Z If `in_placements` is specified, `local_map` would examine whether the 2024-06-26T05:54:27.0331494Z placements of each :class:`DTensor` argument is the same as the required 2024-06-26T05:54:27.0331743Z placements or not. If the placements are not the same and 2024-06-26T05:54:27.0332056Z `redistribute_inputs` is `False`, an exception will be raised. Otherwise if 2024-06-26T05:54:27.0332376Z `redistribute_inputs` is `True`, the argument will be first redistributed to 2024-06-26T05:54:27.0332716Z the required sharding placements before passing its local tensor to `func`. 2024-06-26T05:54:27.0333008Z The only exception is when required placements are not `None` and the 2024-06-26T05:54:27.0333348Z argument is a :class:`torch.Tensor`. In this case, the placements examination 2024-06-26T05:54:27.0333771Z will be skipped and the argument will be directly passed to `func`. 2024-06-26T05:54:27.0334076Z If `in_placements` is `None`, no placements examination will be performed. 2024-06-26T05:54:27.0334201Z Default: `None` 2024-06-26T05:54:27.0334376Z device_mesh (:class:`DeviceMesh`, optional): 2024-06-26T05:54:27.0334667Z the device mesh that all the :class:`DTensor`s are placed on. If not 2024-06-26T05:54:27.0335058Z specified, this will be inferred from the input :class:`DTensor`s' device 2024-06-26T05:54:27.0335371Z mesh. `local_map` requires every :class:`DTensor`s to be placed on the same 2024-06-26T05:54:27.0335506Z device mesh. Default: `None`. 2024-06-26T05:54:27.0335677Z redistribute_inputs (bool, optional): 2024-06-26T05:54:27.0336012Z the bool value indicating whether to reshard the input :class:`DTensor`s when 2024-06-26T05:54:27.0336351Z their placements are different from the required input placements. If this 2024-06-26T05:54:27.0336666Z value is `False` and some :class:`DTensor` input has a different placement, 2024-06-26T05:54:27.0336852Z an exception will be raised. Default: `False`. 2024-06-26T05:54:27.0336953Z 2024-06-26T05:54:27.0337051Z Returns: 2024-06-26T05:54:27.0337395Z A `Callable` that applies `func` to each local shard of the input :class:`DTensor` 2024-06-26T05:54:27.0337717Z and returns a :class:`DTensor` constructed from the return value of `func`. 2024-06-26T05:54:27.0337860Z 2024-06-26T05:54:27.0337956Z Raises: 2024-06-26T05:54:27.0338320Z AssertionError: If the input :class:`DTensor`s are not placed on the same device 2024-06-26T05:54:27.0338644Z mesh, or if they are placed on a different device mesh than the `device_mesh` 2024-06-26T05:54:27.0338772Z argument passed in. 2024-06-26T05:54:27.0338904Z 2024-06-26T05:54:27.0339322Z AssertionError: For any non-:class:`DTensor` output, we require its corresponding 2024-06-26T05:54:27.0339675Z output placement in `out_placements` be `None`. An AssertionError will be raised 2024-06-26T05:54:27.0339831Z if this is not the case. 2024-06-26T05:54:27.0339917Z 2024-06-26T05:54:27.0340298Z ValueError: If `redistribute_inputs=False` but the input :class:`DTensor` needs 2024-06-26T05:54:27.0340488Z a redistribution according to `in_placements`. 2024-06-26T05:54:27.0340574Z 2024-06-26T05:54:27.0340684Z Example: 2024-06-26T05:54:27.0340835Z >>> # xdoctest: +SKIP("distributed") 2024-06-26T05:54:27.0341014Z >>> def mm_allreduce_forward(device_mesh, W, X): 2024-06-26T05:54:27.0341184Z >>> partial_sum_tensor = torch.mm(W, X) 2024-06-26T05:54:27.0341497Z >>> reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh) 2024-06-26T05:54:27.0341623Z >>> return reduced_tensor 2024-06-26T05:54:27.0341725Z >>> 2024-06-26T05:54:27.0341895Z >>> W = torch.randn(12, 8, requires_grad=False) 2024-06-26T05:54:27.0342075Z >>> X = torch.randn(8, 16, requires_grad=False) 2024-06-26T05:54:27.0342190Z >>> Y = torch.mm(W, X) 2024-06-26T05:54:27.0342515Z >>> row_wise = [Shard(0)] # row-wise sharding placements on 1-d mesh 2024-06-26T05:54:27.0342844Z >>> col_wise = [Shard(1)] # col-wise sharding placements on 1-d mesh 2024-06-26T05:54:27.0342936Z >>> 2024-06-26T05:54:27.0343292Z >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor convertion 2024-06-26T05:54:27.0343462Z >>> local_mm_allreduce_forward = local_map( 2024-06-26T05:54:27.0343584Z >>> mm_allreduce_forward, 2024-06-26T05:54:27.0343729Z >>> out_placements=[Replicate()], 2024-06-26T05:54:27.0343889Z >>> in_placements=[col_wise, row_wise], 2024-06-26T05:54:27.0344020Z >>> device_mesh=device_mesh, 2024-06-26T05:54:27.0344112Z >>> ) 2024-06-26T05:54:27.0344214Z >>> 2024-06-26T05:54:27.0344636Z >>> W_dt = distribute_tensor(W, device_mesh, (col_wise)) # col-wisely sharded W tensor 2024-06-26T05:54:27.0345069Z >>> X_dt = distribute_tensor(X, device_mesh, (row_wise)) # row-wisely sharded X tensor 2024-06-26T05:54:27.0345525Z >>> Y_dt = local_mm_allreduce_forward(device_mesh, W_dt, X_dt) # apply local_mm_allreduce_forward to DTensors 2024-06-26T05:54:27.0345614Z 2024-06-26T05:54:27.0345879Z NOTE: This API is currently experimental and subject to change 2024-06-26T05:54:27.0345968Z 2024-06-26T05:54:27.0346351Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0346447Z 2024-06-26T05:54:27.0346555Z warnings.warn(msg) 2024-06-26T05:54:27.0346641Z 2024-06-26T05:54:27.0346852Z --- Parse Warning: 29 / 90 --- 2024-06-26T05:54:27.0348472Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=post_localSGD_hook in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/post_localSGD_hook.py line=72. 2024-06-26T05:54:27.0348870Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0348968Z 2024-06-26T05:54:27.0349128Z Run post-localSGD algorithm. 2024-06-26T05:54:27.0349223Z 2024-06-26T05:54:27.0349591Z This DDP communication hook is used for running post-localSGD algorithm, 2024-06-26T05:54:27.0349822Z by combining with a model averaging component (e.g., 2024-06-26T05:54:27.0350267Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`) 2024-06-26T05:54:27.0350404Z that runs after the optimizer step. 2024-06-26T05:54:27.0350491Z 2024-06-26T05:54:27.0350595Z Args: 2024-06-26T05:54:27.0350966Z state (PostLocalSGDState): State information to run post-localSGD. 2024-06-26T05:54:27.0351337Z Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD. 2024-06-26T05:54:27.0351998Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2024-06-26T05:54:27.0352393Z Note that since DDP comm hook only supports single process single device mode, 2024-06-26T05:54:27.0352601Z only exactly one tensor is stored in this bucket. 2024-06-26T05:54:27.0352687Z 2024-06-26T05:54:27.0352782Z Returns: 2024-06-26T05:54:27.0353118Z Future handler of the communication, which updates the gradients in place. 2024-06-26T05:54:27.0353203Z 2024-06-26T05:54:27.0353310Z Example:: 2024-06-26T05:54:27.0353433Z >>> # xdoctest: +SKIP 2024-06-26T05:54:27.0353749Z >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup, 2024-06-26T05:54:27.0353909Z start_localSGD_iter=10) 2024-06-26T05:54:27.0354145Z >>> ddp_model.register_comm_hook(state, post_localSGD_hook) 2024-06-26T05:54:27.0354603Z >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``. 2024-06-26T05:54:27.0355081Z >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module. 2024-06-26T05:54:27.0355182Z 2024-06-26T05:54:27.0355569Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0355667Z 2024-06-26T05:54:27.0355775Z warnings.warn(msg) 2024-06-26T05:54:27.0355863Z 2024-06-26T05:54:27.0356073Z --- Parse Warning: 30 / 90 --- 2024-06-26T05:54:27.0357633Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=powerSGD_hook in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py line=342. 2024-06-26T05:54:27.0358033Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0358134Z 2024-06-26T05:54:27.0358266Z Implement PowerSGD algorithm. 2024-06-26T05:54:27.0358351Z 2024-06-26T05:54:27.0358655Z This DDP communication hook implements PowerSGD gradient compression 2024-06-26T05:54:27.0358964Z algorithm described in the `paper `_. 2024-06-26T05:54:27.0359280Z Once gradient tensors are aggregated across all workers, this hook applies 2024-06-26T05:54:27.0359413Z compression as follows: 2024-06-26T05:54:27.0359501Z 2024-06-26T05:54:27.0360206Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups: 2024-06-26T05:54:27.0360293Z 2024-06-26T05:54:27.0360950Z 1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth. 2024-06-26T05:54:27.0361054Z 2024-06-26T05:54:27.0361616Z 1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases). 2024-06-26T05:54:27.0361704Z 2024-06-26T05:54:27.0361849Z 2. Handles uncompressed tensors: 2024-06-26T05:54:27.0361939Z 2024-06-26T05:54:27.0362610Z 2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression; 2024-06-26T05:54:27.0362711Z 2024-06-26T05:54:27.0363203Z 2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor. 2024-06-26T05:54:27.0363326Z 2024-06-26T05:54:27.0363649Z 3. Handles the tensors that should be compressed by PowerSGD compression: 2024-06-26T05:54:27.0363737Z 2024-06-26T05:54:27.0364159Z 3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M, 2024-06-26T05:54:27.0364632Z such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized; 2024-06-26T05:54:27.0364719Z 2024-06-26T05:54:27.0364927Z 3.2. Computes each P in Ps, which is equal to MQ; 2024-06-26T05:54:27.0365048Z 2024-06-26T05:54:27.0365178Z 3.3. Allreduces Ps as a batch; 2024-06-26T05:54:27.0365278Z 2024-06-26T05:54:27.0365445Z 3.4. Orthogonalizes each P in Ps; 2024-06-26T05:54:27.0365536Z 2024-06-26T05:54:27.0365818Z 3.5. Computes each Q in Qs, which is approximately equal to M^TP; 2024-06-26T05:54:27.0365906Z 2024-06-26T05:54:27.0366035Z 3.6. Allreduces Qs as a batch; 2024-06-26T05:54:27.0366134Z 2024-06-26T05:54:27.0366547Z 3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T. 2024-06-26T05:54:27.0366634Z 2024-06-26T05:54:27.0367189Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations. 2024-06-26T05:54:27.0367583Z This not only gives the user more control over the tradeoff between speedup and accuracy, 2024-06-26T05:54:27.0368169Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers. 2024-06-26T05:54:27.0368257Z 2024-06-26T05:54:27.0368349Z Args: 2024-06-26T05:54:27.0368924Z state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc. 2024-06-26T05:54:27.0369403Z To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter`` 2024-06-26T05:54:27.0369538Z and ``min_compression_rate``. 2024-06-26T05:54:27.0370201Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2024-06-26T05:54:27.0370544Z Note that since DDP comm hook only supports single process single device mode, 2024-06-26T05:54:27.0370753Z only exactly one tensor is stored in this bucket. 2024-06-26T05:54:27.0370841Z 2024-06-26T05:54:27.0370939Z Returns: 2024-06-26T05:54:27.0371275Z Future handler of the communication, which updates the gradients in place. 2024-06-26T05:54:27.0371361Z 2024-06-26T05:54:27.0371462Z Example:: 2024-06-26T05:54:27.0371589Z >>> # xdoctest: +SKIP 2024-06-26T05:54:27.0371931Z >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, 2024-06-26T05:54:27.0372149Z start_powerSGD_iter=10, min_compression_rate=0.5) 2024-06-26T05:54:27.0372372Z >>> ddp_model.register_comm_hook(state, powerSGD_hook) 2024-06-26T05:54:27.0372461Z 2024-06-26T05:54:27.0372841Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0372939Z 2024-06-26T05:54:27.0373050Z warnings.warn(msg) 2024-06-26T05:54:27.0373135Z 2024-06-26T05:54:27.0373346Z --- Parse Warning: 31 / 90 --- 2024-06-26T05:54:27.0375077Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PeriodicModelAverager in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/model_averaging/averagers.py line=36. 2024-06-26T05:54:27.0375494Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0375581Z 2024-06-26T05:54:27.0375866Z Averages parameters periodically after the warm-up stage. 2024-06-26T05:54:27.0375970Z 2024-06-26T05:54:27.0376445Z This can be used for running `post-local SGD `_, 2024-06-26T05:54:27.0376700Z by running :class:`~torch.nn.DistributedDataParallel` (DDP) 2024-06-26T05:54:27.0377025Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`. 2024-06-26T05:54:27.0377113Z 2024-06-26T05:54:27.0377206Z Args: 2024-06-26T05:54:27.0377471Z period (int): The number of steps per model averaging. 2024-06-26T05:54:27.0377837Z Usually the period should be greater than ``1`` to reduce the communication cost. 2024-06-26T05:54:27.0378059Z Otherwise, only DDP needs to be used. 2024-06-26T05:54:27.0378445Z warmup_steps (int): The number of warm-up steps. During this stage, 2024-06-26T05:54:27.0378603Z model averaging is skipped. 2024-06-26T05:54:27.0378911Z process_group: The process group to be used for all-reduce. 2024-06-26T05:54:27.0379105Z If ``None``, the default process group, which 2024-06-26T05:54:27.0379361Z is created by :func:`torch.distributed.init_process_group`, 2024-06-26T05:54:27.0379535Z will be used. (default: ``None``) 2024-06-26T05:54:27.0379621Z 2024-06-26T05:54:27.0379723Z Example:: 2024-06-26T05:54:27.0379826Z 2024-06-26T05:54:27.0380000Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:27.0380106Z >>> import torch 2024-06-26T05:54:27.0380275Z >>> import torch.distributed as dist 2024-06-26T05:54:27.0380679Z >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD 2024-06-26T05:54:27.0381048Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2024-06-26T05:54:27.0381169Z >>> import torch.nn as nn 2024-06-26T05:54:27.0381261Z >>> 2024-06-26T05:54:27.0381501Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2024-06-26T05:54:27.0381632Z >>> torch.cuda.set_device(rank) 2024-06-26T05:54:27.0381801Z >>> module = nn.Linear(1, 1, bias=False).cuda() 2024-06-26T05:54:27.0382018Z >>> model = nn.parallel.DistributedDataParallel( 2024-06-26T05:54:27.0382202Z >>> module, device_ids=[rank], output_device=rank 2024-06-26T05:54:27.0382294Z >>> ) 2024-06-26T05:54:27.0382548Z >>> # Register a post-localSGD communication hook. 2024-06-26T05:54:27.0382923Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2024-06-26T05:54:27.0383128Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-06-26T05:54:27.0383234Z >>> 2024-06-26T05:54:27.0383605Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2024-06-26T05:54:27.0383825Z >>> # After 100 steps, run model averaging every 4 steps. 2024-06-26T05:54:27.0384261Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-06-26T05:54:27.0384581Z >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2024-06-26T05:54:27.0384717Z >>> for step in range(0, 200): 2024-06-26T05:54:27.0384841Z >>> optimizer.zero_grad() 2024-06-26T05:54:27.0384982Z >>> loss = loss_fn(output, labels) 2024-06-26T05:54:27.0385109Z >>> loss.backward() 2024-06-26T05:54:27.0385226Z >>> optimizer.step() 2024-06-26T05:54:27.0385480Z >>> # Will average model parameters globally every 4 steps. Thus, 2024-06-26T05:54:27.0385825Z >>> # inter-node communication only occurs every 4 iterations after 2024-06-26T05:54:27.0385988Z >>> # the initial ``warmup_steps`` period. 2024-06-26T05:54:27.0386193Z >>> averager.average_parameters(model.parameters()) 2024-06-26T05:54:27.0386292Z 2024-06-26T05:54:27.0386676Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0386774Z 2024-06-26T05:54:27.0386915Z warnings.warn(msg) 2024-06-26T05:54:27.0387004Z 2024-06-26T05:54:27.0387213Z --- Parse Warning: 32 / 90 --- 2024-06-26T05:54:27.0388949Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=HierarchicalModelAverager in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/model_averaging/hierarchical_model_averager.py line=18. 2024-06-26T05:54:27.0389377Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0389508Z 2024-06-26T05:54:27.0389960Z Runs hierarchical model averaging (`hierarchical SGD `_). 2024-06-26T05:54:27.0390048Z 2024-06-26T05:54:27.0390466Z Process groups of different sizes are organized in a hierarchy, and they average parameters 2024-06-26T05:54:27.0390790Z by using different periods concurrently after the warm-up stage. 2024-06-26T05:54:27.0391356Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager` 2024-06-26T05:54:27.0391880Z that supports `post-local SGD `_, which essentially only supports 2024-06-26T05:54:27.0392363Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine 2024-06-26T05:54:27.0392851Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`. 2024-06-26T05:54:27.0393323Z Similarly, the process groups within this class do not have such an intra-machine process 2024-06-26T05:54:27.0393758Z subgroup, which should be embedded by the post-local SGD communication hook instead. 2024-06-26T05:54:27.0393861Z 2024-06-26T05:54:27.0393955Z Args: 2024-06-26T05:54:27.0394309Z period_group_size_dict: An ordered dict mapping keys of model averaging period to 2024-06-26T05:54:27.0394581Z process group size, used for initializing process groups of 2024-06-26T05:54:27.0394887Z different sizes in a hierarchy to average parameters concurrently. 2024-06-26T05:54:27.0395186Z Particularly, at each iteration, there will be at most a single 2024-06-26T05:54:27.0395571Z process group that runs averaging -- the period of such group should 2024-06-26T05:54:27.0395868Z have the largest period which the current step can be divided by. 2024-06-26T05:54:27.0396107Z For example, if the dict has three keys: 2, 4, and 8, 2024-06-26T05:54:27.0396392Z then this means totally three process groups will be created to 2024-06-26T05:54:27.0396690Z average parameters every 2, 4, and 8 iterations, respectively. 2024-06-26T05:54:27.0396960Z At the 4th iteration, only the second process group will run 2024-06-26T05:54:27.0397198Z averaging, because the first process group should be a 2024-06-26T05:54:27.0397517Z subset of the second process group, and no need to execute the first 2024-06-26T05:54:27.0397682Z process group redundantly. 2024-06-26T05:54:27.0397969Z On the other hand, the third process group can only be triggered 2024-06-26T05:54:27.0398291Z every 8 iterations, so it will not be triggered at the 4th iteration. 2024-06-26T05:54:27.0398789Z warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped. 2024-06-26T05:54:27.0399368Z process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging. 2024-06-26T05:54:27.0399656Z If ``None``, the default process group, which is created 2024-06-26T05:54:27.0399950Z by :func:`torch.distributed.init_process_group`, will be used. 2024-06-26T05:54:27.0400127Z (default: ``None``) 2024-06-26T05:54:27.0400215Z 2024-06-26T05:54:27.0400317Z Example:: 2024-06-26T05:54:27.0400557Z >>> # xdoctest: +SKIP('undefined rank') 2024-06-26T05:54:27.0400793Z >>> from collections import OrderedDict 2024-06-26T05:54:27.0400900Z >>> import torch 2024-06-26T05:54:27.0401063Z >>> import torch.distributed as dist 2024-06-26T05:54:27.0401481Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2024-06-26T05:54:27.0401620Z >>> PostLocalSGDState, 2024-06-26T05:54:27.0401739Z >>> post_localSGD_hook, 2024-06-26T05:54:27.0401831Z >>> ) 2024-06-26T05:54:27.0402324Z >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD 2024-06-26T05:54:27.0402441Z >>> import torch.nn as nn 2024-06-26T05:54:27.0402534Z >>> 2024-06-26T05:54:27.0402775Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2024-06-26T05:54:27.0402902Z >>> torch.cuda.set_device(rank) 2024-06-26T05:54:27.0403077Z >>> module = nn.Linear(1, 1, bias=False).to(rank) 2024-06-26T05:54:27.0403297Z >>> model = nn.parallel.DistributedDataParallel( 2024-06-26T05:54:27.0403482Z >>> module, device_ids=[rank], output_device=rank 2024-06-26T05:54:27.0403578Z >>> ) 2024-06-26T05:54:27.0403833Z >>> # Register a post-localSGD communication hook. 2024-06-26T05:54:27.0404296Z >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4. 2024-06-26T05:54:27.0404448Z >>> subgroup, _ = dist.new_subgroups() 2024-06-26T05:54:27.0404858Z >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100) 2024-06-26T05:54:27.0405069Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-06-26T05:54:27.0405172Z >>> 2024-06-26T05:54:27.0405557Z >>> # Average parameters among each group of 8 processes every 4 iterations, and among all 2024-06-26T05:54:27.0405716Z >>> # the 16 processes every 16 iterations. 2024-06-26T05:54:27.0405984Z >>> averager = hierarchicalSGD.HierarchicalModelAverager( 2024-06-26T05:54:27.0406284Z >>> period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100) 2024-06-26T05:54:27.0406723Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-06-26T05:54:27.0407111Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2024-06-26T05:54:27.0407317Z >>> # After 100 steps, run model averaging at two levels. 2024-06-26T05:54:27.0407458Z >>> for step in range(0, 200): 2024-06-26T05:54:27.0407584Z >>> optimizer.zero_grad() 2024-06-26T05:54:27.0407724Z >>> loss = loss_fn(output, labels) 2024-06-26T05:54:27.0407851Z >>> loss.backward() 2024-06-26T05:54:27.0407969Z >>> optimizer.step() 2024-06-26T05:54:27.0408170Z >>> # Average parameters after ``optimizer.step()``. 2024-06-26T05:54:27.0408638Z >>> # Thus, the inter-node communication only occurs periodically after ``warmup_steps``. 2024-06-26T05:54:27.0408844Z >>> averager.average_parameters(model.parameters()) 2024-06-26T05:54:27.0408932Z 2024-06-26T05:54:27.0409046Z .. warning :: 2024-06-26T05:54:27.0409412Z The last group size in the dict must be the size of the provided ``process_group``, 2024-06-26T05:54:27.0409712Z which indicates model averaging at the highest level of the hierarchy. 2024-06-26T05:54:27.0410145Z If ``process_group`` is not provided, then the last group size should be equal to the world size. 2024-06-26T05:54:27.0410233Z 2024-06-26T05:54:27.0410375Z .. warning :: 2024-06-26T05:54:27.0410664Z `HierarchicalModelAverager` is experimental and subject to change. 2024-06-26T05:54:27.0410752Z 2024-06-26T05:54:27.0411149Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0411236Z 2024-06-26T05:54:27.0411374Z warnings.warn(msg) 2024-06-26T05:54:27.0411473Z 2024-06-26T05:54:27.0411673Z --- Parse Warning: 33 / 90 --- 2024-06-26T05:54:27.0413269Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=BroadcastingTorchSaveReader in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/format_utils.py line=40. 2024-06-26T05:54:27.0413827Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0413917Z 2024-06-26T05:54:27.0414312Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint 2024-06-26T05:54:27.0414661Z on the coordinator rank, and then broadcast and shard each tensor to all ranks. 2024-06-26T05:54:27.0414750Z 2024-06-26T05:54:27.0414980Z . N.B. Intended to be used with DynamicMetaLoadPlanner 2024-06-26T05:54:27.0415067Z 2024-06-26T05:54:27.0415171Z .. warning:: 2024-06-26T05:54:27.0415404Z Current implementation only supports loading Tensors. 2024-06-26T05:54:27.0415491Z 2024-06-26T05:54:27.0415635Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:27.0415759Z >>> sd = {"mode": model} 2024-06-26T05:54:27.0415860Z >>> dcp.load( 2024-06-26T05:54:27.0415955Z >>> sd, 2024-06-26T05:54:27.0416165Z >>> storage_reader=BroadcastingTorchSaveReader(), 2024-06-26T05:54:27.0416323Z >>> planner=DynamicMetaLoadPlanner(), 2024-06-26T05:54:27.0416463Z >>> checkpoint_id="path_to_model.pt" 2024-06-26T05:54:27.0416565Z >>> ) 2024-06-26T05:54:27.0416654Z 2024-06-26T05:54:27.0417047Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0417149Z 2024-06-26T05:54:27.0417260Z warnings.warn(msg) 2024-06-26T05:54:27.0417348Z 2024-06-26T05:54:27.0417560Z --- Parse Warning: 34 / 90 --- 2024-06-26T05:54:27.0419070Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DynamicMetaLoadPlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/format_utils.py line=148. 2024-06-26T05:54:27.0419490Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0419578Z 2024-06-26T05:54:27.0420054Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, 2024-06-26T05:54:27.0420598Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a 2024-06-26T05:54:27.0420745Z metadata file, like Torch Save files. 2024-06-26T05:54:27.0420831Z 2024-06-26T05:54:27.0421091Z . N.B. Intended to be used with BroadcastingTorchSaveReader 2024-06-26T05:54:27.0421180Z 2024-06-26T05:54:27.0421283Z .. warning:: 2024-06-26T05:54:27.0421516Z Current implementation only supports loading Tensors. 2024-06-26T05:54:27.0421608Z 2024-06-26T05:54:27.0421762Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:27.0421873Z >>> sd = {"mode": model} 2024-06-26T05:54:27.0421974Z >>> dcp.load( 2024-06-26T05:54:27.0422084Z >>> sd, 2024-06-26T05:54:27.0422286Z >>> storage_reader=BroadcastingTorchSaveReader(), 2024-06-26T05:54:27.0422447Z >>> planner=DynamicMetaLoadPlanner(), 2024-06-26T05:54:27.0422601Z >>> checkpoint_id="path_to_model.pt" 2024-06-26T05:54:27.0422693Z >>> ) 2024-06-26T05:54:27.0422782Z 2024-06-26T05:54:27.0423181Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0423325Z 2024-06-26T05:54:27.0423439Z warnings.warn(msg) 2024-06-26T05:54:27.0423541Z 2024-06-26T05:54:27.0423742Z --- Parse Warning: 35 / 90 --- 2024-06-26T05:54:27.0425291Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load_sharded_optimizer_state_dict in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/optimizer.py line=213. 2024-06-26T05:54:27.0425748Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0425873Z 2024-06-26T05:54:27.0426167Z Load a state_dict in conjunction with FSDP sharded optimizer state. 2024-06-26T05:54:27.0426286Z 2024-06-26T05:54:27.0426510Z This is the current recommended way to checkpoint FSDP. 2024-06-26T05:54:27.0426638Z >>> # xdoctest: +SKIP 2024-06-26T05:54:27.0426834Z >>> import torch.distributed.checkpoint as dist_cp 2024-06-26T05:54:27.0426928Z >>> # Save 2024-06-26T05:54:27.0427060Z >>> model: torch.nn.Model 2024-06-26T05:54:27.0427206Z >>> optim_params = model.parameters() 2024-06-26T05:54:27.0427390Z >>> optim = torch.optim.SGD(optim_params, lr=0.01) 2024-06-26T05:54:27.0427498Z >>> # Save 2024-06-26T05:54:27.0427786Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT): 2024-06-26T05:54:27.0427893Z >>> state_dict = { 2024-06-26T05:54:27.0428107Z >>> "optimizer": FSDP.optim_state_dict(model, optim), 2024-06-26T05:54:27.0428243Z >>> "model": model.state_dict() 2024-06-26T05:54:27.0428338Z >>> } 2024-06-26T05:54:27.0428474Z >>> dist_cp.save_state_dict( 2024-06-26T05:54:27.0428602Z >>> state_dict=optim_state, 2024-06-26T05:54:27.0428845Z >>> storage_writer=dist_cp.FileSystemWriter("checkpoint"), 2024-06-26T05:54:27.0429019Z >>> planner=dist_cp.DefaultSavePlanner(), 2024-06-26T05:54:27.0429112Z >>> ) 2024-06-26T05:54:27.0429215Z >>> 2024-06-26T05:54:27.0429311Z >>> # Load 2024-06-26T05:54:27.0429613Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT): 2024-06-26T05:54:27.0429791Z >>> model_state_dict = model_tp.state_dict() 2024-06-26T05:54:27.0429902Z >>> checkpoint = { 2024-06-26T05:54:27.0430030Z >>> "model": model_state_dict 2024-06-26T05:54:27.0430139Z >>> } 2024-06-26T05:54:27.0430258Z >>> dist_cp.load_state_dict( 2024-06-26T05:54:27.0430383Z >>> state_dict=checkpoint, 2024-06-26T05:54:27.0430636Z >>> storage_reader=dist_cp.FileSystemReader(checkpoint_file), 2024-06-26T05:54:27.0430811Z >>> planner=dist_cp.DefaultLoadPlanner(), 2024-06-26T05:54:27.0430901Z >>> ) 2024-06-26T05:54:27.0431103Z >>> model.load_state_dict(checkpoint["model_state"]) 2024-06-26T05:54:27.0431194Z >>> 2024-06-26T05:54:27.0431416Z >>> optim_state = dist_cp.load_sharded_optimizer_state_dict( 2024-06-26T05:54:27.0431542Z >>> model_state_dict, 2024-06-26T05:54:27.0431678Z >>> optimizer_key="optimizer", 2024-06-26T05:54:27.0431916Z >>> storage_reader=dist_cp.FileSystemReader("checkpoint"), 2024-06-26T05:54:27.0432010Z >>> ) 2024-06-26T05:54:27.0432102Z >>> 2024-06-26T05:54:27.0432299Z >>> flattened_osd = FSDP.optim_state_dict_to_load( 2024-06-26T05:54:27.0432466Z >>> model, optim, optim_state["optimizer"] 2024-06-26T05:54:27.0432557Z >>> ) 2024-06-26T05:54:27.0432657Z >>> 2024-06-26T05:54:27.0432808Z >>> optim.load_state_dict(flattened_osd) 2024-06-26T05:54:27.0432897Z 2024-06-26T05:54:27.0433295Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0433382Z 2024-06-26T05:54:27.0433492Z warnings.warn(msg) 2024-06-26T05:54:27.0433591Z 2024-06-26T05:54:27.0433789Z --- Parse Warning: 36 / 90 --- 2024-06-26T05:54:27.0435235Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SavePlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/planner.py line=110. 2024-06-26T05:54:27.0435648Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0435767Z 2024-06-26T05:54:27.0436157Z Abstract class defining the protocol used by save_state_dict to plan the save process. 2024-06-26T05:54:27.0436245Z 2024-06-26T05:54:27.0436625Z SavePlanners are stateful objects that can be used to customize the whole save process. 2024-06-26T05:54:27.0436750Z 2024-06-26T05:54:27.0437167Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it 2024-06-26T05:54:27.0437311Z will be visible to the whole process. 2024-06-26T05:54:27.0437411Z 2024-06-26T05:54:27.0437782Z A planner subclass can expect the following sequence of calls during save_state_dict: 2024-06-26T05:54:27.0437873Z 2024-06-26T05:54:27.0438077Z 1) set_up_planner - called on all ranks. 2024-06-26T05:54:27.0438238Z Signals the start of a checkpoint save. 2024-06-26T05:54:27.0438326Z 2024-06-26T05:54:27.0438541Z 2) create_local_plan - called on all ranks. 2024-06-26T05:54:27.0438926Z Process the state_dict and produces a `SavePlan` that will be sent for global planning. 2024-06-26T05:54:27.0439016Z 2024-06-26T05:54:27.0439315Z 3) create_global_plan - called on the coordinator rank only. 2024-06-26T05:54:27.0439575Z Takes the SavePlan from all ranks and make any global decision. 2024-06-26T05:54:27.0439679Z 2024-06-26T05:54:27.0439860Z 4) finish_plan - called on all ranks. 2024-06-26T05:54:27.0440154Z This gives each rank a chance to adjust to global planning decisions. 2024-06-26T05:54:27.0440253Z 2024-06-26T05:54:27.0440492Z 5) resolve_data - called multiple times on each rank 2024-06-26T05:54:27.0440854Z Lookups a value on the `state_dict` for the storage layer to write. 2024-06-26T05:54:27.0440959Z 2024-06-26T05:54:27.0441358Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as 2024-06-26T05:54:27.0441599Z most changes can be expressed by changes in a single method. 2024-06-26T05:54:27.0441702Z 2024-06-26T05:54:27.0441862Z There are 3 usual patterns of extension: 2024-06-26T05:54:27.0441948Z 2024-06-26T05:54:27.0442299Z Rewriting state_dict. This is the simplest way to extend the save process as it 2024-06-26T05:54:27.0442654Z doesn't requite understanding the intrincacies of how SavePlan works: 2024-06-26T05:54:27.0442745Z 2024-06-26T05:54:27.0442905Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:27.0443077Z >>> class RenamePlanner(DefaultSavePlanner): 2024-06-26T05:54:27.0443206Z >>> def set_up_planner( 2024-06-26T05:54:27.0443303Z >>> self, 2024-06-26T05:54:27.0443447Z >>> state_dict: STATE_DICT_TYPE, 2024-06-26T05:54:27.0443627Z >>> storage_meta: Optional[StorageMeta], 2024-06-26T05:54:27.0443749Z >>> is_coordinator: bool, 2024-06-26T05:54:27.0443878Z >>> ) -> None: 2024-06-26T05:54:27.0444038Z >>> # prefix all keys with `foo_`` 2024-06-26T05:54:27.0444455Z >>> super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator) 2024-06-26T05:54:27.0444547Z 2024-06-26T05:54:27.0445009Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted 2024-06-26T05:54:27.0445101Z 2024-06-26T05:54:27.0445244Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:27.0445423Z >>> class FP16Planner(DefaultSavePlanner): 2024-06-26T05:54:27.0445555Z >>> def create_local_plan(self): 2024-06-26T05:54:27.0445707Z >>> plan = super().create_local_plan() 2024-06-26T05:54:27.0445836Z >>> for p in plan: 2024-06-26T05:54:27.0446028Z >>> if p.tensor_data is not None: 2024-06-26T05:54:27.0446252Z >>> p.tensor_data.properties.dtype = torch.float16 2024-06-26T05:54:27.0446359Z >>> return plan 2024-06-26T05:54:27.0446451Z >>> 2024-06-26T05:54:27.0446617Z >>> def resolve_data(self, write_item): 2024-06-26T05:54:27.0446781Z >>> item = super().resolve_data(write_item) 2024-06-26T05:54:27.0447180Z >>> return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16) 2024-06-26T05:54:27.0447284Z 2024-06-26T05:54:27.0447853Z Using the global planning step to make central decisions that can't be made individually by each rank 2024-06-26T05:54:27.0447942Z 2024-06-26T05:54:27.0448120Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:27.0448250Z >>> from itertools import islice 2024-06-26T05:54:27.0448384Z >>> from dataclasses import replace 2024-06-26T05:54:27.0448617Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner): 2024-06-26T05:54:27.0449077Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0 2024-06-26T05:54:27.0449314Z >>> # This sample doesn't handle ShardedTensors 2024-06-26T05:54:27.0449479Z >>> def create_global_plan(self, all_plans): 2024-06-26T05:54:27.0449598Z >>> def chunk(it, size): 2024-06-26T05:54:27.0449723Z >>> it = iter(it) 2024-06-26T05:54:27.0449943Z >>> return list(iter(lambda: tuple(islice(it, size)), ())) 2024-06-26T05:54:27.0450052Z >>> all_plans = [ 2024-06-26T05:54:27.0450263Z >>> replace(plan, items=items) for plan, items in 2024-06-26T05:54:27.0450501Z >>> zip(all_plans, chunk(all_plans[0].items, len(all_plans))) 2024-06-26T05:54:27.0450599Z >>> ] 2024-06-26T05:54:27.0450795Z >>> return super().create_global_plan(all_plans) 2024-06-26T05:54:27.0450883Z 2024-06-26T05:54:27.0451239Z Finally, some planners need to save additional metadata in the checkpoint, this is 2024-06-26T05:54:27.0451611Z accomplished by having each rank contribute their data items in the local plan and 2024-06-26T05:54:27.0451747Z the global planner aggregate them: 2024-06-26T05:54:27.0451846Z 2024-06-26T05:54:27.0451988Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:27.0452194Z >>> class SaveExtraDataPlanner(DefaultSavePlanner): 2024-06-26T05:54:27.0452425Z >>> def create_local_plan(self) -> SavePlan: 2024-06-26T05:54:27.0452579Z >>> plan = super().create_local_plan() 2024-06-26T05:54:27.0452850Z >>> return replace(plan, planner_data="per-rank-data") 2024-06-26T05:54:27.0452954Z >>> 2024-06-26T05:54:27.0453428Z >>> def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]: 2024-06-26T05:54:27.0453793Z >>> global_plan, metadata = super().create_global_plan(all_plans) 2024-06-26T05:54:27.0454021Z >>> merged_data = [p.planner_data for p in global_plan] 2024-06-26T05:54:27.0454242Z >>> metadata = replace(metadata, planner_data=merged_data) 2024-06-26T05:54:27.0454385Z >>> return global_plan, metadata 2024-06-26T05:54:27.0454490Z 2024-06-26T05:54:27.0454874Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0454965Z 2024-06-26T05:54:27.0455089Z warnings.warn(msg) 2024-06-26T05:54:27.0455177Z 2024-06-26T05:54:27.0455392Z --- Parse Warning: 37 / 90 --- 2024-06-26T05:54:27.0456809Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=LoadPlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/planner.py line=275. 2024-06-26T05:54:27.0457207Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0457304Z 2024-06-26T05:54:27.0457725Z Abstract class defining the protocol used by load_state_dict to plan the load process. 2024-06-26T05:54:27.0457817Z 2024-06-26T05:54:27.0458214Z LoadPlanner are stateful objects that can be used to customize the whole load process. 2024-06-26T05:54:27.0458302Z 2024-06-26T05:54:27.0458685Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it 2024-06-26T05:54:27.0458878Z will be visible to the whole process. 2024-06-26T05:54:27.0458965Z 2024-06-26T05:54:27.0459349Z A planner subclass can expect the following sequence of calls during load_state_dict: 2024-06-26T05:54:27.0459474Z 2024-06-26T05:54:27.0459670Z 1) set_up_planner - called on all ranks. 2024-06-26T05:54:27.0459892Z Signals the start of loading a checkpoint. 2024-06-26T05:54:27.0459982Z 2024-06-26T05:54:27.0460186Z 2) create_local_plan - called on all ranks. 2024-06-26T05:54:27.0460587Z Process the state_dict and produces a `LoadPlan` that will be sent for global planning. 2024-06-26T05:54:27.0460681Z 2024-06-26T05:54:27.0460970Z 3) create_global_plan - called on the coordinator rank only. 2024-06-26T05:54:27.0461246Z Takes the LoadPlan from all ranks and make any global decision. 2024-06-26T05:54:27.0461334Z 2024-06-26T05:54:27.0461569Z 4) load_bytes - called multiple times on each rank 2024-06-26T05:54:27.0461857Z This is called once per non-tensor value in state_dict. 2024-06-26T05:54:27.0461947Z 2024-06-26T05:54:27.0462300Z 5) resolve_tensor and commit_tensor - called multiple times on each rank 2024-06-26T05:54:27.0462561Z They are called in pair for each Tensor value in state_dict. 2024-06-26T05:54:27.0462648Z 2024-06-26T05:54:27.0463060Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as 2024-06-26T05:54:27.0463301Z most changes can be expressed by changes in a single method. 2024-06-26T05:54:27.0463390Z 2024-06-26T05:54:27.0463566Z There are two usual patterns of extension: 2024-06-26T05:54:27.0463657Z 2024-06-26T05:54:27.0463993Z Rewriting state_dict. This is the simplest way to extend the load process as it 2024-06-26T05:54:27.0464403Z doesn't requite understanding the intrincacies of how LoadPlan works. We need 2024-06-26T05:54:27.0464717Z to keep a reference to the original state_dict as load happens in place so 2024-06-26T05:54:27.0464879Z we need to be able to perform it in place 2024-06-26T05:54:27.0464981Z 2024-06-26T05:54:27.0465125Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:27.0465299Z >>> class RenamePlanner(DefaultLoadPlanner): 2024-06-26T05:54:27.0465426Z >>> def set_up_planner( 2024-06-26T05:54:27.0465530Z >>> self, 2024-06-26T05:54:27.0465670Z >>> state_dict: STATE_DICT_TYPE, 2024-06-26T05:54:27.0465800Z >>> metadata: Metadata, 2024-06-26T05:54:27.0465926Z >>> is_coordinator: bool, 2024-06-26T05:54:27.0466069Z >>> ) -> None: 2024-06-26T05:54:27.0466232Z >>> self.original_state_dict = state_dict 2024-06-26T05:54:27.0466466Z >>> state_dict = {"foo_" + k: v for k, v in state_dict.items()} 2024-06-26T05:54:27.0466570Z >>> 2024-06-26T05:54:27.0466720Z >>> if self.flatten_sharded_tensors: 2024-06-26T05:54:27.0466924Z >>> state_dict = _flatten_sharded_tensors(state_dict) 2024-06-26T05:54:27.0467028Z >>> 2024-06-26T05:54:27.0467164Z >>> if self.flatten_state_dict: 2024-06-26T05:54:27.0467405Z >>> state_dict, self.mappings = flatten_state_dict(state_dict) 2024-06-26T05:54:27.0467510Z >>> 2024-06-26T05:54:27.0467650Z >>> self.state_dict = state_dict 2024-06-26T05:54:27.0467784Z >>> self.metadata = metadata 2024-06-26T05:54:27.0467958Z >>> self.is_coordinator = is_coordinator 2024-06-26T05:54:27.0468048Z >>> 2024-06-26T05:54:27.0468204Z >>> def load_bytes(self, read_item, value): 2024-06-26T05:54:27.0468375Z >>> # Remove the "foo_" prefix 2024-06-26T05:54:27.0468694Z >>> self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value) 2024-06-26T05:54:27.0468796Z 2024-06-26T05:54:27.0468882Z 2024-06-26T05:54:27.0469214Z Modifying resolve_tensor and commit_tensor to handle load time transformation. 2024-06-26T05:54:27.0469340Z 2024-06-26T05:54:27.0469483Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:27.0469684Z >>> class MetaModelMaterialize(DefaultSavePlanner): 2024-06-26T05:54:27.0469848Z >>> def resolve_tensor(self, read_item): 2024-06-26T05:54:27.0470051Z >>> tensor = super().resolve_tensor(read_item) 2024-06-26T05:54:27.0470266Z >>> return torch.empty_like(tensor, device="cpu") 2024-06-26T05:54:27.0470371Z >>> 2024-06-26T05:54:27.0470542Z >>> def commit_tensor(self, read_item, tensor): 2024-06-26T05:54:27.0470746Z >>> self.state_dict[read_item.dest_index.fqn] = tensor 2024-06-26T05:54:27.0470844Z 2024-06-26T05:54:27.0471233Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0471320Z 2024-06-26T05:54:27.0471442Z warnings.warn(msg) 2024-06-26T05:54:27.0471528Z 2024-06-26T05:54:27.0471726Z --- Parse Warning: 38 / 90 --- 2024-06-26T05:54:27.0473174Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_loader.py line=60. 2024-06-26T05:54:27.0473575Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0473677Z 2024-06-26T05:54:27.0473862Z Load a distributed ``state_dict`` in SPMD style. 2024-06-26T05:54:27.0473949Z 2024-06-26T05:54:27.0474207Z Each rank will try to read the least amount of data necessary 2024-06-26T05:54:27.0474525Z to fullfill the requested `state_dict`. When loading :class:`ShardedTensor` 2024-06-26T05:54:27.0474855Z or :class:`DTensor` instances, each rank only reads data for their local shards. 2024-06-26T05:54:27.0474954Z 2024-06-26T05:54:27.0475304Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2024-06-26T05:54:27.0475645Z load will first call ``state_dict`` before attempting deserialization, followed by 2024-06-26T05:54:27.0475874Z ``load_state_dict`` once the deserialization is complete. 2024-06-26T05:54:27.0475961Z 2024-06-26T05:54:27.0476081Z .. warning:: 2024-06-26T05:54:27.0476305Z All tensors in ``state_dict`` must be allocated on their 2024-06-26T05:54:27.0476514Z destination device *prior to* calling this function. 2024-06-26T05:54:27.0476612Z 2024-06-26T05:54:27.0476985Z All non-tensor data is loaded using `torch.load()` and modified in place 2024-06-26T05:54:27.0477089Z on state_dict. 2024-06-26T05:54:27.0477191Z 2024-06-26T05:54:27.0477293Z .. warning:: 2024-06-26T05:54:27.0477566Z Users must call `load_state_dict` on the root module to ensure load 2024-06-26T05:54:27.0477867Z pos-processing and non-tensor data properly propagates. 2024-06-26T05:54:27.0477955Z 2024-06-26T05:54:27.0478048Z .. note: 2024-06-26T05:54:27.0478365Z If no process group is initialized, this function will assume the intent 2024-06-26T05:54:27.0478678Z is to load a checkpoint into the local process. This can be useful in the 2024-06-26T05:54:27.0479014Z case of local inference, and when using regular Tensors (as opposed to DTensor 2024-06-26T05:54:27.0479142Z or ShardedTensor) 2024-06-26T05:54:27.0479230Z 2024-06-26T05:54:27.0479338Z .. note: 2024-06-26T05:54:27.0479518Z Rank 0 is assumed to be the coordinator rank. 2024-06-26T05:54:27.0479605Z 2024-06-26T05:54:27.0479710Z Args: 2024-06-26T05:54:27.0479908Z state_dict (Dict[str, Any]): The state_dict to save. 2024-06-26T05:54:27.0480123Z checkpoint_id (Union[str, os.PathLike, None]): 2024-06-26T05:54:27.0480428Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-06-26T05:54:27.0480791Z depends on the storage. It can be a path to a folder or to a file. 2024-06-26T05:54:27.0481091Z It can also be a key if the storage is a key-value store. 2024-06-26T05:54:27.0481270Z (Default: ``None``) 2024-06-26T05:54:27.0481435Z storage_reader (Optional[StorageReader]): 2024-06-26T05:54:27.0481708Z Instance of StorageWriter used to perform reads. If this is not 2024-06-26T05:54:27.0482046Z specified, DCP will automatically infer the reader based on the 2024-06-26T05:54:27.0482312Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-06-26T05:54:27.0482460Z be raised. (Default: ``None``) 2024-06-26T05:54:27.0482606Z planner (Optional[LoadPlanner]): 2024-06-26T05:54:27.0482875Z Instance of LoadPlanner. If this is not specificed, the default 2024-06-26T05:54:27.0483056Z planner will be used. (Default: ``None``) 2024-06-26T05:54:27.0483220Z process_group (Optional[ProcessGroup]): 2024-06-26T05:54:27.0483515Z ProcessGroup to be used for cross-rank synchronization. 2024-06-26T05:54:27.0483642Z (Default: ``None``) 2024-06-26T05:54:27.0483731Z 2024-06-26T05:54:27.0483827Z Returns: 2024-06-26T05:54:27.0483936Z None. 2024-06-26T05:54:27.0484024Z 2024-06-26T05:54:27.0484118Z Examples 2024-06-26T05:54:27.0484246Z >>> # xdoctest: +SKIP 2024-06-26T05:54:27.0484365Z >>> my_model = MyModule() 2024-06-26T05:54:27.0484550Z >>> optimizer = Adagrad(my_model.parameters()) 2024-06-26T05:54:27.0484726Z >>> model_state_dict = my_model.state_dict() 2024-06-26T05:54:27.0485125Z >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader("/checkpoint/1") 2024-06-26T05:54:27.0485212Z 2024-06-26T05:54:27.0485431Z >>> torch.distributed.checkpoint.load_state_dict( 2024-06-26T05:54:27.0485571Z >>> state_dict=model_state_dict, 2024-06-26T05:54:27.0485733Z >>> storage_reader=fs_storage_reader, 2024-06-26T05:54:27.0485825Z >>> ) 2024-06-26T05:54:27.0485911Z 2024-06-26T05:54:27.0486183Z >>> # module.load_state_dict() function might have customized steps 2024-06-26T05:54:27.0486347Z >>> # to flush the state_dict, must call it to 2024-06-26T05:54:27.0486478Z >>> # ensure correct behavior. 2024-06-26T05:54:27.0486656Z >>> my_model.load_state_dict(model_state_dict) 2024-06-26T05:54:27.0486743Z 2024-06-26T05:54:27.0486841Z .. note:: 2024-06-26T05:54:27.0487124Z load_state_dict uses collectives to coordinate reads across ranks. 2024-06-26T05:54:27.0487464Z For NCCL-based process groups, internal tensor representations of 2024-06-26T05:54:27.0487780Z objects must be moved to the GPU device before communication takes place. 2024-06-26T05:54:27.0488098Z In this case, the device used is given by ``torch.cuda.current_device()`` 2024-06-26T05:54:27.0488485Z and it is the user's responsibility to ensure that this is set so that each 2024-06-26T05:54:27.0488745Z rank has an individual GPU, via ``torch.cuda.set_device()``. 2024-06-26T05:54:27.0488834Z 2024-06-26T05:54:27.0489215Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0489316Z 2024-06-26T05:54:27.0489424Z warnings.warn(msg) 2024-06-26T05:54:27.0489515Z 2024-06-26T05:54:27.0489726Z --- Parse Warning: 39 / 90 --- 2024-06-26T05:54:27.0491154Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=save in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=66. 2024-06-26T05:54:27.0491586Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0491687Z 2024-06-26T05:54:27.0491839Z Save a distributed model in SPMD style. 2024-06-26T05:54:27.0491926Z 2024-06-26T05:54:27.0492186Z This function is different from ``torch.save()`` as it handles 2024-06-26T05:54:27.0492526Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards. 2024-06-26T05:54:27.0492653Z 2024-06-26T05:54:27.0492998Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2024-06-26T05:54:27.0493215Z save will call ``state_dict`` before serialization. 2024-06-26T05:54:27.0493316Z 2024-06-26T05:54:27.0493417Z .. warning:: 2024-06-26T05:54:27.0493897Z There is no guarantees of Backwards Compatibility across PyTorch versions 2024-06-26T05:54:27.0494030Z for saved state_dicts. 2024-06-26T05:54:27.0494117Z 2024-06-26T05:54:27.0494221Z .. warning:: 2024-06-26T05:54:27.0494522Z If using the `process_group` argument, make sure that only its ranks 2024-06-26T05:54:27.0494796Z call `save_state_dict` and that all data in state_dict belong to it. 2024-06-26T05:54:27.0494885Z 2024-06-26T05:54:27.0494997Z .. note:: 2024-06-26T05:54:27.0495418Z When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of 2024-06-26T05:54:27.0495770Z the shard_group should be calling `save_state_dict` and the corresponding process 2024-06-26T05:54:27.0495913Z group needs to be passed in. 2024-06-26T05:54:27.0496001Z 2024-06-26T05:54:27.0496111Z .. note:: 2024-06-26T05:54:27.0496483Z If no process group is available, this function assumes the intention is to save the 2024-06-26T05:54:27.0496618Z state_dict in the local process. 2024-06-26T05:54:27.0496718Z 2024-06-26T05:54:27.0496813Z .. note: 2024-06-26T05:54:27.0496993Z Rank 0 is assumed to be the coordinator rank. 2024-06-26T05:54:27.0497095Z 2024-06-26T05:54:27.0497185Z 2024-06-26T05:54:27.0497277Z Args: 2024-06-26T05:54:27.0497489Z state_dict (Dict[str, Any]): The state_dict to save. 2024-06-26T05:54:27.0497671Z checkpoint_id (Union[str, os.PathLike, None]): 2024-06-26T05:54:27.0497964Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-06-26T05:54:27.0498261Z depends on the storage. It can be a path to a folder or to a file. 2024-06-26T05:54:27.0498552Z It can also be a key if the storage is a key-value store. 2024-06-26T05:54:27.0498665Z (Default: ``None``) 2024-06-26T05:54:27.0498846Z storage_writer (Optional[StorageWriter]): 2024-06-26T05:54:27.0499126Z Instance of StorageWriter used to perform writes. If this is not 2024-06-26T05:54:27.0499406Z specified, DCP will automatically infer the writer based on the 2024-06-26T05:54:27.0499669Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-06-26T05:54:27.0499807Z be raised. (Default: ``None``) 2024-06-26T05:54:27.0499960Z planner (Optional[SavePlanner]): 2024-06-26T05:54:27.0500231Z Instance of SavePlanner. If this is not specificed, the default 2024-06-26T05:54:27.0500397Z planner will be used. (Default: ``None``) 2024-06-26T05:54:27.0500575Z process_group (Optional[ProcessGroup]): 2024-06-26T05:54:27.0500868Z ProcessGroup to be used for cross-rank synchronization. 2024-06-26T05:54:27.0500981Z (Default: ``None``) 2024-06-26T05:54:27.0501081Z 2024-06-26T05:54:27.0501179Z Returns: 2024-06-26T05:54:27.0501382Z Metadata: Metadata object for the saved checkpoint. 2024-06-26T05:54:27.0501488Z 2024-06-26T05:54:27.0501583Z Example: 2024-06-26T05:54:27.0501699Z >>> # xdoctest: +SKIP 2024-06-26T05:54:27.0501831Z >>> my_model = MyModule() 2024-06-26T05:54:27.0501919Z 2024-06-26T05:54:27.0502061Z >>> state_dict = {"model": my_model} 2024-06-26T05:54:27.0502204Z 2024-06-26T05:54:27.0502603Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2024-06-26T05:54:27.0502790Z >>> torch.distributed.checkpoint.save( 2024-06-26T05:54:27.0502911Z >>> state_dict=state_dict, 2024-06-26T05:54:27.0503062Z >>> storage_writer=fs_storage_writer, 2024-06-26T05:54:27.0503234Z >>> ) 2024-06-26T05:54:27.0503323Z 2024-06-26T05:54:27.0503419Z .. note:: 2024-06-26T05:54:27.0503711Z save_state_dict uses collectives to coordinate writes across ranks. 2024-06-26T05:54:27.0504089Z For NCCL-based process groups, internal tensor representations of 2024-06-26T05:54:27.0504481Z objects must be moved to the GPU device before communication takes place. 2024-06-26T05:54:27.0504919Z In this case, the device used is given by ``torch.cuda.current_device()`` 2024-06-26T05:54:27.0505350Z and it is the user's responsibility to ensure that this is set so that 2024-06-26T05:54:27.0505654Z each rank has an individual GPU, via ``torch.cuda.set_device()``. 2024-06-26T05:54:27.0512620Z 2024-06-26T05:54:27.0513108Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0513211Z 2024-06-26T05:54:27.0513326Z warnings.warn(msg) 2024-06-26T05:54:27.0513420Z 2024-06-26T05:54:27.0513645Z --- Parse Warning: 40 / 90 --- 2024-06-26T05:54:27.0515118Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=async_save in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=169. 2024-06-26T05:54:27.0515525Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0515961Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the 2024-06-26T05:54:27.0516358Z staging storage (defaults to CPU memory), and then calls the `save` in a separate thread. 2024-06-26T05:54:27.0516448Z 2024-06-26T05:54:27.0516573Z .. warning:: 2024-06-26T05:54:27.0516784Z This feature is experimental and subject to change. 2024-06-26T05:54:27.0516889Z 2024-06-26T05:54:27.0516986Z Args: 2024-06-26T05:54:27.0517193Z state_dict (Dict[str, Any]): The state_dict to save. 2024-06-26T05:54:27.0517397Z checkpoint_id (Union[str, os.PathLike, None]): 2024-06-26T05:54:27.0517695Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-06-26T05:54:27.0517984Z depends on the storage. It can be a path to a folder or to a file. 2024-06-26T05:54:27.0518300Z It can also be a key if the storage is a key-value store. 2024-06-26T05:54:27.0518417Z (Default: ``None``) 2024-06-26T05:54:27.0518590Z storage_writer (Optional[StorageWriter]): 2024-06-26T05:54:27.0518954Z Instance of StorageWriter used to perform 'stage' and 'save'. If 2024-06-26T05:54:27.0519282Z this is not specified, DCP will automatically infer the writer based on the 2024-06-26T05:54:27.0519553Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-06-26T05:54:27.0519712Z be raised. (Default: ``None``) 2024-06-26T05:54:27.0519863Z planner (Optional[SavePlanner]): 2024-06-26T05:54:27.0520151Z Instance of SavePlanner. If this is not specificed, the default 2024-06-26T05:54:27.0520324Z planner will be used. (Default: ``None``) 2024-06-26T05:54:27.0520491Z process_group (Optional[ProcessGroup]): 2024-06-26T05:54:27.0520890Z ProcessGroup to be used for cross-rank synchronization. 2024-06-26T05:54:27.0521008Z (Default: ``None``) 2024-06-26T05:54:27.0521097Z 2024-06-26T05:54:27.0521211Z Returns: 2024-06-26T05:54:27.0521585Z Future: A future holding the resultant Metadata object from `save`. 2024-06-26T05:54:27.0521674Z 2024-06-26T05:54:27.0521783Z Example: 2024-06-26T05:54:27.0521904Z >>> # xdoctest: +SKIP 2024-06-26T05:54:27.0522027Z >>> my_model = MyModule() 2024-06-26T05:54:27.0522126Z 2024-06-26T05:54:27.0522274Z >>> state_dict = {"model": my_model} 2024-06-26T05:54:27.0522396Z 2024-06-26T05:54:27.0522812Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2024-06-26T05:54:27.0523088Z >>> checkpoint_future = torch.distributed.checkpoint.async_save( 2024-06-26T05:54:27.0523269Z >>> state_dict=state_dict, 2024-06-26T05:54:27.0523463Z >>> storage_writer=fs_storage_writer, 2024-06-26T05:54:27.0523561Z >>> ) 2024-06-26T05:54:27.0523668Z >>> 2024-06-26T05:54:27.0523790Z >>> # ... do some work ... 2024-06-26T05:54:27.0523881Z >>> 2024-06-26T05:54:27.0524033Z >>> checkpoint_future.result() 2024-06-26T05:54:27.0524121Z 2024-06-26T05:54:27.0524214Z 2024-06-26T05:54:27.0524620Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0524707Z 2024-06-26T05:54:27.0524818Z warnings.warn(msg) 2024-06-26T05:54:27.0524920Z 2024-06-26T05:54:27.0525121Z --- Parse Warning: 41 / 90 --- 2024-06-26T05:54:27.0526656Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=construct_and_record_rdzv_event in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/elastic/events/__init__.py line=91. 2024-06-26T05:54:27.0527075Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0527165Z 2024-06-26T05:54:27.0527427Z Initialize rendezvous event object and record its operations. 2024-06-26T05:54:27.0527516Z 2024-06-26T05:54:27.0527611Z Args: 2024-06-26T05:54:27.0527793Z run_id (str): The run id of the rendezvous. 2024-06-26T05:54:27.0527982Z message (str): The message describing the event. 2024-06-26T05:54:27.0528321Z node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED). 2024-06-26T05:54:27.0528589Z name (str): Event name. (E.g. Current action being performed). 2024-06-26T05:54:27.0528744Z hostname (str): Hostname of the node. 2024-06-26T05:54:27.0528934Z pid (Optional[int]): The process id of the node. 2024-06-26T05:54:27.0529275Z master_endpoint (str): The master endpoint for the rendezvous store, if known. 2024-06-26T05:54:27.0529649Z local_id (Optional[int]): The local_id of the node, if defined in dynamic_rendezvous.py 2024-06-26T05:54:27.0529865Z rank (Optional[int]): The rank of the node, if known. 2024-06-26T05:54:27.0529961Z Returns: 2024-06-26T05:54:27.0530055Z None 2024-06-26T05:54:27.0530163Z Example: 2024-06-26T05:54:27.0530333Z >>> # See DynamicRendezvousHandler class 2024-06-26T05:54:27.0530437Z >>> def _record( 2024-06-26T05:54:27.0530549Z ... self, 2024-06-26T05:54:27.0530660Z ... message: str, 2024-06-26T05:54:27.0530845Z ... node_state: NodeState = NodeState.RUNNING, 2024-06-26T05:54:27.0531001Z ... rank: Optional[int] = None, 2024-06-26T05:54:27.0531135Z ... ) -> None: 2024-06-26T05:54:27.0531287Z ... construct_and_record_rdzv_event( 2024-06-26T05:54:27.0531522Z ... name=f"{self.__class__.__name__}.{get_method_name()}", 2024-06-26T05:54:27.0531673Z ... run_id=self._settings.run_id, 2024-06-26T05:54:27.0531790Z ... message=message, 2024-06-26T05:54:27.0531929Z ... node_state=node_state, 2024-06-26T05:54:27.0532080Z ... hostname=self._this_node.addr, 2024-06-26T05:54:27.0532217Z ... pid=self._this_node.pid, 2024-06-26T05:54:27.0532421Z ... local_id=self._this_node.local_id, 2024-06-26T05:54:27.0532531Z ... rank=rank, 2024-06-26T05:54:27.0532641Z ... ) 2024-06-26T05:54:27.0532729Z 2024-06-26T05:54:27.0533118Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0533221Z 2024-06-26T05:54:27.0533359Z warnings.warn(msg) 2024-06-26T05:54:27.0533446Z 2024-06-26T05:54:27.0533805Z --- Parse Warning: 42 / 90 --- 2024-06-26T05:54:27.0535231Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MixedPrecision in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/api.py line=113. 2024-06-26T05:54:27.0535668Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0535771Z 2024-06-26T05:54:27.0536033Z This configures FSDP-native mixed precision training. 2024-06-26T05:54:27.0536125Z 2024-06-26T05:54:27.0536243Z Attributes: 2024-06-26T05:54:27.0536548Z param_dtype (Optional[torch.dtype]): This specifies the dtype for model 2024-06-26T05:54:27.0536807Z parameters during forward and backward and thus the dtype for 2024-06-26T05:54:27.0537113Z forward and backward computation. Outside forward and backward, the 2024-06-26T05:54:27.0537369Z *sharded* parameters are kept in full precision (e.g. for the 2024-06-26T05:54:27.0537658Z optimizer step), and for model checkpointing, the parameters are 2024-06-26T05:54:27.0537864Z always saved in full precision. (Default: ``None``) 2024-06-26T05:54:27.0538145Z reduce_dtype (Optional[torch.dtype]): This specifies the dtype for 2024-06-26T05:54:27.0538504Z gradient reduction (i.e. reduce-scatter or all-reduce). If this is 2024-06-26T05:54:27.0538751Z ``None`` but ``param_dtype`` is not ``None``, then this takes on 2024-06-26T05:54:27.0539017Z the ``param_dtype`` value, still running gradient reduction in low 2024-06-26T05:54:27.0539311Z precision. This is permitted to differ from ``param_dtype``, e.g. 2024-06-26T05:54:27.0539577Z to force gradient reduction to run in full precision. (Default: 2024-06-26T05:54:27.0539691Z ``None``) 2024-06-26T05:54:27.0539965Z buffer_dtype (Optional[torch.dtype]): This specifies the dtype for 2024-06-26T05:54:27.0540233Z buffers. FSDP does not shard buffers. Rather, FSDP casts them to 2024-06-26T05:54:27.0540507Z ``buffer_dtype`` in the first forward pass and keeps them in that 2024-06-26T05:54:27.0540781Z dtype thereafter. For model checkpointing, the buffers are saved 2024-06-26T05:54:27.0541024Z in full precision except for ``LOCAL_STATE_DICT``. (Default: 2024-06-26T05:54:27.0541137Z ``None``) 2024-06-26T05:54:27.0541387Z keep_low_precision_grads (bool): If ``False``, then FSDP upcasts 2024-06-26T05:54:27.0541674Z gradients to full precision after the backward pass in preparation 2024-06-26T05:54:27.0541966Z for the optimizer step. If ``True``, then FSDP keeps the gradients 2024-06-26T05:54:27.0542246Z in the dtype used for gradient reduction, which can save memory if 2024-06-26T05:54:27.0542531Z using a custom optimizer that supports running in low precision. 2024-06-26T05:54:27.0542646Z (Default: ``False``) 2024-06-26T05:54:27.0542912Z cast_forward_inputs (bool): If ``True``, then this FSDP module casts 2024-06-26T05:54:27.0543199Z its forward args and kwargs to ``param_dtype``. This is to ensure 2024-06-26T05:54:27.0543475Z that parameter and input dtypes match for forward computation, as 2024-06-26T05:54:27.0543758Z required by many ops. This may need to be set to ``True`` when only 2024-06-26T05:54:27.0544064Z applying mixed precision to some but not all FSDP modules, in which 2024-06-26T05:54:27.0544442Z case a mixed-precision FSDP submodule needs to recast its inputs. 2024-06-26T05:54:27.0544564Z (Default: ``False``) 2024-06-26T05:54:27.0544860Z cast_root_forward_inputs (bool): If ``True``, then the root FSDP module 2024-06-26T05:54:27.0545131Z casts its forward args and kwargs to ``param_dtype``, overriding 2024-06-26T05:54:27.0545511Z the value of ``cast_forward_inputs``. For non-root FSDP modules, 2024-06-26T05:54:27.0545696Z this does not do anything. (Default: ``True``) 2024-06-26T05:54:27.0546005Z _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies 2024-06-26T05:54:27.0546291Z module classes to ignore for mixed precision when using an 2024-06-26T05:54:27.0546531Z ``auto_wrap_policy``: Modules of these classes will have FSDP 2024-06-26T05:54:27.0546811Z applied to them separately with mixed precision disabled (meaning 2024-06-26T05:54:27.0547103Z that the final FSDP construction would deviate from the specified 2024-06-26T05:54:27.0547361Z policy). If ``auto_wrap_policy`` is not specified, then this does 2024-06-26T05:54:27.0547649Z not do anything. This API is experimental and subject to change. 2024-06-26T05:54:27.0547781Z (Default: ``(_BatchNorm,)``) 2024-06-26T05:54:27.0547873Z 2024-06-26T05:54:27.0548110Z .. note:: This API is experimental and subject to change. 2024-06-26T05:54:27.0548200Z 2024-06-26T05:54:27.0548492Z .. note:: Only floating point tensors are cast to their specified dtypes. 2024-06-26T05:54:27.0548595Z 2024-06-26T05:54:27.0548845Z .. note:: In ``summon_full_params``, parameters are forced to full 2024-06-26T05:54:27.0548977Z precision, but buffers are not. 2024-06-26T05:54:27.0549082Z 2024-06-26T05:54:27.0549362Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when 2024-06-26T05:54:27.0549652Z their inputs are in a low precision like ``float16`` or ``bfloat16``. 2024-06-26T05:54:27.0550033Z Disabling FSDP's mixed precision for those norm modules only means that 2024-06-26T05:54:27.0550311Z the affine parameters are kept in ``float32``. However, this incurs 2024-06-26T05:54:27.0550692Z separate all-gathers and reduce-scatters for those norm modules, which 2024-06-26T05:54:27.0550991Z may be inefficient, so if the workload permits, the user should prefer 2024-06-26T05:54:27.0551183Z to still apply mixed precision to those modules. 2024-06-26T05:54:27.0551288Z 2024-06-26T05:54:27.0551578Z .. note:: By default, if the user passes a model with any ``_BatchNorm`` 2024-06-26T05:54:27.0551852Z modules and specifies an ``auto_wrap_policy``, then the batch norm 2024-06-26T05:54:27.0552167Z modules will have FSDP applied to them separately with mixed precision 2024-06-26T05:54:27.0552393Z disabled. See the ``_module_classes_to_ignore`` argument. 2024-06-26T05:54:27.0552483Z 2024-06-26T05:54:27.0552766Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and 2024-06-26T05:54:27.0553043Z ``cast_forward_inputs=False`` by default. For the root FSDP instance, 2024-06-26T05:54:27.0553281Z its ``cast_root_forward_inputs`` takes precedence over its 2024-06-26T05:54:27.0553567Z ``cast_forward_inputs``. For non-root FSDP instances, their 2024-06-26T05:54:27.0553849Z ``cast_root_forward_inputs`` values are ignored. The default setting is 2024-06-26T05:54:27.0554152Z sufficient for the typical case where each FSDP instance has the same 2024-06-26T05:54:27.0554444Z ``MixedPrecision`` configuration and only needs to cast inputs to the 2024-06-26T05:54:27.0554740Z ``param_dtype`` at the beginning of the model's forward pass. 2024-06-26T05:54:27.0554840Z 2024-06-26T05:54:27.0555122Z .. note:: For nested FSDP instances with different ``MixedPrecision`` 2024-06-26T05:54:27.0555465Z configurations, we recommend setting individual ``cast_forward_inputs`` 2024-06-26T05:54:27.0555767Z values to configure casting inputs or not before each instance's 2024-06-26T05:54:27.0556031Z forward. In such a case, since the casts happen before each FSDP 2024-06-26T05:54:27.0556384Z instance's forward, a parent FSDP instance should have its non-FSDP 2024-06-26T05:54:27.0556727Z submodules run before its FSDP submodules to avoid the activation dtype 2024-06-26T05:54:27.0557015Z being changed due to a different ``MixedPrecision`` configuration. 2024-06-26T05:54:27.0557144Z 2024-06-26T05:54:27.0557248Z Example:: 2024-06-26T05:54:27.0557336Z 2024-06-26T05:54:27.0557547Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:27.0557773Z >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3)) 2024-06-26T05:54:27.0557888Z >>> model[1] = FSDP( 2024-06-26T05:54:27.0558007Z >>> model[1], 2024-06-26T05:54:27.0558393Z >>> mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True), 2024-06-26T05:54:27.0558501Z >>> ) 2024-06-26T05:54:27.0558611Z >>> model = FSDP( 2024-06-26T05:54:27.0558714Z >>> model, 2024-06-26T05:54:27.0559113Z >>> mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), 2024-06-26T05:54:27.0559210Z >>> ) 2024-06-26T05:54:27.0559296Z 2024-06-26T05:54:27.0559597Z The above shows a working example. On the other hand, if ``model[1]`` 2024-06-26T05:54:27.0559864Z were replaced with ``model[0]``, meaning that the submodule using 2024-06-26T05:54:27.0560148Z different ``MixedPrecision`` ran its forward first, then ``model[1]`` 2024-06-26T05:54:27.0560445Z would incorrectly see ``float16`` activations instead of ``bfloat16`` 2024-06-26T05:54:27.0560541Z ones. 2024-06-26T05:54:27.0560708Z 2024-06-26T05:54:27.0560814Z 2024-06-26T05:54:27.0561208Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0561294Z 2024-06-26T05:54:27.0561418Z warnings.warn(msg) 2024-06-26T05:54:27.0561506Z 2024-06-26T05:54:27.0561709Z --- Parse Warning: 43 / 90 --- 2024-06-26T05:54:27.0563446Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.set_state_dict_type in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=648. 2024-06-26T05:54:27.0563853Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0564213Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2024-06-26T05:54:27.0564301Z 2024-06-26T05:54:27.0564719Z Also takes (optional) configuration for the model's and optimizer's state dict. 2024-06-26T05:54:27.0565014Z The target module does not have to be a FSDP module. If the target 2024-06-26T05:54:27.0565305Z module is a FSDP module, its ``state_dict_type`` will also be changed. 2024-06-26T05:54:27.0565393Z 2024-06-26T05:54:27.0565733Z .. note:: This API should be called for only the top-level (root) 2024-06-26T05:54:27.0565836Z module. 2024-06-26T05:54:27.0565937Z 2024-06-26T05:54:27.0566227Z .. note:: This API enables users to transparently use the conventional 2024-06-26T05:54:27.0566486Z ``state_dict`` API to take model checkpoints in cases where the 2024-06-26T05:54:27.0566780Z root FSDP module is wrapped by another ``nn.Module``. For example, 2024-06-26T05:54:27.0567120Z the following will ensure ``state_dict`` is called on all non-FSDP 2024-06-26T05:54:27.0567420Z instances, while dispatching into `sharded_state_dict` implementation 2024-06-26T05:54:27.0567594Z for FSDP: 2024-06-26T05:54:27.0567682Z 2024-06-26T05:54:27.0567787Z Example:: 2024-06-26T05:54:27.0567886Z 2024-06-26T05:54:27.0568066Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:27.0568199Z >>> model = DDP(FSDP(...)) 2024-06-26T05:54:27.0568381Z >>> FSDP.set_state_dict_type( 2024-06-26T05:54:27.0568488Z >>> model, 2024-06-26T05:54:27.0568681Z >>> StateDictType.SHARDED_STATE_DICT, 2024-06-26T05:54:27.0568992Z >>> state_dict_config = ShardedStateDictConfig(offload_to_cpu=True), 2024-06-26T05:54:27.0569319Z >>> optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True), 2024-06-26T05:54:27.0569429Z >>> ) 2024-06-26T05:54:27.0569598Z >>> param_state_dict = model.state_dict() 2024-06-26T05:54:27.0569826Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2024-06-26T05:54:27.0569926Z 2024-06-26T05:54:27.0570023Z Args: 2024-06-26T05:54:27.0570190Z module (torch.nn.Module): Root module. 2024-06-26T05:54:27.0570506Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2024-06-26T05:54:27.0570815Z state_dict_config (Optional[StateDictConfig]): the configuration for the 2024-06-26T05:54:27.0570973Z target ``state_dict_type``. 2024-06-26T05:54:27.0571298Z optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration 2024-06-26T05:54:27.0571451Z for the optimizer state dict. 2024-06-26T05:54:27.0571554Z 2024-06-26T05:54:27.0571655Z Returns: 2024-06-26T05:54:27.0571942Z A StateDictSettings that include the previous state_dict type and 2024-06-26T05:54:27.0572098Z configuration for the module. 2024-06-26T05:54:27.0572192Z 2024-06-26T05:54:27.0572580Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0572681Z 2024-06-26T05:54:27.0572793Z warnings.warn(msg) 2024-06-26T05:54:27.0572881Z 2024-06-26T05:54:27.0573092Z --- Parse Warning: 44 / 90 --- 2024-06-26T05:54:27.0574893Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.state_dict_type in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=804. 2024-06-26T05:54:27.0575313Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0575661Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2024-06-26T05:54:27.0575750Z 2024-06-26T05:54:27.0576192Z This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of 2024-06-26T05:54:27.0576364Z :meth:`set_state_dict_type` for the detail. 2024-06-26T05:54:27.0576453Z 2024-06-26T05:54:27.0576570Z Example:: 2024-06-26T05:54:27.0576657Z 2024-06-26T05:54:27.0576836Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:27.0576982Z >>> model = DDP(FSDP(...)) 2024-06-26T05:54:27.0577132Z >>> with FSDP.state_dict_type( 2024-06-26T05:54:27.0577236Z >>> model, 2024-06-26T05:54:27.0577430Z >>> StateDictType.SHARDED_STATE_DICT, 2024-06-26T05:54:27.0577528Z >>> ): 2024-06-26T05:54:27.0577690Z >>> checkpoint = model.state_dict() 2024-06-26T05:54:27.0577793Z 2024-06-26T05:54:27.0577889Z Args: 2024-06-26T05:54:27.0578065Z module (torch.nn.Module): Root module. 2024-06-26T05:54:27.0578369Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2024-06-26T05:54:27.0578718Z state_dict_config (Optional[StateDictConfig]): the model ``state_dict`` 2024-06-26T05:54:27.0578939Z configuration for the target ``state_dict_type``. 2024-06-26T05:54:27.0579243Z optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer 2024-06-26T05:54:27.0579504Z ``state_dict`` configuration for the target ``state_dict_type``. 2024-06-26T05:54:27.0579642Z 2024-06-26T05:54:27.0580030Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0580156Z 2024-06-26T05:54:27.0580286Z warnings.warn(msg) 2024-06-26T05:54:27.0580374Z 2024-06-26T05:54:27.0580606Z --- Parse Warning: 45 / 90 --- 2024-06-26T05:54:27.0582314Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.optim_state_dict in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1801. 2024-06-26T05:54:27.0582712Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0582812Z 2024-06-26T05:54:27.0583197Z Transform the state-dict of an optimizer corresponding to a sharded model. 2024-06-26T05:54:27.0583286Z 2024-06-26T05:54:27.0583612Z The given state-dict can be transformed to one of three types: 2024-06-26T05:54:27.0584006Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict. 2024-06-26T05:54:27.0584097Z 2024-06-26T05:54:27.0584422Z For full optimizer state_dict, all states are unflattened and not sharded. 2024-06-26T05:54:27.0584723Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to 2024-06-26T05:54:27.0584834Z avoid OOM. 2024-06-26T05:54:27.0584923Z 2024-06-26T05:54:27.0585230Z For sharded optimizer state_dict, all states are unflattened but sharded. 2024-06-26T05:54:27.0585526Z CPU only can be specified via :meth:`state_dict_type` to further save 2024-06-26T05:54:27.0585622Z memory. 2024-06-26T05:54:27.0585711Z 2024-06-26T05:54:27.0586017Z For local state_dict, no transformation will be performed. But a state 2024-06-26T05:54:27.0586340Z will be converted from nn.Tensor to ShardedTensor to represent its sharding 2024-06-26T05:54:27.0586480Z nature (this is not supported yet). 2024-06-26T05:54:27.0586581Z 2024-06-26T05:54:27.0586681Z Example:: 2024-06-26T05:54:27.0586768Z 2024-06-26T05:54:27.0586954Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:27.0587272Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2024-06-26T05:54:27.0587482Z >>> from torch.distributed.fsdp import StateDictType 2024-06-26T05:54:27.0587736Z >>> from torch.distributed.fsdp import FullStateDictConfig 2024-06-26T05:54:27.0588003Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2024-06-26T05:54:27.0588135Z >>> # Save a checkpoint 2024-06-26T05:54:27.0588251Z >>> model, optim = ... 2024-06-26T05:54:27.0588379Z >>> FSDP.set_state_dict_type( 2024-06-26T05:54:27.0588489Z >>> model, 2024-06-26T05:54:27.0588645Z >>> StateDictType.FULL_STATE_DICT, 2024-06-26T05:54:27.0588819Z >>> FullStateDictConfig(rank0_only=False), 2024-06-26T05:54:27.0589032Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-06-26T05:54:27.0589125Z >>> ) 2024-06-26T05:54:27.0589264Z >>> state_dict = model.state_dict() 2024-06-26T05:54:27.0589496Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2024-06-26T05:54:27.0589685Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2024-06-26T05:54:27.0589798Z >>> # Load a checkpoint 2024-06-26T05:54:27.0589924Z >>> model, optim = ... 2024-06-26T05:54:27.0590123Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2024-06-26T05:54:27.0590278Z >>> FSDP.set_state_dict_type( 2024-06-26T05:54:27.0590390Z >>> model, 2024-06-26T05:54:27.0590544Z >>> StateDictType.FULL_STATE_DICT, 2024-06-26T05:54:27.0590716Z >>> FullStateDictConfig(rank0_only=False), 2024-06-26T05:54:27.0590926Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-06-26T05:54:27.0591047Z >>> ) 2024-06-26T05:54:27.0591205Z >>> model.load_state_dict(state_dict) 2024-06-26T05:54:27.0591402Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2024-06-26T05:54:27.0591547Z >>> model, optim, optim_state_dict 2024-06-26T05:54:27.0591679Z >>> ) 2024-06-26T05:54:27.0591875Z >>> optim.load_state_dict(optim_state_dict) 2024-06-26T05:54:27.0591965Z 2024-06-26T05:54:27.0592075Z Args: 2024-06-26T05:54:27.0592338Z model (torch.nn.Module): Root module (which may or may not be a 2024-06-26T05:54:27.0592598Z :class:`FullyShardedDataParallel` instance) whose parameters 2024-06-26T05:54:27.0592790Z were passed into the optimizer ``optim``. 2024-06-26T05:54:27.0593058Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2024-06-26T05:54:27.0593163Z parameters. 2024-06-26T05:54:27.0593457Z optim_state_dict (Dict[str, Any]): the target optimizer state_dict to 2024-06-26T05:54:27.0593743Z transform. If the value is None, optim.state_dict() will be used. ( 2024-06-26T05:54:27.0593869Z Default: ``None``) 2024-06-26T05:54:27.0594248Z group (dist.ProcessGroup): Model's process group across which parameters 2024-06-26T05:54:27.0594503Z are sharded or ``None`` if using the default process group. ( 2024-06-26T05:54:27.0594629Z Default: ``None``) 2024-06-26T05:54:27.0594717Z 2024-06-26T05:54:27.0594813Z Returns: 2024-06-26T05:54:27.0595092Z Dict[str, Any]: A :class:`dict` containing the optimizer state for 2024-06-26T05:54:27.0595319Z ``model``. The sharding of the optimizer state is based on 2024-06-26T05:54:27.0595429Z ``state_dict_type``. 2024-06-26T05:54:27.0595530Z 2024-06-26T05:54:27.0595917Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0596004Z 2024-06-26T05:54:27.0596129Z warnings.warn(msg) 2024-06-26T05:54:27.0596218Z 2024-06-26T05:54:27.0596420Z --- Parse Warning: 46 / 90 --- 2024-06-26T05:54:27.0598172Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.optim_state_dict_to_load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1899. 2024-06-26T05:54:27.0598576Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0598681Z 2024-06-26T05:54:27.0599266Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model. 2024-06-26T05:54:27.0599355Z 2024-06-26T05:54:27.0599587Z Given a ``optim_state_dict`` that is transformed through 2024-06-26T05:54:27.0599866Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer 2024-06-26T05:54:27.0600152Z state_dict that can be loaded to ``optim`` which is the optimizer for 2024-06-26T05:54:27.0600421Z ``model``. ``model`` must be sharded by FullyShardedDataParallel. 2024-06-26T05:54:27.0600510Z 2024-06-26T05:54:27.0600762Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:27.0601095Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2024-06-26T05:54:27.0601309Z >>> from torch.distributed.fsdp import StateDictType 2024-06-26T05:54:27.0601560Z >>> from torch.distributed.fsdp import FullStateDictConfig 2024-06-26T05:54:27.0601826Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2024-06-26T05:54:27.0601942Z >>> # Save a checkpoint 2024-06-26T05:54:27.0602103Z >>> model, optim = ... 2024-06-26T05:54:27.0602231Z >>> FSDP.set_state_dict_type( 2024-06-26T05:54:27.0602333Z >>> model, 2024-06-26T05:54:27.0602498Z >>> StateDictType.FULL_STATE_DICT, 2024-06-26T05:54:27.0602672Z >>> FullStateDictConfig(rank0_only=False), 2024-06-26T05:54:27.0602896Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-06-26T05:54:27.0603007Z >>> ) 2024-06-26T05:54:27.0603146Z >>> state_dict = model.state_dict() 2024-06-26T05:54:27.0603295Z >>> original_osd = optim.state_dict() 2024-06-26T05:54:27.0603504Z >>> optim_state_dict = FSDP.optim_state_dict( 2024-06-26T05:54:27.0603631Z >>> model, 2024-06-26T05:54:27.0603732Z >>> optim, 2024-06-26T05:54:27.0603890Z >>> optim_state_dict=original_osd 2024-06-26T05:54:27.0603983Z >>> ) 2024-06-26T05:54:27.0604183Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2024-06-26T05:54:27.0604301Z >>> # Load a checkpoint 2024-06-26T05:54:27.0604413Z >>> model, optim = ... 2024-06-26T05:54:27.0604627Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2024-06-26T05:54:27.0604754Z >>> FSDP.set_state_dict_type( 2024-06-26T05:54:27.0604852Z >>> model, 2024-06-26T05:54:27.0605017Z >>> StateDictType.FULL_STATE_DICT, 2024-06-26T05:54:27.0605188Z >>> FullStateDictConfig(rank0_only=False), 2024-06-26T05:54:27.0605380Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-06-26T05:54:27.0605490Z >>> ) 2024-06-26T05:54:27.0605637Z >>> model.load_state_dict(state_dict) 2024-06-26T05:54:27.0605832Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2024-06-26T05:54:27.0605990Z >>> model, optim, optim_state_dict 2024-06-26T05:54:27.0606082Z >>> ) 2024-06-26T05:54:27.0606239Z >>> optim.load_state_dict(optim_state_dict) 2024-06-26T05:54:27.0606342Z 2024-06-26T05:54:27.0606439Z Args: 2024-06-26T05:54:27.0606700Z model (torch.nn.Module): Root module (which may or may not be a 2024-06-26T05:54:27.0606972Z :class:`FullyShardedDataParallel` instance) whose parameters 2024-06-26T05:54:27.0607143Z were passed into the optimizer ``optim``. 2024-06-26T05:54:27.0607431Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2024-06-26T05:54:27.0607536Z parameters. 2024-06-26T05:54:27.0607820Z optim_state_dict (Dict[str, Any]): The optimizer states to be loaded. 2024-06-26T05:54:27.0608093Z is_named_optimizer (bool): Is this optimizer a NamedOptimizer or 2024-06-26T05:54:27.0608377Z KeyedOptimizer. Only set to True if ``optim`` is TorchRec's 2024-06-26T05:54:27.0608659Z KeyedOptimizer or torch.distributed's NamedOptimizer. 2024-06-26T05:54:27.0608931Z load_directly (bool): If this is set to True, this API will also 2024-06-26T05:54:27.0609198Z call optim.load_state_dict(result) before returning the result. 2024-06-26T05:54:27.0609483Z Otherwise, users are responsible to call ``optim.load_state_dict()`` 2024-06-26T05:54:27.0609611Z (Default: ``False``) 2024-06-26T05:54:27.0609989Z group (dist.ProcessGroup): Model's process group across which parameters 2024-06-26T05:54:27.0610256Z are sharded or ``None`` if using the default process group. ( 2024-06-26T05:54:27.0610370Z Default: ``None``) 2024-06-26T05:54:27.0610457Z 2024-06-26T05:54:27.0610851Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0610942Z 2024-06-26T05:54:27.0611052Z warnings.warn(msg) 2024-06-26T05:54:27.0611153Z 2024-06-26T05:54:27.0611352Z --- Parse Warning: 47 / 90 --- 2024-06-26T05:54:27.0612854Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_RemoteModule.__init__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=137. 2024-06-26T05:54:27.0613270Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0613360Z 2024-06-26T05:54:27.0613775Z RemoteModule instance can only be created after RPC initialization. 2024-06-26T05:54:27.0613910Z 2024-06-26T05:54:27.0614228Z It creates a user-specified module on a specified remote node. 2024-06-26T05:54:27.0614558Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2024-06-26T05:54:27.0614726Z executed on the remote node. 2024-06-26T05:54:27.0615075Z It takes care of autograd recording to ensure the backward pass propagates 2024-06-26T05:54:27.0615285Z gradients back to the corresponding remote module. 2024-06-26T05:54:27.0615759Z It can be shared across processors using `RPC framework `__, 2024-06-26T05:54:27.0616012Z without incurring any overheads of copying the actual module, 2024-06-26T05:54:27.0616293Z which is equivalent to an :class:`~torch.distributed.rpc.RRef` 2024-06-26T05:54:27.0616422Z pointing to the remote module. 2024-06-26T05:54:27.0616510Z 2024-06-26T05:54:27.0616783Z The arguments of ``forward_async`` and ``forward`` are the same as 2024-06-26T05:54:27.0617055Z the ``forward`` method of the module returned by the ``module_cls``. 2024-06-26T05:54:27.0617155Z 2024-06-26T05:54:27.0617576Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now. 2024-06-26T05:54:27.0617668Z 2024-06-26T05:54:27.0618017Z Particularly, to create a hybrid model, typically the local modules should be 2024-06-26T05:54:27.0618518Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``). 2024-06-26T05:54:27.0618624Z Hybrid Example: 2024-06-26T05:54:27.0618783Z >>> class HybridModel(nn.Module): 2024-06-26T05:54:27.0618907Z >>> def __init__(self): 2024-06-26T05:54:27.0619051Z >>> nn.Module.__init__(self) 2024-06-26T05:54:27.0619256Z >>> self.remote_embedding = RemoteModule(...) 2024-06-26T05:54:27.0619426Z >>> self.local_linear = nn.Linear(...) 2024-06-26T05:54:27.0619516Z 2024-06-26T05:54:27.0619800Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2024-06-26T05:54:27.0620205Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``, 2024-06-26T05:54:27.0620490Z the generated ``RemoteModule`` will have 2 methods in signature of 2024-06-26T05:54:27.0620703Z ``def forward(input: Tensor) -> Tensor:`` and 2024-06-26T05:54:27.0620963Z ``def forward_async(input: Tensor) -> Future[Tensor]:``. 2024-06-26T05:54:27.0621063Z 2024-06-26T05:54:27.0621167Z .. note:: 2024-06-26T05:54:27.0621357Z If the remote module is placed on a cuda device, 2024-06-26T05:54:27.0621691Z any input CPU tensors will be automatically moved to the same cuda device, 2024-06-26T05:54:27.0622253Z and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend. 2024-06-26T05:54:27.0622342Z 2024-06-26T05:54:27.0622446Z Args: 2024-06-26T05:54:27.0622928Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-06-26T05:54:27.0623356Z The device can be a local device or a remote device specified by one of the following remote 2024-06-26T05:54:27.0623457Z formats: 2024-06-26T05:54:27.0623544Z 2024-06-26T05:54:27.0623745Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2024-06-26T05:54:27.0623943Z 2. "/" (ex: "trainer0/cuda:0"). 2024-06-26T05:54:27.0624031Z 2024-06-26T05:54:27.0624379Z In addition, the device field can be optional and the default value is "cpu". 2024-06-26T05:54:27.0624563Z module_cls (nn.Module): For example, 2024-06-26T05:54:27.0624704Z >>> class MyModule(nn.Module): 2024-06-26T05:54:27.0624842Z >>> def forward(input): 2024-06-26T05:54:27.0624969Z >>> return input + 1 2024-06-26T05:54:27.0625062Z >>> 2024-06-26T05:54:27.0625225Z >>> module_cls = MyModule 2024-06-26T05:54:27.0625482Z args (Sequence, optional): args to be passed to ``module_cls``. 2024-06-26T05:54:27.0625736Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2024-06-26T05:54:27.0626140Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2024-06-26T05:54:27.0626498Z to be created. The type object should be decorated by @torch.jit.interface. 2024-06-26T05:54:27.0626879Z If not provided, the generated RemoteModule is not torchscript-able. 2024-06-26T05:54:27.0627200Z Warning, this is an experimental API and susceptible to frequent changes. 2024-06-26T05:54:27.0627290Z 2024-06-26T05:54:27.0627396Z Returns: 2024-06-26T05:54:27.0627715Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-06-26T05:54:27.0628080Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2024-06-26T05:54:27.0628446Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-06-26T05:54:27.0628680Z on the user-provided module on the remote side. 2024-06-26T05:54:27.0628770Z 2024-06-26T05:54:27.0628888Z Example:: 2024-06-26T05:54:27.0629090Z Run the following code in two different processes: 2024-06-26T05:54:27.0629181Z 2024-06-26T05:54:27.0629347Z >>> # xdoctest: +SKIP("distributed") 2024-06-26T05:54:27.0629458Z >>> # On worker 0: 2024-06-26T05:54:27.0629578Z >>> import torch 2024-06-26T05:54:27.0629743Z >>> import torch.distributed.rpc as rpc 2024-06-26T05:54:27.0629882Z >>> from torch import nn, Tensor 2024-06-26T05:54:27.0630185Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-06-26T05:54:27.0630280Z >>> 2024-06-26T05:54:27.0630458Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-06-26T05:54:27.0630627Z >>> remote_linear_module = RemoteModule( 2024-06-26T05:54:27.0630800Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-06-26T05:54:27.0630895Z >>> ) 2024-06-26T05:54:27.0631038Z >>> input = torch.randn(128, 20) 2024-06-26T05:54:27.0631244Z >>> ret_fut = remote_linear_module.forward_async(input) 2024-06-26T05:54:27.0631360Z >>> ret = ret_fut.wait() 2024-06-26T05:54:27.0631485Z >>> rpc.shutdown() 2024-06-26T05:54:27.0631576Z 2024-06-26T05:54:27.0631683Z >>> # On worker 1: 2024-06-26T05:54:27.0631801Z >>> import torch 2024-06-26T05:54:27.0631966Z >>> import torch.distributed.rpc as rpc 2024-06-26T05:54:27.0632059Z >>> 2024-06-26T05:54:27.0632251Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-06-26T05:54:27.0632360Z >>> rpc.shutdown() 2024-06-26T05:54:27.0632460Z 2024-06-26T05:54:27.0632847Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0632936Z 2024-06-26T05:54:27.0633063Z warnings.warn(msg) 2024-06-26T05:54:27.0633150Z 2024-06-26T05:54:27.0633349Z --- Parse Warning: 48 / 90 --- 2024-06-26T05:54:27.0634919Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_RemoteModule.init_from_module_rref in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=514. 2024-06-26T05:54:27.0635322Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0635411Z 2024-06-26T05:54:27.0635869Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef. 2024-06-26T05:54:27.0635962Z 2024-06-26T05:54:27.0636380Z This alternate initialization method can be particularly useful if we want to create multiple 2024-06-26T05:54:27.0636800Z RemoteModule instances that share the same underlying module and reduce memory consumption. 2024-06-26T05:54:27.0636930Z 2024-06-26T05:54:27.0637307Z Moreover, this also provides a workaround for passing script RemoteModule over RPC, 2024-06-26T05:54:27.0637542Z which is not supported. The recommended way is as follows: 2024-06-26T05:54:27.0637657Z 2024-06-26T05:54:27.0637825Z 1. the sender creates a RemoteModule; 2024-06-26T05:54:27.0638044Z 2. the sender sends its ``module_rref`` over RPC; 2024-06-26T05:54:27.0638498Z 3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``. 2024-06-26T05:54:27.0638600Z 2024-06-26T05:54:27.0638702Z Example:: 2024-06-26T05:54:27.0638903Z Run the following code in two different processes: 2024-06-26T05:54:27.0639004Z 2024-06-26T05:54:27.0639153Z >>> # xdoctest: +SKIP("distributed") 2024-06-26T05:54:27.0639261Z >>> # On worker 0: 2024-06-26T05:54:27.0639382Z >>> import torch 2024-06-26T05:54:27.0639547Z >>> import torch.distributed.rpc as rpc 2024-06-26T05:54:27.0639686Z >>> from torch import nn, Tensor 2024-06-26T05:54:27.0639993Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-06-26T05:54:27.0640087Z >>> 2024-06-26T05:54:27.0640277Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-06-26T05:54:27.0640417Z >>> remote_module = RemoteModule( 2024-06-26T05:54:27.0640663Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-06-26T05:54:27.0640779Z >>> ) 2024-06-26T05:54:27.0640871Z >>> 2024-06-26T05:54:27.0641007Z >>> remote_module1 = rpc.rpc_sync( 2024-06-26T05:54:27.0641129Z >>> "worker1/cpu", 2024-06-26T05:54:27.0641291Z >>> RemoteModule.init_from_module_rref, 2024-06-26T05:54:27.0641493Z >>> ("worker1/cpu", remote_module1.get_module_rref()), 2024-06-26T05:54:27.0641599Z >>> ) 2024-06-26T05:54:27.0641708Z >>> rpc.shutdown() 2024-06-26T05:54:27.0641796Z 2024-06-26T05:54:27.0641918Z >>> # On worker 1: 2024-06-26T05:54:27.0642026Z >>> import torch 2024-06-26T05:54:27.0642188Z >>> import torch.distributed.rpc as rpc 2024-06-26T05:54:27.0642295Z >>> 2024-06-26T05:54:27.0642476Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-06-26T05:54:27.0642588Z >>> rpc.shutdown() 2024-06-26T05:54:27.0642686Z 2024-06-26T05:54:27.0642782Z Args: 2024-06-26T05:54:27.0643264Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-06-26T05:54:27.0643693Z The device can be a local device or a remote device specified by one of the following remote 2024-06-26T05:54:27.0643792Z formats: 2024-06-26T05:54:27.0643895Z 2024-06-26T05:54:27.0644081Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2024-06-26T05:54:27.0644277Z 2. "/" (ex: "trainer0/cuda:0"). 2024-06-26T05:54:27.0644378Z 2024-06-26T05:54:27.0644715Z In addition, the device field can be optional and the default value is "cpu". 2024-06-26T05:54:27.0645055Z module_rref (RRef[nn.Module]): The module reference shared by both the caller and 2024-06-26T05:54:27.0645194Z the created remote module. 2024-06-26T05:54:27.0645552Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2024-06-26T05:54:27.0645882Z to be created. The type object should be decorated by @torch.jit.interface. 2024-06-26T05:54:27.0646257Z If not provided, the generated RemoteModule is not torchscript-able. 2024-06-26T05:54:27.0646615Z Warning, this is an experimental API and susceptible to frequent changes. 2024-06-26T05:54:27.0646717Z 2024-06-26T05:54:27.0646813Z Returns: 2024-06-26T05:54:27.0647133Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-06-26T05:54:27.0647516Z user-provided ``module_rref``, it has a blocking ``forward`` method and an 2024-06-26T05:54:27.0647892Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-06-26T05:54:27.0648128Z on the user-provided module on the remote side. 2024-06-26T05:54:27.0648232Z 2024-06-26T05:54:27.0648646Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0648735Z 2024-06-26T05:54:27.0648902Z warnings.warn(msg) 2024-06-26T05:54:27.0648993Z 2024-06-26T05:54:27.0649194Z --- Parse Warning: 49 / 90 --- 2024-06-26T05:54:27.0650642Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RemoteModule in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=606. 2024-06-26T05:54:27.0651040Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0651141Z 2024-06-26T05:54:27.0651436Z A RemoteModule instance can only be created after RPC initialization. 2024-06-26T05:54:27.0651528Z 2024-06-26T05:54:27.0651862Z It creates a user-specified module on a specified remote node. 2024-06-26T05:54:27.0652187Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2024-06-26T05:54:27.0652317Z executed on the remote node. 2024-06-26T05:54:27.0652652Z It takes care of autograd recording to ensure the backward pass propagates 2024-06-26T05:54:27.0652852Z gradients back to the corresponding remote module. 2024-06-26T05:54:27.0652940Z 2024-06-26T05:54:27.0653251Z It generates two methods ``forward_async`` and ``forward`` based on the 2024-06-26T05:54:27.0653668Z signature of the ``forward`` method of ``module_cls``. ``forward_async`` 2024-06-26T05:54:27.0654007Z runs asynchronously and returns a Future. The arguments of ``forward_async`` 2024-06-26T05:54:27.0654277Z and ``forward`` are the same as the ``forward`` method of the module 2024-06-26T05:54:27.0654415Z returned by the ``module_cls``. 2024-06-26T05:54:27.0654516Z 2024-06-26T05:54:27.0654791Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2024-06-26T05:54:27.0655201Z that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``, 2024-06-26T05:54:27.0655513Z the generated ``RemoteModule`` will have 2 methods with the signatures: 2024-06-26T05:54:27.0655602Z 2024-06-26T05:54:27.0655809Z | ``def forward(input: Tensor) -> Tensor:`` 2024-06-26T05:54:27.0656087Z | ``def forward_async(input: Tensor) -> Future[Tensor]:`` 2024-06-26T05:54:27.0656176Z 2024-06-26T05:54:27.0656270Z Args: 2024-06-26T05:54:27.0656759Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-06-26T05:54:27.0657216Z The format should be "/", where the device field can be parsed as torch.device type. 2024-06-26T05:54:27.0657421Z E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0". 2024-06-26T05:54:27.0657759Z In addition, the device field can be optional and the default value is "cpu". 2024-06-26T05:54:27.0658102Z module_cls (nn.Module): Class for the module to be created remotely. For example, 2024-06-26T05:54:27.0658206Z 2024-06-26T05:54:27.0658344Z >>> class MyModule(nn.Module): 2024-06-26T05:54:27.0658467Z >>> def forward(input): 2024-06-26T05:54:27.0658606Z >>> return input + 1 2024-06-26T05:54:27.0658700Z >>> 2024-06-26T05:54:27.0658820Z >>> module_cls = MyModule 2024-06-26T05:54:27.0658973Z 2024-06-26T05:54:27.0659231Z args (Sequence, optional): args to be passed to ``module_cls``. 2024-06-26T05:54:27.0659483Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2024-06-26T05:54:27.0659582Z 2024-06-26T05:54:27.0659678Z Returns: 2024-06-26T05:54:27.0660009Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-06-26T05:54:27.0660412Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2024-06-26T05:54:27.0660763Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-06-26T05:54:27.0661046Z on the user-provided module on the remote side. 2024-06-26T05:54:27.0661167Z 2024-06-26T05:54:27.0661273Z Example:: 2024-06-26T05:54:27.0661485Z Run the following code in two different processes: 2024-06-26T05:54:27.0661573Z 2024-06-26T05:54:27.0661722Z >>> # xdoctest: +SKIP("distributed") 2024-06-26T05:54:27.0661846Z >>> # On worker 0: 2024-06-26T05:54:27.0661953Z >>> import torch 2024-06-26T05:54:27.0662119Z >>> import torch.distributed.rpc as rpc 2024-06-26T05:54:27.0662266Z >>> from torch import nn, Tensor 2024-06-26T05:54:27.0662556Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-06-26T05:54:27.0662652Z >>> 2024-06-26T05:54:27.0662846Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-06-26T05:54:27.0663003Z >>> remote_linear_module = RemoteModule( 2024-06-26T05:54:27.0663185Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-06-26T05:54:27.0663282Z >>> ) 2024-06-26T05:54:27.0663416Z >>> input = torch.randn(128, 20) 2024-06-26T05:54:27.0663632Z >>> ret_fut = remote_linear_module.forward_async(input) 2024-06-26T05:54:27.0663748Z >>> ret = ret_fut.wait() 2024-06-26T05:54:27.0663857Z >>> rpc.shutdown() 2024-06-26T05:54:27.0663960Z 2024-06-26T05:54:27.0664067Z >>> # On worker 1: 2024-06-26T05:54:27.0664174Z >>> import torch 2024-06-26T05:54:27.0664350Z >>> import torch.distributed.rpc as rpc 2024-06-26T05:54:27.0664442Z >>> 2024-06-26T05:54:27.0664619Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-06-26T05:54:27.0664740Z >>> rpc.shutdown() 2024-06-26T05:54:27.0664830Z 2024-06-26T05:54:27.0665074Z Furthermore, a more practical example that is combined with 2024-06-26T05:54:27.0665700Z `DistributedDataParallel `__ (DDP) 2024-06-26T05:54:27.0666150Z can be found in this `tutorial `__. 2024-06-26T05:54:27.0666241Z 2024-06-26T05:54:27.0666637Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0666726Z 2024-06-26T05:54:27.0666849Z warnings.warn(msg) 2024-06-26T05:54:27.0666938Z 2024-06-26T05:54:27.0667139Z --- Parse Warning: 50 / 90 --- 2024-06-26T05:54:27.0668606Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/optimizer.py line=129. 2024-06-26T05:54:27.0669009Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0669098Z 2024-06-26T05:54:27.0669407Z DistributedOptimizer takes remote references to parameters scattered 2024-06-26T05:54:27.0669731Z across workers and applies the given optimizer locally for each parameter. 2024-06-26T05:54:27.0669822Z 2024-06-26T05:54:27.0670158Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order 2024-06-26T05:54:27.0670353Z to retrieve the gradients for specific parameters. 2024-06-26T05:54:27.0670457Z 2024-06-26T05:54:27.0670568Z Concurrent calls to 2024-06-26T05:54:27.0670877Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`, 2024-06-26T05:54:27.0671072Z either from the same or different clients, will 2024-06-26T05:54:27.0671437Z be serialized on each worker -- as each worker's optimizer can only work 2024-06-26T05:54:27.0671728Z on one set of gradients at a time. However, there is no guarantee that 2024-06-26T05:54:27.0672153Z the full forward-backward-optimizer sequence will execute for one client 2024-06-26T05:54:27.0672459Z at a time. This means that the gradients being applied may not correspond 2024-06-26T05:54:27.0672785Z to the latest forward pass executed on a given worker. Also, there is no 2024-06-26T05:54:27.0672970Z guaranteed ordering across workers. 2024-06-26T05:54:27.0673061Z 2024-06-26T05:54:27.0673387Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled 2024-06-26T05:54:27.0673710Z by default, so that optimizer updates are not blocked by the Python Global 2024-06-26T05:54:27.0674041Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed 2024-06-26T05:54:27.0674368Z Model Parallel). This feature is currently enabled for most optimizers. You 2024-06-26T05:54:27.0674709Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support 2024-06-26T05:54:27.0674841Z for your own custom optimizers. 2024-06-26T05:54:27.0674944Z 2024-06-26T05:54:27.0675037Z Args: 2024-06-26T05:54:27.0675290Z optimizer_class (optim.Optimizer): the class of optimizer to 2024-06-26T05:54:27.0675436Z instantiate on each worker. 2024-06-26T05:54:27.0675718Z params_rref (list[RRef]): list of RRefs to local or remote parameters 2024-06-26T05:54:27.0675824Z to optimize. 2024-06-26T05:54:27.0676124Z args: arguments to pass to the optimizer constructor on each worker. 2024-06-26T05:54:27.0676419Z kwargs: arguments to pass to the optimizer constructor on each worker. 2024-06-26T05:54:27.0676508Z 2024-06-26T05:54:27.0676622Z Example:: 2024-06-26T05:54:27.0676772Z >>> # xdoctest: +SKIP("distributed") 2024-06-26T05:54:27.0677002Z >>> import torch.distributed.autograd as dist_autograd 2024-06-26T05:54:27.0677167Z >>> import torch.distributed.rpc as rpc 2024-06-26T05:54:27.0677285Z >>> from torch import optim 2024-06-26T05:54:27.0677540Z >>> from torch.distributed.optim import DistributedOptimizer 2024-06-26T05:54:27.0677628Z >>> 2024-06-26T05:54:27.0677798Z >>> with dist_autograd.context() as context_id: 2024-06-26T05:54:27.0677919Z >>> # Forward pass. 2024-06-26T05:54:27.0678190Z >>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3)) 2024-06-26T05:54:27.0678452Z >>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1)) 2024-06-26T05:54:27.0678632Z >>> loss = rref1.to_here() + rref2.to_here() 2024-06-26T05:54:27.0678724Z >>> 2024-06-26T05:54:27.0678838Z >>> # Backward pass. 2024-06-26T05:54:27.0679049Z >>> dist_autograd.backward(context_id, [loss.sum()]) 2024-06-26T05:54:27.0679143Z >>> 2024-06-26T05:54:27.0679253Z >>> # Optimizer. 2024-06-26T05:54:27.0679429Z >>> dist_optim = DistributedOptimizer( 2024-06-26T05:54:27.0679535Z >>> optim.SGD, 2024-06-26T05:54:27.0679662Z >>> [rref1, rref2], 2024-06-26T05:54:27.0679762Z >>> lr=0.05, 2024-06-26T05:54:27.0679856Z >>> ) 2024-06-26T05:54:27.0680005Z >>> dist_optim.step(context_id) 2024-06-26T05:54:27.0680094Z 2024-06-26T05:54:27.0680289Z __ https://github.com/pytorch/tutorials/pull/1465 2024-06-26T05:54:27.0680394Z 2024-06-26T05:54:27.0680857Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0680947Z 2024-06-26T05:54:27.0681073Z warnings.warn(msg) 2024-06-26T05:54:27.0681161Z 2024-06-26T05:54:27.0681396Z --- Parse Warning: 51 / 90 --- 2024-06-26T05:54:27.0682956Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PostLocalSGDOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/post_localSGD_optimizer.py line=9. 2024-06-26T05:54:27.0683355Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0683474Z 2024-06-26T05:54:27.0684094Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD `_, 2024-06-26T05:54:27.0684320Z This optimizer runs local optimizer at every step. 2024-06-26T05:54:27.0684883Z After the warm-up stage, it averages parameters periodically afer the local optimizer is applied. 2024-06-26T05:54:27.0684972Z 2024-06-26T05:54:27.0685064Z Args: 2024-06-26T05:54:27.0685208Z optim: The local optimizer. 2024-06-26T05:54:27.0685557Z averager: A model averager instance to run post-localSGD algorithm. 2024-06-26T05:54:27.0685645Z 2024-06-26T05:54:27.0685762Z Example:: 2024-06-26T05:54:27.0685849Z 2024-06-26T05:54:27.0686015Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:27.0686135Z >>> import torch 2024-06-26T05:54:27.0686287Z >>> import torch.distributed as dist 2024-06-26T05:54:27.0686644Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2024-06-26T05:54:27.0686778Z >>> import torch.nn as nn 2024-06-26T05:54:27.0687032Z >>> from torch.distributed.optim import PostLocalSGDOptimizer 2024-06-26T05:54:27.0687404Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2024-06-26T05:54:27.0687528Z >>> PostLocalSGDState, 2024-06-26T05:54:27.0687644Z >>> post_localSGD_hook, 2024-06-26T05:54:27.0687750Z >>> ) 2024-06-26T05:54:27.0687842Z >>> 2024-06-26T05:54:27.0688051Z >>> model = nn.parallel.DistributedDataParallel( 2024-06-26T05:54:27.0688256Z >>> module, device_ids=[rank], output_device=rank 2024-06-26T05:54:27.0688352Z >>> ) 2024-06-26T05:54:27.0688447Z >>> 2024-06-26T05:54:27.0688704Z >>> # Register a post-localSGD communication hook. 2024-06-26T05:54:27.0689082Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2024-06-26T05:54:27.0689291Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-06-26T05:54:27.0689399Z >>> 2024-06-26T05:54:27.0689732Z >>> # Create a post-localSGD optimizer that wraps a local optimizer. 2024-06-26T05:54:27.0690083Z >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as 2024-06-26T05:54:27.0690318Z >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-06-26T05:54:27.0690591Z >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01) 2024-06-26T05:54:27.0690751Z >>> opt = PostLocalSGDOptimizer( 2024-06-26T05:54:27.0690868Z >>> optim=local_optim, 2024-06-26T05:54:27.0691189Z >>> averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2024-06-26T05:54:27.0691296Z >>> ) 2024-06-26T05:54:27.0691386Z >>> 2024-06-26T05:54:27.0691704Z >>> # In the first 100 steps, DDP runs global gradient averaging at every step. 2024-06-26T05:54:27.0692204Z >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default), 2024-06-26T05:54:27.0692790Z >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer. 2024-06-26T05:54:27.0692923Z >>> for step in range(0, 200): 2024-06-26T05:54:27.0693048Z >>> opt.zero_grad() 2024-06-26T05:54:27.0693193Z >>> loss = loss_fn(output, labels) 2024-06-26T05:54:27.0693307Z >>> loss.backward() 2024-06-26T05:54:27.0693428Z >>> opt.step() 2024-06-26T05:54:27.0693672Z 2024-06-26T05:54:27.0694077Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0694166Z 2024-06-26T05:54:27.0694276Z warnings.warn(msg) 2024-06-26T05:54:27.0694375Z 2024-06-26T05:54:27.0694575Z --- Parse Warning: 52 / 90 --- 2024-06-26T05:54:27.0696215Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ZeroRedundancyOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/zero_redundancy_optimizer.py line=282. 2024-06-26T05:54:27.0696700Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0696791Z 2024-06-26T05:54:27.0697322Z Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group. 2024-06-26T05:54:27.0697424Z 2024-06-26T05:54:27.0697584Z The sharing is done as described by ZeRO_. 2024-06-26T05:54:27.0697673Z 2024-06-26T05:54:27.0697878Z The local optimizer instance in each rank is only 2024-06-26T05:54:27.0698187Z responsible for updating approximately ``1 / world_size`` parameters and 2024-06-26T05:54:27.0698469Z hence only needs to keep ``1 / world_size`` optimizer states. After 2024-06-26T05:54:27.0698795Z parameters are updated locally, each rank will broadcast its parameters to 2024-06-26T05:54:27.0699044Z all other peers to keep all model replicas in the same state. 2024-06-26T05:54:27.0699301Z ``ZeroRedundancyOptimizer`` can be used in conjunction with 2024-06-26T05:54:27.0699717Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak 2024-06-26T05:54:27.0699830Z memory consumption. 2024-06-26T05:54:27.0699929Z 2024-06-26T05:54:27.0700317Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number 2024-06-26T05:54:27.0700630Z of parameters at each rank. Each parameter belongs to a single rank and is 2024-06-26T05:54:27.0700967Z not divided among ranks. The partition is arbitrary and might not match the 2024-06-26T05:54:27.0701131Z the parameter registration or usage order. 2024-06-26T05:54:27.0701231Z 2024-06-26T05:54:27.0701332Z Arguments: 2024-06-26T05:54:27.0701587Z params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s 2024-06-26T05:54:27.0701861Z or :class:`dict` s giving all parameters, which will be sharded 2024-06-26T05:54:27.0701968Z across ranks. 2024-06-26T05:54:27.0702053Z 2024-06-26T05:54:27.0702167Z Keyword Args: 2024-06-26T05:54:27.0702454Z optimizer_class (:class:`torch.nn.Optimizer`): the class of the local 2024-06-26T05:54:27.0702559Z optimizer. 2024-06-26T05:54:27.0702843Z process_group (``ProcessGroup``, optional): ``torch.distributed`` 2024-06-26T05:54:27.0703093Z ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by 2024-06-26T05:54:27.0703287Z :meth:`torch.distributed.init_process_group`). 2024-06-26T05:54:27.0703594Z parameters_as_bucket_view (bool, optional): if ``True``, parameters are 2024-06-26T05:54:27.0703877Z packed into buckets to speed up communication, and ``param.data`` 2024-06-26T05:54:27.0704157Z fields point to bucket views at different offsets; if ``False``, 2024-06-26T05:54:27.0704424Z each individual parameter is communicated separately, and each 2024-06-26T05:54:27.0704621Z ``params.data`` stays intact (default: ``False``). 2024-06-26T05:54:27.0704888Z overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is 2024-06-26T05:54:27.0705212Z overlapped with :class:`DistributedDataParallel` 's gradient 2024-06-26T05:54:27.0705477Z synchronization; this requires (1) either a functional optimizer 2024-06-26T05:54:27.0705736Z for the ``optimizer_class`` argument or one with a functional 2024-06-26T05:54:27.0705992Z equivalent and (2) registering a DDP communication hook 2024-06-26T05:54:27.0706255Z constructed from one of the functions in ``ddp_zero_hook.py``; 2024-06-26T05:54:27.0706482Z parameters are packed into buckets matching those in 2024-06-26T05:54:27.0706696Z :class:`DistributedDataParallel`, meaning that the 2024-06-26T05:54:27.0706933Z ``parameters_as_bucket_view`` argument is ignored. 2024-06-26T05:54:27.0707190Z If ``False``, :meth:`step` runs disjointly after the backward pass 2024-06-26T05:54:27.0707294Z (per normal). 2024-06-26T05:54:27.0707447Z (default: ``False``) 2024-06-26T05:54:27.0707751Z **defaults: any trailing arguments, which are forwarded to the local 2024-06-26T05:54:27.0707854Z optimizer. 2024-06-26T05:54:27.0707952Z 2024-06-26T05:54:27.0708055Z Example:: 2024-06-26T05:54:27.0708139Z 2024-06-26T05:54:27.0708265Z >>> # xdoctest: +SKIP 2024-06-26T05:54:27.0708386Z >>> import torch.nn as nn 2024-06-26T05:54:27.0708656Z >>> from torch.distributed.optim import ZeroRedundancyOptimizer 2024-06-26T05:54:27.0708940Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2024-06-26T05:54:27.0709243Z >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)]) 2024-06-26T05:54:27.0709397Z >>> ddp = DDP(model, device_ids=[rank]) 2024-06-26T05:54:27.0709562Z >>> opt = ZeroRedundancyOptimizer( 2024-06-26T05:54:27.0709680Z >>> ddp.parameters(), 2024-06-26T05:54:27.0709852Z >>> optimizer_class=torch.optim.Adam, 2024-06-26T05:54:27.0709951Z >>> lr=0.01 2024-06-26T05:54:27.0710047Z >>> ) 2024-06-26T05:54:27.0710193Z >>> ddp(inputs).sum().backward() 2024-06-26T05:54:27.0710296Z >>> opt.step() 2024-06-26T05:54:27.0710382Z 2024-06-26T05:54:27.0710496Z .. warning:: 2024-06-26T05:54:27.0710768Z Currently, ``ZeroRedundancyOptimizer`` requires that all of the 2024-06-26T05:54:27.0710999Z passed-in parameters are the same dense type. 2024-06-26T05:54:27.0711101Z 2024-06-26T05:54:27.0711203Z .. warning:: 2024-06-26T05:54:27.0711488Z If you pass ``overlap_with_ddp=True``, be wary of the following: Given 2024-06-26T05:54:27.0711771Z the way that overlapping :class:`DistributedDataParallel` with 2024-06-26T05:54:27.0712069Z :class:`ZeroRedundancyOptimizer` is currently implemented, the first 2024-06-26T05:54:27.0712360Z two or three training iterations do not perform parameter updates in 2024-06-26T05:54:27.0712627Z the optimizer step, depending on if ``static_graph=False`` or 2024-06-26T05:54:27.0712865Z ``static_graph=True``, respectively. This is because it needs 2024-06-26T05:54:27.0713112Z information about the gradient bucketing strategy used by 2024-06-26T05:54:27.0713394Z :class:`DistributedDataParallel`, which is not finalized until the 2024-06-26T05:54:27.0713659Z second forward pass if ``static_graph=False`` or until the third 2024-06-26T05:54:27.0713952Z forward pass if ``static_graph=True``. To adjust for this, one option 2024-06-26T05:54:27.0714080Z is to prepend dummy inputs. 2024-06-26T05:54:27.0714167Z 2024-06-26T05:54:27.0714505Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change. 2024-06-26T05:54:27.0714597Z 2024-06-26T05:54:27.0714760Z .. _ZeRO: https://arxiv.org/abs/1910.02054 2024-06-26T05:54:27.0714862Z 2024-06-26T05:54:27.0714950Z 2024-06-26T05:54:27.0715336Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0715439Z 2024-06-26T05:54:27.0715550Z warnings.warn(msg) 2024-06-26T05:54:27.0715637Z 2024-06-26T05:54:27.0715851Z --- Parse Warning: 53 / 90 --- 2024-06-26T05:54:27.0717324Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_CustomReducer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/pipelining/microbatch.py line=28. 2024-06-26T05:54:27.0717736Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0717828Z 2024-06-26T05:54:27.0718135Z Custom reducer class that can be used to specify a custom operation that 2024-06-26T05:54:27.0718405Z reduces losses of multiple microbatches into one value. 2024-06-26T05:54:27.0718495Z 2024-06-26T05:54:27.0718591Z Example: 2024-06-26T05:54:27.0718715Z >>> # xdoctest: +SKIP 2024-06-26T05:54:27.0718877Z >>> sum_reducer = _CustomReducer( 2024-06-26T05:54:27.0718992Z >>> torch.tensor(0.0), 2024-06-26T05:54:27.0719146Z >>> lambda a, b: a + b 2024-06-26T05:54:27.0719237Z >>> ) 2024-06-26T05:54:27.0719323Z 2024-06-26T05:54:27.0719725Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0719810Z 2024-06-26T05:54:27.0719936Z warnings.warn(msg) 2024-06-26T05:54:27.0720025Z 2024-06-26T05:54:27.0720223Z --- Parse Warning: 54 / 90 --- 2024-06-26T05:54:27.0721682Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=async_execution in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/functions.py line=6. 2024-06-26T05:54:27.0722088Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0722175Z 2024-06-26T05:54:27.0722513Z A decorator for a function indicating that the return value of the function 2024-06-26T05:54:27.0722801Z is guaranteed to be a :class:`~torch.futures.Future` object and this 2024-06-26T05:54:27.0723119Z function can run asynchronously on the RPC callee. More specifically, the 2024-06-26T05:54:27.0723446Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped 2024-06-26T05:54:27.0723749Z function and installs subsequent processing steps as a callback to that 2024-06-26T05:54:27.0724073Z :class:`~torch.futures.Future`. The installed callback will read the value 2024-06-26T05:54:27.0724352Z from the :class:`~torch.futures.Future` when completed and send the 2024-06-26T05:54:27.0724593Z value back as the RPC response. That also means the returned 2024-06-26T05:54:27.0724917Z :class:`~torch.futures.Future` only exists on the callee side and is never 2024-06-26T05:54:27.0725247Z sent through RPC. This decorator is useful when the wrapped function's 2024-06-26T05:54:27.0725512Z (``fn``) execution needs to pause and resume due to, e.g., containing 2024-06-26T05:54:27.0725824Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals. 2024-06-26T05:54:27.0725914Z 2024-06-26T05:54:27.0726195Z .. note:: To enable asynchronous execution, applications must pass the 2024-06-26T05:54:27.0726518Z function object returned by this decorator to RPC APIs. If RPC detected 2024-06-26T05:54:27.0726801Z attributes installed by this decorator, it knows that this function 2024-06-26T05:54:27.0727057Z returns a ``Future`` object and will handle that accordingly. 2024-06-26T05:54:27.0727343Z However, this does not mean this decorator has to be outmost one when 2024-06-26T05:54:27.0727642Z defining a function. For example, when combined with ``@staticmethod`` 2024-06-26T05:54:27.0727937Z or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the 2024-06-26T05:54:27.0728234Z inner decorator to allow the target function be recognized as a static 2024-06-26T05:54:27.0728541Z or class function. This target function can still execute asynchronously 2024-06-26T05:54:27.0728858Z because, when accessed, the static or class method preserves attributes 2024-06-26T05:54:27.0729052Z installed by ``@rpc.functions.async_execution``. 2024-06-26T05:54:27.0729156Z 2024-06-26T05:54:27.0729244Z 2024-06-26T05:54:27.0729378Z Example:: 2024-06-26T05:54:27.0729665Z The returned :class:`~torch.futures.Future` object can come from 2024-06-26T05:54:27.0729834Z :meth:`~torch.distributed.rpc.rpc_async`, 2024-06-26T05:54:27.0730128Z :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future` 2024-06-26T05:54:27.0730398Z constructor. The example below shows directly using the 2024-06-26T05:54:27.0730563Z :class:`~torch.futures.Future` returned by 2024-06-26T05:54:27.0730713Z :meth:`~torch.futures.Future.then`. 2024-06-26T05:54:27.0730841Z 2024-06-26T05:54:27.0730997Z >>> from torch.distributed import rpc 2024-06-26T05:54:27.0731090Z >>> 2024-06-26T05:54:27.0731282Z >>> # omitting setup and shutdown RPC 2024-06-26T05:54:27.0731375Z >>> 2024-06-26T05:54:27.0731485Z >>> # On all workers 2024-06-26T05:54:27.0731640Z >>> @rpc.functions.async_execution 2024-06-26T05:54:27.0731793Z >>> def async_add_chained(to, x, y, z): 2024-06-26T05:54:27.0732052Z >>> # This function runs on "worker1" and returns immediately when 2024-06-26T05:54:27.0732321Z >>> # the callback is installed through the `then(cb)` API. In the 2024-06-26T05:54:27.0732576Z >>> # mean time, the `rpc_async` to "worker2" can run concurrently. 2024-06-26T05:54:27.0732806Z >>> # When the return value of that `rpc_async` arrives at 2024-06-26T05:54:27.0733054Z >>> # "worker1", "worker1" will run the lambda function accordingly 2024-06-26T05:54:27.0733317Z >>> # and set the value for the previously returned `Future`, which 2024-06-26T05:54:27.0733699Z >>> # will then trigger RPC to send the result back to "worker0". 2024-06-26T05:54:27.0733919Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-06-26T05:54:27.0734066Z >>> lambda fut: fut.wait() + z 2024-06-26T05:54:27.0734175Z >>> ) 2024-06-26T05:54:27.0734265Z >>> 2024-06-26T05:54:27.0734368Z >>> # On worker0 2024-06-26T05:54:27.0734496Z >>> # xdoctest: +SKIP 2024-06-26T05:54:27.0734610Z >>> ret = rpc.rpc_sync( 2024-06-26T05:54:27.0734711Z >>> "worker1", 2024-06-26T05:54:27.0734842Z >>> async_add_chained, 2024-06-26T05:54:27.0735003Z >>> args=("worker2", torch.ones(2), 1, 1) 2024-06-26T05:54:27.0735114Z >>> ) 2024-06-26T05:54:27.0735266Z >>> print(ret) # prints tensor([3., 3.]) 2024-06-26T05:54:27.0735354Z 2024-06-26T05:54:27.0735668Z When combined with TorchScript decorators, this decorator must be the 2024-06-26T05:54:27.0735774Z outmost one. 2024-06-26T05:54:27.0735860Z 2024-06-26T05:54:27.0735999Z >>> from torch import Tensor 2024-06-26T05:54:27.0736148Z >>> from torch.futures import Future 2024-06-26T05:54:27.0736303Z >>> from torch.distributed import rpc 2024-06-26T05:54:27.0736408Z >>> 2024-06-26T05:54:27.0736556Z >>> # omitting setup and shutdown RPC 2024-06-26T05:54:27.0736647Z >>> 2024-06-26T05:54:27.0736770Z >>> # On all workers 2024-06-26T05:54:27.0736881Z >>> @torch.jit.script 2024-06-26T05:54:27.0737131Z >>> def script_add(x: Tensor, y: Tensor) -> Tensor: 2024-06-26T05:54:27.0737252Z >>> return x + y 2024-06-26T05:54:27.0737346Z >>> 2024-06-26T05:54:27.0737488Z >>> @rpc.functions.async_execution 2024-06-26T05:54:27.0737614Z >>> @torch.jit.script 2024-06-26T05:54:27.0737929Z >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]: 2024-06-26T05:54:27.0738116Z >>> return rpc.rpc_async(to, script_add, (x, y)) 2024-06-26T05:54:27.0738224Z >>> 2024-06-26T05:54:27.0738328Z >>> # On worker0 2024-06-26T05:54:27.0738456Z >>> ret = rpc.rpc_sync( 2024-06-26T05:54:27.0738562Z >>> "worker1", 2024-06-26T05:54:27.0738665Z >>> async_add, 2024-06-26T05:54:27.0738877Z >>> args=("worker2", torch.ones(2), 1) 2024-06-26T05:54:27.0738969Z >>> ) 2024-06-26T05:54:27.0739122Z >>> print(ret) # prints tensor([2., 2.]) 2024-06-26T05:54:27.0739221Z 2024-06-26T05:54:27.0739520Z When combined with static or class method, this decorator must be the 2024-06-26T05:54:27.0739620Z inner one. 2024-06-26T05:54:27.0739754Z 2024-06-26T05:54:27.0739909Z >>> from torch.distributed import rpc 2024-06-26T05:54:27.0740001Z >>> 2024-06-26T05:54:27.0740159Z >>> # omitting setup and shutdown RPC 2024-06-26T05:54:27.0740299Z >>> 2024-06-26T05:54:27.0740407Z >>> # On all workers 2024-06-26T05:54:27.0740588Z >>> class AsyncExecutionClass: 2024-06-26T05:54:27.0740684Z >>> 2024-06-26T05:54:27.0740798Z >>> @staticmethod 2024-06-26T05:54:27.0740961Z >>> @rpc.functions.async_execution 2024-06-26T05:54:27.0741112Z >>> def static_async_add(to, x, y, z): 2024-06-26T05:54:27.0741339Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-06-26T05:54:27.0741499Z >>> lambda fut: fut.wait() + z 2024-06-26T05:54:27.0741598Z >>> ) 2024-06-26T05:54:27.0741701Z >>> 2024-06-26T05:54:27.0741807Z >>> @classmethod 2024-06-26T05:54:27.0741956Z >>> @rpc.functions.async_execution 2024-06-26T05:54:27.0742132Z >>> def class_async_add(cls, to, x, y, z): 2024-06-26T05:54:27.0742296Z >>> ret_fut = torch.futures.Future() 2024-06-26T05:54:27.0742494Z >>> rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-06-26T05:54:27.0742710Z >>> lambda fut: ret_fut.set_result(fut.wait() + z) 2024-06-26T05:54:27.0742806Z >>> ) 2024-06-26T05:54:27.0742917Z >>> return ret_fut 2024-06-26T05:54:27.0743026Z >>> 2024-06-26T05:54:27.0743174Z >>> @rpc.functions.async_execution 2024-06-26T05:54:27.0743340Z >>> def bound_async_add(self, to, x, y, z): 2024-06-26T05:54:27.0743574Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-06-26T05:54:27.0743722Z >>> lambda fut: fut.wait() + z 2024-06-26T05:54:27.0743819Z >>> ) 2024-06-26T05:54:27.0743918Z >>> 2024-06-26T05:54:27.0744020Z >>> # On worker0 2024-06-26T05:54:27.0744150Z >>> ret = rpc.rpc_sync( 2024-06-26T05:54:27.0744253Z >>> "worker1", 2024-06-26T05:54:27.0744433Z >>> AsyncExecutionClass.static_async_add, 2024-06-26T05:54:27.0744603Z >>> args=("worker2", torch.ones(2), 1, 2) 2024-06-26T05:54:27.0744697Z >>> ) 2024-06-26T05:54:27.0744853Z >>> print(ret) # prints tensor([4., 4.]) 2024-06-26T05:54:27.0744954Z >>> 2024-06-26T05:54:27.0745070Z >>> ret = rpc.rpc_sync( 2024-06-26T05:54:27.0745174Z >>> "worker1", 2024-06-26T05:54:27.0745362Z >>> AsyncExecutionClass.class_async_add, 2024-06-26T05:54:27.0745520Z >>> args=("worker2", torch.ones(2), 1, 2) 2024-06-26T05:54:27.0745611Z >>> ) 2024-06-26T05:54:27.0745776Z >>> print(ret) # prints tensor([4., 4.]) 2024-06-26T05:54:27.0745863Z 2024-06-26T05:54:27.0746072Z This decorator also works with RRef helpers, i.e., . 2024-06-26T05:54:27.0746272Z :meth:`torch.distributed.rpc.RRef.rpc_sync`, 2024-06-26T05:54:27.0746481Z :meth:`torch.distributed.rpc.RRef.rpc_async`, and 2024-06-26T05:54:27.0746662Z :meth:`torch.distributed.rpc.RRef.remote`. 2024-06-26T05:54:27.0746767Z 2024-06-26T05:54:27.0746919Z >>> from torch.distributed import rpc 2024-06-26T05:54:27.0747011Z >>> 2024-06-26T05:54:27.0747204Z >>> # reuse the AsyncExecutionClass class above 2024-06-26T05:54:27.0747411Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-06-26T05:54:27.0747701Z >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2) 2024-06-26T05:54:27.0747880Z >>> print(ret) # prints tensor([4., 4.]) 2024-06-26T05:54:27.0747974Z >>> 2024-06-26T05:54:27.0748189Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-06-26T05:54:27.0748505Z >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait() 2024-06-26T05:54:27.0748659Z >>> print(ret) # prints tensor([4., 4.]) 2024-06-26T05:54:27.0748790Z >>> 2024-06-26T05:54:27.0748992Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-06-26T05:54:27.0749308Z >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here() 2024-06-26T05:54:27.0749500Z >>> print(ret) # prints tensor([4., 4.]) 2024-06-26T05:54:27.0749640Z 2024-06-26T05:54:27.0750035Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0750135Z 2024-06-26T05:54:27.0750247Z warnings.warn(msg) 2024-06-26T05:54:27.0750344Z 2024-06-26T05:54:27.0750546Z --- Parse Warning: 55 / 90 --- 2024-06-26T05:54:27.0752117Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=TensorPipeRpcBackendOptions.set_device_map in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/options.py line=108. 2024-06-26T05:54:27.0752531Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0752619Z 2024-06-26T05:54:27.0752886Z Set device mapping between each RPC caller and callee pair. This 2024-06-26T05:54:27.0753134Z function can be called multiple times to incrementally add 2024-06-26T05:54:27.0753269Z device placement configurations. 2024-06-26T05:54:27.0753356Z 2024-06-26T05:54:27.0753462Z Args: 2024-06-26T05:54:27.0753578Z to (str): Callee name. 2024-06-26T05:54:27.0753840Z device_map (Dict of int, str, or torch.device): Device placement 2024-06-26T05:54:27.0754091Z mappings from this worker to the callee. This map must be 2024-06-26T05:54:27.0754198Z invertible. 2024-06-26T05:54:27.0754297Z 2024-06-26T05:54:27.0754397Z Example: 2024-06-26T05:54:27.0754544Z >>> # xdoctest: +SKIP("distributed") 2024-06-26T05:54:27.0754661Z >>> # both workers 2024-06-26T05:54:27.0754769Z >>> def add(x, y): 2024-06-26T05:54:27.0755007Z >>> print(x) # tensor([1., 1.], device='cuda:1') 2024-06-26T05:54:27.0755154Z >>> return x + y, (x + y).to(2) 2024-06-26T05:54:27.0755249Z >>> 2024-06-26T05:54:27.0755353Z >>> # on worker 0 2024-06-26T05:54:27.0755549Z >>> options = TensorPipeRpcBackendOptions( 2024-06-26T05:54:27.0755673Z >>> num_worker_threads=8, 2024-06-26T05:54:27.0755818Z >>> device_maps={"worker1": {0: 1}} 2024-06-26T05:54:27.0756064Z >>> # maps worker0's cuda:0 to worker1's cuda:1 2024-06-26T05:54:27.0756158Z >>> ) 2024-06-26T05:54:27.0756326Z >>> options.set_device_map("worker1", {1: 2}) 2024-06-26T05:54:27.0756562Z >>> # maps worker0's cuda:1 to worker1's cuda:2 2024-06-26T05:54:27.0756658Z >>> 2024-06-26T05:54:27.0756763Z >>> rpc.init_rpc( 2024-06-26T05:54:27.0756880Z >>> "worker0", 2024-06-26T05:54:27.0756979Z >>> rank=0, 2024-06-26T05:54:27.0757102Z >>> world_size=2, 2024-06-26T05:54:27.0757272Z >>> backend=rpc.BackendType.TENSORPIPE, 2024-06-26T05:54:27.0757406Z >>> rpc_backend_options=options 2024-06-26T05:54:27.0757516Z >>> ) 2024-06-26T05:54:27.0757611Z >>> 2024-06-26T05:54:27.0757723Z >>> x = torch.ones(2) 2024-06-26T05:54:27.0757948Z >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1)) 2024-06-26T05:54:27.0758198Z >>> # The first argument will be moved to cuda:1 on worker1. When 2024-06-26T05:54:27.0758444Z >>> # sending the return value back, it will follow the invert of 2024-06-26T05:54:27.0758731Z >>> # the device map, and hence will be moved back to cuda:0 and 2024-06-26T05:54:27.0758845Z >>> # cuda:1 on worker0 2024-06-26T05:54:27.0759094Z >>> print(rets[0]) # tensor([2., 2.], device='cuda:0') 2024-06-26T05:54:27.0759352Z >>> print(rets[1]) # tensor([2., 2.], device='cuda:1') 2024-06-26T05:54:27.0759442Z 2024-06-26T05:54:27.0759858Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0759965Z 2024-06-26T05:54:27.0760076Z warnings.warn(msg) 2024-06-26T05:54:27.0760161Z 2024-06-26T05:54:27.0760400Z --- Parse Warning: 56 / 90 --- 2024-06-26T05:54:27.0762005Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PrepareModuleInput in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py line=366. 2024-06-26T05:54:27.0762423Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0762517Z 2024-06-26T05:54:27.0763128Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to 2024-06-26T05:54:27.0763553Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``. 2024-06-26T05:54:27.0763645Z 2024-06-26T05:54:27.0763747Z Keyword Args: 2024-06-26T05:54:27.0764009Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2024-06-26T05:54:27.0764465Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2024-06-26T05:54:27.0764985Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2024-06-26T05:54:27.0765135Z as a placeholder. default: None. 2024-06-26T05:54:27.0765419Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2024-06-26T05:54:27.0765956Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2024-06-26T05:54:27.0766494Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2024-06-26T05:54:27.0766659Z input_kwarg_layouts (Dict[str, Placement]): 2024-06-26T05:54:27.0767205Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2024-06-26T05:54:27.0767316Z default: None 2024-06-26T05:54:27.0767535Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2024-06-26T05:54:27.0768057Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2024-06-26T05:54:27.0768254Z have the desired DTensor layouts. default: None. 2024-06-26T05:54:27.0768407Z use_local_output (bool, optional): 2024-06-26T05:54:27.0768903Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2024-06-26T05:54:27.0768998Z Returns: 2024-06-26T05:54:27.0769503Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs. 2024-06-26T05:54:27.0769592Z 2024-06-26T05:54:27.0769696Z Example:: 2024-06-26T05:54:27.0769840Z >>> # xdoctest: +SKIP(failing) 2024-06-26T05:54:27.0770254Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput 2024-06-26T05:54:27.0770517Z >>> from torch.distributed.device_mesh import init_device_mesh 2024-06-26T05:54:27.0770613Z >>> ... 2024-06-26T05:54:27.0771029Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2024-06-26T05:54:27.0771204Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2024-06-26T05:54:27.0771299Z >>> 2024-06-26T05:54:27.0771788Z >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor 2024-06-26T05:54:27.0771996Z >>> # and then redistributed to Replicated DTensor. 2024-06-26T05:54:27.0772113Z >>> parallelize_module( 2024-06-26T05:54:27.0772293Z >>> block, # this can be a submodule or module 2024-06-26T05:54:27.0772413Z >>> tp_mesh, 2024-06-26T05:54:27.0772558Z >>> parallelize_plan={ 2024-06-26T05:54:27.0772714Z >>> "attn": PrepareModuleInput( 2024-06-26T05:54:27.0772916Z >>> input_layouts=(Shard(0), None, None, ...), 2024-06-26T05:54:27.0773164Z >>> desired_input_layouts=(Replicate(), None, None, ...) 2024-06-26T05:54:27.0773300Z >>> ), 2024-06-26T05:54:27.0773397Z >>> } 2024-06-26T05:54:27.0773604Z >>> ) 2024-06-26T05:54:27.0773701Z 2024-06-26T05:54:27.0774092Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0774179Z 2024-06-26T05:54:27.0774307Z warnings.warn(msg) 2024-06-26T05:54:27.0774395Z 2024-06-26T05:54:27.0774594Z --- Parse Warning: 57 / 90 --- 2024-06-26T05:54:27.0776103Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PrepareModuleOutput in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py line=521. 2024-06-26T05:54:27.0776504Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0776595Z 2024-06-26T05:54:27.0777239Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to 2024-06-26T05:54:27.0777661Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``. 2024-06-26T05:54:27.0777762Z 2024-06-26T05:54:27.0777864Z Keyword Args: 2024-06-26T05:54:27.0778073Z output_layouts (Union[Placement, Tuple[Placement]]): 2024-06-26T05:54:27.0778551Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2024-06-26T05:54:27.0779082Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2024-06-26T05:54:27.0779270Z ``None`` need to be specified as a placeholder. 2024-06-26T05:54:27.0779531Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2024-06-26T05:54:27.0780069Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2024-06-26T05:54:27.0780233Z have the desired DTensor layouts. 2024-06-26T05:54:27.0780376Z use_local_output (bool, optional): 2024-06-26T05:54:27.0780869Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2024-06-26T05:54:27.0780978Z Returns: 2024-06-26T05:54:27.0781441Z A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs. 2024-06-26T05:54:27.0781528Z 2024-06-26T05:54:27.0781643Z Example:: 2024-06-26T05:54:27.0781770Z >>> # xdoctest: +SKIP(failing) 2024-06-26T05:54:27.0782184Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput 2024-06-26T05:54:27.0782450Z >>> from torch.distributed.device_mesh import init_device_mesh 2024-06-26T05:54:27.0782542Z >>> ... 2024-06-26T05:54:27.0782957Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2024-06-26T05:54:27.0783139Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2024-06-26T05:54:27.0783231Z >>> 2024-06-26T05:54:27.0783787Z >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor 2024-06-26T05:54:27.0783967Z >>> # and then redistributed to Sharded DTensor. 2024-06-26T05:54:27.0784138Z >>> parallelize_module( 2024-06-26T05:54:27.0784332Z >>> block, # this can be a submodule or module 2024-06-26T05:54:27.0784434Z >>> tp_mesh, 2024-06-26T05:54:27.0784613Z >>> parallelize_plan = PrepareModuleOutput( 2024-06-26T05:54:27.0784774Z >>> output_layouts=Replicate(), 2024-06-26T05:54:27.0784962Z >>> desired_output_layouts=Shard(0) 2024-06-26T05:54:27.0785057Z >>> ) 2024-06-26T05:54:27.0785165Z >>> ) 2024-06-26T05:54:27.0785252Z 2024-06-26T05:54:27.0785670Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0785772Z 2024-06-26T05:54:27.0785909Z warnings.warn(msg) 2024-06-26T05:54:27.0785999Z 2024-06-26T05:54:27.0786212Z --- Parse Warning: 58 / 90 --- 2024-06-26T05:54:27.0787671Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MixtureSameFamily in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/mixture_same_family.py line=12. 2024-06-26T05:54:27.0788083Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0788171Z 2024-06-26T05:54:27.0788453Z The `MixtureSameFamily` distribution implements a (batch of) mixture 2024-06-26T05:54:27.0788784Z distribution where all component are from different parameterizations of 2024-06-26T05:54:27.0789062Z the same distribution type. It is parameterized by a `Categorical` 2024-06-26T05:54:27.0789311Z "selecting distribution" (over `k` component) and a component 2024-06-26T05:54:27.0789600Z distribution, i.e., a `Distribution` with a rightmost batch shape 2024-06-26T05:54:27.0789812Z (equal to `[k]`) which indexes each (batch of) component. 2024-06-26T05:54:27.0789899Z 2024-06-26T05:54:27.0790016Z Examples:: 2024-06-26T05:54:27.0790104Z 2024-06-26T05:54:27.0790261Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:27.0790539Z >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally 2024-06-26T05:54:27.0790681Z >>> # weighted normal distributions 2024-06-26T05:54:27.0790848Z >>> mix = D.Categorical(torch.ones(5,)) 2024-06-26T05:54:27.0791043Z >>> comp = D.Normal(torch.randn(5,), torch.rand(5,)) 2024-06-26T05:54:27.0791201Z >>> gmm = MixtureSameFamily(mix, comp) 2024-06-26T05:54:27.0791304Z 2024-06-26T05:54:27.0791570Z >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally 2024-06-26T05:54:27.0791742Z >>> # weighted bivariate normal distributions 2024-06-26T05:54:27.0791912Z >>> mix = D.Categorical(torch.ones(5,)) 2024-06-26T05:54:27.0792050Z >>> comp = D.Independent(D.Normal( 2024-06-26T05:54:27.0792220Z ... torch.randn(5,2), torch.rand(5,2)), 1) 2024-06-26T05:54:27.0792388Z >>> gmm = MixtureSameFamily(mix, comp) 2024-06-26T05:54:27.0792475Z 2024-06-26T05:54:27.0792714Z >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each 2024-06-26T05:54:27.0792994Z >>> # consisting of 5 random weighted bivariate normal distributions 2024-06-26T05:54:27.0793146Z >>> mix = D.Categorical(torch.rand(3,5)) 2024-06-26T05:54:27.0793301Z >>> comp = D.Independent(D.Normal( 2024-06-26T05:54:27.0793481Z ... torch.randn(3,5,2), torch.rand(3,5,2)), 1) 2024-06-26T05:54:27.0793633Z >>> gmm = MixtureSameFamily(mix, comp) 2024-06-26T05:54:27.0793733Z 2024-06-26T05:54:27.0793830Z Args: 2024-06-26T05:54:27.0794151Z mixture_distribution: `torch.distributions.Categorical`-like 2024-06-26T05:54:27.0794409Z instance. Manages the probability of selecting component. 2024-06-26T05:54:27.0794633Z The number of categories must match the rightmost batch 2024-06-26T05:54:27.0794877Z dimension of the `component_distribution`. Must have either 2024-06-26T05:54:27.0795107Z scalar `batch_shape` or `batch_shape` matching 2024-06-26T05:54:27.0795331Z `component_distribution.batch_shape[:-1]` 2024-06-26T05:54:27.0795666Z component_distribution: `torch.distributions.Distribution`-like 2024-06-26T05:54:27.0795961Z instance. Right-most batch dimension indexes component. 2024-06-26T05:54:27.0796089Z 2024-06-26T05:54:27.0796490Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0796577Z 2024-06-26T05:54:27.0796689Z warnings.warn(msg) 2024-06-26T05:54:27.0796820Z 2024-06-26T05:54:27.0797022Z --- Parse Warning: 59 / 90 --- 2024-06-26T05:54:27.0798495Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RelaxedBernoulli in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/relaxed_bernoulli.py line=109. 2024-06-26T05:54:27.0798916Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0799004Z 2024-06-26T05:54:27.0799237Z Creates a RelaxedBernoulli distribution, parametrized by 2024-06-26T05:54:27.0799494Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits` 2024-06-26T05:54:27.0799796Z (but not both). This is a relaxed version of the `Bernoulli` distribution, 2024-06-26T05:54:27.0800062Z so the values are in (0, 1), and has reparametrizable samples. 2024-06-26T05:54:27.0800151Z 2024-06-26T05:54:27.0800251Z Example:: 2024-06-26T05:54:27.0800352Z 2024-06-26T05:54:27.0800662Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-06-26T05:54:27.0800844Z >>> m = RelaxedBernoulli(torch.tensor([2.2]), 2024-06-26T05:54:27.0801031Z ... torch.tensor([0.1, 0.2, 0.3, 0.99])) 2024-06-26T05:54:27.0801135Z >>> m.sample() 2024-06-26T05:54:27.0801288Z tensor([ 0.2951, 0.3442, 0.8918, 0.9021]) 2024-06-26T05:54:27.0801389Z 2024-06-26T05:54:27.0801483Z Args: 2024-06-26T05:54:27.0801661Z temperature (Tensor): relaxation temperature 2024-06-26T05:54:27.0801897Z probs (Number, Tensor): the probability of sampling `1` 2024-06-26T05:54:27.0802158Z logits (Number, Tensor): the log-odds of sampling `1` 2024-06-26T05:54:27.0802245Z 2024-06-26T05:54:27.0802647Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0802734Z 2024-06-26T05:54:27.0802845Z warnings.warn(msg) 2024-06-26T05:54:27.0802944Z 2024-06-26T05:54:27.0803142Z --- Parse Warning: 60 / 90 --- 2024-06-26T05:54:27.0804666Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RelaxedOneHotCategorical in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/relaxed_categorical.py line=97. 2024-06-26T05:54:27.0805066Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0805155Z 2024-06-26T05:54:27.0805444Z Creates a RelaxedOneHotCategorical distribution parametrized by 2024-06-26T05:54:27.0805693Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`. 2024-06-26T05:54:27.0806017Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so 2024-06-26T05:54:27.0806241Z its samples are on simplex, and are reparametrizable. 2024-06-26T05:54:27.0806328Z 2024-06-26T05:54:27.0806428Z Example:: 2024-06-26T05:54:27.0806526Z 2024-06-26T05:54:27.0806761Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-06-26T05:54:27.0806982Z >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]), 2024-06-26T05:54:27.0807165Z ... torch.tensor([0.1, 0.2, 0.3, 0.4])) 2024-06-26T05:54:27.0807267Z >>> m.sample() 2024-06-26T05:54:27.0807427Z tensor([ 0.1294, 0.2324, 0.3859, 0.2523]) 2024-06-26T05:54:27.0807513Z 2024-06-26T05:54:27.0807641Z Args: 2024-06-26T05:54:27.0807834Z temperature (Tensor): relaxation temperature 2024-06-26T05:54:27.0807980Z probs (Tensor): event probabilities 2024-06-26T05:54:27.0808226Z logits (Tensor): unnormalized log probability for each event 2024-06-26T05:54:27.0808324Z 2024-06-26T05:54:27.0808708Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0808823Z 2024-06-26T05:54:27.0808944Z warnings.warn(msg) 2024-06-26T05:54:27.0809032Z 2024-06-26T05:54:27.0809231Z --- Parse Warning: 61 / 90 --- 2024-06-26T05:54:27.0810821Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assoc_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=230. 2024-06-26T05:54:27.0811220Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0811487Z Return a new dict with new, potentially nested, key value pair 2024-06-26T05:54:27.0811574Z 2024-06-26T05:54:27.0811742Z >>> purchase = {'name': 'Alice', 2024-06-26T05:54:27.0811980Z ... 'order': {'items': ['Apple', 'Orange'], 2024-06-26T05:54:27.0812185Z ... 'costs': [0.50, 1.25]}, 2024-06-26T05:54:27.0812400Z ... 'credit card': '5555-1234-1234-1234'} 2024-06-26T05:54:27.0812750Z >>> assoc_in(purchase, ['order', 'costs'], [0.25, 1.00]) # doctest: +SKIP 2024-06-26T05:54:27.0812935Z {'credit card': '5555-1234-1234-1234', 2024-06-26T05:54:27.0813067Z 'name': 'Alice', 2024-06-26T05:54:27.0813366Z 'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}} 2024-06-26T05:54:27.0813458Z 2024-06-26T05:54:27.0813965Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0814069Z 2024-06-26T05:54:27.0814179Z warnings.warn(msg) 2024-06-26T05:54:27.0814282Z 2024-06-26T05:54:27.0814482Z --- Parse Warning: 62 / 90 --- 2024-06-26T05:54:27.0816002Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=update_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=245. 2024-06-26T05:54:27.0816415Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0816608Z Update value in a (potentially) nested dictionary 2024-06-26T05:54:27.0816700Z 2024-06-26T05:54:27.0816811Z inputs: 2024-06-26T05:54:27.0816996Z d - dictionary on which to operate 2024-06-26T05:54:27.0817359Z keys - list or tuple giving the location of the value to be changed in d 2024-06-26T05:54:27.0817574Z func - function to operate on that value 2024-06-26T05:54:27.0817662Z 2024-06-26T05:54:27.0817950Z If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the 2024-06-26T05:54:27.0818269Z original dictionary with v replaced by func(v), but does not mutate the 2024-06-26T05:54:27.0818382Z original dictionary. 2024-06-26T05:54:27.0818479Z 2024-06-26T05:54:27.0818788Z If k0 is not a key in d, update_in creates nested dictionaries to the depth 2024-06-26T05:54:27.0819082Z specified by the keys, with the innermost value set to func(default). 2024-06-26T05:54:27.0819181Z 2024-06-26T05:54:27.0819300Z >>> inc = lambda x: x + 1 2024-06-26T05:54:27.0819475Z >>> update_in({'a': 0}, ['a'], inc) 2024-06-26T05:54:27.0819607Z {'a': 1} 2024-06-26T05:54:27.0819695Z 2024-06-26T05:54:27.0819870Z >>> transaction = {'name': 'Alice', 2024-06-26T05:54:27.0820130Z ... 'purchase': {'items': ['Apple', 'Orange'], 2024-06-26T05:54:27.0820347Z ... 'costs': [0.50, 1.25]}, 2024-06-26T05:54:27.0820624Z ... 'credit card': '5555-1234-1234-1234'} 2024-06-26T05:54:27.0820980Z >>> update_in(transaction, ['purchase', 'costs'], sum) # doctest: +SKIP 2024-06-26T05:54:27.0821165Z {'credit card': '5555-1234-1234-1234', 2024-06-26T05:54:27.0821312Z 'name': 'Alice', 2024-06-26T05:54:27.0821586Z 'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}} 2024-06-26T05:54:27.0821715Z 2024-06-26T05:54:27.0821885Z >>> # updating a value when k0 is not in d 2024-06-26T05:54:27.0822053Z >>> update_in({}, [1, 2, 3], str, default="bar") 2024-06-26T05:54:27.0822229Z {1: {2: {3: 'bar'}}} 2024-06-26T05:54:27.0822440Z >>> update_in({1: 'foo'}, [2, 3, 4], inc, 0) 2024-06-26T05:54:27.0822619Z {1: 'foo', 2: {3: {4: 1}}} 2024-06-26T05:54:27.0822712Z 2024-06-26T05:54:27.0823110Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0823198Z 2024-06-26T05:54:27.0823306Z warnings.warn(msg) 2024-06-26T05:54:27.0823406Z 2024-06-26T05:54:27.0823602Z --- Parse Warning: 63 / 90 --- 2024-06-26T05:54:27.0825087Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=get_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=303. 2024-06-26T05:54:27.0825496Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0825708Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys. 2024-06-26T05:54:27.0825811Z 2024-06-26T05:54:27.0826071Z If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless 2024-06-26T05:54:27.0826335Z ``no_default`` is specified, then it raises KeyError or IndexError. 2024-06-26T05:54:27.0826436Z 2024-06-26T05:54:27.0826716Z ``get_in`` is a generalization of ``operator.getitem`` for nested data 2024-06-26T05:54:27.0826883Z structures such as dictionaries and lists. 2024-06-26T05:54:27.0826981Z 2024-06-26T05:54:27.0827158Z >>> transaction = {'name': 'Alice', 2024-06-26T05:54:27.0827405Z ... 'purchase': {'items': ['Apple', 'Orange'], 2024-06-26T05:54:27.0827633Z ... 'costs': [0.50, 1.25]}, 2024-06-26T05:54:27.0827859Z ... 'credit card': '5555-1234-1234-1234'} 2024-06-26T05:54:27.0828085Z >>> get_in(['purchase', 'items', 0], transaction) 2024-06-26T05:54:27.0828217Z 'Apple' 2024-06-26T05:54:27.0828387Z >>> get_in(['name'], transaction) 2024-06-26T05:54:27.0828518Z 'Alice' 2024-06-26T05:54:27.0828732Z >>> get_in(['purchase', 'total'], transaction) 2024-06-26T05:54:27.0828980Z >>> get_in(['purchase', 'items', 'apple'], transaction) 2024-06-26T05:54:27.0829213Z >>> get_in(['purchase', 'items', 10], transaction) 2024-06-26T05:54:27.0829438Z >>> get_in(['purchase', 'total'], transaction, 0) 2024-06-26T05:54:27.0829532Z 0 2024-06-26T05:54:27.0829722Z >>> get_in(['y'], {}, no_default=True) 2024-06-26T05:54:27.0829861Z Traceback (most recent call last): 2024-06-26T05:54:27.0829956Z ... 2024-06-26T05:54:27.0830098Z KeyError: 'y' 2024-06-26T05:54:27.0830184Z 2024-06-26T05:54:27.0830280Z See Also: 2024-06-26T05:54:27.0830399Z itertoolz.get 2024-06-26T05:54:27.0830509Z operator.getitem 2024-06-26T05:54:27.0830598Z 2024-06-26T05:54:27.0830994Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0831082Z 2024-06-26T05:54:27.0831194Z warnings.warn(msg) 2024-06-26T05:54:27.0831293Z 2024-06-26T05:54:27.0831493Z --- Parse Warning: 64 / 90 --- 2024-06-26T05:54:27.0833039Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=groupby in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=355. 2024-06-26T05:54:27.0833439Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0833580Z Group a collection by a key function 2024-06-26T05:54:27.0833680Z 2024-06-26T05:54:27.0833969Z >>> names = ['Alice', 'Bob', 'Charlie', 'Dan', 'Edith', 'Frank'] 2024-06-26T05:54:27.0834172Z >>> groupby(len, names) # doctest: +SKIP 2024-06-26T05:54:27.0834475Z {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} 2024-06-26T05:54:27.0834563Z 2024-06-26T05:54:27.0834720Z >>> iseven = lambda x: x % 2 == 0 2024-06-26T05:54:27.0834998Z >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP 2024-06-26T05:54:27.0835146Z {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} 2024-06-26T05:54:27.0835233Z 2024-06-26T05:54:27.0835472Z Non-callable keys imply grouping on a member. 2024-06-26T05:54:27.0835560Z 2024-06-26T05:54:27.0835819Z >>> groupby('gender', [{'name': 'Alice', 'gender': 'F'}, 2024-06-26T05:54:27.0836050Z ... {'name': 'Bob', 'gender': 'M'}, 2024-06-26T05:54:27.0836329Z ... {'name': 'Charlie', 'gender': 'M'}]) # doctest:+SKIP 2024-06-26T05:54:27.0836532Z {'F': [{'gender': 'F', 'name': 'Alice'}], 2024-06-26T05:54:27.0836718Z 'M': [{'gender': 'M', 'name': 'Bob'}, 2024-06-26T05:54:27.0836910Z {'gender': 'M', 'name': 'Charlie'}]} 2024-06-26T05:54:27.0837009Z 2024-06-26T05:54:27.0837192Z Not to be confused with ``itertools.groupby`` 2024-06-26T05:54:27.0837279Z 2024-06-26T05:54:27.0837387Z See Also: 2024-06-26T05:54:27.0837484Z countby 2024-06-26T05:54:27.0837573Z 2024-06-26T05:54:27.0837966Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0838054Z 2024-06-26T05:54:27.0838164Z warnings.warn(msg) 2024-06-26T05:54:27.0838264Z 2024-06-26T05:54:27.0838464Z --- Parse Warning: 65 / 90 --- 2024-06-26T05:54:27.0839814Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SyncBatchNorm in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py line=601. 2024-06-26T05:54:27.0840227Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0840502Z Applies Batch Normalization over a N-Dimensional input. 2024-06-26T05:54:27.0840674Z 2024-06-26T05:54:27.0841261Z The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper 2024-06-26T05:54:27.0841555Z `Batch Normalization: Accelerating Deep Network Training by Reducing 2024-06-26T05:54:27.0841843Z Internal Covariate Shift `__ . 2024-06-26T05:54:27.0841931Z 2024-06-26T05:54:27.0842034Z .. math:: 2024-06-26T05:54:27.0842135Z 2024-06-26T05:54:27.0842540Z y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta 2024-06-26T05:54:27.0842627Z 2024-06-26T05:54:27.0843005Z The mean and standard-deviation are calculated per-dimension over all 2024-06-26T05:54:27.0843383Z mini-batches of the same process groups. :math:`\gamma` and :math:`\beta` 2024-06-26T05:54:27.0843715Z are learnable parameter vectors of size `C` (where `C` is the input size). 2024-06-26T05:54:27.0843950Z By default, the elements of :math:`\gamma` are sampled from 2024-06-26T05:54:27.0844239Z :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. 2024-06-26T05:54:27.0844661Z The standard-deviation is calculated via the biased estimator, equivalent to 2024-06-26T05:54:27.0844807Z `torch.var(input, unbiased=False)`. 2024-06-26T05:54:27.0844894Z 2024-06-26T05:54:27.0845220Z Also by default, during training this layer keeps running estimates of its 2024-06-26T05:54:27.0845564Z computed mean and variance, which are then used for normalization during 2024-06-26T05:54:27.0845880Z evaluation. The running estimates are kept with a default :attr:`momentum` 2024-06-26T05:54:27.0845986Z of 0.1. 2024-06-26T05:54:27.0846074Z 2024-06-26T05:54:27.0846378Z If :attr:`track_running_stats` is set to ``False``, this layer then does not 2024-06-26T05:54:27.0846705Z keep running estimates, and batch statistics are instead used during 2024-06-26T05:54:27.0846827Z evaluation time as well. 2024-06-26T05:54:27.0846950Z 2024-06-26T05:54:27.0847048Z .. note:: 2024-06-26T05:54:27.0847364Z This :attr:`momentum` argument is different from one used in optimizer 2024-06-26T05:54:27.0847673Z classes and the conventional notion of momentum. Mathematically, the 2024-06-26T05:54:27.0847845Z update rule for running statistics here is 2024-06-26T05:54:27.0848324Z :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, 2024-06-26T05:54:27.0848626Z where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the 2024-06-26T05:54:27.0848741Z new observed value. 2024-06-26T05:54:27.0848825Z 2024-06-26T05:54:27.0849240Z Because the Batch Normalization is done for each channel in the ``C`` dimension, computing 2024-06-26T05:54:27.0849658Z statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch 2024-06-26T05:54:27.0849943Z Normalization or Spatio-temporal Batch Normalization. 2024-06-26T05:54:27.0850033Z 2024-06-26T05:54:27.0850218Z Currently :class:`SyncBatchNorm` only supports 2024-06-26T05:54:27.0850601Z :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use 2024-06-26T05:54:27.0850881Z :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert 2024-06-26T05:54:27.0851149Z :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping 2024-06-26T05:54:27.0851273Z Network with DDP. 2024-06-26T05:54:27.0851359Z 2024-06-26T05:54:27.0851452Z Args: 2024-06-26T05:54:27.0851676Z num_features: :math:`C` from an expected input of size 2024-06-26T05:54:27.0851788Z :math:`(N, C, +)` 2024-06-26T05:54:27.0852050Z eps: a value added to the denominator for numerical stability. 2024-06-26T05:54:27.0852211Z Default: ``1e-5`` 2024-06-26T05:54:27.0852462Z momentum: the value used for the running_mean and running_var 2024-06-26T05:54:27.0852752Z computation. Can be set to ``None`` for cumulative moving average 2024-06-26T05:54:27.0852907Z (i.e. simple average). Default: 0.1 2024-06-26T05:54:27.0853177Z affine: a boolean value that when set to ``True``, this module has 2024-06-26T05:54:27.0853382Z learnable affine parameters. Default: ``True`` 2024-06-26T05:54:27.0853778Z track_running_stats: a boolean value that when set to ``True``, this 2024-06-26T05:54:27.0854084Z module tracks the running mean and variance, and when set to ``False``, 2024-06-26T05:54:27.0854405Z this module does not track such statistics, and initializes statistics 2024-06-26T05:54:27.0854673Z buffers :attr:`running_mean` and :attr:`running_var` as ``None``. 2024-06-26T05:54:27.0854993Z When these buffers are ``None``, this module always uses batch statistics. 2024-06-26T05:54:27.0855211Z in both training and eval modes. Default: ``True`` 2024-06-26T05:54:27.0855529Z process_group: synchronization of stats happen within each process group 2024-06-26T05:54:27.0855828Z individually. Default behavior is synchronization across the whole 2024-06-26T05:54:27.0855929Z world 2024-06-26T05:54:27.0856020Z 2024-06-26T05:54:27.0856129Z Shape: 2024-06-26T05:54:27.0856346Z - Input: :math:`(N, C, +)` 2024-06-26T05:54:27.0856592Z - Output: :math:`(N, C, +)` (same shape as input) 2024-06-26T05:54:27.0856693Z 2024-06-26T05:54:27.0856793Z .. note:: 2024-06-26T05:54:27.0857112Z Synchronization of batchnorm statistics occurs only while training, i.e. 2024-06-26T05:54:27.0857436Z synchronization is disabled when ``model.eval()`` is set or if 2024-06-26T05:54:27.0857603Z ``self.training`` is otherwise ``False``. 2024-06-26T05:54:27.0857693Z 2024-06-26T05:54:27.0857847Z Examples:: 2024-06-26T05:54:27.0857934Z 2024-06-26T05:54:27.0858050Z >>> # xdoctest: +SKIP 2024-06-26T05:54:27.0858235Z >>> # With Learnable Parameters 2024-06-26T05:54:27.0858374Z >>> m = nn.SyncBatchNorm(100) 2024-06-26T05:54:27.0858549Z >>> # creating process group (optional) 2024-06-26T05:54:27.0858738Z >>> # ranks is a list of int identifying rank ids. 2024-06-26T05:54:27.0858864Z >>> ranks = list(range(8)) 2024-06-26T05:54:27.0859013Z >>> r1, r2 = ranks[:4], ranks[4:] 2024-06-26T05:54:27.0859209Z >>> # Note: every rank calls into new_group for every 2024-06-26T05:54:27.0859409Z >>> # process group created, even if that rank is not 2024-06-26T05:54:27.0859544Z >>> # part of the group. 2024-06-26T05:54:27.0859864Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2024-06-26T05:54:27.0860129Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2024-06-26T05:54:27.0860294Z >>> # Without Learnable Parameters 2024-06-26T05:54:27.0860576Z >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group) 2024-06-26T05:54:27.0860740Z >>> input = torch.randn(20, 100, 35, 45, 10) 2024-06-26T05:54:27.0860865Z >>> output = m(input) 2024-06-26T05:54:27.0860952Z 2024-06-26T05:54:27.0861115Z >>> # network is nn.BatchNorm layer 2024-06-26T05:54:27.0861476Z >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group) 2024-06-26T05:54:27.0861694Z >>> # only single gpu per process is currently supported 2024-06-26T05:54:27.0862003Z >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel( 2024-06-26T05:54:27.0862157Z >>> sync_bn_network, 2024-06-26T05:54:27.0862333Z >>> device_ids=[args.local_rank], 2024-06-26T05:54:27.0862522Z >>> output_device=args.local_rank) 2024-06-26T05:54:27.0862614Z 2024-06-26T05:54:27.0863003Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0863103Z 2024-06-26T05:54:27.0863213Z warnings.warn(msg) 2024-06-26T05:54:27.0863300Z 2024-06-26T05:54:27.0863510Z --- Parse Warning: 66 / 90 --- 2024-06-26T05:54:27.0864994Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SyncBatchNorm.convert_sync_batchnorm in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py line=824. 2024-06-26T05:54:27.0865400Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0865818Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers. 2024-06-26T05:54:27.0865906Z 2024-06-26T05:54:27.0866012Z Args: 2024-06-26T05:54:27.0866340Z module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers 2024-06-26T05:54:27.0866621Z process_group (optional): process group to scope synchronization, 2024-06-26T05:54:27.0866776Z default is the whole world 2024-06-26T05:54:27.0866863Z 2024-06-26T05:54:27.0866961Z Returns: 2024-06-26T05:54:27.0867352Z The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm` 2024-06-26T05:54:27.0867649Z layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer, 2024-06-26T05:54:27.0867948Z a new :class:`torch.nn.SyncBatchNorm` layer object will be returned 2024-06-26T05:54:27.0868077Z instead. 2024-06-26T05:54:27.0868163Z 2024-06-26T05:54:27.0868277Z Example:: 2024-06-26T05:54:27.0868364Z 2024-06-26T05:54:27.0868529Z >>> # Network with nn.BatchNorm layer 2024-06-26T05:54:27.0868754Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-06-26T05:54:27.0868934Z >>> module = torch.nn.Sequential( 2024-06-26T05:54:27.0869096Z >>> torch.nn.Linear(20, 100), 2024-06-26T05:54:27.0869272Z >>> torch.nn.BatchNorm1d(100), 2024-06-26T05:54:27.0869384Z >>> ).cuda() 2024-06-26T05:54:27.0869552Z >>> # creating process group (optional) 2024-06-26T05:54:27.0869758Z >>> # ranks is a list of int identifying rank ids. 2024-06-26T05:54:27.0869886Z >>> ranks = list(range(8)) 2024-06-26T05:54:27.0870039Z >>> r1, r2 = ranks[:4], ranks[4:] 2024-06-26T05:54:27.0870241Z >>> # Note: every rank calls into new_group for every 2024-06-26T05:54:27.0870444Z >>> # process group created, even if that rank is not 2024-06-26T05:54:27.0870585Z >>> # part of the group. 2024-06-26T05:54:27.0870745Z >>> # xdoctest: +SKIP("distributed") 2024-06-26T05:54:27.0871073Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2024-06-26T05:54:27.0871347Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2024-06-26T05:54:27.0871741Z >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group) 2024-06-26T05:54:27.0871831Z 2024-06-26T05:54:27.0871934Z 2024-06-26T05:54:27.0872319Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0872406Z 2024-06-26T05:54:27.0872525Z warnings.warn(msg) 2024-06-26T05:54:27.0872610Z 2024-06-26T05:54:27.0872821Z --- Parse Warning: 67 / 90 --- 2024-06-26T05:54:27.0874129Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Unflatten in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py line=60. 2024-06-26T05:54:27.0874532Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0874632Z 2024-06-26T05:54:27.0875053Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`. 2024-06-26T05:54:27.0875141Z 2024-06-26T05:54:27.0875520Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can 2024-06-26T05:54:27.0875838Z be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively. 2024-06-26T05:54:27.0875924Z 2024-06-26T05:54:27.0876365Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be 2024-06-26T05:54:27.0876742Z a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input; a `NamedShape` 2024-06-26T05:54:27.0876969Z (tuple of `(name, size)` tuples) for `NamedTensor` input. 2024-06-26T05:54:27.0877054Z 2024-06-26T05:54:27.0877150Z Shape: 2024-06-26T05:54:27.0877553Z - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at 2024-06-26T05:54:27.0877892Z dimension :attr:`dim` and :math:`*` means any number of dimensions including none. 2024-06-26T05:54:27.0878270Z - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and 2024-06-26T05:54:27.0878476Z :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`. 2024-06-26T05:54:27.0878563Z 2024-06-26T05:54:27.0878656Z Args: 2024-06-26T05:54:27.0878861Z dim (Union[int, str]): Dimension to be unflattened 2024-06-26T05:54:27.0879311Z unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension 2024-06-26T05:54:27.0879436Z 2024-06-26T05:54:27.0879549Z Examples: 2024-06-26T05:54:27.0879680Z >>> input = torch.randn(2, 50) 2024-06-26T05:54:27.0879808Z >>> # With tuple of ints 2024-06-26T05:54:27.0879922Z >>> m = nn.Sequential( 2024-06-26T05:54:27.0880060Z >>> nn.Linear(50, 50), 2024-06-26T05:54:27.0880229Z >>> nn.Unflatten(1, (2, 5, 5)) 2024-06-26T05:54:27.0880322Z >>> ) 2024-06-26T05:54:27.0880434Z >>> output = m(input) 2024-06-26T05:54:27.0880552Z >>> output.size() 2024-06-26T05:54:27.0880742Z torch.Size([2, 2, 5, 5]) 2024-06-26T05:54:27.0880855Z >>> # With torch.Size 2024-06-26T05:54:27.0880987Z >>> m = nn.Sequential( 2024-06-26T05:54:27.0881101Z >>> nn.Linear(50, 50), 2024-06-26T05:54:27.0881263Z >>> nn.Unflatten(1, torch.Size([2, 5, 5])) 2024-06-26T05:54:27.0881368Z >>> ) 2024-06-26T05:54:27.0881480Z >>> output = m(input) 2024-06-26T05:54:27.0881587Z >>> output.size() 2024-06-26T05:54:27.0881716Z torch.Size([2, 2, 5, 5]) 2024-06-26T05:54:27.0881871Z >>> # With namedshape (tuple of tuples) 2024-06-26T05:54:27.0882133Z >>> input = torch.randn(2, 50, names=('N', 'features')) 2024-06-26T05:54:27.0882493Z >>> unflatten = nn.Unflatten('features', (('C', 2), ('H', 5), ('W', 5))) 2024-06-26T05:54:27.0882625Z >>> output = unflatten(input) 2024-06-26T05:54:27.0882747Z >>> output.size() 2024-06-26T05:54:27.0882861Z torch.Size([2, 2, 5, 5]) 2024-06-26T05:54:27.0882951Z 2024-06-26T05:54:27.0883348Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0883440Z 2024-06-26T05:54:27.0883551Z warnings.warn(msg) 2024-06-26T05:54:27.0883648Z 2024-06-26T05:54:27.0883847Z --- Parse Warning: 68 / 90 --- 2024-06-26T05:54:27.0885277Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=TripletMarginWithDistanceLoss in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py line=1696. 2024-06-26T05:54:27.0885692Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0885949Z Creates a criterion that measures the triplet loss given input 2024-06-26T05:54:27.0886207Z tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor, 2024-06-26T05:54:27.0886499Z positive, and negative examples, respectively), and a nonnegative, 2024-06-26T05:54:27.0886888Z real-valued function ("distance function") used to compute the relationship 2024-06-26T05:54:27.0887194Z between the anchor and positive example ("positive distance") and the 2024-06-26T05:54:27.0887389Z anchor and negative example ("negative distance"). 2024-06-26T05:54:27.0887477Z 2024-06-26T05:54:27.0887829Z The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``) 2024-06-26T05:54:27.0887944Z can be described as: 2024-06-26T05:54:27.0888030Z 2024-06-26T05:54:27.0888144Z .. math:: 2024-06-26T05:54:27.0888338Z \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad 2024-06-26T05:54:27.0888627Z l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} 2024-06-26T05:54:27.0888728Z 2024-06-26T05:54:27.0889147Z where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function 2024-06-26T05:54:27.0889533Z quantifying the closeness of two tensors, referred to as the :attr:`distance_function`; 2024-06-26T05:54:27.0889916Z and :math:`margin` is a nonnegative margin representing the minimum difference 2024-06-26T05:54:27.0890245Z between the positive and negative distances that is required for the loss to 2024-06-26T05:54:27.0890576Z be 0. The input tensors have :math:`N` elements each and can be of any shape 2024-06-26T05:54:27.0890729Z that the distance function can handle. 2024-06-26T05:54:27.0890849Z 2024-06-26T05:54:27.0891052Z If :attr:`reduction` is not ``'none'`` 2024-06-26T05:54:27.0891207Z (default ``'mean'``), then: 2024-06-26T05:54:27.0891295Z 2024-06-26T05:54:27.0891408Z .. math:: 2024-06-26T05:54:27.0891591Z \ell(x, y) = 2024-06-26T05:54:27.0891724Z \begin{cases} 2024-06-26T05:54:27.0892132Z \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ 2024-06-26T05:54:27.0892672Z \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} 2024-06-26T05:54:27.0892803Z \end{cases} 2024-06-26T05:54:27.0892917Z 2024-06-26T05:54:27.0900154Z See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet 2024-06-26T05:54:27.0900569Z loss for input tensors using the :math:`l_p` distance as the distance function. 2024-06-26T05:54:27.0900673Z 2024-06-26T05:54:27.0900771Z Args: 2024-06-26T05:54:27.0901247Z distance_function (Callable, optional): A nonnegative, real-valued function that 2024-06-26T05:54:27.0901502Z quantifies the closeness of two tensors. If not specified, 2024-06-26T05:54:27.0901732Z `nn.PairwiseDistance` will be used. Default: ``None`` 2024-06-26T05:54:27.0902103Z margin (float, optional): A nonnegative margin representing the minimum difference 2024-06-26T05:54:27.0902477Z between the positive and negative distances required for the loss to be 0. Larger 2024-06-26T05:54:27.0902848Z margins penalize cases where the negative examples are not distant enough from the 2024-06-26T05:54:27.0903100Z anchors, relative to the positives. Default: :math:`1`. 2024-06-26T05:54:27.0903433Z swap (bool, optional): Whether to use the distance swap described in the paper 2024-06-26T05:54:27.0903781Z `Learning shallow convolutional feature descriptors with triplet losses` by 2024-06-26T05:54:27.0904126Z V. Balntas, E. Riba et al. If True, and if the positive example is closer to the 2024-06-26T05:54:27.0904492Z negative example than the anchor is, swaps the positive example and the anchor in 2024-06-26T05:54:27.0904683Z the loss computation. Default: ``False``. 2024-06-26T05:54:27.0905057Z reduction (str, optional): Specifies the (optional) reduction to apply to the output: 2024-06-26T05:54:27.0905401Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2024-06-26T05:54:27.0905735Z ``'mean'``: the sum of the output will be divided by the number of 2024-06-26T05:54:27.0906142Z elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'`` 2024-06-26T05:54:27.0906233Z 2024-06-26T05:54:27.0906333Z 2024-06-26T05:54:27.0906430Z Shape: 2024-06-26T05:54:27.0906852Z - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions 2024-06-26T05:54:27.0907021Z as supported by the distance function. 2024-06-26T05:54:27.0907451Z - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar 2024-06-26T05:54:27.0907571Z otherwise. 2024-06-26T05:54:27.0907657Z 2024-06-26T05:54:27.0907769Z Examples:: 2024-06-26T05:54:27.0907868Z 2024-06-26T05:54:27.0907993Z >>> # Initialize embeddings 2024-06-26T05:54:27.0908146Z >>> embedding = nn.Embedding(1000, 128) 2024-06-26T05:54:27.0908328Z >>> anchor_ids = torch.randint(0, 1000, (1,)) 2024-06-26T05:54:27.0908616Z >>> positive_ids = torch.randint(0, 1000, (1,)) 2024-06-26T05:54:27.0908785Z >>> negative_ids = torch.randint(0, 1000, (1,)) 2024-06-26T05:54:27.0908937Z >>> anchor = embedding(anchor_ids) 2024-06-26T05:54:27.0909091Z >>> positive = embedding(positive_ids) 2024-06-26T05:54:27.0909243Z >>> negative = embedding(negative_ids) 2024-06-26T05:54:27.0909393Z >>> 2024-06-26T05:54:27.0909569Z >>> # Built-in Distance Function 2024-06-26T05:54:27.0909694Z >>> triplet_loss = \ 2024-06-26T05:54:27.0910046Z >>> nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance()) 2024-06-26T05:54:27.0910298Z >>> output = triplet_loss(anchor, positive, negative) 2024-06-26T05:54:27.0910466Z >>> output.backward() 2024-06-26T05:54:27.0910561Z >>> 2024-06-26T05:54:27.0910688Z >>> # Custom Distance Function 2024-06-26T05:54:27.0910823Z >>> def l_infinity(x1, x2): 2024-06-26T05:54:27.0911086Z >>> return torch.max(torch.abs(x1 - x2), dim=1).values 2024-06-26T05:54:27.0911180Z >>> 2024-06-26T05:54:27.0911446Z >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time") 2024-06-26T05:54:27.0911558Z >>> triplet_loss = ( 2024-06-26T05:54:27.0911911Z >>> nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5)) 2024-06-26T05:54:27.0912129Z >>> output = triplet_loss(anchor, positive, negative) 2024-06-26T05:54:27.0912244Z >>> output.backward() 2024-06-26T05:54:27.0912336Z >>> 2024-06-26T05:54:27.0912505Z >>> # Custom Distance Function (Lambda) 2024-06-26T05:54:27.0912620Z >>> triplet_loss = ( 2024-06-26T05:54:27.0912808Z >>> nn.TripletMarginWithDistanceLoss( 2024-06-26T05:54:27.0913151Z >>> distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y))) 2024-06-26T05:54:27.0913348Z >>> output = triplet_loss(anchor, positive, negative) 2024-06-26T05:54:27.0913474Z >>> output.backward() 2024-06-26T05:54:27.0913559Z 2024-06-26T05:54:27.0913662Z Reference: 2024-06-26T05:54:27.0914081Z V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses: 2024-06-26T05:54:27.0914327Z http://www.bmva.org/bmvc/2016/papers/paper119/index.html 2024-06-26T05:54:27.0914419Z 2024-06-26T05:54:27.0914827Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17)) 2024-06-26T05:54:27.0914916Z 2024-06-26T05:54:27.0915028Z warnings.warn(msg) 2024-06-26T05:54:27.0915129Z 2024-06-26T05:54:27.0915331Z --- Parse Warning: 69 / 90 --- 2024-06-26T05:54:27.0916654Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MaxUnpool2d in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py line=395. 2024-06-26T05:54:27.0917067Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0917261Z Computes a partial inverse of :class:`MaxPool2d`. 2024-06-26T05:54:27.0917362Z 2024-06-26T05:54:27.0917780Z :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost. 2024-06-26T05:54:27.0917867Z 2024-06-26T05:54:27.0918177Z :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d` 2024-06-26T05:54:27.0918499Z including the indices of the maximal values and computes a partial inverse 2024-06-26T05:54:27.0918735Z in which all non-maximal values are set to zero. 2024-06-26T05:54:27.0918841Z 2024-06-26T05:54:27.0918937Z Note: 2024-06-26T05:54:27.0919345Z This operation may behave nondeterministically when the input indices has repeat values. 2024-06-26T05:54:27.0919853Z See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. 2024-06-26T05:54:27.0919941Z 2024-06-26T05:54:27.0920286Z .. note:: :class:`MaxPool2d` can map several input sizes to the same output 2024-06-26T05:54:27.0920517Z sizes. Hence, the inversion process can get ambiguous. 2024-06-26T05:54:27.0920852Z To accommodate this, you can provide the needed output size 2024-06-26T05:54:27.0921149Z as an additional argument :attr:`output_size` in the forward call. 2024-06-26T05:54:27.0921352Z See the Inputs and Example below. 2024-06-26T05:54:27.0921438Z 2024-06-26T05:54:27.0921548Z Args: 2024-06-26T05:54:27.0921786Z kernel_size (int or tuple): Size of the max pooling window. 2024-06-26T05:54:27.0922042Z stride (int or tuple): Stride of the max pooling window. 2024-06-26T05:54:27.0922264Z It is set to :attr:`kernel_size` by default. 2024-06-26T05:54:27.0922515Z padding (int or tuple): Padding that was added to the input 2024-06-26T05:54:27.0922615Z 2024-06-26T05:54:27.0922712Z Inputs: 2024-06-26T05:54:27.0922917Z - `input`: the input Tensor to invert 2024-06-26T05:54:27.0923261Z - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d` 2024-06-26T05:54:27.0923515Z - `output_size` (optional): the targeted output size 2024-06-26T05:54:27.0923601Z 2024-06-26T05:54:27.0923712Z Shape: 2024-06-26T05:54:27.0924041Z - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. 2024-06-26T05:54:27.0924415Z - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where 2024-06-26T05:54:27.0924519Z 2024-06-26T05:54:27.0924623Z .. math:: 2024-06-26T05:54:27.0925121Z H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} 2024-06-26T05:54:27.0925223Z 2024-06-26T05:54:27.0925323Z .. math:: 2024-06-26T05:54:27.0925802Z W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} 2024-06-26T05:54:27.0925908Z 2024-06-26T05:54:27.0926134Z or as given by :attr:`output_size` in the call operator 2024-06-26T05:54:27.0926233Z 2024-06-26T05:54:27.0926336Z Example:: 2024-06-26T05:54:27.0926422Z 2024-06-26T05:54:27.0926654Z >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True) 2024-06-26T05:54:27.0926821Z >>> unpool = nn.MaxUnpool2d(2, stride=2) 2024-06-26T05:54:27.0926997Z >>> input = torch.tensor([[[[ 1., 2., 3., 4.], 2024-06-26T05:54:27.0927163Z [ 5., 6., 7., 8.], 2024-06-26T05:54:27.0927317Z [ 9., 10., 11., 12.], 2024-06-26T05:54:27.0927474Z [13., 14., 15., 16.]]]]) 2024-06-26T05:54:27.0927641Z >>> output, indices = pool(input) 2024-06-26T05:54:27.0927771Z >>> unpool(output, indices) 2024-06-26T05:54:27.0927911Z tensor([[[[ 0., 0., 0., 0.], 2024-06-26T05:54:27.0928059Z [ 0., 6., 0., 8.], 2024-06-26T05:54:27.0928190Z [ 0., 0., 0., 0.], 2024-06-26T05:54:27.0928323Z [ 0., 14., 0., 16.]]]]) 2024-06-26T05:54:27.0928623Z >>> # Now using output_size to resolve an ambiguous size for the inverse 2024-06-26T05:54:27.0928812Z >>> input = torch.tensor([[[[ 1., 2., 3., 4., 5.], 2024-06-26T05:54:27.0928985Z [ 6., 7., 8., 9., 10.], 2024-06-26T05:54:27.0929142Z [11., 12., 13., 14., 15.], 2024-06-26T05:54:27.0929303Z [16., 17., 18., 19., 20.]]]]) 2024-06-26T05:54:27.0929465Z >>> output, indices = pool(input) 2024-06-26T05:54:27.0929697Z >>> # This call will not work without specifying output_size 2024-06-26T05:54:27.0929900Z >>> unpool(output, indices, output_size=input.size()) 2024-06-26T05:54:27.0930082Z tensor([[[[ 0., 0., 0., 0., 0.], 2024-06-26T05:54:27.0930217Z [ 0., 7., 0., 9., 0.], 2024-06-26T05:54:27.0930348Z [ 0., 0., 0., 0., 0.], 2024-06-26T05:54:27.0930498Z [ 0., 17., 0., 19., 0.]]]]) 2024-06-26T05:54:27.0930585Z 2024-06-26T05:54:27.0930714Z 2024-06-26T05:54:27.0930803Z 2024-06-26T05:54:27.0931192Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0931293Z 2024-06-26T05:54:27.0931406Z warnings.warn(msg) 2024-06-26T05:54:27.0931521Z 2024-06-26T05:54:27.0931735Z --- Parse Warning: 70 / 90 --- 2024-06-26T05:54:27.0933098Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=EmbeddingBag in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py line=270. 2024-06-26T05:54:27.0933642Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0934170Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings. 2024-06-26T05:54:27.0934260Z 2024-06-26T05:54:27.0934701Z For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`, 2024-06-26T05:54:27.0934851Z and with 2D inputs, this class 2024-06-26T05:54:27.0934941Z 2024-06-26T05:54:27.0935382Z * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``, 2024-06-26T05:54:27.0935817Z * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``, 2024-06-26T05:54:27.0936241Z * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``. 2024-06-26T05:54:27.0936345Z 2024-06-26T05:54:27.0936827Z However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these 2024-06-26T05:54:27.0936930Z operations. 2024-06-26T05:54:27.0937027Z 2024-06-26T05:54:27.0937422Z EmbeddingBag also supports per-sample weights as an argument to the forward 2024-06-26T05:54:27.0937740Z pass. This scales the output of the Embedding before performing a weighted 2024-06-26T05:54:27.0938091Z reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the 2024-06-26T05:54:27.0938415Z only supported ``mode`` is ``"sum"``, which computes a weighted sum according to 2024-06-26T05:54:27.0938555Z :attr:`per_sample_weights`. 2024-06-26T05:54:27.0938644Z 2024-06-26T05:54:27.0938739Z Args: 2024-06-26T05:54:27.0938992Z num_embeddings (int): size of the dictionary of embeddings 2024-06-26T05:54:27.0939209Z embedding_dim (int): the size of each embedding vector 2024-06-26T05:54:27.0939639Z max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm` 2024-06-26T05:54:27.0939868Z is renormalized to have norm :attr:`max_norm`. 2024-06-26T05:54:27.0940436Z norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``. 2024-06-26T05:54:27.0940884Z scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of 2024-06-26T05:54:27.0941204Z the words in the mini-batch. Default ``False``. 2024-06-26T05:54:27.0941465Z Note: this option is not supported when ``mode="max"``. 2024-06-26T05:54:27.0941838Z mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag. 2024-06-26T05:54:27.0942139Z ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights` 2024-06-26T05:54:27.0942484Z into consideration. ``"mean"`` computes the average of the values 2024-06-26T05:54:27.0942751Z in the bag, ``"max"`` computes the max value over each bag. 2024-06-26T05:54:27.0942906Z Default: ``"mean"`` 2024-06-26T05:54:27.0943372Z sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See 2024-06-26T05:54:27.0943756Z Notes for more details regarding sparse gradients. Note: this option is not 2024-06-26T05:54:27.0943965Z supported when ``mode="max"``. 2024-06-26T05:54:27.0944512Z include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element 2024-06-26T05:54:27.0944830Z is equivalent to the size of `indices`. This matches the CSR format. 2024-06-26T05:54:27.0945282Z padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the 2024-06-26T05:54:27.0945660Z gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated 2024-06-26T05:54:27.0946003Z during training, i.e. it remains as a fixed "pad". For a newly constructed 2024-06-26T05:54:27.0946379Z EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all 2024-06-26T05:54:27.0946729Z zeros, but can be updated to another value to be used as the padding vector. 2024-06-26T05:54:27.0947077Z Note that the embedding vector at :attr:`padding_idx` is excluded from the 2024-06-26T05:54:27.0947229Z reduction. 2024-06-26T05:54:27.0947316Z 2024-06-26T05:54:27.0947421Z Attributes: 2024-06-26T05:54:27.0947865Z weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)` 2024-06-26T05:54:27.0948061Z initialized from :math:`\mathcal{N}(0, 1)`. 2024-06-26T05:54:27.0948165Z 2024-06-26T05:54:27.0948271Z Examples:: 2024-06-26T05:54:27.0948360Z 2024-06-26T05:54:27.0948605Z >>> # an EmbeddingBag module containing 10 tensors of size 3 2024-06-26T05:54:27.0948874Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum') 2024-06-26T05:54:27.0949045Z >>> # a batch of 2 samples of 4 indices each 2024-06-26T05:54:27.0949319Z >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long) 2024-06-26T05:54:27.0949513Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2024-06-26T05:54:27.0949755Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-06-26T05:54:27.0949913Z >>> embedding_sum(input, offsets) 2024-06-26T05:54:27.0950104Z tensor([[-0.8861, -5.4350, -0.0523], 2024-06-26T05:54:27.0950288Z [ 1.1306, -2.5798, -1.0044]]) 2024-06-26T05:54:27.0950391Z 2024-06-26T05:54:27.0950528Z >>> # Example with padding_idx 2024-06-26T05:54:27.0950870Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2) 2024-06-26T05:54:27.0951140Z >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long) 2024-06-26T05:54:27.0951333Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2024-06-26T05:54:27.0951488Z >>> embedding_sum(input, offsets) 2024-06-26T05:54:27.0951624Z tensor([[ 0.0000, 0.0000, 0.0000], 2024-06-26T05:54:27.0951804Z [-0.7082, 3.2145, -2.6251]]) 2024-06-26T05:54:27.0951907Z 2024-06-26T05:54:27.0952141Z >>> # An EmbeddingBag can be loaded from an Embedding like so 2024-06-26T05:54:27.0952336Z >>> embedding = nn.Embedding(10, 3, padding_idx=2) 2024-06-26T05:54:27.0952584Z >>> embedding_sum = nn.EmbeddingBag.from_pretrained( 2024-06-26T05:54:27.0952711Z embedding.weight, 2024-06-26T05:54:27.0952880Z padding_idx=embedding.padding_idx, 2024-06-26T05:54:27.0953037Z mode='sum') 2024-06-26T05:54:27.0953129Z 2024-06-26T05:54:27.0953543Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0953644Z 2024-06-26T05:54:27.0953756Z warnings.warn(msg) 2024-06-26T05:54:27.0953857Z 2024-06-26T05:54:27.0954083Z --- Parse Warning: 71 / 90 --- 2024-06-26T05:54:27.0955604Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedDataParallel.join in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py line=1743. 2024-06-26T05:54:27.0956021Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0956113Z 2024-06-26T05:54:27.0956423Z Context manager for training with uneven inputs across processes in DDP. 2024-06-26T05:54:27.0956526Z 2024-06-26T05:54:27.0956879Z This context manager will keep track of already-joined DDP processes, 2024-06-26T05:54:27.0957159Z and "shadow" the forward and backward passes by inserting collective 2024-06-26T05:54:27.0957528Z communication operations to match with the ones created by non-joined 2024-06-26T05:54:27.0957833Z DDP processes. This will ensure each collective call has a corresponding 2024-06-26T05:54:27.0958202Z call by already-joined DDP processes, preventing hangs or errors that 2024-06-26T05:54:27.0958458Z would otherwise happen when training with uneven inputs across 2024-06-26T05:54:27.0958753Z processes. Alternatively, if the flag ``throw_on_early_termination`` is 2024-06-26T05:54:27.0959055Z specified to be ``True``, all trainers will throw an error once one rank 2024-06-26T05:54:27.0959327Z runs out of inputs, allowing these errors to be caught and handled 2024-06-26T05:54:27.0959459Z according to application logic. 2024-06-26T05:54:27.0959560Z 2024-06-26T05:54:27.0959851Z Once all DDP processes have joined, the context manager will broadcast 2024-06-26T05:54:27.0960149Z the model corresponding to the last joined process to all processes to 2024-06-26T05:54:27.0960353Z ensure the model is the same across all processes 2024-06-26T05:54:27.0960479Z (which is guaranteed by DDP). 2024-06-26T05:54:27.0960566Z 2024-06-26T05:54:27.0960958Z To use this to enable training with uneven inputs across processes, 2024-06-26T05:54:27.0961251Z simply wrap this context manager around your training loop. No further 2024-06-26T05:54:27.0961482Z modifications to the model or data loading is required. 2024-06-26T05:54:27.0961571Z 2024-06-26T05:54:27.0961678Z .. warning:: 2024-06-26T05:54:27.0961983Z If the model or training loop this context manager is wrapped around 2024-06-26T05:54:27.0962216Z has additional distributed collective operations, such as 2024-06-26T05:54:27.0962518Z ``SyncBatchNorm`` in the model's forward pass, then the flag 2024-06-26T05:54:27.0962799Z ``throw_on_early_termination`` must be enabled. This is because this 2024-06-26T05:54:27.0963136Z context manager is not aware of non-DDP collective communication. 2024-06-26T05:54:27.0963364Z This flag will cause all ranks to throw when any one rank 2024-06-26T05:54:27.0963648Z exhausts inputs, allowing these errors to be caught and recovered 2024-06-26T05:54:27.0963765Z from across all ranks. 2024-06-26T05:54:27.0963866Z 2024-06-26T05:54:27.0963965Z Args: 2024-06-26T05:54:27.0964204Z divide_by_initial_world_size (bool): If ``True``, will divide 2024-06-26T05:54:27.0964489Z gradients by the initial ``world_size`` DDP training was launched 2024-06-26T05:54:27.0964762Z with. If ``False``, will compute the effective world size 2024-06-26T05:54:27.0965013Z (number of ranks that have not depleted their inputs yet) and 2024-06-26T05:54:27.0965215Z divide gradients by that during allreduce. Set 2024-06-26T05:54:27.0965445Z ``divide_by_initial_world_size=True`` to ensure every input 2024-06-26T05:54:27.0965755Z sample including the uneven inputs have equal weight in terms of 2024-06-26T05:54:27.0965998Z how much they contribute to the global gradient. This is 2024-06-26T05:54:27.0966228Z achieved by always dividing the gradient by the initial 2024-06-26T05:54:27.0966555Z ``world_size`` even when we encounter uneven inputs. If you set 2024-06-26T05:54:27.0966803Z this to ``False``, we divide the gradient by the remaining 2024-06-26T05:54:27.0967066Z number of nodes. This ensures parity with training on a smaller 2024-06-26T05:54:27.0967319Z ``world_size`` although it also means the uneven inputs would 2024-06-26T05:54:27.0967568Z contribute more towards the global gradient. Typically, you 2024-06-26T05:54:27.0967822Z would want to set this to ``True`` for cases where the last few 2024-06-26T05:54:27.0968097Z inputs of your training job are uneven. In extreme cases, where 2024-06-26T05:54:27.0968354Z there is a large discrepancy in the number of inputs, setting 2024-06-26T05:54:27.0968537Z this to ``False`` might provide better results. 2024-06-26T05:54:27.0968834Z enable (bool): Whether to enable uneven input detection or not. Pass 2024-06-26T05:54:27.0969070Z in ``enable=False`` to disable in cases where you know that 2024-06-26T05:54:27.0969331Z inputs are even across participating processes. Default is 2024-06-26T05:54:27.0969429Z ``True``. 2024-06-26T05:54:27.0969666Z throw_on_early_termination (bool): Whether to throw an error 2024-06-26T05:54:27.0969916Z or continue training when at least one rank has exhausted 2024-06-26T05:54:27.0970175Z inputs. If ``True``, will throw upon the first rank reaching end 2024-06-26T05:54:27.0970411Z of data. If ``False``, will continue training with a smaller 2024-06-26T05:54:27.0970682Z effective world size until all ranks are joined. Note that if 2024-06-26T05:54:27.0970841Z this flag is specified, then the flag 2024-06-26T05:54:27.0971065Z ``divide_by_initial_world_size`` would be ignored. Default 2024-06-26T05:54:27.0971188Z is ``False``. 2024-06-26T05:54:27.0971279Z 2024-06-26T05:54:27.0971366Z 2024-06-26T05:54:27.0971479Z Example:: 2024-06-26T05:54:27.0971568Z 2024-06-26T05:54:27.0971717Z >>> # xdoctest: +SKIP("Distributed") 2024-06-26T05:54:27.0971839Z >>> import torch 2024-06-26T05:54:27.0971989Z >>> import torch.distributed as dist 2024-06-26T05:54:27.0972101Z >>> import os 2024-06-26T05:54:27.0972264Z >>> import torch.multiprocessing as mp 2024-06-26T05:54:27.0972383Z >>> import torch.nn as nn 2024-06-26T05:54:27.0972520Z >>> # On each spawned worker 2024-06-26T05:54:27.0972633Z >>> def worker(rank): 2024-06-26T05:54:27.0972863Z >>> dist.init_process_group("nccl", rank=rank, world_size=2) 2024-06-26T05:54:27.0973014Z >>> torch.cuda.set_device(rank) 2024-06-26T05:54:27.0973191Z >>> model = nn.Linear(1, 1, bias=False).to(rank) 2024-06-26T05:54:27.0973429Z >>> model = torch.nn.parallel.DistributedDataParallel( 2024-06-26T05:54:27.0973756Z >>> model, device_ids=[rank], output_device=rank 2024-06-26T05:54:27.0973856Z >>> ) 2024-06-26T05:54:27.0974029Z >>> # Rank 1 gets one more input than rank 0. 2024-06-26T05:54:27.0974296Z >>> inputs = [torch.tensor([1]).float() for _ in range(10 + rank)] 2024-06-26T05:54:27.0974414Z >>> with model.join(): 2024-06-26T05:54:27.0974584Z >>> for _ in range(5): 2024-06-26T05:54:27.0974731Z >>> for inp in inputs: 2024-06-26T05:54:27.0974879Z >>> loss = model(inp).sum() 2024-06-26T05:54:27.0975021Z >>> loss.backward() 2024-06-26T05:54:27.0975278Z >>> # Without the join() API, the below synchronization will hang 2024-06-26T05:54:27.0975576Z >>> # blocking for rank 1's allreduce to complete. 2024-06-26T05:54:27.0975752Z >>> torch.cuda.synchronize(device=rank) 2024-06-26T05:54:27.0975842Z 2024-06-26T05:54:27.0976271Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0976374Z 2024-06-26T05:54:27.0976518Z warnings.warn(msg) 2024-06-26T05:54:27.0976606Z 2024-06-26T05:54:27.0976823Z --- Parse Warning: 72 / 90 --- 2024-06-26T05:54:27.0978402Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedDataParallel._register_fused_optim in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py line=2034. 2024-06-26T05:54:27.0978820Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0978909Z 2024-06-26T05:54:27.0979321Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction. 2024-06-26T05:54:27.0979424Z 2024-06-26T05:54:27.0979696Z Registers an optimizer with DDP such that the optimization for a 2024-06-26T05:54:27.0980014Z parameter will run immediately when that parameter's gradient is 2024-06-26T05:54:27.0980289Z finished with reduction, instead of waiting for all parameters' 2024-06-26T05:54:27.0980573Z gradients to finish reduction. This can result in a training speedup 2024-06-26T05:54:27.0980863Z depending on your workload since the optimizer can run while gradient 2024-06-26T05:54:27.0981177Z reduction for other parameters are still ongoing. In addition, this has 2024-06-26T05:54:27.0981472Z the potential to reduce peak memory consumption during training, as it 2024-06-26T05:54:27.0981811Z only needs to load the per-parameter optimizer states of a single 2024-06-26T05:54:27.0982149Z parameter at a time, instead of loading all per-parameter optimizer 2024-06-26T05:54:27.0982256Z states at once. 2024-06-26T05:54:27.0982358Z 2024-06-26T05:54:27.0982453Z Args: 2024-06-26T05:54:27.0982718Z optim (Type): a ``torch.optim.Optimizer`` class to be registered 2024-06-26T05:54:27.0982852Z as a fused optimizer. 2024-06-26T05:54:27.0983063Z *args (Sequence[Any]): Arguments to forward to `optim`. 2024-06-26T05:54:27.0983337Z optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters 2024-06-26T05:54:27.0983653Z to optimize, similar to `params` argument of traditional `torch.optim` 2024-06-26T05:54:27.0983921Z Optimizers. If this is omitted, all DDP model parameters will be 2024-06-26T05:54:27.0984022Z optimized. 2024-06-26T05:54:27.0984308Z **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`. 2024-06-26T05:54:27.0984399Z 2024-06-26T05:54:27.0984502Z .. warning :: 2024-06-26T05:54:27.0984789Z _register_fused_optim should only be called once on a DDP instance, 2024-06-26T05:54:27.0985060Z and registering multiple fused optimizers for the same DDP model 2024-06-26T05:54:27.0985236Z is not currently supported. Please ping 2024-06-26T05:54:27.0985536Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-06-26T05:54:27.0985653Z for your use case. 2024-06-26T05:54:27.0985757Z 2024-06-26T05:54:27.0985858Z .. warning :: 2024-06-26T05:54:27.0986103Z _register_fused_optim and register_comm_hook currently do not 2024-06-26T05:54:27.0986394Z compose together, meaning that custom DDP communication hooks are 2024-06-26T05:54:27.0986642Z not supported with overlapped optimizers. Please ping 2024-06-26T05:54:27.0986939Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-06-26T05:54:27.0987067Z for your use case. 2024-06-26T05:54:27.0987155Z 2024-06-26T05:54:27.0987256Z .. warning :: 2024-06-26T05:54:27.0987556Z Gradient accumulation and DDP `no_sync` are currently not supported 2024-06-26T05:54:27.0987744Z with overlapped optimizer. Please ping 2024-06-26T05:54:27.0988049Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-06-26T05:54:27.0988188Z for your use case. 2024-06-26T05:54:27.0988276Z 2024-06-26T05:54:27.0988389Z Example:: 2024-06-26T05:54:27.0988504Z 2024-06-26T05:54:27.0988677Z >>> # xdoctest: +SKIP("No rendezvous handler") 2024-06-26T05:54:27.0989153Z >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...') 2024-06-26T05:54:27.0989428Z >>> net = torch.nn.parallel.DistributedDataParallel(model, pg) 2024-06-26T05:54:27.0989543Z >>> lr = 1e-2 2024-06-26T05:54:27.0989671Z >>> betas = (0.9, 0.99) 2024-06-26T05:54:27.0989788Z >>> eps = 1e-6 2024-06-26T05:54:27.0990083Z >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps) 2024-06-26T05:54:27.0990254Z >>> # Example with subset of parameters 2024-06-26T05:54:27.0990432Z >>> params_to_opt = [list(net.parameters())[0]] 2024-06-26T05:54:27.0990562Z >>> net._register_fused_optim( 2024-06-26T05:54:27.0990889Z ... torch.optim.Adam, lr, optim_params=params_to_opt, betas=betas, eps=eps 2024-06-26T05:54:27.0990984Z ... ) 2024-06-26T05:54:27.0991087Z 2024-06-26T05:54:27.0991473Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.0991562Z 2024-06-26T05:54:27.0991687Z warnings.warn(msg) 2024-06-26T05:54:27.0991775Z 2024-06-26T05:54:27.0991976Z --- Parse Warning: 73 / 90 --- 2024-06-26T05:54:27.0993437Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=convert_conv2d_weight_memory_format in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/memory_format.py line=6. 2024-06-26T05:54:27.0993837Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.0994117Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``. 2024-06-26T05:54:27.0994220Z 2024-06-26T05:54:27.0994584Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2024-06-26T05:54:27.0994976Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2024-06-26T05:54:27.0995325Z This function is used to facilitate the computation to adopt NHWC kernels, which 2024-06-26T05:54:27.0995747Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2024-06-26T05:54:27.0995848Z 2024-06-26T05:54:27.0995946Z .. note:: 2024-06-26T05:54:27.0996257Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2024-06-26T05:54:27.0996566Z than the utility function ``convert_conv2d_weight_memory_format``. Any 2024-06-26T05:54:27.0996863Z layer with 4d weight will be affected by ``model.to``, which does not 2024-06-26T05:54:27.0997150Z necessarily benefit from conversion to specified ``memory_format``. 2024-06-26T05:54:27.0997470Z One place we are confident in is that NHWC(channels_last) conversion for 2024-06-26T05:54:27.0997767Z convolution in cuDNN, As it is beneficial to run convolution in NHWC, 2024-06-26T05:54:27.0998063Z even in cases where we have to apply permutation to input tensors. 2024-06-26T05:54:27.0998151Z 2024-06-26T05:54:27.0998455Z Hence our strategy here is to convert only the weight of convolution to 2024-06-26T05:54:27.0998650Z channels_last. This ensures that; 2024-06-26T05:54:27.0998942Z 1. Fast convolution kernels will be used, the benefit of which could 2024-06-26T05:54:27.0999238Z outweigh overhead of permutation (if input is not in the same format) 2024-06-26T05:54:27.0999562Z 2. No unnecessary permutations are applied on layers that do not benefit 2024-06-26T05:54:27.0999741Z from memory_format conversion. 2024-06-26T05:54:27.0999828Z 2024-06-26T05:54:27.1000153Z The optimal case is that, layers between convolution layers are channels 2024-06-26T05:54:27.1000522Z last compatible. Input tensor would be permuted to channels last when it 2024-06-26T05:54:27.1000902Z encounters the first convolution layer and stay in that memory format. 2024-06-26T05:54:27.1001211Z Hence following convolutions will not need to permute its input tensor. 2024-06-26T05:54:27.1001298Z 2024-06-26T05:54:27.1001613Z In case where a channels last incompatible layer is between convolution 2024-06-26T05:54:27.1001902Z layers, we need to permute the input tensor back to contiguous format 2024-06-26T05:54:27.1002210Z for that layer. The input tensor will go through the remaining layers in 2024-06-26T05:54:27.1002526Z contiguous format and be permuted to channels last when it encounters 2024-06-26T05:54:27.1002868Z another convolution layer. There's no point in propagating that 2024-06-26T05:54:27.1003163Z permutation to an earlier layer, as most layers are quite agnostic to 2024-06-26T05:54:27.1003289Z ``memory_format``. 2024-06-26T05:54:27.1003380Z 2024-06-26T05:54:27.1003704Z This claim might change when PyTorch supports fusion of permutation, as 2024-06-26T05:54:27.1004007Z there might have been a better spot to fuse the permutation other than 2024-06-26T05:54:27.1004163Z immediately before a convolution. 2024-06-26T05:54:27.1004267Z 2024-06-26T05:54:27.1004364Z Args: 2024-06-26T05:54:27.1004656Z module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container 2024-06-26T05:54:27.1004797Z ``nn.Module`` 2024-06-26T05:54:27.1004988Z memory_format: user specified ``memory_format``, 2024-06-26T05:54:27.1005235Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2024-06-26T05:54:27.1005334Z 2024-06-26T05:54:27.1005430Z Returns: 2024-06-26T05:54:27.1005614Z The original module with updated ``nn.Conv2d`` 2024-06-26T05:54:27.1005717Z 2024-06-26T05:54:27.1005815Z Example: 2024-06-26T05:54:27.1005998Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-06-26T05:54:27.1006213Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2024-06-26T05:54:27.1006535Z >>> input = torch.randint(1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda") 2024-06-26T05:54:27.1006676Z >>> model = nn.Sequential( 2024-06-26T05:54:27.1006831Z >>> nn.Conv2d(8, 4, 3)).cuda().half() 2024-06-26T05:54:27.1006957Z >>> # This is identical to: 2024-06-26T05:54:27.1007291Z >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2024-06-26T05:54:27.1007645Z >>> model = nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2024-06-26T05:54:27.1007759Z >>> out = model(input) 2024-06-26T05:54:27.1007865Z 2024-06-26T05:54:27.1008256Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.1008344Z 2024-06-26T05:54:27.1008472Z warnings.warn(msg) 2024-06-26T05:54:27.1008560Z 2024-06-26T05:54:27.1008759Z --- Parse Warning: 74 / 90 --- 2024-06-26T05:54:27.1010281Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=convert_conv3d_weight_memory_format in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/memory_format.py line=81. 2024-06-26T05:54:27.1010684Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.1010971Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format`` 2024-06-26T05:54:27.1011354Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2024-06-26T05:54:27.1011733Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2024-06-26T05:54:27.1012128Z This function is used to facilitate the computation to adopt NHWC kernels, which 2024-06-26T05:54:27.1012572Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2024-06-26T05:54:27.1012663Z 2024-06-26T05:54:27.1012778Z .. note:: 2024-06-26T05:54:27.1013094Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2024-06-26T05:54:27.1013401Z than the utility function ``convert_conv3d_weight_memory_format``. Any 2024-06-26T05:54:27.1013813Z layer with 4d weight will be affected by ``model.to``, which does not 2024-06-26T05:54:27.1014102Z necessarily benefit from conversion to specified ``memory_format``. 2024-06-26T05:54:27.1014425Z One place we are confident in is that NHWC(channels_last) conversion for 2024-06-26T05:54:27.1014719Z convolution in cuDNN, As it is beneficial to run convolution in NHWC, 2024-06-26T05:54:27.1015003Z even in cases where we have to apply permutation to input tensors. 2024-06-26T05:54:27.1015102Z 2024-06-26T05:54:27.1015410Z Hence our strategy here is to convert only the weight of convolution to 2024-06-26T05:54:27.1015559Z channels_last. This ensures that; 2024-06-26T05:54:27.1015860Z 1. Fast convolution kernels will be used, the benefit of which could 2024-06-26T05:54:27.1016160Z outweigh overhead of permutation (if input is not in the same format) 2024-06-26T05:54:27.1016488Z 2. No unnecessary permutations are applied on layers that do not benefit 2024-06-26T05:54:27.1016628Z from memory_format conversion. 2024-06-26T05:54:27.1016717Z 2024-06-26T05:54:27.1017043Z The optimal case is that, layers between convolution layers are channels 2024-06-26T05:54:27.1017352Z last compatible. Input tensor would be permuted to channels last when it 2024-06-26T05:54:27.1017655Z encounters the first convolution layer and stay in that memory format. 2024-06-26T05:54:27.1017978Z Hence following convolutions will not need to permute its input tensor. 2024-06-26T05:54:27.1018066Z 2024-06-26T05:54:27.1018369Z In case where a channels last incompatible layer is between convolution 2024-06-26T05:54:27.1018672Z layers, we need to permute the input tensor back to contiguous format 2024-06-26T05:54:27.1018982Z for that layer. The input tensor will go through the remaining layers in 2024-06-26T05:54:27.1019298Z contiguous format and be permuted to channels last when it encounters 2024-06-26T05:54:27.1019635Z another convolution layer. There's no point in propagating that 2024-06-26T05:54:27.1019932Z permutation to an earlier layer, as most layers are quite agnostic to 2024-06-26T05:54:27.1020060Z ``memory_format``. 2024-06-26T05:54:27.1020149Z 2024-06-26T05:54:27.1020463Z This claim might change when PyTorch supports fusion of permutation, as 2024-06-26T05:54:27.1020779Z there might have been a better spot to fuse the permutation other than 2024-06-26T05:54:27.1020934Z immediately before a convolution. 2024-06-26T05:54:27.1021023Z 2024-06-26T05:54:27.1021129Z Args: 2024-06-26T05:54:27.1021475Z module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container 2024-06-26T05:54:27.1021612Z ``nn.Module`` 2024-06-26T05:54:27.1021820Z memory_format: user specified ``memory_format``, 2024-06-26T05:54:27.1022065Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2024-06-26T05:54:27.1022167Z 2024-06-26T05:54:27.1022298Z Returns: 2024-06-26T05:54:27.1022488Z The original module with updated ``nn.Conv3d`` 2024-06-26T05:54:27.1022588Z 2024-06-26T05:54:27.1022688Z Example: 2024-06-26T05:54:27.1022873Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-06-26T05:54:27.1023125Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2024-06-26T05:54:27.1023493Z >>> input = torch.randint(1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda") 2024-06-26T05:54:27.1023622Z >>> model = nn.Sequential( 2024-06-26T05:54:27.1023792Z >>> nn.Conv3d(8, 4, 3)).cuda().half() 2024-06-26T05:54:27.1023921Z >>> # This is identical to: 2024-06-26T05:54:27.1024239Z >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last) 2024-06-26T05:54:27.1024605Z >>> model = nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last) 2024-06-26T05:54:27.1024722Z >>> out = model(input) 2024-06-26T05:54:27.1024832Z 2024-06-26T05:54:27.1025222Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.1025312Z 2024-06-26T05:54:27.1025437Z warnings.warn(msg) 2024-06-26T05:54:27.1025526Z 2024-06-26T05:54:27.1025727Z --- Parse Warning: 75 / 90 --- 2024-06-26T05:54:27.1027069Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=random_structured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=937. 2024-06-26T05:54:27.1027468Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.1027770Z Prune tensor by removing random channels along the specified dimension. 2024-06-26T05:54:27.1027871Z 2024-06-26T05:54:27.1028174Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2024-06-26T05:54:27.1028479Z by removing the specified ``amount`` of (currently unpruned) channels 2024-06-26T05:54:27.1028663Z along the specified ``dim`` selected at random. 2024-06-26T05:54:27.1028923Z Modifies module in place (and also return the modified module) 2024-06-26T05:54:27.1029033Z by: 2024-06-26T05:54:27.1029142Z 2024-06-26T05:54:27.1029492Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-06-26T05:54:27.1029795Z binary mask applied to the parameter ``name`` by the pruning method. 2024-06-26T05:54:27.1030080Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-06-26T05:54:27.1030350Z original (unpruned) parameter is stored in a new parameter named 2024-06-26T05:54:27.1030503Z ``name+'_orig'``. 2024-06-26T05:54:27.1030591Z 2024-06-26T05:54:27.1030686Z Args: 2024-06-26T05:54:27.1030935Z module (nn.Module): module containing the tensor to prune 2024-06-26T05:54:27.1031181Z name (str): parameter name within ``module`` on which pruning 2024-06-26T05:54:27.1031288Z will act. 2024-06-26T05:54:27.1031527Z amount (int or float): quantity of parameters to prune. 2024-06-26T05:54:27.1031770Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-06-26T05:54:27.1032051Z fraction of parameters to prune. If ``int``, it represents the 2024-06-26T05:54:27.1032222Z absolute number of parameters to prune. 2024-06-26T05:54:27.1032501Z dim (int): index of the dim along which we define channels to prune. 2024-06-26T05:54:27.1032602Z 2024-06-26T05:54:27.1032727Z Returns: 2024-06-26T05:54:27.1033026Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-06-26T05:54:27.1033127Z 2024-06-26T05:54:27.1033226Z Examples: 2024-06-26T05:54:27.1033344Z >>> # xdoctest: +SKIP 2024-06-26T05:54:27.1033505Z >>> m = prune.random_structured( 2024-06-26T05:54:27.1033774Z ... nn.Linear(5, 3), 'weight', amount=3, dim=1 2024-06-26T05:54:27.1033872Z ... ) 2024-06-26T05:54:27.1034124Z >>> columns_pruned = int(sum(torch.sum(m.weight, dim=0) == 0)) 2024-06-26T05:54:27.1034276Z >>> print(columns_pruned) 2024-06-26T05:54:27.1034384Z 3 2024-06-26T05:54:27.1034508Z 2024-06-26T05:54:27.1034895Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.1035001Z 2024-06-26T05:54:27.1035114Z warnings.warn(msg) 2024-06-26T05:54:27.1035201Z 2024-06-26T05:54:27.1035417Z --- Parse Warning: 76 / 90 --- 2024-06-26T05:54:27.1036718Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ln_structured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=978. 2024-06-26T05:54:27.1037117Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.1037629Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension. 2024-06-26T05:54:27.1037718Z 2024-06-26T05:54:27.1038024Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2024-06-26T05:54:27.1038334Z by removing the specified ``amount`` of (currently unpruned) channels 2024-06-26T05:54:27.1038621Z along the specified ``dim`` with the lowest L\ ``n``-norm. 2024-06-26T05:54:27.1038893Z Modifies module in place (and also return the modified module) 2024-06-26T05:54:27.1038986Z by: 2024-06-26T05:54:27.1039076Z 2024-06-26T05:54:27.1039436Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-06-26T05:54:27.1039727Z binary mask applied to the parameter ``name`` by the pruning method. 2024-06-26T05:54:27.1040009Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-06-26T05:54:27.1040295Z original (unpruned) parameter is stored in a new parameter named 2024-06-26T05:54:27.1040434Z ``name+'_orig'``. 2024-06-26T05:54:27.1040522Z 2024-06-26T05:54:27.1040706Z Args: 2024-06-26T05:54:27.1040945Z module (nn.Module): module containing the tensor to prune 2024-06-26T05:54:27.1041192Z name (str): parameter name within ``module`` on which pruning 2024-06-26T05:54:27.1041313Z will act. 2024-06-26T05:54:27.1041537Z amount (int or float): quantity of parameters to prune. 2024-06-26T05:54:27.1041794Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-06-26T05:54:27.1042059Z fraction of parameters to prune. If ``int``, it represents the 2024-06-26T05:54:27.1042233Z absolute number of parameters to prune. 2024-06-26T05:54:27.1042581Z n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid 2024-06-26T05:54:27.1042785Z entries for argument ``p`` in :func:`torch.norm`. 2024-06-26T05:54:27.1043067Z dim (int): index of the dim along which we define channels to prune. 2024-06-26T05:54:27.1043384Z importance_scores (torch.Tensor): tensor of importance scores (of same 2024-06-26T05:54:27.1043647Z shape as module parameter) used to compute mask for pruning. 2024-06-26T05:54:27.1043952Z The values in this tensor indicate the importance of the corresponding 2024-06-26T05:54:27.1044137Z elements in the parameter being pruned. 2024-06-26T05:54:27.1044486Z If unspecified or None, the module parameter will be used in its place. 2024-06-26T05:54:27.1044592Z 2024-06-26T05:54:27.1044690Z Returns: 2024-06-26T05:54:27.1044988Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-06-26T05:54:27.1045095Z 2024-06-26T05:54:27.1045195Z Examples: 2024-06-26T05:54:27.1045378Z >>> from torch.nn.utils import prune 2024-06-26T05:54:27.1045523Z >>> m = prune.ln_structured( 2024-06-26T05:54:27.1045854Z ... nn.Conv2d(5, 3, 2), 'weight', amount=0.3, dim=1, n=float('-inf') 2024-06-26T05:54:27.1045982Z ... ) 2024-06-26T05:54:27.1046086Z 2024-06-26T05:54:27.1046497Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.1046587Z 2024-06-26T05:54:27.1046709Z warnings.warn(msg) 2024-06-26T05:54:27.1046797Z 2024-06-26T05:54:27.1046996Z --- Parse Warning: 77 / 90 --- 2024-06-26T05:54:27.1048369Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=global_unstructured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=1025. 2024-06-26T05:54:27.1048768Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.1048871Z 2024-06-26T05:54:27.1049427Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``. 2024-06-26T05:54:27.1049515Z 2024-06-26T05:54:27.1049655Z Modifies modules in place by: 2024-06-26T05:54:27.1049744Z 2024-06-26T05:54:27.1050091Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-06-26T05:54:27.1050392Z binary mask applied to the parameter ``name`` by the pruning method. 2024-06-26T05:54:27.1050669Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-06-26T05:54:27.1050937Z original (unpruned) parameter is stored in a new parameter named 2024-06-26T05:54:27.1051087Z ``name+'_orig'``. 2024-06-26T05:54:27.1051177Z 2024-06-26T05:54:27.1051271Z Args: 2024-06-26T05:54:27.1051537Z parameters (Iterable of (module, name) tuples): parameters of 2024-06-26T05:54:27.1051806Z the model to prune in a global fashion, i.e. by aggregating all 2024-06-26T05:54:27.1052093Z weights prior to deciding which ones to prune. module must be of 2024-06-26T05:54:27.1052291Z type :class:`nn.Module`, and name must be a string. 2024-06-26T05:54:27.1052580Z pruning_method (function): a valid pruning function from this module, 2024-06-26T05:54:27.1052836Z or a custom one implemented by the user that satisfies the 2024-06-26T05:54:27.1053190Z implementation guidelines and has ``PRUNING_TYPE='unstructured'``. 2024-06-26T05:54:27.1053619Z importance_scores (dict): a dictionary mapping (module, name) tuples to 2024-06-26T05:54:27.1053985Z the corresponding parameter's importance scores tensor. The tensor 2024-06-26T05:54:27.1054278Z should be the same shape as the parameter, and is used for computing 2024-06-26T05:54:27.1054408Z mask for pruning. 2024-06-26T05:54:27.1054689Z If unspecified or None, the parameter will be used in place of its 2024-06-26T05:54:27.1054805Z importance scores. 2024-06-26T05:54:27.1054980Z kwargs: other keyword arguments such as: 2024-06-26T05:54:27.1055252Z amount (int or float): quantity of parameters to prune across the 2024-06-26T05:54:27.1055375Z specified parameters. 2024-06-26T05:54:27.1055629Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-06-26T05:54:27.1055889Z fraction of parameters to prune. If ``int``, it represents the 2024-06-26T05:54:27.1056052Z absolute number of parameters to prune. 2024-06-26T05:54:27.1056154Z 2024-06-26T05:54:27.1056247Z Raises: 2024-06-26T05:54:27.1056537Z TypeError: if ``PRUNING_TYPE != 'unstructured'`` 2024-06-26T05:54:27.1056640Z 2024-06-26T05:54:27.1056736Z Note: 2024-06-26T05:54:27.1057075Z Since global structured pruning doesn't make much sense unless the 2024-06-26T05:54:27.1057357Z norm is normalized by the size of the parameter, we now limit the 2024-06-26T05:54:27.1057595Z scope of global pruning to unstructured methods. 2024-06-26T05:54:27.1057697Z 2024-06-26T05:54:27.1057797Z Examples: 2024-06-26T05:54:27.1057947Z >>> from torch.nn.utils import prune 2024-06-26T05:54:27.1058168Z >>> from collections import OrderedDict 2024-06-26T05:54:27.1058349Z >>> net = nn.Sequential(OrderedDict([ 2024-06-26T05:54:27.1058532Z ... ('first', nn.Linear(10, 4)), 2024-06-26T05:54:27.1058723Z ... ('second', nn.Linear(4, 1)), 2024-06-26T05:54:27.1058817Z ... ])) 2024-06-26T05:54:27.1058941Z >>> parameters_to_prune = ( 2024-06-26T05:54:27.1059118Z ... (net.first, 'weight'), 2024-06-26T05:54:27.1059282Z ... (net.second, 'weight'), 2024-06-26T05:54:27.1059374Z ... ) 2024-06-26T05:54:27.1059518Z >>> prune.global_unstructured( 2024-06-26T05:54:27.1059640Z ... parameters_to_prune, 2024-06-26T05:54:27.1059815Z ... pruning_method=prune.L1Unstructured, 2024-06-26T05:54:27.1059941Z ... amount=10, 2024-06-26T05:54:27.1060035Z ... ) 2024-06-26T05:54:27.1060332Z >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0)) 2024-06-26T05:54:27.1060448Z tensor(10) 2024-06-26T05:54:27.1060536Z 2024-06-26T05:54:27.1060622Z 2024-06-26T05:54:27.1061024Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.1061111Z 2024-06-26T05:54:27.1061240Z warnings.warn(msg) 2024-06-26T05:54:27.1061327Z 2024-06-26T05:54:27.1061528Z --- Parse Warning: 78 / 90 --- 2024-06-26T05:54:27.1062873Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=custom_from_mask in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=1144. 2024-06-26T05:54:27.1063272Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.1063883Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``. 2024-06-26T05:54:27.1063984Z 2024-06-26T05:54:27.1064264Z Modifies module in place (and also return the modified module) by: 2024-06-26T05:54:27.1064355Z 2024-06-26T05:54:27.1064725Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-06-26T05:54:27.1065018Z binary mask applied to the parameter ``name`` by the pruning method. 2024-06-26T05:54:27.1065319Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-06-26T05:54:27.1065589Z original (unpruned) parameter is stored in a new parameter named 2024-06-26T05:54:27.1065732Z ``name+'_orig'``. 2024-06-26T05:54:27.1065833Z 2024-06-26T05:54:27.1065929Z Args: 2024-06-26T05:54:27.1066166Z module (nn.Module): module containing the tensor to prune 2024-06-26T05:54:27.1066428Z name (str): parameter name within ``module`` on which pruning 2024-06-26T05:54:27.1066531Z will act. 2024-06-26T05:54:27.1066772Z mask (Tensor): binary mask to be applied to the parameter. 2024-06-26T05:54:27.1066874Z 2024-06-26T05:54:27.1066972Z Returns: 2024-06-26T05:54:27.1067270Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-06-26T05:54:27.1067371Z 2024-06-26T05:54:27.1067470Z Examples: 2024-06-26T05:54:27.1067626Z >>> from torch.nn.utils import prune 2024-06-26T05:54:27.1067779Z >>> m = prune.custom_from_mask( 2024-06-26T05:54:27.1068118Z ... nn.Linear(5, 3), name='bias', mask=torch.tensor([0, 1, 0]) 2024-06-26T05:54:27.1068227Z ... ) 2024-06-26T05:54:27.1068344Z >>> print(m.bias_mask) 2024-06-26T05:54:27.1068456Z tensor([0., 1., 0.]) 2024-06-26T05:54:27.1068558Z 2024-06-26T05:54:27.1068649Z 2024-06-26T05:54:27.1069062Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.1069165Z 2024-06-26T05:54:27.1069276Z warnings.warn(msg) 2024-06-26T05:54:27.1069364Z 2024-06-26T05:54:27.1069607Z --- Parse Warning: 79 / 90 --- 2024-06-26T05:54:27.1070961Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=AveragedModel in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py line=103. 2024-06-26T05:54:27.1071360Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.1071845Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA). 2024-06-26T05:54:27.1071936Z 2024-06-26T05:54:27.1072249Z Stochastic Weight Averaging was proposed in `Averaging Weights Leads to 2024-06-26T05:54:27.1072550Z Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii 2024-06-26T05:54:27.1072826Z Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson 2024-06-26T05:54:27.1072940Z (UAI 2018). 2024-06-26T05:54:27.1073028Z 2024-06-26T05:54:27.1073301Z Exponential Moving Average is a variation of `Polyak averaging`_, 2024-06-26T05:54:27.1073635Z but using exponential weights instead of equal weights across iterations. 2024-06-26T05:54:27.1073721Z 2024-06-26T05:54:27.1074031Z AveragedModel class creates a copy of the provided module :attr:`model` 2024-06-26T05:54:27.1074352Z on the device :attr:`device` and allows to compute running averages of the 2024-06-26T05:54:27.1074493Z parameters of the :attr:`model`. 2024-06-26T05:54:27.1074581Z 2024-06-26T05:54:27.1074689Z Args: 2024-06-26T05:54:27.1074896Z model (torch.nn.Module): model to use with SWA/EMA 2024-06-26T05:54:27.1075209Z device (torch.device, optional): if provided, the averaged model will be 2024-06-26T05:54:27.1075365Z stored on the :attr:`device` 2024-06-26T05:54:27.1075637Z avg_fn (function, optional): the averaging function used to update 2024-06-26T05:54:27.1075920Z parameters; the function must take in the current value of the 2024-06-26T05:54:27.1076209Z :class:`AveragedModel` parameter, the current value of :attr:`model` 2024-06-26T05:54:27.1076474Z parameter, and the number of models already averaged; if None, 2024-06-26T05:54:27.1076698Z an equally weighted average is used (default: None) 2024-06-26T05:54:27.1076998Z multi_avg_fn (function, optional): the averaging function used to update 2024-06-26T05:54:27.1077306Z parameters inplace; the function must take in the current values of the 2024-06-26T05:54:27.1077675Z :class:`AveragedModel` parameters as a list, the current values of :attr:`model` 2024-06-26T05:54:27.1077997Z parameters as a list, and the number of models already averaged; if None, 2024-06-26T05:54:27.1078221Z an equally weighted average is used (default: None) 2024-06-26T05:54:27.1078499Z use_buffers (bool): if ``True``, it will compute running averages for 2024-06-26T05:54:27.1078804Z both the parameters and the buffers of the model. (default: ``False``) 2024-06-26T05:54:27.1078908Z 2024-06-26T05:54:27.1079005Z Example: 2024-06-26T05:54:27.1079179Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:27.1079361Z >>> loader, optimizer, model, loss_fn = ... 2024-06-26T05:54:27.1079617Z >>> swa_model = torch.optim.swa_utils.AveragedModel(model) 2024-06-26T05:54:27.1079912Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 2024-06-26T05:54:27.1080085Z >>> T_max=300) 2024-06-26T05:54:27.1080198Z >>> swa_start = 160 2024-06-26T05:54:27.1080424Z >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05) 2024-06-26T05:54:27.1080558Z >>> for i in range(300): 2024-06-26T05:54:27.1080783Z >>> for input, target in loader: 2024-06-26T05:54:27.1080979Z >>> optimizer.zero_grad() 2024-06-26T05:54:27.1081159Z >>> loss_fn(model(input), target).backward() 2024-06-26T05:54:27.1081316Z >>> optimizer.step() 2024-06-26T05:54:27.1081451Z >>> if i > swa_start: 2024-06-26T05:54:27.1081622Z >>> swa_model.update_parameters(model) 2024-06-26T05:54:27.1081765Z >>> swa_scheduler.step() 2024-06-26T05:54:27.1081882Z >>> else: 2024-06-26T05:54:27.1082010Z >>> scheduler.step() 2024-06-26T05:54:27.1082104Z >>> 2024-06-26T05:54:27.1082329Z >>> # Update bn statistics for the swa_model at the end 2024-06-26T05:54:27.1082542Z >>> torch.optim.swa_utils.update_bn(loader, swa_model) 2024-06-26T05:54:27.1082631Z 2024-06-26T05:54:27.1083056Z You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters. 2024-06-26T05:54:27.1083315Z If no averaging function is provided, the default is to compute 2024-06-26T05:54:27.1083575Z equally-weighted average of the weights (SWA). 2024-06-26T05:54:27.1083665Z 2024-06-26T05:54:27.1083759Z Example: 2024-06-26T05:54:27.1083945Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:27.1084216Z >>> # Compute exponential moving averages of the weights and buffers 2024-06-26T05:54:27.1084444Z >>> ema_model = torch.optim.swa_utils.AveragedModel(model, 2024-06-26T05:54:27.1084749Z >>> torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True) 2024-06-26T05:54:27.1084836Z 2024-06-26T05:54:27.1084942Z .. note:: 2024-06-26T05:54:27.1085257Z When using SWA/EMA with models containing Batch Normalization you may 2024-06-26T05:54:27.1085537Z need to update the activation statistics for Batch Normalization. 2024-06-26T05:54:27.1085866Z This can be done either by using the :meth:`torch.optim.swa_utils.update_bn` 2024-06-26T05:54:27.1086194Z or by setting :attr:`use_buffers` to `True`. The first approach updates the 2024-06-26T05:54:27.1086584Z statistics in a post-training step by passing data through the model. The 2024-06-26T05:54:27.1086919Z second does it during the parameter update phase by averaging all buffers. 2024-06-26T05:54:27.1087249Z Empirical evidence has shown that updating the statistics in normalization 2024-06-26T05:54:27.1087543Z layers increases accuracy, but you may wish to empirically test which 2024-06-26T05:54:27.1087758Z approach yields the best results in your problem. 2024-06-26T05:54:27.1087845Z 2024-06-26T05:54:27.1087943Z .. note:: 2024-06-26T05:54:27.1088321Z :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model. 2024-06-26T05:54:27.1088408Z 2024-06-26T05:54:27.1088506Z .. note:: 2024-06-26T05:54:27.1088785Z When :meth:`update_parameters` is called for the first time (i.e. 2024-06-26T05:54:27.1089036Z :attr:`n_averaged` is `0`) the parameters of `model` are copied 2024-06-26T05:54:27.1089323Z to the parameters of :class:`AveragedModel`. For every subsequent 2024-06-26T05:54:27.1089576Z call of :meth:`update_parameters` the function `avg_fn` is used 2024-06-26T05:54:27.1089737Z to update the parameters. 2024-06-26T05:54:27.1089837Z 2024-06-26T05:54:27.1090127Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2024-06-26T05:54:27.1090276Z https://arxiv.org/abs/1803.05407 2024-06-26T05:54:27.1090604Z .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should 2024-06-26T05:54:27.1090728Z Average: 2024-06-26T05:54:27.1090874Z https://arxiv.org/abs/1806.05594 2024-06-26T05:54:27.1091215Z .. _SWALP: Stochastic Weight Averaging in Low-Precision Training: 2024-06-26T05:54:27.1091386Z https://arxiv.org/abs/1904.11943 2024-06-26T05:54:27.1091766Z .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That 2024-06-26T05:54:27.1091898Z Generalizes Well: 2024-06-26T05:54:27.1092041Z https://arxiv.org/abs/2001.02312 2024-06-26T05:54:27.1092168Z .. _Polyak averaging: 2024-06-26T05:54:27.1092445Z https://paperswithcode.com/method/polyak-averaging 2024-06-26T05:54:27.1092539Z 2024-06-26T05:54:27.1092939Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.1093027Z 2024-06-26T05:54:27.1093136Z warnings.warn(msg) 2024-06-26T05:54:27.1093241Z 2024-06-26T05:54:27.1093440Z --- Parse Warning: 80 / 90 --- 2024-06-26T05:54:27.1094840Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SWALR in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py line=354. 2024-06-26T05:54:27.1095255Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.1095541Z Anneals the learning rate in each parameter group to a fixed value. 2024-06-26T05:54:27.1095631Z 2024-06-26T05:54:27.1095950Z This learning rate scheduler is meant to be used with Stochastic Weight 2024-06-26T05:54:27.1096234Z Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`). 2024-06-26T05:54:27.1096336Z 2024-06-26T05:54:27.1096433Z Args: 2024-06-26T05:54:27.1096651Z optimizer (torch.optim.Optimizer): wrapped optimizer 2024-06-26T05:54:27.1096950Z swa_lrs (float or list): the learning rate value for all param groups 2024-06-26T05:54:27.1097122Z together or separately for each group. 2024-06-26T05:54:27.1097379Z annealing_epochs (int): number of epochs in the annealing phase 2024-06-26T05:54:27.1097502Z (default: 10) 2024-06-26T05:54:27.1097783Z annealing_strategy (str): "cos" or "linear"; specifies the annealing 2024-06-26T05:54:27.1098062Z strategy: "cos" for cosine annealing, "linear" for linear annealing 2024-06-26T05:54:27.1098192Z (default: "cos") 2024-06-26T05:54:27.1098489Z last_epoch (int): the index of the last epoch (default: -1) 2024-06-26T05:54:27.1098578Z 2024-06-26T05:54:27.1098832Z The :class:`SWALR` scheduler can be used together with other 2024-06-26T05:54:27.1099124Z schedulers to switch to a constant learning rate late in the training 2024-06-26T05:54:27.1099260Z as in the example below. 2024-06-26T05:54:27.1099348Z 2024-06-26T05:54:27.1099445Z Example: 2024-06-26T05:54:27.1099632Z >>> # xdoctest: +SKIP("Undefined variables") 2024-06-26T05:54:27.1099781Z >>> loader, optimizer, model = ... 2024-06-26T05:54:27.1099926Z >>> lr_lambda = lambda epoch: 0.9 2024-06-26T05:54:27.1100229Z >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, 2024-06-26T05:54:27.1100365Z >>> lr_lambda=lr_lambda) 2024-06-26T05:54:27.1100589Z >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, 2024-06-26T05:54:27.1100833Z >>> anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05) 2024-06-26T05:54:27.1100945Z >>> swa_start = 160 2024-06-26T05:54:27.1101114Z >>> for i in range(300): 2024-06-26T05:54:27.1101280Z >>> for input, target in loader: 2024-06-26T05:54:27.1101426Z >>> optimizer.zero_grad() 2024-06-26T05:54:27.1101619Z >>> loss_fn(model(input), target).backward() 2024-06-26T05:54:27.1101749Z >>> optimizer.step() 2024-06-26T05:54:27.1101903Z >>> if i > swa_start: 2024-06-26T05:54:27.1102057Z >>> swa_scheduler.step() 2024-06-26T05:54:27.1102157Z >>> else: 2024-06-26T05:54:27.1102323Z >>> scheduler.step() 2024-06-26T05:54:27.1102428Z 2024-06-26T05:54:27.1102749Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2024-06-26T05:54:27.1102904Z https://arxiv.org/abs/1803.05407 2024-06-26T05:54:27.1103009Z 2024-06-26T05:54:27.1103393Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.1103482Z 2024-06-26T05:54:27.1103610Z warnings.warn(msg) 2024-06-26T05:54:27.1103696Z 2024-06-26T05:54:27.1103893Z --- Parse Warning: 81 / 90 --- 2024-06-26T05:54:27.1105261Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_close in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_comparison.py line=1268. 2024-06-26T05:54:27.1105660Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.1105864Z Asserts that ``actual`` and ``expected`` are close. 2024-06-26T05:54:27.1105955Z 2024-06-26T05:54:27.1106534Z If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if 2024-06-26T05:54:27.1106637Z 2024-06-26T05:54:27.1106740Z .. math:: 2024-06-26T05:54:27.1106829Z 2024-06-26T05:54:27.1107466Z \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert 2024-06-26T05:54:27.1107552Z 2024-06-26T05:54:27.1108119Z Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are 2024-06-26T05:54:27.1108402Z only considered equal to each other if ``equal_nan`` is ``True``. 2024-06-26T05:54:27.1108491Z 2024-06-26T05:54:27.1108771Z In addition, they are only considered close if they have the same 2024-06-26T05:54:27.1108857Z 2024-06-26T05:54:27.1109169Z - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``), 2024-06-26T05:54:27.1109397Z - ``dtype`` (if ``check_dtype`` is ``True``), 2024-06-26T05:54:27.1109631Z - ``layout`` (if ``check_layout`` is ``True``), and 2024-06-26T05:54:27.1109835Z - stride (if ``check_stride`` is ``True``). 2024-06-26T05:54:27.1109937Z 2024-06-26T05:54:27.1110358Z If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed. 2024-06-26T05:54:27.1110447Z 2024-06-26T05:54:27.1110972Z If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are 2024-06-26T05:54:27.1111463Z checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR, 2024-06-26T05:54:27.1111774Z or ``ccol_indices`` and ``row_indices`` for CSC and BSC layouts, respectively, 2024-06-26T05:54:27.1112310Z are always checked for equality whereas the values are checked for closeness according to the definition above. 2024-06-26T05:54:27.1112400Z 2024-06-26T05:54:27.1112802Z If ``actual`` and ``expected`` are quantized, they are considered close if they have the same 2024-06-26T05:54:27.1113286Z :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the 2024-06-26T05:54:27.1113401Z definition above. 2024-06-26T05:54:27.1113503Z 2024-06-26T05:54:27.1114042Z ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which 2024-06-26T05:54:27.1114644Z :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types 2024-06-26T05:54:27.1115209Z have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s 2024-06-26T05:54:27.1115854Z or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all 2024-06-26T05:54:27.1116196Z their elements are considered close according to the above definition. 2024-06-26T05:54:27.1116329Z 2024-06-26T05:54:27.1116433Z .. note:: 2024-06-26T05:54:27.1116535Z 2024-06-26T05:54:27.1116987Z Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e. 2024-06-26T05:54:27.1117525Z :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus, 2024-06-26T05:54:27.1117918Z Python scalars of different types can be checked, but require ``check_dtype=False``. 2024-06-26T05:54:27.1118008Z 2024-06-26T05:54:27.1118102Z Args: 2024-06-26T05:54:27.1118246Z actual (Any): Actual input. 2024-06-26T05:54:27.1118392Z expected (Any): Expected input. 2024-06-26T05:54:27.1118881Z allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types 2024-06-26T05:54:27.1119094Z are allowed. Otherwise type equality is required. 2024-06-26T05:54:27.1119584Z rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default 2024-06-26T05:54:27.1119964Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2024-06-26T05:54:27.1120446Z atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default 2024-06-26T05:54:27.1120884Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2024-06-26T05:54:27.1121251Z equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal. 2024-06-26T05:54:27.1121645Z check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same 2024-06-26T05:54:27.1121992Z :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different 2024-06-26T05:54:27.1122388Z :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared. 2024-06-26T05:54:27.1122864Z check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this 2024-06-26T05:54:27.1123433Z check is disabled, tensors with different ``dtype``'s are promoted to a common ``dtype`` (according to 2024-06-26T05:54:27.1123644Z :func:`torch.promote_types`) before being compared. 2024-06-26T05:54:27.1124122Z check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this 2024-06-26T05:54:27.1124666Z check is disabled, tensors with different ``layout``'s are converted to strided tensors before being 2024-06-26T05:54:27.1124774Z compared. 2024-06-26T05:54:27.1125272Z check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride. 2024-06-26T05:54:27.1125757Z msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during 2024-06-26T05:54:27.1126248Z the comparison. Can also passed as callable in which case it will be called with the generated message and 2024-06-26T05:54:27.1126410Z should return the new message. 2024-06-26T05:54:27.1126499Z 2024-06-26T05:54:27.1126627Z Raises: 2024-06-26T05:54:27.1126953Z ValueError: If no :class:`torch.Tensor` can be constructed from an input. 2024-06-26T05:54:27.1127170Z ValueError: If only ``rtol`` or ``atol`` is specified. 2024-06-26T05:54:27.1127601Z AssertionError: If corresponding inputs are not Python scalars and are not directly related. 2024-06-26T05:54:27.1128104Z AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have 2024-06-26T05:54:27.1128222Z different types. 2024-06-26T05:54:27.1128863Z AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match. 2024-06-26T05:54:27.1129442Z AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match. 2024-06-26T05:54:27.1129850Z AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`. 2024-06-26T05:54:27.1130266Z AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same 2024-06-26T05:54:27.1130418Z :attr:`~torch.Tensor.layout`. 2024-06-26T05:54:27.1130716Z AssertionError: If only one of corresponding tensors is quantized. 2024-06-26T05:54:27.1131307Z AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s. 2024-06-26T05:54:27.1131702Z AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same 2024-06-26T05:54:27.1131868Z :attr:`~torch.Tensor.device`. 2024-06-26T05:54:27.1132316Z AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``. 2024-06-26T05:54:27.1132794Z AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride. 2024-06-26T05:54:27.1133285Z AssertionError: If the values of corresponding tensors are not close according to the definition above. 2024-06-26T05:54:27.1133374Z 2024-06-26T05:54:27.1134094Z The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching 2024-06-26T05:54:27.1134349Z ``dtype``'s, the maximum of both tolerances is used. 2024-06-26T05:54:27.1134439Z 2024-06-26T05:54:27.1134666Z +---------------------------+------------+----------+ 2024-06-26T05:54:27.1134842Z | ``dtype`` | ``rtol`` | ``atol`` | 2024-06-26T05:54:27.1134989Z +===========================+============+==========+ 2024-06-26T05:54:27.1135244Z | :attr:`~torch.float16` | ``1e-3`` | ``1e-5`` | 2024-06-26T05:54:27.1135448Z +---------------------------+------------+----------+ 2024-06-26T05:54:27.1135685Z | :attr:`~torch.bfloat16` | ``1.6e-2`` | ``1e-5`` | 2024-06-26T05:54:27.1135904Z +---------------------------+------------+----------+ 2024-06-26T05:54:27.1136141Z | :attr:`~torch.float32` | ``1.3e-6`` | ``1e-5`` | 2024-06-26T05:54:27.1136360Z +---------------------------+------------+----------+ 2024-06-26T05:54:27.1136595Z | :attr:`~torch.float64` | ``1e-7`` | ``1e-7`` | 2024-06-26T05:54:27.1136800Z +---------------------------+------------+----------+ 2024-06-26T05:54:27.1137052Z | :attr:`~torch.complex32` | ``1e-3`` | ``1e-5`` | 2024-06-26T05:54:27.1137252Z +---------------------------+------------+----------+ 2024-06-26T05:54:27.1137492Z | :attr:`~torch.complex64` | ``1.3e-6`` | ``1e-5`` | 2024-06-26T05:54:27.1137709Z +---------------------------+------------+----------+ 2024-06-26T05:54:27.1137949Z | :attr:`~torch.complex128` | ``1e-7`` | ``1e-7`` | 2024-06-26T05:54:27.1138150Z +---------------------------+------------+----------+ 2024-06-26T05:54:27.1138397Z | :attr:`~torch.quint8` | ``1.3e-6`` | ``1e-5`` | 2024-06-26T05:54:27.1138644Z +---------------------------+------------+----------+ 2024-06-26T05:54:27.1138893Z | :attr:`~torch.quint2x4` | ``1.3e-6`` | ``1e-5`` | 2024-06-26T05:54:27.1139092Z +---------------------------+------------+----------+ 2024-06-26T05:54:27.1139325Z | :attr:`~torch.quint4x2` | ``1.3e-6`` | ``1e-5`` | 2024-06-26T05:54:27.1139579Z +---------------------------+------------+----------+ 2024-06-26T05:54:27.1139811Z | :attr:`~torch.qint8` | ``1.3e-6`` | ``1e-5`` | 2024-06-26T05:54:27.1140013Z +---------------------------+------------+----------+ 2024-06-26T05:54:27.1140296Z | :attr:`~torch.qint32` | ``1.3e-6`` | ``1e-5`` | 2024-06-26T05:54:27.1140529Z +---------------------------+------------+----------+ 2024-06-26T05:54:27.1140696Z | other | ``0.0`` | ``0.0`` | 2024-06-26T05:54:27.1140913Z +---------------------------+------------+----------+ 2024-06-26T05:54:27.1141000Z 2024-06-26T05:54:27.1141104Z .. note:: 2024-06-26T05:54:27.1141204Z 2024-06-26T05:54:27.1141712Z :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged 2024-06-26T05:54:27.1142225Z to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might 2024-06-26T05:54:27.1142586Z define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default: 2024-06-26T05:54:27.1142672Z 2024-06-26T05:54:27.1142803Z >>> import functools 2024-06-26T05:54:27.1143149Z >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0) 2024-06-26T05:54:27.1143322Z >>> assert_equal(1e-9, 1e-10) 2024-06-26T05:54:27.1143488Z Traceback (most recent call last): 2024-06-26T05:54:27.1143583Z ... 2024-06-26T05:54:27.1143748Z AssertionError: Scalars are not equal! 2024-06-26T05:54:27.1143867Z 2024-06-26T05:54:27.1144043Z Expected 1e-10 but got 1e-09. 2024-06-26T05:54:27.1144258Z Absolute difference: 9.000000000000001e-10 2024-06-26T05:54:27.1144396Z Relative difference: 9.0 2024-06-26T05:54:27.1144483Z 2024-06-26T05:54:27.1144583Z Examples: 2024-06-26T05:54:27.1144748Z >>> # tensor to tensor comparison 2024-06-26T05:54:27.1144973Z >>> expected = torch.tensor([1e0, 1e-1, 1e-2]) 2024-06-26T05:54:27.1145160Z >>> actual = torch.acos(torch.cos(expected)) 2024-06-26T05:54:27.1145351Z >>> torch.testing.assert_close(actual, expected) 2024-06-26T05:54:27.1145440Z 2024-06-26T05:54:27.1145600Z >>> # scalar to scalar comparison 2024-06-26T05:54:27.1145708Z >>> import math 2024-06-26T05:54:27.1145840Z >>> expected = math.sqrt(2.0) 2024-06-26T05:54:27.1145987Z >>> actual = 2.0 / math.sqrt(2.0) 2024-06-26T05:54:27.1146177Z >>> torch.testing.assert_close(actual, expected) 2024-06-26T05:54:27.1146264Z 2024-06-26T05:54:27.1146447Z >>> # numpy array to numpy array comparison 2024-06-26T05:54:27.1146567Z >>> import numpy as np 2024-06-26T05:54:27.1146782Z >>> expected = np.array([1e0, 1e-1, 1e-2]) 2024-06-26T05:54:27.1146959Z >>> actual = np.arccos(np.cos(expected)) 2024-06-26T05:54:27.1147147Z >>> torch.testing.assert_close(actual, expected) 2024-06-26T05:54:27.1147236Z 2024-06-26T05:54:27.1147404Z >>> # sequence to sequence comparison 2024-06-26T05:54:27.1147524Z >>> import numpy as np 2024-06-26T05:54:27.1147894Z >>> # The types of the sequences do not have to match. They only have to have the same 2024-06-26T05:54:27.1148072Z >>> # length and their elements have to match. 2024-06-26T05:54:27.1148279Z >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)] 2024-06-26T05:54:27.1148455Z >>> actual = tuple(expected) 2024-06-26T05:54:27.1148648Z >>> torch.testing.assert_close(actual, expected) 2024-06-26T05:54:27.1148735Z 2024-06-26T05:54:27.1148902Z >>> # mapping to mapping comparison 2024-06-26T05:54:27.1149061Z >>> from collections import OrderedDict 2024-06-26T05:54:27.1149231Z >>> import numpy as np 2024-06-26T05:54:27.1149370Z >>> foo = torch.tensor(1.0) 2024-06-26T05:54:27.1149473Z >>> bar = 2.0 2024-06-26T05:54:27.1149589Z >>> baz = np.array(3.0) 2024-06-26T05:54:27.1149982Z >>> # The types and a possible ordering of mappings do not have to match. They only 2024-06-26T05:54:27.1150304Z >>> # have to have the same set of keys and their elements have to match. 2024-06-26T05:54:27.1150587Z >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)]) 2024-06-26T05:54:27.1150770Z >>> actual = {"baz": baz, "bar": bar, "foo": foo} 2024-06-26T05:54:27.1150964Z >>> torch.testing.assert_close(actual, expected) 2024-06-26T05:54:27.1151065Z 2024-06-26T05:54:27.1151232Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2024-06-26T05:54:27.1151370Z >>> actual = expected.clone() 2024-06-26T05:54:27.1151616Z >>> # By default, directly related instances can be compared 2024-06-26T05:54:27.1151908Z >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected) 2024-06-26T05:54:27.1152175Z >>> # This check can be made more strict with allow_subclasses=False 2024-06-26T05:54:27.1152335Z >>> torch.testing.assert_close( 2024-06-26T05:54:27.1152606Z ... torch.nn.Parameter(actual), expected, allow_subclasses=False 2024-06-26T05:54:27.1152701Z ... ) 2024-06-26T05:54:27.1152866Z Traceback (most recent call last): 2024-06-26T05:54:27.1152964Z ... 2024-06-26T05:54:27.1153245Z TypeError: No comparison pair was able to handle inputs of type 2024-06-26T05:54:27.1153601Z and . 2024-06-26T05:54:27.1153913Z >>> # If the inputs are not directly related, they are never considered close 2024-06-26T05:54:27.1154153Z >>> torch.testing.assert_close(actual.numpy(), expected) 2024-06-26T05:54:27.1154306Z Traceback (most recent call last): 2024-06-26T05:54:27.1154400Z ... 2024-06-26T05:54:27.1154885Z TypeError: No comparison pair was able to handle inputs of type 2024-06-26T05:54:27.1155055Z and . 2024-06-26T05:54:27.1155415Z >>> # Exceptions to these rules are Python scalars. They can be checked regardless of 2024-06-26T05:54:27.1155587Z >>> # their type if check_dtype=False. 2024-06-26T05:54:27.1155812Z >>> torch.testing.assert_close(1.0, 1, check_dtype=False) 2024-06-26T05:54:27.1155912Z 2024-06-26T05:54:27.1156046Z >>> # NaN != NaN by default. 2024-06-26T05:54:27.1156214Z >>> expected = torch.tensor(float("Nan")) 2024-06-26T05:54:27.1156365Z >>> actual = expected.clone() 2024-06-26T05:54:27.1156558Z >>> torch.testing.assert_close(actual, expected) 2024-06-26T05:54:27.1156709Z Traceback (most recent call last): 2024-06-26T05:54:27.1156820Z ... 2024-06-26T05:54:27.1156985Z AssertionError: Scalars are not close! 2024-06-26T05:54:27.1157085Z 2024-06-26T05:54:27.1157232Z Expected nan but got nan. 2024-06-26T05:54:27.1157473Z Absolute difference: nan (up to 1e-05 allowed) 2024-06-26T05:54:27.1157722Z Relative difference: nan (up to 1.3e-06 allowed) 2024-06-26T05:54:27.1157994Z >>> torch.testing.assert_close(actual, expected, equal_nan=True) 2024-06-26T05:54:27.1158083Z 2024-06-26T05:54:27.1158251Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2024-06-26T05:54:27.1158452Z >>> actual = torch.tensor([1.0, 4.0, 5.0]) 2024-06-26T05:54:27.1158648Z >>> # The default error message can be overwritten. 2024-06-26T05:54:27.1159040Z >>> torch.testing.assert_close(actual, expected, msg="Argh, the tensors are not close!") 2024-06-26T05:54:27.1159194Z Traceback (most recent call last): 2024-06-26T05:54:27.1159322Z ... 2024-06-26T05:54:27.1159537Z AssertionError: Argh, the tensors are not close! 2024-06-26T05:54:27.1159862Z >>> # If msg is a callable, it can be used to augment the generated message with 2024-06-26T05:54:27.1160009Z >>> # extra information 2024-06-26T05:54:27.1160195Z >>> torch.testing.assert_close( 2024-06-26T05:54:27.1160462Z ... actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter" 2024-06-26T05:54:27.1160553Z ... ) 2024-06-26T05:54:27.1160797Z Traceback (most recent call last): 2024-06-26T05:54:27.1160893Z ... 2024-06-26T05:54:27.1161016Z AssertionError: Header 2024-06-26T05:54:27.1161133Z 2024-06-26T05:54:27.1161308Z Tensor-likes are not close! 2024-06-26T05:54:27.1161408Z 2024-06-26T05:54:27.1161572Z Mismatched elements: 2 / 3 (66.7%) 2024-06-26T05:54:27.1161934Z Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed) 2024-06-26T05:54:27.1162312Z Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed) 2024-06-26T05:54:27.1162416Z 2024-06-26T05:54:27.1162512Z Footer 2024-06-26T05:54:27.1162616Z 2024-06-26T05:54:27.1163001Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.1163089Z 2024-06-26T05:54:27.1163216Z warnings.warn(msg) 2024-06-26T05:54:27.1163305Z 2024-06-26T05:54:27.1163502Z --- Parse Warning: 82 / 90 --- 2024-06-26T05:54:27.1164878Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_pytree_node in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py line=115. 2024-06-26T05:54:27.1165276Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.1165515Z Register a container-like type as pytree node. 2024-06-26T05:54:27.1165605Z 2024-06-26T05:54:27.1165699Z Args: 2024-06-26T05:54:27.1165966Z cls (type): A Python type to treat as an internal pytree node. 2024-06-26T05:54:27.1166339Z flatten_fn (callable): A function to be used during flattening, taking an instance of 2024-06-26T05:54:27.1166691Z ``cls`` and returning a pair, with (1) an iterable for the children to be flattened 2024-06-26T05:54:27.1167095Z recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be 2024-06-26T05:54:27.1167248Z passed to the ``unflatten_fn``. 2024-06-26T05:54:27.1167614Z unflatten_fn (callable): A function taking two arguments: the auxiliary data that was 2024-06-26T05:54:27.1167993Z returned by ``flatten_fn`` and stored in the treespec, and the unflattened children. 2024-06-26T05:54:27.1168202Z The function should return an instance of ``cls``. 2024-06-26T05:54:27.1168562Z serialized_type_name (str, optional): A keyword argument used to specify the fully 2024-06-26T05:54:27.1168778Z qualified name used when serializing the tree spec. 2024-06-26T05:54:27.1169184Z to_dumpable_context (callable, optional): An optional keyword argument to custom specify how 2024-06-26T05:54:27.1169584Z to convert the context of the pytree to a custom json dumpable representation. This is 2024-06-26T05:54:27.1169981Z used for json serialization, which is being used in :mod:`torch.export` right now. 2024-06-26T05:54:27.1170373Z from_dumpable_context (callable, optional): An optional keyword argument to custom specify 2024-06-26T05:54:27.1170750Z how to convert the custom json dumpable representation of the context back to the 2024-06-26T05:54:27.1171104Z original context. This is used for json deserialization, which is being used in 2024-06-26T05:54:27.1171290Z :mod:`torch.export` right now. 2024-06-26T05:54:27.1171380Z 2024-06-26T05:54:27.1171486Z Example:: 2024-06-26T05:54:27.1171611Z 2024-06-26T05:54:27.1171730Z >>> # xdoctest: +SKIP 2024-06-26T05:54:27.1171954Z >>> # Registry a Python type with lambda functions 2024-06-26T05:54:27.1172091Z >>> register_pytree_node( 2024-06-26T05:54:27.1172197Z ... set, 2024-06-26T05:54:27.1172360Z ... lambda s: (sorted(s), None, None), 2024-06-26T05:54:27.1172540Z ... lambda children, _: set(children), 2024-06-26T05:54:27.1172637Z ... ) 2024-06-26T05:54:27.1172729Z 2024-06-26T05:54:27.1173126Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.1173216Z 2024-06-26T05:54:27.1173324Z warnings.warn(msg) 2024-06-26T05:54:27.1173423Z 2024-06-26T05:54:27.1173735Z --- Parse Warning: 83 / 90 --- 2024-06-26T05:54:27.1175176Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SelectiveCheckpointContext in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py line=1186. 2024-06-26T05:54:27.1175578Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.1175669Z 2024-06-26T05:54:27.1175955Z Context passed to policy function during selective checkpointing. 2024-06-26T05:54:27.1176045Z 2024-06-26T05:54:27.1176364Z This class is used to pass relevant metadata to the policy function during 2024-06-26T05:54:27.1176709Z selective checkpointing. The metadata includes whether the current invocation 2024-06-26T05:54:27.1176923Z of the policy function is during recomputation or not. 2024-06-26T05:54:27.1177008Z 2024-06-26T05:54:27.1177116Z Example: 2024-06-26T05:54:27.1177240Z >>> # xdoctest: +SKIP(stub) 2024-06-26T05:54:27.1177336Z >>> 2024-06-26T05:54:27.1177516Z >>> def policy_fn(ctx, op, *args, **kwargs): 2024-06-26T05:54:27.1177643Z >>> print(ctx.is_recompute) 2024-06-26T05:54:27.1177737Z >>> 2024-06-26T05:54:27.1178099Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2024-06-26T05:54:27.1178190Z >>> 2024-06-26T05:54:27.1178389Z >>> out = torch.utils.checkpoint.checkpoint( 2024-06-26T05:54:27.1178495Z >>> fn, x, y, 2024-06-26T05:54:27.1178612Z >>> use_reentrant=False, 2024-06-26T05:54:27.1178750Z >>> context_fn=context_fn, 2024-06-26T05:54:27.1178845Z >>> ) 2024-06-26T05:54:27.1178932Z 2024-06-26T05:54:27.1179331Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.1179421Z 2024-06-26T05:54:27.1179531Z warnings.warn(msg) 2024-06-26T05:54:27.1179631Z 2024-06-26T05:54:27.1179830Z --- Parse Warning: 84 / 90 --- 2024-06-26T05:54:27.1181283Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=create_selective_checkpoint_contexts in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py line=1319. 2024-06-26T05:54:27.1181702Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.1181792Z 2024-06-26T05:54:27.1182111Z Helper to avoid recomputing certain ops during activation checkpointing. 2024-06-26T05:54:27.1182200Z 2024-06-26T05:54:27.1182545Z Use this with `torch.utils.checkpoint.checkpoint` to control which 2024-06-26T05:54:27.1182757Z operations are recomputed during the backward pass. 2024-06-26T05:54:27.1182844Z 2024-06-26T05:54:27.1182936Z Args: 2024-06-26T05:54:27.1183102Z policy_fn_or_list (Callable or List): 2024-06-26T05:54:27.1183372Z - If a policy function is provided, it should accept a 2024-06-26T05:54:27.1183713Z :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and 2024-06-26T05:54:27.1184014Z kwargs to the op, and return a :class:`CheckpointPolicy` enum value 2024-06-26T05:54:27.1184359Z indicating whether the execution of the op should be recomputed or not. 2024-06-26T05:54:27.1184729Z - If a list of operations is provided, it is equivalent to a policy 2024-06-26T05:54:27.1184980Z returning `CheckpointPolicy.MUST_SAVE` for the specified 2024-06-26T05:54:27.1185265Z operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other 2024-06-26T05:54:27.1185383Z operations. 2024-06-26T05:54:27.1185660Z allow_cache_entry_mutation (bool, optional): By default, an error is 2024-06-26T05:54:27.1185943Z raised if any tensors cached by selective activation checkpoint are 2024-06-26T05:54:27.1186241Z mutated in order to ensure correctness. If set to `True`, this check 2024-06-26T05:54:27.1186350Z is disabled. 2024-06-26T05:54:27.1186448Z Returns: 2024-06-26T05:54:27.1186596Z A tuple of two context managers. 2024-06-26T05:54:27.1186684Z 2024-06-26T05:54:27.1186780Z Example: 2024-06-26T05:54:27.1186925Z >>> # xdoctest: +REQUIRES(LINUX) 2024-06-26T05:54:27.1187037Z >>> import functools 2024-06-26T05:54:27.1187130Z >>> 2024-06-26T05:54:27.1187309Z >>> x = torch.rand(10, 10, requires_grad=True) 2024-06-26T05:54:27.1187469Z >>> y = torch.rand(10, 10, requires_grad=True) 2024-06-26T05:54:27.1187562Z >>> 2024-06-26T05:54:27.1187684Z >>> ops_to_save = [ 2024-06-26T05:54:27.1187827Z >>> torch.ops.aten.mm.default, 2024-06-26T05:54:27.1187919Z >>> ] 2024-06-26T05:54:27.1188025Z >>> 2024-06-26T05:54:27.1188192Z >>> def policy_fn(ctx, op, *args, **kwargs): 2024-06-26T05:54:27.1188311Z >>> if op in ops_to_save: 2024-06-26T05:54:27.1188496Z >>> return CheckpointPolicy.MUST_SAVE 2024-06-26T05:54:27.1188596Z >>> else: 2024-06-26T05:54:27.1188798Z >>> return CheckpointPolicy.PREFER_RECOMPUTE 2024-06-26T05:54:27.1188892Z >>> 2024-06-26T05:54:27.1189234Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2024-06-26T05:54:27.1189341Z >>> 2024-06-26T05:54:27.1189459Z >>> # or equivalently 2024-06-26T05:54:27.1189812Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save) 2024-06-26T05:54:27.1189914Z >>> 2024-06-26T05:54:27.1190021Z >>> def fn(x, y): 2024-06-26T05:54:27.1190277Z >>> return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y 2024-06-26T05:54:27.1190382Z >>> 2024-06-26T05:54:27.1190571Z >>> out = torch.utils.checkpoint.checkpoint( 2024-06-26T05:54:27.1190672Z >>> fn, x, y, 2024-06-26T05:54:27.1190804Z >>> use_reentrant=False, 2024-06-26T05:54:27.1190929Z >>> context_fn=context_fn, 2024-06-26T05:54:27.1191021Z >>> ) 2024-06-26T05:54:27.1191120Z 2024-06-26T05:54:27.1191508Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.1191598Z 2024-06-26T05:54:27.1191722Z warnings.warn(msg) 2024-06-26T05:54:27.1191813Z 2024-06-26T05:54:27.1192026Z --- Parse Warning: 85 / 90 --- 2024-06-26T05:54:27.1193389Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CppExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=923. 2024-06-26T05:54:27.1193787Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.1193888Z 2024-06-26T05:54:27.1194071Z Create a :class:`setuptools.Extension` for C++. 2024-06-26T05:54:27.1194159Z 2024-06-26T05:54:27.1194485Z Convenience method that creates a :class:`setuptools.Extension` with the 2024-06-26T05:54:27.1194805Z bare minimum (but often sufficient) arguments to build a C++ extension. 2024-06-26T05:54:27.1194891Z 2024-06-26T05:54:27.1195198Z All arguments are forwarded to the :class:`setuptools.Extension` 2024-06-26T05:54:27.1195425Z constructor. Full list arguments can be found at 2024-06-26T05:54:27.1195930Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2024-06-26T05:54:27.1196017Z 2024-06-26T05:54:27.1196115Z Example: 2024-06-26T05:54:27.1196240Z >>> # xdoctest: +SKIP 2024-06-26T05:54:27.1196437Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-06-26T05:54:27.1196572Z >>> from setuptools import setup 2024-06-26T05:54:27.1196873Z >>> from torch.utils.cpp_extension import BuildExtension, CppExtension 2024-06-26T05:54:27.1196971Z >>> setup( 2024-06-26T05:54:27.1197122Z ... name='extension', 2024-06-26T05:54:27.1197247Z ... ext_modules=[ 2024-06-26T05:54:27.1197361Z ... CppExtension( 2024-06-26T05:54:27.1197526Z ... name='extension', 2024-06-26T05:54:27.1197742Z ... sources=['extension.cpp'], 2024-06-26T05:54:27.1197934Z ... extra_compile_args=['-g'], 2024-06-26T05:54:27.1198194Z ... extra_link_flags=['-Wl,--no-as-needed', '-lm']) 2024-06-26T05:54:27.1198301Z ... ], 2024-06-26T05:54:27.1198406Z ... cmdclass={ 2024-06-26T05:54:27.1198608Z ... 'build_ext': BuildExtension 2024-06-26T05:54:27.1198704Z ... }) 2024-06-26T05:54:27.1198794Z 2024-06-26T05:54:27.1199193Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.1199281Z 2024-06-26T05:54:27.1199393Z warnings.warn(msg) 2024-06-26T05:54:27.1199493Z 2024-06-26T05:54:27.1199690Z --- Parse Warning: 86 / 90 --- 2024-06-26T05:54:27.1201113Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CUDAExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=974. 2024-06-26T05:54:27.1201534Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.1201624Z 2024-06-26T05:54:27.1201832Z Create a :class:`setuptools.Extension` for CUDA/C++. 2024-06-26T05:54:27.1201932Z 2024-06-26T05:54:27.1202241Z Convenience method that creates a :class:`setuptools.Extension` with the 2024-06-26T05:54:27.1202510Z bare minimum (but often sufficient) arguments to build a CUDA/C++ 2024-06-26T05:54:27.1202828Z extension. This includes the CUDA include path, library path and runtime 2024-06-26T05:54:27.1202921Z library. 2024-06-26T05:54:27.1203023Z 2024-06-26T05:54:27.1203289Z All arguments are forwarded to the :class:`setuptools.Extension` 2024-06-26T05:54:27.1203479Z constructor. Full list arguments can be found at 2024-06-26T05:54:27.1203985Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2024-06-26T05:54:27.1204074Z 2024-06-26T05:54:27.1204173Z Example: 2024-06-26T05:54:27.1204299Z >>> # xdoctest: +SKIP 2024-06-26T05:54:27.1204496Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-06-26T05:54:27.1204636Z >>> from setuptools import setup 2024-06-26T05:54:27.1204943Z >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension 2024-06-26T05:54:27.1205040Z >>> setup( 2024-06-26T05:54:27.1205240Z ... name='cuda_extension', 2024-06-26T05:54:27.1205359Z ... ext_modules=[ 2024-06-26T05:54:27.1205473Z ... CUDAExtension( 2024-06-26T05:54:27.1205662Z ... name='cuda_extension', 2024-06-26T05:54:27.1205949Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-06-26T05:54:27.1206202Z ... extra_compile_args={'cxx': ['-g'], 2024-06-26T05:54:27.1206434Z ... 'nvcc': ['-O2']}, 2024-06-26T05:54:27.1206708Z ... extra_link_flags=['-Wl,--no-as-needed', '-lcuda']) 2024-06-26T05:54:27.1206834Z ... ], 2024-06-26T05:54:27.1206980Z ... cmdclass={ 2024-06-26T05:54:27.1207167Z ... 'build_ext': BuildExtension 2024-06-26T05:54:27.1207263Z ... }) 2024-06-26T05:54:27.1207362Z 2024-06-26T05:54:27.1207474Z Compute capabilities: 2024-06-26T05:54:27.1207560Z 2024-06-26T05:54:27.1207989Z By default the extension will be compiled to run on all archs of the cards visible during the 2024-06-26T05:54:27.1208385Z building process of the extension, plus PTX. If down the road a new card is installed the 2024-06-26T05:54:27.1208826Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's 2024-06-26T05:54:27.1209325Z newer than the newest version for which your nvcc can build fully-compiled binaries, Pytorch 2024-06-26T05:54:27.1209720Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does 2024-06-26T05:54:27.1209885Z support (see below for details on PTX). 2024-06-26T05:54:27.1209972Z 2024-06-26T05:54:27.1210390Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which 2024-06-26T05:54:27.1210546Z CCs you want the extension to support: 2024-06-26T05:54:27.1210631Z 2024-06-26T05:54:27.1210868Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py`` 2024-06-26T05:54:27.1211224Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py`` 2024-06-26T05:54:27.1211308Z 2024-06-26T05:54:27.1211733Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified 2024-06-26T05:54:27.1212244Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >= 2024-06-26T05:54:27.1212741Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with 2024-06-26T05:54:27.1213235Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to 2024-06-26T05:54:27.1213853Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on 2024-06-26T05:54:27.1214333Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better 2024-06-26T05:54:27.1214782Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6, 2024-06-26T05:54:27.1215284Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but 2024-06-26T05:54:27.1215398Z "8.0 8.6" would be better. 2024-06-26T05:54:27.1215499Z 2024-06-26T05:54:27.1215983Z Note that while it's possible to include all supported archs, the more archs get included the 2024-06-26T05:54:27.1216408Z slower the building process will be, as it will build a separate kernel image for each arch. 2024-06-26T05:54:27.1216497Z 2024-06-26T05:54:27.1217033Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows. 2024-06-26T05:54:27.1217338Z To workaround the issue, move python binding logic to pure C++ file. 2024-06-26T05:54:27.1217427Z 2024-06-26T05:54:27.1217532Z Example use: 2024-06-26T05:54:27.1217657Z #include 2024-06-26T05:54:27.1217856Z at::Tensor SigmoidAlphaBlendForwardCuda(....) 2024-06-26T05:54:27.1217991Z 2024-06-26T05:54:27.1218107Z Instead of: 2024-06-26T05:54:27.1218233Z #include 2024-06-26T05:54:27.1218436Z torch::Tensor SigmoidAlphaBlendForwardCuda(...) 2024-06-26T05:54:27.1218541Z 2024-06-26T05:54:27.1218904Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460 2024-06-26T05:54:27.1219581Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48 2024-06-26T05:54:27.1219686Z 2024-06-26T05:54:27.1219852Z Relocatable device code linking: 2024-06-26T05:54:27.1219950Z 2024-06-26T05:54:27.1220362Z If you want to reference device symbols across compilation units (across object files), 2024-06-26T05:54:27.1220796Z the object files need to be built with `relocatable device code` (-rdc=true or -dc). 2024-06-26T05:54:27.1221300Z An exception to this rule is "dynamic parallelism" (nested kernel launches) which is not used a lot anymore. 2024-06-26T05:54:27.1221748Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it. 2024-06-26T05:54:27.1222251Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step 2024-06-26T05:54:27.1222545Z help reduce the protentional perf degradation of `-rdc`. 2024-06-26T05:54:27.1222770Z Note that it needs to be used at both steps to be useful. 2024-06-26T05:54:27.1222857Z 2024-06-26T05:54:27.1223490Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step. 2024-06-26T05:54:27.1223780Z There is also a case where `-dlink` is used without `-rdc`: 2024-06-26T05:54:27.1224201Z when an extension is linked against a static lib containing rdc-compiled objects 2024-06-26T05:54:27.1224477Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem). 2024-06-26T05:54:27.1224564Z 2024-06-26T05:54:27.1224853Z Note: Ninja is required to build a CUDA Extension with RDC linking. 2024-06-26T05:54:27.1224939Z 2024-06-26T05:54:27.1225033Z Example: 2024-06-26T05:54:27.1225157Z >>> # xdoctest: +SKIP 2024-06-26T05:54:27.1225351Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-06-26T05:54:27.1225463Z >>> CUDAExtension( 2024-06-26T05:54:27.1225642Z ... name='cuda_extension', 2024-06-26T05:54:27.1225907Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-06-26T05:54:27.1226013Z ... dlink=True, 2024-06-26T05:54:27.1226177Z ... dlink_libraries=["dlink_lib"], 2024-06-26T05:54:27.1226385Z ... extra_compile_args={'cxx': ['-g'], 2024-06-26T05:54:27.1226628Z ... 'nvcc': ['-O2', '-rdc=true']}) 2024-06-26T05:54:27.1226717Z 2024-06-26T05:54:27.1227100Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.1227198Z 2024-06-26T05:54:27.1227309Z warnings.warn(msg) 2024-06-26T05:54:27.1227397Z 2024-06-26T05:54:27.1227605Z --- Parse Warning: 87 / 90 --- 2024-06-26T05:54:27.1228893Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1232. 2024-06-26T05:54:27.1229292Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.1229392Z 2024-06-26T05:54:27.1229627Z Load a PyTorch C++ extension just-in-time (JIT). 2024-06-26T05:54:27.1229716Z 2024-06-26T05:54:27.1230014Z To load an extension, a Ninja build file is emitted, which is used to 2024-06-26T05:54:27.1230280Z compile the given sources into a dynamic library. This library is 2024-06-26T05:54:27.1230570Z subsequently loaded into the current Python process as a module and 2024-06-26T05:54:27.1230766Z returned from this function, ready for use. 2024-06-26T05:54:27.1230854Z 2024-06-26T05:54:27.1231147Z By default, the directory to which the build file is emitted and the 2024-06-26T05:54:27.1231449Z resulting library compiled to is ``/torch_extensions/``, where 2024-06-26T05:54:27.1231724Z ```` is the temporary folder on the current platform and ```` 2024-06-26T05:54:27.1232067Z the name of the extension. This location can be overridden in two ways. 2024-06-26T05:54:27.1232342Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it 2024-06-26T05:54:27.1232651Z replaces ``/torch_extensions`` and all extensions will be compiled 2024-06-26T05:54:27.1232977Z into subfolders of this directory. Second, if the ``build_directory`` 2024-06-26T05:54:27.1233290Z argument to this function is supplied, it overrides the entire path, i.e. 2024-06-26T05:54:27.1233519Z the library will be compiled into that folder directly. 2024-06-26T05:54:27.1233605Z 2024-06-26T05:54:27.1233889Z To compile the sources, the default system compiler (``c++``) is used, 2024-06-26T05:54:27.1234226Z which can be overridden by setting the ``CXX`` environment variable. To pass 2024-06-26T05:54:27.1234509Z additional arguments to the compilation process, ``extra_cflags`` or 2024-06-26T05:54:27.1234800Z ``extra_ldflags`` can be provided. For example, to compile your extension 2024-06-26T05:54:27.1235141Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use 2024-06-26T05:54:27.1235338Z ``extra_cflags`` to pass further include directories. 2024-06-26T05:54:27.1235429Z 2024-06-26T05:54:27.1235753Z CUDA support with mixed compilation is provided. Simply pass CUDA source 2024-06-26T05:54:27.1236013Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be 2024-06-26T05:54:27.1236331Z detected and compiled with nvcc rather than the C++ compiler. This includes 2024-06-26T05:54:27.1236627Z passing the CUDA lib64 directory as a library directory, and linking 2024-06-26T05:54:27.1236822Z ``cudart``. You can pass additional flags to nvcc via 2024-06-26T05:54:27.1237103Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various 2024-06-26T05:54:27.1237409Z heuristics for finding the CUDA install directory are used, which usually 2024-06-26T05:54:27.1237701Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the 2024-06-26T05:54:27.1237815Z safest option. 2024-06-26T05:54:27.1237901Z 2024-06-26T05:54:27.1237991Z Args: 2024-06-26T05:54:27.1238299Z name: The name of the extension to build. This MUST be the same as the 2024-06-26T05:54:27.1238434Z name of the pybind11 module! 2024-06-26T05:54:27.1238707Z sources: A list of relative or absolute paths to C++ source files. 2024-06-26T05:54:27.1239011Z extra_cflags: optional list of compiler flags to forward to the build. 2024-06-26T05:54:27.1239298Z extra_cuda_cflags: optional list of compiler flags to forward to nvcc 2024-06-26T05:54:27.1239438Z when building CUDA sources. 2024-06-26T05:54:27.1239720Z extra_ldflags: optional list of linker flags to forward to the build. 2024-06-26T05:54:27.1239996Z extra_include_paths: optional list of include directories to forward 2024-06-26T05:54:27.1240114Z to the build. 2024-06-26T05:54:27.1240342Z build_directory: optional path to use as build workspace. 2024-06-26T05:54:27.1240580Z verbose: If ``True``, turns on verbose logging of load steps. 2024-06-26T05:54:27.1240961Z with_cuda: Determines whether CUDA headers and libraries are added to 2024-06-26T05:54:27.1241180Z the build. If set to ``None`` (default), this value is 2024-06-26T05:54:27.1241439Z automatically determined based on the existence of ``.cu`` or 2024-06-26T05:54:27.1241692Z ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers 2024-06-26T05:54:27.1241882Z and libraries to be included. 2024-06-26T05:54:27.1242167Z is_python_module: If ``True`` (default), imports the produced shared 2024-06-26T05:54:27.1242419Z library as a Python module. If ``False``, behavior depends on 2024-06-26T05:54:27.1242531Z ``is_standalone``. 2024-06-26T05:54:27.1242819Z is_standalone: If ``False`` (default) loads the constructed extension 2024-06-26T05:54:27.1243132Z into the process as a plain dynamic library. If ``True``, build a 2024-06-26T05:54:27.1243254Z standalone executable. 2024-06-26T05:54:27.1243387Z 2024-06-26T05:54:27.1243483Z Returns: 2024-06-26T05:54:27.1243627Z If ``is_python_module`` is ``True``: 2024-06-26T05:54:27.1243901Z Returns the loaded PyTorch extension as a Python module. 2024-06-26T05:54:27.1243990Z 2024-06-26T05:54:27.1244267Z If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``: 2024-06-26T05:54:27.1244556Z Returns nothing. (The shared library is loaded into the process as 2024-06-26T05:54:27.1244665Z a side effect.) 2024-06-26T05:54:27.1244751Z 2024-06-26T05:54:27.1244897Z If ``is_standalone`` is ``True``. 2024-06-26T05:54:27.1245172Z Return the path to the executable. (On Windows, TORCH_LIB_PATH is 2024-06-26T05:54:27.1245424Z added to the PATH environment variable as a side effect.) 2024-06-26T05:54:27.1245516Z 2024-06-26T05:54:27.1245613Z Example: 2024-06-26T05:54:27.1245739Z >>> # xdoctest: +SKIP 2024-06-26T05:54:27.1245920Z >>> from torch.utils.cpp_extension import load 2024-06-26T05:54:27.1246028Z >>> module = load( 2024-06-26T05:54:27.1246206Z ... name='extension', 2024-06-26T05:54:27.1246468Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-06-26T05:54:27.1246626Z ... extra_cflags=['-O2'], 2024-06-26T05:54:27.1246748Z ... verbose=True) 2024-06-26T05:54:27.1246834Z 2024-06-26T05:54:27.1247220Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.1247317Z 2024-06-26T05:54:27.1247429Z warnings.warn(msg) 2024-06-26T05:54:27.1247514Z 2024-06-26T05:54:27.1247725Z --- Parse Warning: 88 / 90 --- 2024-06-26T05:54:27.1249051Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load_inline in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1524. 2024-06-26T05:54:27.1249466Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.1249554Z 2024-06-26T05:54:27.1249895Z Load a PyTorch C++ extension just-in-time (JIT) from string sources. 2024-06-26T05:54:27.1249996Z 2024-06-26T05:54:27.1250302Z This function behaves exactly like :func:`load`, but takes its sources as 2024-06-26T05:54:27.1250603Z strings rather than filenames. These strings are stored to files in the 2024-06-26T05:54:27.1250892Z build directory, after which the behavior of :func:`load_inline` is 2024-06-26T05:54:27.1251011Z identical to :func:`load`. 2024-06-26T05:54:27.1251095Z 2024-06-26T05:54:27.1251201Z See `the 2024-06-26T05:54:27.1251619Z tests `_ 2024-06-26T05:54:27.1251779Z for good examples of using this function. 2024-06-26T05:54:27.1251875Z 2024-06-26T05:54:27.1252252Z Sources may omit two required parts of a typical non-inline C++ extension: 2024-06-26T05:54:27.1252584Z the necessary header includes, as well as the (pybind11) binding code. More 2024-06-26T05:54:27.1252899Z precisely, strings passed to ``cpp_sources`` are first concatenated into a 2024-06-26T05:54:27.1253154Z single ``.cpp`` file. This file is then prepended with ``#include 2024-06-26T05:54:27.1253278Z ``. 2024-06-26T05:54:27.1253365Z 2024-06-26T05:54:27.1253817Z Furthermore, if the ``functions`` argument is supplied, bindings will be 2024-06-26T05:54:27.1254134Z automatically generated for each function specified. ``functions`` can 2024-06-26T05:54:27.1254439Z either be a list of function names, or a dictionary mapping from function 2024-06-26T05:54:27.1254745Z names to docstrings. If a list is given, the name of each function is used 2024-06-26T05:54:27.1254901Z as its docstring. 2024-06-26T05:54:27.1254989Z 2024-06-26T05:54:27.1255281Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu`` 2024-06-26T05:54:27.1255559Z file and prepended with ``torch/types.h``, ``cuda.h`` and 2024-06-26T05:54:27.1255869Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled 2024-06-26T05:54:27.1256165Z separately, but ultimately linked into a single library. Note that no 2024-06-26T05:54:27.1256468Z bindings are generated for functions in ``cuda_sources`` per se. To bind 2024-06-26T05:54:27.1256771Z to a CUDA kernel, you must create a C++ function that calls it, and either 2024-06-26T05:54:27.1257071Z declare or define this C++ function in one of the ``cpp_sources`` (and 2024-06-26T05:54:27.1257208Z include its name in ``functions``). 2024-06-26T05:54:27.1257293Z 2024-06-26T05:54:27.1257551Z See :func:`load` for a description of arguments omitted below. 2024-06-26T05:54:27.1257641Z 2024-06-26T05:54:27.1257732Z Args: 2024-06-26T05:54:27.1258032Z cpp_sources: A string, or list of strings, containing C++ source code. 2024-06-26T05:54:27.1258329Z cuda_sources: A string, or list of strings, containing CUDA source code. 2024-06-26T05:54:27.1258601Z functions: A list of function names for which to generate function 2024-06-26T05:54:27.1258895Z bindings. If a dictionary is given, it should map function names to 2024-06-26T05:54:27.1259126Z docstrings (which are otherwise just the function names). 2024-06-26T05:54:27.1259424Z with_cuda: Determines whether CUDA headers and libraries are added to 2024-06-26T05:54:27.1259638Z the build. If set to ``None`` (default), this value is 2024-06-26T05:54:27.1259894Z automatically determined based on whether ``cuda_sources`` is 2024-06-26T05:54:27.1260107Z provided. Set it to ``True`` to force CUDA headers 2024-06-26T05:54:27.1260244Z and libraries to be included. 2024-06-26T05:54:27.1260507Z with_pytorch_error_handling: Determines whether pytorch error and 2024-06-26T05:54:27.1260779Z warning macros are handled by pytorch instead of pybind. To do 2024-06-26T05:54:27.1261076Z this, each function ``foo`` is called via an intermediary ``_safe_foo`` 2024-06-26T05:54:27.1261349Z function. This redirection might cause issues in obscure cases 2024-06-26T05:54:27.1261606Z of cpp. This flag should be set to ``False`` when this redirect 2024-06-26T05:54:27.1261712Z causes issues. 2024-06-26T05:54:27.1261814Z 2024-06-26T05:54:27.1261911Z Example: 2024-06-26T05:54:27.1262104Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-06-26T05:54:27.1262319Z >>> from torch.utils.cpp_extension import load_inline 2024-06-26T05:54:27.1262424Z >>> source = """ 2024-06-26T05:54:27.1262613Z at::Tensor sin_add(at::Tensor x, at::Tensor y) { 2024-06-26T05:54:27.1262741Z return x.sin() + y.sin(); 2024-06-26T05:54:27.1262833Z } 2024-06-26T05:54:27.1262923Z """ 2024-06-26T05:54:27.1263171Z >>> module = load_inline(name='inline_extension', 2024-06-26T05:54:27.1263332Z ... cpp_sources=[source], 2024-06-26T05:54:27.1263538Z ... functions=['sin_add']) 2024-06-26T05:54:27.1263636Z 2024-06-26T05:54:27.1263733Z .. note:: 2024-06-26T05:54:27.1263999Z By default, the Ninja backend uses #CPUS + 2 workers to build the 2024-06-26T05:54:27.1264318Z extension. This may use up too many resources on some systems. One 2024-06-26T05:54:27.1264622Z can control the number of workers by setting the `MAX_JOBS` environment 2024-06-26T05:54:27.1264819Z variable to a non-negative number. 2024-06-26T05:54:27.1264908Z 2024-06-26T05:54:27.1265290Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.1265417Z 2024-06-26T05:54:27.1265525Z warnings.warn(msg) 2024-06-26T05:54:27.1265610Z 2024-06-26T05:54:27.1265821Z --- Parse Warning: 89 / 90 --- 2024-06-26T05:54:27.1267309Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ThroughputBenchmark in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/throughput_benchmark.py line=61. 2024-06-26T05:54:27.1267706Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.1267806Z 2024-06-26T05:54:27.1268213Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark. 2024-06-26T05:54:27.1268312Z 2024-06-26T05:54:27.1268694Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible 2024-06-26T05:54:27.1269025Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference 2024-06-26T05:54:27.1269361Z server like load. It can emulate multiple calling threads to a single module 2024-06-26T05:54:27.1269695Z provided. In the future we plan to enhance this component to support inter and 2024-06-26T05:54:27.1270086Z intra-op parallelism as well as multiple models running in a single process. 2024-06-26T05:54:27.1270187Z 2024-06-26T05:54:27.1270528Z Please note that even though nn.Module is supported, it might incur an overhead 2024-06-26T05:54:27.1270849Z from the need to hold GIL every time we execute Python code or pass around 2024-06-26T05:54:27.1271171Z inputs as Python objects. As soon as you have a ScriptModule version of your 2024-06-26T05:54:27.1271480Z model for inference deployment it is better to switch to using it in this 2024-06-26T05:54:27.1271590Z benchmark. 2024-06-26T05:54:27.1271677Z 2024-06-26T05:54:27.1271773Z Example:: 2024-06-26T05:54:27.1271870Z 2024-06-26T05:54:27.1272026Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:27.1272212Z >>> from torch.utils import ThroughputBenchmark 2024-06-26T05:54:27.1272386Z >>> bench = ThroughputBenchmark(my_module) 2024-06-26T05:54:27.1272649Z >>> # Pre-populate benchmark's data set with the inputs 2024-06-26T05:54:27.1272767Z >>> for input in inputs: 2024-06-26T05:54:27.1273083Z ... # Both args and kwargs work, same as any PyTorch Module / ScriptModule 2024-06-26T05:54:27.1273249Z ... bench.add_input(input[0], x2=input[1]) 2024-06-26T05:54:27.1273503Z >>> # Inputs supplied above are randomly used during the execution 2024-06-26T05:54:27.1273643Z >>> stats = bench.benchmark( 2024-06-26T05:54:27.1273764Z ... num_calling_threads=4, 2024-06-26T05:54:27.1273896Z ... num_warmup_iters = 100, 2024-06-26T05:54:27.1274007Z ... num_iters = 1000, 2024-06-26T05:54:27.1274099Z ... ) 2024-06-26T05:54:27.1274343Z >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms)) 2024-06-26T05:54:27.1274576Z >>> print("Number of iterations: {}".format(stats.num_iters)) 2024-06-26T05:54:27.1274662Z 2024-06-26T05:54:27.1275058Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.1275147Z 2024-06-26T05:54:27.1275256Z warnings.warn(msg) 2024-06-26T05:54:27.1275356Z 2024-06-26T05:54:27.1275554Z --- Parse Warning: 90 / 90 --- 2024-06-26T05:54:27.1276974Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedSampler in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/distributed.py line=14. 2024-06-26T05:54:27.1277386Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:27.1277641Z Sampler that restricts data loading to a subset of the dataset. 2024-06-26T05:54:27.1277738Z 2024-06-26T05:54:27.1277935Z It is especially useful in conjunction with 2024-06-26T05:54:27.1278274Z :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each 2024-06-26T05:54:27.1278643Z process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a 2024-06-26T05:54:27.1279010Z :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the 2024-06-26T05:54:27.1279173Z original dataset that is exclusive to it. 2024-06-26T05:54:27.1279272Z 2024-06-26T05:54:27.1279371Z .. note:: 2024-06-26T05:54:27.1279699Z Dataset is assumed to be of constant size and that any instance of it always 2024-06-26T05:54:27.1279891Z returns the same elements in the same order. 2024-06-26T05:54:27.1279982Z 2024-06-26T05:54:27.1280075Z Args: 2024-06-26T05:54:27.1280241Z dataset: Dataset used for sampling. 2024-06-26T05:54:27.1280519Z num_replicas (int, optional): Number of processes participating in 2024-06-26T05:54:27.1280933Z distributed training. By default, :attr:`world_size` is retrieved from the 2024-06-26T05:54:27.1281072Z current distributed group. 2024-06-26T05:54:27.1281401Z rank (int, optional): Rank of the current process within :attr:`num_replicas`. 2024-06-26T05:54:27.1281690Z By default, :attr:`rank` is retrieved from the current distributed 2024-06-26T05:54:27.1281790Z group. 2024-06-26T05:54:27.1282090Z shuffle (bool, optional): If ``True`` (default), sampler will shuffle the 2024-06-26T05:54:27.1282205Z indices. 2024-06-26T05:54:27.1282468Z seed (int, optional): random seed used to shuffle the sampler if 2024-06-26T05:54:27.1282729Z :attr:`shuffle=True`. This number should be identical across all 2024-06-26T05:54:27.1282959Z processes in the distributed group. Default: ``0``. 2024-06-26T05:54:27.1283246Z drop_last (bool, optional): if ``True``, then the sampler will drop the 2024-06-26T05:54:27.1283537Z tail of the data to make it evenly divisible across the number of 2024-06-26T05:54:27.1283811Z replicas. If ``False``, the sampler will add extra indices to make 2024-06-26T05:54:27.1284089Z the data evenly divisible across the replicas. Default: ``False``. 2024-06-26T05:54:27.1284193Z 2024-06-26T05:54:27.1284297Z .. warning:: 2024-06-26T05:54:27.1284547Z In distributed mode, calling the :meth:`set_epoch` method at 2024-06-26T05:54:27.1284906Z the beginning of each epoch **before** creating the :class:`DataLoader` iterator 2024-06-26T05:54:27.1285252Z is necessary to make shuffling work properly across multiple epochs. Otherwise, 2024-06-26T05:54:27.1285406Z the same ordering will be always used. 2024-06-26T05:54:27.1285506Z 2024-06-26T05:54:27.1285607Z Example:: 2024-06-26T05:54:27.1285693Z 2024-06-26T05:54:27.1285826Z >>> # xdoctest: +SKIP 2024-06-26T05:54:27.1286111Z >>> sampler = DistributedSampler(dataset) if is_distributed else None 2024-06-26T05:54:27.1286346Z >>> loader = DataLoader(dataset, shuffle=(sampler is None), 2024-06-26T05:54:27.1286503Z ... sampler=sampler) 2024-06-26T05:54:27.1286682Z >>> for epoch in range(start_epoch, n_epochs): 2024-06-26T05:54:27.1286817Z ... if is_distributed: 2024-06-26T05:54:27.1286961Z ... sampler.set_epoch(epoch) 2024-06-26T05:54:27.1287072Z ... train(loader) 2024-06-26T05:54:27.1287175Z 2024-06-26T05:54:27.1287600Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:27.1287691Z 2024-06-26T05:54:27.1287814Z warnings.warn(msg) 2024-06-26T05:54:27.1287902Z 2024-06-26T05:54:27.1288009Z  2024-06-26T05:54:27.1288214Z === Found 9 run-time warnings === 2024-06-26T05:54:27.1288444Z --- Runtime Warning: 1 / 9 --- 2024-06-26T05:54:27.1288777Z example = 2024-06-26T05:54:27.1290830Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py:1241: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /var/lib/jenkins/workspace/c10/core/TensorImpl.h:1928.) 2024-06-26T05:54:27.1290997Z return super().refine_names(names) 2024-06-26T05:54:27.1291100Z 2024-06-26T05:54:27.1291301Z --- Runtime Warning: 2 / 9 --- 2024-06-26T05:54:27.1291696Z example = 2024-06-26T05:54:27.1292655Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py:225: UserWarning: Warning only once for all operators, other operators may also be overrided. 2024-06-26T05:54:27.1293060Z Overriding a previously registered kernel for the same operator and the same dispatch key 2024-06-26T05:54:27.1293392Z operator: aten::div.Tensor(Tensor self, Tensor other) -> Tensor 2024-06-26T05:54:27.1293895Z registered at /var/lib/jenkins/workspace/build/aten/src/ATen/RegisterSchema.cpp:6 2024-06-26T05:54:27.1294009Z dispatch key: CPU 2024-06-26T05:54:27.1294584Z previous kernel: registered at /var/lib/jenkins/workspace/aten/src/ATen/LegacyBatchingRegistrations.cpp:1079 2024-06-26T05:54:27.1295323Z new kernel: registered at /dev/null:811 (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/core/dispatch/OperatorEntry.cpp:160.) 2024-06-26T05:54:27.1295578Z impl_fn(self.ns, name.split("::")[-1], dispatch_key) 2024-06-26T05:54:27.1295681Z 2024-06-26T05:54:27.1295879Z --- Runtime Warning: 3 / 9 --- 2024-06-26T05:54:27.1296189Z example = 2024-06-26T05:54:27.1297840Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py:106: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/NestedTensorImpl.cpp:180.) 2024-06-26T05:54:27.1298159Z return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None) 2024-06-26T05:54:27.1298262Z 2024-06-26T05:54:27.1298456Z --- Runtime Warning: 4 / 9 --- 2024-06-26T05:54:27.1298783Z example = 2024-06-26T05:54:27.1301230Z :1: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) 2024-06-26T05:54:27.1301322Z 2024-06-26T05:54:27.1301534Z --- Runtime Warning: 5 / 9 --- 2024-06-26T05:54:27.1301921Z example = 2024-06-26T05:54:27.1304187Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/const_fold.py:251: UserWarning: Attempted to insert a get_attr Node with no underlying reference in the owning GraphModule! Call GraphModule.add_submodule to add the necessary submodule, GraphModule.add_parameter to add the necessary Parameter, or nn.Module.register_buffer to add the necessary buffer 2024-06-26T05:54:27.1304406Z new_node = root_const_gm.graph.get_attr(in_node.target) 2024-06-26T05:54:27.1304491Z 2024-06-26T05:54:27.1304705Z --- Runtime Warning: 6 / 9 --- 2024-06-26T05:54:27.1305083Z example = 2024-06-26T05:54:27.1306725Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py:379: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance) 2024-06-26T05:54:27.1306881Z warnings.warn( 2024-06-26T05:54:27.1307000Z 2024-06-26T05:54:27.1307209Z --- Runtime Warning: 7 / 9 --- 2024-06-26T05:54:27.1307639Z example = 2024-06-26T05:54:27.1309233Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py:379: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance) 2024-06-26T05:54:27.1309354Z warnings.warn( 2024-06-26T05:54:27.1309441Z 2024-06-26T05:54:27.1309637Z --- Runtime Warning: 8 / 9 --- 2024-06-26T05:54:27.1316744Z example = 2024-06-26T05:54:27.1318049Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py:143: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`. 2024-06-26T05:54:27.1318218Z WeightNorm.apply(module, name, dim) 2024-06-26T05:54:27.1318309Z 2024-06-26T05:54:27.1318513Z --- Runtime Warning: 9 / 9 --- 2024-06-26T05:54:27.1318930Z example = 2024-06-26T05:54:27.1320157Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py:143: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`. 2024-06-26T05:54:27.1320302Z WeightNorm.apply(module, name, dim) 2024-06-26T05:54:27.1320406Z 2024-06-26T05:54:27.1320844Z === 331 passed, 365 skipped, 99 warnings in 10.95 seconds === 2024-06-26T05:54:27.1321208Z Running test_cpp_extensions_aot_no_ninja 1/1 ... [2024-06-26 05:54:26.958148] 2024-06-26T05:54:29.1270519Z running install 2024-06-26T05:54:29.1272485Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2024-06-26T05:54:29.1273523Z !! 2024-06-26T05:54:29.1273655Z 2024-06-26T05:54:29.1273849Z ******************************************************************************** 2024-06-26T05:54:29.1274375Z Please avoid running ``setup.py`` directly. 2024-06-26T05:54:29.1274914Z Instead, use pypa/build, pypa/installer or other 2024-06-26T05:54:29.1275438Z standards-based tools. 2024-06-26T05:54:29.1275677Z 2024-06-26T05:54:29.1276147Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2024-06-26T05:54:29.1276890Z ******************************************************************************** 2024-06-26T05:54:29.1277237Z 2024-06-26T05:54:29.1277338Z !! 2024-06-26T05:54:29.1277582Z self.initialize_options() 2024-06-26T05:54:29.1384260Z running build 2024-06-26T05:54:29.1384546Z running build_py 2024-06-26T05:54:29.1444184Z creating build 2024-06-26T05:54:29.1445604Z creating build/lib.linux-x86_64-cpython-312 2024-06-26T05:54:29.1446791Z creating build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension 2024-06-26T05:54:29.1448615Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension 2024-06-26T05:54:29.1450621Z running build_ext 2024-06-26T05:54:29.1466216Z building 'torch_test_cpp_extension.cpp' extension 2024-06-26T05:54:29.1467223Z creating build/temp.linux-x86_64-cpython-312 2024-06-26T05:54:29.1475141Z gcc -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c extension.cpp -o build/temp.linux-x86_64-cpython-312/extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_clang\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1002\" -DTORCH_EXTENSION_NAME=cpp -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2024-06-26T05:54:30.3457629Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/Exceptions.h:12, 2024-06-26T05:54:30.3459698Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11, 2024-06-26T05:54:30.3460980Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:9, 2024-06-26T05:54:30.3461734Z from extension.cpp:1: 2024-06-26T05:54:30.3464918Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_’: 2024-06-26T05:54:30.3466046Z extension.cpp:45:53: required from here 2024-06-26T05:54:30.3467827Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h:1555:7: warning: ‘pybind11::class_’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes] 2024-06-26T05:54:30.3469420Z 1555 | class class_ : public detail::generic_type { 2024-06-26T05:54:30.3469875Z | ^~~~~~ 2024-06-26T05:54:30.3474724Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cpp.cpython-312-x86_64-linux-gnu.so 2024-06-26T05:54:30.7497347Z building 'torch_test_cpp_extension.maia' extension 2024-06-26T05:54:30.7503116Z gcc -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c maia_extension.cpp -o build/temp.linux-x86_64-cpython-312/maia_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_clang\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1002\" -DTORCH_EXTENSION_NAME=maia -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2024-06-26T05:54:31.9443609Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/maia_extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/maia.cpython-312-x86_64-linux-gnu.so 2024-06-26T05:54:32.3105478Z building 'torch_test_cpp_extension.rng' extension 2024-06-26T05:54:32.3111431Z gcc -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c rng_extension.cpp -o build/temp.linux-x86_64-cpython-312/rng_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_clang\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1002\" -DTORCH_EXTENSION_NAME=rng -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2024-06-26T05:54:33.6451489Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-06-26T05:54:33.6454074Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:6, 2024-06-26T05:54:33.6455641Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-06-26T05:54:33.6457185Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-06-26T05:54:33.6458688Z from rng_extension.cpp:6: 2024-06-26T05:54:33.6459909Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1106: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-06-26T05:54:33.6460953Z 1106 | # pragma unroll 2024-06-26T05:54:33.6461255Z | 2024-06-26T05:54:33.6462057Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1141, 2024-06-26T05:54:33.6463370Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-06-26T05:54:33.6464557Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:6, 2024-06-26T05:54:33.6465722Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-06-26T05:54:33.6467033Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-06-26T05:54:33.6467904Z from rng_extension.cpp:6: 2024-06-26T05:54:33.6469059Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_n.h:59: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-06-26T05:54:33.6470073Z 59 | #pragma unroll 2024-06-26T05:54:33.6470358Z | 2024-06-26T05:54:33.6471358Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_n.h:72: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-06-26T05:54:33.6472377Z 72 | #pragma unroll 2024-06-26T05:54:33.6472660Z | 2024-06-26T05:54:33.6473461Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1142, 2024-06-26T05:54:33.6474798Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-06-26T05:54:33.6475972Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:6, 2024-06-26T05:54:33.6477260Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-06-26T05:54:33.6478584Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-06-26T05:54:33.6479463Z from rng_extension.cpp:6: 2024-06-26T05:54:33.6480711Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_mask.h:131: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-06-26T05:54:33.6481818Z 131 | #pragma unroll 2024-06-26T05:54:33.6482118Z | 2024-06-26T05:54:33.6485639Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/rng_extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/rng.cpython-312-x86_64-linux-gnu.so 2024-06-26T05:54:34.0821688Z running install_lib 2024-06-26T05:54:34.0884386Z creating install 2024-06-26T05:54:34.0884861Z creating install/opt 2024-06-26T05:54:34.0885344Z creating install/opt/conda 2024-06-26T05:54:34.0885870Z creating install/opt/conda/envs 2024-06-26T05:54:34.0886457Z creating install/opt/conda/envs/py_3.12 2024-06-26T05:54:34.0887148Z creating install/opt/conda/envs/py_3.12/lib 2024-06-26T05:54:34.0887819Z creating install/opt/conda/envs/py_3.12/lib/python3.12 2024-06-26T05:54:34.0889258Z creating install/opt/conda/envs/py_3.12/lib/python3.12/site-packages 2024-06-26T05:54:34.0890464Z creating install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2024-06-26T05:54:34.0892261Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/__init__.py -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2024-06-26T05:54:34.0894521Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cpp.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2024-06-26T05:54:34.0979112Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/maia.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2024-06-26T05:54:34.1065777Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/rng.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2024-06-26T05:54:34.1159581Z byte-compiling ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension/__init__.py to __init__.cpython-312.pyc 2024-06-26T05:54:34.1161111Z running install_egg_info 2024-06-26T05:54:34.1299167Z running egg_info 2024-06-26T05:54:34.1299882Z creating torch_test_cpp_extension.egg-info 2024-06-26T05:54:34.1353554Z writing torch_test_cpp_extension.egg-info/PKG-INFO 2024-06-26T05:54:34.1356865Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt 2024-06-26T05:54:34.1358476Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt 2024-06-26T05:54:34.1359826Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-06-26T05:54:34.1422989Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-06-26T05:54:34.1427928Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-06-26T05:54:34.1429770Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension-0.0.0-py3.12.egg-info 2024-06-26T05:54:34.1432819Z running install_scripts 2024-06-26T05:54:36.0630749Z running install 2024-06-26T05:54:36.0632572Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2024-06-26T05:54:36.0633793Z !! 2024-06-26T05:54:36.0633942Z 2024-06-26T05:54:36.0634123Z ******************************************************************************** 2024-06-26T05:54:36.0634657Z Please avoid running ``setup.py`` directly. 2024-06-26T05:54:36.0635198Z Instead, use pypa/build, pypa/installer or other 2024-06-26T05:54:36.0635770Z standards-based tools. 2024-06-26T05:54:36.0636024Z 2024-06-26T05:54:36.0636501Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2024-06-26T05:54:36.0637291Z ******************************************************************************** 2024-06-26T05:54:36.0637641Z 2024-06-26T05:54:36.0637792Z !! 2024-06-26T05:54:36.0638061Z self.initialize_options() 2024-06-26T05:54:36.0742485Z running build 2024-06-26T05:54:36.0742764Z running build_ext 2024-06-26T05:54:36.1054168Z building 'no_python_abi_suffix_test' extension 2024-06-26T05:54:36.1055063Z creating /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build 2024-06-26T05:54:36.1056332Z creating /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-312 2024-06-26T05:54:36.1351869Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-312/build.ninja... 2024-06-26T05:54:36.1352932Z Compiling objects... 2024-06-26T05:54:36.1353342Z Using envvar MAX_JOBS (6) as the number of workers... 2024-06-26T05:54:36.2459396Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-312/no_python_abi_suffix_test.o.d -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -I/opt/conda/envs/py_3.12/include/python3.12 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/no_python_abi_suffix_test.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-312/no_python_abi_suffix_test.o -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=no_python_abi_suffix_test -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2024-06-26T05:54:36.2499653Z creating build/lib.linux-x86_64-cpython-312 2024-06-26T05:54:36.2503818Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-312/no_python_abi_suffix_test.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/no_python_abi_suffix_test.so 2024-06-26T05:54:36.3046548Z running install_lib 2024-06-26T05:54:36.3106351Z creating install 2024-06-26T05:54:36.3107267Z creating install/opt 2024-06-26T05:54:36.3107830Z creating install/opt/conda 2024-06-26T05:54:36.3108303Z creating install/opt/conda/envs 2024-06-26T05:54:36.3108842Z creating install/opt/conda/envs/py_3.12 2024-06-26T05:54:36.3109517Z creating install/opt/conda/envs/py_3.12/lib 2024-06-26T05:54:36.3110173Z creating install/opt/conda/envs/py_3.12/lib/python3.12 2024-06-26T05:54:36.3111098Z creating install/opt/conda/envs/py_3.12/lib/python3.12/site-packages 2024-06-26T05:54:36.3112472Z copying build/lib.linux-x86_64-cpython-312/no_python_abi_suffix_test.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages 2024-06-26T05:54:36.3116532Z running install_egg_info 2024-06-26T05:54:36.3261288Z running egg_info 2024-06-26T05:54:36.3261772Z creating no_python_abi_suffix_test.egg-info 2024-06-26T05:54:36.3315415Z writing no_python_abi_suffix_test.egg-info/PKG-INFO 2024-06-26T05:54:36.3318977Z writing dependency_links to no_python_abi_suffix_test.egg-info/dependency_links.txt 2024-06-26T05:54:36.3321997Z writing top-level names to no_python_abi_suffix_test.egg-info/top_level.txt 2024-06-26T05:54:36.3323660Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2024-06-26T05:54:36.3380731Z reading manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2024-06-26T05:54:36.3386295Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2024-06-26T05:54:36.3388791Z Copying no_python_abi_suffix_test.egg-info to ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/no_python_abi_suffix_test-0.0.0-py3.12.egg-info 2024-06-26T05:54:36.3392581Z running install_scripts 2024-06-26T05:54:36.7942796Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cpp_extensions_aot_no_ninja.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:54:36.793757] 2024-06-26T05:54:40.9479716Z 2024-06-26T05:54:40.9481825Z test_cpp_extensions_aot_no_ninja 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_extensions_aot_no_ninja_1.1_af0a04dedcc9fedc_.log 2024-06-26T05:54:40.9490706Z Running 17 items in this shard: test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_backward, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cublas_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cuda_dlink_libs, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cuda_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cusolver_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_extension_function, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_extension_module, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_mps_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_no_python_abi_suffix_sets_the_correct_library_name, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_optional, test/test_cpp_extensions_aot_no_ninja.py::TestPybindTypeCasters::test_pybind_return_types, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_add, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_conv_backend_override, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_unregistered, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_zeros, test/test_cpp_extensions_aot_no_ninja.py::TestRNGExtension::test_rng, test/test_cpp_extensions_aot_no_ninja.py::TestTorchLibrary::test_torch_library 2024-06-26T05:54:40.9498351Z 2024-06-26T05:54:40.9498749Z Running test_cpp_extensions_aot_ninja 1/1 ... [2024-06-26 05:54:40.948356] 2024-06-26T05:54:43.4632674Z running install 2024-06-26T05:54:43.4634439Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2024-06-26T05:54:43.4635469Z !! 2024-06-26T05:54:43.4635615Z 2024-06-26T05:54:43.4635796Z ******************************************************************************** 2024-06-26T05:54:43.4636327Z Please avoid running ``setup.py`` directly. 2024-06-26T05:54:43.4636861Z Instead, use pypa/build, pypa/installer or other 2024-06-26T05:54:43.4637380Z standards-based tools. 2024-06-26T05:54:43.4637616Z 2024-06-26T05:54:43.4638105Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2024-06-26T05:54:43.4639055Z ******************************************************************************** 2024-06-26T05:54:43.4639404Z 2024-06-26T05:54:43.4639494Z !! 2024-06-26T05:54:43.4639751Z self.initialize_options() 2024-06-26T05:54:43.4755886Z running build 2024-06-26T05:54:43.4756165Z running build_py 2024-06-26T05:54:43.4823771Z creating build 2024-06-26T05:54:43.4824602Z creating build/lib.linux-x86_64-cpython-312 2024-06-26T05:54:43.4825284Z creating build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension 2024-06-26T05:54:43.4826387Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension 2024-06-26T05:54:43.4827282Z running build_ext 2024-06-26T05:54:43.5172848Z building 'torch_test_cpp_extension.cpp' extension 2024-06-26T05:54:43.5174021Z creating /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312 2024-06-26T05:54:43.5496168Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/build.ninja... 2024-06-26T05:54:43.5497086Z Compiling objects... 2024-06-26T05:54:43.5497497Z Using envvar MAX_JOBS (6) as the number of workers... 2024-06-26T05:54:44.3437250Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/extension.o.d -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -I/var/lib/jenkins/workspace/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/extension.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=cpp -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2024-06-26T05:54:44.3538501Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cpp.cpython-312-x86_64-linux-gnu.so 2024-06-26T05:54:44.7008872Z building 'torch_test_cpp_extension.maia' extension 2024-06-26T05:54:44.7336587Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/build.ninja... 2024-06-26T05:54:44.7343706Z Compiling objects... 2024-06-26T05:54:44.7344390Z Using envvar MAX_JOBS (6) as the number of workers... 2024-06-26T05:54:45.4997080Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/maia_extension.o.d -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -I/var/lib/jenkins/workspace/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/maia_extension.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/maia_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=maia -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2024-06-26T05:54:45.5045607Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/maia_extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/maia.cpython-312-x86_64-linux-gnu.so 2024-06-26T05:54:45.8320129Z building 'torch_test_cpp_extension.rng' extension 2024-06-26T05:54:45.8646202Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/build.ninja... 2024-06-26T05:54:45.8647121Z Compiling objects... 2024-06-26T05:54:45.8647528Z Using envvar MAX_JOBS (6) as the number of workers... 2024-06-26T05:54:46.7768272Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/rng_extension.o.d -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -I/var/lib/jenkins/workspace/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/rng_extension.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/rng_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=rng -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2024-06-26T05:54:46.7816986Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/rng_extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/rng.cpython-312-x86_64-linux-gnu.so 2024-06-26T05:54:47.0332166Z running install_lib 2024-06-26T05:54:47.0396130Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cpp.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2024-06-26T05:54:47.0442756Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/maia.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2024-06-26T05:54:47.0490767Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/rng.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2024-06-26T05:54:47.0544065Z running install_egg_info 2024-06-26T05:54:47.0680704Z running egg_info 2024-06-26T05:54:47.0733328Z writing torch_test_cpp_extension.egg-info/PKG-INFO 2024-06-26T05:54:47.0736564Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt 2024-06-26T05:54:47.0746826Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt 2024-06-26T05:54:47.0811598Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-06-26T05:54:47.0817122Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-06-26T05:54:47.0827212Z removing './install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension-0.0.0-py3.12.egg-info' (and everything under it) 2024-06-26T05:54:47.0829093Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension-0.0.0-py3.12.egg-info 2024-06-26T05:54:47.0832600Z running install_scripts 2024-06-26T05:54:48.9397214Z running install 2024-06-26T05:54:48.9399637Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2024-06-26T05:54:48.9400708Z !! 2024-06-26T05:54:48.9400855Z 2024-06-26T05:54:48.9401039Z ******************************************************************************** 2024-06-26T05:54:48.9401573Z Please avoid running ``setup.py`` directly. 2024-06-26T05:54:48.9402092Z Instead, use pypa/build, pypa/installer or other 2024-06-26T05:54:48.9402628Z standards-based tools. 2024-06-26T05:54:48.9402885Z 2024-06-26T05:54:48.9403356Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2024-06-26T05:54:48.9404083Z ******************************************************************************** 2024-06-26T05:54:48.9404438Z 2024-06-26T05:54:48.9404525Z !! 2024-06-26T05:54:48.9404791Z self.initialize_options() 2024-06-26T05:54:48.9511937Z running build 2024-06-26T05:54:48.9512211Z running build_ext 2024-06-26T05:54:48.9829133Z building 'no_python_abi_suffix_test' extension 2024-06-26T05:54:49.0122547Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-312/build.ninja... 2024-06-26T05:54:49.0123586Z Compiling objects... 2024-06-26T05:54:49.0123991Z Using envvar MAX_JOBS (6) as the number of workers... 2024-06-26T05:54:49.0381372Z ninja: no work to do. 2024-06-26T05:54:49.0428645Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-312/no_python_abi_suffix_test.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/no_python_abi_suffix_test.so 2024-06-26T05:54:49.0994016Z running install_lib 2024-06-26T05:54:49.1054125Z copying build/lib.linux-x86_64-cpython-312/no_python_abi_suffix_test.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages 2024-06-26T05:54:49.1058326Z running install_egg_info 2024-06-26T05:54:49.1200536Z running egg_info 2024-06-26T05:54:49.1254108Z writing no_python_abi_suffix_test.egg-info/PKG-INFO 2024-06-26T05:54:49.1256837Z writing dependency_links to no_python_abi_suffix_test.egg-info/dependency_links.txt 2024-06-26T05:54:49.1266980Z writing top-level names to no_python_abi_suffix_test.egg-info/top_level.txt 2024-06-26T05:54:49.1333007Z reading manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2024-06-26T05:54:49.1338051Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2024-06-26T05:54:49.1347129Z removing './install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/no_python_abi_suffix_test-0.0.0-py3.12.egg-info' (and everything under it) 2024-06-26T05:54:49.1349453Z Copying no_python_abi_suffix_test.egg-info to ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/no_python_abi_suffix_test-0.0.0-py3.12.egg-info 2024-06-26T05:54:49.1352534Z running install_scripts 2024-06-26T05:54:49.5301877Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cpp_extensions_aot_ninja.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:54:49.529698] 2024-06-26T05:54:53.5175004Z 2024-06-26T05:54:53.5176603Z test_cpp_extensions_aot_ninja 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_extensions_aot_ninja_1.1_b11b579adcc3153c_.log 2024-06-26T05:54:53.5186405Z Running 17 items in this shard: test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_backward, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cublas_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cuda_dlink_libs, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cuda_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cusolver_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_extension_function, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_extension_module, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_mps_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_no_python_abi_suffix_sets_the_correct_library_name, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_optional, test/test_cpp_extensions_aot_ninja.py::TestPybindTypeCasters::test_pybind_return_types, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_add, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_conv_backend_override, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_unregistered, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_zeros, test/test_cpp_extensions_aot_ninja.py::TestRNGExtension::test_rng, test/test_cpp_extensions_aot_ninja.py::TestTorchLibrary::test_torch_library 2024-06-26T05:54:53.5199536Z 2024-06-26T05:54:53.5200261Z Running dynamo/test_dynamic_shapes 1/1 ... [2024-06-26 05:54:53.517713] 2024-06-26T05:54:53.5203779Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_dynamic_shapes.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:54:53.518009] 2024-06-26T05:54:57.4843571Z 2024-06-26T05:54:57.4845181Z dynamo/test_dynamic_shapes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_dynamic_shapes_1.1_6f0b33925bc11e1e_.log 2024-06-26T05:54:57.4846494Z 2024-06-26T05:54:57.4846988Z Running dynamo/test_fx_passes_pre_grad 1/1 ... [2024-06-26 05:54:57.484442] 2024-06-26T05:54:57.4851720Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_fx_passes_pre_grad.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:54:57.484723] 2024-06-26T05:54:59.6846744Z 2024-06-26T05:54:59.6848631Z dynamo/test_fx_passes_pre_grad 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_fx_passes_pre_grad_1.1_ffd2d8b8fabca2eb_.log 2024-06-26T05:54:59.6850207Z 2024-06-26T05:54:59.6850554Z Running dynamo/test_frame_init 1/1 ... [2024-06-26 05:54:59.684773] 2024-06-26T05:54:59.6853826Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_frame_init.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:54:59.685062] 2024-06-26T05:55:01.9751403Z 2024-06-26T05:55:01.9752762Z dynamo/test_frame_init 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_frame_init_1.1_a039f92502bd633f_.log 2024-06-26T05:55:01.9753914Z 2024-06-26T05:55:01.9754642Z Running dynamo/test_sdpa 1/1 ... [2024-06-26 05:55:01.975297] 2024-06-26T05:55:01.9759254Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_sdpa.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:01.975616] 2024-06-26T05:55:04.1908292Z 2024-06-26T05:55:04.1909658Z dynamo/test_sdpa 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_sdpa_1.1_fb11f26323820e7e_.log 2024-06-26T05:55:04.1910882Z 2024-06-26T05:55:04.1911625Z Running dynamo/test_exceptions 1/1 ... [2024-06-26 05:55:04.190965] 2024-06-26T05:55:04.1915910Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_exceptions.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:04.191267] 2024-06-26T05:55:06.3404104Z 2024-06-26T05:55:06.3405584Z dynamo/test_exceptions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_exceptions_1.1_8b79ad684a1bfd74_.log 2024-06-26T05:55:06.3406756Z 2024-06-26T05:55:06.3407186Z Running dynamo/test_repros 1/1 ... [2024-06-26 05:55:06.340527] 2024-06-26T05:55:06.3411167Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_repros.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:06.340851] 2024-06-26T05:55:08.4935806Z 2024-06-26T05:55:08.4937225Z dynamo/test_repros 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_repros_1.1_ec3c1887602d0509_.log 2024-06-26T05:55:08.4938250Z 2024-06-26T05:55:08.4938619Z Running dynamo/test_nops 1/1 ... [2024-06-26 05:55:08.493677] 2024-06-26T05:55:08.4943150Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_nops.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:08.493979] 2024-06-26T05:55:10.6815100Z 2024-06-26T05:55:10.6816378Z dynamo/test_nops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_nops_1.1_7008a2aa2da396c7_.log 2024-06-26T05:55:10.6817246Z 2024-06-26T05:55:10.6818719Z Running dynamo/test_config 1/1 ... [2024-06-26 05:55:10.681663] 2024-06-26T05:55:10.6822915Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_config.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:10.681994] 2024-06-26T05:55:12.8188405Z 2024-06-26T05:55:12.8190033Z dynamo/test_config 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_config_1.1_60fb99aa2fdb3a48_.log 2024-06-26T05:55:12.8191063Z 2024-06-26T05:55:12.8191463Z Running test_jiterator 1/1 ... [2024-06-26 05:55:12.818975] 2024-06-26T05:55:12.8195361Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_jiterator.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:12.819273] 2024-06-26T05:55:15.0372649Z 2024-06-26T05:55:15.0374378Z test_jiterator 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jiterator_1.1_92e485b309a5427c_.log 2024-06-26T05:55:15.0375549Z Running 0 items in this shard: 2024-06-26T05:55:15.0375821Z 2024-06-26T05:55:15.0376120Z Running test_matmul_cuda 1/1 ... [2024-06-26 05:55:15.037447] 2024-06-26T05:55:15.0380472Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_matmul_cuda.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:15.037739] 2024-06-26T05:55:17.2869125Z 2024-06-26T05:55:17.2871006Z test_matmul_cuda 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_matmul_cuda_1.1_b98a30f9b25e9e01_.log 2024-06-26T05:55:17.2872494Z Running 0 items in this shard: 2024-06-26T05:55:17.2872744Z 2024-06-26T05:55:17.2873169Z Running dynamo/test_sources 1/1 ... [2024-06-26 05:55:17.287023] 2024-06-26T05:55:17.2876282Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_sources.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:17.287306] 2024-06-26T05:55:19.4907739Z 2024-06-26T05:55:19.4909191Z dynamo/test_sources 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_sources_1.1_291ab6c42efbddcb_.log 2024-06-26T05:55:19.4910214Z 2024-06-26T05:55:19.4910946Z Running xpu/test_conv 1/1 ... [2024-06-26 05:55:19.490867] 2024-06-26T05:55:19.4914317Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'xpu/test_conv.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:19.491158] 2024-06-26T05:55:22.1603703Z 2024-06-26T05:55:22.1605369Z xpu/test_conv 1/1 was successful, full logs can be found in artifacts with path test/test-reports/xpu.test_conv_1.1_a78de2b012917b92_.log 2024-06-26T05:55:22.1606744Z Running 0 items in this shard: 2024-06-26T05:55:22.1607155Z 2024-06-26T05:55:22.1607410Z Running test_cuda 1/1 ... [2024-06-26 05:55:22.160468] 2024-06-26T05:55:22.1610937Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cuda.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:22.160771] 2024-06-26T05:55:25.4924151Z 2024-06-26T05:55:25.4925654Z test_cuda 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_1.1_071b591b138c6f39_.log 2024-06-26T05:55:25.4926722Z Running 0 items in this shard: 2024-06-26T05:55:25.4927091Z 2024-06-26T05:55:25.4927677Z Running dynamo/test_verify_correctness 1/1 ... [2024-06-26 05:55:25.492564] 2024-06-26T05:55:25.4931604Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_verify_correctness.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:25.492856] 2024-06-26T05:55:27.6594265Z 2024-06-26T05:55:27.6596146Z 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_dfb91f1f8e547716_.log 2024-06-26T05:55:27.6597391Z 2024-06-26T05:55:27.6597748Z Running dynamo/test_profiler 1/1 ... [2024-06-26 05:55:27.659512] 2024-06-26T05:55:27.6600936Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_profiler.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:27.659798] 2024-06-26T05:55:29.7969331Z 2024-06-26T05:55:29.7971009Z dynamo/test_profiler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_profiler_1.1_33b8d9d67f14e555_.log 2024-06-26T05:55:29.7972057Z 2024-06-26T05:55:29.7972424Z Running dynamo/test_reorder_logs 1/1 ... [2024-06-26 05:55:29.797034] 2024-06-26T05:55:29.7976325Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_reorder_logs.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:29.797326] 2024-06-26T05:55:31.9330293Z 2024-06-26T05:55:31.9331697Z dynamo/test_reorder_logs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_reorder_logs_1.1_5688035919cd1c53_.log 2024-06-26T05:55:31.9332869Z 2024-06-26T05:55:31.9333970Z Running test_hub 1/1 ... [2024-06-26 05:55:31.933213] 2024-06-26T05:55:31.9338303Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_hub.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:31.933549] 2024-06-26T05:55:34.1147247Z 2024-06-26T05:55:34.1148594Z test_hub 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_hub_1.1_8bc055d42587c735_.log 2024-06-26T05:55:34.1149467Z 2024-06-26T05:55:34.1149944Z Running dynamo/test_minifier 1/1 ... [2024-06-26 05:55:34.114812] 2024-06-26T05:55:34.1153870Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_minifier.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:34.115113] 2024-06-26T05:55:36.3155431Z 2024-06-26T05:55:36.3157407Z dynamo/test_minifier 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_minifier_1.1_41ee298638884c45_.log 2024-06-26T05:55:36.3158903Z 2024-06-26T05:55:36.3159630Z Running dynamo/test_activation_checkpointing 1/1 ... [2024-06-26 05:55:36.315622] 2024-06-26T05:55:36.3163491Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_activation_checkpointing.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:36.316008] 2024-06-26T05:55:38.6026071Z 2024-06-26T05:55:38.6034717Z 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_94bc5c25e06f6dbf_.log 2024-06-26T05:55:38.6042814Z 2024-06-26T05:55:38.6058455Z Running dynamo/test_recompile_ux 1/1 ... [2024-06-26 05:55:38.605438] 2024-06-26T05:55:38.6076740Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_recompile_ux.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:38.606893] 2024-06-26T05:55:40.8277947Z 2024-06-26T05:55:40.8279937Z 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_5d2d76508736e45b_.log 2024-06-26T05:55:40.8281062Z 2024-06-26T05:55:40.8281414Z Running dynamo/test_subclasses 1/1 ... [2024-06-26 05:55:40.827849] 2024-06-26T05:55:40.8284593Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_subclasses.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:40.828135] 2024-06-26T05:55:43.1126256Z 2024-06-26T05:55:43.1134050Z dynamo/test_subclasses 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_subclasses_1.1_daf3d7f22389b3f3_.log 2024-06-26T05:55:43.1141055Z 2024-06-26T05:55:43.1152420Z Running lazy/test_extract_compiled_graph 1/1 ... [2024-06-26 05:55:43.114874] 2024-06-26T05:55:43.1165252Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'lazy/test_extract_compiled_graph.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:43.115941] 2024-06-26T05:55:44.5220232Z 2024-06-26T05:55:44.5222048Z lazy/test_extract_compiled_graph 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_extract_compiled_graph_1.1_85c7eb82d0d1e683_.log 2024-06-26T05:55:44.5223466Z 2024-06-26T05:55:44.5223873Z Running dynamo/test_aot_autograd_cache 1/1 ... [2024-06-26 05:55:44.522063] 2024-06-26T05:55:44.5227222Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_aot_autograd_cache.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:44.522370] 2024-06-26T05:55:46.8008685Z 2024-06-26T05:55:46.8013846Z dynamo/test_aot_autograd_cache 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_aot_autograd_cache_1.1_80c7dc5c105efb67_.log 2024-06-26T05:55:46.8018300Z 2024-06-26T05:55:46.8023124Z Running test_cuda_multigpu 1/1 ... [2024-06-26 05:55:46.801112] 2024-06-26T05:55:46.8034548Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cuda_multigpu.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:46.801638] 2024-06-26T05:55:49.2801446Z 2024-06-26T05:55:49.2803292Z test_cuda_multigpu 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_multigpu_1.1_6a49901e08a3a55a_.log 2024-06-26T05:55:49.2804831Z Running 0 items in this shard: 2024-06-26T05:55:49.2805083Z 2024-06-26T05:55:49.2805372Z Running test_linalg 3/3 ... [2024-06-26 05:55:49.280219] 2024-06-26T05:55:49.2808467Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_linalg.py', '-m', 'serial', '--shard-id=3', '--num-shards=3', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:49.280525] 2024-06-26T05:55:53.5536011Z 2024-06-26T05:55:53.5537833Z test_linalg 3/3 was successful, full logs can be found in artifacts with path test/test-reports/test_linalg_3.3_87f66cd6a0bcbf9b_.log 2024-06-26T05:55:53.5539899Z Running 1 items in this shard: test/test_linalg.py::TestLinalgCPU::test_svd_cpu_complex64 2024-06-26T05:55:53.5540851Z 2024-06-26T05:55:53.5541766Z Running dynamo/test_python_autograd 1/1 ... [2024-06-26 05:55:53.553776] 2024-06-26T05:55:53.5545012Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_python_autograd.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:53.554101] 2024-06-26T05:55:55.8795829Z 2024-06-26T05:55:55.8797340Z 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_a87a52b111aa6f14_.log 2024-06-26T05:55:55.8798342Z 2024-06-26T05:55:55.8860425Z Running dynamo/test_dynamic_shapes 1/1 ... [2024-06-26 05:55:55.885721] 2024-06-26T05:55:55.8863677Z Running dynamo/test_fx_passes_pre_grad 1/1 ... [2024-06-26 05:55:55.886092] 2024-06-26T05:55:55.8865685Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_dynamic_shapes.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:55.886167] 2024-06-26T05:55:55.8867715Z Running dynamo/test_frame_init 1/1 ... [2024-06-26 05:55:55.886214] 2024-06-26T05:55:55.8869881Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_frame_init.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:55.886636] 2024-06-26T05:55:55.8872751Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_fx_passes_pre_grad.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:55.886655] 2024-06-26T05:55:58.3154707Z 2024-06-26T05:55:58.3156198Z dynamo/test_frame_init 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_frame_init_1.1_16f16892ff499805_.log 2024-06-26T05:55:58.3157556Z 2024-06-26T05:55:58.3353612Z 2024-06-26T05:55:58.3362362Z dynamo/test_fx_passes_pre_grad 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_fx_passes_pre_grad_1.1_9ab100515d147b04_.log 2024-06-26T05:55:58.3370385Z 2024-06-26T05:56:00.0204121Z 2024-06-26T05:56:00.0206223Z dynamo/test_dynamic_shapes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_dynamic_shapes_1.1_86c9cae756e2096c_.log 2024-06-26T05:56:00.0207704Z 2024-06-26T05:56:00.9702366Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:00.9844298Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:01.0687743Z Running dynamo/test_sdpa 1/1 ... [2024-06-26 05:56:01.067941] 2024-06-26T05:56:01.0691667Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_sdpa.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:01.068403] 2024-06-26T05:56:01.0873979Z Running dynamo/test_exceptions 1/1 ... [2024-06-26 05:56:01.086920] 2024-06-26T05:56:01.0878241Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_exceptions.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:01.087469] 2024-06-26T05:56:02.5664594Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:02.6251643Z Running dynamo/test_repros 1/1 ... [2024-06-26 05:56:02.624680] 2024-06-26T05:56:02.6255125Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_repros.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:02.625024] 2024-06-26T05:56:03.6003371Z 2024-06-26T05:56:03.6005306Z dynamo/test_exceptions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_exceptions_1.1_ea06718918b9f36f_.log 2024-06-26T05:56:03.6006753Z 2024-06-26T05:56:03.6103099Z 2024-06-26T05:56:03.6105553Z dynamo/test_sdpa 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_sdpa_1.1_84619ccd504b118a_.log 2024-06-26T05:56:03.6107657Z 2024-06-26T05:56:05.1908381Z 2024-06-26T05:56:05.1910478Z dynamo/test_repros 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_repros_1.1_756626a27f4d89d9_.log 2024-06-26T05:56:05.1912077Z 2024-06-26T05:56:06.1333823Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:06.1460801Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:06.2314671Z Running dynamo/test_nops 1/1 ... [2024-06-26 05:56:06.230799] 2024-06-26T05:56:06.2324357Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_nops.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:06.231304] 2024-06-26T05:56:06.2519139Z Running dynamo/test_config 1/1 ... [2024-06-26 05:56:06.251408] 2024-06-26T05:56:06.2523442Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_config.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:06.251804] 2024-06-26T05:56:07.7823434Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:07.8418183Z Running test_jiterator 1/1 ... [2024-06-26 05:56:07.841387] 2024-06-26T05:56:07.8421630Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_jiterator.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:07.841739] 2024-06-26T05:56:08.7707670Z 2024-06-26T05:56:08.7709411Z dynamo/test_config 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_config_1.1_8c8be10d7f45542f_.log 2024-06-26T05:56:08.7710343Z 2024-06-26T05:56:08.8246578Z 2024-06-26T05:56:08.8248278Z dynamo/test_nops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_nops_1.1_b2c21895c482ee7c_.log 2024-06-26T05:56:08.8249659Z 2024-06-26T05:56:10.4734235Z 2024-06-26T05:56:10.4736302Z test_jiterator 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jiterator_1.1_45e642bc46f091df_.log 2024-06-26T05:56:10.4737811Z Running 0 items in this shard: 2024-06-26T05:56:10.4738201Z 2024-06-26T05:56:11.2891253Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:11.3429026Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:11.3491434Z Running test_matmul_cuda 1/1 ... [2024-06-26 05:56:11.348756] 2024-06-26T05:56:11.3495317Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_matmul_cuda.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:11.349106] 2024-06-26T05:56:11.4037754Z Running dynamo/test_sources 1/1 ... [2024-06-26 05:56:11.403330] 2024-06-26T05:56:11.4041186Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_sources.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:11.403703] 2024-06-26T05:56:13.0267888Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:13.0853659Z Running xpu/test_conv 1/1 ... [2024-06-26 05:56:13.084937] 2024-06-26T05:56:13.0858039Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'xpu/test_conv.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:13.085306] 2024-06-26T05:56:13.8844255Z 2024-06-26T05:56:13.8846356Z dynamo/test_sources 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_sources_1.1_d351657bb66b6511_.log 2024-06-26T05:56:13.8847995Z 2024-06-26T05:56:13.9971420Z 2024-06-26T05:56:13.9973702Z test_matmul_cuda 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_matmul_cuda_1.1_b11dd42baaa2bf85_.log 2024-06-26T05:56:13.9975917Z Running 0 items in this shard: 2024-06-26T05:56:13.9976397Z 2024-06-26T05:56:16.1797071Z 2024-06-26T05:56:16.1799166Z xpu/test_conv 1/1 was successful, full logs can be found in artifacts with path test/test-reports/xpu.test_conv_1.1_672dd7924fa70b11_.log 2024-06-26T05:56:16.1801061Z Running 0 items in this shard: 2024-06-26T05:56:16.1801509Z 2024-06-26T05:56:16.4165683Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:16.4749600Z Running test_cuda 1/1 ... [2024-06-26 05:56:16.474468] 2024-06-26T05:56:16.4752525Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cuda.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:16.474809] 2024-06-26T05:56:16.5424726Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:16.6017183Z Running dynamo/test_verify_correctness 1/1 ... [2024-06-26 05:56:16.601273] 2024-06-26T05:56:16.6020141Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_verify_correctness.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:16.601634] 2024-06-26T05:56:18.7829706Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:18.8426587Z Running dynamo/test_profiler 1/1 ... [2024-06-26 05:56:18.842220] 2024-06-26T05:56:18.8430219Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_profiler.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:18.842568] 2024-06-26T05:56:19.1386475Z 2024-06-26T05:56:19.1388442Z 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_14a5e006808ed3de_.log 2024-06-26T05:56:19.1389453Z 2024-06-26T05:56:20.3466332Z 2024-06-26T05:56:20.3468161Z test_cuda 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_1.1_af83e754e03a69b8_.log 2024-06-26T05:56:20.3469110Z Running 0 items in this shard: 2024-06-26T05:56:20.3469361Z 2024-06-26T05:56:21.2522543Z 2024-06-26T05:56:21.2524286Z dynamo/test_profiler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_profiler_1.1_4b76a2e4417a9818_.log 2024-06-26T05:56:21.2525225Z 2024-06-26T05:56:21.6491689Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:21.7083918Z Running dynamo/test_reorder_logs 1/1 ... [2024-06-26 05:56:21.708013] 2024-06-26T05:56:21.7087212Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_reorder_logs.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:21.708355] 2024-06-26T05:56:22.8627649Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:22.9216953Z Running test_hub 1/1 ... [2024-06-26 05:56:22.921248] 2024-06-26T05:56:22.9220750Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_hub.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:22.921639] 2024-06-26T05:56:23.8383551Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:23.8984140Z Running dynamo/test_minifier 1/1 ... [2024-06-26 05:56:23.897923] 2024-06-26T05:56:23.8987073Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_minifier.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:23.898282] 2024-06-26T05:56:24.1271657Z 2024-06-26T05:56:24.1273793Z dynamo/test_reorder_logs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_reorder_logs_1.1_456e1b44baceeb99_.log 2024-06-26T05:56:24.1274949Z 2024-06-26T05:56:25.2979805Z 2024-06-26T05:56:25.2981859Z test_hub 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_hub_1.1_66aa5e8391e631a4_.log 2024-06-26T05:56:25.2983425Z 2024-06-26T05:56:26.2498657Z 2024-06-26T05:56:26.2507335Z dynamo/test_minifier 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_minifier_1.1_77fce51aecd4a704_.log 2024-06-26T05:56:26.2514998Z 2024-06-26T05:56:26.5803896Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:26.6394766Z Running dynamo/test_activation_checkpointing 1/1 ... [2024-06-26 05:56:26.639000] 2024-06-26T05:56:26.6398593Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_activation_checkpointing.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:26.639355] 2024-06-26T05:56:27.7367366Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:27.7947154Z Running dynamo/test_recompile_ux 1/1 ... [2024-06-26 05:56:27.794280] 2024-06-26T05:56:27.7950863Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_recompile_ux.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:27.794624] 2024-06-26T05:56:28.7023105Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:28.7606835Z Running dynamo/test_subclasses 1/1 ... [2024-06-26 05:56:28.760295] 2024-06-26T05:56:28.7610687Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_subclasses.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:28.760666] 2024-06-26T05:56:29.1134484Z 2024-06-26T05:56:29.1143928Z 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_50ea8085013573fa_.log 2024-06-26T05:56:29.1146762Z 2024-06-26T05:56:30.2287048Z 2024-06-26T05:56:30.2289292Z 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_1694fb078d4e70e2_.log 2024-06-26T05:56:30.2291049Z 2024-06-26T05:56:31.3674304Z 2024-06-26T05:56:31.3681803Z dynamo/test_subclasses 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_subclasses_1.1_a71b1440c3bbf576_.log 2024-06-26T05:56:31.3688435Z 2024-06-26T05:56:31.6957237Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:31.7546159Z Running lazy/test_extract_compiled_graph 1/1 ... [2024-06-26 05:56:31.754037] 2024-06-26T05:56:31.7550209Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'lazy/test_extract_compiled_graph.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:31.754386] 2024-06-26T05:56:32.7860003Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:32.8451543Z Running dynamo/test_aot_autograd_cache 1/1 ... [2024-06-26 05:56:32.844642] 2024-06-26T05:56:32.8455442Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_aot_autograd_cache.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:32.844997] 2024-06-26T05:56:33.3103047Z 2024-06-26T05:56:33.3110234Z lazy/test_extract_compiled_graph 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_extract_compiled_graph_1.1_b3f9d2e3bc8bd33b_.log 2024-06-26T05:56:33.3116335Z 2024-06-26T05:56:33.9384127Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:33.9985827Z Running test_cuda_multigpu 1/1 ... [2024-06-26 05:56:33.998144] 2024-06-26T05:56:33.9989622Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cuda_multigpu.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:33.998498] 2024-06-26T05:56:35.3014453Z 2024-06-26T05:56:35.3016766Z dynamo/test_aot_autograd_cache 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_aot_autograd_cache_1.1_c049b65b6f9dfda7_.log 2024-06-26T05:56:35.3018597Z 2024-06-26T05:56:35.7896634Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:35.8480021Z Running test_linalg 3/3 ... [2024-06-26 05:56:35.847550] 2024-06-26T05:56:35.8483273Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_linalg.py', '-m', 'not serial', '--shard-id=3', '--num-shards=3', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:35.847875] 2024-06-26T05:56:36.6518165Z 2024-06-26T05:56:36.6520230Z test_cuda_multigpu 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_multigpu_1.1_c4c98500186cb55a_.log 2024-06-26T05:56:36.6522237Z Running 0 items in this shard: 2024-06-26T05:56:36.6522709Z 2024-06-26T05:56:37.8591778Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:37.9175916Z Running dynamo/test_python_autograd 1/1 ... [2024-06-26 05:56:37.917196] 2024-06-26T05:56:37.9179763Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_python_autograd.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:37.917549] 2024-06-26T05:56:39.0925131Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:40.1442878Z 2024-06-26T05:56:40.1444639Z 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_6a0b50a3b1a38ac3_.log 2024-06-26T05:56:40.1445645Z 2024-06-26T05:56:42.4513230Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:03:00.5008628Z 2024-06-26T06:03:00.5009862Z test_linalg 3/3 was successful, full logs can be found in artifacts with path test/test-reports/test_linalg_3.3_d9fdf9f4f25ed762_.log 2024-06-26T06:03:00.5296137Z Running 351 items in this shard: test/test_linalg.py::TestLinalgCPU::test_1_sized_with_0_strided_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_1_sized_with_0_strided_cpu_float64, test/test_linalg.py::TestLinalgCPU::test__int4_mm_m_32_k_64_n_48_cpu, test/test_linalg.py::TestLinalgCPU::test__int4_mm_m_32_k_64_n_64_cpu, test/test_linalg.py::TestLinalgCPU::test__int4_mm_m_64_k_32_n_48_cpu, test/test_linalg.py::TestLinalgCPU::test__int4_mm_m_64_k_32_n_64_cpu, test/test_linalg.py::TestLinalgCPU::test__int4_mm_m_64_k_64_n_64_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_32_k_64_n_48_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_32_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__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_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_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_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_0_n_16_use_transpose_a_True_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_0_n_16_use_transpose_a_True_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_0_n_16_use_transpose_a_True_use_transpose_b_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_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_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_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_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_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_0_n_32_use_transpose_a_True_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_16_n_16_use_transpose_a_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_False_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_False_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_True_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_16_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_16_n_32_use_transpose_a_False_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_16_n_32_use_transpose_a_True_use_transpose_b_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_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_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_32_n_16_use_transpose_a_False_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_32_n_16_use_transpose_a_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_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_False_use_transpose_b_True_non_contig_type_1_cpu, 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test/test_linalg.py::TestLinalgCPU::test_eigvalsh_errors_and_warnings_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_einsum_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_einsum_error_cases_cpu, test/test_linalg.py::TestLinalgCPU::test_einsum_random_cpu_float64, 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_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_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_36_k_64_cpu, test/test_linalg.py::TestLinalgCPU::test_fp16_mv_transposed_first_argument_arm_cpu_m_40_k_35_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_35_cpu, test/test_linalg.py::TestLinalgCPU::test_geqrf_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_geqrf_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_householder_product_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_householder_product_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_inv_errors_and_warnings_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_inv_errors_and_warnings_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_inv_errors_and_warnings_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_inv_ex_info_device_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_inv_ex_info_device_cpu_float32, 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_float32, test/test_linalg.py::TestLinalgCPU::test_inverse_errors_large_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_inverse_many_batches_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_kron_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_kron_empty_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_kron_empty_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_lapack_empty_cpu, test/test_linalg.py::TestLinalgCPU::test_linalg_cross_with_and_without_dim_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_linalg_lstsq_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_linalg_lstsq_input_checks_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_linalg_lu_cpu_errors_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_linalg_lu_family_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_linalg_lu_solve_cpu_complex128, 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_complex128, test/test_linalg.py::TestLinalgCPU::test_linalg_matrix_exp_analytic_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_linalg_matrix_exp_batch_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_linalg_matrix_exp_batch_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_linalg_matrix_exp_boundary_cases_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_linalg_matrix_exp_compare_with_taylor_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_linalg_matrix_exp_compare_with_taylor_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_linalg_matrix_exp_no_warnings_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_linalg_solve_triangular_broadcasting_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_linalg_solve_triangular_broadcasting_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_linalg_solve_triangular_large_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_linalg_solve_triangular_large_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_lobpcg_scipy_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_lstsq_removed_error_cpu, test/test_linalg.py::TestLinalgCPU::test_lu_solve_batched_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_lu_solve_batched_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_lu_solve_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_lu_solve_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_lu_solve_large_matrices_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_matmul_45724_cpu, test/test_linalg.py::TestLinalgCPU::test_matmul_small_brute_force_1d_Nd_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_matmul_small_brute_force_2d_Nd_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_matmul_small_brute_force_2d_Nd_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_matmul_small_brute_force_3d_Nd_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_matmul_small_brute_force_tunableop_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_matmul_small_brute_force_tunableop_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_matmul_small_brute_force_tunableop_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_matrix_power_negative_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_matrix_power_non_negative_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_matrix_rank_atol_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_matrix_rank_basic_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_matrix_rank_basic_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_matrix_rank_empty_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_matrix_rank_out_errors_and_warnings_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_matrix_rank_removed_error_cpu, test/test_linalg.py::TestLinalgCPU::test_mm_conjtranspose_cpu, test/test_linalg.py::TestLinalgCPU::test_mm_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_mm_empty_inputs_mixed_dtype_errors_cpu, test/test_linalg.py::TestLinalgCPU::test_multi_dot_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_norm_bfloat16_and_half_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_norm_complex_old_cpu, test/test_linalg.py::TestLinalgCPU::test_norm_dtype_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_norm_dtype_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_norm_dtype_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_norm_errors_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_norm_fastpaths_cpu, test/test_linalg.py::TestLinalgCPU::test_norm_fro_2_equivalence_old_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_norm_fused_type_promotion_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_norm_matrix_degenerate_shapes_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_norm_vector_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_nuclear_norm_axes_small_brute_force_old_cpu, test/test_linalg.py::TestLinalgCPU::test_nuclear_norm_exceptions_old_cpu, test/test_linalg.py::TestLinalgCPU::test_old_cholesky_batched_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_old_cholesky_batched_upper_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_outer_cpu_bool, test/test_linalg.py::TestLinalgCPU::test_outer_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_outer_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_outer_cpu_int16, test/test_linalg.py::TestLinalgCPU::test_outer_cpu_int8, test/test_linalg.py::TestLinalgCPU::test_outer_ger_addr_legacy_tests_cpu, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_bfloat16_float16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_bfloat16_int32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_bfloat16_int8, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_bool_complex64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_bool_int8, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex128_bfloat16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex128_complex128, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex128_int16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex64_complex128, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex64_complex64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex64_float16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex64_int8, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float16_float64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float16_int16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float32_bfloat16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float32_complex64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float32_float64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float32_int8, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float64_float16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float64_int32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float64_uint8, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int16_complex128, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int16_int16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int16_int32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int32_float16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int32_float64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int32_int32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int64_bfloat16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int64_bool, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int64_complex64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int64_float64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int64_int16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int8_bfloat16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int8_float16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_uint8_bfloat16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_uint8_complex128, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_uint8_float32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_uint8_int64, test/test_linalg.py::TestLinalgCPU::test_pca_lowrank_cpu, test/test_linalg.py::TestLinalgCPU::test_pinv_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_pinv_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_pinv_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_pinverse_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_pinverse_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_pinverse_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_qr_batched_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_qr_batched_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_qr_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_renorm_ps_cpu, test/test_linalg.py::TestLinalgCPU::test_slogdet_errors_and_warnings_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_slogdet_errors_and_warnings_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_strided_mm_bmm_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_strided_mm_bmm_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_svd_lowrank_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_tensordot_cpu, test/test_linalg.py::TestLinalgCPU::test_tensorinv_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_tensorinv_errors_and_warnings_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_tensorinv_singular_input_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_tensorsolve_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_tensorsolve_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_tensorsolve_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_tensorsolve_empty_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_tensorsolve_empty_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_triangular_solve_batched_broadcasting_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_triangular_solve_batched_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_triangular_solve_batched_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_triangular_solve_batched_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_triangular_solve_batched_many_batches_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_triangular_solve_batched_many_batches_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_triangular_solve_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_triangular_solve_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_triangular_solve_out_errors_and_warnings_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_vector_norm_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_vector_norm_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_vector_norm_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_vector_norm_dim_tuple_arg_cpu, test/test_linalg.py::TestLinalgCPU::test_vector_norm_reduce_over_1D_vector_cpu_float32 2024-06-26T06:03:00.5513606Z 2024-06-26T06:03:01.5715447Z 2024-06-26T06:03:01.5716312Z real 62m8.617s 2024-06-26T06:03:01.5716844Z user 82m12.971s 2024-06-26T06:03:01.5717260Z sys 6m33.630s 2024-06-26T06:03:01.5717691Z + assert_git_not_dirty 2024-06-26T06:03:01.5718811Z + [[ linux-focal-py3.12-clang10 != *rocm* ]] 2024-06-26T06:03:01.5719590Z + [[ linux-focal-py3.12-clang10 != *xla* ]] 2024-06-26T06:03:01.5723421Z ++ grep -v '?? third_party' 2024-06-26T06:03:01.5727686Z ++ git status --porcelain 2024-06-26T06:03:19.3271522Z ++ true 2024-06-26T06:03:19.3271960Z + git_status= 2024-06-26T06:03:19.3272501Z + [[ -n '' ]] 2024-06-26T06:03:19.3272870Z + test_aten 2024-06-26T06:03:19.3273453Z + echo 'Running ATen tests with pytorch lib' 2024-06-26T06:03:19.3274010Z Running ATen tests with pytorch lib 2024-06-26T06:03:19.3274405Z + [[ -n '' ]] 2024-06-26T06:03:19.3274774Z + echo 'Running test with the build folder' 2024-06-26T06:03:19.3275223Z Running test with the build folder 2024-06-26T06:03:19.3275596Z + TEST_BASE_DIR=build/bin 2024-06-26T06:03:19.3276323Z + ln -sf /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libc10.so build/bin 2024-06-26T06:03:19.3306859Z + ln -sf '/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libcaffe2*' build/bin 2024-06-26T06:03:19.3315886Z + ln -sf '/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libmkldnn*' build/bin 2024-06-26T06:03:19.3323917Z + ln -sf '/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libnccl*' build/bin 2024-06-26T06:03:19.3334564Z + ln -sf /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_global_deps.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_python.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorchbind_test.so build/bin 2024-06-26T06:03:19.3339802Z + ls build/bin 2024-06-26T06:03:19.3369214Z CMakeFiles cpu_rng_test 2024-06-26T06:03:19.3370158Z CTestTestfile.cmake dispatch_key_set_test 2024-06-26T06:03:19.3370938Z CppSignature_test dlconvertor_test 2024-06-26T06:03:19.3371391Z Dict_test example_allreduce 2024-06-26T06:03:19.3371911Z Dimname_test extension_backend_test 2024-06-26T06:03:19.3372492Z FileStoreTest half_test 2024-06-26T06:03:19.3375103Z HashStoreTest inline_container_test 2024-06-26T06:03:19.3375665Z IListRef_test ivalue_test 2024-06-26T06:03:19.3376156Z KernelFunction_test kernel_function_legacy_test 2024-06-26T06:03:19.3376818Z List_test kernel_function_test 2024-06-26T06:03:19.3377480Z Makefile kernel_lambda_legacy_test 2024-06-26T06:03:19.3378225Z MaybeOwned_test kernel_lambda_test 2024-06-26T06:03:19.3378889Z NamedTensor_test kernel_stackbased_test 2024-06-26T06:03:19.3379485Z ProcessGroupGlooTest lazy_tensor_test 2024-06-26T06:03:19.3379952Z StorageUtils_test legacy_vmap_test 2024-06-26T06:03:19.3380446Z TCPStoreTest libc10.so 2024-06-26T06:03:19.3381337Z aot_model_compiler_test 'libcaffe2*' 2024-06-26T06:03:19.3382245Z apply_utils_test 'libmkldnn*' 2024-06-26T06:03:19.3383072Z atest 'libnccl*' 2024-06-26T06:03:19.3383769Z backend_fallback_test libtorch.so 2024-06-26T06:03:19.3384571Z basic libtorch_cpu.so 2024-06-26T06:03:19.3385340Z broadcast_test libtorch_global_deps.so 2024-06-26T06:03:19.3386249Z c10_Bitset_test libtorch_python.so 2024-06-26T06:03:19.3387291Z c10_CompileTimeFunctionPointer_test libtorchbind_test.so 2024-06-26T06:03:19.3388023Z c10_ConstexprCrc_test make_boxed_from_unboxed_functor_test 2024-06-26T06:03:19.3388619Z c10_DeadlockDetection_test math_kernel_test 2024-06-26T06:03:19.3389127Z c10_DeviceGuard_test memory_format_test 2024-06-26T06:03:19.3389604Z c10_Device_test memory_overlapping_test 2024-06-26T06:03:19.3390123Z c10_DispatchKeySet_test mobile_memory_cleanup 2024-06-26T06:03:19.3390598Z c10_Half_test native_test 2024-06-26T06:03:19.3391033Z c10_InlineDeviceGuard_test op_allowlist_test 2024-06-26T06:03:19.3391584Z c10_InlineStreamGuard_test op_registration_test 2024-06-26T06:03:19.3392112Z c10_LeftRight_test operator_name_test 2024-06-26T06:03:19.3392582Z c10_Metaprogramming_test operators_test 2024-06-26T06:03:19.3393086Z c10_Scalar_test packedtensoraccessor_test 2024-06-26T06:03:19.3393608Z c10_SizesAndStrides_test parallel_benchmark 2024-06-26T06:03:19.3394083Z c10_StreamGuard_test pow_test 2024-06-26T06:03:19.3394469Z c10_SymInt_test protoc 2024-06-26T06:03:19.3394942Z c10_Synchronized_test protoc-3.13.0.0 2024-06-26T06:03:19.3395416Z c10_ThreadLocal_test quantized_test 2024-06-26T06:03:19.3395860Z c10_TypeIndex_test reduce_ops_test 2024-06-26T06:03:19.3396338Z c10_TypeList_test reportMemoryUsage_test 2024-06-26T06:03:19.3396842Z c10_TypeTraits_test scalar_tensor_test 2024-06-26T06:03:19.3397284Z c10_accumulate_test scalar_test 2024-06-26T06:03:19.3397734Z c10_bfloat16_test static_runtime_bench 2024-06-26T06:03:19.3398201Z c10_bit_cast_test static_runtime_test 2024-06-26T06:03:19.3398685Z c10_complex_math_test stride_properties_test 2024-06-26T06:03:19.3399191Z c10_complex_test tensor_iterator_test 2024-06-26T06:03:19.3399625Z c10_cow_test test_api 2024-06-26T06:03:19.3399992Z c10_exception_test test_cpp_rpc 2024-06-26T06:03:19.3400422Z c10_flags_test test_dist_autograd 2024-06-26T06:03:19.3401139Z c10_generic_math_test test_edge_op_registration 2024-06-26T06:03:19.3401652Z c10_intrusive_ptr_benchmark test_jit 2024-06-26T06:03:19.3402105Z c10_intrusive_ptr_test test_lazy 2024-06-26T06:03:19.3402637Z c10_irange_test test_mobile_nnc 2024-06-26T06:03:19.3403107Z c10_lazy_test test_parallel 2024-06-26T06:03:19.3403795Z c10_logging_test test_tensorexpr 2024-06-26T06:03:19.3404311Z c10_optional_test thread_init_test 2024-06-26T06:03:19.3404882Z c10_ordered_preserving_dict_test torch_shm_manager 2024-06-26T06:03:19.3405517Z c10_registry_test tutorial_tensorexpr 2024-06-26T06:03:19.3406128Z c10_small_vector_test type_ptr_test 2024-06-26T06:03:19.3406845Z c10_ssize_test type_test 2024-06-26T06:03:19.3407553Z c10_string_util_test undefined_tensor_test 2024-06-26T06:03:19.3408463Z c10_string_view_test vec_test_all_types_AVX2 2024-06-26T06:03:19.3409404Z c10_tempfile_test vec_test_all_types_AVX512 2024-06-26T06:03:19.3410333Z c10_typeid_test vec_test_all_types_DEFAULT 2024-06-26T06:03:19.3411300Z cmake_install.cmake verify_api_visibility 2024-06-26T06:03:19.3412374Z cpu_allocator_test weakref_test 2024-06-26T06:03:19.3413130Z cpu_generator_test wrapdim_test 2024-06-26T06:03:19.3413857Z cpu_profiling_allocator_test xla_tensor_test 2024-06-26T06:03:19.3414626Z + aten/tools/run_tests.sh build/bin 2024-06-26T06:03:19.3415248Z + set -e 2024-06-26T06:03:19.3415749Z ++ dirname aten/tools/run_tests.sh 2024-06-26T06:03:19.3416363Z + VALGRIND_SUP=/var/lib/jenkins/workspace/aten/tools/valgrind.sup 2024-06-26T06:03:19.3417225Z + export CPP_TESTS_DIR=build/bin 2024-06-26T06:03:19.3417848Z + CPP_TESTS_DIR=build/bin 2024-06-26T06:03:19.3418374Z + VALGRIND=ON 2024-06-26T06:03:19.3423127Z + python test/run_test.py --cpp --verbose -i cpp/basic cpp/atest cpp/scalar_test cpp/broadcast_test cpp/wrapdim_test cpp/apply_utils_test cpp/dlconvertor_test cpp/native_test cpp/scalar_tensor_test cpp/undefined_tensor_test cpp/extension_backend_test cpp/lazy_tensor_test cpp/tensor_iterator_test cpp/Dimname_test cpp/Dict_test cpp/NamedTensor_test cpp/cpu_generator_test cpp/legacy_vmap_test cpp/operators_test 2024-06-26T06:03:19.4411140Z /var/lib/jenkins/workspace/test/run_test.py:21: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html 2024-06-26T06:03:19.4413248Z import pkg_resources 2024-06-26T06:03:21.1934597Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:03:22.0770436Z Downloading https://ossci-metrics.s3.amazonaws.com/slow-tests.json to /var/lib/jenkins/workspace/test/.pytorch-slow-tests.json 2024-06-26T06:03:22.0774303Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2024-06-26T06:03:22.0857523Z Found test times from artifacts 2024-06-26T06:03:22.1242645Z Found test times from artifacts 2024-06-26T06:03:22.1255205Z Running all tests 2024-06-26T06:03:22.1259729Z Running parallel tests on 3 processes 2024-06-26T06:03:22.1261427Z Name: tests to run (est. time: 0.0min) 2024-06-26T06:03:22.1262020Z Serial tests (0): 2024-06-26T06:03:22.1262351Z Parallel tests (19): 2024-06-26T06:03:22.1262683Z cpp/Dict_test 1/1 2024-06-26T06:03:22.1263002Z cpp/Dimname_test 1/1 2024-06-26T06:03:22.1263327Z cpp/NamedTensor_test 1/1 2024-06-26T06:03:22.1263681Z cpp/apply_utils_test 1/1 2024-06-26T06:03:22.1264018Z cpp/atest 1/1 2024-06-26T06:03:22.1264294Z cpp/basic 1/1 2024-06-26T06:03:22.1264593Z cpp/broadcast_test 1/1 2024-06-26T06:03:22.1264953Z cpp/cpu_generator_test 1/1 2024-06-26T06:03:22.1265305Z cpp/dlconvertor_test 1/1 2024-06-26T06:03:22.1265662Z cpp/extension_backend_test 1/1 2024-06-26T06:03:22.1266038Z cpp/lazy_tensor_test 1/1 2024-06-26T06:03:22.1266368Z cpp/legacy_vmap_test 1/1 2024-06-26T06:03:22.1266862Z cpp/native_test 1/1 2024-06-26T06:03:22.1267182Z cpp/operators_test 1/1 2024-06-26T06:03:22.1267527Z cpp/scalar_tensor_test 1/1 2024-06-26T06:03:22.1267876Z cpp/scalar_test 1/1 2024-06-26T06:03:22.1268194Z cpp/tensor_iterator_test 1/1 2024-06-26T06:03:22.1268576Z cpp/undefined_tensor_test 1/1 2024-06-26T06:03:22.1269016Z cpp/wrapdim_test 1/1 2024-06-26T06:03:22.1269346Z Name: excluded (est. time: 0.0min) 2024-06-26T06:03:22.1269718Z Serial tests (0): 2024-06-26T06:03:22.1270019Z Parallel tests (0): 2024-06-26T06:03:22.1270657Z Starting test batch 'tests to run' 0.0 seconds after initiating testing 2024-06-26T06:03:22.1315316Z Running cpp/Dict_test 1/1 ... [2024-06-26 06:03:22.131209] 2024-06-26T06:03:22.1323385Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/Dict_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-93af65b15c3ff81a.xml', '-x', '--reruns=2'] ... [2024-06-26 06:03:22.131813] 2024-06-26T06:03:24.3018690Z 2024-06-26T06:03:24.3020645Z cpp/Dict_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dict_test_1.1_fcdb0adcc1c7a496_.log 2024-06-26T06:03:24.3022205Z 2024-06-26T06:03:24.3022785Z Running cpp/Dimname_test 1/1 ... [2024-06-26 06:03:24.301976] 2024-06-26T06:03:24.3028603Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/Dimname_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-b87d4e4b9f9a7db9.xml', '-x', '--reruns=2'] ... [2024-06-26 06:03:24.302337] 2024-06-26T06:03:24.3624314Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:03:24.4308845Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:03:24.4387968Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:03:25.8193190Z 2024-06-26T06:03:25.8194883Z cpp/Dimname_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dimname_test_1.1_2573306d52629893_.log 2024-06-26T06:03:25.8195943Z 2024-06-26T06:03:25.8196450Z Running cpp/NamedTensor_test 1/1 ... [2024-06-26 06:03:25.819163] 2024-06-26T06:03:25.8198803Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/NamedTensor_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-7f1475377e42ca68.xml', '-x', '--reruns=2'] ... [2024-06-26 06:03:25.819499] 2024-06-26T06:03:27.3363949Z 2024-06-26T06:03:27.3365484Z cpp/NamedTensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.NamedTensor_test_1.1_f404b7768289904b_.log 2024-06-26T06:03:27.3366415Z 2024-06-26T06:03:27.3366805Z Running cpp/apply_utils_test 1/1 ... [2024-06-26 06:03:27.336216] 2024-06-26T06:03:27.3368926Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/apply_utils_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-9bf728b58d5d2e23.xml', '-x', '--reruns=2'] ... [2024-06-26 06:03:27.336541] 2024-06-26T06:03:28.8536927Z 2024-06-26T06:03:28.8538839Z cpp/apply_utils_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.apply_utils_test_1.1_2ddb5421916ed90d_.log 2024-06-26T06:03:28.8539989Z 2024-06-26T06:03:28.8540475Z Running cpp/atest 1/1 ... [2024-06-26 06:03:28.853519] 2024-06-26T06:03:28.8542296Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/atest', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-97cb23e84573f493.xml', '-x', '--reruns=2'] ... [2024-06-26 06:03:28.853816] 2024-06-26T06:03:30.3706755Z 2024-06-26T06:03:30.3708255Z cpp/atest 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.atest_1.1_575ca072bd70a3c5_.log 2024-06-26T06:03:30.3709163Z 2024-06-26T06:03:30.3709492Z Running cpp/basic 1/1 ... [2024-06-26 06:03:30.370492] 2024-06-26T06:03:30.3711714Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/basic', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-9a9b6a05167e389c.xml', '-x', '--reruns=2'] ... [2024-06-26 06:03:30.370807] 2024-06-26T06:03:31.8878923Z 2024-06-26T06:03:31.8880215Z cpp/basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.basic_1.1_67c0b80879051ef3_.log 2024-06-26T06:03:31.8881413Z 2024-06-26T06:03:31.8881891Z Running cpp/broadcast_test 1/1 ... [2024-06-26 06:03:31.887774] 2024-06-26T06:03:31.8884641Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/broadcast_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-59d876a595b2fb60.xml', '-x', '--reruns=2'] ... [2024-06-26 06:03:31.888143] 2024-06-26T06:03:33.4051341Z 2024-06-26T06:03:33.4053068Z cpp/broadcast_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.broadcast_test_1.1_70f019d52e751f5d_.log 2024-06-26T06:03:33.4054146Z 2024-06-26T06:03:33.4054532Z Running cpp/cpu_generator_test 1/1 ... [2024-06-26 06:03:33.405010] 2024-06-26T06:03:33.4057030Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/cpu_generator_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-373afe56d220aa04.xml', '-x', '--reruns=2'] ... [2024-06-26 06:03:33.405339] 2024-06-26T06:03:34.9220864Z 2024-06-26T06:03:34.9222504Z cpp/cpu_generator_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.cpu_generator_test_1.1_80062f24abd91fcd_.log 2024-06-26T06:03:34.9223565Z 2024-06-26T06:03:34.9224004Z Running cpp/dlconvertor_test 1/1 ... [2024-06-26 06:03:34.921914] 2024-06-26T06:03:34.9225936Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/dlconvertor_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-4564a92089c8f854.xml', '-x', '--reruns=2'] ... [2024-06-26 06:03:34.922247] 2024-06-26T06:03:36.4391142Z 2024-06-26T06:03:36.4393137Z cpp/dlconvertor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.dlconvertor_test_1.1_e3fdcd74c0f704a3_.log 2024-06-26T06:03:36.4394715Z 2024-06-26T06:03:36.4395462Z Running cpp/extension_backend_test 1/1 ... [2024-06-26 06:03:36.438964] 2024-06-26T06:03:36.4398006Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/extension_backend_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-4876a874fc3ffdad.xml', '-x', '--reruns=2'] ... [2024-06-26 06:03:36.439330] 2024-06-26T06:03:37.9563014Z 2024-06-26T06:03:37.9564779Z cpp/extension_backend_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.extension_backend_test_1.1_e695808f49fdf321_.log 2024-06-26T06:03:37.9565919Z 2024-06-26T06:03:37.9566255Z Running cpp/lazy_tensor_test 1/1 ... [2024-06-26 06:03:37.956121] 2024-06-26T06:03:37.9568174Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/lazy_tensor_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-f518fd9c38a8ff45.xml', '-x', '--reruns=2'] ... [2024-06-26 06:03:37.956451] 2024-06-26T06:03:39.4733857Z 2024-06-26T06:03:39.4735847Z cpp/lazy_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.lazy_tensor_test_1.1_3c3dae66ec4ba06b_.log 2024-06-26T06:03:39.4737391Z 2024-06-26T06:03:39.4738039Z Running cpp/legacy_vmap_test 1/1 ... [2024-06-26 06:03:39.473209] 2024-06-26T06:03:39.4741548Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/legacy_vmap_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-c31c03575cc19803.xml', '-x', '--reruns=2'] ... [2024-06-26 06:03:39.473598] 2024-06-26T06:03:40.9905404Z 2024-06-26T06:03:40.9906915Z cpp/legacy_vmap_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.legacy_vmap_test_1.1_8b48d502b65c8bc9_.log 2024-06-26T06:03:40.9908171Z 2024-06-26T06:03:40.9908513Z Running cpp/native_test 1/1 ... [2024-06-26 06:03:40.990369] 2024-06-26T06:03:40.9911502Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/native_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-b63d7aec9a5e588a.xml', '-x', '--reruns=2'] ... [2024-06-26 06:03:40.990696] 2024-06-26T06:03:42.5076024Z 2024-06-26T06:03:42.5077397Z cpp/native_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.native_test_1.1_a3ab5697f9f9c4be_.log 2024-06-26T06:03:42.5078268Z 2024-06-26T06:03:42.5078596Z Running cpp/operators_test 1/1 ... [2024-06-26 06:03:42.507454] 2024-06-26T06:03:42.5080943Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/operators_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-60520716c5d3545f.xml', '-x', '--reruns=2'] ... [2024-06-26 06:03:42.507782] 2024-06-26T06:03:43.9745054Z 2024-06-26T06:03:43.9746872Z cpp/operators_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.operators_test_1.1_224749529274f746_.log 2024-06-26T06:03:43.9747815Z 2024-06-26T06:03:43.9748287Z Running cpp/scalar_tensor_test 1/1 ... [2024-06-26 06:03:43.974355] 2024-06-26T06:03:43.9750260Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/scalar_tensor_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-89eb17f1a8ec1887.xml', '-x', '--reruns=2'] ... [2024-06-26 06:03:43.974671] 2024-06-26T06:03:45.4915217Z 2024-06-26T06:03:45.4917237Z cpp/scalar_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_tensor_test_1.1_2a555cadd5c8c463_.log 2024-06-26T06:03:45.4918931Z 2024-06-26T06:03:45.4919485Z Running cpp/scalar_test 1/1 ... [2024-06-26 06:03:45.491336] 2024-06-26T06:03:45.4922751Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/scalar_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-98b0584c2b332423.xml', '-x', '--reruns=2'] ... [2024-06-26 06:03:45.491691] 2024-06-26T06:03:47.0084281Z 2024-06-26T06:03:47.0086241Z cpp/scalar_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_test_1.1_c3286307930c80d6_.log 2024-06-26T06:03:47.0087728Z 2024-06-26T06:03:47.0088446Z Running cpp/tensor_iterator_test 1/1 ... [2024-06-26 06:03:47.008235] 2024-06-26T06:03:47.0090796Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/tensor_iterator_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-31d306fdcd810852.xml', '-x', '--reruns=2'] ... [2024-06-26 06:03:47.008590] 2024-06-26T06:03:48.5255345Z 2024-06-26T06:03:48.5256999Z cpp/tensor_iterator_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.tensor_iterator_test_1.1_244c3dc204adf155_.log 2024-06-26T06:03:48.5258040Z 2024-06-26T06:03:48.5258545Z Running cpp/undefined_tensor_test 1/1 ... [2024-06-26 06:03:48.525432] 2024-06-26T06:03:48.5261047Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/undefined_tensor_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-40aade6a611ce5eb.xml', '-x', '--reruns=2'] ... [2024-06-26 06:03:48.525774] 2024-06-26T06:03:50.0427742Z 2024-06-26T06:03:50.0429455Z cpp/undefined_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.undefined_tensor_test_1.1_3a9726314aacdb90_.log 2024-06-26T06:03:50.0430729Z 2024-06-26T06:03:50.0431062Z Running cpp/wrapdim_test 1/1 ... [2024-06-26 06:03:50.042613] 2024-06-26T06:03:50.0433210Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/wrapdim_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-f79d27246c68a3f5.xml', '-x', '--reruns=2'] ... [2024-06-26 06:03:50.042962] 2024-06-26T06:03:51.5097071Z 2024-06-26T06:03:51.5099050Z cpp/wrapdim_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.wrapdim_test_1.1_07013e2eeeb2fd30_.log 2024-06-26T06:03:51.5099935Z 2024-06-26T06:03:51.5109628Z Running cpp/Dict_test 1/1 ... [2024-06-26 06:03:51.510660] 2024-06-26T06:03:51.5110937Z Running cpp/Dimname_test 1/1 ... [2024-06-26 06:03:51.510862] 2024-06-26T06:03:51.5112104Z Running cpp/NamedTensor_test 1/1 ... [2024-06-26 06:03:51.510926] 2024-06-26T06:03:51.5116438Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/Dict_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-2f45c03f37e2fb8b.xml', '-x', '--reruns=2'] ... [2024-06-26 06:03:51.511230] 2024-06-26T06:03:51.5120732Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/NamedTensor_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-7724529ecdba64c3.xml', '-x', '--reruns=2'] ... [2024-06-26 06:03:51.511433] 2024-06-26T06:03:51.5123845Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/Dimname_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-1a0326bac018d0dc.xml', '-x', '--reruns=2'] ... [2024-06-26 06:03:51.511468] 2024-06-26T06:03:55.2345345Z 2024-06-26T06:03:55.2347677Z cpp/Dimname_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dimname_test_1.1_3889348c26369e0e_.log 2024-06-26T06:03:55.2349520Z 2024-06-26T06:03:56.0344648Z 2024-06-26T06:03:56.0346521Z cpp/NamedTensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.NamedTensor_test_1.1_bc3a803ab28475e7_.log 2024-06-26T06:03:56.0347498Z 2024-06-26T06:03:58.1220277Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:03:58.1825959Z Running cpp/apply_utils_test 1/1 ... [2024-06-26 06:03:58.182106] 2024-06-26T06:03:58.1830346Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/apply_utils_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-2e5dca6e765fc70f.xml', '-x', '--reruns=2'] ... [2024-06-26 06:03:58.182582] 2024-06-26T06:03:59.1600017Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:03:59.2650786Z Running cpp/atest 1/1 ... [2024-06-26 06:03:59.264594] 2024-06-26T06:03:59.2659006Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/atest', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-2b6c5a80baa17c9c.xml', '-x', '--reruns=2'] ... [2024-06-26 06:03:59.265161] 2024-06-26T06:04:02.0572717Z 2024-06-26T06:04:02.0577320Z cpp/apply_utils_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.apply_utils_test_1.1_4255ad3eae03506d_.log 2024-06-26T06:04:02.0578807Z 2024-06-26T06:04:02.3035524Z 2024-06-26T06:04:02.3038275Z cpp/Dict_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dict_test_1.1_5267ef562a04bc40_.log 2024-06-26T06:04:02.3039837Z 2024-06-26T06:04:04.6902331Z 2024-06-26T06:04:04.6904184Z cpp/atest 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.atest_1.1_6fceb5963e2a8c6f_.log 2024-06-26T06:04:04.6905059Z 2024-06-26T06:04:04.7210643Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:04:04.7809214Z Running cpp/basic 1/1 ... [2024-06-26 06:04:04.780572] 2024-06-26T06:04:04.7813890Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/basic', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-44f4e5e5baa16ecb.xml', '-x', '--reruns=2'] ... [2024-06-26 06:04:04.781016] 2024-06-26T06:04:05.1588574Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:04:05.2195456Z Running cpp/broadcast_test 1/1 ... [2024-06-26 06:04:05.219094] 2024-06-26T06:04:05.2201546Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/broadcast_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-318ec5aeebeeaf52.xml', '-x', '--reruns=2'] ... [2024-06-26 06:04:05.219559] 2024-06-26T06:04:07.7499275Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:04:07.8438287Z Running cpp/cpu_generator_test 1/1 ... [2024-06-26 06:04:07.843315] 2024-06-26T06:04:07.8444259Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/cpu_generator_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-8cc7e665536cf9d4.xml', '-x', '--reruns=2'] ... [2024-06-26 06:04:07.843856] 2024-06-26T06:04:07.8511938Z 2024-06-26T06:04:07.8514221Z cpp/basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.basic_1.1_41276235819fdbc6_.log 2024-06-26T06:04:07.8515641Z 2024-06-26T06:04:07.9890759Z 2024-06-26T06:04:07.9892899Z cpp/broadcast_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.broadcast_test_1.1_9272d0456dc19b54_.log 2024-06-26T06:04:07.9894505Z 2024-06-26T06:04:10.6676016Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:04:10.7294502Z Running cpp/dlconvertor_test 1/1 ... [2024-06-26 06:04:10.728970] 2024-06-26T06:04:10.7300473Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/dlconvertor_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-5fa84552de97544a.xml', '-x', '--reruns=2'] ... [2024-06-26 06:04:10.729471] 2024-06-26T06:04:10.7531399Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:04:10.8524885Z Running cpp/extension_backend_test 1/1 ... [2024-06-26 06:04:10.851990] 2024-06-26T06:04:10.8530604Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/extension_backend_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-03bf9c062b1d6ff3.xml', '-x', '--reruns=2'] ... [2024-06-26 06:04:10.852559] 2024-06-26T06:04:12.6705841Z 2024-06-26T06:04:12.6708714Z cpp/cpu_generator_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.cpu_generator_test_1.1_83ba4d08214577b5_.log 2024-06-26T06:04:12.6710640Z 2024-06-26T06:04:13.4492247Z 2024-06-26T06:04:13.4495771Z cpp/dlconvertor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.dlconvertor_test_1.1_9a7597e54929f86a_.log 2024-06-26T06:04:13.4498129Z 2024-06-26T06:04:13.5721939Z 2024-06-26T06:04:13.5723967Z cpp/extension_backend_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.extension_backend_test_1.1_3edad47a584aacfa_.log 2024-06-26T06:04:13.5724958Z 2024-06-26T06:04:15.2577120Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:04:15.3166065Z Running cpp/lazy_tensor_test 1/1 ... [2024-06-26 06:04:15.316187] 2024-06-26T06:04:15.3170436Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/lazy_tensor_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-3a5b0f478b28c397.xml', '-x', '--reruns=2'] ... [2024-06-26 06:04:15.316637] 2024-06-26T06:04:15.8671807Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:04:15.9269539Z Running cpp/legacy_vmap_test 1/1 ... [2024-06-26 06:04:15.926449] 2024-06-26T06:04:15.9273959Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/legacy_vmap_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-aad19c7dc316abb6.xml', '-x', '--reruns=2'] ... [2024-06-26 06:04:15.926928] 2024-06-26T06:04:16.2049718Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:04:16.2654361Z Running cpp/native_test 1/1 ... [2024-06-26 06:04:16.264965] 2024-06-26T06:04:16.2659764Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/native_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-5c1de2d70b189516.xml', '-x', '--reruns=2'] ... [2024-06-26 06:04:16.265462] 2024-06-26T06:04:18.0930717Z 2024-06-26T06:04:18.0932922Z cpp/lazy_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.lazy_tensor_test_1.1_8975e97b9417cfcb_.log 2024-06-26T06:04:18.0935231Z 2024-06-26T06:04:19.1368400Z 2024-06-26T06:04:19.1370517Z cpp/native_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.native_test_1.1_86d442e08fea433d_.log 2024-06-26T06:04:19.1372192Z 2024-06-26T06:04:21.0449740Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:04:21.1390117Z Running cpp/operators_test 1/1 ... [2024-06-26 06:04:21.138528] 2024-06-26T06:04:21.1396053Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/operators_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-b5e7d4a248c087c9.xml', '-x', '--reruns=2'] ... [2024-06-26 06:04:21.139084] 2024-06-26T06:04:21.6673548Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:04:21.7570707Z Running cpp/scalar_tensor_test 1/1 ... [2024-06-26 06:04:21.756571] 2024-06-26T06:04:21.7575443Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/scalar_tensor_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-18bfbf67e9b897b3.xml', '-x', '--reruns=2'] ... [2024-06-26 06:04:21.757033] 2024-06-26T06:04:22.0531459Z 2024-06-26T06:04:22.0535308Z cpp/legacy_vmap_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.legacy_vmap_test_1.1_a4051984096db178_.log 2024-06-26T06:04:22.0537070Z 2024-06-26T06:04:24.3598611Z 2024-06-26T06:04:24.3600477Z cpp/operators_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.operators_test_1.1_8233132a97df46e6_.log 2024-06-26T06:04:24.3601473Z 2024-06-26T06:04:24.3764343Z 2024-06-26T06:04:24.3766261Z cpp/scalar_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_tensor_test_1.1_cb509ded9cc294db_.log 2024-06-26T06:04:24.3767815Z 2024-06-26T06:04:25.0639634Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:04:25.1240302Z Running cpp/scalar_test 1/1 ... [2024-06-26 06:04:25.123628] 2024-06-26T06:04:25.1245309Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/scalar_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-e5a5b76739991589.xml', '-x', '--reruns=2'] ... [2024-06-26 06:04:25.124086] 2024-06-26T06:04:26.8972599Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:04:26.9057550Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:04:26.9565556Z Running cpp/tensor_iterator_test 1/1 ... [2024-06-26 06:04:26.956161] 2024-06-26T06:04:26.9569353Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/tensor_iterator_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-8dc6a3204fe76168.xml', '-x', '--reruns=2'] ... [2024-06-26 06:04:26.956575] 2024-06-26T06:04:26.9649703Z Running cpp/undefined_tensor_test 1/1 ... [2024-06-26 06:04:26.964573] 2024-06-26T06:04:26.9654087Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/undefined_tensor_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-9f777bcb35f16296.xml', '-x', '--reruns=2'] ... [2024-06-26 06:04:26.965000] 2024-06-26T06:04:28.3449196Z 2024-06-26T06:04:28.3451011Z cpp/scalar_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_test_1.1_1c1843d74863dc3a_.log 2024-06-26T06:04:28.3452299Z 2024-06-26T06:04:29.6343646Z 2024-06-26T06:04:29.6345721Z cpp/undefined_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.undefined_tensor_test_1.1_d1d7e77328e4c22f_.log 2024-06-26T06:04:29.6347226Z 2024-06-26T06:04:31.0942287Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:04:31.1544741Z Running cpp/wrapdim_test 1/1 ... [2024-06-26 06:04:31.154006] 2024-06-26T06:04:31.1550212Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/wrapdim_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-94555ccf0d4bfd7e.xml', '-x', '--reruns=2'] ... [2024-06-26 06:04:31.154483] 2024-06-26T06:04:32.6143438Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:04:33.6739297Z 2024-06-26T06:04:33.6741241Z cpp/wrapdim_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.wrapdim_test_1.1_39f9311cf576a1b0_.log 2024-06-26T06:04:33.6742509Z 2024-06-26T06:04:36.2129836Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:04:38.4613825Z 2024-06-26T06:04:38.4615572Z cpp/tensor_iterator_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.tensor_iterator_test_1.1_e0a78401184e3796_.log 2024-06-26T06:04:38.4616598Z 2024-06-26T06:04:39.3319266Z + [[ -x ./tensor_interop_test ]] 2024-06-26T06:04:39.3320060Z + [[ -x ./cudnn_test ]] 2024-06-26T06:04:39.3320665Z + [[ -x ./cuda_generator_test ]] 2024-06-26T06:04:39.3321137Z + [[ -x ./apply_test ]] 2024-06-26T06:04:39.3321560Z + [[ -x ./stream_test ]] 2024-06-26T06:04:39.3322260Z + [[ -x ./cuda_half_test ]] 2024-06-26T06:04:39.3322653Z + [[ -x ./cuda_vectorized_test ]] 2024-06-26T06:04:39.3323081Z + [[ -x ./cuda_distributions_test ]] 2024-06-26T06:04:39.3323512Z + [[ -x ./cuda_optional_test ]] 2024-06-26T06:04:39.3323928Z + [[ -x ./cuda_tensor_interop_test ]] 2024-06-26T06:04:39.3324356Z + [[ -x ./cuda_complex_test ]] 2024-06-26T06:04:39.3325293Z + [[ -x ./cuda_complex_math_test ]] 2024-06-26T06:04:39.3325929Z + [[ -x ./cuda_cub_test ]] 2024-06-26T06:04:39.3326452Z + [[ -x ./cuda_atomic_ops_test ]] 2024-06-26T06:04:39.3326837Z + '[' ON == ON ']' 2024-06-26T06:04:39.3327839Z + valgrind --suppressions=/var/lib/jenkins/workspace/aten/tools/valgrind.sup --error-exitcode=1 build/bin/basic '--gtest_filter=-*CUDA' 2024-06-26T06:04:39.3683668Z ==5808== Memcheck, a memory error detector 2024-06-26T06:04:39.3684813Z ==5808== Copyright (C) 2002-2022, and GNU GPL'd, by Julian Seward et al. 2024-06-26T06:04:39.3685652Z ==5808== Using Valgrind-3.20.0 and LibVEX; rerun with -h for copyright info 2024-06-26T06:04:39.3686548Z ==5808== Command: build/bin/basic --gtest_filter=-*CUDA 2024-06-26T06:04:39.3687060Z ==5808== 2024-06-26T06:05:07.6542958Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc 2024-06-26T06:05:07.6758323Z Note: Google Test filter = -*CUDA 2024-06-26T06:05:07.6808995Z [==========] Running 4 tests from 1 test suite. 2024-06-26T06:05:07.6834734Z [----------] Global test environment set-up. 2024-06-26T06:05:07.6873496Z [----------] 4 tests from BasicTest 2024-06-26T06:05:07.6894756Z [ RUN ] BasicTest.BasicTestCPU 2024-06-26T06:05:09.0922651Z 342 ms 2024-06-26T06:05:09.1752192Z 52 ms 2024-06-26T06:05:09.2533243Z 70 ms 2024-06-26T06:05:09.7838017Z [ OK ] BasicTest.BasicTestCPU (2092 ms) 2024-06-26T06:05:09.7844807Z [ RUN ] BasicTest.BasicTestHalfCPU 2024-06-26T06:05:09.9114636Z 82 ms 2024-06-26T06:05:09.9603253Z 44 ms 2024-06-26T06:05:10.0263436Z 64 ms 2024-06-26T06:05:10.0804196Z [ OK ] BasicTest.BasicTestHalfCPU (295 ms) 2024-06-26T06:05:10.0804880Z [ RUN ] BasicTest.FactoryMethodsTest 2024-06-26T06:05:10.1117697Z [ OK ] BasicTest.FactoryMethodsTest (31 ms) 2024-06-26T06:05:10.1118326Z [ RUN ] BasicTest.BasicStdTestCPU 2024-06-26T06:05:10.1953413Z Simple example: called once 2024-06-26T06:05:10.2847477Z throw: call_once will retry 2024-06-26T06:05:10.2862000Z throw: call_once will retry 2024-06-26T06:05:10.2868217Z Didn't throw, call_once will not attempt again 2024-06-26T06:05:10.3295869Z [ OK ] BasicTest.BasicStdTestCPU (217 ms) 2024-06-26T06:05:10.3317867Z [----------] 4 tests from BasicTest (2641 ms total) 2024-06-26T06:05:10.3318319Z 2024-06-26T06:05:10.3328957Z [----------] Global test environment tear-down 2024-06-26T06:05:10.3360509Z [==========] 4 tests from 1 test suite ran. (2661 ms total) 2024-06-26T06:05:10.3371501Z [ PASSED ] 4 tests. 2024-06-26T06:05:12.0977714Z ==5808== 2024-06-26T06:05:12.0981342Z ==5808== HEAP SUMMARY: 2024-06-26T06:05:12.0981874Z ==5808== in use at exit: 239,520 bytes in 3,995 blocks 2024-06-26T06:05:12.0984470Z ==5808== total heap usage: 730,491 allocs, 726,496 frees, 212,868,424 bytes allocated 2024-06-26T06:05:12.0985277Z ==5808== 2024-06-26T06:05:12.1353037Z ==5808== LEAK SUMMARY: 2024-06-26T06:05:12.1353776Z ==5808== definitely lost: 0 bytes in 0 blocks 2024-06-26T06:05:12.1354280Z ==5808== indirectly lost: 0 bytes in 0 blocks 2024-06-26T06:05:12.1354776Z ==5808== possibly lost: 0 bytes in 0 blocks 2024-06-26T06:05:12.1355289Z ==5808== still reachable: 239,520 bytes in 3,995 blocks 2024-06-26T06:05:12.1355795Z ==5808== suppressed: 0 bytes in 0 blocks 2024-06-26T06:05:12.1356537Z ==5808== Rerun with --leak-check=full to see details of leaked memory 2024-06-26T06:05:12.1357060Z ==5808== 2024-06-26T06:05:12.1357497Z ==5808== For lists of detected and suppressed errors, rerun with: -s 2024-06-26T06:05:12.1358221Z ==5808== ERROR SUMMARY: 0 errors from 0 contexts (suppressed: 0 from 0) 2024-06-26T06:05:12.1663537Z + [[ -x ./tensor_interop_test ]] 2024-06-26T06:05:12.1664926Z + [[ -n '' ]] 2024-06-26T06:05:12.1665467Z + assert_git_not_dirty 2024-06-26T06:05:12.1666037Z + [[ linux-focal-py3.12-clang10 != *rocm* ]] 2024-06-26T06:05:12.1666758Z + [[ linux-focal-py3.12-clang10 != *xla* ]] 2024-06-26T06:05:12.1671041Z ++ git status --porcelain 2024-06-26T06:05:12.1671479Z ++ grep -v '?? third_party' 2024-06-26T06:05:12.3371479Z ++ true 2024-06-26T06:05:12.3371951Z + git_status= 2024-06-26T06:05:12.3372625Z + [[ -n '' ]] 2024-06-26T06:05:12.3373683Z + cleanup_workspace 2024-06-26T06:05:12.3374933Z + echo 'sudo may print the following warning message that can be ignored. The chown command will still run.' 2024-06-26T06:05:12.3376239Z sudo may print the following warning message that can be ignored. The chown command will still run. 2024-06-26T06:05:12.3377232Z + echo ' sudo: setrlimit(RLIMIT_STACK): Operation not permitted' 2024-06-26T06:05:12.3377912Z sudo: setrlimit(RLIMIT_STACK): Operation not permitted 2024-06-26T06:05:12.3378706Z + echo 'For more details refer to https://github.com/sudo-project/sudo/issues/42' 2024-06-26T06:05:12.3379578Z For more details refer to https://github.com/sudo-project/sudo/issues/42 2024-06-26T06:05:12.3380275Z + sudo chown -R 1000 /var/lib/jenkins/workspace 2024-06-26T06:05:13.9666018Z ##[group]Run cat test/**/*_toprint.log || true 2024-06-26T06:05:13.9666523Z cat test/**/*_toprint.log || true 2024-06-26T06:05:13.9752675Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T06:05:13.9753160Z env: 2024-06-26T06:05:13.9753425Z GIT_DEFAULT_BRANCH: main 2024-06-26T06:05:13.9754047Z DOCKER_CONTAINER_ID: dece8529f48a7f61fcf0b1a68620de6340e6da1a183eee9f3a1750c5234902db 2024-06-26T06:05:13.9754700Z ##[endgroup] 2024-06-26T06:05:13.9824656Z cat: test/**/*_toprint.log: No such file or directory 2024-06-26T06:05:13.9855111Z ##[group]Run kill "$MONITOR_SCRIPT_PID" 2024-06-26T06:05:13.9855546Z kill "$MONITOR_SCRIPT_PID" 2024-06-26T06:05:13.9862410Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T06:05:13.9862893Z env: 2024-06-26T06:05:13.9863138Z GIT_DEFAULT_BRANCH: main 2024-06-26T06:05:13.9863746Z DOCKER_CONTAINER_ID: dece8529f48a7f61fcf0b1a68620de6340e6da1a183eee9f3a1750c5234902db 2024-06-26T06:05:13.9864418Z MONITOR_SCRIPT_PID: 31817 2024-06-26T06:05:13.9864737Z ##[endgroup] 2024-06-26T06:05:14.0062495Z Prepare all required actions 2024-06-26T06:05:14.0062942Z Getting action download info 2024-06-26T06:05:14.1524879Z Download action repository 'actions/upload-artifact@v3' (SHA:a8a3f3ad30e3422c9c7b888a15615d19a852ae32) 2024-06-26T06:05:14.3027463Z ##[group]Run ./.github/actions/upload-test-artifacts 2024-06-26T06:05:14.3027936Z with: 2024-06-26T06:05:14.3028285Z file-suffix: test-dynamo-1-3-linux.2xlarge_26688259850 2024-06-26T06:05:14.3028784Z s3-bucket: gha-artifacts 2024-06-26T06:05:14.3029115Z env: 2024-06-26T06:05:14.3029359Z GIT_DEFAULT_BRANCH: main 2024-06-26T06:05:14.3029964Z DOCKER_CONTAINER_ID: dece8529f48a7f61fcf0b1a68620de6340e6da1a183eee9f3a1750c5234902db 2024-06-26T06:05:14.3030624Z ##[endgroup] 2024-06-26T06:05:14.3056963Z ##[group]Run # Remove any previous test jsons if they exist 2024-06-26T06:05:14.3057565Z # Remove any previous test jsons if they exist 2024-06-26T06:05:14.3058065Z rm -f test-jsons-*.zip 2024-06-26T06:05:14.3058610Z zip -r "test-jsons-${FILE_SUFFIX}.zip" test -i '*.json' 2024-06-26T06:05:14.3066147Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T06:05:14.3066636Z env: 2024-06-26T06:05:14.3066895Z GIT_DEFAULT_BRANCH: main 2024-06-26T06:05:14.3067503Z DOCKER_CONTAINER_ID: dece8529f48a7f61fcf0b1a68620de6340e6da1a183eee9f3a1750c5234902db 2024-06-26T06:05:14.3068272Z FILE_SUFFIX: test-dynamo-1-3-linux.2xlarge_26688259850 2024-06-26T06:05:14.3068743Z ##[endgroup] 2024-06-26T06:05:14.3304554Z adding: test/allowlist_for_publicAPI.json (deflated 79%) 2024-06-26T06:05:14.3331390Z adding: test/benchmark_utils/callgrind_artifacts.json (deflated 92%) 2024-06-26T06:05:14.3332082Z adding: test/minioptest_failures_dict.json (deflated 70%) 2024-06-26T06:05:14.3338070Z adding: test/profiler/profiler_utils_mock_events.json (deflated 87%) 2024-06-26T06:05:14.3340271Z adding: test/test-reports/td_exclusions-95cca5d70cd487bd7f53.json (deflated 81%) 2024-06-26T06:05:14.3341249Z adding: test/test-reports/td_exclusions-4619455a2f5e6b3b7fbd.json (deflated 73%) 2024-06-26T06:05:14.3342033Z adding: test/.pytorch-slow-tests.json (deflated 77%) 2024-06-26T06:05:14.3348494Z adding: test/.pytorch-disabled-tests.json (deflated 84%) 2024-06-26T06:05:14.3375070Z ##[group]Run # Remove any previous test reports if they exist 2024-06-26T06:05:14.3375693Z # Remove any previous test reports if they exist 2024-06-26T06:05:14.3376208Z rm -f test-reports-*.zip 2024-06-26T06:05:14.3376846Z zip -r "test-reports-${FILE_SUFFIX}.zip" test -i '*.xml' -i '*.csv' 2024-06-26T06:05:14.3383995Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T06:05:14.3384476Z env: 2024-06-26T06:05:14.3384733Z GIT_DEFAULT_BRANCH: main 2024-06-26T06:05:14.3385325Z DOCKER_CONTAINER_ID: dece8529f48a7f61fcf0b1a68620de6340e6da1a183eee9f3a1750c5234902db 2024-06-26T06:05:14.3386085Z FILE_SUFFIX: test-dynamo-1-3-linux.2xlarge_26688259850 2024-06-26T06:05:14.3386557Z ##[endgroup] 2024-06-26T06:05:14.3618552Z adding: test/test-reports/python-pytest/test_nn/test_nn-1fa663c2ffaf501e.xml (deflated 96%) 2024-06-26T06:05:14.3674912Z adding: test/test-reports/python-pytest/test_cpp_api_parity/test_cpp_api_parity-ad0beebd1cc50fd0.xml (deflated 99%) 2024-06-26T06:05:14.3708493Z adding: test/test-reports/python-pytest/test_torch/test_torch-d4c445c26dbc102f.xml (deflated 95%) 2024-06-26T06:05:14.3709775Z adding: test/test-reports/python-pytest/test_show_pickle/test_show_pickle-43256f6880d49abe.xml (deflated 37%) 2024-06-26T06:05:14.3711001Z adding: test/test-reports/python-pytest/test_autocast/test_autocast-a073314785f69fe1.xml (deflated 86%) 2024-06-26T06:05:14.3818291Z adding: test/test-reports/python-pytest/test_utils/test_utils-ac1aa5d7acda9e87.xml (deflated 98%) 2024-06-26T06:05:14.3819779Z adding: test/test-reports/python-pytest/test_tensorexpr/test_tensorexpr-b9b8e3cf84b5d6eb.xml (deflated 95%) 2024-06-26T06:05:14.3821370Z adding: test/test-reports/python-pytest/test_autograd_fallback/test_autograd_fallback-f196e4a0780dd441.xml (deflated 88%) 2024-06-26T06:05:14.3825996Z adding: test/test-reports/python-pytest/test_python_dispatch/test_python_dispatch-57cbc27ee963ea91.xml (deflated 92%) 2024-06-26T06:05:14.3827604Z adding: test/test-reports/python-pytest/test_cpp_extensions_stream_and_event/test_cpp_extensions_stream_and_event-5e3fc8ee0f5e1ea7.xml (deflated 59%) 2024-06-26T06:05:14.3829259Z adding: test/test-reports/python-pytest/test_cpp_extensions_mtia_backend/test_cpp_extensions_mtia_backend-59e1cd707ade3617.xml (deflated 79%) 2024-06-26T06:05:14.3849594Z adding: test/test-reports/python-pytest/test_overrides/test_overrides-f1fbc7cdf31faf1a.xml (deflated 96%) 2024-06-26T06:05:14.3851497Z adding: test/test-reports/python-pytest/test_jit_disabled/test_jit_disabled-82c1d06487e3b15f.xml (deflated 56%) 2024-06-26T06:05:14.3852953Z adding: test/test-reports/python-pytest/test_native_mha/test_native_mha-a31dd1525ef3eb75.xml (deflated 95%) 2024-06-26T06:05:14.3855107Z adding: test/test-reports/python-pytest/test_cpp_extensions_jit/test_cpp_extensions_jit-a8132ac5776f2dd6.xml (deflated 89%) 2024-06-26T06:05:14.3856764Z adding: test/test-reports/python-pytest/test_cpp_extensions_open_device_registration/test_cpp_extensions_open_device_registration-ec77a082fb30cb7f.xml (deflated 84%) 2024-06-26T06:05:14.3859333Z adding: test/test-reports/python-pytest/test_sort_and_select/test_sort_and_select-98dafc4f35106201.xml (deflated 92%) 2024-06-26T06:05:14.3861217Z adding: test/test-reports/python-pytest/test_multiprocessing/test_multiprocessing-c5bc5b913d65ba85.xml (deflated 88%) 2024-06-26T06:05:14.3862591Z adding: test/test-reports/python-pytest/test_mobile_optimizer/test_mobile_optimizer-19353497e9986f18.xml (deflated 59%) 2024-06-26T06:05:14.3864093Z adding: test/test-reports/python-pytest/nn.test_pooling/nn.test_pooling-8e3177e07bf68d43.xml (deflated 90%) 2024-06-26T06:05:14.3884154Z adding: test/test-reports/python-pytest/test_tensor_creation_ops/test_tensor_creation_ops-d70101dad43be097.xml (deflated 94%) 2024-06-26T06:05:14.3980834Z adding: test/test-reports/python-pytest/test_reductions/test_reductions-978df75f88633ec3.xml (deflated 98%) 2024-06-26T06:05:14.3982333Z adding: test/test-reports/python-pytest/test_dispatch/test_dispatch-eb77493022283de0.xml (deflated 93%) 2024-06-26T06:05:14.3983853Z adding: test/test-reports/python-pytest/test_multiprocessing_spawn/test_multiprocessing_spawn-282fb1fb74357c31.xml (deflated 77%) 2024-06-26T06:05:14.3990558Z adding: test/test-reports/python-pytest/test_spectral_ops/test_spectral_ops-52f349666a6b2725.xml (deflated 95%) 2024-06-26T06:05:14.3993748Z adding: test/test-reports/python-pytest/distributions.test_distributions/distributions.test_distributions-023639b9db39000e.xml (deflated 94%) 2024-06-26T06:05:14.3996431Z adding: test/test-reports/python-pytest/distributions.test_distributions/distributions.test_distributions-a10044577b0fd80b.xml (deflated 93%) 2024-06-26T06:05:14.3998076Z adding: test/test-reports/python-pytest/test_cpp_extensions_aot_no_ninja/test_cpp_extensions_aot_no_ninja-fe21bdb096763c04.xml (deflated 85%) 2024-06-26T06:05:14.3999658Z adding: test/test-reports/python-pytest/test_cpp_extensions_aot_ninja/test_cpp_extensions_aot_ninja-293847e0735b6e0b.xml (deflated 85%) 2024-06-26T06:05:14.4001099Z adding: 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test/test-reports/python-pytest/test.run_test/test.run_test-8dc6a3204fe76168.xml (deflated 90%) 2024-06-26T06:05:14.4089700Z ##[group]Run # Remove any previous usage logs if they exist 2024-06-26T06:05:14.4090578Z # Remove any previous usage logs if they exist 2024-06-26T06:05:14.4091068Z rm -f logs-*.zip 2024-06-26T06:05:14.4091723Z # this workflow is also run in bazel build test, but we dont generate usage reports for it 2024-06-26T06:05:14.4092587Z # so check to see if the file exists first 2024-06-26T06:05:14.4093062Z if [ -f 'usage_log.txt' ]; then 2024-06-26T06:05:14.4093707Z  zip "logs-${FILE_SUFFIX}.zip" 'usage_log.txt' 2024-06-26T06:05:14.4094234Z fi 2024-06-26T06:05:14.4094558Z if ls test/**/*.log 1> /dev/null 2>&1; then 2024-06-26T06:05:14.4095111Z  zip -r "logs-${FILE_SUFFIX}.zip" test -i '*.log' 2024-06-26T06:05:14.4095588Z fi 2024-06-26T06:05:14.4102578Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T06:05:14.4103060Z env: 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test/test-reports/dynamo.test_profiler_1.1_4b76a2e4417a9818_.log (stored 0%) 2024-06-26T06:05:14.4860201Z adding: test/test-reports/cpp.undefined_tensor_test_1.1_3a9726314aacdb90_.log (deflated 49%) 2024-06-26T06:05:14.4861206Z adding: test/test-reports/cpp.operators_test_1.1_8233132a97df46e6_.log (deflated 60%) 2024-06-26T06:05:14.4862371Z adding: test/test-reports/dynamo.test_reorder_logs_1.1_456e1b44baceeb99_.log (stored 0%) 2024-06-26T06:05:14.4863399Z adding: test/test-reports/dynamo.test_minifier_1.1_77fce51aecd4a704_.log (stored 0%) 2024-06-26T06:05:14.4864337Z adding: test/test-reports/cpp.native_test_1.1_a3ab5697f9f9c4be_.log (deflated 48%) 2024-06-26T06:05:14.4865272Z adding: test/test-reports/cpp.scalar_test_1.1_1c1843d74863dc3a_.log (deflated 59%) 2024-06-26T06:05:14.4866196Z adding: test/test-reports/cpp.Dict_test_1.1_5267ef562a04bc40_.log (deflated 84%) 2024-06-26T06:05:14.4867215Z adding: test/test-reports/dynamo.test_activation_checkpointing_1.1_50ea8085013573fa_.log (stored 0%) 2024-06-26T06:05:14.4868277Z adding: test/test-reports/dynamo.test_recompile_ux_1.1_1694fb078d4e70e2_.log (stored 0%) 2024-06-26T06:05:14.4869264Z adding: test/test-reports/dynamo.test_subclasses_1.1_a71b1440c3bbf576_.log (stored 0%) 2024-06-26T06:05:14.4870248Z adding: test/test-reports/cpp.scalar_tensor_test_1.1_2a555cadd5c8c463_.log (deflated 48%) 2024-06-26T06:05:14.4871226Z adding: test/test-reports/cpp.Dimname_test_1.1_3889348c26369e0e_.log (deflated 59%) 2024-06-26T06:05:14.4872243Z adding: test/test-reports/lazy.test_extract_compiled_graph_1.1_b3f9d2e3bc8bd33b_.log (stored 0%) 2024-06-26T06:05:14.4873328Z adding: test/test-reports/dynamo.test_aot_autograd_cache_1.1_c049b65b6f9dfda7_.log (stored 0%) 2024-06-26T06:05:14.4874393Z adding: test/test-reports/test_cuda_multigpu_1.1_c4c98500186cb55a_.log (deflated 49%) 2024-06-26T06:05:14.4875415Z adding: test/test-reports/cpp.extension_backend_test_1.1_3edad47a584aacfa_.log (deflated 50%) 2024-06-26T06:05:14.4876515Z adding: test/test-reports/dynamo.test_python_autograd_1.1_6a0b50a3b1a38ac3_.log (stored 0%) 2024-06-26T06:05:14.4877475Z adding: test/test-reports/cpp.Dict_test_1.1_fcdb0adcc1c7a496_.log (deflated 48%) 2024-06-26T06:05:14.4878407Z adding: test/test-reports/cpp.wrapdim_test_1.1_39f9311cf576a1b0_.log (deflated 49%) 2024-06-26T06:05:14.4879341Z adding: test/test-reports/cpp.Dimname_test_1.1_2573306d52629893_.log (deflated 48%) 2024-06-26T06:05:14.4880250Z adding: test/test-reports/cpp.basic_1.1_41276235819fdbc6_.log (deflated 61%) 2024-06-26T06:05:14.4881223Z adding: test/test-reports/cpp.NamedTensor_test_1.1_f404b7768289904b_.log (deflated 48%) 2024-06-26T06:05:14.4882205Z adding: test/test-reports/cpp.broadcast_test_1.1_9272d0456dc19b54_.log (deflated 50%) 2024-06-26T06:05:14.4883191Z adding: test/test-reports/cpp.apply_utils_test_1.1_2ddb5421916ed90d_.log (deflated 48%) 2024-06-26T06:05:14.4884162Z adding: test/test-reports/cpp.broadcast_test_1.1_70f019d52e751f5d_.log (deflated 48%) 2024-06-26T06:05:14.4885160Z adding: test/test-reports/cpp.scalar_tensor_test_1.1_cb509ded9cc294db_.log (deflated 60%) 2024-06-26T06:05:14.4886173Z adding: test/test-reports/cpp.cpu_generator_test_1.1_80062f24abd91fcd_.log (deflated 48%) 2024-06-26T06:05:14.4887164Z adding: test/test-reports/cpp.cpu_generator_test_1.1_83ba4d08214577b5_.log (deflated 78%) 2024-06-26T06:05:14.4888167Z adding: test/test-reports/cpp.dlconvertor_test_1.1_e3fdcd74c0f704a3_.log (deflated 48%) 2024-06-26T06:05:14.4889227Z adding: test/test-reports/cpp.dlconvertor_test_1.1_9a7597e54929f86a_.log (deflated 56%) 2024-06-26T06:05:14.4890252Z adding: test/test-reports/cpp.extension_backend_test_1.1_e695808f49fdf321_.log (deflated 48%) 2024-06-26T06:05:14.4891307Z adding: test/test-reports/cpp.undefined_tensor_test_1.1_d1d7e77328e4c22f_.log (deflated 50%) 2024-06-26T06:05:14.4892326Z adding: test/test-reports/cpp.lazy_tensor_test_1.1_3c3dae66ec4ba06b_.log (deflated 48%) 2024-06-26T06:05:14.4893315Z adding: test/test-reports/cpp.lazy_tensor_test_1.1_8975e97b9417cfcb_.log (deflated 54%) 2024-06-26T06:05:14.4894410Z adding: test/test-reports/cpp.legacy_vmap_test_1.1_8b48d502b65c8bc9_.log (deflated 48%) 2024-06-26T06:05:14.4895374Z adding: test/test-reports/cpp.operators_test_1.1_224749529274f746_.log (deflated 48%) 2024-06-26T06:05:14.4896330Z adding: test/test-reports/cpp.wrapdim_test_1.1_07013e2eeeb2fd30_.log (deflated 48%) 2024-06-26T06:05:14.4897276Z adding: test/test-reports/cpp.native_test_1.1_86d442e08fea433d_.log (deflated 53%) 2024-06-26T06:05:14.4898227Z adding: test/test-reports/cpp.legacy_vmap_test_1.1_a4051984096db178_.log (deflated 80%) 2024-06-26T06:05:14.4899231Z adding: test/test-reports/cpp.tensor_iterator_test_1.1_e0a78401184e3796_.log (deflated 88%) 2024-06-26T06:05:14.4923763Z ##[group]Run # Remove any previous debugging artifacts if they exist 2024-06-26T06:05:14.4924460Z # Remove any previous debugging artifacts if they exist 2024-06-26T06:05:14.4925004Z rm -f debug-*.zip 2024-06-26T06:05:14.4925367Z if [ -d 'test/debug' ]; then 2024-06-26T06:05:14.4925843Z  zip -r "debug-${FILE_SUFFIX}.zip" test/debug 2024-06-26T06:05:14.4926302Z fi 2024-06-26T06:05:14.4933205Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T06:05:14.4933830Z env: 2024-06-26T06:05:14.4934095Z GIT_DEFAULT_BRANCH: main 2024-06-26T06:05:14.4934724Z DOCKER_CONTAINER_ID: dece8529f48a7f61fcf0b1a68620de6340e6da1a183eee9f3a1750c5234902db 2024-06-26T06:05:14.4935484Z FILE_SUFFIX: test-dynamo-1-3-linux.2xlarge_26688259850 2024-06-26T06:05:14.4935958Z ##[endgroup] 2024-06-26T06:05:14.5034572Z ##[group]Run seemethere/upload-artifact-s3@v5 2024-06-26T06:05:14.5034997Z with: 2024-06-26T06:05:14.5035327Z s3-bucket: gha-artifacts 2024-06-26T06:05:14.5035734Z s3-prefix: pytorch/pytorch/9673645538/1/artifact 2024-06-26T06:05:14.5036188Z retention-days: 14 2024-06-26T06:05:14.5036489Z if-no-files-found: warn 2024-06-26T06:05:14.5036872Z path: test-jsons-*.zip 2024-06-26T06:05:14.5037184Z name: artifact 2024-06-26T06:05:14.5037449Z region: us-east-1 2024-06-26T06:05:14.5037725Z env: 2024-06-26T06:05:14.5037978Z GIT_DEFAULT_BRANCH: main 2024-06-26T06:05:14.5038566Z DOCKER_CONTAINER_ID: dece8529f48a7f61fcf0b1a68620de6340e6da1a183eee9f3a1750c5234902db 2024-06-26T06:05:14.5039222Z ##[endgroup] 2024-06-26T06:05:14.8755302Z NOTE: s3-prefix specified, ignoring name parameter 2024-06-26T06:05:14.8756358Z With the provided path, there will be 1 file uploaded 2024-06-26T06:05:14.8757141Z Uploading to s3 prefix: pytorch/pytorch/9673645538/1/artifact 2024-06-26T06:05:14.9922326Z Starting upload of test-jsons-test-dynamo-1-3-linux.2xlarge_26688259850.zip 2024-06-26T06:05:15.1138796Z Finished upload of test-jsons-test-dynamo-1-3-linux.2xlarge_26688259850.zip 2024-06-26T06:05:15.1273206Z ##[group]Run seemethere/upload-artifact-s3@v5 2024-06-26T06:05:15.1273622Z with: 2024-06-26T06:05:15.1273886Z s3-bucket: gha-artifacts 2024-06-26T06:05:15.1274300Z s3-prefix: pytorch/pytorch/9673645538/1/artifact 2024-06-26T06:05:15.1274741Z retention-days: 14 2024-06-26T06:05:15.1275058Z if-no-files-found: error 2024-06-26T06:05:15.1275402Z path: test-reports-*.zip 2024-06-26T06:05:15.1275715Z name: artifact 2024-06-26T06:05:15.1275994Z region: us-east-1 2024-06-26T06:05:15.1276268Z env: 2024-06-26T06:05:15.1276507Z GIT_DEFAULT_BRANCH: main 2024-06-26T06:05:15.1277236Z DOCKER_CONTAINER_ID: dece8529f48a7f61fcf0b1a68620de6340e6da1a183eee9f3a1750c5234902db 2024-06-26T06:05:15.1277902Z ##[endgroup] 2024-06-26T06:05:15.4646440Z NOTE: s3-prefix specified, ignoring name parameter 2024-06-26T06:05:15.4647448Z With the provided path, there will be 1 file uploaded 2024-06-26T06:05:15.4648561Z Uploading to s3 prefix: pytorch/pytorch/9673645538/1/artifact 2024-06-26T06:05:15.4684386Z Starting upload of test-reports-test-dynamo-1-3-linux.2xlarge_26688259850.zip 2024-06-26T06:05:15.6622171Z Finished upload of test-reports-test-dynamo-1-3-linux.2xlarge_26688259850.zip 2024-06-26T06:05:15.6755628Z ##[group]Run seemethere/upload-artifact-s3@v5 2024-06-26T06:05:15.6756052Z with: 2024-06-26T06:05:15.6756315Z s3-bucket: gha-artifacts 2024-06-26T06:05:15.6756722Z s3-prefix: pytorch/pytorch/9673645538/1/artifact 2024-06-26T06:05:15.6757163Z retention-days: 14 2024-06-26T06:05:15.6757479Z if-no-files-found: ignore 2024-06-26T06:05:15.6757816Z path: logs-*.zip 2024-06-26T06:05:15.6758093Z name: artifact 2024-06-26T06:05:15.6758374Z region: us-east-1 2024-06-26T06:05:15.6758665Z env: 2024-06-26T06:05:15.6758909Z GIT_DEFAULT_BRANCH: main 2024-06-26T06:05:15.6759511Z DOCKER_CONTAINER_ID: dece8529f48a7f61fcf0b1a68620de6340e6da1a183eee9f3a1750c5234902db 2024-06-26T06:05:15.6760168Z ##[endgroup] 2024-06-26T06:05:16.0126983Z NOTE: s3-prefix specified, ignoring name parameter 2024-06-26T06:05:16.0128169Z With the provided path, there will be 1 file uploaded 2024-06-26T06:05:16.0128973Z Uploading to s3 prefix: pytorch/pytorch/9673645538/1/artifact 2024-06-26T06:05:16.0162961Z Starting upload of logs-test-dynamo-1-3-linux.2xlarge_26688259850.zip 2024-06-26T06:05:16.2411958Z Finished upload of logs-test-dynamo-1-3-linux.2xlarge_26688259850.zip 2024-06-26T06:05:16.2546201Z ##[group]Run seemethere/upload-artifact-s3@v5 2024-06-26T06:05:16.2546620Z with: 2024-06-26T06:05:16.2546891Z s3-bucket: gha-artifacts 2024-06-26T06:05:16.2547301Z s3-prefix: pytorch/pytorch/9673645538/1/artifact 2024-06-26T06:05:16.2547740Z retention-days: 14 2024-06-26T06:05:16.2548070Z if-no-files-found: ignore 2024-06-26T06:05:16.2548407Z path: debug-*.zip 2024-06-26T06:05:16.2548681Z name: artifact 2024-06-26T06:05:16.2548957Z region: us-east-1 2024-06-26T06:05:16.2549236Z env: 2024-06-26T06:05:16.2549477Z GIT_DEFAULT_BRANCH: main 2024-06-26T06:05:16.2550184Z DOCKER_CONTAINER_ID: dece8529f48a7f61fcf0b1a68620de6340e6da1a183eee9f3a1750c5234902db 2024-06-26T06:05:16.2550843Z ##[endgroup] 2024-06-26T06:05:16.5860829Z No files were found with the provided path: debug-*.zip. No artifacts will be uploaded. 2024-06-26T06:05:16.5993175Z ##[group]Run # shellcheck disable=SC2156 2024-06-26T06:05:16.5993647Z # shellcheck disable=SC2156 2024-06-26T06:05:16.5994441Z find . -iname "core.[1-9]*" -exec docker exec "${DOCKER_CONTAINER_ID}" sh -c "gdb python {} -ex 'bt' -ex 'q'" \; 2024-06-26T06:05:16.6002046Z shell: /usr/bin/bash -e {0} 2024-06-26T06:05:16.6002381Z env: 2024-06-26T06:05:16.6002635Z GIT_DEFAULT_BRANCH: main 2024-06-26T06:05:16.6003234Z DOCKER_CONTAINER_ID: dece8529f48a7f61fcf0b1a68620de6340e6da1a183eee9f3a1750c5234902db 2024-06-26T06:05:16.6003894Z ##[endgroup] 2024-06-26T06:05:16.9746539Z GNU gdb (Ubuntu 9.2-0ubuntu1~20.04.2) 9.2 2024-06-26T06:05:16.9747186Z Copyright (C) 2020 Free Software Foundation, Inc. 2024-06-26T06:05:16.9748303Z License GPLv3+: GNU GPL version 3 or later 2024-06-26T06:05:16.9749397Z This is free software: you are free to change and redistribute it. 2024-06-26T06:05:16.9750650Z There is NO WARRANTY, to the extent permitted by law. 2024-06-26T06:05:16.9751502Z Type "show copying" and "show warranty" for details. 2024-06-26T06:05:16.9752094Z This GDB was configured as "x86_64-linux-gnu". 2024-06-26T06:05:16.9752616Z Type "show configuration" for configuration details. 2024-06-26T06:05:16.9753117Z For bug reporting instructions, please see: 2024-06-26T06:05:16.9753594Z . 2024-06-26T06:05:16.9754154Z Find the GDB manual and other documentation resources online at: 2024-06-26T06:05:16.9754772Z . 2024-06-26T06:05:16.9755121Z 2024-06-26T06:05:16.9755235Z For help, type "help". 2024-06-26T06:05:16.9755690Z Type "apropos word" to search for commands related to "word"... 2024-06-26T06:05:17.1031876Z Reading symbols from python... 2024-06-26T06:05:18.2671333Z 2024-06-26T06:05:18.2672191Z warning: core file may not match specified executable file. 2024-06-26T06:05:18.2872296Z [New LWP 1215] 2024-06-26T06:05:18.2872816Z [New LWP 1217] 2024-06-26T06:05:18.2873344Z [New LWP 1216] 2024-06-26T06:05:18.2873851Z [New LWP 1218] 2024-06-26T06:05:18.2874282Z [New LWP 1222] 2024-06-26T06:05:18.2874535Z [New LWP 1221] 2024-06-26T06:05:18.2874805Z [New LWP 1220] 2024-06-26T06:05:18.2875073Z [New LWP 1219] 2024-06-26T06:05:18.2911100Z [Thread debugging using libthread_db enabled] 2024-06-26T06:05:18.2911933Z Using host libthread_db library "/lib/x86_64-linux-gnu/libthread_db.so.1". 2024-06-26T06:05:22.1648949Z 50 ../sysdeps/unix/sysv/linux/raise.c: No such file or directory. 2024-06-26T06:05:22.1650381Z warning: File "/var/lib/jenkins/workspace/.gdbinit" auto-loading has been declined by your `auto-load safe-path' set to "$debugdir:$datadir/auto-load". 2024-06-26T06:05:22.1651870Z Core was generated by `/opt/conda/envs/py_3.12/bin/python -c import os; os.environ["TORCH_CUSTOM_TERMI'. 2024-06-26T06:05:22.1652716Z Program terminated with signal SIGABRT, Aborted. 2024-06-26T06:05:22.1653322Z #0 __GI_raise (sig=sig@entry=6) at ../sysdeps/unix/sysv/linux/raise.c:50 2024-06-26T06:05:22.1654134Z [Current thread is 1 (Thread 0x7f1da7403280 (LWP 1215))] 2024-06-26T06:05:22.1713542Z To enable execution of this file add 2024-06-26T06:05:22.1714668Z add-auto-load-safe-path /var/lib/jenkins/workspace/.gdbinit 2024-06-26T06:05:22.1715306Z line to your configuration file "/var/lib/jenkins/.gdbinit". 2024-06-26T06:05:22.1715894Z To completely disable this security protection add 2024-06-26T06:05:22.1716409Z set auto-load safe-path / 2024-06-26T06:05:22.1716860Z line to your configuration file "/var/lib/jenkins/.gdbinit". 2024-06-26T06:05:22.1717485Z For more information about this security protection see the 2024-06-26T06:05:22.1718269Z "Auto-loading safe path" section in the GDB manual. E.g., run from the shell: 2024-06-26T06:05:22.1719146Z info "(gdb)Auto-loading safe path" 2024-06-26T06:05:22.1719700Z #0 __GI_raise (sig=sig@entry=6) at ../sysdeps/unix/sysv/linux/raise.c:50 2024-06-26T06:05:22.1720321Z #1 0x00007f1da7426859 in __GI_abort () at abort.c:79 2024-06-26T06:05:22.2309754Z #2 0x00007f1d8ce83090 in __gnu_cxx::__verbose_terminate_handler () 2024-06-26T06:05:22.2310589Z at ../../../../libstdc++-v3/libsupc++/vterminate.cc:95 2024-06-26T06:05:22.2316142Z #3 0x00007f1d9d3877ac in c10::detail::terminate_handler() () 2024-06-26T06:05:22.2317072Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_python.so 2024-06-26T06:05:22.2337570Z #4 0x00007f1d8ce8157e in __cxxabiv1::__terminate (handler=) 2024-06-26T06:05:22.2338362Z at ../../../../libstdc++-v3/libsupc++/eh_terminate.cc:48 2024-06-26T06:05:22.2338882Z #5 0x00007f1d8ce815d0 in std::terminate () 2024-06-26T06:05:22.2339431Z at ../../../../libstdc++-v3/libsupc++/eh_terminate.cc:58 2024-06-26T06:05:22.2343425Z #6 0x00007f1d9d374fb6 in THPModule_abort(_object*, _object*) () 2024-06-26T06:05:22.2344399Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_python.so 2024-06-26T06:05:22.2348757Z #7 0x000000000051e164 in cfunction_vectorcall_NOARGS (func=0x7f1da6be42c0, 2024-06-26T06:05:22.2349864Z args=, nargsf=, kwnames=) 2024-06-26T06:05:22.2350722Z at /usr/local/src/conda/python-3.12.4/Include/cpython/methodobject.h:50 2024-06-26T06:05:22.2363882Z #8 0x000000000053ee61 in _PyObject_VectorcallTstate (kwnames=0x0, 2024-06-26T06:05:22.2364911Z kwnames@entry=, nargsf=9223372036854775808, 2024-06-26T06:05:22.2366202Z nargsf@entry=, args=0x7f1da7779078, 2024-06-26T06:05:22.2367471Z args@entry=, callable=0x7f1da6be42c0, 2024-06-26T06:05:22.2369080Z callable@entry=, tstate=0x9c11f8 <_PyRuntime+459704>, 2024-06-26T06:05:22.2370345Z tstate@entry=) 2024-06-26T06:05:22.2371331Z at /usr/local/src/conda/python-3.12.4/Include/internal/pycore_call.h:77 2024-06-26T06:05:22.2372133Z #9 PyObject_Vectorcall (callable=0x7f1da6be42c0, args=0x7f1da7779078, 2024-06-26T06:05:22.2372921Z nargsf=9223372036854775808, kwnames=0x0) 2024-06-26T06:05:22.2373719Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:325 2024-06-26T06:05:22.2374412Z #10 0x0000000000525e75 in _PyEval_EvalFrameDefault (tstate=, 2024-06-26T06:05:22.2375071Z frame=0x7f1da7779020, throwflag=) 2024-06-26T06:05:22.2375522Z at Python/bytecodes.c:2714 2024-06-26T06:05:22.2376466Z #11 0x00000000005e4dfe in PyEval_EvalCode (co=, 2024-06-26T06:05:22.2377057Z globals=0x7f1da73c9c00, locals=) 2024-06-26T06:05:22.2377690Z at /usr/local/src/conda/python-3.12.4/Python/ceval.c:578 2024-06-26T06:05:22.2381670Z #12 0x000000000060a5d7 in run_eval_code_obj ( 2024-06-26T06:05:22.2382294Z tstate=0x9c11f8 <_PyRuntime+459704>, co=0x7f1da739d460, 2024-06-26T06:05:22.2382842Z globals=0x7f1da73c9c00, locals=0x7f1da73c9c00) 2024-06-26T06:05:22.2383490Z at /usr/local/src/conda/python-3.12.4/Python/pythonrun.c:1722 2024-06-26T06:05:22.2405005Z #13 0x0000000000605ca7 in run_mod (mod=, 2024-06-26T06:05:22.2405773Z filename=0x956ca0 <_PyRuntime+24160>, globals=0x7f1da73c9c00, 2024-06-26T06:05:22.2406437Z locals=0x7f1da73c9c00, flags=0x7ffc5b731170, arena=0x7f1da72ebc70) 2024-06-26T06:05:22.2407196Z at /usr/local/src/conda/python-3.12.4/Python/pythonrun.c:1743 2024-06-26T06:05:22.2410697Z #14 0x00000000005f58cf in PyRun_StringFlags (str=, start=257, 2024-06-26T06:05:22.2411630Z globals=0x7f1da73c9c00, locals=0x7f1da73c9c00, flags=0x7ffc5b731170) 2024-06-26T06:05:22.2412385Z at /usr/local/src/conda/python-3.12.4/Python/pythonrun.c:1618 2024-06-26T06:05:22.2431550Z #15 0x00000000005f57fa in PyRun_SimpleStringFlags ( 2024-06-26T06:05:22.2433628Z command=0x7f1da73aa3f0 "import os; os.environ[\"TORCH_CUSTOM_TERMINATE\"] ='1';", ' ' , "import torch; import torch._C; torch._C._abort()\n", flags=0x7ffc5b731170) 2024-06-26T06:05:22.2435464Z at /usr/local/src/conda/python-3.12.4/Python/pythonrun.c:480 2024-06-26T06:05:22.2436025Z #16 0x0000000000615c9f in pymain_run_command ( 2024-06-26T06:05:22.2437169Z command=) at /usr/local/src/conda/python-3.12.4/Modules/main.c:255 2024-06-26T06:05:22.2438214Z #17 pymain_run_python (exitcode=0x7ffc5b731144) 2024-06-26T06:05:22.2438804Z at /usr/local/src/conda/python-3.12.4/Modules/main.c:620 2024-06-26T06:05:22.2439541Z #18 Py_RunMain () at /usr/local/src/conda/python-3.12.4/Modules/main.c:709 2024-06-26T06:05:22.2448305Z #19 0x00000000005cc259 in Py_BytesMain (argc=, 2024-06-26T06:05:22.2448822Z argv=) 2024-06-26T06:05:22.2449340Z at /usr/local/src/conda/python-3.12.4/Modules/main.c:763 2024-06-26T06:05:22.2453050Z #20 0x00007f1da7428083 in __libc_start_main (main=0x5cc190
, argc=3, 2024-06-26T06:05:22.2453965Z argv=0x7ffc5b7313a8, init=, fini=, 2024-06-26T06:05:22.2454580Z rtld_fini=, stack_end=0x7ffc5b731398) 2024-06-26T06:05:22.2455122Z at ../csu/libc-start.c:308 2024-06-26T06:05:22.2455487Z #21 0x00000000005cc089 in _start () 2024-06-26T06:05:22.2456041Z at /usr/local/src/conda/python-3.12.4/Parser/parser.c:41555 2024-06-26T06:05:22.3878752Z GNU gdb (Ubuntu 9.2-0ubuntu1~20.04.2) 9.2 2024-06-26T06:05:22.3879773Z Copyright (C) 2020 Free Software Foundation, Inc. 2024-06-26T06:05:22.3880702Z License GPLv3+: GNU GPL version 3 or later 2024-06-26T06:05:22.3881477Z This is free software: you are free to change and redistribute it. 2024-06-26T06:05:22.3882155Z There is NO WARRANTY, to the extent permitted by law. 2024-06-26T06:05:22.3882724Z Type "show copying" and "show warranty" for details. 2024-06-26T06:05:22.3883287Z This GDB was configured as "x86_64-linux-gnu". 2024-06-26T06:05:22.3883801Z Type "show configuration" for configuration details. 2024-06-26T06:05:22.3884319Z For bug reporting instructions, please see: 2024-06-26T06:05:22.3884771Z . 2024-06-26T06:05:22.3885332Z Find the GDB manual and other documentation resources online at: 2024-06-26T06:05:22.3885954Z . 2024-06-26T06:05:22.3886305Z 2024-06-26T06:05:22.3886419Z For help, type "help". 2024-06-26T06:05:22.3886874Z Type "apropos word" to search for commands related to "word"... 2024-06-26T06:05:22.5101232Z Reading symbols from python... 2024-06-26T06:05:23.6076195Z 2024-06-26T06:05:23.6077153Z warning: core file may not match specified executable file. 2024-06-26T06:05:23.6267659Z [New LWP 2945] 2024-06-26T06:05:23.6268253Z [New LWP 2948] 2024-06-26T06:05:23.6268568Z [New LWP 2949] 2024-06-26T06:05:23.6268858Z [New LWP 2947] 2024-06-26T06:05:23.6269114Z [New LWP 2951] 2024-06-26T06:05:23.6269380Z [New LWP 2950] 2024-06-26T06:05:23.6269644Z [New LWP 2952] 2024-06-26T06:05:23.6269898Z [New LWP 2953] 2024-06-26T06:05:23.6294760Z [Thread debugging using libthread_db enabled] 2024-06-26T06:05:23.6295930Z Using host libthread_db library "/lib/x86_64-linux-gnu/libthread_db.so.1". 2024-06-26T06:05:27.3626673Z 78 ../sysdeps/unix/syscall-template.S: No such file or directory. 2024-06-26T06:05:27.3627739Z Core was generated by `/opt/conda/envs/py_3.12/bin/python -bb -c from multiprocessing.spawn import spa'. 2024-06-26T06:05:27.3628565Z Program terminated with signal SIGABRT, Aborted. 2024-06-26T06:05:27.3629998Z warning: File "/var/lib/jenkins/workspace/.gdbinit" auto-loading has been declined by your `auto-load safe-path' set to "$debugdir:$datadir/auto-load". 2024-06-26T06:05:27.3631271Z #0 0x00007fd591ac63db in kill () at ../sysdeps/unix/syscall-template.S:78 2024-06-26T06:05:27.3632121Z [Current thread is 1 (Thread 0x7fd591a82280 (LWP 2945))] 2024-06-26T06:05:27.3662354Z To enable execution of this file add 2024-06-26T06:05:27.3663387Z add-auto-load-safe-path /var/lib/jenkins/workspace/.gdbinit 2024-06-26T06:05:27.3664396Z line to your configuration file "/var/lib/jenkins/.gdbinit". 2024-06-26T06:05:27.3665293Z To completely disable this security protection add 2024-06-26T06:05:27.3665999Z set auto-load safe-path / 2024-06-26T06:05:27.3666712Z line to your configuration file "/var/lib/jenkins/.gdbinit". 2024-06-26T06:05:27.3667634Z For more information about this security protection see the 2024-06-26T06:05:27.3668417Z "Auto-loading safe path" section in the GDB manual. E.g., run from the shell: 2024-06-26T06:05:27.3669085Z info "(gdb)Auto-loading safe path" 2024-06-26T06:05:27.3669716Z #0 0x00007fd591ac63db in kill () at ../sysdeps/unix/syscall-template.S:78 2024-06-26T06:05:27.3670318Z #1 0x00000000004f712d in os_kill_impl ( 2024-06-26T06:05:27.3671049Z module=, 2024-06-26T06:05:27.3672197Z signal=, 2024-06-26T06:05:27.3673548Z pid=) 2024-06-26T06:05:27.3674455Z at /usr/local/src/conda/python-3.12.4/Modules/posixmodule.c:8949 2024-06-26T06:05:27.3675108Z #2 os_kill (module=, args=, 2024-06-26T06:05:27.3675583Z nargs=) 2024-06-26T06:05:27.3676202Z at /usr/local/src/conda/python-3.12.4/Modules/clinic/posixmodule.c.h:5035 2024-06-26T06:05:27.3676905Z #3 0x000000000054b25c in cfunction_vectorcall_FASTCALL ( 2024-06-26T06:05:27.3677550Z func=, args=0x7fd591df8430, nargsf=9223372036854775810, 2024-06-26T06:05:27.3678178Z kwnames=) 2024-06-26T06:05:27.3679208Z at /usr/local/src/conda/python-3.12.4/Objects/methodobject.c:424 2024-06-26T06:05:27.3680554Z #4 0x000000000053ee61 in _PyObject_VectorcallTstate (kwnames=0x0, 2024-06-26T06:05:27.3681876Z kwnames@entry=, nargsf=9223372036854775810, 2024-06-26T06:05:27.3683310Z nargsf@entry=, args=0x7fd591df8430, 2024-06-26T06:05:27.3685166Z args@entry=, callable=0x7fd591a12930, 2024-06-26T06:05:27.3686540Z callable@entry=, tstate=0x9c11f8 <_PyRuntime+459704>, 2024-06-26T06:05:27.3688339Z tstate@entry=) 2024-06-26T06:05:27.3689347Z at /usr/local/src/conda/python-3.12.4/Include/internal/pycore_call.h:77 2024-06-26T06:05:27.3690125Z #5 PyObject_Vectorcall (callable=0x7fd591a12930, args=0x7fd591df8430, 2024-06-26T06:05:27.3690778Z nargsf=9223372036854775810, kwnames=0x0) 2024-06-26T06:05:27.3691502Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:325 2024-06-26T06:05:27.3692180Z #6 0x0000000000525e75 in _PyEval_EvalFrameDefault (tstate=, 2024-06-26T06:05:27.3692815Z frame=0x7fd591df83d0, throwflag=) 2024-06-26T06:05:27.3693277Z at Python/bytecodes.c:2714 2024-06-26T06:05:27.3694049Z #7 0x00000000005e4dfe in PyEval_EvalCode (co=, 2024-06-26T06:05:27.3694622Z globals=0x7fd591a49cc0, locals=) 2024-06-26T06:05:27.3695220Z at /usr/local/src/conda/python-3.12.4/Python/ceval.c:578 2024-06-26T06:05:27.3697086Z #8 0x000000000060a5d7 in run_eval_code_obj ( 2024-06-26T06:05:27.3697976Z tstate=0x9c11f8 <_PyRuntime+459704>, co=0x7fd5919952f0, 2024-06-26T06:05:27.3698503Z globals=0x7fd591a49cc0, locals=0x7fd591a49cc0) 2024-06-26T06:05:27.3699355Z at /usr/local/src/conda/python-3.12.4/Python/pythonrun.c:1722 2024-06-26T06:05:27.3720902Z #9 0x0000000000605ca7 in run_mod (mod=, 2024-06-26T06:05:27.3721741Z filename=0x956ca0 <_PyRuntime+24160>, globals=0x7fd591a49cc0, 2024-06-26T06:05:27.3722410Z locals=0x7fd591a49cc0, flags=0x7ffe201c3bf0, arena=0x7fd59196bcb0) 2024-06-26T06:05:27.3723172Z at /usr/local/src/conda/python-3.12.4/Python/pythonrun.c:1743 2024-06-26T06:05:27.3726817Z #10 0x00000000005f58cf in PyRun_StringFlags (str=, start=257, 2024-06-26T06:05:27.3727745Z globals=0x7fd591a49cc0, locals=0x7fd591a49cc0, flags=0x7ffe201c3bf0) 2024-06-26T06:05:27.3728516Z at /usr/local/src/conda/python-3.12.4/Python/pythonrun.c:1618 2024-06-26T06:05:27.3732839Z #11 0x00000000005f57fa in PyRun_SimpleStringFlags ( 2024-06-26T06:05:27.3734401Z command=0x7fd591a01fd0 "from multiprocessing.spawn import spawn_main; spawn_main(tracker_fd=7, pipe_handle=9)\n", flags=0x7ffe201c3bf0) 2024-06-26T06:05:27.3735936Z at /usr/local/src/conda/python-3.12.4/Python/pythonrun.c:480 2024-06-26T06:05:27.3736507Z #12 0x0000000000615c9f in pymain_run_command ( 2024-06-26T06:05:27.3737637Z command=) at /usr/local/src/conda/python-3.12.4/Modules/main.c:255 2024-06-26T06:05:27.3738664Z #13 pymain_run_python (exitcode=0x7ffe201c3bc4) 2024-06-26T06:05:27.3739263Z at /usr/local/src/conda/python-3.12.4/Modules/main.c:620 2024-06-26T06:05:27.3739994Z #14 Py_RunMain () at /usr/local/src/conda/python-3.12.4/Modules/main.c:709 2024-06-26T06:05:27.3855862Z #15 0x00000000005cc259 in Py_BytesMain (argc=, 2024-06-26T06:05:27.3856822Z argv=) 2024-06-26T06:05:27.3857797Z at /usr/local/src/conda/python-3.12.4/Modules/main.c:763 2024-06-26T06:05:27.3861201Z #16 0x00007fd591aa7083 in __libc_start_main (main=0x5cc190
, argc=5, 2024-06-26T06:05:27.3862437Z argv=0x7ffe201c3e28, init=, fini=, 2024-06-26T06:05:27.3863413Z rtld_fini=, stack_end=0x7ffe201c3e18) 2024-06-26T06:05:27.3864125Z at ../csu/libc-start.c:308 2024-06-26T06:05:27.3864743Z #17 0x00000000005cc089 in _start () 2024-06-26T06:05:27.3865819Z at /usr/local/src/conda/python-3.12.4/Parser/parser.c:41555 2024-06-26T06:05:27.5239487Z GNU gdb (Ubuntu 9.2-0ubuntu1~20.04.2) 9.2 2024-06-26T06:05:27.5240401Z Copyright (C) 2020 Free Software Foundation, Inc. 2024-06-26T06:05:27.5263485Z License GPLv3+: GNU GPL version 3 or later 2024-06-26T06:05:27.5265060Z This is free software: you are free to change and redistribute it. 2024-06-26T06:05:27.5266207Z There is NO WARRANTY, to the extent permitted by law. 2024-06-26T06:05:27.5267209Z Type "show copying" and "show warranty" for details. 2024-06-26T06:05:27.5268322Z This GDB was configured as "x86_64-linux-gnu". 2024-06-26T06:05:27.5269258Z Type "show configuration" for configuration details. 2024-06-26T06:05:27.5270195Z For bug reporting instructions, please see: 2024-06-26T06:05:27.5271047Z . 2024-06-26T06:05:27.5272043Z Find the GDB manual and other documentation resources online at: 2024-06-26T06:05:27.5273109Z . 2024-06-26T06:05:27.5273718Z 2024-06-26T06:05:27.5273927Z For help, type "help". 2024-06-26T06:05:27.5274738Z Type "apropos word" to search for commands related to "word"... 2024-06-26T06:05:27.6431847Z Reading symbols from python... 2024-06-26T06:05:28.7158372Z 2024-06-26T06:05:28.7159165Z warning: core file may not match specified executable file. 2024-06-26T06:05:28.7354689Z [New LWP 2979] 2024-06-26T06:05:28.7381382Z [Thread debugging using libthread_db enabled] 2024-06-26T06:05:28.7383192Z Using host libthread_db library "/lib/x86_64-linux-gnu/libthread_db.so.1". 2024-06-26T06:05:32.5963550Z 50 ../sysdeps/unix/sysv/linux/raise.c: No such file or directory. 2024-06-26T06:05:32.5965399Z warning: File "/var/lib/jenkins/workspace/.gdbinit" auto-loading has been declined by your `auto-load safe-path' set to "$debugdir:$datadir/auto-load". 2024-06-26T06:05:32.5966899Z Core was generated by `/opt/conda/envs/py_3.12/bin/python -bb test_multiprocessing_spawn.py --shard-id'. 2024-06-26T06:05:32.5967723Z Program terminated with signal SIGABRT, Aborted. 2024-06-26T06:05:32.5968350Z #0 raise (sig=) at ../sysdeps/unix/sysv/linux/raise.c:50 2024-06-26T06:05:32.6001927Z To enable execution of this file add 2024-06-26T06:05:32.6002813Z add-auto-load-safe-path /var/lib/jenkins/workspace/.gdbinit 2024-06-26T06:05:32.6003496Z line to your configuration file "/var/lib/jenkins/.gdbinit". 2024-06-26T06:05:32.6004088Z To completely disable this security protection add 2024-06-26T06:05:32.6004593Z set auto-load safe-path / 2024-06-26T06:05:32.6005062Z line to your configuration file "/var/lib/jenkins/.gdbinit". 2024-06-26T06:05:32.6005690Z For more information about this security protection see the 2024-06-26T06:05:32.6006486Z "Auto-loading safe path" section in the GDB manual. E.g., run from the shell: 2024-06-26T06:05:32.6007156Z info "(gdb)Auto-loading safe path" 2024-06-26T06:05:32.6007713Z #0 raise (sig=) at ../sysdeps/unix/sysv/linux/raise.c:50 2024-06-26T06:05:32.6010738Z #1 2024-06-26T06:05:32.6011359Z #2 0x00007fa7ed1893db in kill () at ../sysdeps/unix/syscall-template.S:78 2024-06-26T06:05:32.6013395Z #3 0x00000000004f712d in os_kill_impl ( 2024-06-26T06:05:32.6014328Z module=, 2024-06-26T06:05:32.6015413Z signal=, 2024-06-26T06:05:32.6016552Z pid=) 2024-06-26T06:05:32.6017471Z at /usr/local/src/conda/python-3.12.4/Modules/posixmodule.c:8949 2024-06-26T06:05:32.6018142Z #4 os_kill (module=, args=, 2024-06-26T06:05:32.6018628Z nargs=) 2024-06-26T06:05:32.6019256Z at /usr/local/src/conda/python-3.12.4/Modules/clinic/posixmodule.c.h:5035 2024-06-26T06:05:32.6021516Z #5 0x000000000054b25c in cfunction_vectorcall_FASTCALL ( 2024-06-26T06:05:32.6022231Z func=, args=0x7fa7ed4bd100, nargsf=9223372036854775810, 2024-06-26T06:05:32.6022809Z kwnames=) 2024-06-26T06:05:32.6023402Z at /usr/local/src/conda/python-3.12.4/Objects/methodobject.c:424 2024-06-26T06:05:32.6027890Z #6 0x000000000053ee61 in _PyObject_VectorcallTstate (kwnames=0x0, 2024-06-26T06:05:32.6029297Z kwnames@entry=, nargsf=9223372036854775810, 2024-06-26T06:05:32.6030617Z nargsf@entry=, args=0x7fa7ed4bd100, 2024-06-26T06:05:32.6032046Z args@entry=, callable=0x7fa7ed0da9d0, 2024-06-26T06:05:32.6033870Z callable@entry=, tstate=0x9c11f8 <_PyRuntime+459704>, 2024-06-26T06:05:32.6035125Z tstate@entry=) 2024-06-26T06:05:32.6036131Z at /usr/local/src/conda/python-3.12.4/Include/internal/pycore_call.h:77 2024-06-26T06:05:32.6036905Z #7 PyObject_Vectorcall (callable=0x7fa7ed0da9d0, args=0x7fa7ed4bd100, 2024-06-26T06:05:32.6037476Z nargsf=9223372036854775810, kwnames=0x0) 2024-06-26T06:05:32.6038051Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:325 2024-06-26T06:05:32.6164774Z #8 0x0000000000525e75 in _PyEval_EvalFrameDefault (tstate=, 2024-06-26T06:05:32.6165680Z frame=0x7fa7ed4bd0a0, throwflag=) 2024-06-26T06:05:32.6166445Z at Python/bytecodes.c:2714 2024-06-26T06:05:32.6166875Z #9 0x00007fa7e31a484c in custom_eval_frame_shim () 2024-06-26T06:05:32.6167698Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_python.so 2024-06-26T06:05:32.6168932Z #10 0x000000000052b0a2 in PyCFunction_Call ( 2024-06-26T06:05:32.6169760Z kwargs=, 2024-06-26T06:05:32.6170766Z args=, 2024-06-26T06:05:32.6172166Z callable=) at /usr/local/src/conda/python-3.12.4/Objects/call.c:387 2024-06-26T06:05:32.6179724Z #11 _PyEval_EvalFrameDefault (tstate=, frame=0x7fa7ed4bcff0, 2024-06-26T06:05:32.6180517Z throwflag=) at Python/bytecodes.c:3262 2024-06-26T06:05:32.6181212Z #12 0x00007fa7e31a484c in custom_eval_frame_shim () 2024-06-26T06:05:32.6182101Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_python.so 2024-06-26T06:05:32.6183803Z #13 0x000000000052b0a2 in PyCFunction_Call ( 2024-06-26T06:05:32.6184650Z kwargs=, 2024-06-26T06:05:32.6185640Z args=, 2024-06-26T06:05:32.6187064Z callable=) at /usr/local/src/conda/python-3.12.4/Objects/call.c:387 2024-06-26T06:05:32.6193062Z #14 _PyEval_EvalFrameDefault (tstate=, frame=0x7fa7ed4bcf78, 2024-06-26T06:05:32.6193801Z throwflag=) at Python/bytecodes.c:3262 2024-06-26T06:05:32.6194488Z #15 0x00007fa7e31a484c in custom_eval_frame_shim () 2024-06-26T06:05:32.6195317Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_python.so 2024-06-26T06:05:32.6201297Z #16 0x000000000053ee61 in _PyObject_VectorcallTstate (kwnames=0x0, 2024-06-26T06:05:32.6202637Z kwnames@entry=, nargsf=9223372036854775809, 2024-06-26T06:05:32.6203942Z nargsf@entry=, args=0x7fa7ed4bcf50, 2024-06-26T06:05:32.6205268Z args@entry=, callable=0x7fa78d413560, 2024-06-26T06:05:32.6206962Z callable@entry=, tstate=0x9c11f8 <_PyRuntime+459704>, 2024-06-26T06:05:32.6208207Z tstate@entry=) 2024-06-26T06:05:32.6209219Z at /usr/local/src/conda/python-3.12.4/Include/internal/pycore_call.h:77 2024-06-26T06:05:32.6210000Z #17 PyObject_Vectorcall (callable=0x7fa78d413560, args=0x7fa7ed4bcf50, 2024-06-26T06:05:32.6210585Z nargsf=9223372036854775809, kwnames=0x0) 2024-06-26T06:05:32.6211153Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:325 2024-06-26T06:05:32.6215926Z #18 0x0000000000525e75 in _PyEval_EvalFrameDefault (tstate=, 2024-06-26T06:05:32.6216782Z frame=0x7fa7ed4bcec0, throwflag=) 2024-06-26T06:05:32.6217282Z at Python/bytecodes.c:2714 2024-06-26T06:05:32.6217767Z #19 0x00007fa7e31a484c in custom_eval_frame_shim () 2024-06-26T06:05:32.6218595Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_python.so 2024-06-26T06:05:32.6223895Z #20 0x000000000053ee61 in _PyObject_VectorcallTstate (kwnames=0x7fa7867511b0, 2024-06-26T06:05:32.6225158Z kwnames@entry=, nargsf=9223372036854775809, 2024-06-26T06:05:32.6226570Z nargsf@entry=, args=0x7fa7ed4bce98, 2024-06-26T06:05:32.6228096Z args@entry=, callable=0x7fa78d41c040, 2024-06-26T06:05:32.6229635Z callable@entry=, tstate=0x9c11f8 <_PyRuntime+459704>, 2024-06-26T06:05:32.6230896Z tstate@entry=) 2024-06-26T06:05:32.6232130Z at /usr/local/src/conda/python-3.12.4/Include/internal/pycore_call.h:77 2024-06-26T06:05:32.6233359Z #21 PyObject_Vectorcall (callable=0x7fa78d41c040, args=0x7fa7ed4bce98, 2024-06-26T06:05:32.6233981Z nargsf=9223372036854775809, kwnames=0x7fa7867511b0) 2024-06-26T06:05:32.6234609Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:325 2024-06-26T06:05:32.6235295Z #22 0x0000000000525e75 in _PyEval_EvalFrameDefault (tstate=, 2024-06-26T06:05:32.6235940Z frame=0x7fa7ed4bce10, throwflag=) 2024-06-26T06:05:32.6236408Z at Python/bytecodes.c:2714 2024-06-26T06:05:32.6237858Z #23 0x000000000053ee61 in _PyObject_VectorcallTstate (kwnames=0x0, 2024-06-26T06:05:32.6239288Z kwnames@entry=, nargsf=9223372036854775810, 2024-06-26T06:05:32.6240595Z nargsf@entry=, args=0x7fa7ed4bce00, 2024-06-26T06:05:32.6241952Z args@entry=, callable=0x7fa7867639c0, 2024-06-26T06:05:32.6243381Z callable@entry=, tstate=0x9c11f8 <_PyRuntime+459704>, 2024-06-26T06:05:32.6244648Z tstate@entry=) 2024-06-26T06:05:32.6245641Z at /usr/local/src/conda/python-3.12.4/Include/internal/pycore_call.h:77 2024-06-26T06:05:32.6246461Z #24 PyObject_Vectorcall (callable=0x7fa7867639c0, args=0x7fa7ed4bce00, 2024-06-26T06:05:32.6247191Z nargsf=9223372036854775810, kwnames=0x0) 2024-06-26T06:05:32.6247773Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:325 2024-06-26T06:05:32.6248460Z #25 0x0000000000525e75 in _PyEval_EvalFrameDefault (tstate=, 2024-06-26T06:05:32.6249118Z frame=0x7fa7ed4bcda0, throwflag=) 2024-06-26T06:05:32.6249577Z at Python/bytecodes.c:2714 2024-06-26T06:05:32.6252398Z #26 0x000000000042cb39 in _PyEval_Vector (kwnames=0x0, 2024-06-26T06:05:32.6253019Z argcount=, args=0x7ffda4a112c0, locals=0x0, 2024-06-26T06:05:32.6253788Z func=0x7fa786763560, tstate=0x9c11f8 <_PyRuntime+459704>) 2024-06-26T06:05:32.6254572Z at /usr/local/src/conda/python-3.12.4/Include/internal/pycore_ceval.h:91 2024-06-26T06:05:32.6257018Z #27 _PyFunction_Vectorcall (kwnames=0x0, nargsf=, 2024-06-26T06:05:32.6257845Z stack=0x7ffda4a112c0, func=0x7fa786763560) 2024-06-26T06:05:32.6258440Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:419 2024-06-26T06:05:32.6259602Z #28 _PyObject_FastCallDictTstate (tstate=, 2024-06-26T06:05:32.6260902Z callable=, args=0x7ffda4a112c0, 2024-06-26T06:05:32.6262021Z nargsf=, 2024-06-26T06:05:32.6263420Z kwargs=) at /usr/local/src/conda/python-3.12.4/Objects/call.c:133 2024-06-26T06:05:32.6265106Z #29 0x0000000000558334 in _PyObject_Call_Prepend (kwargs=0x0, 2024-06-26T06:05:32.6265798Z args=0x7fa78629efb0, obj=0x7fa78639e720, callable=0x7fa786763560, 2024-06-26T06:05:32.6266512Z tstate=0x9c11f8 <_PyRuntime+459704>) 2024-06-26T06:05:32.6267098Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:508 2024-06-26T06:05:32.6269757Z #30 slot_tp_init (self=0x7fa78639e720, args=0x7fa78629efb0, kwds=0x0) 2024-06-26T06:05:32.6270521Z at /usr/local/src/conda/python-3.12.4/Objects/typeobject.c:9020 2024-06-26T06:05:32.6272831Z #31 0x000000000051b2db in type_call (kwds=0x0, args=0x7fa78629efb0, 2024-06-26T06:05:32.6273375Z type=) 2024-06-26T06:05:32.6273884Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:250 2024-06-26T06:05:32.6277203Z #32 _PyObject_MakeTpCall (tstate=0x9c11f8 <_PyRuntime+459704>, 2024-06-26T06:05:32.6277934Z callable=0x7d7e610, args=, nargs=, 2024-06-26T06:05:32.6278738Z keywords=0x0) at /usr/local/src/conda/python-3.12.4/Objects/call.c:240 2024-06-26T06:05:32.6281136Z #33 0x0000000000525e75 in _PyEval_EvalFrameDefault (tstate=, 2024-06-26T06:05:32.6281785Z frame=0x7fa7ed4bcd30, throwflag=) 2024-06-26T06:05:32.6282275Z at Python/bytecodes.c:2714 2024-06-26T06:05:32.6286434Z #34 0x000000000053ee61 in _PyObject_VectorcallTstate (kwnames=0x0, 2024-06-26T06:05:32.6287805Z kwnames@entry=, nargsf=9223372036854775809, 2024-06-26T06:05:32.6289112Z nargsf@entry=, args=0x7fa7ed4bcd28, 2024-06-26T06:05:32.6290454Z args@entry=, callable=0x7fa78d41ec00, 2024-06-26T06:05:32.6291951Z callable@entry=, tstate=0x9c11f8 <_PyRuntime+459704>, 2024-06-26T06:05:32.6293198Z tstate@entry=) 2024-06-26T06:05:32.6294558Z at /usr/local/src/conda/python-3.12.4/Include/internal/pycore_call.h:77 2024-06-26T06:05:32.6295398Z #35 PyObject_Vectorcall (callable=0x7fa78d41ec00, args=0x7fa7ed4bcd28, 2024-06-26T06:05:32.6295969Z nargsf=9223372036854775809, kwnames=0x0) 2024-06-26T06:05:32.6296551Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:325 2024-06-26T06:05:32.6297228Z #36 0x0000000000525e75 in _PyEval_EvalFrameDefault (tstate=, 2024-06-26T06:05:32.6297885Z frame=0x7fa7ed4bccc8, throwflag=) 2024-06-26T06:05:32.6298342Z at Python/bytecodes.c:2714 2024-06-26T06:05:32.6299469Z #37 0x000000000053ee61 in _PyObject_VectorcallTstate (kwnames=0x0, 2024-06-26T06:05:32.6300880Z kwnames@entry=, nargsf=9223372036854775809, 2024-06-26T06:05:32.6302184Z nargsf@entry=, args=0x7fa7ed4bcc98, 2024-06-26T06:05:32.6303514Z args@entry=, callable=0x7fa78d413600, 2024-06-26T06:05:32.6305438Z callable@entry=, tstate=0x9c11f8 <_PyRuntime+459704>, 2024-06-26T06:05:32.6306690Z tstate@entry=) 2024-06-26T06:05:32.6308026Z at /usr/local/src/conda/python-3.12.4/Include/internal/pycore_call.h:77 2024-06-26T06:05:32.6308919Z #38 PyObject_Vectorcall (callable=0x7fa78d413600, args=0x7fa7ed4bcc98, 2024-06-26T06:05:32.6309503Z nargsf=9223372036854775809, kwnames=0x0) 2024-06-26T06:05:32.6310067Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:325 2024-06-26T06:05:32.6310866Z #39 0x0000000000525e75 in _PyEval_EvalFrameDefault (tstate=, 2024-06-26T06:05:32.6311524Z frame=0x7fa7ed4bcbd8, throwflag=) 2024-06-26T06:05:32.6311991Z at Python/bytecodes.c:2714 2024-06-26T06:05:32.6313108Z #40 0x000000000053ee61 in _PyObject_VectorcallTstate (kwnames=0x7fa7ecf75dc0, 2024-06-26T06:05:32.6314497Z kwnames@entry=, nargsf=9223372036854775809, 2024-06-26T06:05:32.6315796Z nargsf@entry=, args=0x7fa7ed4bcbc0, 2024-06-26T06:05:32.6317060Z args@entry=, callable=0x7fa78d1b22a0, 2024-06-26T06:05:32.6318681Z callable@entry=, tstate=0x9c11f8 <_PyRuntime+459704>, 2024-06-26T06:05:32.6319934Z tstate@entry=) 2024-06-26T06:05:32.6321007Z at /usr/local/src/conda/python-3.12.4/Include/internal/pycore_call.h:77 2024-06-26T06:05:32.6321774Z #41 PyObject_Vectorcall (callable=0x7fa78d1b22a0, args=0x7fa7ed4bcbc0, 2024-06-26T06:05:32.6322403Z nargsf=9223372036854775809, kwnames=0x7fa7ecf75dc0) 2024-06-26T06:05:32.6323027Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:325 2024-06-26T06:05:32.6328761Z #42 0x0000000000525e75 in _PyEval_EvalFrameDefault (tstate=, 2024-06-26T06:05:32.6329588Z frame=0x7fa7ed4bcb48, throwflag=) 2024-06-26T06:05:32.6330176Z at Python/bytecodes.c:2714 2024-06-26T06:05:32.6330653Z #43 0x00007fa7e31a484c in custom_eval_frame_shim () 2024-06-26T06:05:32.6331463Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_python.so 2024-06-26T06:05:32.6337525Z #44 0x000000000053ee61 in _PyObject_VectorcallTstate (kwnames=0x0, 2024-06-26T06:05:32.6338603Z kwnames@entry=, nargsf=9223372036854775810, 2024-06-26T06:05:32.6339902Z nargsf@entry=, args=0x7fa7ed4bcb38, 2024-06-26T06:05:32.6341188Z args@entry=, callable=0x7fa7865e5800, 2024-06-26T06:05:32.6342783Z callable@entry=, tstate=0x9c11f8 <_PyRuntime+459704>, 2024-06-26T06:05:32.6344040Z tstate@entry=) 2024-06-26T06:05:32.6345038Z at /usr/local/src/conda/python-3.12.4/Include/internal/pycore_call.h:77 2024-06-26T06:05:32.6345804Z #45 PyObject_Vectorcall (callable=0x7fa7865e5800, args=0x7fa7ed4bcb38, 2024-06-26T06:05:32.6346393Z nargsf=9223372036854775810, kwnames=0x0) 2024-06-26T06:05:32.6346959Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:325 2024-06-26T06:05:32.6352105Z #46 0x0000000000525e75 in _PyEval_EvalFrameDefault (tstate=, 2024-06-26T06:05:32.6352826Z frame=0x7fa7ed4bcac8, throwflag=) 2024-06-26T06:05:32.6353357Z at Python/bytecodes.c:2714 2024-06-26T06:05:32.6353878Z #47 0x00007fa7e31a4a4b in eval_custom_code () 2024-06-26T06:05:32.6354672Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_python.so 2024-06-26T06:05:32.6359010Z #48 0x00007fa7e31a46c4 in custom_eval_frame_shim () 2024-06-26T06:05:32.6359813Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_python.so 2024-06-26T06:05:32.6362901Z #49 0x000000000045985e in _PyEval_EvalFrame ( 2024-06-26T06:05:32.6363777Z throwflag=, frame=0x7fa7ed4bca40, 2024-06-26T06:05:32.6364724Z tstate=0x9c11f8 <_PyRuntime+459704>) 2024-06-26T06:05:32.6365319Z at /usr/local/src/conda/python-3.12.4/Python/compile.c:2838 2024-06-26T06:05:32.6367423Z #50 _PyEval_Vector (kwnames=, argcount=, 2024-06-26T06:05:32.6368121Z args=0x7ffda4a11e98, locals=0x0, func=0x7fa788cab240, 2024-06-26T06:05:32.6368615Z tstate=0x9c11f8 <_PyRuntime+459704>) 2024-06-26T06:05:32.6369204Z at /usr/local/src/conda/python-3.12.4/Python/ceval.c:1683 2024-06-26T06:05:32.6370141Z #51 _PyFunction_Vectorcall (kwnames=, nargsf=, 2024-06-26T06:05:32.6370780Z stack=0x7ffda4a11e98, func=0x7fa788cab240) 2024-06-26T06:05:32.6371363Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:419 2024-06-26T06:05:32.6372651Z #52 _PyObject_VectorcallTstate (tstate=, 2024-06-26T06:05:32.6373730Z callable=, args=0x7ffda4a11e98, 2024-06-26T06:05:32.6374979Z nargsf=, kwnames=) 2024-06-26T06:05:32.6376338Z at /usr/local/src/conda/python-3.12.4/Include/internal/pycore_call.h:92 2024-06-26T06:05:32.6377314Z #53 0x0000000000575e80 in method_vectorcall (method=, 2024-06-26T06:05:32.6378117Z args=0x963870 <_PyRuntime+76336>, nargsf=0, kwnames=0x0) 2024-06-26T06:05:32.6378805Z at /usr/local/src/conda/python-3.12.4/Objects/classobject.c:69 2024-06-26T06:05:32.6379385Z #54 0x000000000052b0a2 in PyCFunction_Call ( 2024-06-26T06:05:32.6380123Z kwargs=, 2024-06-26T06:05:32.6381280Z args=, 2024-06-26T06:05:32.6382685Z callable=) at /usr/local/src/conda/python-3.12.4/Objects/call.c:387 2024-06-26T06:05:32.6383863Z #55 _PyEval_EvalFrameDefault (tstate=, frame=0x7fa7ed4bc9a0, 2024-06-26T06:05:32.6384544Z throwflag=) at Python/bytecodes.c:3262 2024-06-26T06:05:32.6386453Z #56 0x000000000053ee61 in _PyObject_VectorcallTstate (kwnames=0x0, 2024-06-26T06:05:32.6387545Z kwnames@entry=, nargsf=9223372036854775809, 2024-06-26T06:05:32.6388855Z nargsf@entry=, args=0x7fa7ed4bc980, 2024-06-26T06:05:32.6390138Z args@entry=, callable=0x7fa786762700, 2024-06-26T06:05:32.6391689Z callable@entry=, tstate=0x9c11f8 <_PyRuntime+459704>, 2024-06-26T06:05:32.6392944Z tstate@entry=) 2024-06-26T06:05:32.6393949Z at /usr/local/src/conda/python-3.12.4/Include/internal/pycore_call.h:77 2024-06-26T06:05:32.6394759Z #57 PyObject_Vectorcall (callable=0x7fa786762700, args=0x7fa7ed4bc980, 2024-06-26T06:05:32.6395403Z nargsf=9223372036854775809, kwnames=0x0) 2024-06-26T06:05:32.6395992Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:325 2024-06-26T06:05:32.6396675Z #58 0x0000000000525e75 in _PyEval_EvalFrameDefault (tstate=, 2024-06-26T06:05:32.6397309Z frame=0x7fa7ed4bc920, throwflag=) 2024-06-26T06:05:32.6397777Z at Python/bytecodes.c:2714 2024-06-26T06:05:32.6401021Z #59 0x000000000053ee61 in _PyObject_VectorcallTstate (kwnames=0x0, 2024-06-26T06:05:32.6402048Z kwnames@entry=, nargsf=9223372036854775810, 2024-06-26T06:05:32.6403356Z nargsf@entry=, args=0x7fa7ed4bc900, 2024-06-26T06:05:32.6404731Z args@entry=, callable=0x7fa7eccfc720, 2024-06-26T06:05:32.6406420Z callable@entry=, tstate=0x9c11f8 <_PyRuntime+459704>, 2024-06-26T06:05:32.6407669Z tstate@entry=) 2024-06-26T06:05:32.6408771Z at /usr/local/src/conda/python-3.12.4/Include/internal/pycore_call.h:77 2024-06-26T06:05:32.6409733Z #60 PyObject_Vectorcall (callable=0x7fa7eccfc720, args=0x7fa7ed4bc900, 2024-06-26T06:05:32.6410326Z nargsf=9223372036854775810, kwnames=0x0) 2024-06-26T06:05:32.6410935Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:325 2024-06-26T06:05:32.6411860Z #61 0x0000000000525e75 in _PyEval_EvalFrameDefault (tstate=, 2024-06-26T06:05:32.6412516Z frame=0x7fa7ed4bc860, throwflag=) 2024-06-26T06:05:32.6412973Z at Python/bytecodes.c:2714 2024-06-26T06:05:32.6413381Z #62 0x000000000045985e in _PyEval_EvalFrame ( 2024-06-26T06:05:32.6414383Z throwflag=, frame=0x7fa7ed4bc860, 2024-06-26T06:05:32.6415218Z tstate=0x9c11f8 <_PyRuntime+459704>) 2024-06-26T06:05:32.6415853Z at /usr/local/src/conda/python-3.12.4/Python/compile.c:2838 2024-06-26T06:05:32.6416670Z #63 _PyEval_Vector (kwnames=, argcount=, 2024-06-26T06:05:32.6417307Z args=0x7fa786791360, locals=0x0, func=0x7fa7eccfc900, 2024-06-26T06:05:32.6417811Z tstate=0x9c11f8 <_PyRuntime+459704>) 2024-06-26T06:05:32.6418390Z at /usr/local/src/conda/python-3.12.4/Python/ceval.c:1683 2024-06-26T06:05:32.6419317Z #64 _PyFunction_Vectorcall (kwnames=, nargsf=, 2024-06-26T06:05:32.6419944Z stack=0x7fa786791360, func=0x7fa7eccfc900) 2024-06-26T06:05:32.6420535Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:419 2024-06-26T06:05:32.6421556Z #65 _PyObject_VectorcallTstate (tstate=, 2024-06-26T06:05:32.6422548Z callable=, args=0x7fa786791360, 2024-06-26T06:05:32.6423791Z nargsf=, kwnames=) 2024-06-26T06:05:32.6424882Z at /usr/local/src/conda/python-3.12.4/Include/internal/pycore_call.h:92 2024-06-26T06:05:32.6427024Z #66 0x0000000000575e4d in method_vectorcall ( 2024-06-26T06:05:32.6427667Z method=method@entry=0x7fa7865ffa80, args=args@entry=0x7fa786791368, 2024-06-26T06:05:32.6428287Z nargsf=, kwnames=0x7fa78629e0e0) 2024-06-26T06:05:32.6428958Z at /usr/local/src/conda/python-3.12.4/Objects/classobject.c:61 2024-06-26T06:05:32.6430341Z #67 0x000000000055b492 in _PyVectorcall_Call (kwargs=, 2024-06-26T06:05:32.6431072Z tuple=, callable=0x7fa7865ffa80, 2024-06-26T06:05:32.6431710Z func=0x575b60 , tstate=0x9c11f8 <_PyRuntime+459704>) 2024-06-26T06:05:32.6432711Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:283 2024-06-26T06:05:32.6433485Z #68 _PyObject_Call (tstate=0x9c11f8 <_PyRuntime+459704>, 2024-06-26T06:05:32.6434322Z callable=0x7fa7865ffa80, args=, kwargs=) 2024-06-26T06:05:32.6435063Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:354 2024-06-26T06:05:32.6435589Z #69 0x000000000052b0a2 in PyCFunction_Call ( 2024-06-26T06:05:32.6436322Z kwargs=, 2024-06-26T06:05:32.6437318Z args=, 2024-06-26T06:05:32.6438716Z callable=) at /usr/local/src/conda/python-3.12.4/Objects/call.c:387 2024-06-26T06:05:32.6443378Z #70 _PyEval_EvalFrameDefault (tstate=, frame=0x7fa7ed4bc7c8, 2024-06-26T06:05:32.6444429Z throwflag=) at Python/bytecodes.c:3262 2024-06-26T06:05:32.6445077Z #71 0x00007fa7e31a4a4b in eval_custom_code () 2024-06-26T06:05:32.6445857Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_python.so 2024-06-26T06:05:32.6450071Z #72 0x00007fa7e31a46c4 in custom_eval_frame_shim () 2024-06-26T06:05:32.6450892Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_python.so 2024-06-26T06:05:32.6455009Z #73 0x000000000055b492 in _PyVectorcall_Call (kwargs=, 2024-06-26T06:05:32.6455644Z tuple=, callable=0x7fa786a5ab60, 2024-06-26T06:05:32.6456195Z func=0x553210 <_PyFunction_Vectorcall>, 2024-06-26T06:05:32.6456737Z tstate=0x9c11f8 <_PyRuntime+459704>) 2024-06-26T06:05:32.6457429Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:283 2024-06-26T06:05:32.6458071Z #74 _PyObject_Call (tstate=0x9c11f8 <_PyRuntime+459704>, 2024-06-26T06:05:32.6459170Z callable=0x7fa786a5ab60, args=, kwargs=) 2024-06-26T06:05:32.6459902Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:354 2024-06-26T06:05:32.6460466Z #75 0x000000000052b0a2 in PyCFunction_Call ( 2024-06-26T06:05:32.6461339Z kwargs=, 2024-06-26T06:05:32.6462319Z args=, 2024-06-26T06:05:32.6464019Z callable=) at /usr/local/src/conda/python-3.12.4/Objects/call.c:387 2024-06-26T06:05:32.6465204Z #76 _PyEval_EvalFrameDefault (tstate=, frame=0x7fa7ed4bc670, 2024-06-26T06:05:32.6465892Z throwflag=) at Python/bytecodes.c:3262 2024-06-26T06:05:32.6466400Z #77 0x000000000057633d in _PyEval_EvalFrame ( 2024-06-26T06:05:32.6467258Z throwflag=, frame=0x7fa7ed4bc488, 2024-06-26T06:05:32.6468313Z tstate=0x9c11f8 <_PyRuntime+459704>) 2024-06-26T06:05:32.6469028Z at /usr/local/src/conda/python-3.12.4/Include/internal/pycore_ceval.h:85 2024-06-26T06:05:32.6469804Z #78 _PyEval_Vector (kwnames=, argcount=, 2024-06-26T06:05:32.6470569Z args=0x7fa789576eb0, locals=0x0, func=0x7fa788e99c60, 2024-06-26T06:05:32.6471348Z tstate=0x9c11f8 <_PyRuntime+459704>) 2024-06-26T06:05:32.6472055Z at /usr/local/src/conda/python-3.12.4/Python/ceval.c:1683 2024-06-26T06:05:32.6472755Z #79 _PyFunction_Vectorcall (kwnames=, nargsf=, 2024-06-26T06:05:32.6473386Z stack=0x7fa789576eb0, func=0x7fa788e99c60) 2024-06-26T06:05:32.6474052Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:419 2024-06-26T06:05:32.6474745Z #80 _PyObject_VectorcallTstate (tstate=, 2024-06-26T06:05:32.6475410Z callable=0x7fa788e99c60, args=0x7fa789576eb0, nargsf=, 2024-06-26T06:05:32.6475973Z kwnames=) 2024-06-26T06:05:32.6476592Z at /usr/local/src/conda/python-3.12.4/Include/internal/pycore_call.h:92 2024-06-26T06:05:32.6479953Z #81 0x0000000000575e4d in method_vectorcall ( 2024-06-26T06:05:32.6480546Z method=method@entry=0x7fa7865ff2c0, args=args@entry=0x7fa789576eb8, 2024-06-26T06:05:32.6481245Z nargsf=, kwnames=0x7fa78629c700) 2024-06-26T06:05:32.6481929Z at /usr/local/src/conda/python-3.12.4/Objects/classobject.c:61 2024-06-26T06:05:32.6483300Z #82 0x000000000055b492 in _PyVectorcall_Call (kwargs=, 2024-06-26T06:05:32.6483920Z tuple=, callable=0x7fa7865ff2c0, 2024-06-26T06:05:32.6484626Z func=0x575b60 , tstate=0x9c11f8 <_PyRuntime+459704>) 2024-06-26T06:05:32.6485705Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:283 2024-06-26T06:05:32.6486537Z #83 _PyObject_Call (tstate=0x9c11f8 <_PyRuntime+459704>, 2024-06-26T06:05:32.6487418Z callable=0x7fa7865ff2c0, args=, kwargs=) 2024-06-26T06:05:32.6488163Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:354 2024-06-26T06:05:32.6488708Z #84 0x000000000052b0a2 in PyCFunction_Call ( 2024-06-26T06:05:32.6489686Z kwargs=, 2024-06-26T06:05:32.6490708Z args=, 2024-06-26T06:05:32.6492116Z callable=) at /usr/local/src/conda/python-3.12.4/Objects/call.c:387 2024-06-26T06:05:32.6493279Z #85 _PyEval_EvalFrameDefault (tstate=, frame=0x7fa7ed4bc400, 2024-06-26T06:05:32.6494336Z throwflag=) at Python/bytecodes.c:3262 2024-06-26T06:05:32.6495076Z #86 0x000000000051df35 in _PyObject_FastCallDictTstate ( 2024-06-26T06:05:32.6495980Z tstate=0x9c11f8 <_PyRuntime+459704>, callable=0x7fa7eccfcae0, 2024-06-26T06:05:32.6496661Z args=0x7ffda4a12e50, nargsf=, kwargs=) 2024-06-26T06:05:32.6497392Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:144 2024-06-26T06:05:32.6499956Z #87 0x0000000000558836 in _PyObject_Call_Prepend ( 2024-06-26T06:05:32.6500539Z tstate=0x9c11f8 <_PyRuntime+459704>, callable=0x7fa7eccfcae0, 2024-06-26T06:05:32.6501209Z obj=0x7fa78639df10, args=, kwargs=0x7fa7865c7300) 2024-06-26T06:05:32.6501912Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:508 2024-06-26T06:05:32.6503980Z #88 0x000000000062f5e6 in slot_tp_call (self=0x7fa78639df10, 2024-06-26T06:05:32.6504578Z args=0x963858 <_PyRuntime+76312>, kwds=0x7fa7865c7300) 2024-06-26T06:05:32.6505281Z at /usr/local/src/conda/python-3.12.4/Objects/typeobject.c:8776 2024-06-26T06:05:32.6508463Z #89 0x000000000051b30b in _PyObject_MakeTpCall ( 2024-06-26T06:05:32.6509350Z tstate=0x9c11f8 <_PyRuntime+459704>, callable=0x7fa78639df10, 2024-06-26T06:05:32.6510044Z args=, nargs=, keywords=0x7fa788cea380) 2024-06-26T06:05:32.6510801Z at /usr/local/src/conda/python-3.12.4/Include/object.h:704 2024-06-26T06:05:32.6512149Z #90 0x0000000000525e75 in _PyEval_EvalFrameDefault (tstate=, 2024-06-26T06:05:32.6512969Z frame=0x7fa7ed4bc370, throwflag=) 2024-06-26T06:05:32.6513442Z at Python/bytecodes.c:2714 2024-06-26T06:05:32.6516491Z #91 0x000000000051df35 in _PyObject_FastCallDictTstate ( 2024-06-26T06:05:32.6517378Z tstate=0x9c11f8 <_PyRuntime+459704>, callable=0x7fa78b18a980, 2024-06-26T06:05:32.6518064Z args=0x7ffda4a131b0, nargsf=, kwargs=) 2024-06-26T06:05:32.6518808Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:144 2024-06-26T06:05:32.6521674Z #92 0x0000000000558836 in _PyObject_Call_Prepend ( 2024-06-26T06:05:32.6522558Z tstate=0x9c11f8 <_PyRuntime+459704>, callable=0x7fa78b18a980, 2024-06-26T06:05:32.6523227Z obj=0x7fa788e774c0, args=, kwargs=0x7fa786781c80) 2024-06-26T06:05:32.6523925Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:508 2024-06-26T06:05:32.6525350Z #93 0x000000000062f5e6 in slot_tp_call (self=0x7fa788e774c0, 2024-06-26T06:05:32.6526232Z args=0x963858 <_PyRuntime+76312>, kwds=0x7fa786781c80) 2024-06-26T06:05:32.6526932Z at /usr/local/src/conda/python-3.12.4/Objects/typeobject.c:8776 2024-06-26T06:05:32.6529433Z #94 0x000000000055b425 in _PyObject_Call ( 2024-06-26T06:05:32.6530140Z tstate=0x9c11f8 <_PyRuntime+459704>, callable=0x7fa788e774c0, 2024-06-26T06:05:32.6530950Z args=0x963858 <_PyRuntime+76312>, kwargs=) 2024-06-26T06:05:32.6531656Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:367 2024-06-26T06:05:32.6532342Z #95 0x000000000052b0a2 in PyCFunction_Call ( 2024-06-26T06:05:32.6533088Z kwargs=, 2024-06-26T06:05:32.6534577Z args=, 2024-06-26T06:05:32.6535994Z callable=) at /usr/local/src/conda/python-3.12.4/Objects/call.c:387 2024-06-26T06:05:32.6537180Z #96 _PyEval_EvalFrameDefault (tstate=, frame=0x7fa7ed4bc008, 2024-06-26T06:05:32.6537873Z throwflag=) at Python/bytecodes.c:3262 2024-06-26T06:05:32.6538960Z #97 0x000000000051df35 in _PyObject_FastCallDictTstate ( 2024-06-26T06:05:32.6539989Z tstate=0x9c11f8 <_PyRuntime+459704>, callable=0x7fa78b18a980, 2024-06-26T06:05:32.6540683Z args=0x7ffda4a13510, nargsf=, kwargs=) 2024-06-26T06:05:32.6541418Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:144 2024-06-26T06:05:32.6544507Z #98 0x0000000000558836 in _PyObject_Call_Prepend ( 2024-06-26T06:05:32.6545508Z tstate=0x9c11f8 <_PyRuntime+459704>, callable=0x7fa78b18a980, 2024-06-26T06:05:32.6546177Z obj=0x7fa788e77650, args=, kwargs=0x7fa78871f000) 2024-06-26T06:05:32.6546875Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:508 2024-06-26T06:05:32.6548657Z #99 0x000000000062f5e6 in slot_tp_call (self=0x7fa788e77650, 2024-06-26T06:05:32.6549551Z args=0x963858 <_PyRuntime+76312>, kwds=0x7fa78871f000) 2024-06-26T06:05:32.6550239Z at /usr/local/src/conda/python-3.12.4/Objects/typeobject.c:8776 2024-06-26T06:05:32.6552954Z #100 0x000000000051b30b in _PyObject_MakeTpCall ( 2024-06-26T06:05:32.6553863Z tstate=0x9c11f8 <_PyRuntime+459704>, callable=0x7fa788e77650, 2024-06-26T06:05:32.6554557Z args=, nargs=, keywords=0x7fa78adf2d40) 2024-06-26T06:05:32.6555301Z at /usr/local/src/conda/python-3.12.4/Include/object.h:704 2024-06-26T06:05:32.6556506Z #101 0x0000000000525e75 in _PyEval_EvalFrameDefault (tstate=, 2024-06-26T06:05:32.6557782Z frame=0x7fa7ed4bb968, throwflag=) 2024-06-26T06:05:32.6558543Z at Python/bytecodes.c:2714 2024-06-26T06:05:32.6561027Z #102 0x000000000051df35 in _PyObject_FastCallDictTstate ( 2024-06-26T06:05:32.6562026Z tstate=0x9c11f8 <_PyRuntime+459704>, callable=0x7fa78b18a980, 2024-06-26T06:05:32.6562717Z args=0x7ffda4a13870, nargsf=, kwargs=) 2024-06-26T06:05:32.6563441Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:144 2024-06-26T06:05:32.6566438Z #103 0x0000000000558836 in _PyObject_Call_Prepend ( 2024-06-26T06:05:32.6567408Z tstate=0x9c11f8 <_PyRuntime+459704>, callable=0x7fa78b18a980, 2024-06-26T06:05:32.6568058Z obj=0x7fa788e77740, args=, kwargs=0x7fa788cd8f40) 2024-06-26T06:05:32.6568771Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:508 2024-06-26T06:05:32.6570490Z #104 0x000000000062f5e6 in slot_tp_call (self=0x7fa788e77740, 2024-06-26T06:05:32.6571347Z args=0x963858 <_PyRuntime+76312>, kwds=0x7fa788cd8f40) 2024-06-26T06:05:32.6572043Z at /usr/local/src/conda/python-3.12.4/Objects/typeobject.c:8776 2024-06-26T06:05:32.6574965Z #105 0x000000000051b30b in _PyObject_MakeTpCall ( 2024-06-26T06:05:32.6575968Z tstate=0x9c11f8 <_PyRuntime+459704>, callable=0x7fa788e77740, 2024-06-26T06:05:32.6576646Z args=, nargs=, keywords=0x7fa78ada6f50) 2024-06-26T06:05:32.6577402Z at /usr/local/src/conda/python-3.12.4/Include/object.h:704 2024-06-26T06:05:32.6578694Z #106 0x0000000000525e75 in _PyEval_EvalFrameDefault (tstate=, 2024-06-26T06:05:32.6579568Z frame=0x7fa7ed4bb690, throwflag=) 2024-06-26T06:05:32.6580045Z at Python/bytecodes.c:2714 2024-06-26T06:05:32.6583105Z #107 0x000000000051df35 in _PyObject_FastCallDictTstate ( 2024-06-26T06:05:32.6584128Z tstate=0x9c11f8 <_PyRuntime+459704>, callable=0x7fa78b18a980, 2024-06-26T06:05:32.6584815Z args=0x7ffda4a13bd0, nargsf=, kwargs=) 2024-06-26T06:05:32.6585699Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:144 2024-06-26T06:05:32.6588287Z #108 0x0000000000558836 in _PyObject_Call_Prepend ( 2024-06-26T06:05:32.6589181Z tstate=0x9c11f8 <_PyRuntime+459704>, callable=0x7fa78b18a980, 2024-06-26T06:05:32.6589843Z obj=0x7fa788e76930, args=, kwargs=0x7fa788aab500) 2024-06-26T06:05:32.6590549Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:508 2024-06-26T06:05:32.6592231Z #109 0x000000000062f5e6 in slot_tp_call (self=0x7fa788e76930, 2024-06-26T06:05:32.6593085Z args=0x963858 <_PyRuntime+76312>, kwds=0x7fa788aab500) 2024-06-26T06:05:32.6593782Z at /usr/local/src/conda/python-3.12.4/Objects/typeobject.c:8776 2024-06-26T06:05:32.6596655Z #110 0x000000000051b30b in _PyObject_MakeTpCall ( 2024-06-26T06:05:32.6597597Z tstate=0x9c11f8 <_PyRuntime+459704>, callable=0x7fa788e76930, 2024-06-26T06:05:32.6598287Z args=, nargs=, keywords=0x7fa78ada7610) 2024-06-26T06:05:32.6599045Z at /usr/local/src/conda/python-3.12.4/Include/object.h:704 2024-06-26T06:05:32.6600176Z #111 0x0000000000525e75 in _PyEval_EvalFrameDefault (tstate=, 2024-06-26T06:05:32.6601068Z frame=0x7fa7ed4bb218, throwflag=) 2024-06-26T06:05:32.6601544Z at Python/bytecodes.c:2714 2024-06-26T06:05:32.6603414Z #112 0x00000000005e4dfe in PyEval_EvalCode (co=, 2024-06-26T06:05:32.6604294Z globals=0x7fa7ed10df80, locals=) 2024-06-26T06:05:32.6604928Z at /usr/local/src/conda/python-3.12.4/Python/ceval.c:578 2024-06-26T06:05:32.6608341Z #113 0x000000000060a5d7 in run_eval_code_obj ( 2024-06-26T06:05:32.6609172Z tstate=0x9c11f8 <_PyRuntime+459704>, co=0x1d03ed0, 2024-06-26T06:05:32.6609698Z globals=0x7fa7ed10df80, locals=0x7fa7ed10df80) 2024-06-26T06:05:32.6610368Z at /usr/local/src/conda/python-3.12.4/Python/pythonrun.c:1722 2024-06-26T06:05:32.6632237Z #114 0x0000000000605ca7 in run_mod (mod=, 2024-06-26T06:05:32.6633173Z filename=0x7fa7ed05f590, globals=0x7fa7ed10df80, locals=0x7fa7ed10df80, 2024-06-26T06:05:32.6633808Z flags=0x7ffda4a140f0, arena=0x7fa7ed02fcb0) 2024-06-26T06:05:32.6634461Z at /usr/local/src/conda/python-3.12.4/Python/pythonrun.c:1743 2024-06-26T06:05:32.6639694Z #115 0x000000000061db12 in pyrun_file (fp=fp@entry=0x1c47490, 2024-06-26T06:05:32.6640625Z filename=filename@entry=0x7fa7ed05f590, start=start@entry=257, 2024-06-26T06:05:32.6641417Z globals=globals@entry=0x7fa7ed10df80, locals=locals@entry=0x7fa7ed10df80, 2024-06-26T06:05:32.6642066Z closeit=closeit@entry=1, flags=0x7ffda4a140f0) 2024-06-26T06:05:32.6642723Z at /usr/local/src/conda/python-3.12.4/Python/pythonrun.c:1643 2024-06-26T06:05:32.6645179Z #116 0x000000000061d3b0 in _PyRun_SimpleFileObject (fp=0x1c47490, 2024-06-26T06:05:32.6646095Z filename=0x7fa7ed05f590, closeit=1, flags=0x7ffda4a140f0) 2024-06-26T06:05:32.6646797Z at /usr/local/src/conda/python-3.12.4/Python/pythonrun.c:433 2024-06-26T06:05:32.6650391Z #117 0x000000000061d123 in _PyRun_AnyFileObject (fp=0x1c47490, 2024-06-26T06:05:32.6651326Z filename=0x7fa7ed05f590, closeit=1, flags=0x7ffda4a140f0) 2024-06-26T06:05:32.6652012Z at /usr/local/src/conda/python-3.12.4/Python/pythonrun.c:78 2024-06-26T06:05:32.6654763Z #118 0x0000000000615b43 in pymain_run_file_obj (skip_source_first_line=0, 2024-06-26T06:05:32.6655670Z filename=0x7fa7ed05f590, program_name=0x7fa7ecec6ec0) 2024-06-26T06:05:32.6656612Z at /usr/local/src/conda/python-3.12.4/Modules/main.c:360 2024-06-26T06:05:32.6657353Z #119 pymain_run_file (config=0x963dd8 <_PyRuntime+77720>) 2024-06-26T06:05:32.6658008Z at /usr/local/src/conda/python-3.12.4/Modules/main.c:379 2024-06-26T06:05:32.6658567Z #120 pymain_run_python (exitcode=0x7ffda4a140c4) 2024-06-26T06:05:32.6659281Z at /usr/local/src/conda/python-3.12.4/Modules/main.c:629 2024-06-26T06:05:32.6660023Z #121 Py_RunMain () at /usr/local/src/conda/python-3.12.4/Modules/main.c:709 2024-06-26T06:05:32.6773943Z #122 0x00000000005cc259 in Py_BytesMain (argc=, 2024-06-26T06:05:32.6774525Z argv=) 2024-06-26T06:05:32.6775053Z at /usr/local/src/conda/python-3.12.4/Modules/main.c:763 2024-06-26T06:05:32.6778342Z #123 0x00007fa7ed16a083 in __libc_start_main (main=0x5cc190
, argc=17, 2024-06-26T06:05:32.6779095Z argv=0x7ffda4a14328, init=, fini=, 2024-06-26T06:05:32.6779712Z rtld_fini=, stack_end=0x7ffda4a14318) 2024-06-26T06:05:32.6780244Z at ../csu/libc-start.c:308 2024-06-26T06:05:32.6780606Z #124 0x00000000005cc089 in _start () 2024-06-26T06:05:32.6781165Z at /usr/local/src/conda/python-3.12.4/Parser/parser.c:41555 2024-06-26T06:05:32.8762544Z ##[group]Run pytorch/test-infra/.github/actions/teardown-linux@main 2024-06-26T06:05:32.8763086Z with: 2024-06-26T06:05:32.8763327Z env: 2024-06-26T06:05:32.8763583Z GIT_DEFAULT_BRANCH: main 2024-06-26T06:05:32.8764178Z DOCKER_CONTAINER_ID: dece8529f48a7f61fcf0b1a68620de6340e6da1a183eee9f3a1750c5234902db 2024-06-26T06:05:32.8764844Z ##[endgroup] 2024-06-26T06:05:32.8790619Z ##[group]Run set -eou pipefail 2024-06-26T06:05:32.8791012Z set -eou pipefail 2024-06-26T06:05:32.8791339Z  2024-06-26T06:05:32.8791812Z echo "Holding runner for 2 hours until all ssh sessions have logged out" 2024-06-26T06:05:32.8792442Z for _ in $(seq 1440); do 2024-06-26T06:05:32.8792889Z  # Break if no ssh session exists anymore 2024-06-26T06:05:32.8793353Z  if [ "$(who)" = "" ]; then 2024-06-26T06:05:32.8793736Z  break 2024-06-26T06:05:32.8794022Z  fi 2024-06-26T06:05:32.8794331Z  echo "." 2024-06-26T06:05:32.8794622Z  sleep 5 2024-06-26T06:05:32.8794922Z done 2024-06-26T06:05:32.8802045Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T06:05:32.8802514Z env: 2024-06-26T06:05:32.8802775Z GIT_DEFAULT_BRANCH: main 2024-06-26T06:05:32.8803384Z DOCKER_CONTAINER_ID: dece8529f48a7f61fcf0b1a68620de6340e6da1a183eee9f3a1750c5234902db 2024-06-26T06:05:32.8804035Z ##[endgroup] 2024-06-26T06:05:32.8824551Z Holding runner for 2 hours until all ssh sessions have logged out 2024-06-26T06:05:32.8906432Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2024-06-26T06:05:32.8907191Z # ignore expansion of "docker ps -q" since it could be empty 2024-06-26T06:05:32.8907775Z # shellcheck disable=SC2046 2024-06-26T06:05:32.8908215Z docker stop $(docker ps -q) || true 2024-06-26T06:05:32.8908662Z # Prune all of the docker images 2024-06-26T06:05:32.8909089Z docker system prune -af 2024-06-26T06:05:32.8915876Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T06:05:32.8916355Z env: 2024-06-26T06:05:32.8916617Z GIT_DEFAULT_BRANCH: main 2024-06-26T06:05:32.8917225Z DOCKER_CONTAINER_ID: dece8529f48a7f61fcf0b1a68620de6340e6da1a183eee9f3a1750c5234902db 2024-06-26T06:05:32.8917867Z ##[endgroup] 2024-06-26T06:05:33.1805902Z dece8529f48a 2024-06-26T06:05:33.6479902Z Deleted Containers: 2024-06-26T06:05:33.6480662Z dece8529f48a7f61fcf0b1a68620de6340e6da1a183eee9f3a1750c5234902db 2024-06-26T06:05:33.6481345Z 2024-06-26T06:05:38.4534052Z Deleted Images: 2024-06-26T06:05:38.4536212Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T06:05:38.4538728Z untagged: 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2024-06-26T06:05:38.5985853Z Adding repository directory to the temporary git global config as a safe directory 2024-06-26T06:05:38.5989394Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-06-26T06:05:38.6034212Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2024-06-26T06:05:38.6064593Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'core\.sshCommand' && git config --local --unset-all 'core.sshCommand' || :" 2024-06-26T06:05:38.6475454Z Entering 'android/libs/fbjni' 2024-06-26T06:05:38.6524618Z Entering 'third_party/FP16' 2024-06-26T06:05:38.6571992Z Entering 'third_party/FXdiv' 2024-06-26T06:05:38.6618850Z Entering 'third_party/NNPACK' 2024-06-26T06:05:38.6666907Z Entering 'third_party/VulkanMemoryAllocator' 2024-06-26T06:05:38.6708729Z Entering 'third_party/XNNPACK' 2024-06-26T06:05:38.6847736Z Entering 'third_party/benchmark' 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'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2024-06-26T06:05:38.9361699Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-06-26T06:05:38.9424936Z Entering 'third_party/pocketfft' 2024-06-26T06:05:38.9466919Z Entering 'third_party/protobuf' 2024-06-26T06:05:38.9512143Z Entering 'third_party/protobuf/third_party/benchmark' 2024-06-26T06:05:38.9552606Z Entering 'third_party/protobuf/third_party/googletest' 2024-06-26T06:05:38.9595247Z Entering 'third_party/psimd' 2024-06-26T06:05:38.9637432Z Entering 'third_party/pthreadpool' 2024-06-26T06:05:38.9678391Z Entering 'third_party/pybind11' 2024-06-26T06:05:38.9720570Z Entering 'third_party/python-peachpy' 2024-06-26T06:05:38.9761652Z Entering 'third_party/sleef' 2024-06-26T06:05:38.9803296Z Entering 'third_party/tensorpipe' 2024-06-26T06:05:38.9844832Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-06-26T06:05:38.9885794Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-06-26T06:05:38.9926755Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-06-26T06:05:38.9967449Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-06-26T06:05:39.0007318Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-06-26T06:05:39.0065880Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2024-06-26T06:05:39.0089722Z http.https://github.com/.extraheader 2024-06-26T06:05:39.0098921Z [command]/usr/bin/git config --local --unset-all http.https://github.com/.extraheader 2024-06-26T06:05:39.0130568Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'http\.https\:\/\/github\.com\/\.extraheader' && git config --local --unset-all 'http.https://github.com/.extraheader' || :" 2024-06-26T06:05:39.0386709Z Entering 'android/libs/fbjni' 2024-06-26T06:05:39.0413771Z http.https://github.com/.extraheader 2024-06-26T06:05:39.0441441Z Entering 'third_party/FP16' 2024-06-26T06:05:39.0468981Z http.https://github.com/.extraheader 2024-06-26T06:05:39.0498079Z Entering 'third_party/FXdiv' 2024-06-26T06:05:39.0524904Z http.https://github.com/.extraheader 2024-06-26T06:05:39.0553212Z Entering 'third_party/NNPACK' 2024-06-26T06:05:39.0579605Z http.https://github.com/.extraheader 2024-06-26T06:05:39.0608400Z Entering 'third_party/VulkanMemoryAllocator' 2024-06-26T06:05:39.0635393Z http.https://github.com/.extraheader 2024-06-26T06:05:39.0663611Z Entering 'third_party/XNNPACK' 2024-06-26T06:05:39.0690211Z http.https://github.com/.extraheader 2024-06-26T06:05:39.0735128Z Entering 'third_party/benchmark' 2024-06-26T06:05:39.0761894Z http.https://github.com/.extraheader 2024-06-26T06:05:39.0789599Z Entering 'third_party/cpp-httplib' 2024-06-26T06:05:39.0816948Z http.https://github.com/.extraheader 2024-06-26T06:05:39.0844369Z Entering 'third_party/cpuinfo' 2024-06-26T06:05:39.0871241Z http.https://github.com/.extraheader 2024-06-26T06:05:39.0900591Z Entering 'third_party/cudnn_frontend' 2024-06-26T06:05:39.0927645Z http.https://github.com/.extraheader 2024-06-26T06:05:39.0955757Z Entering 'third_party/cutlass' 2024-06-26T06:05:39.0982336Z http.https://github.com/.extraheader 2024-06-26T06:05:39.1018321Z Entering 'third_party/eigen' 2024-06-26T06:05:39.1044698Z http.https://github.com/.extraheader 2024-06-26T06:05:39.1076307Z Entering 'third_party/fbgemm' 2024-06-26T06:05:39.1103728Z http.https://github.com/.extraheader 2024-06-26T06:05:39.1131273Z Entering 'third_party/fbgemm/third_party/asmjit' 2024-06-26T06:05:39.1158691Z http.https://github.com/.extraheader 2024-06-26T06:05:39.1186343Z Entering 'third_party/fbgemm/third_party/cpuinfo' 2024-06-26T06:05:39.1212171Z http.https://github.com/.extraheader 2024-06-26T06:05:39.1240348Z Entering 'third_party/fbgemm/third_party/cutlass' 2024-06-26T06:05:39.1266442Z http.https://github.com/.extraheader 2024-06-26T06:05:39.1300674Z Entering 'third_party/fbgemm/third_party/googletest' 2024-06-26T06:05:39.1326883Z http.https://github.com/.extraheader 2024-06-26T06:05:39.1355500Z Entering 'third_party/fbgemm/third_party/hipify_torch' 2024-06-26T06:05:39.1382340Z http.https://github.com/.extraheader 2024-06-26T06:05:39.1410155Z Entering 'third_party/flatbuffers' 2024-06-26T06:05:39.1438280Z http.https://github.com/.extraheader 2024-06-26T06:05:39.1469373Z Entering 'third_party/fmt' 2024-06-26T06:05:39.1496174Z http.https://github.com/.extraheader 2024-06-26T06:05:39.1524106Z Entering 'third_party/foxi' 2024-06-26T06:05:39.1551874Z http.https://github.com/.extraheader 2024-06-26T06:05:39.1579679Z Entering 'third_party/gemmlowp/gemmlowp' 2024-06-26T06:05:39.1605703Z http.https://github.com/.extraheader 2024-06-26T06:05:39.1633510Z Entering 'third_party/gloo' 2024-06-26T06:05:39.1661038Z http.https://github.com/.extraheader 2024-06-26T06:05:39.1689345Z Entering 'third_party/googletest' 2024-06-26T06:05:39.1715884Z http.https://github.com/.extraheader 2024-06-26T06:05:39.1743747Z Entering 'third_party/ideep' 2024-06-26T06:05:39.1770717Z http.https://github.com/.extraheader 2024-06-26T06:05:39.1796938Z Entering 'third_party/ideep/mkl-dnn' 2024-06-26T06:05:39.1823454Z http.https://github.com/.extraheader 2024-06-26T06:05:39.1858996Z Entering 'third_party/ittapi' 2024-06-26T06:05:39.1886063Z http.https://github.com/.extraheader 2024-06-26T06:05:39.1913870Z Entering 'third_party/kineto' 2024-06-26T06:05:39.1941712Z http.https://github.com/.extraheader 2024-06-26T06:05:39.1970173Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2024-06-26T06:05:39.1996819Z http.https://github.com/.extraheader 2024-06-26T06:05:39.2025032Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2024-06-26T06:05:39.2051586Z http.https://github.com/.extraheader 2024-06-26T06:05:39.2081046Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2024-06-26T06:05:39.2107959Z http.https://github.com/.extraheader 2024-06-26T06:05:39.2137116Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2024-06-26T06:05:39.2163734Z http.https://github.com/.extraheader 2024-06-26T06:05:39.2191767Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-06-26T06:05:39.2218064Z http.https://github.com/.extraheader 2024-06-26T06:05:39.2246037Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2024-06-26T06:05:39.2273117Z http.https://github.com/.extraheader 2024-06-26T06:05:39.2302507Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2024-06-26T06:05:39.2328796Z http.https://github.com/.extraheader 2024-06-26T06:05:39.2356775Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2024-06-26T06:05:39.2384012Z http.https://github.com/.extraheader 2024-06-26T06:05:39.2411825Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2024-06-26T06:05:39.2437936Z http.https://github.com/.extraheader 2024-06-26T06:05:39.2467776Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2024-06-26T06:05:39.2493303Z http.https://github.com/.extraheader 2024-06-26T06:05:39.2522156Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2024-06-26T06:05:39.2548567Z http.https://github.com/.extraheader 2024-06-26T06:05:39.2575936Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2024-06-26T06:05:39.2602041Z http.https://github.com/.extraheader 2024-06-26T06:05:39.2631659Z Entering 'third_party/mimalloc' 2024-06-26T06:05:39.2659256Z http.https://github.com/.extraheader 2024-06-26T06:05:39.2686777Z Entering 'third_party/nccl/nccl' 2024-06-26T06:05:39.2713908Z http.https://github.com/.extraheader 2024-06-26T06:05:39.2742137Z Entering 'third_party/nlohmann' 2024-06-26T06:05:39.2768965Z http.https://github.com/.extraheader 2024-06-26T06:05:39.2796828Z Entering 'third_party/onnx' 2024-06-26T06:05:39.2824356Z http.https://github.com/.extraheader 2024-06-26T06:05:39.2868317Z Entering 'third_party/onnx/third_party/benchmark' 2024-06-26T06:05:39.2895316Z http.https://github.com/.extraheader 2024-06-26T06:05:39.2923212Z Entering 'third_party/onnx/third_party/pybind11' 2024-06-26T06:05:39.2949574Z http.https://github.com/.extraheader 2024-06-26T06:05:39.2980701Z Entering 'third_party/opentelemetry-cpp' 2024-06-26T06:05:39.3008330Z http.https://github.com/.extraheader 2024-06-26T06:05:39.3038528Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2024-06-26T06:05:39.3064806Z http.https://github.com/.extraheader 2024-06-26T06:05:39.3093004Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2024-06-26T06:05:39.3119495Z http.https://github.com/.extraheader 2024-06-26T06:05:39.3147309Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2024-06-26T06:05:39.3173328Z http.https://github.com/.extraheader 2024-06-26T06:05:39.3200518Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2024-06-26T06:05:39.3226485Z http.https://github.com/.extraheader 2024-06-26T06:05:39.3256528Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2024-06-26T06:05:39.3282898Z http.https://github.com/.extraheader 2024-06-26T06:05:39.3311094Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-06-26T06:05:39.3337712Z http.https://github.com/.extraheader 2024-06-26T06:05:39.3365462Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-06-26T06:05:39.3392989Z http.https://github.com/.extraheader 2024-06-26T06:05:39.3419307Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2024-06-26T06:05:39.3445101Z http.https://github.com/.extraheader 2024-06-26T06:05:39.3475215Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2024-06-26T06:05:39.3501578Z http.https://github.com/.extraheader 2024-06-26T06:05:39.3529843Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-06-26T06:05:39.3556022Z http.https://github.com/.extraheader 2024-06-26T06:05:39.3604235Z Entering 'third_party/pocketfft' 2024-06-26T06:05:39.3631550Z http.https://github.com/.extraheader 2024-06-26T06:05:39.3658879Z Entering 'third_party/protobuf' 2024-06-26T06:05:39.3685929Z http.https://github.com/.extraheader 2024-06-26T06:05:39.3716871Z Entering 'third_party/protobuf/third_party/benchmark' 2024-06-26T06:05:39.3743233Z http.https://github.com/.extraheader 2024-06-26T06:05:39.3770407Z Entering 'third_party/protobuf/third_party/googletest' 2024-06-26T06:05:39.3796500Z http.https://github.com/.extraheader 2024-06-26T06:05:39.3824999Z Entering 'third_party/psimd' 2024-06-26T06:05:39.3851922Z http.https://github.com/.extraheader 2024-06-26T06:05:39.3879336Z Entering 'third_party/pthreadpool' 2024-06-26T06:05:39.3906127Z http.https://github.com/.extraheader 2024-06-26T06:05:39.3932810Z Entering 'third_party/pybind11' 2024-06-26T06:05:39.3959790Z http.https://github.com/.extraheader 2024-06-26T06:05:39.3986960Z Entering 'third_party/python-peachpy' 2024-06-26T06:05:39.4015324Z http.https://github.com/.extraheader 2024-06-26T06:05:39.4042238Z Entering 'third_party/sleef' 2024-06-26T06:05:39.4068736Z http.https://github.com/.extraheader 2024-06-26T06:05:39.4095824Z Entering 'third_party/tensorpipe' 2024-06-26T06:05:39.4122650Z http.https://github.com/.extraheader 2024-06-26T06:05:39.4150660Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-06-26T06:05:39.4176445Z http.https://github.com/.extraheader 2024-06-26T06:05:39.4203122Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-06-26T06:05:39.4229443Z http.https://github.com/.extraheader 2024-06-26T06:05:39.4257098Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-06-26T06:05:39.4283145Z http.https://github.com/.extraheader 2024-06-26T06:05:39.4310499Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-06-26T06:05:39.4337309Z http.https://github.com/.extraheader 2024-06-26T06:05:39.4362939Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-06-26T06:05:39.4390128Z http.https://github.com/.extraheader 2024-06-26T06:05:39.4494904Z A job completed hook has been configured by the self-hosted runner administrator 2024-06-26T06:05:39.4514156Z ##[group]Run '/home/ec2-user/runner-scripts/cleanup.sh' 2024-06-26T06:05:39.4520689Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T06:05:39.4521287Z ##[endgroup] 2024-06-26T06:05:40.8408031Z Cleaning up orphan processes