2024-08-20T21:31:10.6754397Z Current runner version: '2.319.1' 2024-08-20T21:31:10.6760901Z Runner name: 'i-025b30bca34dcaa23' 2024-08-20T21:31:10.6761724Z Runner group name: 'Default' 2024-08-20T21:31:10.6762504Z Machine name: 'ip-10-0-33-210' 2024-08-20T21:31:10.6779587Z Testing runner upgrade compatibility 2024-08-20T21:31:10.8089919Z ##[group]GITHUB_TOKEN Permissions 2024-08-20T21:31:10.8092277Z Actions: read 2024-08-20T21:31:10.8092718Z Attestations: read 2024-08-20T21:31:10.8093271Z Checks: read 2024-08-20T21:31:10.8093714Z Contents: read 2024-08-20T21:31:10.8094126Z Deployments: read 2024-08-20T21:31:10.8094630Z Discussions: read 2024-08-20T21:31:10.8095082Z Issues: read 2024-08-20T21:31:10.8095465Z Metadata: read 2024-08-20T21:31:10.8095955Z Packages: read 2024-08-20T21:31:10.8096393Z Pages: read 2024-08-20T21:31:10.8096779Z PullRequests: read 2024-08-20T21:31:10.8097315Z RepositoryProjects: read 2024-08-20T21:31:10.8097844Z SecurityEvents: read 2024-08-20T21:31:10.8098330Z Statuses: read 2024-08-20T21:31:10.8099015Z ##[endgroup] 2024-08-20T21:31:10.8102413Z Secret source: Actions 2024-08-20T21:31:10.8103268Z Prepare workflow directory 2024-08-20T21:31:11.1730618Z Prepare all required actions 2024-08-20T21:31:11.1900091Z Getting action download info 2024-08-20T21:31:11.4143638Z Download action repository 'pytorch/test-infra@main' (SHA:0c3a2634aaa2f638c8f640e743f03d696ce1191f) 2024-08-20T21:31:11.7472968Z Download action repository 'pytorch/pytorch@main' (SHA:15b5a0b67fc3f34fb0bf1afa6f91e0c4c2b7fd8d) 2024-08-20T21:31:14.7571136Z Download action repository 'aws-actions/configure-aws-credentials@v3' (SHA:50ac8dd1e1b10d09dac7b8727528b91bed831ac0) 2024-08-20T21:31:14.9067810Z Download action repository 'seemethere/upload-artifact-s3@v5' (SHA:baba72d0712b404f646cebe0730933554ebce96a) 2024-08-20T21:31:15.2222887Z Getting action download info 2024-08-20T21:31:15.3455901Z Download action repository 'malfet/checkout@silent-checkout' (SHA:e07af140b3ccefc05679e3755b9db68f4ee4589c) 2024-08-20T21:31:15.5346904Z Getting action download info 2024-08-20T21:31:15.6267399Z Download action repository 'nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482' (SHA:3e91a01664abd3c5cd539100d10d33b9c5b68482) 2024-08-20T21:31:15.7863909Z Uses: pytorch/pytorch/.github/workflows/_linux-test.yml@refs/pull/133712/merge (f2fb9405c2fa9f9502a76363091cce6fd8179736) 2024-08-20T21:31:15.7866258Z ##[group] Inputs 2024-08-20T21:31:15.7866679Z build-environment: linux-focal-py3.12-clang10 2024-08-20T21:31:15.7869688Z test-matrix: {"include": [{"config": "default", "shard": 1, "num_shards": 4, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 2, "num_shards": 4, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 3, "num_shards": 4, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 4, "num_shards": 4, "runner": "amz2023.linux.2xlarge"}, {"config": "dynamo", "shard": 1, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "dynamo", "shard": 2, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "dynamo", "shard": 3, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}]} 2024-08-20T21:31:15.7873184Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:31:15.7874188Z sync-tag: 2024-08-20T21:31:15.7874967Z timeout-minutes: 600 2024-08-20T21:31:15.7875295Z use-gha: 2024-08-20T21:31:15.7875550Z dashboard-tag: 2024-08-20T21:31:15.7875854Z s3-bucket: gha-artifacts 2024-08-20T21:31:15.7876196Z aws-role-to-assume: 2024-08-20T21:31:15.7876500Z ##[endgroup] 2024-08-20T21:31:15.7877503Z Complete job name: linux-focal-py3.12-clang10 / test (default, 1, 4, amz2023.linux.2xlarge) 2024-08-20T21:31:15.8461060Z A job started hook has been configured by the self-hosted runner administrator 2024-08-20T21:31:15.8604704Z ##[group]Run '/home/ec2-user/runner-scripts/before_job.sh' 2024-08-20T21:31:15.8614714Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:31:15.8615254Z ##[endgroup] 2024-08-20T21:31:17.5419871Z Runner Type: amz2023.linux.2xlarge 2024-08-20T21:31:17.5420392Z Instance Type: c5.2xlarge 2024-08-20T21:31:17.5421064Z AMI Name: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64 2024-08-20T21:31:17.5421605Z AMI ID: ami-06c68f701d8090592 2024-08-20T21:31:23.3628723Z ##[group]Run pytorch/test-infra/.github/actions/setup-ssh@main 2024-08-20T21:31:23.3629326Z with: 2024-08-20T21:31:23.3630042Z github-secret: *** 2024-08-20T21:31:23.3631009Z instructions: All testing is done inside the container, to start an interactive session run: docker exec -it $(docker container ps --format '{{.ID}}') bash 2024-08-20T21:31:23.3632059Z activate-with-label: false 2024-08-20T21:31:23.3632396Z label: with-ssh 2024-08-20T21:31:23.3632701Z remove-existing-keys: true 2024-08-20T21:31:23.3633049Z fail-silently: true 2024-08-20T21:31:23.3633330Z env: 2024-08-20T21:31:23.3633586Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:31:23.3633912Z ##[endgroup] 2024-08-20T21:31:23.4501462Z Please see https://github.com/pytorch/pytorch/wiki/Debugging-using-with-ssh-for-Github-Actions for more info. 2024-08-20T21:31:23.6852432Z Grabbing public ssh keys from https://github.com/pytorchmergebot.keys 2024-08-20T21:31:23.7680886Z No SSH keys found for user pytorchmergebot 2024-08-20T21:31:23.7681871Z Grabbing public ssh keys from https://github.com/XuehaiPan.keys 2024-08-20T21:31:23.8383675Z ~/.ssh/authorized_keys file found on node, removing ~/.ssh and starting fresh 2024-08-20T21:31:23.8397382Z Public keys pulled and installed to /home/ec2-user/.ssh/authorized_keys 2024-08-20T21:31:23.8422145Z Login using: ssh ec2-user@ec2-52-23-240-155.compute-1.amazonaws.com 2024-08-20T21:31:23.8423398Z All testing is done inside the container, to start an interactive session run: 2024-08-20T21:31:23.8424375Z docker exec -it $(docker container ps --format '{{.ID}}') bash 2024-08-20T21:31:23.8539845Z ##[group]Run pytorch/pytorch/.github/actions/checkout-pytorch@main 2024-08-20T21:31:23.8540384Z with: 2024-08-20T21:31:23.8540640Z submodules: recursive 2024-08-20T21:31:23.8540973Z fetch-depth: 0 2024-08-20T21:31:23.8541232Z env: 2024-08-20T21:31:23.8541479Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:31:23.8541801Z ##[endgroup] 2024-08-20T21:31:23.8766711Z ##[group]Run retry () { 2024-08-20T21:31:23.8767085Z retry () { 2024-08-20T21:31:23.8767559Z  $* || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*) 2024-08-20T21:31:23.8768114Z } 2024-08-20T21:31:23.8768406Z echo "${GITHUB_WORKSPACE}" 2024-08-20T21:31:23.8768801Z if [ -z "${NO_SUDO}" ]; then 2024-08-20T21:31:23.8769270Z  retry sudo rm -rf "${GITHUB_WORKSPACE}" 2024-08-20T21:31:23.8769717Z else 2024-08-20T21:31:23.8770033Z  retry rm -rf "${GITHUB_WORKSPACE}" 2024-08-20T21:31:23.8770452Z fi 2024-08-20T21:31:23.8770779Z mkdir "${GITHUB_WORKSPACE}" 2024-08-20T21:31:23.8778535Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:31:23.8779058Z env: 2024-08-20T21:31:23.8779305Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:31:23.8779636Z NO_SUDO: 2024-08-20T21:31:23.8779896Z ##[endgroup] 2024-08-20T21:31:23.8809735Z /home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-08-20T21:31:26.5929288Z ##[group]Run malfet/checkout@silent-checkout 2024-08-20T21:31:26.5929719Z with: 2024-08-20T21:31:26.5930011Z ref: 40ec5f6ddd9787aca0449b24128343ff4c4a88b3 2024-08-20T21:31:26.5930548Z fetch-depth: 0 2024-08-20T21:31:26.5930838Z submodules: recursive 2024-08-20T21:31:26.5931144Z quiet-checkout: true 2024-08-20T21:31:26.5931475Z repository: pytorch/pytorch 2024-08-20T21:31:26.5931962Z token: *** 2024-08-20T21:31:26.5932231Z ssh-strict: true 2024-08-20T21:31:26.5932523Z persist-credentials: true 2024-08-20T21:31:26.5932861Z clean: true 2024-08-20T21:31:26.5933175Z sparse-checkout-cone-mode: true 2024-08-20T21:31:26.5933530Z lfs: false 2024-08-20T21:31:26.5933806Z set-safe-directory: true 2024-08-20T21:31:26.5934108Z env: 2024-08-20T21:31:26.5934576Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:31:26.5934897Z ##[endgroup] 2024-08-20T21:31:26.6864758Z Syncing repository: pytorch/pytorch 2024-08-20T21:31:26.6866349Z ##[group]Getting Git version info 2024-08-20T21:31:26.6867115Z Working directory is '/home/ec2-user/actions-runner/_work/pytorch/pytorch' 2024-08-20T21:31:26.6867967Z [command]/usr/bin/git version 2024-08-20T21:31:26.6868301Z git version 2.40.1 2024-08-20T21:31:26.6869560Z ##[endgroup] 2024-08-20T21:31:26.6880709Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/45836a5d-f7be-4a6a-a528-bf737351899c' before making global git config changes 2024-08-20T21:31:26.6881997Z Adding repository directory to the temporary git global config as a safe directory 2024-08-20T21:31:26.6883159Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-08-20T21:31:26.6917091Z Deleting the contents of '/home/ec2-user/actions-runner/_work/pytorch/pytorch' 2024-08-20T21:31:26.6920699Z ##[group]Initializing the repository 2024-08-20T21:31:26.6923407Z [command]/usr/bin/git init /home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-08-20T21:31:26.6952721Z hint: Using 'master' as the name for the initial branch. This default branch name 2024-08-20T21:31:26.6954020Z hint: is subject to change. To configure the initial branch name to use in all 2024-08-20T21:31:26.6954831Z hint: of your new repositories, which will suppress this warning, call: 2024-08-20T21:31:26.6955378Z hint: 2024-08-20T21:31:26.6955933Z hint: git config --global init.defaultBranch 2024-08-20T21:31:26.6956390Z hint: 2024-08-20T21:31:26.6956906Z hint: Names commonly chosen instead of 'master' are 'main', 'trunk' and 2024-08-20T21:31:26.6957750Z hint: 'development'. The just-created branch can be renamed via this command: 2024-08-20T21:31:26.6958535Z hint: 2024-08-20T21:31:26.6958841Z hint: git branch -m 2024-08-20T21:31:26.6959580Z Initialized empty Git repository in /home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/ 2024-08-20T21:31:26.6962890Z [command]/usr/bin/git remote add origin https://github.com/pytorch/pytorch 2024-08-20T21:31:26.6992072Z ##[endgroup] 2024-08-20T21:31:26.6992617Z ##[group]Disabling automatic garbage collection 2024-08-20T21:31:26.6994796Z [command]/usr/bin/git config --local gc.auto 0 2024-08-20T21:31:26.7023492Z ##[endgroup] 2024-08-20T21:31:26.7023967Z ##[group]Setting up auth 2024-08-20T21:31:26.7029413Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2024-08-20T21:31:26.7058477Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'core\.sshCommand' && git config --local --unset-all 'core.sshCommand' || :" 2024-08-20T21:31:26.7317080Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2024-08-20T21:31:26.7350048Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'http\.https\:\/\/github\.com\/\.extraheader' && git config --local --unset-all 'http.https://github.com/.extraheader' || :" 2024-08-20T21:31:26.7604906Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic *** 2024-08-20T21:31:26.7650038Z ##[endgroup] 2024-08-20T21:31:26.7650578Z ##[group]Fetching the repository 2024-08-20T21:31:26.7656448Z [command]/usr/bin/git -c protocol.version=2 fetch --prune --progress --no-recurse-submodules --quiet origin +refs/heads/*:refs/remotes/origin/* +refs/tags/*:refs/tags/* 2024-08-20T21:31:29.2608541Z remote: Enumerating objects: 1008455 2024-08-20T21:31:29.2609404Z remote: Enumerating objects: 1010082, done. 2024-08-20T21:31:29.2610365Z remote: Counting objects: 0% (1/1627) 2024-08-20T21:31:29.2611064Z remote: Counting objects: 1% (17/1627) 2024-08-20T21:31:29.2611801Z remote: Counting objects: 2% (33/1627) 2024-08-20T21:31:29.2612520Z remote: Counting objects: 3% (49/1627) 2024-08-20T21:31:29.2613672Z remote: Counting objects: 4% (66/1627) 2024-08-20T21:31:29.2614456Z remote: Counting objects: 5% (82/1627) 2024-08-20T21:31:29.2615203Z remote: Counting objects: 6% (98/1627) 2024-08-20T21:31:29.2615935Z remote: Counting objects: 7% (114/1627) 2024-08-20T21:31:29.2616679Z remote: Counting objects: 8% (131/1627) 2024-08-20T21:31:29.2617165Z remote: Counting objects: 9% (147/1627) 2024-08-20T21:31:29.2617685Z remote: Counting objects: 10% (163/1627) 2024-08-20T21:31:29.2618177Z remote: Counting objects: 11% (179/1627) 2024-08-20T21:31:29.2618675Z remote: Counting objects: 12% (196/1627) 2024-08-20T21:31:29.2619380Z remote: Counting objects: 13% (212/1627) 2024-08-20T21:31:29.2620225Z remote: Counting objects: 14% (228/1627) 2024-08-20T21:31:29.2620837Z remote: Counting objects: 15% (245/1627) 2024-08-20T21:31:29.2621488Z remote: Counting objects: 16% (261/1627) 2024-08-20T21:31:29.2622322Z remote: Counting objects: 17% (277/1627) 2024-08-20T21:31:29.2622909Z remote: Counting objects: 18% (293/1627) 2024-08-20T21:31:29.2623643Z remote: Counting objects: 19% (310/1627) 2024-08-20T21:31:29.2624347Z remote: Counting objects: 20% (326/1627) 2024-08-20T21:31:29.2625231Z remote: Counting objects: 21% (342/1627) 2024-08-20T21:31:29.2625917Z remote: Counting objects: 22% (358/1627) 2024-08-20T21:31:29.2626507Z remote: Counting objects: 23% (375/1627) 2024-08-20T21:31:29.2627226Z remote: Counting objects: 24% (391/1627) 2024-08-20T21:31:29.2627836Z remote: Counting objects: 25% (407/1627) 2024-08-20T21:31:29.2628565Z remote: Counting objects: 26% (424/1627) 2024-08-20T21:31:29.2629283Z remote: Counting objects: 27% (440/1627) 2024-08-20T21:31:29.2630284Z remote: Counting objects: 28% (456/1627) 2024-08-20T21:31:29.2631169Z remote: Counting objects: 29% (472/1627) 2024-08-20T21:31:29.2631854Z remote: Counting objects: 30% (489/1627) 2024-08-20T21:31:29.2632610Z remote: Counting objects: 31% (505/1627) 2024-08-20T21:31:29.2633346Z remote: Counting objects: 32% (521/1627) 2024-08-20T21:31:29.2633984Z remote: Counting objects: 33% (537/1627) 2024-08-20T21:31:29.2634478Z remote: Counting objects: 34% (554/1627) 2024-08-20T21:31:29.2634952Z remote: Counting objects: 35% (570/1627) 2024-08-20T21:31:29.2635438Z remote: Counting objects: 36% (586/1627) 2024-08-20T21:31:29.2635921Z remote: Counting objects: 37% (602/1627) 2024-08-20T21:31:29.2636402Z remote: Counting objects: 38% (619/1627) 2024-08-20T21:31:29.2636868Z remote: Counting objects: 39% (635/1627) 2024-08-20T21:31:29.2637355Z remote: Counting objects: 40% (651/1627) 2024-08-20T21:31:29.2637849Z remote: Counting objects: 41% (668/1627) 2024-08-20T21:31:29.2638320Z remote: Counting objects: 42% (684/1627) 2024-08-20T21:31:29.2638820Z remote: Counting objects: 43% (700/1627) 2024-08-20T21:31:29.2639309Z remote: Counting objects: 44% (716/1627) 2024-08-20T21:31:29.2639777Z remote: Counting objects: 45% (733/1627) 2024-08-20T21:31:29.2640266Z remote: Counting objects: 46% (749/1627) 2024-08-20T21:31:29.2640750Z remote: Counting objects: 47% (765/1627) 2024-08-20T21:31:29.2641221Z remote: Counting objects: 48% (781/1627) 2024-08-20T21:31:29.2641706Z remote: Counting objects: 49% (798/1627) 2024-08-20T21:31:29.2642186Z remote: Counting objects: 50% (814/1627) 2024-08-20T21:31:29.2642654Z remote: Counting objects: 51% (830/1627) 2024-08-20T21:31:29.2643138Z remote: Counting objects: 52% (847/1627) 2024-08-20T21:31:29.2643620Z remote: Counting objects: 53% (863/1627) 2024-08-20T21:31:29.2644097Z remote: Counting objects: 54% (879/1627) 2024-08-20T21:31:29.2644583Z remote: Counting objects: 55% (895/1627) 2024-08-20T21:31:29.2645212Z remote: Counting objects: 56% (912/1627) 2024-08-20T21:31:29.2645680Z remote: Counting objects: 57% (928/1627) 2024-08-20T21:31:29.2646162Z remote: Counting objects: 58% (944/1627) 2024-08-20T21:31:29.2646962Z remote: Counting objects: 59% (960/1627) 2024-08-20T21:31:29.2647459Z remote: Counting objects: 60% (977/1627) 2024-08-20T21:31:29.2647930Z remote: Counting objects: 61% (993/1627) 2024-08-20T21:31:29.2648431Z remote: Counting objects: 62% (1009/1627) 2024-08-20T21:31:29.2648955Z remote: Counting objects: 63% (1026/1627) 2024-08-20T21:31:29.2649466Z remote: Counting objects: 64% (1042/1627) 2024-08-20T21:31:29.2649960Z remote: Counting objects: 65% (1058/1627) 2024-08-20T21:31:29.2650472Z remote: Counting objects: 66% (1074/1627) 2024-08-20T21:31:29.2650982Z remote: Counting objects: 67% (1091/1627) 2024-08-20T21:31:29.2651475Z remote: Counting objects: 68% (1107/1627) 2024-08-20T21:31:29.2651983Z remote: Counting objects: 69% (1123/1627) 2024-08-20T21:31:29.2652484Z remote: Counting objects: 70% (1139/1627) 2024-08-20T21:31:29.2652977Z remote: Counting objects: 71% (1156/1627) 2024-08-20T21:31:29.2653483Z remote: Counting objects: 72% (1172/1627) 2024-08-20T21:31:29.2653982Z remote: Counting objects: 73% (1188/1627) 2024-08-20T21:31:29.2654472Z remote: Counting objects: 74% (1204/1627) 2024-08-20T21:31:29.2655043Z remote: Counting objects: 75% (1221/1627) 2024-08-20T21:31:29.2655545Z remote: Counting objects: 76% (1237/1627) 2024-08-20T21:31:29.2656036Z remote: Counting objects: 77% (1253/1627) 2024-08-20T21:31:29.2656536Z remote: Counting objects: 78% (1270/1627) 2024-08-20T21:31:29.2657190Z remote: Counting objects: 79% (1286/1627) 2024-08-20T21:31:29.2657719Z remote: Counting objects: 80% (1302/1627) 2024-08-20T21:31:29.2658208Z remote: Counting objects: 81% (1318/1627) 2024-08-20T21:31:29.2658716Z remote: Counting objects: 82% (1335/1627) 2024-08-20T21:31:29.2659222Z remote: Counting objects: 83% (1351/1627) 2024-08-20T21:31:29.2659712Z remote: Counting objects: 84% (1367/1627) 2024-08-20T21:31:29.2660213Z remote: Counting objects: 85% (1383/1627) 2024-08-20T21:31:29.2660716Z remote: Counting objects: 86% (1400/1627) 2024-08-20T21:31:29.2661205Z remote: Counting objects: 87% (1416/1627) 2024-08-20T21:31:29.2661705Z remote: Counting objects: 88% (1432/1627) 2024-08-20T21:31:29.2662208Z remote: Counting objects: 89% (1449/1627) 2024-08-20T21:31:29.2662699Z remote: Counting objects: 90% (1465/1627) 2024-08-20T21:31:29.2663199Z remote: Counting objects: 91% (1481/1627) 2024-08-20T21:31:29.2663705Z remote: Counting objects: 92% (1497/1627) 2024-08-20T21:31:29.2664196Z remote: Counting objects: 93% (1514/1627) 2024-08-20T21:31:29.2664701Z remote: Counting objects: 94% (1530/1627) 2024-08-20T21:31:29.2665208Z remote: Counting objects: 95% (1546/1627) 2024-08-20T21:31:29.2665713Z remote: Counting objects: 96% (1562/1627) 2024-08-20T21:31:29.2666204Z remote: Counting objects: 97% (1579/1627) 2024-08-20T21:31:29.2666706Z remote: Counting objects: 98% (1595/1627) 2024-08-20T21:31:29.2667209Z remote: Counting objects: 99% (1611/1627) 2024-08-20T21:31:29.2667698Z remote: Counting objects: 100% (1627/1627) 2024-08-20T21:31:29.2668240Z remote: Counting objects: 100% (1627/1627), done. 2024-08-20T21:31:29.2802534Z remote: Compressing objects: 0% (1/812) 2024-08-20T21:31:29.3033326Z remote: Compressing objects: 1% (9/812) 2024-08-20T21:31:29.3278823Z remote: Compressing objects: 2% (17/812) 2024-08-20T21:31:29.3448023Z remote: Compressing objects: 3% (25/812) 2024-08-20T21:31:29.4106506Z remote: Compressing objects: 4% (33/812) 2024-08-20T21:31:29.4796096Z remote: Compressing objects: 5% (41/812) 2024-08-20T21:31:29.5236629Z remote: Compressing objects: 6% (49/812) 2024-08-20T21:31:29.5648100Z remote: Compressing objects: 7% (57/812) 2024-08-20T21:31:29.6062724Z remote: Compressing objects: 8% (65/812) 2024-08-20T21:31:29.6365499Z remote: Compressing objects: 9% (74/812) 2024-08-20T21:31:29.6616481Z remote: Compressing objects: 10% (82/812) 2024-08-20T21:31:29.6869675Z remote: Compressing objects: 11% (90/812) 2024-08-20T21:31:29.7045856Z remote: Compressing objects: 12% (98/812) 2024-08-20T21:31:29.7187905Z remote: Compressing objects: 13% (106/812) 2024-08-20T21:31:29.7311778Z remote: Compressing objects: 14% (114/812) 2024-08-20T21:31:29.7404435Z remote: Compressing objects: 15% (122/812) 2024-08-20T21:31:29.7490396Z remote: Compressing objects: 16% (130/812) 2024-08-20T21:31:29.7563817Z remote: Compressing objects: 17% (139/812) 2024-08-20T21:31:29.7613912Z remote: Compressing objects: 18% (147/812) 2024-08-20T21:31:29.7653113Z remote: Compressing objects: 19% (155/812) 2024-08-20T21:31:29.7685166Z remote: Compressing objects: 20% (163/812) 2024-08-20T21:31:29.7711085Z remote: Compressing objects: 21% (171/812) 2024-08-20T21:31:29.7721584Z remote: Compressing objects: 22% (179/812) 2024-08-20T21:31:29.7724139Z remote: Compressing objects: 23% (187/812) 2024-08-20T21:31:29.7726828Z remote: Compressing objects: 24% (195/812) 2024-08-20T21:31:29.7736214Z remote: Compressing objects: 25% (203/812) 2024-08-20T21:31:29.7738730Z remote: Compressing objects: 26% (212/812) 2024-08-20T21:31:29.7742308Z remote: Compressing objects: 27% (220/812) 2024-08-20T21:31:29.7751415Z remote: Compressing objects: 28% (228/812) 2024-08-20T21:31:29.7754653Z remote: Compressing objects: 29% (236/812) 2024-08-20T21:31:29.7763903Z remote: Compressing objects: 30% (244/812) 2024-08-20T21:31:29.7775239Z remote: Compressing objects: 31% (252/812) 2024-08-20T21:31:29.7783680Z remote: Compressing objects: 32% (260/812) 2024-08-20T21:31:29.7794664Z remote: Compressing objects: 33% (268/812) 2024-08-20T21:31:29.7798517Z remote: Compressing objects: 34% (277/812) 2024-08-20T21:31:29.7808920Z remote: Compressing objects: 35% (285/812) 2024-08-20T21:31:29.7812587Z remote: Compressing objects: 36% (293/812) 2024-08-20T21:31:29.7819292Z remote: Compressing objects: 37% (301/812) 2024-08-20T21:31:29.7826442Z remote: Compressing objects: 38% (309/812) 2024-08-20T21:31:29.7831481Z remote: Compressing objects: 39% (317/812) 2024-08-20T21:31:29.7839176Z remote: Compressing objects: 40% (325/812) 2024-08-20T21:31:29.7845246Z remote: Compressing objects: 41% (333/812) 2024-08-20T21:31:29.7849980Z remote: Compressing objects: 42% (342/812) 2024-08-20T21:31:29.7858940Z remote: Compressing objects: 43% (350/812) 2024-08-20T21:31:29.7865728Z remote: Compressing objects: 44% (358/812) 2024-08-20T21:31:29.7870339Z remote: Compressing objects: 45% (366/812) 2024-08-20T21:31:29.7877043Z remote: Compressing objects: 46% (374/812) 2024-08-20T21:31:29.7881525Z remote: Compressing objects: 47% (382/812) 2024-08-20T21:31:29.7886781Z remote: Compressing objects: 48% (390/812) 2024-08-20T21:31:29.7889745Z remote: Compressing objects: 49% (398/812) 2024-08-20T21:31:29.7893541Z remote: Compressing objects: 50% (406/812) 2024-08-20T21:31:29.7896730Z remote: Compressing objects: 51% (415/812) 2024-08-20T21:31:29.7899312Z remote: Compressing objects: 52% (423/812) 2024-08-20T21:31:29.7904588Z remote: Compressing objects: 53% (431/812) 2024-08-20T21:31:29.7905109Z remote: Compressing objects: 54% (439/812) 2024-08-20T21:31:29.7907050Z remote: Compressing objects: 55% (447/812) 2024-08-20T21:31:29.7909633Z remote: Compressing objects: 56% (455/812) 2024-08-20T21:31:29.7910160Z remote: Compressing objects: 57% (463/812) 2024-08-20T21:31:29.7912151Z remote: Compressing objects: 58% (471/812) 2024-08-20T21:31:29.7912703Z remote: Compressing objects: 59% (480/812) 2024-08-20T21:31:29.7913213Z remote: Compressing objects: 60% (488/812) 2024-08-20T21:31:29.7913960Z remote: Compressing objects: 61% (496/812) 2024-08-20T21:31:29.7914888Z remote: Compressing objects: 62% (504/812) 2024-08-20T21:31:29.7915463Z remote: Compressing objects: 63% (512/812) 2024-08-20T21:31:29.7915977Z remote: Compressing objects: 64% (520/812) 2024-08-20T21:31:29.7917046Z remote: Compressing objects: 65% (528/812) 2024-08-20T21:31:29.7925183Z remote: Compressing objects: 66% (536/812) 2024-08-20T21:31:29.7929305Z remote: Compressing objects: 67% (545/812) 2024-08-20T21:31:29.7933005Z remote: Compressing objects: 68% (553/812) 2024-08-20T21:31:29.7936943Z remote: Compressing objects: 69% (561/812) 2024-08-20T21:31:29.7940319Z remote: Compressing objects: 70% (569/812) 2024-08-20T21:31:29.7943185Z remote: Compressing objects: 71% (577/812) 2024-08-20T21:31:29.7945953Z remote: Compressing objects: 72% (585/812) 2024-08-20T21:31:29.7949576Z remote: Compressing objects: 73% (593/812) 2024-08-20T21:31:29.7952507Z remote: Compressing objects: 74% (601/812) 2024-08-20T21:31:29.7955633Z remote: Compressing objects: 75% (609/812) 2024-08-20T21:31:29.7958605Z remote: Compressing objects: 76% (618/812) 2024-08-20T21:31:29.7961095Z remote: Compressing objects: 77% (626/812) 2024-08-20T21:31:29.7963715Z remote: Compressing objects: 78% (634/812) 2024-08-20T21:31:29.7966418Z remote: Compressing objects: 79% (642/812) 2024-08-20T21:31:29.7968837Z remote: Compressing objects: 80% (650/812) 2024-08-20T21:31:29.7971214Z remote: Compressing objects: 81% (658/812) 2024-08-20T21:31:29.7971720Z remote: Compressing objects: 82% (666/812) 2024-08-20T21:31:29.7973760Z remote: Compressing objects: 83% (674/812) 2024-08-20T21:31:29.7976438Z remote: Compressing objects: 84% (683/812) 2024-08-20T21:31:29.7976947Z remote: Compressing objects: 85% (691/812) 2024-08-20T21:31:29.7978989Z remote: Compressing objects: 86% (699/812) 2024-08-20T21:31:29.7979509Z remote: Compressing objects: 87% (707/812) 2024-08-20T21:31:29.7981860Z remote: Compressing objects: 88% (715/812) 2024-08-20T21:31:29.7984142Z remote: Compressing objects: 89% (723/812) 2024-08-20T21:31:29.7986831Z remote: Compressing objects: 90% (731/812) 2024-08-20T21:31:29.7987693Z remote: Compressing objects: 91% (739/812) 2024-08-20T21:31:29.7988213Z remote: Compressing objects: 92% (748/812) 2024-08-20T21:31:29.7988731Z remote: Compressing objects: 93% (756/812) 2024-08-20T21:31:29.7989442Z remote: Compressing objects: 94% (764/812) 2024-08-20T21:31:29.7990218Z remote: Compressing objects: 95% (772/812) 2024-08-20T21:31:29.7991982Z remote: Compressing objects: 96% (780/812) 2024-08-20T21:31:29.7992765Z remote: Compressing objects: 97% (788/812) 2024-08-20T21:31:29.7994518Z remote: Compressing objects: 98% (796/812) 2024-08-20T21:31:29.7995297Z remote: Compressing objects: 99% (804/812) 2024-08-20T21:31:29.7995817Z remote: Compressing objects: 100% (812/812) 2024-08-20T21:31:29.7996377Z remote: Compressing objects: 100% (812/812), done. 2024-08-20T21:31:50.9567795Z remote: Total 1010082 (delta 1062), reused 1263 (delta 812), pack-reused 1008455 (from 1) 2024-08-20T21:32:17.7744451Z [command]/usr/bin/git rev-parse --verify --quiet 40ec5f6ddd9787aca0449b24128343ff4c4a88b3^{object} 2024-08-20T21:32:17.7772717Z 40ec5f6ddd9787aca0449b24128343ff4c4a88b3 2024-08-20T21:32:17.7778035Z ##[endgroup] 2024-08-20T21:32:17.7780906Z ##[group]Determining the checkout info 2024-08-20T21:32:17.7782030Z ##[endgroup] 2024-08-20T21:32:17.7782941Z ##[group]Checking out the ref 2024-08-20T21:32:17.7784818Z [command]/usr/bin/git checkout --quiet --force 40ec5f6ddd9787aca0449b24128343ff4c4a88b3 2024-08-20T21:32:19.1581248Z ##[endgroup] 2024-08-20T21:32:19.1584006Z ##[group]Setting up auth for fetching submodules 2024-08-20T21:32:19.1585147Z [command]/usr/bin/git config --global http.https://github.com/.extraheader AUTHORIZATION: basic *** 2024-08-20T21:32:19.1632925Z [command]/usr/bin/git config --global --unset-all url.https://github.com/.insteadOf 2024-08-20T21:32:19.1663414Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf git@github.com: 2024-08-20T21:32:19.1693136Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf org-21003710@github.com: 2024-08-20T21:32:19.1718929Z ##[endgroup] 2024-08-20T21:32:19.1719421Z ##[group]Fetching submodules 2024-08-20T21:32:19.1722683Z [command]/usr/bin/git submodule sync --recursive 2024-08-20T21:32:19.2003601Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --recursive 2024-08-20T21:32:19.2273942Z Submodule 'android/libs/fbjni' (https://github.com/facebookincubator/fbjni.git) registered for path 'android/libs/fbjni' 2024-08-20T21:32:19.2275511Z Submodule 'third_party/NNPACK_deps/FP16' (https://github.com/Maratyszcza/FP16.git) registered for path 'third_party/FP16' 2024-08-20T21:32:19.2277258Z Submodule 'third_party/NNPACK_deps/FXdiv' (https://github.com/Maratyszcza/FXdiv.git) registered for path 'third_party/FXdiv' 2024-08-20T21:32:19.2279520Z Submodule 'third_party/NNPACK' (https://github.com/Maratyszcza/NNPACK.git) registered for path 'third_party/NNPACK' 2024-08-20T21:32:19.2282809Z Submodule 'third_party/VulkanMemoryAllocator' (https://github.com/GPUOpen-LibrariesAndSDKs/VulkanMemoryAllocator.git) registered for path 'third_party/VulkanMemoryAllocator' 2024-08-20T21:32:19.2284909Z Submodule 'third_party/XNNPACK' (https://github.com/google/XNNPACK.git) registered for path 'third_party/XNNPACK' 2024-08-20T21:32:19.2287452Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/benchmark' 2024-08-20T21:32:19.2290158Z Submodule 'third_party/cpp-httplib' (https://github.com/yhirose/cpp-httplib.git) registered for path 'third_party/cpp-httplib' 2024-08-20T21:32:19.2292958Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo.git) registered for path 'third_party/cpuinfo' 2024-08-20T21:32:19.2295884Z Submodule 'third_party/cudnn_frontend' (https://github.com/NVIDIA/cudnn-frontend.git) registered for path 'third_party/cudnn_frontend' 2024-08-20T21:32:19.2298859Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/cutlass' 2024-08-20T21:32:19.2301815Z Submodule 'third_party/eigen' (https://gitlab.com/libeigen/eigen.git) registered for path 'third_party/eigen' 2024-08-20T21:32:19.2305015Z Submodule 'third_party/fbgemm' (https://github.com/pytorch/fbgemm) registered for path 'third_party/fbgemm' 2024-08-20T21:32:19.2308486Z Submodule 'third_party/flatbuffers' (https://github.com/google/flatbuffers.git) registered for path 'third_party/flatbuffers' 2024-08-20T21:32:19.2311844Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/fmt' 2024-08-20T21:32:19.2315916Z Submodule 'third_party/gemmlowp/gemmlowp' (https://github.com/google/gemmlowp.git) registered for path 'third_party/gemmlowp/gemmlowp' 2024-08-20T21:32:19.2320991Z Submodule 'third_party/gloo' (https://github.com/facebookincubator/gloo) registered for path 'third_party/gloo' 2024-08-20T21:32:19.2324731Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/googletest' 2024-08-20T21:32:19.2328542Z Submodule 'third_party/ideep' (https://github.com/intel/ideep) registered for path 'third_party/ideep' 2024-08-20T21:32:19.2332453Z Submodule 'third_party/ittapi' (https://github.com/intel/ittapi.git) registered for path 'third_party/ittapi' 2024-08-20T21:32:19.2336502Z Submodule 'third_party/kineto' (https://github.com/pytorch/kineto) registered for path 'third_party/kineto' 2024-08-20T21:32:19.2340623Z Submodule 'third_party/mimalloc' (https://github.com/microsoft/mimalloc.git) registered for path 'third_party/mimalloc' 2024-08-20T21:32:19.2344540Z Submodule 'third_party/nccl/nccl' (https://github.com/NVIDIA/nccl) registered for path 'third_party/nccl/nccl' 2024-08-20T21:32:19.2349065Z Submodule 'third_party/nlohmann' (https://github.com/nlohmann/json.git) registered for path 'third_party/nlohmann' 2024-08-20T21:32:19.2353260Z Submodule 'third_party/onnx' (https://github.com/onnx/onnx.git) registered for path 'third_party/onnx' 2024-08-20T21:32:19.2357899Z Submodule 'third_party/opentelemetry-cpp' (https://github.com/open-telemetry/opentelemetry-cpp.git) registered for path 'third_party/opentelemetry-cpp' 2024-08-20T21:32:19.2362173Z Submodule 'third_party/pocketfft' (https://github.com/mreineck/pocketfft) registered for path 'third_party/pocketfft' 2024-08-20T21:32:19.2366996Z Submodule 'third_party/protobuf' (https://github.com/protocolbuffers/protobuf.git) registered for path 'third_party/protobuf' 2024-08-20T21:32:19.2371713Z Submodule 'third_party/NNPACK_deps/psimd' (https://github.com/Maratyszcza/psimd.git) registered for path 'third_party/psimd' 2024-08-20T21:32:19.2376484Z Submodule 'third_party/NNPACK_deps/pthreadpool' (https://github.com/Maratyszcza/pthreadpool.git) registered for path 'third_party/pthreadpool' 2024-08-20T21:32:19.2381230Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/pybind11' 2024-08-20T21:32:19.2386333Z Submodule 'third_party/python-peachpy' (https://github.com/malfet/PeachPy.git) registered for path 'third_party/python-peachpy' 2024-08-20T21:32:19.2391304Z Submodule 'third_party/sleef' (https://github.com/shibatch/sleef) registered for path 'third_party/sleef' 2024-08-20T21:32:19.2397832Z Submodule 'third_party/tensorpipe' (https://github.com/pytorch/tensorpipe.git) registered for path 'third_party/tensorpipe' 2024-08-20T21:32:19.2425836Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/android/libs/fbjni'... 2024-08-20T21:32:19.5198139Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FP16'... 2024-08-20T21:32:19.6877600Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FXdiv'... 2024-08-20T21:32:19.9017053Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/NNPACK'... 2024-08-20T21:32:20.1339547Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/VulkanMemoryAllocator'... 2024-08-20T21:32:22.3558719Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/XNNPACK'... 2024-08-20T21:32:36.2141541Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/benchmark'... 2024-08-20T21:32:36.6304584Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpp-httplib'... 2024-08-20T21:32:37.1082125Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpuinfo'... 2024-08-20T21:32:37.7161314Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cudnn_frontend'... 2024-08-20T21:32:38.8874645Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cutlass'... 2024-08-20T21:32:40.9033742Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/eigen'... 2024-08-20T21:32:46.1505678Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm'... 2024-08-20T21:32:47.6767873Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flatbuffers'... 2024-08-20T21:32:49.5138605Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fmt'... 2024-08-20T21:32:51.0250989Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gemmlowp/gemmlowp'... 2024-08-20T21:32:51.4669910Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gloo'... 2024-08-20T21:32:51.8267082Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/googletest'... 2024-08-20T21:32:53.2647322Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep'... 2024-08-20T21:32:53.6118836Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ittapi'... 2024-08-20T21:32:53.8546461Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto'... 2024-08-20T21:32:55.3316280Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/mimalloc'... 2024-08-20T21:32:56.0596595Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/nccl/nccl'... 2024-08-20T21:32:56.9493494Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/nlohmann'... 2024-08-20T21:33:03.2377503Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx'... 2024-08-20T21:33:05.4539949Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp'... 2024-08-20T21:33:11.6652569Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pocketfft'... 2024-08-20T21:33:11.8934702Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/protobuf'... 2024-08-20T21:33:21.7028744Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/psimd'... 2024-08-20T21:33:21.8658517Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pthreadpool'... 2024-08-20T21:33:22.0744340Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pybind11'... 2024-08-20T21:33:23.0046956Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/python-peachpy'... 2024-08-20T21:33:23.3104353Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/sleef'... 2024-08-20T21:33:23.9480332Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe'... 2024-08-20T21:33:24.3525374Z Submodule path 'android/libs/fbjni': checked out '7e1e1fe3858c63c251c637ae41a20de425dde96f' 2024-08-20T21:33:24.3636881Z Submodule path 'third_party/FP16': checked out '4dfe081cf6bcd15db339cf2680b9281b8451eeb3' 2024-08-20T21:33:24.3722578Z Submodule path 'third_party/FXdiv': checked out 'b408327ac2a15ec3e43352421954f5b1967701d1' 2024-08-20T21:33:24.3960403Z Submodule path 'third_party/NNPACK': checked out 'c07e3a0400713d546e0dea2d5466dd22ea389c73' 2024-08-20T21:33:24.4318118Z Submodule path 'third_party/VulkanMemoryAllocator': checked out 'a6bfc237255a6bac1513f7c1ebde6d8aed6b5191' 2024-08-20T21:33:25.3535822Z Submodule path 'third_party/XNNPACK': checked out 'fcbf55af6cf28a4627bcd1f703ab7ad843f0f3a2' 2024-08-20T21:33:25.3762279Z Submodule path 'third_party/benchmark': checked out '0d98dba29d66e93259db7daa53a9327df767a415' 2024-08-20T21:33:25.4197468Z Submodule path 'third_party/cpp-httplib': checked out '3b6597bba913d51161383657829b7e644e59c006' 2024-08-20T21:33:25.5140480Z Submodule path 'third_party/cpuinfo': checked out '3c8b1533ac03dd6531ab6e7b9245d488f13a82a5' 2024-08-20T21:33:25.5475585Z Submodule path 'third_party/cudnn_frontend': checked out '23511ba176243f27b3b275da1fb3814ea805a171' 2024-08-20T21:33:26.0322226Z Submodule path 'third_party/cutlass': checked out 'bbe579a9e3beb6ea6626d9227ec32d0dae119a49' 2024-08-20T21:33:26.2774026Z Submodule path 'third_party/eigen': checked out '3147391d946bb4b6c68edd901f2add6ac1f31f8c' 2024-08-20T21:33:26.3568234Z Submodule path 'third_party/fbgemm': checked out 'dbc3157bf256f1339b3fa1fef2be89ac4078be0e' 2024-08-20T21:33:26.3585491Z Submodule 'third_party/asmjit' (https://github.com/asmjit/asmjit.git) registered for path 'third_party/fbgemm/third_party/asmjit' 2024-08-20T21:33:26.3587720Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo) registered for path 'third_party/fbgemm/third_party/cpuinfo' 2024-08-20T21:33:26.3590098Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/fbgemm/third_party/cutlass' 2024-08-20T21:33:26.3592539Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/fbgemm/third_party/googletest' 2024-08-20T21:33:26.3595096Z Submodule 'third_party/hipify_torch' (https://github.com/ROCmSoftwarePlatform/hipify_torch.git) registered for path 'third_party/fbgemm/third_party/hipify_torch' 2024-08-20T21:33:26.3620857Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/asmjit'... 2024-08-20T21:33:27.3604880Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/cpuinfo'... 2024-08-20T21:33:27.9944051Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/cutlass'... 2024-08-20T21:33:29.9946515Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/googletest'... 2024-08-20T21:33:31.2162340Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/hipify_torch'... 2024-08-20T21:33:31.5448633Z Submodule path 'third_party/fbgemm/third_party/asmjit': checked out 'd3fbf7c9bc7c1d1365a94a45614b91c5a3706b81' 2024-08-20T21:33:31.6377547Z Submodule path 'third_party/fbgemm/third_party/cpuinfo': checked out 'ed8b86a253800bafdb7b25c5c399f91bff9cb1f3' 2024-08-20T21:33:32.0251752Z Submodule path 'third_party/fbgemm/third_party/cutlass': checked out 'fc9ebc645b63f3a6bc80aaefde5c063fb72110d6' 2024-08-20T21:33:32.0862995Z Submodule path 'third_party/fbgemm/third_party/googletest': checked out 'cbf019de22c8dd37b2108da35b2748fd702d1796' 2024-08-20T21:33:32.0985907Z Submodule path 'third_party/fbgemm/third_party/hipify_torch': checked out '23f53b025b466d8ec3c45d52290d3442f7fbe6b1' 2024-08-20T21:33:32.2124057Z Submodule path 'third_party/flatbuffers': checked out '01834de25e4bf3975a9a00e816292b1ad0fe184b' 2024-08-20T21:33:32.2523421Z Submodule path 'third_party/fmt': checked out '0c9fce2ffefecfdce794e1859584e25877b7b592' 2024-08-20T21:33:32.2921933Z Submodule path 'third_party/gemmlowp/gemmlowp': checked out '3fb5c176c17c765a3492cd2f0321b0dab712f350' 2024-08-20T21:33:32.3172183Z Submodule path 'third_party/gloo': checked out '5354032ea08eadd7fc4456477f7f7c6308818509' 2024-08-20T21:33:32.3616318Z Submodule path 'third_party/googletest': checked out 'e2239ee6043f73722e7aa812a459f54a28552929' 2024-08-20T21:33:32.3740381Z Submodule path 'third_party/ideep': checked out '55ca0191687aaf19aca5cdb7881c791e3bea442b' 2024-08-20T21:33:32.3753982Z Submodule 'mkl-dnn' (https://github.com/intel/mkl-dnn.git) registered for path 'third_party/ideep/mkl-dnn' 2024-08-20T21:33:32.3776894Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep/mkl-dnn'... 2024-08-20T21:33:46.6044017Z Submodule path 'third_party/ideep/mkl-dnn': checked out '1137e04ec0b5251ca2b4400a4fd3c667ce843d67' 2024-08-20T21:33:46.6222985Z Submodule path 'third_party/ittapi': checked out '5b8a7d7422611c3a0d799fb5fc5dd4abfae35b42' 2024-08-20T21:33:46.7070007Z Submodule path 'third_party/kineto': checked out 'd9753139d181b9ff42872465aac0e5d3018be415' 2024-08-20T21:33:46.7086278Z Submodule 'libkineto/third_party/dynolog' (https://github.com/facebookincubator/dynolog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog' 2024-08-20T21:33:46.7088171Z Submodule 'libkineto/third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/fmt' 2024-08-20T21:33:46.7090379Z Submodule 'libkineto/third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/googletest' 2024-08-20T21:33:46.7115405Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog'... 2024-08-20T21:33:47.3145465Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/fmt'... 2024-08-20T21:33:48.6644136Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/googletest'... 2024-08-20T21:33:49.8716623Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog': checked out '7d04a0053a845370ae06ce317a22a48e9edcc74e' 2024-08-20T21:33:49.8733244Z Submodule 'third_party/DCGM' (https://github.com/NVIDIA/DCGM.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2024-08-20T21:33:49.8735868Z Submodule 'third_party/cpr' (https://github.com/libcpr/cpr.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2024-08-20T21:33:49.8738840Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2024-08-20T21:33:49.8741941Z Submodule 'third_party/gflags' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-08-20T21:33:49.8744997Z Submodule 'third_party/glog' (https://github.com/google/glog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2024-08-20T21:33:49.8748949Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2024-08-20T21:33:49.8751965Z Submodule 'third_party/json' (https://github.com/nlohmann/json.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2024-08-20T21:33:49.8755336Z Submodule 'third_party/pfs' (https://github.com/dtrugman/pfs.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2024-08-20T21:33:49.8781712Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'... 2024-08-20T21:33:50.6921206Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'... 2024-08-20T21:33:51.0597260Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'... 2024-08-20T21:33:52.4702369Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'... 2024-08-20T21:33:52.7424272Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/glog'... 2024-08-20T21:33:53.2709693Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'... 2024-08-20T21:33:54.4017393Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/json'... 2024-08-20T21:34:02.3458777Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'... 2024-08-20T21:34:02.7697676Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM': checked out 'ffde4e54bc7249a6039a5e6b45b395141e1217f9' 2024-08-20T21:34:02.7882284Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr': checked out '871ed52d350214a034f6ef8a3b8f51c5ce1bd400' 2024-08-20T21:34:02.8259326Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt': checked out 'cd4af11efc9c622896a3e4cb599fa28668ca3d05' 2024-08-20T21:34:02.8393442Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags': checked out 'e171aa2d15ed9eb17054558e0b3a6a413bb01067' 2024-08-20T21:34:02.8409757Z Submodule 'doc' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2024-08-20T21:34:02.8434910Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'... 2024-08-20T21:34:03.3246602Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc': checked out '8411df715cf522606e3b1aca386ddfc0b63d34b4' 2024-08-20T21:34:03.3429081Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog': checked out 'b33e3bad4c46c8a6345525fd822af355e5ef9446' 2024-08-20T21:34:03.3828645Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest': checked out '58d77fa8070e8cec2dc1ed015d66b454c8d78850' 2024-08-20T21:34:03.4838968Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json': checked out '4f8fba14066156b73f1189a2b8bd568bde5284c5' 2024-08-20T21:34:03.4999005Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs': checked out 'f68a2fa8ea36c783bdd760371411fcb495aa3150' 2024-08-20T21:34:03.5396596Z Submodule path 'third_party/kineto/libkineto/third_party/fmt': checked out '0041a40c1350ba702d475b9c4ad62da77caea164' 2024-08-20T21:34:03.5981817Z Submodule path 'third_party/kineto/libkineto/third_party/googletest': checked out '7aca84427f224eeed3144123d5230d5871e93347' 2024-08-20T21:34:03.6346490Z Submodule path 'third_party/mimalloc': checked out 'b66e3214d8a104669c2ec05ae91ebc26a8f5ab78' 2024-08-20T21:34:03.6584182Z Submodule path 'third_party/nccl/nccl': checked out 'ab2b89c4c339bd7f816fbc114a4b05d386b66290' 2024-08-20T21:34:03.7587179Z Submodule path 'third_party/nlohmann': checked out '87cda1d6646592ac5866dc703c8e1839046a6806' 2024-08-20T21:34:04.1038522Z Submodule path 'third_party/onnx': checked out '3bf92c03a9f27eba3bda1e5b9e63ea20ec213557' 2024-08-20T21:34:04.1074723Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/onnx/third_party/benchmark' 2024-08-20T21:34:04.1077042Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/onnx/third_party/pybind11' 2024-08-20T21:34:04.1103471Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx/third_party/benchmark'... 2024-08-20T21:34:04.6129014Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx/third_party/pybind11'... 2024-08-20T21:34:05.5775010Z Submodule path 'third_party/onnx/third_party/benchmark': checked out '2dd015dfef425c866d9a43f2c67d8b52d709acb6' 2024-08-20T21:34:05.6105552Z Submodule path 'third_party/onnx/third_party/pybind11': checked out '5b0a6fc2017fcc176545afe3e09c9f9885283242' 2024-08-20T21:34:05.6762840Z Submodule path 'third_party/opentelemetry-cpp': checked out 'a799f4aed9c94b765dcdaabaeab7d5e7e2310878' 2024-08-20T21:34:05.6782262Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark) registered for path 'third_party/opentelemetry-cpp/third_party/benchmark' 2024-08-20T21:34:05.6784457Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/opentelemetry-cpp/third_party/googletest' 2024-08-20T21:34:05.6786545Z Submodule 'third_party/ms-gsl' (https://github.com/microsoft/GSL) registered for path 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2024-08-20T21:34:05.6788746Z Submodule 'third_party/nlohmann-json' (https://github.com/nlohmann/json) registered for path 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2024-08-20T21:34:05.6791515Z Submodule 'third_party/opentelemetry-proto' (https://github.com/open-telemetry/opentelemetry-proto) registered for path 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2024-08-20T21:34:05.6794087Z Submodule 'third_party/opentracing-cpp' (https://github.com/opentracing/opentracing-cpp.git) registered for path 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-08-20T21:34:05.6796746Z Submodule 'third_party/prometheus-cpp' (https://github.com/jupp0r/prometheus-cpp) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-08-20T21:34:05.6799514Z Submodule 'tools/vcpkg' (https://github.com/Microsoft/vcpkg) registered for path 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-08-20T21:34:05.6826143Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/benchmark'... 2024-08-20T21:34:06.0938541Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/googletest'... 2024-08-20T21:34:07.2211381Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/ms-gsl'... 2024-08-20T21:34:07.5173272Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/nlohmann-json'... 2024-08-20T21:34:13.7399957Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/opentelemetry-proto'... 2024-08-20T21:34:14.0344604Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/opentracing-cpp'... 2024-08-20T21:34:14.2233226Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/prometheus-cpp'... 2024-08-20T21:34:14.4969442Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/tools/vcpkg'... 2024-08-20T21:34:21.1521251Z Submodule path 'third_party/opentelemetry-cpp/third_party/benchmark': checked out 'd572f4777349d43653b21d6c2fc63020ab326db2' 2024-08-20T21:34:21.1897185Z Submodule path 'third_party/opentelemetry-cpp/third_party/googletest': checked out 'b796f7d44681514f58a683a3a71ff17c94edb0c1' 2024-08-20T21:34:21.2049430Z Submodule path 'third_party/opentelemetry-cpp/third_party/ms-gsl': checked out '6f4529395c5b7c2d661812257cd6780c67e54afa' 2024-08-20T21:34:21.3005752Z Submodule path 'third_party/opentelemetry-cpp/third_party/nlohmann-json': checked out 'bc889afb4c5bf1c0d8ee29ef35eaaf4c8bef8a5d' 2024-08-20T21:34:21.3140419Z Submodule path 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto': checked out '4ca4f0335c63cda7ab31ea7ed70d6553aee14dce' 2024-08-20T21:34:21.3284844Z Submodule path 'third_party/opentelemetry-cpp/third_party/opentracing-cpp': checked out '06b57f48ded1fa3bdd3d4346f6ef29e40e08eaf5' 2024-08-20T21:34:21.3434839Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp': checked out 'c9ffcdda9086ffd9e1283ea7a0276d831f3c8a8d' 2024-08-20T21:34:21.3449793Z Submodule 'civetweb' (https://github.com/civetweb/civetweb.git) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2024-08-20T21:34:21.3452100Z Submodule 'googletest' (https://github.com/google/googletest.git) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2024-08-20T21:34:21.3476669Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'... 2024-08-20T21:34:23.5213262Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'... 2024-08-20T21:34:24.9600369Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb': checked out 'eefb26f82b233268fc98577d265352720d477ba4' 2024-08-20T21:34:25.0052625Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest': checked out 'e2239ee6043f73722e7aa812a459f54a28552929' 2024-08-20T21:34:25.4545564Z Submodule path 'third_party/opentelemetry-cpp/tools/vcpkg': checked out '8eb57355a4ffb410a2e94c07b4dca2dffbee8e50' 2024-08-20T21:34:25.4661374Z Submodule path 'third_party/pocketfft': checked out '9d3ab05a7fffbc71a492bc6a17be034e83e8f0fe' 2024-08-20T21:34:25.7228521Z Submodule path 'third_party/protobuf': checked out 'd1eca4e4b421cd2997495c4b4e65cea6be4e9b8a' 2024-08-20T21:34:25.7250563Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/protobuf/third_party/benchmark' 2024-08-20T21:34:25.7252991Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/protobuf/third_party/googletest' 2024-08-20T21:34:25.7278594Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/protobuf/third_party/benchmark'... 2024-08-20T21:34:26.1885042Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/protobuf/third_party/googletest'... 2024-08-20T21:34:27.3513880Z Submodule path 'third_party/protobuf/third_party/benchmark': checked out '5b7683f49e1e9223cf9927b24f6fd3d6bd82e3f8' 2024-08-20T21:34:27.4206047Z Submodule path 'third_party/protobuf/third_party/googletest': checked out '5ec7f0c4a113e2f18ac2c6cc7df51ad6afc24081' 2024-08-20T21:34:27.4293887Z Submodule path 'third_party/psimd': checked out '072586a71b55b7f8c584153d223e95687148a900' 2024-08-20T21:34:27.4413125Z Submodule path 'third_party/pthreadpool': checked out '4fe0e1e183925bf8cfa6aae24237e724a96479b8' 2024-08-20T21:34:27.4771891Z Submodule path 'third_party/pybind11': checked out '941f45bcb51457884fa1afd6e24a67377d70f75c' 2024-08-20T21:34:27.5045635Z Submodule path 'third_party/python-peachpy': checked out 'f45429b087dd7d5bc78bb40dc7cf06425c252d67' 2024-08-20T21:34:27.5454190Z Submodule path 'third_party/sleef': checked out '60e76d2bce17d278b439d9da17177c8f957a9e9b' 2024-08-20T21:34:27.5709571Z Submodule path 'third_party/tensorpipe': checked out '52791a2fd214b2a9dc5759d36725909c1daa7f2e' 2024-08-20T21:34:27.5725524Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/tensorpipe/third_party/googletest' 2024-08-20T21:34:27.5727797Z Submodule 'third_party/libnop' (https://github.com/google/libnop.git) registered for path 'third_party/tensorpipe/third_party/libnop' 2024-08-20T21:34:27.5730406Z Submodule 'third_party/libuv' (https://github.com/libuv/libuv.git) registered for path 'third_party/tensorpipe/third_party/libuv' 2024-08-20T21:34:27.5733090Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/tensorpipe/third_party/pybind11' 2024-08-20T21:34:27.5757495Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe/third_party/googletest'... 2024-08-20T21:34:28.7385009Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe/third_party/libnop'... 2024-08-20T21:34:28.9656689Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe/third_party/libuv'... 2024-08-20T21:34:30.1546464Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe/third_party/pybind11'... 2024-08-20T21:34:31.1367154Z Submodule path 'third_party/tensorpipe/third_party/googletest': checked out 'aee0f9d9b5b87796ee8a0ab26b7587ec30e8858e' 2024-08-20T21:34:31.1513471Z Submodule path 'third_party/tensorpipe/third_party/libnop': checked out '910b55815be16109f04f4180e9adee14fb4ce281' 2024-08-20T21:34:31.2181549Z Submodule path 'third_party/tensorpipe/third_party/libuv': checked out '1dff88e5161cba5c59276d2070d2e304e4dcb242' 2024-08-20T21:34:31.2458685Z Submodule path 'third_party/tensorpipe/third_party/pybind11': checked out 'a23996fce38ff6ccfbcdc09f1e63f2c4be5ea2ef' 2024-08-20T21:34:31.2472035Z Submodule 'tools/clang' (https://github.com/wjakob/clang-cindex-python3) registered for path 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-08-20T21:34:31.2498130Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe/third_party/pybind11/tools/clang'... 2024-08-20T21:34:31.4322445Z Submodule path 'third_party/tensorpipe/third_party/pybind11/tools/clang': checked out '6a00cbc4a9b8e68b71caf7f774b3f9c753ae84d5' 2024-08-20T21:34:31.4354764Z [command]/usr/bin/git submodule foreach --recursive git config --local gc.auto 0 2024-08-20T21:34:31.4632580Z Entering 'android/libs/fbjni' 2024-08-20T21:34:31.4670796Z Entering 'third_party/FP16' 2024-08-20T21:34:31.4710406Z Entering 'third_party/FXdiv' 2024-08-20T21:34:31.4748196Z Entering 'third_party/NNPACK' 2024-08-20T21:34:31.4786993Z Entering 'third_party/VulkanMemoryAllocator' 2024-08-20T21:34:31.4824244Z Entering 'third_party/XNNPACK' 2024-08-20T21:34:31.4878444Z Entering 'third_party/benchmark' 2024-08-20T21:34:31.4916152Z Entering 'third_party/cpp-httplib' 2024-08-20T21:34:31.4955994Z Entering 'third_party/cpuinfo' 2024-08-20T21:34:31.4994145Z Entering 'third_party/cudnn_frontend' 2024-08-20T21:34:31.5032863Z Entering 'third_party/cutlass' 2024-08-20T21:34:31.5077683Z Entering 'third_party/eigen' 2024-08-20T21:34:31.5117210Z Entering 'third_party/fbgemm' 2024-08-20T21:34:31.5155634Z Entering 'third_party/fbgemm/third_party/asmjit' 2024-08-20T21:34:31.5192520Z Entering 'third_party/fbgemm/third_party/cpuinfo' 2024-08-20T21:34:31.5230065Z Entering 'third_party/fbgemm/third_party/cutlass' 2024-08-20T21:34:31.5273024Z Entering 'third_party/fbgemm/third_party/googletest' 2024-08-20T21:34:31.5310475Z Entering 'third_party/fbgemm/third_party/hipify_torch' 2024-08-20T21:34:31.5349415Z Entering 'third_party/flatbuffers' 2024-08-20T21:34:31.5392712Z Entering 'third_party/fmt' 2024-08-20T21:34:31.5431524Z Entering 'third_party/gemmlowp/gemmlowp' 2024-08-20T21:34:31.5472252Z Entering 'third_party/gloo' 2024-08-20T21:34:31.5511208Z Entering 'third_party/googletest' 2024-08-20T21:34:31.5550929Z Entering 'third_party/ideep' 2024-08-20T21:34:31.5588975Z Entering 'third_party/ideep/mkl-dnn' 2024-08-20T21:34:31.5633756Z Entering 'third_party/ittapi' 2024-08-20T21:34:31.5673244Z Entering 'third_party/kineto' 2024-08-20T21:34:31.5712674Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2024-08-20T21:34:31.5750502Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2024-08-20T21:34:31.5789938Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2024-08-20T21:34:31.5827448Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2024-08-20T21:34:31.5866693Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-08-20T21:34:31.5903811Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2024-08-20T21:34:31.5944480Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2024-08-20T21:34:31.5982648Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2024-08-20T21:34:31.6021475Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2024-08-20T21:34:31.6062693Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2024-08-20T21:34:31.6102669Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2024-08-20T21:34:31.6139964Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2024-08-20T21:34:31.6180837Z Entering 'third_party/mimalloc' 2024-08-20T21:34:31.6220620Z Entering 'third_party/nccl/nccl' 2024-08-20T21:34:31.6260916Z Entering 'third_party/nlohmann' 2024-08-20T21:34:31.6300877Z Entering 'third_party/onnx' 2024-08-20T21:34:31.6355860Z Entering 'third_party/onnx/third_party/benchmark' 2024-08-20T21:34:31.6395324Z Entering 'third_party/onnx/third_party/pybind11' 2024-08-20T21:34:31.6436387Z Entering 'third_party/opentelemetry-cpp' 2024-08-20T21:34:31.6477797Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2024-08-20T21:34:31.6514972Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2024-08-20T21:34:31.6552423Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2024-08-20T21:34:31.6590490Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2024-08-20T21:34:31.6630648Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2024-08-20T21:34:31.6668472Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-08-20T21:34:31.6705752Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-08-20T21:34:31.6743384Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2024-08-20T21:34:31.6783818Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2024-08-20T21:34:31.6823532Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-08-20T21:34:31.6881425Z Entering 'third_party/pocketfft' 2024-08-20T21:34:31.6921289Z Entering 'third_party/protobuf' 2024-08-20T21:34:31.6965066Z Entering 'third_party/protobuf/third_party/benchmark' 2024-08-20T21:34:31.7002194Z Entering 'third_party/protobuf/third_party/googletest' 2024-08-20T21:34:31.7042136Z Entering 'third_party/psimd' 2024-08-20T21:34:31.7080615Z Entering 'third_party/pthreadpool' 2024-08-20T21:34:31.7119122Z Entering 'third_party/pybind11' 2024-08-20T21:34:31.7158395Z Entering 'third_party/python-peachpy' 2024-08-20T21:34:31.7196833Z Entering 'third_party/sleef' 2024-08-20T21:34:31.7235683Z Entering 'third_party/tensorpipe' 2024-08-20T21:34:31.7276175Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-08-20T21:34:31.7314104Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-08-20T21:34:31.7351854Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-08-20T21:34:31.7390223Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-08-20T21:34:31.7427017Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-08-20T21:34:31.7487854Z ##[endgroup] 2024-08-20T21:34:31.7489843Z ##[group]Persisting credentials for submodules 2024-08-20T21:34:31.7493325Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'url\.https\:\/\/github\.com\/\.insteadOf' && git config --local --unset-all 'url.https://github.com/.insteadOf' || :" 2024-08-20T21:34:31.7773538Z Entering 'android/libs/fbjni' 2024-08-20T21:34:31.7824085Z Entering 'third_party/FP16' 2024-08-20T21:34:31.7874797Z Entering 'third_party/FXdiv' 2024-08-20T21:34:31.7924718Z Entering 'third_party/NNPACK' 2024-08-20T21:34:31.7977296Z Entering 'third_party/VulkanMemoryAllocator' 2024-08-20T21:34:31.8026695Z Entering 'third_party/XNNPACK' 2024-08-20T21:34:31.8093128Z Entering 'third_party/benchmark' 2024-08-20T21:34:31.8143404Z Entering 'third_party/cpp-httplib' 2024-08-20T21:34:31.8193719Z Entering 'third_party/cpuinfo' 2024-08-20T21:34:31.8243858Z Entering 'third_party/cudnn_frontend' 2024-08-20T21:34:31.8293460Z Entering 'third_party/cutlass' 2024-08-20T21:34:31.8350472Z Entering 'third_party/eigen' 2024-08-20T21:34:31.8401379Z Entering 'third_party/fbgemm' 2024-08-20T21:34:31.8450829Z Entering 'third_party/fbgemm/third_party/asmjit' 2024-08-20T21:34:31.8500726Z Entering 'third_party/fbgemm/third_party/cpuinfo' 2024-08-20T21:34:31.8551471Z Entering 'third_party/fbgemm/third_party/cutlass' 2024-08-20T21:34:31.8607517Z Entering 'third_party/fbgemm/third_party/googletest' 2024-08-20T21:34:31.8656572Z Entering 'third_party/fbgemm/third_party/hipify_torch' 2024-08-20T21:34:31.8707311Z Entering 'third_party/flatbuffers' 2024-08-20T21:34:31.8761001Z Entering 'third_party/fmt' 2024-08-20T21:34:31.8811888Z Entering 'third_party/gemmlowp/gemmlowp' 2024-08-20T21:34:31.8862638Z Entering 'third_party/gloo' 2024-08-20T21:34:31.8912840Z Entering 'third_party/googletest' 2024-08-20T21:34:31.8961506Z Entering 'third_party/ideep' 2024-08-20T21:34:31.9010261Z Entering 'third_party/ideep/mkl-dnn' 2024-08-20T21:34:31.9068337Z Entering 'third_party/ittapi' 2024-08-20T21:34:31.9118016Z Entering 'third_party/kineto' 2024-08-20T21:34:31.9172256Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2024-08-20T21:34:31.9221370Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2024-08-20T21:34:31.9272403Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2024-08-20T21:34:31.9321826Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2024-08-20T21:34:31.9371906Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-08-20T21:34:31.9419857Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2024-08-20T21:34:31.9473108Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2024-08-20T21:34:31.9522751Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2024-08-20T21:34:31.9573129Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2024-08-20T21:34:31.9623365Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2024-08-20T21:34:31.9676677Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2024-08-20T21:34:31.9726038Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2024-08-20T21:34:31.9777355Z Entering 'third_party/mimalloc' 2024-08-20T21:34:31.9827604Z Entering 'third_party/nccl/nccl' 2024-08-20T21:34:31.9878877Z Entering 'third_party/nlohmann' 2024-08-20T21:34:31.9929479Z Entering 'third_party/onnx' 2024-08-20T21:34:31.9994457Z Entering 'third_party/onnx/third_party/benchmark' 2024-08-20T21:34:32.0044117Z Entering 'third_party/onnx/third_party/pybind11' 2024-08-20T21:34:32.0096555Z Entering 'third_party/opentelemetry-cpp' 2024-08-20T21:34:32.0148346Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2024-08-20T21:34:32.0196791Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2024-08-20T21:34:32.0245127Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2024-08-20T21:34:32.0293448Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2024-08-20T21:34:32.0342772Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2024-08-20T21:34:32.0391734Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-08-20T21:34:32.0440494Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-08-20T21:34:32.0488955Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2024-08-20T21:34:32.0539279Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2024-08-20T21:34:32.0590099Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-08-20T21:34:32.0658642Z Entering 'third_party/pocketfft' 2024-08-20T21:34:32.0708243Z Entering 'third_party/protobuf' 2024-08-20T21:34:32.0760762Z Entering 'third_party/protobuf/third_party/benchmark' 2024-08-20T21:34:32.0808907Z Entering 'third_party/protobuf/third_party/googletest' 2024-08-20T21:34:32.0859207Z Entering 'third_party/psimd' 2024-08-20T21:34:32.0908277Z Entering 'third_party/pthreadpool' 2024-08-20T21:34:32.0958266Z Entering 'third_party/pybind11' 2024-08-20T21:34:32.1007960Z Entering 'third_party/python-peachpy' 2024-08-20T21:34:32.1057899Z Entering 'third_party/sleef' 2024-08-20T21:34:32.1107187Z Entering 'third_party/tensorpipe' 2024-08-20T21:34:32.1155910Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-08-20T21:34:32.1204353Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-08-20T21:34:32.1253567Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-08-20T21:34:32.1301967Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-08-20T21:34:32.1350027Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-08-20T21:34:32.1414564Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local 'http.https://github.com/.extraheader' 'AUTHORIZATION: basic ***' && git config --local --show-origin --name-only --get-regexp remote.origin.url" 2024-08-20T21:34:32.1686529Z Entering 'android/libs/fbjni' 2024-08-20T21:34:32.1732872Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/android/libs/fbjni/config remote.origin.url 2024-08-20T21:34:32.1748109Z Entering 'third_party/FP16' 2024-08-20T21:34:32.1794790Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FP16/config remote.origin.url 2024-08-20T21:34:32.1818586Z Entering 'third_party/FXdiv' 2024-08-20T21:34:32.1857527Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FXdiv/config remote.origin.url 2024-08-20T21:34:32.1872165Z Entering 'third_party/NNPACK' 2024-08-20T21:34:32.1918545Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK/config remote.origin.url 2024-08-20T21:34:32.1933079Z Entering 'third_party/VulkanMemoryAllocator' 2024-08-20T21:34:32.1979916Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/VulkanMemoryAllocator/config remote.origin.url 2024-08-20T21:34:32.1994227Z Entering 'third_party/XNNPACK' 2024-08-20T21:34:32.2040904Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/XNNPACK/config remote.origin.url 2024-08-20T21:34:32.2072239Z Entering 'third_party/benchmark' 2024-08-20T21:34:32.2120777Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/benchmark/config remote.origin.url 2024-08-20T21:34:32.2135063Z Entering 'third_party/cpp-httplib' 2024-08-20T21:34:32.2183947Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cpp-httplib/config remote.origin.url 2024-08-20T21:34:32.2198058Z Entering 'third_party/cpuinfo' 2024-08-20T21:34:32.2243766Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cpuinfo/config remote.origin.url 2024-08-20T21:34:32.2258651Z Entering 'third_party/cudnn_frontend' 2024-08-20T21:34:32.2304309Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cudnn_frontend/config remote.origin.url 2024-08-20T21:34:32.2318441Z Entering 'third_party/cutlass' 2024-08-20T21:34:32.2365519Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cutlass/config remote.origin.url 2024-08-20T21:34:32.2387373Z Entering 'third_party/eigen' 2024-08-20T21:34:32.2433652Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/eigen/config remote.origin.url 2024-08-20T21:34:32.2451008Z Entering 'third_party/fbgemm' 2024-08-20T21:34:32.2498367Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/config remote.origin.url 2024-08-20T21:34:32.2512559Z Entering 'third_party/fbgemm/third_party/asmjit' 2024-08-20T21:34:32.2559118Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/asmjit/config remote.origin.url 2024-08-20T21:34:32.2573727Z Entering 'third_party/fbgemm/third_party/cpuinfo' 2024-08-20T21:34:32.2619614Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/cpuinfo/config remote.origin.url 2024-08-20T21:34:32.2633993Z Entering 'third_party/fbgemm/third_party/cutlass' 2024-08-20T21:34:32.2680585Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/cutlass/config remote.origin.url 2024-08-20T21:34:32.2702113Z Entering 'third_party/fbgemm/third_party/googletest' 2024-08-20T21:34:32.2748617Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/googletest/config remote.origin.url 2024-08-20T21:34:32.2763681Z Entering 'third_party/fbgemm/third_party/hipify_torch' 2024-08-20T21:34:32.2809216Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/hipify_torch/config remote.origin.url 2024-08-20T21:34:32.2824685Z Entering 'third_party/flatbuffers' 2024-08-20T21:34:32.2870971Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/flatbuffers/config remote.origin.url 2024-08-20T21:34:32.2889150Z Entering 'third_party/fmt' 2024-08-20T21:34:32.2935688Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fmt/config remote.origin.url 2024-08-20T21:34:32.2952208Z Entering 'third_party/gemmlowp/gemmlowp' 2024-08-20T21:34:32.2999170Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/gemmlowp/gemmlowp/config remote.origin.url 2024-08-20T21:34:32.3015382Z Entering 'third_party/gloo' 2024-08-20T21:34:32.3060873Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/gloo/config remote.origin.url 2024-08-20T21:34:32.3076123Z Entering 'third_party/googletest' 2024-08-20T21:34:32.3121267Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/googletest/config remote.origin.url 2024-08-20T21:34:32.3136117Z Entering 'third_party/ideep' 2024-08-20T21:34:32.3182228Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/config remote.origin.url 2024-08-20T21:34:32.3195768Z Entering 'third_party/ideep/mkl-dnn' 2024-08-20T21:34:32.3241661Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/modules/mkl-dnn/config remote.origin.url 2024-08-20T21:34:32.3265066Z Entering 'third_party/ittapi' 2024-08-20T21:34:32.3311102Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ittapi/config remote.origin.url 2024-08-20T21:34:32.3325446Z Entering 'third_party/kineto' 2024-08-20T21:34:32.3372891Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/config remote.origin.url 2024-08-20T21:34:32.3387135Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2024-08-20T21:34:32.3434049Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/config remote.origin.url 2024-08-20T21:34:32.3448114Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2024-08-20T21:34:32.3494776Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/DCGM/config remote.origin.url 2024-08-20T21:34:32.3510682Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2024-08-20T21:34:32.3559384Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/cpr/config remote.origin.url 2024-08-20T21:34:32.3573367Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2024-08-20T21:34:32.3619339Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/fmt/config remote.origin.url 2024-08-20T21:34:32.3633765Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-08-20T21:34:32.3681221Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/gflags/config remote.origin.url 2024-08-20T21:34:32.3694798Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2024-08-20T21:34:32.3741275Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/gflags/modules/doc/config remote.origin.url 2024-08-20T21:34:32.3757454Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2024-08-20T21:34:32.3803699Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/glog/config remote.origin.url 2024-08-20T21:34:32.3818034Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2024-08-20T21:34:32.3865378Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/googletest/config remote.origin.url 2024-08-20T21:34:32.3879575Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2024-08-20T21:34:32.3925969Z 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 2024-08-20T21:34:32.3941176Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2024-08-20T21:34:32.3987870Z 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 2024-08-20T21:34:32.4004077Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2024-08-20T21:34:32.4050371Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/fmt/config remote.origin.url 2024-08-20T21:34:32.4064536Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2024-08-20T21:34:32.4109753Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/googletest/config remote.origin.url 2024-08-20T21:34:32.4125716Z Entering 'third_party/mimalloc' 2024-08-20T21:34:32.4172244Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/mimalloc/config remote.origin.url 2024-08-20T21:34:32.4187660Z Entering 'third_party/nccl/nccl' 2024-08-20T21:34:32.4234222Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/nccl/nccl/config remote.origin.url 2024-08-20T21:34:32.4249775Z Entering 'third_party/nlohmann' 2024-08-20T21:34:32.4295531Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/nlohmann/config remote.origin.url 2024-08-20T21:34:32.4311637Z Entering 'third_party/onnx' 2024-08-20T21:34:32.4358013Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/config remote.origin.url 2024-08-20T21:34:32.4389699Z Entering 'third_party/onnx/third_party/benchmark' 2024-08-20T21:34:32.4435363Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/modules/third_party/benchmark/config remote.origin.url 2024-08-20T21:34:32.4450343Z Entering 'third_party/onnx/third_party/pybind11' 2024-08-20T21:34:32.4496196Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/modules/third_party/pybind11/config remote.origin.url 2024-08-20T21:34:32.4513656Z Entering 'third_party/opentelemetry-cpp' 2024-08-20T21:34:32.4562168Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/config remote.origin.url 2024-08-20T21:34:32.4578147Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2024-08-20T21:34:32.4623495Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/benchmark/config remote.origin.url 2024-08-20T21:34:32.4637697Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2024-08-20T21:34:32.4682925Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/googletest/config remote.origin.url 2024-08-20T21:34:32.4697098Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2024-08-20T21:34:32.4741888Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/ms-gsl/config remote.origin.url 2024-08-20T21:34:32.4756308Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2024-08-20T21:34:32.4801985Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/nlohmann-json/config remote.origin.url 2024-08-20T21:34:32.4817259Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2024-08-20T21:34:32.4862607Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/opentelemetry-proto/config remote.origin.url 2024-08-20T21:34:32.4876366Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-08-20T21:34:32.4921431Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/opentracing-cpp/config remote.origin.url 2024-08-20T21:34:32.4935440Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-08-20T21:34:32.4981043Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/config remote.origin.url 2024-08-20T21:34:32.4994222Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2024-08-20T21:34:32.5042149Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/modules/civetweb/config remote.origin.url 2024-08-20T21:34:32.5060485Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2024-08-20T21:34:32.5106603Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/modules/googletest/config remote.origin.url 2024-08-20T21:34:32.5122307Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-08-20T21:34:32.5168748Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/tools/vcpkg/config remote.origin.url 2024-08-20T21:34:32.5202131Z Entering 'third_party/pocketfft' 2024-08-20T21:34:32.5249460Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/pocketfft/config remote.origin.url 2024-08-20T21:34:32.5265254Z Entering 'third_party/protobuf' 2024-08-20T21:34:32.5311533Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/config remote.origin.url 2024-08-20T21:34:32.5329584Z Entering 'third_party/protobuf/third_party/benchmark' 2024-08-20T21:34:32.5375836Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/modules/third_party/benchmark/config remote.origin.url 2024-08-20T21:34:32.5390421Z Entering 'third_party/protobuf/third_party/googletest' 2024-08-20T21:34:32.5436662Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/modules/third_party/googletest/config remote.origin.url 2024-08-20T21:34:32.5453377Z Entering 'third_party/psimd' 2024-08-20T21:34:32.5500753Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/psimd/config remote.origin.url 2024-08-20T21:34:32.5515362Z Entering 'third_party/pthreadpool' 2024-08-20T21:34:32.5562558Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/pthreadpool/config remote.origin.url 2024-08-20T21:34:32.5577179Z Entering 'third_party/pybind11' 2024-08-20T21:34:32.5624896Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/pybind11/config remote.origin.url 2024-08-20T21:34:32.5639913Z Entering 'third_party/python-peachpy' 2024-08-20T21:34:32.5687524Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/python-peachpy/config remote.origin.url 2024-08-20T21:34:32.5702917Z Entering 'third_party/sleef' 2024-08-20T21:34:32.5750746Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/sleef/config remote.origin.url 2024-08-20T21:34:32.5765754Z Entering 'third_party/tensorpipe' 2024-08-20T21:34:32.5813318Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/config remote.origin.url 2024-08-20T21:34:32.5827559Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-08-20T21:34:32.5874668Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/googletest/config remote.origin.url 2024-08-20T21:34:32.5889611Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-08-20T21:34:32.5936529Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libnop/config remote.origin.url 2024-08-20T21:34:32.5950963Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-08-20T21:34:32.5996226Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libuv/config remote.origin.url 2024-08-20T21:34:32.6012386Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-08-20T21:34:32.6060621Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/pybind11/config remote.origin.url 2024-08-20T21:34:32.6073961Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-08-20T21:34:32.6120692Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/pybind11/modules/tools/clang/config remote.origin.url 2024-08-20T21:34:32.6850198Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'git@github.com:' 2024-08-20T21:34:32.7129645Z Entering 'android/libs/fbjni' 2024-08-20T21:34:32.7169382Z Entering 'third_party/FP16' 2024-08-20T21:34:32.7207797Z Entering 'third_party/FXdiv' 2024-08-20T21:34:32.7246059Z Entering 'third_party/NNPACK' 2024-08-20T21:34:32.7285421Z Entering 'third_party/VulkanMemoryAllocator' 2024-08-20T21:34:32.7325252Z Entering 'third_party/XNNPACK' 2024-08-20T21:34:32.7382909Z Entering 'third_party/benchmark' 2024-08-20T21:34:32.7424797Z Entering 'third_party/cpp-httplib' 2024-08-20T21:34:32.7466188Z Entering 'third_party/cpuinfo' 2024-08-20T21:34:32.7507207Z Entering 'third_party/cudnn_frontend' 2024-08-20T21:34:32.7548163Z Entering 'third_party/cutlass' 2024-08-20T21:34:32.7597359Z Entering 'third_party/eigen' 2024-08-20T21:34:32.7639155Z Entering 'third_party/fbgemm' 2024-08-20T21:34:32.7680441Z Entering 'third_party/fbgemm/third_party/asmjit' 2024-08-20T21:34:32.7718038Z Entering 'third_party/fbgemm/third_party/cpuinfo' 2024-08-20T21:34:32.7756560Z Entering 'third_party/fbgemm/third_party/cutlass' 2024-08-20T21:34:32.7800602Z Entering 'third_party/fbgemm/third_party/googletest' 2024-08-20T21:34:32.7838094Z Entering 'third_party/fbgemm/third_party/hipify_torch' 2024-08-20T21:34:32.7878153Z Entering 'third_party/flatbuffers' 2024-08-20T21:34:32.7919855Z Entering 'third_party/fmt' 2024-08-20T21:34:32.7958412Z Entering 'third_party/gemmlowp/gemmlowp' 2024-08-20T21:34:32.7996865Z Entering 'third_party/gloo' 2024-08-20T21:34:32.8036219Z Entering 'third_party/googletest' 2024-08-20T21:34:32.8076129Z Entering 'third_party/ideep' 2024-08-20T21:34:32.8114914Z Entering 'third_party/ideep/mkl-dnn' 2024-08-20T21:34:32.8161434Z Entering 'third_party/ittapi' 2024-08-20T21:34:32.8201507Z Entering 'third_party/kineto' 2024-08-20T21:34:32.8242958Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2024-08-20T21:34:32.8285342Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2024-08-20T21:34:32.8326459Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2024-08-20T21:34:32.8366283Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2024-08-20T21:34:32.8405512Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-08-20T21:34:32.8442762Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2024-08-20T21:34:32.8482242Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2024-08-20T21:34:32.8521142Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2024-08-20T21:34:32.8561731Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2024-08-20T21:34:32.8600371Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2024-08-20T21:34:32.8641261Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2024-08-20T21:34:32.8679476Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2024-08-20T21:34:32.8719380Z Entering 'third_party/mimalloc' 2024-08-20T21:34:32.8759897Z Entering 'third_party/nccl/nccl' 2024-08-20T21:34:32.8799707Z Entering 'third_party/nlohmann' 2024-08-20T21:34:32.8839336Z Entering 'third_party/onnx' 2024-08-20T21:34:32.8893954Z Entering 'third_party/onnx/third_party/benchmark' 2024-08-20T21:34:32.8932614Z Entering 'third_party/onnx/third_party/pybind11' 2024-08-20T21:34:32.8972962Z Entering 'third_party/opentelemetry-cpp' 2024-08-20T21:34:32.9013016Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2024-08-20T21:34:32.9050404Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2024-08-20T21:34:32.9087600Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2024-08-20T21:34:32.9124808Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2024-08-20T21:34:32.9163870Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2024-08-20T21:34:32.9200583Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-08-20T21:34:32.9237933Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-08-20T21:34:32.9276402Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2024-08-20T21:34:32.9315580Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2024-08-20T21:34:32.9354748Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-08-20T21:34:32.9411462Z Entering 'third_party/pocketfft' 2024-08-20T21:34:32.9450759Z Entering 'third_party/protobuf' 2024-08-20T21:34:32.9493058Z Entering 'third_party/protobuf/third_party/benchmark' 2024-08-20T21:34:32.9530085Z Entering 'third_party/protobuf/third_party/googletest' 2024-08-20T21:34:32.9570855Z Entering 'third_party/psimd' 2024-08-20T21:34:32.9608520Z Entering 'third_party/pthreadpool' 2024-08-20T21:34:32.9646398Z Entering 'third_party/pybind11' 2024-08-20T21:34:32.9684776Z Entering 'third_party/python-peachpy' 2024-08-20T21:34:32.9722306Z Entering 'third_party/sleef' 2024-08-20T21:34:32.9761595Z Entering 'third_party/tensorpipe' 2024-08-20T21:34:32.9800969Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-08-20T21:34:32.9837015Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-08-20T21:34:32.9874502Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-08-20T21:34:32.9912056Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-08-20T21:34:32.9949102Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-08-20T21:34:33.0003683Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'org-21003710@github.com:' 2024-08-20T21:34:33.0282786Z Entering 'android/libs/fbjni' 2024-08-20T21:34:33.0321337Z Entering 'third_party/FP16' 2024-08-20T21:34:33.0359220Z Entering 'third_party/FXdiv' 2024-08-20T21:34:33.0398974Z Entering 'third_party/NNPACK' 2024-08-20T21:34:33.0436354Z Entering 'third_party/VulkanMemoryAllocator' 2024-08-20T21:34:33.0473776Z Entering 'third_party/XNNPACK' 2024-08-20T21:34:33.0527484Z Entering 'third_party/benchmark' 2024-08-20T21:34:33.0565382Z Entering 'third_party/cpp-httplib' 2024-08-20T21:34:33.0602492Z Entering 'third_party/cpuinfo' 2024-08-20T21:34:33.0640439Z Entering 'third_party/cudnn_frontend' 2024-08-20T21:34:33.0677986Z Entering 'third_party/cutlass' 2024-08-20T21:34:33.0722588Z Entering 'third_party/eigen' 2024-08-20T21:34:33.0763217Z Entering 'third_party/fbgemm' 2024-08-20T21:34:33.0800930Z Entering 'third_party/fbgemm/third_party/asmjit' 2024-08-20T21:34:33.0837654Z Entering 'third_party/fbgemm/third_party/cpuinfo' 2024-08-20T21:34:33.0875578Z Entering 'third_party/fbgemm/third_party/cutlass' 2024-08-20T21:34:33.0919741Z Entering 'third_party/fbgemm/third_party/googletest' 2024-08-20T21:34:33.0956882Z Entering 'third_party/fbgemm/third_party/hipify_torch' 2024-08-20T21:34:33.0995706Z Entering 'third_party/flatbuffers' 2024-08-20T21:34:33.1036471Z Entering 'third_party/fmt' 2024-08-20T21:34:33.1075890Z Entering 'third_party/gemmlowp/gemmlowp' 2024-08-20T21:34:33.1113682Z Entering 'third_party/gloo' 2024-08-20T21:34:33.1153049Z Entering 'third_party/googletest' 2024-08-20T21:34:33.1191783Z Entering 'third_party/ideep' 2024-08-20T21:34:33.1229104Z Entering 'third_party/ideep/mkl-dnn' 2024-08-20T21:34:33.1274678Z Entering 'third_party/ittapi' 2024-08-20T21:34:33.1312750Z Entering 'third_party/kineto' 2024-08-20T21:34:33.1351744Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2024-08-20T21:34:33.1389149Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2024-08-20T21:34:33.1427269Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2024-08-20T21:34:33.1465628Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2024-08-20T21:34:33.1503128Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-08-20T21:34:33.1539734Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2024-08-20T21:34:33.1578951Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2024-08-20T21:34:33.1616800Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2024-08-20T21:34:33.1654804Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2024-08-20T21:34:33.1692993Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2024-08-20T21:34:33.1732349Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2024-08-20T21:34:33.1770261Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2024-08-20T21:34:33.1808920Z Entering 'third_party/mimalloc' 2024-08-20T21:34:33.1847052Z Entering 'third_party/nccl/nccl' 2024-08-20T21:34:33.1886052Z Entering 'third_party/nlohmann' 2024-08-20T21:34:33.1925305Z Entering 'third_party/onnx' 2024-08-20T21:34:33.1979029Z Entering 'third_party/onnx/third_party/benchmark' 2024-08-20T21:34:33.2017237Z Entering 'third_party/onnx/third_party/pybind11' 2024-08-20T21:34:33.2058144Z Entering 'third_party/opentelemetry-cpp' 2024-08-20T21:34:33.2099175Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2024-08-20T21:34:33.2137377Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2024-08-20T21:34:33.2176314Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2024-08-20T21:34:33.2213770Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2024-08-20T21:34:33.2253273Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2024-08-20T21:34:33.2290180Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-08-20T21:34:33.2326676Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-08-20T21:34:33.2365023Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2024-08-20T21:34:33.2404480Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2024-08-20T21:34:33.2443710Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-08-20T21:34:33.2501978Z Entering 'third_party/pocketfft' 2024-08-20T21:34:33.2541868Z Entering 'third_party/protobuf' 2024-08-20T21:34:33.2583269Z Entering 'third_party/protobuf/third_party/benchmark' 2024-08-20T21:34:33.2620887Z Entering 'third_party/protobuf/third_party/googletest' 2024-08-20T21:34:33.2660505Z Entering 'third_party/psimd' 2024-08-20T21:34:33.2699450Z Entering 'third_party/pthreadpool' 2024-08-20T21:34:33.2735824Z Entering 'third_party/pybind11' 2024-08-20T21:34:33.2775787Z Entering 'third_party/python-peachpy' 2024-08-20T21:34:33.2813513Z Entering 'third_party/sleef' 2024-08-20T21:34:33.2851514Z Entering 'third_party/tensorpipe' 2024-08-20T21:34:33.2889829Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-08-20T21:34:33.2926964Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-08-20T21:34:33.2964321Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-08-20T21:34:33.3001159Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-08-20T21:34:33.3037562Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-08-20T21:34:33.3089041Z ##[endgroup] 2024-08-20T21:34:33.3129227Z [command]/usr/bin/git log -1 --format='%H' 2024-08-20T21:34:33.3159171Z '40ec5f6ddd9787aca0449b24128343ff4c4a88b3' 2024-08-20T21:34:33.3359738Z Prepare all required actions 2024-08-20T21:34:33.3360196Z Getting action download info 2024-08-20T21:34:33.4834197Z ##[group]Run ./.github/actions/setup-linux 2024-08-20T21:34:33.4834612Z env: 2024-08-20T21:34:33.4834868Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:34:33.4835181Z ##[endgroup] 2024-08-20T21:34:33.4899801Z ##[group]Run set -euo pipefail 2024-08-20T21:34:33.4900226Z set -euo pipefail 2024-08-20T21:34:33.4900581Z function get_ec2_metadata() { 2024-08-20T21:34:33.4901233Z  # Pulled from instance metadata endpoint for EC2 2024-08-20T21:34:33.4902141Z  # see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html 2024-08-20T21:34:33.4902915Z  category=$1 2024-08-20T21:34:33.4903428Z  # If it is GCP runner (runner name contains gcp), do not run this 2024-08-20T21:34:33.4904057Z  runner_name_str=i-025b30bca34dcaa23 2024-08-20T21:34:33.4904542Z  if [[ -f /.inarc ]]; then 2024-08-20T21:34:33.4905012Z  echo "ARC Runner, no info on ec2 metadata" 2024-08-20T21:34:33.4905549Z  elif [[ $runner_name_str == *"gcp"* ]]; then 2024-08-20T21:34:33.4906233Z  echo "Runner is from Google Cloud Platform, No info on ec2 metadata" 2024-08-20T21:34:33.4906842Z  else 2024-08-20T21:34:33.4907304Z  curl -fsSL "http://169.254.169.254/latest/meta-data/${category}" 2024-08-20T21:34:33.4907877Z  fi 2024-08-20T21:34:33.4908134Z } 2024-08-20T21:34:33.4908456Z echo "ami-id: $(get_ec2_metadata ami-id)" 2024-08-20T21:34:33.4909028Z echo "instance-id: $(get_ec2_metadata instance-id)" 2024-08-20T21:34:33.4909673Z echo "instance-type: $(get_ec2_metadata instance-type)" 2024-08-20T21:34:33.4910216Z echo "system info $(uname -a)" 2024-08-20T21:34:33.4917835Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:34:33.4918323Z env: 2024-08-20T21:34:33.4918576Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:34:33.4918910Z ##[endgroup] 2024-08-20T21:34:33.5006132Z ami-id: ami-06c68f701d8090592 2024-08-20T21:34:33.5056007Z instance-id: i-025b30bca34dcaa23 2024-08-20T21:34:33.5107672Z instance-type: c5.2xlarge 2024-08-20T21:34:33.5117550Z system info Linux ip-10-0-33-210.ec2.internal 6.1.94-99.176.amzn2023.x86_64 #1 SMP PREEMPT_DYNAMIC Tue Jun 18 14:57:56 UTC 2024 x86_64 x86_64 x86_64 GNU/Linux 2024-08-20T21:34:33.5177469Z ##[group]Run echo "IN_ARC_RUNNER=$([ -f /.inarc ] && echo true || echo false)" >> $GITHUB_OUTPUT 2024-08-20T21:34:33.5178407Z echo "IN_ARC_RUNNER=$([ -f /.inarc ] && echo true || echo false)" >> $GITHUB_OUTPUT 2024-08-20T21:34:33.5184251Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:34:33.5184744Z env: 2024-08-20T21:34:33.5184987Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:34:33.5185313Z ##[endgroup] 2024-08-20T21:34:33.5253379Z ##[group]Run if systemctl is-active --quiet docker; then 2024-08-20T21:34:33.5253993Z if systemctl is-active --quiet docker; then 2024-08-20T21:34:33.5254524Z  echo "Docker daemon is running..."; 2024-08-20T21:34:33.5254964Z else 2024-08-20T21:34:33.5255431Z  echo "Starting docker deamon..." && sudo systemctl start docker; 2024-08-20T21:34:33.5256012Z fi 2024-08-20T21:34:33.5261485Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:34:33.5261961Z env: 2024-08-20T21:34:33.5262235Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:34:33.5262621Z ##[endgroup] 2024-08-20T21:34:33.5331240Z Docker daemon is running... 2024-08-20T21:34:33.5383688Z ##[group]Run nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482 2024-08-20T21:34:33.5384247Z with: 2024-08-20T21:34:33.5384494Z shell: bash 2024-08-20T21:34:33.5384757Z timeout_minutes: 5 2024-08-20T21:34:33.5385059Z max_attempts: 3 2024-08-20T21:34:33.5385351Z retry_wait_seconds: 30 2024-08-20T21:34:33.5388697Z command: AWS_ACCOUNT_ID=$(aws sts get-caller-identity|grep Account|cut -f4 -d\") aws ecr get-login-password --region "$AWS_DEFAULT_REGION" | docker login --username AWS \ --password-stdin "$AWS_ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com" # For LF Runners we need to make sure we also login to Meta's ECR docker registry too. META_AWS_ACCOUNT_ID=308535385114 if [ "$AWS_ACCOUNT_ID" != "$META_AWS_ACCOUNT_ID" ] ; then aws ecr get-login-password --region "$AWS_DEFAULT_REGION" | docker login --username AWS \ --password-stdin "$META_AWS_ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com" fi 2024-08-20T21:34:33.5392039Z polling_interval_seconds: 1 2024-08-20T21:34:33.5392385Z warning_on_retry: true 2024-08-20T21:34:33.5392719Z continue_on_error: false 2024-08-20T21:34:33.5393038Z env: 2024-08-20T21:34:33.5393284Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:34:33.5393618Z AWS_RETRY_MODE: standard 2024-08-20T21:34:33.5393942Z AWS_MAX_ATTEMPTS: 5 2024-08-20T21:34:33.5394247Z AWS_DEFAULT_REGION: us-east-1 2024-08-20T21:34:33.5394600Z ##[endgroup] 2024-08-20T21:34:34.6997396Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2024-08-20T21:34:34.6998231Z Configure a credential helper to remove this warning. See 2024-08-20T21:34:34.6999094Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2024-08-20T21:34:34.6999634Z 2024-08-20T21:34:34.6999737Z Login Succeeded 2024-08-20T21:34:35.5935798Z Command completed after 1 attempt(s). 2024-08-20T21:34:35.5987054Z ##[group]Run env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2024-08-20T21:34:35.5987781Z env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2024-08-20T21:34:35.5988429Z env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2024-08-20T21:34:35.5994906Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:34:35.5995386Z env: 2024-08-20T21:34:35.5995646Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:34:35.5995970Z ##[endgroup] 2024-08-20T21:34:35.6081577Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2024-08-20T21:34:35.6082915Z # ignore expansion of "docker ps -q" since it could be empty 2024-08-20T21:34:35.6083965Z # shellcheck disable=SC2046 2024-08-20T21:34:35.6084743Z docker stop $(docker ps -q) || true 2024-08-20T21:34:35.6085258Z # Prune all of the docker images 2024-08-20T21:34:35.6085692Z docker system prune -af 2024-08-20T21:34:35.6091368Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:34:35.6091846Z env: 2024-08-20T21:34:35.6092102Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:34:35.6092426Z ##[endgroup] 2024-08-20T21:34:35.6356807Z "docker stop" requires at least 1 argument. 2024-08-20T21:34:35.6357491Z See 'docker stop --help'. 2024-08-20T21:34:35.6357733Z 2024-08-20T21:34:35.6357949Z Usage: docker stop [OPTIONS] CONTAINER [CONTAINER...] 2024-08-20T21:34:35.6358338Z 2024-08-20T21:34:35.6358477Z Stop one or more running containers 2024-08-20T21:34:35.6540005Z Total reclaimed space: 0B 2024-08-20T21:34:35.6575469Z ##[group]Run set +e 2024-08-20T21:34:35.6575818Z set +e 2024-08-20T21:34:35.6576084Z set -x 2024-08-20T21:34:35.6576353Z  2024-08-20T21:34:35.6576654Z PT_DOMAIN=download.pytorch.org 2024-08-20T21:34:35.6577432Z # TODO: Flaky access to download.pytorch.org https://github.com/pytorch/pytorch/issues/100400, 2024-08-20T21:34:35.6578535Z # cleaning this up once the issue is fixed. There are more than one resolved IP here, the last 2024-08-20T21:34:35.6579306Z # one is returned at random 2024-08-20T21:34:35.6579818Z RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" | tail -n1) 2024-08-20T21:34:35.6580319Z  2024-08-20T21:34:35.6580606Z if [ -z "${RESOLVED_IP}" ]; then 2024-08-20T21:34:35.6581475Z  echo "Couldn't resolve ${PT_DOMAIN}, retrying with Google DNS..." 2024-08-20T21:34:35.6582379Z  RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" @8.8.8.8 | tail -n1) 2024-08-20T21:34:35.6582934Z  2024-08-20T21:34:35.6583229Z  if [ -z "${RESOLVED_IP}" ]; then 2024-08-20T21:34:35.6583753Z  echo "Couldn't resolve ${PT_DOMAIN}, exiting..." 2024-08-20T21:34:35.6584254Z  exit 1 2024-08-20T21:34:35.6584544Z  fi 2024-08-20T21:34:35.6584790Z fi 2024-08-20T21:34:35.6585042Z  2024-08-20T21:34:35.6585374Z if grep -r "${PT_DOMAIN}" /etc/hosts; then 2024-08-20T21:34:35.6585972Z  # Clean up any old records first 2024-08-20T21:34:35.6586473Z  sudo sed -i "/${PT_DOMAIN}/d" /etc/hosts 2024-08-20T21:34:35.6586911Z fi 2024-08-20T21:34:35.6587151Z  2024-08-20T21:34:35.6587558Z echo "${RESOLVED_IP} ${PT_DOMAIN}" | sudo tee -a /etc/hosts 2024-08-20T21:34:35.6588100Z cat /etc/hosts 2024-08-20T21:34:35.6593820Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:34:35.6594309Z env: 2024-08-20T21:34:35.6594572Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:34:35.6594888Z ##[endgroup] 2024-08-20T21:34:35.6618483Z + PT_DOMAIN=download.pytorch.org 2024-08-20T21:34:35.6624594Z ++ dig -4 +short download.pytorch.org 2024-08-20T21:34:35.6625418Z ++ tail -n1 2024-08-20T21:34:35.6917917Z + RESOLVED_IP=18.165.98.47 2024-08-20T21:34:35.6918787Z + '[' -z 18.165.98.47 ']' 2024-08-20T21:34:35.6919208Z + grep -r download.pytorch.org /etc/hosts 2024-08-20T21:34:35.6929295Z 18.165.98.69 download.pytorch.org 2024-08-20T21:34:35.6930509Z + sudo sed -i /download.pytorch.org/d /etc/hosts 2024-08-20T21:34:35.9360750Z + echo '18.165.98.47 download.pytorch.org' 2024-08-20T21:34:35.9361333Z + sudo tee -a /etc/hosts 2024-08-20T21:34:35.9766870Z 18.165.98.47 download.pytorch.org 2024-08-20T21:34:35.9780979Z + cat /etc/hosts 2024-08-20T21:34:35.9788402Z 127.0.0.1 localhost localhost.localdomain localhost4 localhost4.localdomain4 2024-08-20T21:34:35.9800871Z ::1 localhost6 localhost6.localdomain6 2024-08-20T21:34:35.9801412Z 18.165.98.47 download.pytorch.org 2024-08-20T21:34:35.9965776Z ##[group]Run pytorch/test-infra/.github/actions/calculate-docker-image@main 2024-08-20T21:34:35.9966478Z with: 2024-08-20T21:34:35.9967389Z docker-image-name: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:34:35.9968453Z docker-build-dir: .ci/docker 2024-08-20T21:34:35.9968817Z working-directory: . 2024-08-20T21:34:35.9969276Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-20T21:34:35.9969797Z force-push: false 2024-08-20T21:34:35.9970074Z env: 2024-08-20T21:34:35.9970310Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:34:35.9970629Z ##[endgroup] 2024-08-20T21:34:35.9990541Z ##[group]Run set -ex 2024-08-20T21:34:35.9991130Z set -ex 2024-08-20T21:34:35.9991400Z  2024-08-20T21:34:35.9991939Z # If the docker build directory or the build script doesn't exist, the action will 2024-08-20T21:34:35.9992957Z # gracefully return the docker image name as it is. Pulling docker image in Linux 2024-08-20T21:34:35.9993761Z # job could then download the pre-built image as usual 2024-08-20T21:34:35.9994503Z if [[ ! -d "${DOCKER_BUILD_DIR}" ]] || [[ ! -f "${DOCKER_BUILD_DIR}/build.sh" ]]; then 2024-08-20T21:34:35.9995176Z  echo "skip=true" >> "${GITHUB_OUTPUT}" 2024-08-20T21:34:35.9995805Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2024-08-20T21:34:35.9996356Z  2024-08-20T21:34:35.9996853Z  echo "There is no Docker build script in ${REPO_NAME} repo, skipping..." 2024-08-20T21:34:35.9997478Z  exit 0 2024-08-20T21:34:35.9997741Z else 2024-08-20T21:34:35.9998085Z  echo "skip=false" >> "${GITHUB_OUTPUT}" 2024-08-20T21:34:35.9998516Z fi 2024-08-20T21:34:35.9998755Z  2024-08-20T21:34:35.9999203Z if [[ "${DOCKER_IMAGE_NAME}" == *"${DOCKER_REGISTRY}/${REPO_NAME}"* ]]; then 2024-08-20T21:34:36.0000069Z  # The docker image name already includes the ECR prefix and tag, so we can just 2024-08-20T21:34:36.0000841Z  # use it as it is, but first let's extract the tag 2024-08-20T21:34:36.0001547Z  DOCKER_TAG=$(echo "${DOCKER_IMAGE_NAME}" | awk -F '[:,]' '{print $2}') 2024-08-20T21:34:36.0002274Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2024-08-20T21:34:36.0003101Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2024-08-20T21:34:36.0003642Z else 2024-08-20T21:34:36.0004055Z  DOCKER_TAG=$(git rev-parse HEAD:"${DOCKER_BUILD_DIR}") 2024-08-20T21:34:36.0004713Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2024-08-20T21:34:36.0005608Z  echo "docker-image=${DOCKER_REGISTRY}/${REPO_NAME}/${DOCKER_IMAGE_NAME}:${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2024-08-20T21:34:36.0006451Z fi 2024-08-20T21:34:36.0013222Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:34:36.0013699Z env: 2024-08-20T21:34:36.0013952Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:34:36.0014288Z REPO_NAME: pytorch 2024-08-20T21:34:36.0015230Z DOCKER_IMAGE_NAME: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:34:36.0016281Z DOCKER_BUILD_DIR: .ci/docker 2024-08-20T21:34:36.0016762Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-20T21:34:36.0017291Z ##[endgroup] 2024-08-20T21:34:36.0042817Z + [[ ! -d .ci/docker ]] 2024-08-20T21:34:36.0043346Z + [[ ! -f .ci/docker/build.sh ]] 2024-08-20T21:34:36.0043717Z + echo skip=false 2024-08-20T21:34:36.0045382Z + [[ 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 == *\3\0\8\5\3\5\3\8\5\1\1\4\.\d\k\r\.\e\c\r\.\u\s\-\e\a\s\t\-\1\.\a\m\a\z\o\n\a\w\s\.\c\o\m\/\p\y\t\o\r\c\h* ]] 2024-08-20T21:34:36.0049733Z ++ echo 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:34:36.0050768Z ++ awk -F '[:,]' '{print $2}' 2024-08-20T21:34:36.0214294Z + DOCKER_TAG=f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:34:36.0215010Z + echo docker-tag=f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:34:36.0216446Z + echo docker-image=308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:34:36.0262522Z ##[group]Run set +e 2024-08-20T21:34:36.0262860Z set +e 2024-08-20T21:34:36.0263135Z set -x 2024-08-20T21:34:36.0263401Z  2024-08-20T21:34:36.0263639Z login() { 2024-08-20T21:34:36.0264295Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2024-08-20T21:34:36.0265020Z } 2024-08-20T21:34:36.0265257Z  2024-08-20T21:34:36.0265507Z retry () { 2024-08-20T21:34:36.0266132Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2024-08-20T21:34:36.0266549Z } 2024-08-20T21:34:36.0266796Z  2024-08-20T21:34:36.0267085Z retry login "${DOCKER_REGISTRY}" 2024-08-20T21:34:36.0267470Z  2024-08-20T21:34:36.0267923Z # Check if image already exists, if it does then skip building it 2024-08-20T21:34:36.0268624Z if docker manifest inspect "${DOCKER_IMAGE}"; then 2024-08-20T21:34:36.0269112Z  exit 0 2024-08-20T21:34:36.0269387Z fi 2024-08-20T21:34:36.0269635Z  2024-08-20T21:34:36.0270106Z # NB: This part requires a full checkout. Otherwise, the merge base will 2024-08-20T21:34:36.0270940Z # be empty. The default action would be to continue rebuild the image 2024-08-20T21:34:36.0271672Z if [[ "$BASE_REVISION" = "$(git rev-parse HEAD)" ]]; then 2024-08-20T21:34:36.0272335Z  # if we're on the base branch then use the parent commit 2024-08-20T21:34:36.0272903Z  MERGE_BASE=$(git rev-parse HEAD~) 2024-08-20T21:34:36.0273315Z else 2024-08-20T21:34:36.0273763Z  # otherwise we're on a PR, so use the most recent base commit 2024-08-20T21:34:36.0274438Z  MERGE_BASE=$(git merge-base HEAD "$BASE_REVISION") 2024-08-20T21:34:36.0274933Z fi 2024-08-20T21:34:36.0275184Z  2024-08-20T21:34:36.0275459Z if [[ -z "${MERGE_BASE}" ]]; then 2024-08-20T21:34:36.0276067Z  echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2024-08-20T21:34:36.0276513Z  2024-08-20T21:34:36.0277148Z  echo "Finding merge base only works with full checkout, please set fetch-depth to 0, continuing ..." 2024-08-20T21:34:36.0277923Z  exit 0 2024-08-20T21:34:36.0278202Z fi 2024-08-20T21:34:36.0278440Z  2024-08-20T21:34:36.0278854Z if ! git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}"; then 2024-08-20T21:34:36.0279844Z  echo "Directory '${DOCKER_BUILD_DIR}' not found in commit $MERGE_BASE, you should rebase onto a more recent commit" 2024-08-20T21:34:36.0280662Z  exit 1 2024-08-20T21:34:36.0280925Z fi 2024-08-20T21:34:36.0281175Z  2024-08-20T21:34:36.0281644Z PREVIOUS_DOCKER_TAG=$(git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}") 2024-08-20T21:34:36.0282595Z # If no image exists but the hash is the same as the previous hash then we should error out here 2024-08-20T21:34:36.0283485Z if [[ "${PREVIOUS_DOCKER_TAG}" == "${DOCKER_TAG}" ]]; then 2024-08-20T21:34:36.0284469Z  echo "WARNING: Something has gone wrong and the previous image isn't available for the merge-base of your branch" 2024-08-20T21:34:36.0285591Z  echo " Will re-build docker image to store in local cache, TTS may be longer" 2024-08-20T21:34:36.0286310Z fi 2024-08-20T21:34:36.0286568Z  2024-08-20T21:34:36.0287021Z echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2024-08-20T21:34:36.0292553Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:34:36.0293040Z env: 2024-08-20T21:34:36.0293292Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:34:36.0293624Z DOCKER_BUILD_DIR: .ci/docker 2024-08-20T21:34:36.0294072Z BASE_REVISION: 91f3d614142df02f619e44a68e3d9e0dfeba49ec 2024-08-20T21:34:36.0295186Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:34:36.0296281Z DOCKER_TAG: f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:34:36.0296877Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-20T21:34:36.0297385Z ##[endgroup] 2024-08-20T21:34:36.0319904Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-20T21:34:36.0320568Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-20T21:34:36.0322105Z + aws ecr get-login-password --region us-east-1 2024-08-20T21:34:36.0323474Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-20T21:34:36.5759499Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2024-08-20T21:34:36.5760345Z Configure a credential helper to remove this warning. See 2024-08-20T21:34:36.5761426Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2024-08-20T21:34:36.5761968Z 2024-08-20T21:34:36.5762100Z Login Succeeded 2024-08-20T21:34:36.5774092Z + docker manifest inspect 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:34:36.8049490Z { 2024-08-20T21:34:36.8049956Z "schemaVersion": 2, 2024-08-20T21:34:36.8050696Z "mediaType": "application/vnd.docker.distribution.manifest.v2+json", 2024-08-20T21:34:36.8051625Z "config": { 2024-08-20T21:34:36.8052346Z "mediaType": "application/vnd.docker.container.image.v1+json", 2024-08-20T21:34:36.8053440Z "size": 43416, 2024-08-20T21:34:36.8054740Z "digest": "sha256:2aaa7058a7e1d063370f2bda46a0fffde19193dd83023b51f0f1d1c55b4a88e4" 2024-08-20T21:34:36.8056233Z }, 2024-08-20T21:34:36.8056770Z "layers": [ 2024-08-20T21:34:36.8057331Z { 2024-08-20T21:34:36.8058317Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8059630Z "size": 28584223, 2024-08-20T21:34:36.8062618Z "digest": "sha256:560c024910bebac6b404791af28ebd48a8289303b8377d17b67ffdfe52754f2a" 2024-08-20T21:34:36.8064193Z }, 2024-08-20T21:34:36.8064562Z { 2024-08-20T21:34:36.8065211Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8066324Z "size": 1822, 2024-08-20T21:34:36.8067115Z "digest": "sha256:9973d24424a5d77923bf32f3f7cf77d04111177f289c946a51eb77633ac03200" 2024-08-20T21:34:36.8068066Z }, 2024-08-20T21:34:36.8068414Z { 2024-08-20T21:34:36.8069196Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8070312Z "size": 313390323, 2024-08-20T21:34:36.8071260Z "digest": "sha256:31cf2448eae342b481298db0563fe7f68f4840ac62385700a1ef3b6824a54e7b" 2024-08-20T21:34:36.8072206Z }, 2024-08-20T21:34:36.8072560Z { 2024-08-20T21:34:36.8073207Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8074083Z "size": 805, 2024-08-20T21:34:36.8074986Z "digest": "sha256:2cbc8585686fbca1e1a900ffd7ff97a92a6445dbf506a2cfad30233d7f6648cf" 2024-08-20T21:34:36.8076014Z }, 2024-08-20T21:34:36.8076392Z { 2024-08-20T21:34:36.8077091Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8077958Z "size": 79404447, 2024-08-20T21:34:36.8078900Z "digest": "sha256:8c4852dbedbae7b66672521b065738abe5da8a8d97a1bf720cacb3fb8bbaf8b8" 2024-08-20T21:34:36.8079944Z }, 2024-08-20T21:34:36.8080317Z { 2024-08-20T21:34:36.8081059Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8082028Z "size": 545, 2024-08-20T21:34:36.8083221Z "digest": "sha256:dc477a9bbcc668c57f84105691c20adeb11886c57cb1a1189f2ab2b342169ee1" 2024-08-20T21:34:36.8084350Z }, 2024-08-20T21:34:36.8084736Z { 2024-08-20T21:34:36.8085470Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8086530Z "size": 1259, 2024-08-20T21:34:36.8087350Z "digest": "sha256:8c153b899f1d12088fcea7cbf5e519ff80a06514340f15766a9d81a81c067cb0" 2024-08-20T21:34:36.8088396Z }, 2024-08-20T21:34:36.8088939Z + exit 0 2024-08-20T21:34:36.8089315Z { 2024-08-20T21:34:36.8090031Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8090968Z "size": 482, 2024-08-20T21:34:36.8091905Z "digest": "sha256:0b1c4336fa8bd2c1010a17a3bef3e2959d8bb63edccb0ca967b332b6313323bc" 2024-08-20T21:34:36.8093024Z }, 2024-08-20T21:34:36.8093397Z { 2024-08-20T21:34:36.8094071Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8095031Z "size": 110, 2024-08-20T21:34:36.8095956Z "digest": "sha256:b012c0f517e6752698d543b850ac5b96ad9bff3e6e62742e5caa234ba3843d63" 2024-08-20T21:34:36.8097096Z }, 2024-08-20T21:34:36.8097492Z { 2024-08-20T21:34:36.8098248Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8099250Z "size": 3842, 2024-08-20T21:34:36.8100216Z "digest": "sha256:4fe699dadca1a5f4853c531c904484700f344c8cf674d8681811f67d4e2d51f1" 2024-08-20T21:34:36.8101323Z }, 2024-08-20T21:34:36.8101727Z { 2024-08-20T21:34:36.8102489Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8103532Z "size": 1909, 2024-08-20T21:34:36.8104520Z "digest": "sha256:d67ce3a9cb4bcde890a78de673a7653e1170329c91e4c2c42edff9f25eab0879" 2024-08-20T21:34:36.8105686Z }, 2024-08-20T21:34:36.8106055Z { 2024-08-20T21:34:36.8106795Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8107993Z "size": 701, 2024-08-20T21:34:36.8108976Z "digest": "sha256:9841596ece9eebfc9229b66dac9085e17c614f6e2697b9b6787e50cef1fffebe" 2024-08-20T21:34:36.8110134Z }, 2024-08-20T21:34:36.8110528Z { 2024-08-20T21:34:36.8111261Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8112260Z "size": 2609138458, 2024-08-20T21:34:36.8113211Z "digest": "sha256:e72835db40351a3b56e0ca4a982191be176bcd8497f362923ad7e903e4e2ce72" 2024-08-20T21:34:36.8114202Z }, 2024-08-20T21:34:36.8114555Z { 2024-08-20T21:34:36.8115424Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8116355Z "size": 32, 2024-08-20T21:34:36.8117251Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-08-20T21:34:36.8118291Z }, 2024-08-20T21:34:36.8118662Z { 2024-08-20T21:34:36.8119336Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8120285Z "size": 380, 2024-08-20T21:34:36.8121233Z "digest": "sha256:8ef04e16f963f1861c8b9b1d3370bbcfd9d0759722b164656ae8d9d27e8cd69c" 2024-08-20T21:34:36.8122232Z }, 2024-08-20T21:34:36.8122602Z { 2024-08-20T21:34:36.8123284Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8124192Z "size": 104, 2024-08-20T21:34:36.8125095Z "digest": "sha256:5e6c0035b872ae6257af61cf08fb117429b59e9b5e8ad37fbd87ba1a42f0615f" 2024-08-20T21:34:36.8126335Z }, 2024-08-20T21:34:36.8126708Z { 2024-08-20T21:34:36.8127447Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8128406Z "size": 230, 2024-08-20T21:34:36.8129371Z "digest": "sha256:59b7b891e32d1daecd4b72b2f0ebcc56c8262047d6b4ded931b0774ef133e6a1" 2024-08-20T21:34:36.8130521Z }, 2024-08-20T21:34:36.8130914Z { 2024-08-20T21:34:36.8131646Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8132643Z "size": 2912748, 2024-08-20T21:34:36.8133633Z "digest": "sha256:5f4e7925ebfbdc8094468c559f6e04dddc21c34b2f3836dfeb99150d0fc624bf" 2024-08-20T21:34:36.8134752Z }, 2024-08-20T21:34:36.8135109Z { 2024-08-20T21:34:36.8135977Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8136912Z "size": 1805, 2024-08-20T21:34:36.8137815Z "digest": "sha256:12a4509699601b6f70bff1449173c2e63668e0bfca039e6e795a0b206f15f569" 2024-08-20T21:34:36.8138879Z }, 2024-08-20T21:34:36.8139250Z { 2024-08-20T21:34:36.8139923Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8140898Z "size": 105, 2024-08-20T21:34:36.8141806Z "digest": "sha256:b8c108e0e581d467f2bf6f42d629ae9133d1c81f2819bf9aae0e6a61f4899080" 2024-08-20T21:34:36.8142912Z }, 2024-08-20T21:34:36.8143296Z { 2024-08-20T21:34:36.8143979Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8144942Z "size": 563, 2024-08-20T21:34:36.8145854Z "digest": "sha256:d3e5b0194b603125049ad0a4ba6092302c71276b61021ffffeac0839f8d0d5b0" 2024-08-20T21:34:36.8147174Z }, 2024-08-20T21:34:36.8147549Z { 2024-08-20T21:34:36.8148313Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8149288Z "size": 32, 2024-08-20T21:34:36.8150222Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-08-20T21:34:36.8151266Z }, 2024-08-20T21:34:36.8151634Z { 2024-08-20T21:34:36.8152375Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8153371Z "size": 104, 2024-08-20T21:34:36.8154247Z "digest": "sha256:3e4dba8f4febc7776542a5c0df310917594fdd902b5f25ee01e2e11d7dda8dde" 2024-08-20T21:34:36.8155346Z }, 2024-08-20T21:34:36.8155732Z { 2024-08-20T21:34:36.8156454Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8157436Z "size": 504, 2024-08-20T21:34:36.8158320Z "digest": "sha256:4dd8f5b900be084ea5650811264c5995abde8da16fdd9a9e5330b4d6b7c9d2bf" 2024-08-20T21:34:36.8159455Z }, 2024-08-20T21:34:36.8159837Z { 2024-08-20T21:34:36.8160558Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8161553Z "size": 121478498, 2024-08-20T21:34:36.8162471Z "digest": "sha256:f0966db14aac5cadba599cacd437c5c62e774a63909d41795e08ceb102c79ee8" 2024-08-20T21:34:36.8163534Z }, 2024-08-20T21:34:36.8163906Z { 2024-08-20T21:34:36.8164623Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8165555Z "size": 109, 2024-08-20T21:34:36.8166561Z "digest": "sha256:3ca232406eff20a176ec18b81ea360745f258a23b296af5ed9caa7f5f5e0ba9b" 2024-08-20T21:34:36.8167849Z }, 2024-08-20T21:34:36.8168191Z { 2024-08-20T21:34:36.8168911Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8169868Z "size": 488, 2024-08-20T21:34:36.8170798Z "digest": "sha256:93684c5d1426705226d7a8f24fa6a27fa773a59bb4836503d6aedb81da78187b" 2024-08-20T21:34:36.8171904Z }, 2024-08-20T21:34:36.8172299Z { 2024-08-20T21:34:36.8173042Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8174035Z "size": 382, 2024-08-20T21:34:36.8174864Z "digest": "sha256:891f1a671536e6eb04324cf503c429927fad1829d8d17b87576b72b795b598c8" 2024-08-20T21:34:36.8175919Z }, 2024-08-20T21:34:36.8176292Z { 2024-08-20T21:34:36.8176985Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8177895Z "size": 101, 2024-08-20T21:34:36.8178781Z "digest": "sha256:2d1cde488756cc124c53793a09929b80bab99bf795615ff8b3985222590a1a9e" 2024-08-20T21:34:36.8179894Z }, 2024-08-20T21:34:36.8180281Z { 2024-08-20T21:34:36.8181025Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8182022Z "size": 1473, 2024-08-20T21:34:36.8182998Z "digest": "sha256:1b670936c6e7cbe1a7027bdecaf56518c150d4fef9c37e9c8d6028ed24c470cd" 2024-08-20T21:34:36.8184148Z }, 2024-08-20T21:34:36.8184544Z { 2024-08-20T21:34:36.8185284Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8186298Z "size": 594718706, 2024-08-20T21:34:36.8187497Z "digest": "sha256:c89135369bac79807627e2ca0d308b338e5a81d9784725e77e44c85e824c9b44" 2024-08-20T21:34:36.8188607Z }, 2024-08-20T21:34:36.8188918Z { 2024-08-20T21:34:36.8189349Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8189894Z "size": 163, 2024-08-20T21:34:36.8190419Z "digest": "sha256:e32451be40c166669102ba85593c8d7dce7eecf23c4170d78ce9724d0643dda3" 2024-08-20T21:34:36.8191046Z }, 2024-08-20T21:34:36.8191255Z { 2024-08-20T21:34:36.8191672Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8192225Z "size": 2210, 2024-08-20T21:34:36.8192763Z "digest": "sha256:7647e82c5c3f0c4f5cad2572dff08f0bf4a531bbfd9b1a5feb6ce436dd4e49e4" 2024-08-20T21:34:36.8193402Z }, 2024-08-20T21:34:36.8193620Z { 2024-08-20T21:34:36.8194023Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8194568Z "size": 152, 2024-08-20T21:34:36.8195117Z "digest": "sha256:2d334a3c11fd5710fd53edfaaae7b104a0c98d954e58f65b01c7d753c4511eba" 2024-08-20T21:34:36.8195736Z }, 2024-08-20T21:34:36.8195956Z { 2024-08-20T21:34:36.8196372Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8196907Z "size": 646, 2024-08-20T21:34:36.8197450Z "digest": "sha256:e9ea16c3c5d48823f203ff83eff807c5c4bc6fb665fa6ca38062ce17b7f666d9" 2024-08-20T21:34:36.8198076Z }, 2024-08-20T21:34:36.8198286Z { 2024-08-20T21:34:36.8198711Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8199259Z "size": 32, 2024-08-20T21:34:36.8199784Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-08-20T21:34:36.8200409Z }, 2024-08-20T21:34:36.8200629Z { 2024-08-20T21:34:36.8201036Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8201586Z "size": 166, 2024-08-20T21:34:36.8202118Z "digest": "sha256:66a5808f4ad5d4aa00c437341e5d2d79a1831f639ba2975ad7dbc1a19cf59e82" 2024-08-20T21:34:36.8202725Z }, 2024-08-20T21:34:36.8202945Z { 2024-08-20T21:34:36.8203360Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8203895Z "size": 109, 2024-08-20T21:34:36.8204432Z "digest": "sha256:b92de615fef3f321b432cf7abbcc684d72733d3e44836f4b39efe6294cc5e960" 2024-08-20T21:34:36.8205057Z }, 2024-08-20T21:34:36.8205267Z { 2024-08-20T21:34:36.8205685Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8206485Z "size": 102, 2024-08-20T21:34:36.8207003Z "digest": "sha256:56dd63029e04a51ae0ca3da9892051166a52e04aef2ac22ec5f2e7d5e34afb15" 2024-08-20T21:34:36.8207621Z }, 2024-08-20T21:34:36.8207842Z { 2024-08-20T21:34:36.8208244Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8208792Z "size": 422, 2024-08-20T21:34:36.8209312Z "digest": "sha256:0e19dd6ac61155a486662c375d8045665b099ba4433e788e7c1190aa54f22b05" 2024-08-20T21:34:36.8209903Z }, 2024-08-20T21:34:36.8210130Z { 2024-08-20T21:34:36.8210546Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8211078Z "size": 32, 2024-08-20T21:34:36.8211610Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-08-20T21:34:36.8212360Z }, 2024-08-20T21:34:36.8212568Z { 2024-08-20T21:34:36.8212987Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8213540Z "size": 111, 2024-08-20T21:34:36.8214075Z "digest": "sha256:deffd0debe5eeb753ba39d21c285b32e694c57608d36e197b36159a1bbe25904" 2024-08-20T21:34:36.8214707Z }, 2024-08-20T21:34:36.8214926Z { 2024-08-20T21:34:36.8215335Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8215880Z "size": 475, 2024-08-20T21:34:36.8216394Z "digest": "sha256:823a4c61790a03362056ad0704e048b5e346bb14825b282d9a51d20a24f20f24" 2024-08-20T21:34:36.8216980Z }, 2024-08-20T21:34:36.8217201Z { 2024-08-20T21:34:36.8217690Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8218228Z "size": 32, 2024-08-20T21:34:36.8218764Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-08-20T21:34:36.8219388Z }, 2024-08-20T21:34:36.8219595Z { 2024-08-20T21:34:36.8220011Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8220558Z "size": 112, 2024-08-20T21:34:36.8221070Z "digest": "sha256:52c09d8bb1145bf70146f81c269d116f279de800e78599715cca543eb483e42d" 2024-08-20T21:34:36.8221679Z }, 2024-08-20T21:34:36.8221902Z { 2024-08-20T21:34:36.8222306Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8222853Z "size": 564, 2024-08-20T21:34:36.8223395Z "digest": "sha256:fc113046ff5b58aaa39f078222cde0f7aafa5a591ebb9a6b0a0316a92f96d033" 2024-08-20T21:34:36.8224012Z }, 2024-08-20T21:34:36.8224233Z { 2024-08-20T21:34:36.8224651Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8225188Z "size": 43147817, 2024-08-20T21:34:36.8225756Z "digest": "sha256:58fcfcf8962b6535ff551dfc181f884b2f4dc60addbf4f054bdeeb91d584277f" 2024-08-20T21:34:36.8226393Z }, 2024-08-20T21:34:36.8226605Z { 2024-08-20T21:34:36.8227023Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8227568Z "size": 106, 2024-08-20T21:34:36.8228101Z "digest": "sha256:a6bbbb7aff7860a0cdcd2d16dcb5f1a616b92704025f110ea0145903e24c98de" 2024-08-20T21:34:36.8228750Z }, 2024-08-20T21:34:36.8228970Z { 2024-08-20T21:34:36.8229376Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8229917Z "size": 294, 2024-08-20T21:34:36.8230426Z "digest": "sha256:58c762741f8397d210849409ab3166528c26e9030fcfe7c1ae580ce602aa4083" 2024-08-20T21:34:36.8231013Z }, 2024-08-20T21:34:36.8231231Z { 2024-08-20T21:34:36.8231645Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8232192Z "size": 32, 2024-08-20T21:34:36.8232726Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-08-20T21:34:36.8233355Z }, 2024-08-20T21:34:36.8233565Z { 2024-08-20T21:34:36.8233979Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8234526Z "size": 106, 2024-08-20T21:34:36.8235056Z "digest": "sha256:36d6dbd6c3c6a27a1fc5c3cc27f2b80ccb1cb99168d583e9e527ba95b1303495" 2024-08-20T21:34:36.8235793Z }, 2024-08-20T21:34:36.8236013Z { 2024-08-20T21:34:36.8236422Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8236977Z "size": 425, 2024-08-20T21:34:36.8237511Z "digest": "sha256:f6e31afd70a789ecc0d8f950866fae85e550b900a43fc20f10f23926c891e3ab" 2024-08-20T21:34:36.8238132Z }, 2024-08-20T21:34:36.8238339Z { 2024-08-20T21:34:36.8238755Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8239294Z "size": 20262171, 2024-08-20T21:34:36.8239852Z "digest": "sha256:4076a363f04c6788a5ab7f070c8b710f2ce3cab5e2bdd5a88fc826cc1fed41ec" 2024-08-20T21:34:36.8240474Z }, 2024-08-20T21:34:36.8240682Z { 2024-08-20T21:34:36.8241100Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8241650Z "size": 108, 2024-08-20T21:34:36.8242196Z "digest": "sha256:da1b55b13c80cac5c9ef2a0eeb98279caf71d6d9dc0e082c181244e45c034d07" 2024-08-20T21:34:36.8242821Z }, 2024-08-20T21:34:36.8243043Z { 2024-08-20T21:34:36.8243449Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8244001Z "size": 439, 2024-08-20T21:34:36.8244524Z "digest": "sha256:852b7bd929d089cabae97c28bc481262013b52714aca66436277b2923d8ac7a7" 2024-08-20T21:34:36.8245139Z }, 2024-08-20T21:34:36.8245349Z { 2024-08-20T21:34:36.8245769Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8246415Z "size": 701, 2024-08-20T21:34:36.8247312Z "digest": "sha256:9841596ece9eebfc9229b66dac9085e17c614f6e2697b9b6787e50cef1fffebe" 2024-08-20T21:34:36.8247942Z }, 2024-08-20T21:34:36.8248163Z { 2024-08-20T21:34:36.8248567Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8249118Z "size": 143, 2024-08-20T21:34:36.8249641Z "digest": "sha256:43c374b7d87c83c0d9133afd916a9404b9264d90b612e6af3b80d3ff02489a0a" 2024-08-20T21:34:36.8250240Z }, 2024-08-20T21:34:36.8250466Z { 2024-08-20T21:34:36.8250879Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8251415Z "size": 135, 2024-08-20T21:34:36.8251949Z "digest": "sha256:2ae4d1692345e5e2ff7f22f2a51fae87d727f585b1ebc2ceaa06227c7b219489" 2024-08-20T21:34:36.8252571Z }, 2024-08-20T21:34:36.8252779Z { 2024-08-20T21:34:36.8253194Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8253738Z "size": 32, 2024-08-20T21:34:36.8254256Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-08-20T21:34:36.8254878Z }, 2024-08-20T21:34:36.8255096Z { 2024-08-20T21:34:36.8255498Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8256051Z "size": 194, 2024-08-20T21:34:36.8256571Z "digest": "sha256:02fd8160fb6156c3c183324f029e9286283ac4576982eeb6c4b532da8d7cddd7" 2024-08-20T21:34:36.8257162Z }, 2024-08-20T21:34:36.8257386Z { 2024-08-20T21:34:36.8257803Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8258344Z "size": 1380, 2024-08-20T21:34:36.8258867Z "digest": "sha256:1860f83d189475b980ab9ee8b8ee0bba86d85329f6e147944ae7a42157b04160" 2024-08-20T21:34:36.8259486Z }, 2024-08-20T21:34:36.8259696Z { 2024-08-20T21:34:36.8260114Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8260661Z "size": 701, 2024-08-20T21:34:36.8261179Z "digest": "sha256:9841596ece9eebfc9229b66dac9085e17c614f6e2697b9b6787e50cef1fffebe" 2024-08-20T21:34:36.8261795Z }, 2024-08-20T21:34:36.8262019Z { 2024-08-20T21:34:36.8262421Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8262968Z "size": 138, 2024-08-20T21:34:36.8263522Z "digest": "sha256:1beebf68aabb8fb64f447b43f6980ed57c666ada4bfd90faf2569a6da251ae8e" 2024-08-20T21:34:36.8264148Z }, 2024-08-20T21:34:36.8264371Z { 2024-08-20T21:34:36.8264786Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8265425Z "size": 32, 2024-08-20T21:34:36.8265956Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-08-20T21:34:36.8266579Z }, 2024-08-20T21:34:36.8266787Z { 2024-08-20T21:34:36.8267206Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8267754Z "size": 155, 2024-08-20T21:34:36.8268284Z "digest": "sha256:e0cbebc1b4e27811b288dd14edb52e86c01ad1ecb1362e8b74c9521e378e7287" 2024-08-20T21:34:36.8268914Z }, 2024-08-20T21:34:36.8269135Z { 2024-08-20T21:34:36.8269543Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8270093Z "size": 849, 2024-08-20T21:34:36.8270621Z "digest": "sha256:3e8f996f54cd6d79086bd4a75bf8635a86293aef8535806b4561821fbe21692d" 2024-08-20T21:34:36.8271221Z }, 2024-08-20T21:34:36.8271442Z { 2024-08-20T21:34:36.8271856Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8272394Z "size": 701, 2024-08-20T21:34:36.8272927Z "digest": "sha256:9841596ece9eebfc9229b66dac9085e17c614f6e2697b9b6787e50cef1fffebe" 2024-08-20T21:34:36.8273546Z }, 2024-08-20T21:34:36.8273753Z { 2024-08-20T21:34:36.8274170Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8274722Z "size": 139, 2024-08-20T21:34:36.8275242Z "digest": "sha256:689d9bf507d0f1a37ad9857c17601e3ba4ce677cc7bd6a5ec6778e35b7092eb4" 2024-08-20T21:34:36.8275858Z }, 2024-08-20T21:34:36.8276081Z { 2024-08-20T21:34:36.8276547Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8277105Z "size": 32, 2024-08-20T21:34:36.8277644Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-08-20T21:34:36.8278256Z }, 2024-08-20T21:34:36.8278483Z { 2024-08-20T21:34:36.8278903Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8279436Z "size": 160, 2024-08-20T21:34:36.8279983Z "digest": "sha256:ca5838b6767dabfb99553effdca2c070fb09432fc2f2aa55fd32b97c6a3bb69f" 2024-08-20T21:34:36.8280619Z }, 2024-08-20T21:34:36.8280830Z { 2024-08-20T21:34:36.8281246Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8281795Z "size": 908, 2024-08-20T21:34:36.8282320Z "digest": "sha256:2ab493b780e6673f1c4155bc7c7ec7ba0a9aea98bc440070691ebffab08d0499" 2024-08-20T21:34:36.8282939Z }, 2024-08-20T21:34:36.8283159Z { 2024-08-20T21:34:36.8283560Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8284110Z "size": 701, 2024-08-20T21:34:36.8284639Z "digest": "sha256:9841596ece9eebfc9229b66dac9085e17c614f6e2697b9b6787e50cef1fffebe" 2024-08-20T21:34:36.8285239Z }, 2024-08-20T21:34:36.8285461Z { 2024-08-20T21:34:36.8285976Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8286512Z "size": 134, 2024-08-20T21:34:36.8287043Z "digest": "sha256:aa49767ed126d96b9027bf94b97dfc987878b8042215cc45e015f63b12fd8bf6" 2024-08-20T21:34:36.8287655Z }, 2024-08-20T21:34:36.8287864Z { 2024-08-20T21:34:36.8288283Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8288826Z "size": 32, 2024-08-20T21:34:36.8289345Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-08-20T21:34:36.8289962Z }, 2024-08-20T21:34:36.8290180Z { 2024-08-20T21:34:36.8290581Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8291125Z "size": 159, 2024-08-20T21:34:36.8291674Z "digest": "sha256:9c112f6fb43acecc3e1aa531f9c026a74f4bdcf7fa2b1216b5b479a8625a20ae" 2024-08-20T21:34:36.8292291Z }, 2024-08-20T21:34:36.8292548Z { 2024-08-20T21:34:36.8292962Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8293499Z "size": 1579, 2024-08-20T21:34:36.8294031Z "digest": "sha256:056972111ac5414fb416f2254af5eeb1edcb4bb60868dc842aa8dd49ead09d05" 2024-08-20T21:34:36.8294727Z }, 2024-08-20T21:34:36.8294934Z { 2024-08-20T21:34:36.8295349Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8295896Z "size": 32, 2024-08-20T21:34:36.8296418Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-08-20T21:34:36.8297044Z }, 2024-08-20T21:34:36.8297265Z { 2024-08-20T21:34:36.8297669Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8298216Z "size": 135, 2024-08-20T21:34:36.8298747Z "digest": "sha256:37490d77553aec595ade745dcf8b6ade68b094311ca3795d1ff4fd95e6437b2a" 2024-08-20T21:34:36.8299355Z }, 2024-08-20T21:34:36.8299575Z { 2024-08-20T21:34:36.8299991Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8300527Z "size": 378, 2024-08-20T21:34:36.8301080Z "digest": "sha256:55ffcd4cda011d5b6188ebf1385ffe5b8e9ea2ba4d6e31c9bc74fec2dee269e5" 2024-08-20T21:34:36.8301717Z }, 2024-08-20T21:34:36.8301933Z { 2024-08-20T21:34:36.8302350Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8302897Z "size": 32, 2024-08-20T21:34:36.8303417Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-08-20T21:34:36.8304039Z }, 2024-08-20T21:34:36.8304258Z { 2024-08-20T21:34:36.8304663Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8305206Z "size": 104, 2024-08-20T21:34:36.8305728Z "digest": "sha256:4085287a75081f0f03b8d2a1fb53c521a6f56958ca0ddaa918dc34983216bb80" 2024-08-20T21:34:36.8306389Z }, 2024-08-20T21:34:36.8306613Z { 2024-08-20T21:34:36.8307030Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8307561Z "size": 1841, 2024-08-20T21:34:36.8308088Z "digest": "sha256:45f64d78b2480718ab5e00c5717b2b6a0252e60a5bd6e0e6170b9d3cce4b9499" 2024-08-20T21:34:36.8308698Z }, 2024-08-20T21:34:36.8308905Z { 2024-08-20T21:34:36.8309321Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8309872Z "size": 7529599, 2024-08-20T21:34:36.8310407Z "digest": "sha256:c6ce826bc67b42df29739ac9226b1e66b52da7f2a0d29615983421cb51799601" 2024-08-20T21:34:36.8311025Z }, 2024-08-20T21:34:36.8311247Z { 2024-08-20T21:34:36.8311652Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8312201Z "size": 106, 2024-08-20T21:34:36.8312755Z "digest": "sha256:0c6508f0dedcdac9accff0bd8b9a4bfeab224784b95774be2b7f8176822eac7b" 2024-08-20T21:34:36.8313382Z }, 2024-08-20T21:34:36.8313606Z { 2024-08-20T21:34:36.8314020Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8314554Z "size": 164, 2024-08-20T21:34:36.8315098Z "digest": "sha256:af18f4fae28b398c90fab467ce76ba5e34629be182ed51b3df983cbf0ccad439" 2024-08-20T21:34:36.8315725Z }, 2024-08-20T21:34:36.8315935Z { 2024-08-20T21:34:36.8316352Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8316899Z "size": 7943, 2024-08-20T21:34:36.8317439Z "digest": "sha256:e415411fbadd9d6e13dca54a8decfc6b0b31c219fe6362707f6d7aeba1f932c7" 2024-08-20T21:34:36.8318067Z }, 2024-08-20T21:34:36.8318284Z { 2024-08-20T21:34:36.8318686Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8319233Z "size": 8066, 2024-08-20T21:34:36.8319762Z "digest": "sha256:8801c8ab2ed327d5ae39c98fb179f59118a94c6de66189eb9809bb0147464cc0" 2024-08-20T21:34:36.8320366Z }, 2024-08-20T21:34:36.8320590Z { 2024-08-20T21:34:36.8321012Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8321552Z "size": 301, 2024-08-20T21:34:36.8322077Z "digest": "sha256:33d589b886753bb2d002c6d637579ac3e17d9c4de498c0d1d8a17bd2cb738313" 2024-08-20T21:34:36.8322685Z }, 2024-08-20T21:34:36.8322894Z { 2024-08-20T21:34:36.8323310Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8323924Z "size": 32, 2024-08-20T21:34:36.8324445Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-08-20T21:34:36.8325071Z }, 2024-08-20T21:34:36.8325291Z { 2024-08-20T21:34:36.8325692Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8326333Z "size": 108, 2024-08-20T21:34:36.8326850Z "digest": "sha256:005437c751415d116f09202fbaaf1962a998da11c25613e1a0a2420b02fd419d" 2024-08-20T21:34:36.8327439Z }, 2024-08-20T21:34:36.8327661Z { 2024-08-20T21:34:36.8328082Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8328619Z "size": 54145661, 2024-08-20T21:34:36.8329184Z "digest": "sha256:3ec2dad3f44186ff7db7ad2623c04eda6ef73bb99fc66d1ed44a42ec0b7a9402" 2024-08-20T21:34:36.8329827Z }, 2024-08-20T21:34:36.8330038Z { 2024-08-20T21:34:36.8330451Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:34:36.8330997Z "size": 32, 2024-08-20T21:34:36.8331522Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-08-20T21:34:36.8332144Z } 2024-08-20T21:34:36.8332363Z ] 2024-08-20T21:34:36.8332571Z } 2024-08-20T21:34:36.8441011Z ##[group]Run tag=${ECR_DOCKER_IMAGE##*/} 2024-08-20T21:34:36.8441485Z tag=${ECR_DOCKER_IMAGE##*/} 2024-08-20T21:34:36.8442019Z echo "docker pull ghcr.io/pytorch/ci-image:${tag/:/-}" 2024-08-20T21:34:36.8448996Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:34:36.8449489Z env: 2024-08-20T21:34:36.8449744Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:34:36.8450718Z ECR_DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:34:36.8451743Z ##[endgroup] 2024-08-20T21:34:36.8478015Z docker pull ghcr.io/pytorch/ci-image:pytorch-linux-focal-py3.12-clang10-f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:34:36.8531728Z ##[group]Run pytorch/test-infra/.github/actions/pull-docker-image@main 2024-08-20T21:34:36.8532319Z with: 2024-08-20T21:34:36.8533216Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:34:36.8534382Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-20T21:34:36.8534876Z env: 2024-08-20T21:34:36.8535125Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:34:36.8535449Z ##[endgroup] 2024-08-20T21:34:36.8572387Z ##[group]Run set -x 2024-08-20T21:34:36.8572709Z set -x 2024-08-20T21:34:36.8572974Z set +e 2024-08-20T21:34:36.8573245Z  2024-08-20T21:34:36.8573499Z login() { 2024-08-20T21:34:36.8574142Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2024-08-20T21:34:36.8574869Z } 2024-08-20T21:34:36.8575117Z  2024-08-20T21:34:36.8575405Z retry () { 2024-08-20T21:34:36.8575756Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2024-08-20T21:34:36.8576199Z } 2024-08-20T21:34:36.8576444Z  2024-08-20T21:34:36.8576716Z retry login "${DOCKER_REGISTRY}" 2024-08-20T21:34:36.8577118Z  2024-08-20T21:34:36.8577360Z set -e 2024-08-20T21:34:36.8577798Z # ignore output since only exit code is used for conditional 2024-08-20T21:34:36.8578494Z # only pull docker image if it's not available locally 2024-08-20T21:34:36.8579257Z if ! docker inspect --type=image "${DOCKER_IMAGE}" >/dev/null 2>/dev/null; then 2024-08-20T21:34:36.8579948Z  retry docker pull "${DOCKER_IMAGE}" 2024-08-20T21:34:36.8580358Z fi 2024-08-20T21:34:36.8585836Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:34:36.8586324Z env: 2024-08-20T21:34:36.8586561Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:34:36.8587537Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:34:36.8588815Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-20T21:34:36.8589311Z ##[endgroup] 2024-08-20T21:34:36.8612486Z + set +e 2024-08-20T21:34:36.8613116Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-20T21:34:36.8613739Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-20T21:34:36.8616584Z + aws ecr get-login-password --region us-east-1 2024-08-20T21:34:36.8617917Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-20T21:34:37.3988653Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2024-08-20T21:34:37.3989924Z Configure a credential helper to remove this warning. See 2024-08-20T21:34:37.3991194Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2024-08-20T21:34:37.3992138Z 2024-08-20T21:34:37.3992323Z Login Succeeded 2024-08-20T21:34:37.4002721Z + set -e 2024-08-20T21:34:37.4003938Z + docker inspect --type=image 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:34:37.4130533Z + retry docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:34:37.4132365Z + docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:34:37.6399720Z f6d216893d65c7b8ae43df4daaf247db808378e9: Pulling from pytorch/pytorch-linux-focal-py3.12-clang10 2024-08-20T21:34:37.6411904Z 560c024910be: Pulling fs layer 2024-08-20T21:34:37.6412736Z 9973d24424a5: Pulling fs layer 2024-08-20T21:34:37.6413400Z 31cf2448eae3: Pulling fs layer 2024-08-20T21:34:37.6414042Z 2cbc8585686f: Pulling fs layer 2024-08-20T21:34:37.6414663Z 8c4852dbedba: Pulling fs layer 2024-08-20T21:34:37.6415580Z dc477a9bbcc6: Pulling fs layer 2024-08-20T21:34:37.6416388Z 8c153b899f1d: Pulling fs layer 2024-08-20T21:34:37.6416998Z 0b1c4336fa8b: Pulling fs layer 2024-08-20T21:34:37.6417596Z b012c0f517e6: Pulling fs layer 2024-08-20T21:34:37.6420501Z 4fe699dadca1: Pulling fs layer 2024-08-20T21:34:37.6421172Z d67ce3a9cb4b: Pulling fs layer 2024-08-20T21:34:37.6421792Z 9841596ece9e: Pulling fs layer 2024-08-20T21:34:37.6422526Z 8c4852dbedba: Waiting 2024-08-20T21:34:37.6423107Z e72835db4035: Pulling fs layer 2024-08-20T21:34:37.6423993Z dc477a9bbcc6: Waiting 2024-08-20T21:34:37.6424560Z 4f4fb700ef54: Pulling fs layer 2024-08-20T21:34:37.6425160Z 8c153b899f1d: Waiting 2024-08-20T21:34:37.6425639Z 0b1c4336fa8b: Waiting 2024-08-20T21:34:37.6425948Z 8ef04e16f963: Pulling fs layer 2024-08-20T21:34:37.6426305Z 5e6c0035b872: Pulling fs layer 2024-08-20T21:34:37.6426741Z b012c0f517e6: Waiting 2024-08-20T21:34:37.6427117Z 59b7b891e32d: Pulling fs layer 2024-08-20T21:34:37.6427475Z 5f4e7925ebfb: Pulling fs layer 2024-08-20T21:34:37.6427830Z 12a450969960: Pulling fs layer 2024-08-20T21:34:37.6428190Z 4fe699dadca1: Waiting 2024-08-20T21:34:37.6428485Z 9841596ece9e: Waiting 2024-08-20T21:34:37.6428783Z b8c108e0e581: Pulling fs layer 2024-08-20T21:34:37.6429139Z d3e5b0194b60: Pulling fs layer 2024-08-20T21:34:37.6429478Z e72835db4035: Waiting 2024-08-20T21:34:37.6429760Z 4f4fb700ef54: Waiting 2024-08-20T21:34:37.6430055Z d67ce3a9cb4b: Waiting 2024-08-20T21:34:37.6430351Z 2cbc8585686f: Waiting 2024-08-20T21:34:37.6430690Z 5e6c0035b872: Waiting 2024-08-20T21:34:37.6431002Z 3e4dba8f4feb: Pulling fs layer 2024-08-20T21:34:37.6431344Z 59b7b891e32d: Waiting 2024-08-20T21:34:37.6431718Z 8ef04e16f963: Waiting 2024-08-20T21:34:37.6432031Z 4dd8f5b900be: Pulling fs layer 2024-08-20T21:34:37.6432505Z 12a450969960: Waiting 2024-08-20T21:34:37.6432837Z 5f4e7925ebfb: Waiting 2024-08-20T21:34:37.6433130Z b8c108e0e581: Waiting 2024-08-20T21:34:37.6433440Z f0966db14aac: Pulling fs layer 2024-08-20T21:34:37.6433770Z 3e4dba8f4feb: Waiting 2024-08-20T21:34:37.6434095Z 3ca232406eff: Pulling fs layer 2024-08-20T21:34:37.6434648Z f0966db14aac: Waiting 2024-08-20T21:34:37.6434929Z 4dd8f5b900be: Waiting 2024-08-20T21:34:37.6435318Z 93684c5d1426: Pulling fs layer 2024-08-20T21:34:37.6435706Z d3e5b0194b60: Waiting 2024-08-20T21:34:37.6435982Z 3ca232406eff: Waiting 2024-08-20T21:34:37.6436289Z 891f1a671536: Pulling fs layer 2024-08-20T21:34:37.6436643Z 2d1cde488756: Pulling fs layer 2024-08-20T21:34:37.6437043Z 1b670936c6e7: Pulling fs layer 2024-08-20T21:34:37.6437432Z 93684c5d1426: Waiting 2024-08-20T21:34:37.6437810Z c89135369bac: Pulling fs layer 2024-08-20T21:34:37.6438247Z e32451be40c1: Pulling fs layer 2024-08-20T21:34:37.6438677Z 7647e82c5c3f: Pulling fs layer 2024-08-20T21:34:37.6439031Z 2d334a3c11fd: Pulling fs layer 2024-08-20T21:34:37.6439426Z e9ea16c3c5d4: Pulling fs layer 2024-08-20T21:34:37.6439764Z 891f1a671536: Waiting 2024-08-20T21:34:37.6440054Z e32451be40c1: Waiting 2024-08-20T21:34:37.6440334Z 2d1cde488756: Waiting 2024-08-20T21:34:37.6440648Z 66a5808f4ad5: Pulling fs layer 2024-08-20T21:34:37.6440985Z 1b670936c6e7: Waiting 2024-08-20T21:34:37.6441270Z 7647e82c5c3f: Waiting 2024-08-20T21:34:37.6441631Z b92de615fef3: Pulling fs layer 2024-08-20T21:34:37.6442126Z 2d334a3c11fd: Waiting 2024-08-20T21:34:37.6442410Z e9ea16c3c5d4: Waiting 2024-08-20T21:34:37.6442702Z c89135369bac: Waiting 2024-08-20T21:34:37.6443006Z 56dd63029e04: Pulling fs layer 2024-08-20T21:34:37.6443331Z 66a5808f4ad5: Waiting 2024-08-20T21:34:37.6443639Z 0e19dd6ac611: Pulling fs layer 2024-08-20T21:34:37.6443979Z b92de615fef3: Waiting 2024-08-20T21:34:37.6444261Z 56dd63029e04: Waiting 2024-08-20T21:34:37.6444572Z deffd0debe5e: Pulling fs layer 2024-08-20T21:34:37.6444933Z 823a4c61790a: Pulling fs layer 2024-08-20T21:34:37.6445270Z 52c09d8bb114: Pulling fs layer 2024-08-20T21:34:37.6445623Z fc113046ff5b: Pulling fs layer 2024-08-20T21:34:37.6446060Z 58fcfcf8962b: Pulling fs layer 2024-08-20T21:34:37.6446408Z a6bbbb7aff78: Pulling fs layer 2024-08-20T21:34:37.6447142Z 58c762741f83: Pulling fs layer 2024-08-20T21:34:37.6447484Z 0e19dd6ac611: Waiting 2024-08-20T21:34:37.6447794Z 36d6dbd6c3c6: Pulling fs layer 2024-08-20T21:34:37.6448282Z 58fcfcf8962b: Waiting 2024-08-20T21:34:37.6448577Z 52c09d8bb114: Waiting 2024-08-20T21:34:37.6448879Z f6e31afd70a7: Pulling fs layer 2024-08-20T21:34:37.6449304Z fc113046ff5b: Waiting 2024-08-20T21:34:37.6449613Z 4076a363f04c: Pulling fs layer 2024-08-20T21:34:37.6449943Z deffd0debe5e: Waiting 2024-08-20T21:34:37.6450242Z 823a4c61790a: Waiting 2024-08-20T21:34:37.6450550Z da1b55b13c80: Pulling fs layer 2024-08-20T21:34:37.6450894Z 852b7bd929d0: Pulling fs layer 2024-08-20T21:34:37.6451233Z f6e31afd70a7: Waiting 2024-08-20T21:34:37.6451523Z 58c762741f83: Waiting 2024-08-20T21:34:37.6451915Z a6bbbb7aff78: Waiting 2024-08-20T21:34:37.6452226Z 43c374b7d87c: Pulling fs layer 2024-08-20T21:34:37.6452578Z 2ae4d1692345: Pulling fs layer 2024-08-20T21:34:37.6452920Z 02fd8160fb61: Pulling fs layer 2024-08-20T21:34:37.6453262Z 36d6dbd6c3c6: Waiting 2024-08-20T21:34:37.6453561Z 4076a363f04c: Waiting 2024-08-20T21:34:37.6453841Z 43c374b7d87c: Waiting 2024-08-20T21:34:37.6454151Z 1860f83d1894: Pulling fs layer 2024-08-20T21:34:37.6454488Z 2ae4d1692345: Waiting 2024-08-20T21:34:37.6454764Z 02fd8160fb61: Waiting 2024-08-20T21:34:37.6455073Z 1beebf68aabb: Pulling fs layer 2024-08-20T21:34:37.6455417Z da1b55b13c80: Waiting 2024-08-20T21:34:37.6455700Z 1beebf68aabb: Waiting 2024-08-20T21:34:37.6456013Z e0cbebc1b4e2: Pulling fs layer 2024-08-20T21:34:37.6456373Z 3e8f996f54cd: Pulling fs layer 2024-08-20T21:34:37.6456715Z 689d9bf507d0: Pulling fs layer 2024-08-20T21:34:37.6457053Z 852b7bd929d0: Waiting 2024-08-20T21:34:37.6457359Z ca5838b6767d: Pulling fs layer 2024-08-20T21:34:37.6457684Z 3e8f996f54cd: Waiting 2024-08-20T21:34:37.6457991Z 2ab493b780e6: Pulling fs layer 2024-08-20T21:34:37.6458320Z ca5838b6767d: Waiting 2024-08-20T21:34:37.6458686Z aa49767ed126: Pulling fs layer 2024-08-20T21:34:37.6459028Z e0cbebc1b4e2: Waiting 2024-08-20T21:34:37.6459379Z 9c112f6fb43a: Pulling fs layer 2024-08-20T21:34:37.6459757Z 689d9bf507d0: Waiting 2024-08-20T21:34:37.6460340Z 056972111ac5: Pulling fs layer 2024-08-20T21:34:37.6460680Z 37490d77553a: Pulling fs layer 2024-08-20T21:34:37.6461019Z 9c112f6fb43a: Waiting 2024-08-20T21:34:37.6461333Z 55ffcd4cda01: Pulling fs layer 2024-08-20T21:34:37.6461660Z aa49767ed126: Waiting 2024-08-20T21:34:37.6461966Z 4085287a7508: Pulling fs layer 2024-08-20T21:34:37.6462298Z 37490d77553a: Waiting 2024-08-20T21:34:37.6462589Z 45f64d78b248: Pulling fs layer 2024-08-20T21:34:37.6462943Z c6ce826bc67b: Pulling fs layer 2024-08-20T21:34:37.6463301Z 0c6508f0dedc: Pulling fs layer 2024-08-20T21:34:37.6463662Z 55ffcd4cda01: Waiting 2024-08-20T21:34:37.6464134Z af18f4fae28b: Pulling fs layer 2024-08-20T21:34:37.6464610Z e415411fbadd: Pulling fs layer 2024-08-20T21:34:37.6464935Z c6ce826bc67b: Waiting 2024-08-20T21:34:37.6465301Z 8801c8ab2ed3: Pulling fs layer 2024-08-20T21:34:37.6465770Z 0c6508f0dedc: Waiting 2024-08-20T21:34:37.6466365Z 33d589b88675: Pulling fs layer 2024-08-20T21:34:37.6466761Z 005437c75141: Pulling fs layer 2024-08-20T21:34:37.6467099Z 4085287a7508: Waiting 2024-08-20T21:34:37.6467392Z 3ec2dad3f441: Pulling fs layer 2024-08-20T21:34:37.6467727Z 33d589b88675: Waiting 2024-08-20T21:34:37.6468019Z 45f64d78b248: Waiting 2024-08-20T21:34:37.6468300Z af18f4fae28b: Waiting 2024-08-20T21:34:37.6468593Z e415411fbadd: Waiting 2024-08-20T21:34:37.6468883Z 8801c8ab2ed3: Waiting 2024-08-20T21:34:37.6469158Z 005437c75141: Waiting 2024-08-20T21:34:37.6469446Z 3ec2dad3f441: Waiting 2024-08-20T21:34:37.6469736Z 1860f83d1894: Waiting 2024-08-20T21:34:37.7157673Z 9973d24424a5: Verifying Checksum 2024-08-20T21:34:37.7158204Z 9973d24424a5: Download complete 2024-08-20T21:34:37.9830138Z 560c024910be: Verifying Checksum 2024-08-20T21:34:37.9830684Z 560c024910be: Download complete 2024-08-20T21:34:38.0685401Z dc477a9bbcc6: Verifying Checksum 2024-08-20T21:34:38.0686161Z dc477a9bbcc6: Download complete 2024-08-20T21:34:38.1393253Z 8c153b899f1d: Verifying Checksum 2024-08-20T21:34:38.1394287Z 8c153b899f1d: Download complete 2024-08-20T21:34:38.2085319Z 0b1c4336fa8b: Verifying Checksum 2024-08-20T21:34:38.2086013Z 0b1c4336fa8b: Download complete 2024-08-20T21:34:38.2836828Z b012c0f517e6: Verifying Checksum 2024-08-20T21:34:38.2837491Z b012c0f517e6: Download complete 2024-08-20T21:34:38.3652762Z 4fe699dadca1: Verifying Checksum 2024-08-20T21:34:38.3653537Z 4fe699dadca1: Download complete 2024-08-20T21:34:38.4435684Z d67ce3a9cb4b: Verifying Checksum 2024-08-20T21:34:38.4436529Z d67ce3a9cb4b: Download complete 2024-08-20T21:34:38.5014775Z 9841596ece9e: Verifying Checksum 2024-08-20T21:34:38.5017035Z 9841596ece9e: Download complete 2024-08-20T21:34:38.6546848Z 8c4852dbedba: Verifying Checksum 2024-08-20T21:34:38.6547441Z 8c4852dbedba: Download complete 2024-08-20T21:34:38.6633798Z 4f4fb700ef54: Verifying Checksum 2024-08-20T21:34:38.6634287Z 4f4fb700ef54: Download complete 2024-08-20T21:34:38.7288050Z 8ef04e16f963: Verifying Checksum 2024-08-20T21:34:38.7937854Z 8ef04e16f963: Download complete 2024-08-20T21:34:38.7938452Z 5e6c0035b872: Download complete 2024-08-20T21:34:38.8630219Z 560c024910be: Pull complete 2024-08-20T21:34:38.8656892Z 59b7b891e32d: Verifying Checksum 2024-08-20T21:34:38.8657347Z 59b7b891e32d: Download complete 2024-08-20T21:34:38.9067126Z 9973d24424a5: Pull complete 2024-08-20T21:34:38.9537229Z 5f4e7925ebfb: Verifying Checksum 2024-08-20T21:34:38.9537795Z 5f4e7925ebfb: Download complete 2024-08-20T21:34:39.0304456Z 12a450969960: Verifying Checksum 2024-08-20T21:34:39.0304924Z 12a450969960: Download complete 2024-08-20T21:34:39.0911561Z b8c108e0e581: Verifying Checksum 2024-08-20T21:34:39.0912060Z b8c108e0e581: Download complete 2024-08-20T21:34:39.1575446Z d3e5b0194b60: Download complete 2024-08-20T21:34:39.2197227Z 3e4dba8f4feb: Verifying Checksum 2024-08-20T21:34:39.2198060Z 3e4dba8f4feb: Download complete 2024-08-20T21:34:39.3084439Z 4dd8f5b900be: Verifying Checksum 2024-08-20T21:34:39.3084909Z 4dd8f5b900be: Download complete 2024-08-20T21:34:40.5679158Z f0966db14aac: Verifying Checksum 2024-08-20T21:34:40.5679713Z f0966db14aac: Download complete 2024-08-20T21:34:40.6339901Z 3ca232406eff: Download complete 2024-08-20T21:34:40.7086369Z 93684c5d1426: Verifying Checksum 2024-08-20T21:34:40.7086948Z 93684c5d1426: Download complete 2024-08-20T21:34:40.7787053Z 891f1a671536: Verifying Checksum 2024-08-20T21:34:40.7787502Z 891f1a671536: Download complete 2024-08-20T21:34:40.8305240Z 31cf2448eae3: Verifying Checksum 2024-08-20T21:34:40.8305820Z 31cf2448eae3: Download complete 2024-08-20T21:34:40.8491455Z 2d1cde488756: Verifying Checksum 2024-08-20T21:34:40.8491913Z 2d1cde488756: Download complete 2024-08-20T21:34:40.8945466Z 1b670936c6e7: Verifying Checksum 2024-08-20T21:34:40.8946039Z 1b670936c6e7: Download complete 2024-08-20T21:34:40.9797587Z e32451be40c1: Download complete 2024-08-20T21:34:41.0772035Z 7647e82c5c3f: Verifying Checksum 2024-08-20T21:34:41.0772855Z 7647e82c5c3f: Download complete 2024-08-20T21:34:41.1706395Z 2d334a3c11fd: Verifying Checksum 2024-08-20T21:34:41.1707357Z 2d334a3c11fd: Download complete 2024-08-20T21:34:41.2335675Z e9ea16c3c5d4: Verifying Checksum 2024-08-20T21:34:41.2336657Z e9ea16c3c5d4: Download complete 2024-08-20T21:34:41.2988360Z 66a5808f4ad5: Verifying Checksum 2024-08-20T21:34:41.2989173Z 66a5808f4ad5: Download complete 2024-08-20T21:34:41.3578651Z b92de615fef3: Verifying Checksum 2024-08-20T21:34:41.3579197Z b92de615fef3: Download complete 2024-08-20T21:34:41.6000588Z 56dd63029e04: Download complete 2024-08-20T21:34:41.6716183Z 0e19dd6ac611: Verifying Checksum 2024-08-20T21:34:41.6716710Z 0e19dd6ac611: Download complete 2024-08-20T21:34:41.7476871Z deffd0debe5e: Verifying Checksum 2024-08-20T21:34:41.7477672Z deffd0debe5e: Download complete 2024-08-20T21:34:41.8280488Z 823a4c61790a: Download complete 2024-08-20T21:34:41.8906135Z 52c09d8bb114: Verifying Checksum 2024-08-20T21:34:41.9631980Z 52c09d8bb114: Download complete 2024-08-20T21:34:41.9632440Z fc113046ff5b: Download complete 2024-08-20T21:34:42.4736898Z 58fcfcf8962b: Verifying Checksum 2024-08-20T21:34:42.4737931Z 58fcfcf8962b: Download complete 2024-08-20T21:34:42.5419782Z a6bbbb7aff78: Verifying Checksum 2024-08-20T21:34:42.5420580Z a6bbbb7aff78: Download complete 2024-08-20T21:34:42.6159123Z 58c762741f83: Verifying Checksum 2024-08-20T21:34:42.6159788Z 58c762741f83: Download complete 2024-08-20T21:34:42.6920493Z 36d6dbd6c3c6: Verifying Checksum 2024-08-20T21:34:42.6921145Z 36d6dbd6c3c6: Download complete 2024-08-20T21:34:42.7581273Z f6e31afd70a7: Verifying Checksum 2024-08-20T21:34:43.0112299Z 4076a363f04c: Verifying Checksum 2024-08-20T21:34:43.0112980Z 4076a363f04c: Download complete 2024-08-20T21:34:43.0855384Z da1b55b13c80: Verifying Checksum 2024-08-20T21:34:43.0856049Z da1b55b13c80: Download complete 2024-08-20T21:34:43.1724132Z 852b7bd929d0: Verifying Checksum 2024-08-20T21:34:43.1724844Z 852b7bd929d0: Download complete 2024-08-20T21:34:43.2572722Z 43c374b7d87c: Verifying Checksum 2024-08-20T21:34:43.2573276Z 43c374b7d87c: Download complete 2024-08-20T21:34:43.3288856Z 2ae4d1692345: Verifying Checksum 2024-08-20T21:34:43.3289608Z 2ae4d1692345: Download complete 2024-08-20T21:34:43.4032512Z 02fd8160fb61: Download complete 2024-08-20T21:34:43.4777516Z 1860f83d1894: Download complete 2024-08-20T21:34:43.5559546Z 1beebf68aabb: Verifying Checksum 2024-08-20T21:34:43.5560180Z 1beebf68aabb: Download complete 2024-08-20T21:34:43.6210080Z e0cbebc1b4e2: Download complete 2024-08-20T21:34:43.6960602Z 3e8f996f54cd: Verifying Checksum 2024-08-20T21:34:43.6961270Z 3e8f996f54cd: Download complete 2024-08-20T21:34:43.7578892Z 689d9bf507d0: Verifying Checksum 2024-08-20T21:34:43.7579709Z 689d9bf507d0: Download complete 2024-08-20T21:34:43.8333326Z ca5838b6767d: Verifying Checksum 2024-08-20T21:34:43.8333940Z ca5838b6767d: Download complete 2024-08-20T21:34:43.9004883Z 2ab493b780e6: Verifying Checksum 2024-08-20T21:34:43.9005612Z 2ab493b780e6: Download complete 2024-08-20T21:34:43.9795672Z aa49767ed126: Download complete 2024-08-20T21:34:44.0480781Z 9c112f6fb43a: Verifying Checksum 2024-08-20T21:34:44.0481733Z 9c112f6fb43a: Download complete 2024-08-20T21:34:44.1209778Z 056972111ac5: Verifying Checksum 2024-08-20T21:34:44.1210493Z 056972111ac5: Download complete 2024-08-20T21:34:44.1870889Z 37490d77553a: Verifying Checksum 2024-08-20T21:34:44.1871645Z 37490d77553a: Download complete 2024-08-20T21:34:44.2651917Z 55ffcd4cda01: Verifying Checksum 2024-08-20T21:34:44.2652744Z 55ffcd4cda01: Download complete 2024-08-20T21:34:44.3416709Z 4085287a7508: Download complete 2024-08-20T21:34:44.4537250Z 45f64d78b248: Verifying Checksum 2024-08-20T21:34:44.4538013Z 45f64d78b248: Download complete 2024-08-20T21:34:44.5718217Z c6ce826bc67b: Download complete 2024-08-20T21:34:44.6502047Z 0c6508f0dedc: Verifying Checksum 2024-08-20T21:34:44.6502797Z 0c6508f0dedc: Download complete 2024-08-20T21:34:44.7315271Z af18f4fae28b: Verifying Checksum 2024-08-20T21:34:44.7315941Z af18f4fae28b: Download complete 2024-08-20T21:34:44.8113700Z e415411fbadd: Download complete 2024-08-20T21:34:44.8852953Z 8801c8ab2ed3: Download complete 2024-08-20T21:34:44.9498210Z 33d589b88675: Download complete 2024-08-20T21:34:45.0240361Z 005437c75141: Download complete 2024-08-20T21:34:45.6198460Z 3ec2dad3f441: Verifying Checksum 2024-08-20T21:34:45.6199068Z 3ec2dad3f441: Download complete 2024-08-20T21:34:46.8432508Z c89135369bac: Verifying Checksum 2024-08-20T21:34:46.8433209Z c89135369bac: Download complete 2024-08-20T21:34:49.4133425Z 31cf2448eae3: Pull complete 2024-08-20T21:34:49.4828752Z 2cbc8585686f: Pull complete 2024-08-20T21:34:51.1059739Z 8c4852dbedba: Pull complete 2024-08-20T21:34:51.1993202Z dc477a9bbcc6: Pull complete 2024-08-20T21:34:51.3067008Z 8c153b899f1d: Pull complete 2024-08-20T21:34:51.4185603Z 0b1c4336fa8b: Pull complete 2024-08-20T21:34:51.5281518Z b012c0f517e6: Pull complete 2024-08-20T21:34:51.6164169Z 4fe699dadca1: Pull complete 2024-08-20T21:34:51.7128964Z d67ce3a9cb4b: Pull complete 2024-08-20T21:34:51.7764348Z 9841596ece9e: Pull complete 2024-08-20T21:35:04.6403520Z e72835db4035: Verifying Checksum 2024-08-20T21:35:04.6404311Z e72835db4035: Download complete 2024-08-20T21:35:48.1356210Z e72835db4035: Pull complete 2024-08-20T21:35:48.1712968Z 4f4fb700ef54: Pull complete 2024-08-20T21:35:48.2050255Z 8ef04e16f963: Pull complete 2024-08-20T21:35:48.2299045Z 5e6c0035b872: Pull complete 2024-08-20T21:35:48.2541819Z 59b7b891e32d: Pull complete 2024-08-20T21:35:48.3147611Z 5f4e7925ebfb: Pull complete 2024-08-20T21:35:48.3372314Z 12a450969960: Pull complete 2024-08-20T21:35:48.3547585Z b8c108e0e581: Pull complete 2024-08-20T21:35:48.3757356Z d3e5b0194b60: Pull complete 2024-08-20T21:35:48.4240719Z 3e4dba8f4feb: Pull complete 2024-08-20T21:35:48.4473232Z 4dd8f5b900be: Pull complete 2024-08-20T21:35:51.2634698Z f0966db14aac: Pull complete 2024-08-20T21:35:51.4371952Z 3ca232406eff: Pull complete 2024-08-20T21:35:51.5738144Z 93684c5d1426: Pull complete 2024-08-20T21:35:51.7953123Z 891f1a671536: Pull complete 2024-08-20T21:35:52.0130762Z 2d1cde488756: Pull complete 2024-08-20T21:35:52.2021816Z 1b670936c6e7: Pull complete 2024-08-20T21:35:59.7982394Z c89135369bac: Pull complete 2024-08-20T21:35:59.8350860Z e32451be40c1: Pull complete 2024-08-20T21:36:00.0558082Z 7647e82c5c3f: Pull complete 2024-08-20T21:36:00.2680052Z 2d334a3c11fd: Pull complete 2024-08-20T21:36:00.4648061Z e9ea16c3c5d4: Pull complete 2024-08-20T21:36:00.8904644Z 66a5808f4ad5: Pull complete 2024-08-20T21:36:01.0898359Z b92de615fef3: Pull complete 2024-08-20T21:36:01.3171467Z 56dd63029e04: Pull complete 2024-08-20T21:36:01.5306969Z 0e19dd6ac611: Pull complete 2024-08-20T21:36:01.8762431Z deffd0debe5e: Pull complete 2024-08-20T21:36:02.0621477Z 823a4c61790a: Pull complete 2024-08-20T21:36:02.4474895Z 52c09d8bb114: Pull complete 2024-08-20T21:36:02.6419558Z fc113046ff5b: Pull complete 2024-08-20T21:36:04.4632919Z 58fcfcf8962b: Pull complete 2024-08-20T21:36:04.6881155Z a6bbbb7aff78: Pull complete 2024-08-20T21:36:04.9118774Z 58c762741f83: Pull complete 2024-08-20T21:36:05.3405760Z 36d6dbd6c3c6: Pull complete 2024-08-20T21:36:05.5490098Z f6e31afd70a7: Pull complete 2024-08-20T21:36:05.8834685Z 4076a363f04c: Pull complete 2024-08-20T21:36:05.8988848Z da1b55b13c80: Pull complete 2024-08-20T21:36:05.9162774Z 852b7bd929d0: Pull complete 2024-08-20T21:36:05.9463537Z 43c374b7d87c: Pull complete 2024-08-20T21:36:05.9650566Z 2ae4d1692345: Pull complete 2024-08-20T21:36:05.9991054Z 02fd8160fb61: Pull complete 2024-08-20T21:36:06.0157887Z 1860f83d1894: Pull complete 2024-08-20T21:36:06.0466172Z 1beebf68aabb: Pull complete 2024-08-20T21:36:06.0811910Z e0cbebc1b4e2: Pull complete 2024-08-20T21:36:06.0986905Z 3e8f996f54cd: Pull complete 2024-08-20T21:36:06.1345948Z 689d9bf507d0: Pull complete 2024-08-20T21:36:06.1665032Z ca5838b6767d: Pull complete 2024-08-20T21:36:06.1823670Z 2ab493b780e6: Pull complete 2024-08-20T21:36:06.2134063Z aa49767ed126: Pull complete 2024-08-20T21:36:06.2490931Z 9c112f6fb43a: Pull complete 2024-08-20T21:36:06.2701439Z 056972111ac5: Pull complete 2024-08-20T21:36:06.3077668Z 37490d77553a: Pull complete 2024-08-20T21:36:06.3236797Z 55ffcd4cda01: Pull complete 2024-08-20T21:36:06.3593975Z 4085287a7508: Pull complete 2024-08-20T21:36:06.3762400Z 45f64d78b248: Pull complete 2024-08-20T21:36:06.5193736Z c6ce826bc67b: Pull complete 2024-08-20T21:36:06.5336056Z 0c6508f0dedc: Pull complete 2024-08-20T21:36:06.5533569Z af18f4fae28b: Pull complete 2024-08-20T21:36:06.5687674Z e415411fbadd: Pull complete 2024-08-20T21:36:06.5946016Z 8801c8ab2ed3: Pull complete 2024-08-20T21:36:06.6130874Z 33d589b88675: Pull complete 2024-08-20T21:36:06.6429101Z 005437c75141: Pull complete 2024-08-20T21:36:08.1537239Z 3ec2dad3f441: Pull complete 2024-08-20T21:36:08.1849215Z Digest: sha256:53bea9665c81f7bdf502e7eb907e80d01ec46372a842842380b5284cbc52773e 2024-08-20T21:36:08.1880068Z Status: Downloaded newer image for 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:36:08.1920344Z 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:36:08.1970437Z ##[group]Run echo "IN_ARC_RUNNER=$([ -f /.inarc ] && echo true || echo false)" >> "$GITHUB_OUTPUT" 2024-08-20T21:36:08.1971399Z echo "IN_ARC_RUNNER=$([ -f /.inarc ] && echo true || echo false)" >> "$GITHUB_OUTPUT" 2024-08-20T21:36:08.1979549Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:36:08.1980028Z env: 2024-08-20T21:36:08.1980289Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:08.1980614Z ##[endgroup] 2024-08-20T21:36:08.2051707Z ##[group]Run python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 2024-08-20T21:36:08.2052591Z python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 2024-08-20T21:36:08.2053298Z python3 -m tools.stats.monitor > usage_log.txt 2>&1 & 2024-08-20T21:36:08.2053953Z echo "monitor-script-pid=${!}" >> "${GITHUB_OUTPUT}" 2024-08-20T21:36:08.2059681Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:36:08.2060189Z env: 2024-08-20T21:36:08.2060458Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:08.2060783Z ##[endgroup] 2024-08-20T21:36:08.6978169Z Defaulting to user installation because normal site-packages is not writeable 2024-08-20T21:36:08.7258367Z Requirement already satisfied: psutil==5.9.1 in /home/ec2-user/.local/lib/python3.9/site-packages (5.9.1) 2024-08-20T21:36:08.7264212Z Requirement already satisfied: nvidia-ml-py==11.525.84 in /home/ec2-user/.local/lib/python3.9/site-packages (11.525.84) 2024-08-20T21:36:08.9048730Z Prepare all required actions 2024-08-20T21:36:08.9049254Z Getting action download info 2024-08-20T21:36:09.0364361Z Download action repository 'seemethere/download-artifact-s3@v4' (SHA:1da556a7aa0a088e3153970611f6c432d58e80e6) 2024-08-20T21:36:09.2919465Z Download action repository 'actions/download-artifact@v3' (SHA:9bc31d5ccc31df68ecc42ccf4149144866c47d8a) 2024-08-20T21:36:09.4286223Z ##[group]Run ./.github/actions/download-build-artifacts 2024-08-20T21:36:09.4286706Z with: 2024-08-20T21:36:09.4286987Z name: linux-focal-py3.12-clang10 2024-08-20T21:36:09.4287363Z s3-bucket: gha-artifacts 2024-08-20T21:36:09.4287808Z env: 2024-08-20T21:36:09.4288056Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:09.4288368Z ##[endgroup] 2024-08-20T21:36:09.4323136Z ##[group]Run seemethere/download-artifact-s3@v4 2024-08-20T21:36:09.4323564Z with: 2024-08-20T21:36:09.4323840Z name: linux-focal-py3.12-clang10 2024-08-20T21:36:09.4324227Z s3-bucket: gha-artifacts 2024-08-20T21:36:09.4324596Z region: us-east-1 2024-08-20T21:36:09.4324863Z env: 2024-08-20T21:36:09.4325112Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:09.4325440Z ##[endgroup] 2024-08-20T21:36:09.9045143Z (node:363203) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2024-08-20T21:36:09.9046193Z 2024-08-20T21:36:09.9046453Z Please migrate your code to use AWS SDK for JavaScript (v3). 2024-08-20T21:36:09.9047882Z For more information, check the migration guide at https://a.co/7PzMCcy 2024-08-20T21:36:09.9048774Z (Use `node --trace-warnings ...` to show where the warning was created) 2024-08-20T21:36:09.9739860Z Found 1 objects with prefix pytorch/pytorch/10479309237/linux-focal-py3.12-clang10/ 2024-08-20T21:36:09.9740947Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip 2024-08-20T21:36:21.9222454Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip 2024-08-20T21:36:21.9229252Z Artifact download has finished successfully 2024-08-20T21:36:21.9401292Z ##[group]Run unzip -o artifacts.zip 2024-08-20T21:36:21.9401725Z unzip -o artifacts.zip 2024-08-20T21:36:21.9408408Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:36:21.9408896Z env: 2024-08-20T21:36:21.9409164Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:21.9409496Z ##[endgroup] 2024-08-20T21:36:21.9490264Z Archive: artifacts.zip 2024-08-20T21:36:21.9490866Z creating: dist/ 2024-08-20T21:36:22.8815250Z inflating: dist/torch-2.5.0a0+git40ec5f6-cp312-cp312-linux_x86_64.whl 2024-08-20T21:36:22.8815952Z creating: build/custom_test_artifacts/ 2024-08-20T21:36:22.8816569Z creating: build/custom_test_artifacts/custom-op-build/ 2024-08-20T21:36:22.8817286Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/ 2024-08-20T21:36:22.8818184Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/CMakeOutput.log 2024-08-20T21:36:22.8819129Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/ 2024-08-20T21:36:22.8820124Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CMakeSystem.cmake 2024-08-20T21:36:22.8821155Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CompilerIdC/ 2024-08-20T21:36:22.8822171Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CompilerIdC/tmp/ 2024-08-20T21:36:22.8823348Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CompilerIdC/CMakeCCompilerId.c 2024-08-20T21:36:22.8824523Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CompilerIdC/a.out 2024-08-20T21:36:22.8825563Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CompilerIdCXX/ 2024-08-20T21:36:22.8826607Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CompilerIdCXX/tmp/ 2024-08-20T21:36:22.8828135Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CompilerIdCXX/CMakeCXXCompilerId.cpp 2024-08-20T21:36:22.8829365Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CompilerIdCXX/a.out 2024-08-20T21:36:22.8830566Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CMakeDetermineCompilerABI_C.bin 2024-08-20T21:36:22.8831794Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CMakeCCompiler.cmake 2024-08-20T21:36:22.8833031Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CMakeDetermineCompilerABI_CXX.bin 2024-08-20T21:36:22.8834250Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CMakeCXXCompiler.cmake 2024-08-20T21:36:22.8835433Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/CMakeTmp/ 2024-08-20T21:36:22.8836376Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/cmake.check_cache 2024-08-20T21:36:22.8837332Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/ 2024-08-20T21:36:22.8853938Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/depend.make 2024-08-20T21:36:22.8855125Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/link.txt 2024-08-20T21:36:22.8856393Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/cmake_clean.cmake 2024-08-20T21:36:22.8857536Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/build.make 2024-08-20T21:36:22.8858686Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/DependInfo.cmake 2024-08-20T21:36:22.8859835Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/flags.make 2024-08-20T21:36:22.8860961Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/progress.make 2024-08-20T21:36:22.8905842Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/CXX.includecache 2024-08-20T21:36:22.8923786Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/depend.internal 2024-08-20T21:36:22.9015644Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/op.cpp.o 2024-08-20T21:36:22.9016695Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/ 2024-08-20T21:36:22.9040201Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/depend.make 2024-08-20T21:36:22.9041373Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/link.txt 2024-08-20T21:36:22.9042573Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/cmake_clean.cmake 2024-08-20T21:36:22.9043801Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/build.make 2024-08-20T21:36:22.9044994Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/DependInfo.cmake 2024-08-20T21:36:22.9046273Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/flags.make 2024-08-20T21:36:22.9047637Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/progress.make 2024-08-20T21:36:22.9092349Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/CXX.includecache 2024-08-20T21:36:22.9109444Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/depend.internal 2024-08-20T21:36:22.9155927Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/test_custom_ops.cpp.o 2024-08-20T21:36:22.9157211Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/CMakeDirectoryInformation.cmake 2024-08-20T21:36:22.9158341Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/TargetDirectories.txt 2024-08-20T21:36:22.9159645Z extracting: build/custom_test_artifacts/custom-op-build/CMakeFiles/progress.marks 2024-08-20T21:36:22.9160615Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/Makefile2 2024-08-20T21:36:22.9161556Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/Makefile.cmake 2024-08-20T21:36:22.9162454Z inflating: build/custom_test_artifacts/custom-op-build/CMakeCache.txt 2024-08-20T21:36:22.9163260Z inflating: build/custom_test_artifacts/custom-op-build/Makefile 2024-08-20T21:36:22.9164088Z inflating: build/custom_test_artifacts/custom-op-build/cmake_install.cmake 2024-08-20T21:36:22.9247034Z inflating: build/custom_test_artifacts/custom-op-build/libcustom_ops.so 2024-08-20T21:36:22.9285474Z inflating: build/custom_test_artifacts/custom-op-build/test_custom_ops 2024-08-20T21:36:22.9286300Z creating: build/custom_test_artifacts/jit-hook-build/ 2024-08-20T21:36:22.9287001Z creating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/ 2024-08-20T21:36:22.9287959Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/CMakeOutput.log 2024-08-20T21:36:22.9288886Z creating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/ 2024-08-20T21:36:22.9289852Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CMakeSystem.cmake 2024-08-20T21:36:22.9290864Z creating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CompilerIdC/ 2024-08-20T21:36:22.9291872Z creating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CompilerIdC/tmp/ 2024-08-20T21:36:22.9293020Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CompilerIdC/CMakeCCompilerId.c 2024-08-20T21:36:22.9294178Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CompilerIdC/a.out 2024-08-20T21:36:22.9295205Z creating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CompilerIdCXX/ 2024-08-20T21:36:22.9296235Z creating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CompilerIdCXX/tmp/ 2024-08-20T21:36:22.9297455Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CompilerIdCXX/CMakeCXXCompilerId.cpp 2024-08-20T21:36:22.9298676Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CompilerIdCXX/a.out 2024-08-20T21:36:22.9299853Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CMakeDetermineCompilerABI_C.bin 2024-08-20T21:36:22.9301042Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CMakeCCompiler.cmake 2024-08-20T21:36:22.9302266Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CMakeDetermineCompilerABI_CXX.bin 2024-08-20T21:36:22.9303497Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CMakeCXXCompiler.cmake 2024-08-20T21:36:22.9304484Z creating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/CMakeTmp/ 2024-08-20T21:36:22.9305414Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/cmake.check_cache 2024-08-20T21:36:22.9306406Z creating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/ 2024-08-20T21:36:22.9325493Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/depend.make 2024-08-20T21:36:22.9326708Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/link.txt 2024-08-20T21:36:22.9327877Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/cmake_clean.cmake 2024-08-20T21:36:22.9329055Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/build.make 2024-08-20T21:36:22.9330217Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/DependInfo.cmake 2024-08-20T21:36:22.9331397Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/flags.make 2024-08-20T21:36:22.9332551Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/progress.make 2024-08-20T21:36:22.9377980Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/CXX.includecache 2024-08-20T21:36:22.9394870Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/depend.internal 2024-08-20T21:36:22.9425845Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/test_jit_hooks.cpp.o 2024-08-20T21:36:22.9427094Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/CMakeDirectoryInformation.cmake 2024-08-20T21:36:22.9428215Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/TargetDirectories.txt 2024-08-20T21:36:22.9429235Z extracting: build/custom_test_artifacts/jit-hook-build/CMakeFiles/progress.marks 2024-08-20T21:36:22.9430315Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/Makefile2 2024-08-20T21:36:22.9431241Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/Makefile.cmake 2024-08-20T21:36:22.9432121Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeCache.txt 2024-08-20T21:36:22.9432915Z inflating: build/custom_test_artifacts/jit-hook-build/Makefile 2024-08-20T21:36:22.9433735Z inflating: build/custom_test_artifacts/jit-hook-build/cmake_install.cmake 2024-08-20T21:36:22.9456982Z inflating: build/custom_test_artifacts/jit-hook-build/test_jit_hooks 2024-08-20T21:36:22.9457835Z creating: build/custom_test_artifacts/custom-backend-build/ 2024-08-20T21:36:22.9458621Z creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/ 2024-08-20T21:36:22.9459574Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/CMakeOutput.log 2024-08-20T21:36:22.9460555Z creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/ 2024-08-20T21:36:22.9461610Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CMakeSystem.cmake 2024-08-20T21:36:22.9462716Z creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CompilerIdC/ 2024-08-20T21:36:22.9463865Z creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CompilerIdC/tmp/ 2024-08-20T21:36:22.9465095Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CompilerIdC/CMakeCCompilerId.c 2024-08-20T21:36:22.9466337Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CompilerIdC/a.out 2024-08-20T21:36:22.9467455Z creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CompilerIdCXX/ 2024-08-20T21:36:22.9468549Z creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CompilerIdCXX/tmp/ 2024-08-20T21:36:22.9469846Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CompilerIdCXX/CMakeCXXCompilerId.cpp 2024-08-20T21:36:22.9471144Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CompilerIdCXX/a.out 2024-08-20T21:36:22.9472429Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CMakeDetermineCompilerABI_C.bin 2024-08-20T21:36:22.9473692Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CMakeCCompiler.cmake 2024-08-20T21:36:22.9475003Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CMakeDetermineCompilerABI_CXX.bin 2024-08-20T21:36:22.9476302Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CMakeCXXCompiler.cmake 2024-08-20T21:36:22.9477380Z creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/CMakeTmp/ 2024-08-20T21:36:22.9478374Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/cmake.check_cache 2024-08-20T21:36:22.9479478Z creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/ 2024-08-20T21:36:22.9498358Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/depend.make 2024-08-20T21:36:22.9499800Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/link.txt 2024-08-20T21:36:22.9501134Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/cmake_clean.cmake 2024-08-20T21:36:22.9502477Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/build.make 2024-08-20T21:36:22.9503805Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/DependInfo.cmake 2024-08-20T21:36:22.9505119Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/flags.make 2024-08-20T21:36:22.9506537Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/progress.make 2024-08-20T21:36:22.9550358Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/CXX.includecache 2024-08-20T21:36:22.9567534Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/depend.internal 2024-08-20T21:36:22.9590360Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/test_custom_backend.cpp.o 2024-08-20T21:36:22.9591620Z creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/ 2024-08-20T21:36:22.9594592Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/depend.make 2024-08-20T21:36:22.9595813Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/link.txt 2024-08-20T21:36:22.9597061Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/cmake_clean.cmake 2024-08-20T21:36:22.9598343Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/build.make 2024-08-20T21:36:22.9599608Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/DependInfo.cmake 2024-08-20T21:36:22.9600879Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/flags.make 2024-08-20T21:36:22.9602108Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/progress.make 2024-08-20T21:36:22.9604061Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/CXX.includecache 2024-08-20T21:36:22.9607303Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/depend.internal 2024-08-20T21:36:22.9688659Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/custom_backend.cpp.o 2024-08-20T21:36:22.9689993Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/CMakeDirectoryInformation.cmake 2024-08-20T21:36:22.9691213Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/TargetDirectories.txt 2024-08-20T21:36:22.9692306Z extracting: build/custom_test_artifacts/custom-backend-build/CMakeFiles/progress.marks 2024-08-20T21:36:22.9693318Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/Makefile2 2024-08-20T21:36:22.9694315Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/Makefile.cmake 2024-08-20T21:36:22.9695286Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeCache.txt 2024-08-20T21:36:22.9696150Z inflating: build/custom_test_artifacts/custom-backend-build/Makefile 2024-08-20T21:36:22.9697031Z inflating: build/custom_test_artifacts/custom-backend-build/cmake_install.cmake 2024-08-20T21:36:22.9765282Z inflating: build/custom_test_artifacts/custom-backend-build/libcustom_backend.so 2024-08-20T21:36:22.9784082Z inflating: build/custom_test_artifacts/custom-backend-build/test_custom_backend 2024-08-20T21:36:22.9784726Z creating: build/lib/ 2024-08-20T21:36:22.9785066Z inflating: build/lib/libclog.a 2024-08-20T21:36:22.9793508Z inflating: build/lib/libpthreadpool.a 2024-08-20T21:36:22.9800249Z inflating: build/lib/libcpuinfo_internals.a 2024-08-20T21:36:22.9807255Z inflating: build/lib/libcpuinfo.a 2024-08-20T21:36:22.9878711Z inflating: build/lib/libprotobuf-lite.a 2024-08-20T21:36:22.9933746Z inflating: build/lib/libgtest.a 2024-08-20T21:36:22.9940947Z inflating: build/lib/libittnotify.a 2024-08-20T21:36:23.0000512Z inflating: build/lib/libbenchmark.a 2024-08-20T21:36:23.0024317Z inflating: build/lib/libtensorpipe_uv.a 2024-08-20T21:36:23.0102530Z inflating: build/lib/libgloo.a 2024-08-20T21:36:23.0161925Z inflating: build/lib/libasmjit.a 2024-08-20T21:36:23.0531726Z inflating: build/lib/libprotobuf.a 2024-08-20T21:36:23.0550260Z inflating: build/lib/libfmt.a 2024-08-20T21:36:23.0551540Z inflating: build/lib/libtorch_global_deps.so 2024-08-20T21:36:23.0629989Z inflating: build/lib/libc10.so 2024-08-20T21:36:23.0646953Z inflating: build/lib/libpytorch_qnnpack.a 2024-08-20T21:36:23.0649118Z inflating: build/lib/libnnpack_reference_layers.a 2024-08-20T21:36:23.0660250Z inflating: build/lib/libgmock.a 2024-08-20T21:36:23.0660805Z inflating: build/lib/libgtest_main.a 2024-08-20T21:36:23.0661457Z inflating: build/lib/libbenchmark_main.a 2024-08-20T21:36:23.1072923Z inflating: build/lib/libprotoc.a 2024-08-20T21:36:23.1524197Z inflating: build/lib/libtensorpipe.a 2024-08-20T21:36:23.2466394Z inflating: build/lib/libfbgemm.a 2024-08-20T21:36:24.4634135Z inflating: build/lib/libdnnl.a 2024-08-20T21:36:24.4634697Z inflating: build/lib/libgmock_main.a 2024-08-20T21:36:24.4887830Z inflating: build/lib/libkineto.a 2024-08-20T21:36:24.4903785Z inflating: build/lib/libnnpack.a 2024-08-20T21:36:24.4944000Z inflating: build/lib/libonnx_proto.a 2024-08-20T21:36:24.5126137Z inflating: build/lib/libXNNPACK.a 2024-08-20T21:36:24.5760126Z inflating: build/lib/libonnx.a 2024-08-20T21:36:26.7908301Z inflating: build/lib/libtorch_cpu.so 2024-08-20T21:36:26.7909032Z inflating: build/lib/libtorch.so 2024-08-20T21:36:26.7913244Z inflating: build/lib/libunbox_lib.a 2024-08-20T21:36:26.7917059Z inflating: build/lib/libshm.so 2024-08-20T21:36:26.7938668Z inflating: build/lib/libjitbackend_test.so 2024-08-20T21:36:26.8011167Z inflating: build/lib/libtorchbind_test.so 2024-08-20T21:36:26.8036346Z inflating: build/lib/libbackend_with_compiler.so 2024-08-20T21:36:26.8063366Z inflating: build/lib/libaoti_custom_ops.so 2024-08-20T21:36:26.9958250Z inflating: build/lib/libtorch_python.so 2024-08-20T21:36:26.9993722Z inflating: build/lib/libnnapi_backend.so 2024-08-20T21:36:26.9994171Z creating: build/bin/ 2024-08-20T21:36:26.9994525Z creating: build/bin/CMakeFiles/ 2024-08-20T21:36:26.9995110Z inflating: build/bin/CMakeFiles/CMakeDirectoryInformation.cmake 2024-08-20T21:36:26.9995792Z extracting: build/bin/CMakeFiles/progress.marks 2024-08-20T21:36:26.9996280Z inflating: build/bin/Makefile 2024-08-20T21:36:26.9996737Z inflating: build/bin/cmake_install.cmake 2024-08-20T21:36:26.9997217Z inflating: build/bin/CTestTestfile.cmake 2024-08-20T21:36:27.0040086Z inflating: build/bin/c10_TypeTraits_test 2024-08-20T21:36:27.0085554Z inflating: build/bin/c10_Device_test 2024-08-20T21:36:27.0130397Z inflating: build/bin/c10_TypeList_test 2024-08-20T21:36:27.0175959Z inflating: build/bin/c10_TypeIndex_test 2024-08-20T21:36:27.0220357Z inflating: build/bin/c10_Synchronized_test 2024-08-20T21:36:27.0263716Z inflating: build/bin/c10_ConstexprCrc_test 2024-08-20T21:36:27.0308825Z inflating: build/bin/c10_ssize_test 2024-08-20T21:36:27.0358098Z inflating: build/bin/c10_LeftRight_test 2024-08-20T21:36:27.0404765Z inflating: build/bin/c10_DeadlockDetection_test 2024-08-20T21:36:27.0450803Z inflating: build/bin/c10_Half_test 2024-08-20T21:36:27.0502927Z inflating: build/bin/c10_DispatchKeySet_test 2024-08-20T21:36:27.0551461Z inflating: build/bin/c10_ThreadLocal_test 2024-08-20T21:36:27.0606113Z inflating: build/bin/c10_ordered_preserving_dict_test 2024-08-20T21:36:27.0649413Z inflating: build/bin/c10_StreamGuard_test 2024-08-20T21:36:27.0693728Z inflating: build/bin/c10_CompileTimeFunctionPointer_test 2024-08-20T21:36:27.0738350Z inflating: build/bin/c10_tempfile_test 2024-08-20T21:36:27.0783643Z inflating: build/bin/c10_DeviceGuard_test 2024-08-20T21:36:27.0830997Z inflating: build/bin/c10_typeid_test 2024-08-20T21:36:27.0878375Z inflating: build/bin/c10_Scalar_test 2024-08-20T21:36:27.0939059Z inflating: build/bin/c10_cow_test 2024-08-20T21:36:27.0983655Z inflating: build/bin/c10_SymInt_test 2024-08-20T21:36:27.1030194Z inflating: build/bin/c10_Bitset_test 2024-08-20T21:36:27.1080016Z inflating: build/bin/c10_SizesAndStrides_test 2024-08-20T21:36:27.1129418Z inflating: build/bin/c10_InlineStreamGuard_test 2024-08-20T21:36:27.1177409Z inflating: build/bin/c10_InlineDeviceGuard_test 2024-08-20T21:36:27.1223345Z inflating: build/bin/c10_accumulate_test 2024-08-20T21:36:27.1268236Z inflating: build/bin/c10_bit_cast_test 2024-08-20T21:36:27.1318471Z inflating: build/bin/c10_string_view_test 2024-08-20T21:36:27.1363733Z inflating: build/bin/c10_irange_test 2024-08-20T21:36:27.1411848Z inflating: build/bin/c10_bfloat16_test 2024-08-20T21:36:27.1459593Z inflating: build/bin/c10_exception_test 2024-08-20T21:36:27.1507848Z inflating: build/bin/c10_complex_test 2024-08-20T21:36:27.1553771Z inflating: build/bin/c10_flags_test 2024-08-20T21:36:27.1597869Z inflating: build/bin/c10_generic_math_test 2024-08-20T21:36:27.1725175Z inflating: build/bin/c10_intrusive_ptr_test 2024-08-20T21:36:27.1776093Z inflating: build/bin/c10_complex_math_test 2024-08-20T21:36:27.1827912Z inflating: build/bin/c10_logging_test 2024-08-20T21:36:27.1896339Z inflating: build/bin/c10_optional_test 2024-08-20T21:36:27.1943512Z inflating: build/bin/c10_registry_test 2024-08-20T21:36:27.1990523Z inflating: build/bin/c10_lazy_test 2024-08-20T21:36:27.2118749Z inflating: build/bin/c10_small_vector_test 2024-08-20T21:36:27.2164060Z inflating: build/bin/c10_string_util_test 2024-08-20T21:36:27.2202389Z inflating: build/bin/c10_intrusive_ptr_benchmark 2024-08-20T21:36:27.2249603Z inflating: build/bin/c10_Metaprogramming_test 2024-08-20T21:36:27.2608009Z inflating: build/bin/protoc-3.13.0.0 2024-08-20T21:36:27.2963761Z inflating: build/bin/protoc 2024-08-20T21:36:27.3290337Z inflating: build/bin/vec_test_all_types_AVX512 2024-08-20T21:36:27.3630406Z inflating: build/bin/vec_test_all_types_AVX2 2024-08-20T21:36:27.3950549Z inflating: build/bin/vec_test_all_types_DEFAULT 2024-08-20T21:36:27.3997269Z inflating: build/bin/FileStoreTest 2024-08-20T21:36:27.4046091Z inflating: build/bin/TCPStoreTest 2024-08-20T21:36:27.4093271Z inflating: build/bin/HashStoreTest 2024-08-20T21:36:27.4139219Z inflating: build/bin/BackoffTest 2024-08-20T21:36:27.4198944Z inflating: build/bin/ProcessGroupGlooTest 2024-08-20T21:36:27.4201556Z inflating: build/bin/example_allreduce 2024-08-20T21:36:27.4250185Z inflating: build/bin/test_dist_autograd 2024-08-20T21:36:27.4252297Z inflating: build/bin/parallel_benchmark 2024-08-20T21:36:27.4261257Z inflating: build/bin/aot_model_compiler_test 2024-08-20T21:36:27.4321738Z inflating: build/bin/test_cpp_rpc 2024-08-20T21:36:27.4382831Z inflating: build/bin/test_mobile_nnc 2024-08-20T21:36:27.4425867Z inflating: build/bin/op_allowlist_test 2024-08-20T21:36:27.4480476Z inflating: build/bin/kernel_stackbased_test 2024-08-20T21:36:27.4570080Z inflating: build/bin/make_boxed_from_unboxed_functor_test 2024-08-20T21:36:27.4618853Z inflating: build/bin/backend_fallback_test 2024-08-20T21:36:27.4707208Z inflating: build/bin/kernel_function_test 2024-08-20T21:36:27.4826135Z inflating: build/bin/kernel_function_legacy_test 2024-08-20T21:36:27.4879041Z inflating: build/bin/IListRef_test 2024-08-20T21:36:27.4936744Z inflating: build/bin/KernelFunction_test 2024-08-20T21:36:27.4982325Z inflating: build/bin/xla_tensor_test 2024-08-20T21:36:27.5047367Z inflating: build/bin/legacy_vmap_test 2024-08-20T21:36:27.5101722Z inflating: build/bin/type_test 2024-08-20T21:36:27.5148053Z inflating: build/bin/type_ptr_test 2024-08-20T21:36:27.5220875Z inflating: build/bin/tensor_iterator_test 2024-08-20T21:36:27.5267150Z inflating: build/bin/stride_properties_test 2024-08-20T21:36:27.5312748Z inflating: build/bin/StorageUtils_test 2024-08-20T21:36:27.5364161Z inflating: build/bin/apply_utils_test 2024-08-20T21:36:27.5670917Z inflating: build/bin/test_lazy 2024-08-20T21:36:27.5716267Z inflating: build/bin/weakref_test 2024-08-20T21:36:27.5766306Z inflating: build/bin/NamedTensor_test 2024-08-20T21:36:27.5818322Z inflating: build/bin/scalar_test 2024-08-20T21:36:27.5882543Z inflating: build/bin/Dict_test 2024-08-20T21:36:27.5930827Z inflating: build/bin/broadcast_test 2024-08-20T21:36:27.5987941Z inflating: build/bin/basic 2024-08-20T21:36:27.6038949Z inflating: build/bin/cpu_generator_test 2024-08-20T21:36:27.6097435Z inflating: build/bin/MaybeOwned_test 2024-08-20T21:36:27.6144789Z inflating: build/bin/test_parallel 2024-08-20T21:36:27.6239963Z inflating: build/bin/kernel_lambda_test 2024-08-20T21:36:27.6287666Z inflating: build/bin/cpu_profiling_allocator_test 2024-08-20T21:36:27.6334449Z inflating: build/bin/half_test 2024-08-20T21:36:27.6456316Z inflating: build/bin/kernel_lambda_legacy_test 2024-08-20T21:36:27.6500973Z inflating: build/bin/cpu_allocator_test 2024-08-20T21:36:27.6550773Z inflating: build/bin/static_runtime_bench 2024-08-20T21:36:27.6596486Z inflating: build/bin/Dimname_test 2024-08-20T21:36:27.6598005Z inflating: build/bin/verify_api_visibility 2024-08-20T21:36:27.6876939Z inflating: build/bin/static_runtime_test 2024-08-20T21:36:27.6929691Z inflating: build/bin/atest 2024-08-20T21:36:27.6976506Z inflating: build/bin/memory_overlapping_test 2024-08-20T21:36:27.7021114Z inflating: build/bin/dispatch_key_set_test 2024-08-20T21:36:27.7105499Z inflating: build/bin/cpu_rng_test 2024-08-20T21:36:27.7160627Z inflating: build/bin/inline_container_test 2024-08-20T21:36:27.7162927Z inflating: build/bin/thread_init_test 2024-08-20T21:36:27.7208153Z inflating: build/bin/operators_test 2024-08-20T21:36:27.7300559Z inflating: build/bin/List_test 2024-08-20T21:36:27.7345965Z inflating: build/bin/wrapdim_test 2024-08-20T21:36:27.7390967Z inflating: build/bin/operator_name_test 2024-08-20T21:36:27.8634003Z inflating: build/bin/test_api 2024-08-20T21:36:27.8678289Z inflating: build/bin/dlconvertor_test 2024-08-20T21:36:27.8731083Z inflating: build/bin/extension_backend_test 2024-08-20T21:36:27.8775756Z inflating: build/bin/lazy_tensor_test 2024-08-20T21:36:27.8822217Z inflating: build/bin/undefined_tensor_test 2024-08-20T21:36:27.8910741Z inflating: build/bin/ivalue_test 2024-08-20T21:36:27.8955623Z inflating: build/bin/CppSignature_test 2024-08-20T21:36:27.9006033Z inflating: build/bin/scalar_tensor_test 2024-08-20T21:36:27.9053513Z inflating: build/bin/mobile_memory_cleanup 2024-08-20T21:36:27.9358079Z inflating: build/bin/op_registration_test 2024-08-20T21:36:27.9404829Z inflating: build/bin/math_kernel_test 2024-08-20T21:36:27.9451746Z inflating: build/bin/memory_format_test 2024-08-20T21:36:27.9502686Z inflating: build/bin/native_test 2024-08-20T21:36:27.9548532Z inflating: build/bin/packedtensoraccessor_test 2024-08-20T21:36:27.9593026Z inflating: build/bin/reduce_ops_test 2024-08-20T21:36:27.9660339Z inflating: build/bin/pow_test 2024-08-20T21:36:27.9711037Z inflating: build/bin/quantized_test 2024-08-20T21:36:27.9756774Z inflating: build/bin/reportMemoryUsage_test 2024-08-20T21:36:27.9804032Z inflating: build/bin/test_edge_op_registration 2024-08-20T21:36:27.9808196Z inflating: build/bin/torch_shm_manager 2024-08-20T21:36:27.9823860Z inflating: build/bin/tutorial_tensorexpr 2024-08-20T21:36:28.0811690Z inflating: build/bin/test_tensorexpr 2024-08-20T21:36:28.1369351Z inflating: build/bin/test_jit 2024-08-20T21:36:28.1369810Z creating: .additional_ci_files/ 2024-08-20T21:36:28.1426497Z inflating: .additional_ci_files/test-times.json 2024-08-20T21:36:28.1654899Z inflating: .additional_ci_files/test-class-times.json 2024-08-20T21:36:28.1706529Z ##[group]Run rm artifacts.zip 2024-08-20T21:36:28.1706910Z rm artifacts.zip 2024-08-20T21:36:28.1713270Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:36:28.1713916Z env: 2024-08-20T21:36:28.1714173Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:28.1714484Z ##[endgroup] 2024-08-20T21:36:28.2235056Z ##[group]Run df -H 2024-08-20T21:36:28.2235356Z df -H 2024-08-20T21:36:28.2240977Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:36:28.2241455Z env: 2024-08-20T21:36:28.2241724Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:28.2242054Z ##[endgroup] 2024-08-20T21:36:28.2482723Z Filesystem Size Used Avail Use% Mounted on 2024-08-20T21:36:28.2483336Z devtmpfs 4.2M 0 4.2M 0% /dev 2024-08-20T21:36:28.2483808Z tmpfs 8.2G 54k 8.2G 1% /dev/shm 2024-08-20T21:36:28.2484274Z tmpfs 3.3G 488k 3.3G 1% /run 2024-08-20T21:36:28.2484887Z /dev/nvme0n1p1 161G 21G 141G 13% / 2024-08-20T21:36:28.2485366Z tmpfs 8.2G 13k 8.2G 1% /tmp 2024-08-20T21:36:28.2485842Z /dev/nvme0n1p128 11M 1.4M 9.2M 13% /boot/efi 2024-08-20T21:36:28.2531252Z Prepare all required actions 2024-08-20T21:36:28.2531711Z Getting action download info 2024-08-20T21:36:28.3730940Z ##[group]Run ./.github/actions/download-td-artifacts 2024-08-20T21:36:28.3731395Z with: 2024-08-20T21:36:28.3731632Z env: 2024-08-20T21:36:28.3731886Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:28.3732203Z ##[endgroup] 2024-08-20T21:36:28.3837886Z ##[group]Run seemethere/download-artifact-s3@v4 2024-08-20T21:36:28.3838333Z with: 2024-08-20T21:36:28.3838586Z name: td_results 2024-08-20T21:36:28.3838873Z s3-bucket: gha-artifacts 2024-08-20T21:36:28.3839209Z region: us-east-1 2024-08-20T21:36:28.3839488Z env: 2024-08-20T21:36:28.3839725Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:28.3840051Z ##[endgroup] 2024-08-20T21:36:28.8575019Z (node:363227) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2024-08-20T21:36:28.8575760Z 2024-08-20T21:36:28.8576084Z Please migrate your code to use AWS SDK for JavaScript (v3). 2024-08-20T21:36:28.8576841Z For more information, check the migration guide at https://a.co/7PzMCcy 2024-08-20T21:36:28.8578122Z (Use `node --trace-warnings ...` to show where the warning was created) 2024-08-20T21:36:28.9201175Z Found 1 objects with prefix pytorch/pytorch/10479309237/td_results/ 2024-08-20T21:36:28.9202396Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/td_results.json 2024-08-20T21:36:28.9697493Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/td_results.json 2024-08-20T21:36:28.9704217Z Artifact download has finished successfully 2024-08-20T21:36:28.9909350Z ##[group]Run mkdir -p .additional_ci_files 2024-08-20T21:36:28.9909810Z mkdir -p .additional_ci_files 2024-08-20T21:36:28.9910366Z mv td_results.json .additional_ci_files/td_results.json 2024-08-20T21:36:28.9916845Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:36:28.9917323Z env: 2024-08-20T21:36:28.9917593Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:28.9917920Z ##[endgroup] 2024-08-20T21:36:29.0554060Z ##[group]Run .github/scripts/parse_ref.py 2024-08-20T21:36:29.0554528Z .github/scripts/parse_ref.py 2024-08-20T21:36:29.0560064Z shell: /usr/bin/bash -e {0} 2024-08-20T21:36:29.0560405Z env: 2024-08-20T21:36:29.0560661Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:29.0560977Z ##[endgroup] 2024-08-20T21:36:29.0836144Z Prepare all required actions 2024-08-20T21:36:29.0922620Z ##[group]Run ./.github/actions/get-workflow-job-id 2024-08-20T21:36:29.0923080Z with: 2024-08-20T21:36:29.0923659Z github-token: *** 2024-08-20T21:36:29.0923947Z env: 2024-08-20T21:36:29.0924188Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:29.0924518Z ##[endgroup] 2024-08-20T21:36:29.1037182Z ##[group]Run set -eux 2024-08-20T21:36:29.1037500Z set -eux 2024-08-20T21:36:29.1038087Z python3 .github/scripts/get_workflow_job_id.py "${GITHUB_RUN_ID}" "${RUNNER_NAME}" 2024-08-20T21:36:29.1044038Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:36:29.1044660Z env: 2024-08-20T21:36:29.1044919Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:29.1045451Z GITHUB_TOKEN: *** 2024-08-20T21:36:29.1045733Z ##[endgroup] 2024-08-20T21:36:29.1070319Z + python3 .github/scripts/get_workflow_job_id.py 10479309237 i-025b30bca34dcaa23 2024-08-20T21:36:30.8256987Z setting job-id=29025267541 2024-08-20T21:36:30.8257908Z setting job-name=linux-focal-py3.12-clang10 / test (default, 1, 4, amz2023.linux.2xlarge) 2024-08-20T21:36:30.8513096Z Prepare all required actions 2024-08-20T21:36:30.8513535Z Getting action download info 2024-08-20T21:36:30.9669438Z ##[group]Run ./.github/actions/filter-test-configs 2024-08-20T21:36:30.9669899Z with: 2024-08-20T21:36:30.9670343Z github-token: *** 2024-08-20T21:36:30.9673097Z test-matrix: {"include": [{"config": "default", "shard": 1, "num_shards": 4, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 2, "num_shards": 4, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 3, "num_shards": 4, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 4, "num_shards": 4, "runner": "amz2023.linux.2xlarge"}, {"config": "dynamo", "shard": 1, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "dynamo", "shard": 2, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "dynamo", "shard": 3, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}]} 2024-08-20T21:36:30.9676267Z job-name: linux-focal-py3.12-clang10 / test (default, 1, 4, amz2023.linux.2xlarge) 2024-08-20T21:36:30.9676886Z env: 2024-08-20T21:36:30.9677139Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:30.9677460Z ##[endgroup] 2024-08-20T21:36:30.9818291Z ##[group]Run nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482 2024-08-20T21:36:30.9818930Z with: 2024-08-20T21:36:30.9819180Z shell: bash 2024-08-20T21:36:30.9819442Z timeout_minutes: 10 2024-08-20T21:36:30.9819747Z max_attempts: 5 2024-08-20T21:36:30.9820037Z retry_wait_seconds: 30 2024-08-20T21:36:30.9821172Z command: set -eux # PyYAML 6.0 doesn't work with MacOS x86 anymore # This must run on Python-3.7 (AmazonLinux2) so can't use request=3.32.2 python3 -m pip install requests==2.27.1 pyyaml==6.0.1 2024-08-20T21:36:30.9822399Z polling_interval_seconds: 1 2024-08-20T21:36:30.9822753Z warning_on_retry: true 2024-08-20T21:36:30.9823082Z continue_on_error: false 2024-08-20T21:36:30.9823385Z env: 2024-08-20T21:36:30.9823633Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:30.9824171Z GITHUB_TOKEN: *** 2024-08-20T21:36:30.9824443Z ##[endgroup] 2024-08-20T21:36:31.0743470Z + python3 -m pip install requests==2.27.1 pyyaml==6.0.1 2024-08-20T21:36:31.3176447Z Defaulting to user installation because normal site-packages is not writeable 2024-08-20T21:36:31.3344166Z Requirement already satisfied: requests==2.27.1 in /home/ec2-user/.local/lib/python3.9/site-packages (2.27.1) 2024-08-20T21:36:31.3350339Z Requirement already satisfied: pyyaml==6.0.1 in /home/ec2-user/.local/lib/python3.9/site-packages (6.0.1) 2024-08-20T21:36:31.3472134Z Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (2.10) 2024-08-20T21:36:31.3477584Z Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (1.25.10) 2024-08-20T21:36:31.3483202Z Requirement already satisfied: certifi>=2017.4.17 in /home/ec2-user/.local/lib/python3.9/site-packages (from requests==2.27.1) (2024.7.4) 2024-08-20T21:36:31.3495507Z Requirement already satisfied: charset-normalizer~=2.0.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from requests==2.27.1) (2.0.12) 2024-08-20T21:36:32.0352744Z Command completed after 1 attempt(s). 2024-08-20T21:36:32.0403153Z ##[group]Run set -x 2024-08-20T21:36:32.0403471Z set -x 2024-08-20T21:36:32.0403746Z  2024-08-20T21:36:32.0404279Z # Use relative path here as this could be checked out anywhere, not necessarily 2024-08-20T21:36:32.0404947Z # in runner workspace 2024-08-20T21:36:32.0405454Z python3 "${GITHUB_ACTION_PATH}/../../scripts/parse_ref.py" 2024-08-20T21:36:32.0411515Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:36:32.0412006Z env: 2024-08-20T21:36:32.0412262Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:32.0412576Z ##[endgroup] 2024-08-20T21:36:32.0436689Z + python3 /home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/filter-test-configs/../../scripts/parse_ref.py 2024-08-20T21:36:32.0675452Z ##[group]Run echo "Workflow: ${GITHUB_WORKFLOW}" 2024-08-20T21:36:32.0676063Z echo "Workflow: ${GITHUB_WORKFLOW}" 2024-08-20T21:36:32.0676512Z echo "Job name: ${JOB_NAME}" 2024-08-20T21:36:32.0676890Z  2024-08-20T21:36:32.0677406Z # Use relative path here as this could be checked out anywhere, not necessarily 2024-08-20T21:36:32.0678080Z # in runner workspace 2024-08-20T21:36:32.0678638Z python3 "${GITHUB_ACTION_PATH}/../../scripts/filter_test_configs.py" \ 2024-08-20T21:36:32.0679264Z  --workflow "${GITHUB_WORKFLOW}" \ 2024-08-20T21:36:32.0679714Z  --job-name "${JOB_NAME}" \ 2024-08-20T21:36:32.0682620Z  --test-matrix "{"include": [{"config": "default", "shard": 1, "num_shards": 4, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 2, "num_shards": 4, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 3, "num_shards": 4, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 4, "num_shards": 4, "runner": "amz2023.linux.2xlarge"}, {"config": "dynamo", "shard": 1, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "dynamo", "shard": 2, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "dynamo", "shard": 3, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}]}" \ 2024-08-20T21:36:32.0685520Z  --selected-test-configs "" \ 2024-08-20T21:36:32.0686064Z  --pr-number "${PR_NUMBER}" \ 2024-08-20T21:36:32.0686456Z  --tag "${TAG}" \ 2024-08-20T21:36:32.0686828Z  --event-name "${EVENT_NAME}" \ 2024-08-20T21:36:32.0687264Z  --schedule "${SCHEDULE}" \ 2024-08-20T21:36:32.0687675Z  --branch "${HEAD_BRANCH}" 2024-08-20T21:36:32.0693317Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:36:32.0693807Z env: 2024-08-20T21:36:32.0694063Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:32.0694596Z GITHUB_TOKEN: *** 2024-08-20T21:36:32.0695158Z JOB_NAME: linux-focal-py3.12-clang10 / test (default, 1, 4, amz2023.linux.2xlarge) 2024-08-20T21:36:32.0695801Z PR_NUMBER: 133712 2024-08-20T21:36:32.0696070Z TAG: 2024-08-20T21:36:32.0696326Z EVENT_NAME: pull_request 2024-08-20T21:36:32.0696648Z SCHEDULE: 2024-08-20T21:36:32.0696895Z HEAD_BRANCH: 2024-08-20T21:36:32.0697166Z ##[endgroup] 2024-08-20T21:36:32.0720148Z Workflow: pull 2024-08-20T21:36:32.0721190Z Job name: linux-focal-py3.12-clang10 / test (default, 1, 4, amz2023.linux.2xlarge) 2024-08-20T21:36:32.3400967Z INFO:root:Found no test-config label on the PR, so all test configs are included 2024-08-20T21:36:32.4882324Z ##[group]Run echo "Filtered matrix:" 2024-08-20T21:36:32.4882754Z echo "Filtered matrix:" 2024-08-20T21:36:32.4885604Z echo "{"include": [{"config": "default", "shard": 1, "num_shards": 4, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 2, "num_shards": 4, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 3, "num_shards": 4, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 4, "num_shards": 4, "runner": "amz2023.linux.2xlarge"}, {"config": "dynamo", "shard": 1, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "dynamo", "shard": 2, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "dynamo", "shard": 3, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}]}" 2024-08-20T21:36:32.4888497Z  2024-08-20T21:36:32.4888748Z echo 2024-08-20T21:36:32.4889104Z echo "Is the current job unstable? False" 2024-08-20T21:36:32.4889533Z  2024-08-20T21:36:32.4889929Z echo 2024-08-20T21:36:32.4890262Z echo "Is keep-going label set? False" 2024-08-20T21:36:32.4890675Z  2024-08-20T21:36:32.4890920Z echo 2024-08-20T21:36:32.4891214Z echo "Renabled issues? " 2024-08-20T21:36:32.4896876Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:36:32.4897370Z env: 2024-08-20T21:36:32.4897623Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:32.4898069Z ##[endgroup] 2024-08-20T21:36:32.4920636Z Filtered matrix: 2024-08-20T21:36:32.4923813Z {include: [{config: default, shard: 1, num_shards: 4, runner: amz2023.linux.2xlarge}, {config: default, shard: 2, num_shards: 4, runner: amz2023.linux.2xlarge}, {config: default, shard: 3, num_shards: 4, runner: amz2023.linux.2xlarge}, {config: default, shard: 4, num_shards: 4, runner: amz2023.linux.2xlarge}, {config: dynamo, shard: 1, num_shards: 3, runner: amz2023.linux.2xlarge}, {config: dynamo, shard: 2, num_shards: 3, runner: amz2023.linux.2xlarge}, {config: dynamo, shard: 3, num_shards: 3, runner: amz2023.linux.2xlarge}]} 2024-08-20T21:36:32.4926531Z 2024-08-20T21:36:32.4926677Z Is the current job unstable? False 2024-08-20T21:36:32.4926966Z 2024-08-20T21:36:32.4927252Z Is keep-going label set? False 2024-08-20T21:36:32.4927514Z 2024-08-20T21:36:32.4927620Z Renabled issues? 2024-08-20T21:36:32.4982443Z ##[group]Run echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2024-08-20T21:36:32.4983141Z echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2024-08-20T21:36:32.4988659Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:36:32.4989137Z env: 2024-08-20T21:36:32.4989395Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:32.4989724Z JOB_TIMEOUT: 600 2024-08-20T21:36:32.4989994Z ##[endgroup] 2024-08-20T21:36:32.5064118Z ##[group]Run set -x 2024-08-20T21:36:32.5064495Z set -x 2024-08-20T21:36:32.5064773Z  2024-08-20T21:36:32.5065102Z if [[ $TEST_CONFIG == 'multigpu' ]]; then 2024-08-20T21:36:32.5065636Z  TEST_COMMAND=.ci/pytorch/multigpu-test.sh 2024-08-20T21:36:32.5066191Z elif [[ $BUILD_ENVIRONMENT == *onnx* ]]; then 2024-08-20T21:36:32.5066691Z  TEST_COMMAND=.ci/onnx/test.sh 2024-08-20T21:36:32.5067072Z else 2024-08-20T21:36:32.5067390Z  TEST_COMMAND=.ci/pytorch/test.sh 2024-08-20T21:36:32.5067798Z fi 2024-08-20T21:36:32.5068038Z  2024-08-20T21:36:32.5068491Z # detached container should get cleaned up by teardown_ec2_linux 2024-08-20T21:36:32.5069246Z # TODO: Stop building test binaries as part of the build phase 2024-08-20T21:36:32.5069907Z # Used for GPU_FLAG since that doesn't play nice 2024-08-20T21:36:32.5070485Z # shellcheck disable=SC2086,SC2090 2024-08-20T21:36:32.5070917Z container_name=$(docker run \ 2024-08-20T21:36:32.5071316Z  ${GPU_FLAG:-} \ 2024-08-20T21:36:32.5071664Z  -e BUILD_ENVIRONMENT \ 2024-08-20T21:36:32.5072034Z  -e PR_NUMBER \ 2024-08-20T21:36:32.5072383Z  -e GITHUB_ACTIONS \ 2024-08-20T21:36:32.5072752Z  -e GITHUB_REPOSITORY \ 2024-08-20T21:36:32.5073125Z  -e GITHUB_WORKFLOW \ 2024-08-20T21:36:32.5073487Z  -e GITHUB_JOB \ 2024-08-20T21:36:32.5073822Z  -e GITHUB_RUN_ID \ 2024-08-20T21:36:32.5074184Z  -e GITHUB_RUN_NUMBER \ 2024-08-20T21:36:32.5074556Z  -e GITHUB_RUN_ATTEMPT \ 2024-08-20T21:36:32.5074930Z  -e JOB_ID \ 2024-08-20T21:36:32.5075244Z  -e JOB_NAME \ 2024-08-20T21:36:32.5075555Z  -e BASE_SHA \ 2024-08-20T21:36:32.5075872Z  -e BRANCH \ 2024-08-20T21:36:32.5076180Z  -e SHA1 \ 2024-08-20T21:36:32.5076490Z  -e AWS_DEFAULT_REGION \ 2024-08-20T21:36:32.5076873Z  -e IN_WHEEL_TEST \ 2024-08-20T21:36:32.5077230Z  -e SHARD_NUMBER \ 2024-08-20T21:36:32.5077568Z  -e TEST_CONFIG \ 2024-08-20T21:36:32.5077911Z  -e NUM_TEST_SHARDS \ 2024-08-20T21:36:32.5078413Z  -e REENABLED_ISSUES \ 2024-08-20T21:36:32.5078794Z  -e CONTINUE_THROUGH_ERROR \ 2024-08-20T21:36:32.5079203Z  -e VERBOSE_TEST_LOGS \ 2024-08-20T21:36:32.5079586Z  -e TEST_SHOWLOCALS \ 2024-08-20T21:36:32.5079944Z  -e NO_TEST_TIMEOUT \ 2024-08-20T21:36:32.5080299Z  -e NO_TD \ 2024-08-20T21:36:32.5080623Z  -e TD_DISTRIBUTED \ 2024-08-20T21:36:32.5080969Z  -e PR_LABELS \ 2024-08-20T21:36:32.5081359Z  -e MAX_JOBS="$(nproc --ignore=2)" \ 2024-08-20T21:36:32.5081795Z  -e SCCACHE_BUCKET \ 2024-08-20T21:36:32.5082155Z  -e SCCACHE_S3_KEY_PREFIX \ 2024-08-20T21:36:32.5082540Z  -e XLA_CUDA \ 2024-08-20T21:36:32.5082922Z  -e XLA_CLANG_CACHE_S3_BUCKET_NAME \ 2024-08-20T21:36:32.5083403Z  -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK \ 2024-08-20T21:36:32.5083910Z  -e PYTORCH_TEST_RERUN_DISABLED_TESTS \ 2024-08-20T21:36:32.5084418Z  -e SKIP_SCCACHE_INITIALIZATION=1 \ 2024-08-20T21:36:32.5084862Z  -e HUGGING_FACE_HUB_TOKEN \ 2024-08-20T21:36:32.5085303Z  -e SCRIBE_GRAPHQL_ACCESS_TOKEN \ 2024-08-20T21:36:32.5085735Z  -e DASHBOARD_TAG \ 2024-08-20T21:36:32.5086298Z  --env-file="/tmp/github_env_${GITHUB_RUN_ID}" \ 2024-08-20T21:36:32.5086850Z  --security-opt seccomp=unconfined \ 2024-08-20T21:36:32.5087308Z  --cap-add=SYS_PTRACE \ 2024-08-20T21:36:32.5087684Z  --ipc=host \ 2024-08-20T21:36:32.5088009Z  --shm-size="${SHM_SIZE}" \ 2024-08-20T21:36:32.5088392Z  --tty \ 2024-08-20T21:36:32.5088687Z  --detach \ 2024-08-20T21:36:32.5089117Z  --name="${container_name}" \ 2024-08-20T21:36:32.5089526Z  --user jenkins \ 2024-08-20T21:36:32.5090004Z  -v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \ 2024-08-20T21:36:32.5090542Z  -w /var/lib/jenkins/workspace \ 2024-08-20T21:36:32.5090972Z  "${DOCKER_IMAGE}" 2024-08-20T21:36:32.5091308Z ) 2024-08-20T21:36:32.5091684Z # Propagate download.pytorch.org IP to container 2024-08-20T21:36:32.5092629Z grep download.pytorch.org /etc/hosts | docker exec -i "${container_name}" sudo bash -c "/bin/cat >> /etc/hosts" 2024-08-20T21:36:32.5093619Z echo "DOCKER_CONTAINER_ID=${container_name}" >> "${GITHUB_ENV}" 2024-08-20T21:36:32.5094534Z docker exec -t "${container_name}" sh -c "pip install $(echo dist/*.whl)[opt-einsum] && ${TEST_COMMAND}" 2024-08-20T21:36:32.5100085Z shell: /usr/bin/bash -e {0} 2024-08-20T21:36:32.5100417Z env: 2024-08-20T21:36:32.5100660Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:32.5101071Z BUILD_ENVIRONMENT: linux-focal-py3.12-clang10 2024-08-20T21:36:32.5101513Z PR_NUMBER: 133712 2024-08-20T21:36:32.5101819Z GITHUB_REPOSITORY: pytorch/pytorch 2024-08-20T21:36:32.5102213Z GITHUB_WORKFLOW: pull 2024-08-20T21:36:32.5102538Z GITHUB_JOB: test 2024-08-20T21:36:32.5102818Z GITHUB_RUN_ID: 10479309237 2024-08-20T21:36:32.5103157Z GITHUB_RUN_NUMBER: 238988 2024-08-20T21:36:32.5103493Z GITHUB_RUN_ATTEMPT: 1 2024-08-20T21:36:32.5103786Z JOB_ID: 29025267541 2024-08-20T21:36:32.5104336Z JOB_NAME: linux-focal-py3.12-clang10 / test (default, 1, 4, amz2023.linux.2xlarge) 2024-08-20T21:36:32.5104977Z BRANCH: pull/133712 2024-08-20T21:36:32.5105337Z SHA1: 40ec5f6ddd9787aca0449b24128343ff4c4a88b3 2024-08-20T21:36:32.5105830Z BASE_SHA: 91f3d614142df02f619e44a68e3d9e0dfeba49ec 2024-08-20T21:36:32.5106280Z TEST_CONFIG: default 2024-08-20T21:36:32.5106584Z SHARD_NUMBER: 1 2024-08-20T21:36:32.5106855Z NUM_TEST_SHARDS: 4 2024-08-20T21:36:32.5107150Z REENABLED_ISSUES: 2024-08-20T21:36:32.5107465Z CONTINUE_THROUGH_ERROR: False 2024-08-20T21:36:32.5107816Z VERBOSE_TEST_LOGS: False 2024-08-20T21:36:32.5129460Z TEST_SHOWLOCALS: False 2024-08-20T21:36:32.5130031Z NO_TEST_TIMEOUT: False 2024-08-20T21:36:32.5130364Z NO_TD: False 2024-08-20T21:36:32.5130771Z TD_DISTRIBUTED: False 2024-08-20T21:36:32.5131162Z SCCACHE_BUCKET: ossci-compiler-cache-circleci-v2 2024-08-20T21:36:32.5131647Z SCCACHE_S3_KEY_PREFIX: pull 2024-08-20T21:36:32.5131989Z SHM_SIZE: 1g 2024-08-20T21:36:32.5132886Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:36:32.5133884Z XLA_CUDA: 2024-08-20T21:36:32.5134353Z XLA_CLANG_CACHE_S3_BUCKET_NAME: ossci-compiler-clang-cache-circleci-xla 2024-08-20T21:36:32.5134953Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK: 0 2024-08-20T21:36:32.5135367Z PYTORCH_TEST_RERUN_DISABLED_TESTS: 0 2024-08-20T21:36:32.5135751Z DASHBOARD_TAG: 2024-08-20T21:36:32.5136035Z HUGGING_FACE_HUB_TOKEN: 2024-08-20T21:36:32.5136377Z SCRIBE_GRAPHQL_ACCESS_TOKEN: 2024-08-20T21:36:32.5136728Z ##[endgroup] 2024-08-20T21:36:32.5159792Z + [[ default == \m\u\l\t\i\g\p\u ]] 2024-08-20T21:36:32.5160716Z + [[ linux-focal-py3.12-clang10 == *onnx* ]] 2024-08-20T21:36:32.5161185Z + TEST_COMMAND=.ci/pytorch/test.sh 2024-08-20T21:36:32.5168120Z +++ nproc --ignore=2 2024-08-20T21:36:32.5195592Z ++ docker run -e BUILD_ENVIRONMENT -e PR_NUMBER -e GITHUB_ACTIONS -e GITHUB_REPOSITORY -e GITHUB_WORKFLOW -e GITHUB_JOB -e GITHUB_RUN_ID -e GITHUB_RUN_NUMBER -e GITHUB_RUN_ATTEMPT -e JOB_ID -e JOB_NAME -e BASE_SHA -e BRANCH -e SHA1 -e AWS_DEFAULT_REGION -e IN_WHEEL_TEST -e SHARD_NUMBER -e TEST_CONFIG -e NUM_TEST_SHARDS -e REENABLED_ISSUES -e CONTINUE_THROUGH_ERROR -e VERBOSE_TEST_LOGS -e TEST_SHOWLOCALS -e NO_TEST_TIMEOUT -e NO_TD -e TD_DISTRIBUTED -e PR_LABELS -e MAX_JOBS=6 -e SCCACHE_BUCKET -e SCCACHE_S3_KEY_PREFIX -e XLA_CUDA -e XLA_CLANG_CACHE_S3_BUCKET_NAME -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK -e PYTORCH_TEST_RERUN_DISABLED_TESTS -e SKIP_SCCACHE_INITIALIZATION=1 -e HUGGING_FACE_HUB_TOKEN -e SCRIBE_GRAPHQL_ACCESS_TOKEN -e DASHBOARD_TAG --env-file=/tmp/github_env_10479309237 --security-opt seccomp=unconfined --cap-add=SYS_PTRACE --ipc=host --shm-size=1g --tty --detach --name= --user jenkins -v /home/ec2-user/actions-runner/_work/pytorch/pytorch:/var/lib/jenkins/workspace -w /var/lib/jenkins/workspace 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:36:37.1885202Z + container_name=2340f3deffd39d0eed0996837220f02e294a105661d3e11a7b09c171901f5c27 2024-08-20T21:36:37.1887046Z + grep download.pytorch.org /etc/hosts 2024-08-20T21:36:37.1890489Z + docker exec -i 2340f3deffd39d0eed0996837220f02e294a105661d3e11a7b09c171901f5c27 sudo bash -c '/bin/cat >> /etc/hosts' 2024-08-20T21:36:37.2944005Z + echo DOCKER_CONTAINER_ID=2340f3deffd39d0eed0996837220f02e294a105661d3e11a7b09c171901f5c27 2024-08-20T21:36:37.2946432Z ++ echo dist/torch-2.5.0a0+git40ec5f6-cp312-cp312-linux_x86_64.whl 2024-08-20T21:36:37.2948920Z + docker exec -t 2340f3deffd39d0eed0996837220f02e294a105661d3e11a7b09c171901f5c27 sh -c 'pip install dist/torch-2.5.0a0+git40ec5f6-cp312-cp312-linux_x86_64.whl[opt-einsum] && .ci/pytorch/test.sh' 2024-08-20T21:36:38.0958912Z Processing ./dist/torch-2.5.0a0+git40ec5f6-cp312-cp312-linux_x86_64.whl (from torch==2.5.0a0+git40ec5f6) 2024-08-20T21:36:38.5189439Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+git40ec5f6->torch==2.5.0a0+git40ec5f6) (3.13.1) 2024-08-20T21:36:38.5192183Z Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+git40ec5f6->torch==2.5.0a0+git40ec5f6) (4.12.2) 2024-08-20T21:36:38.5194846Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+git40ec5f6->torch==2.5.0a0+git40ec5f6) (2.8.8) 2024-08-20T21:36:38.5198211Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+git40ec5f6->torch==2.5.0a0+git40ec5f6) (3.1.4) 2024-08-20T21:36:38.5202220Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+git40ec5f6->torch==2.5.0a0+git40ec5f6) (2024.6.1) 2024-08-20T21:36:38.5213536Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+git40ec5f6->torch==2.5.0a0+git40ec5f6) (72.1.0) 2024-08-20T21:36:38.5218923Z Requirement already satisfied: sympy==1.13.1 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+git40ec5f6->torch==2.5.0a0+git40ec5f6) (1.13.1) 2024-08-20T21:36:38.5234052Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from sympy==1.13.1->torch==2.5.0a0+git40ec5f6->torch==2.5.0a0+git40ec5f6) (1.3.0) 2024-08-20T21:36:38.5252930Z Requirement already satisfied: opt-einsum>=3.3 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+git40ec5f6->torch==2.5.0a0+git40ec5f6) (3.3.0) 2024-08-20T21:36:38.5271221Z Requirement already satisfied: numpy>=1.7 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from opt-einsum>=3.3->torch==2.5.0a0+git40ec5f6->torch==2.5.0a0+git40ec5f6) (1.26.0) 2024-08-20T21:36:38.5386748Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from jinja2->torch==2.5.0a0+git40ec5f6->torch==2.5.0a0+git40ec5f6) (2.1.5) 2024-08-20T21:36:38.7524562Z Installing collected packages: torch 2024-08-20T21:36:48.0148578Z Successfully installed torch-2.5.0a0+git40ec5f6 2024-08-20T21:36:48.1035814Z + export TERM=vt100 2024-08-20T21:36:48.1036167Z + TERM=vt100 2024-08-20T21:36:48.1037896Z ++ dirname .ci/pytorch/test.sh 2024-08-20T21:36:48.1088437Z + source .ci/pytorch/common.sh 2024-08-20T21:36:48.1091003Z +++ dirname .ci/pytorch/common.sh 2024-08-20T21:36:48.1097643Z ++ source .ci/pytorch/common_utils.sh 2024-08-20T21:36:48.1099001Z +++ declare -f -t trap_add 2024-08-20T21:36:48.1105116Z ++ set -ex 2024-08-20T21:36:48.1105811Z ++ [[ linux-focal-py3.12-clang10 == *rocm* ]] 2024-08-20T21:36:48.1106293Z ++ BUILD_TEST_LIBTORCH=0 2024-08-20T21:36:48.1106755Z + [[ linux-focal-py3.12-clang10 != *rocm* ]] 2024-08-20T21:36:48.1109050Z ++ stat -c %u /var/lib/jenkins/workspace 2024-08-20T21:36:48.1166189Z + WORKSPACE_ORIGINAL_OWNER_ID=1000 2024-08-20T21:36:48.1166963Z + trap_add cleanup_workspace EXIT 2024-08-20T21:36:48.1167368Z + trap_add_cmd=cleanup_workspace 2024-08-20T21:36:48.1167722Z + shift 2024-08-20T21:36:48.1167968Z + for trap_add_name in "$@" 2024-08-20T21:36:48.1173588Z +++ trap -p EXIT 2024-08-20T21:36:48.1176229Z ++ eval 'extract_trap_cmd ' 2024-08-20T21:36:48.1176628Z +++ extract_trap_cmd 2024-08-20T21:36:48.1177025Z +++ printf '%s\n' '' 2024-08-20T21:36:48.1177397Z ++ printf '%s\n' cleanup_workspace 2024-08-20T21:36:48.1179140Z + trap -- ' 2024-08-20T21:36:48.1179773Z cleanup_workspace' EXIT 2024-08-20T21:36:48.1180245Z + sudo chown -R jenkins /var/lib/jenkins/workspace 2024-08-20T21:36:48.5632628Z + git config --global --add safe.directory /var/lib/jenkins/workspace 2024-08-20T21:36:48.5648926Z + echo 'Environment variables:' 2024-08-20T21:36:48.5649567Z Environment variables: 2024-08-20T21:36:48.5649866Z + env 2024-08-20T21:36:48.5669284Z INSTALLED_DB=yes 2024-08-20T21:36:48.5670219Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-08-20T21:36:48.5670959Z CONTINUE_THROUGH_ERROR=False 2024-08-20T21:36:48.5671574Z BUILD_ENVIRONMENT=linux-focal-py3.12-clang10 2024-08-20T21:36:48.5672171Z HOSTNAME=2340f3deffd3 2024-08-20T21:36:48.5674453Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_856aaf90-5051-4db3-9d9e-3b80e87b9cd2 2024-08-20T21:36:48.5675693Z GITHUB_ACTION=__self 2024-08-20T21:36:48.5676189Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2024-08-20T21:36:48.5676788Z GITHUB_RUN_NUMBER=238988 2024-08-20T21:36:48.5677261Z TEST_CONFIG=default 2024-08-20T21:36:48.5677732Z GITHUB_REPOSITORY_OWNER_ID=21003710 2024-08-20T21:36:48.5678511Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all 2024-08-20T21:36:48.5679314Z SCRIBE_GRAPHQL_ACCESS_TOKEN= 2024-08-20T21:36:48.5680255Z GITHUB_TRIGGERING_ACTOR=pytorchmergebot 2024-08-20T21:36:48.5680791Z GITHUB_REF_TYPE=branch 2024-08-20T21:36:48.5681222Z TORCH_CUDA_ARCH_LIST=Maxwell 2024-08-20T21:36:48.5681989Z BASE_SHA=91f3d614142df02f619e44a68e3d9e0dfeba49ec 2024-08-20T21:36:48.5682647Z XLA_CUDA= 2024-08-20T21:36:48.5683024Z HUGGING_FACE_HUB_TOKEN= 2024-08-20T21:36:48.5685660Z *** 2024-08-20T21:36:48.5686344Z GITHUB_REPOSITORY_ID=65600975 2024-08-20T21:36:48.5686899Z GITHUB_ACTIONS=true 2024-08-20T21:36:48.5687257Z SHA1=40ec5f6ddd9787aca0449b24128343ff4c4a88b3 2024-08-20T21:36:48.5687758Z GITHUB_SHA=f2fb9405c2fa9f9502a76363091cce6fd8179736 2024-08-20T21:36:48.5688523Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/pull/133712/merge 2024-08-20T21:36:48.5689165Z UCC_HOME=/usr 2024-08-20T21:36:48.5689486Z VERBOSE_TEST_LOGS=False 2024-08-20T21:36:48.5689817Z GITHUB_REF=refs/pull/133712/merge 2024-08-20T21:36:48.5690175Z SHARD_NUMBER=1 2024-08-20T21:36:48.5690485Z GITHUB_REF_PROTECTED=false 2024-08-20T21:36:48.5691015Z HOME=/var/lib/jenkins 2024-08-20T21:36:48.5691529Z GITHUB_API_URL=https://api.github.com 2024-08-20T21:36:48.5692147Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2024-08-20T21:36:48.5692717Z UCX_COMMIT= 2024-08-20T21:36:48.5693115Z SCCACHE_S3_KEY_PREFIX=pull 2024-08-20T21:36:48.5693599Z NUM_TEST_SHARDS=4 2024-08-20T21:36:48.5694011Z UCX_HOME=/usr 2024-08-20T21:36:48.5695547Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_856aaf90-5051-4db3-9d9e-3b80e87b9cd2 2024-08-20T21:36:48.5696734Z JOB_NAME=linux-focal-py3.12-clang10 / test (default, 1, 4, amz2023.linux.2xlarge) 2024-08-20T21:36:48.5697926Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_856aaf90-5051-4db3-9d9e-3b80e87b9cd2 2024-08-20T21:36:48.5699329Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json 2024-08-20T21:36:48.5700020Z GITHUB_EVENT_NAME=pull_request 2024-08-20T21:36:48.5700482Z DASHBOARD_TAG= 2024-08-20T21:36:48.5700879Z GITHUB_RUN_ID=10479309237 2024-08-20T21:36:48.5701862Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_856aaf90-5051-4db3-9d9e-3b80e87b9cd2 2024-08-20T21:36:48.5702987Z GITHUB_ACTOR=pytorchmergebot 2024-08-20T21:36:48.5703336Z PR_NUMBER=133712 2024-08-20T21:36:48.5703614Z DESIRED_CUDA= 2024-08-20T21:36:48.5703881Z GITHUB_RUN_ATTEMPT=1 2024-08-20T21:36:48.5704238Z ANACONDA_PYTHON_VERSION=3.12 2024-08-20T21:36:48.5704672Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2024-08-20T21:36:48.5705119Z TERM=vt100 2024-08-20T21:36:48.5705508Z INSTALLED_VISION=yes 2024-08-20T21:36:48.5705995Z BRANCH=pull/133712 2024-08-20T21:36:48.5706444Z OPENSSL_ROOT_DIR=/opt/openssl 2024-08-20T21:36:48.5706989Z CUDA_PATH=/usr/local/cuda 2024-08-20T21:36:48.5708149Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux 2024-08-20T21:36:48.5708915Z GITHUB_SERVER_URL=https://github.com 2024-08-20T21:36:48.5709303Z UCC_COMMIT= 2024-08-20T21:36:48.5709565Z REENABLED_ISSUES= 2024-08-20T21:36:48.5709836Z DOCS= 2024-08-20T21:36:48.5710083Z INSTALLED_ANDROID= 2024-08-20T21:36:48.5710361Z SHLVL=1 2024-08-20T21:36:48.5710588Z MAX_JOBS=6 2024-08-20T21:36:48.5710884Z GITHUB_ACTOR_ID=97764156 2024-08-20T21:36:48.5711329Z GITHUB_WORKFLOW_SHA=f2fb9405c2fa9f9502a76363091cce6fd8179736 2024-08-20T21:36:48.5711833Z GITHUB_REF_NAME=133712/merge 2024-08-20T21:36:48.5712537Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla 2024-08-20T21:36:48.5713390Z GITHUB_JOB=test 2024-08-20T21:36:48.5713663Z NO_TEST_TIMEOUT=False 2024-08-20T21:36:48.5713975Z TD_DISTRIBUTED=False 2024-08-20T21:36:48.5714304Z GITHUB_REPOSITORY=pytorch/pytorch 2024-08-20T21:36:48.5714753Z GITHUB_RETENTION_DAYS=90 2024-08-20T21:36:48.5715090Z OPENSSL_DIR=/opt/openssl 2024-08-20T21:36:48.5715425Z GITHUB_ACTION_REPOSITORY= 2024-08-20T21:36:48.5716546Z PATH=/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.12/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2024-08-20T21:36:48.5717889Z GITHUB_BASE_REF=gh/XuehaiPan/146/base 2024-08-20T21:36:48.5718290Z INSTALLED_ACL= 2024-08-20T21:36:48.5718583Z CI=true 2024-08-20T21:36:48.5718851Z GITHUB_REPOSITORY_OWNER=pytorch 2024-08-20T21:36:48.5719252Z JOB_ID=29025267541 2024-08-20T21:36:48.5719539Z INSTALLED_PROTOBUF=yes 2024-08-20T21:36:48.5719879Z GITHUB_HEAD_REF=gh/XuehaiPan/146/head 2024-08-20T21:36:48.5720265Z GITHUB_ACTION_REF= 2024-08-20T21:36:48.5720712Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2 2024-08-20T21:36:48.5721171Z TEST_SHOWLOCALS=False 2024-08-20T21:36:48.5721487Z GITHUB_WORKFLOW=pull 2024-08-20T21:36:48.5721820Z DEBIAN_FRONTEND=noninteractive 2024-08-20T21:36:48.5722853Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_856aaf90-5051-4db3-9d9e-3b80e87b9cd2 2024-08-20T21:36:48.5723696Z NO_TD=False 2024-08-20T21:36:48.5723964Z SKIP_SCCACHE_INITIALIZATION=1 2024-08-20T21:36:48.5724307Z _=/usr/bin/env 2024-08-20T21:36:48.5724775Z ++ python -c 'import site; print(site.getsitepackages()[0])' 2024-08-20T21:36:48.5850982Z + TORCH_INSTALL_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch 2024-08-20T21:36:48.5852422Z + TORCH_BIN_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/bin 2024-08-20T21:36:48.5853340Z + TORCH_LIB_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib 2024-08-20T21:36:48.5854268Z + TORCH_TEST_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/test 2024-08-20T21:36:48.5854868Z + BUILD_DIR=build 2024-08-20T21:36:48.5855169Z + BUILD_RENAMED_DIR=build_renamed 2024-08-20T21:36:48.5855539Z + BUILD_BIN_DIR=build/bin 2024-08-20T21:36:48.5855848Z + SHARD_NUMBER=1 2024-08-20T21:36:48.5856234Z + NUM_TEST_SHARDS=4 2024-08-20T21:36:48.5856835Z + export VALGRIND=ON 2024-08-20T21:36:48.5857128Z + VALGRIND=ON 2024-08-20T21:36:48.5857535Z + [[ linux-focal-py3.12-clang10 == *clang9* ]] 2024-08-20T21:36:48.5857966Z + [[ 0 == \1 ]] 2024-08-20T21:36:48.5858227Z + [[ False == \1 ]] 2024-08-20T21:36:48.5858645Z + [[ linux-focal-py3.12-clang10 != *bazel* ]] 2024-08-20T21:36:48.5859113Z ++ realpath build/custom_test_artifacts 2024-08-20T21:36:48.5881294Z + CUSTOM_TEST_ARTIFACT_BUILD_DIR=/var/lib/jenkins/workspace/build/custom_test_artifacts 2024-08-20T21:36:48.5882101Z + [[ -n '' ]] 2024-08-20T21:36:48.5882447Z + echo 'Environment variables' 2024-08-20T21:36:48.5882802Z Environment variables 2024-08-20T21:36:48.5883100Z + env 2024-08-20T21:36:48.5892560Z INSTALLED_DB=yes 2024-08-20T21:36:48.5894398Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-08-20T21:36:48.5895531Z CONTINUE_THROUGH_ERROR=False 2024-08-20T21:36:48.5896090Z BUILD_ENVIRONMENT=linux-focal-py3.12-clang10 2024-08-20T21:36:48.5896524Z HOSTNAME=2340f3deffd3 2024-08-20T21:36:48.5897390Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_856aaf90-5051-4db3-9d9e-3b80e87b9cd2 2024-08-20T21:36:48.5898217Z GITHUB_ACTION=__self 2024-08-20T21:36:48.5898541Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2024-08-20T21:36:48.5898918Z GITHUB_RUN_NUMBER=238988 2024-08-20T21:36:48.5899235Z TEST_CONFIG=default 2024-08-20T21:36:48.5899557Z GITHUB_REPOSITORY_OWNER_ID=21003710 2024-08-20T21:36:48.5900010Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all 2024-08-20T21:36:48.5900421Z SCRIBE_GRAPHQL_ACCESS_TOKEN= 2024-08-20T21:36:48.5900799Z GITHUB_TRIGGERING_ACTOR=pytorchmergebot 2024-08-20T21:36:48.5901194Z GITHUB_REF_TYPE=branch 2024-08-20T21:36:48.5901512Z TORCH_CUDA_ARCH_LIST=Maxwell 2024-08-20T21:36:48.5901916Z BASE_SHA=91f3d614142df02f619e44a68e3d9e0dfeba49ec 2024-08-20T21:36:48.5902334Z XLA_CUDA= 2024-08-20T21:36:48.5902595Z HUGGING_FACE_HUB_TOKEN= 2024-08-20T21:36:48.5903023Z *** 2024-08-20T21:36:48.5903262Z GITHUB_REPOSITORY_ID=65600975 2024-08-20T21:36:48.5903613Z GITHUB_ACTIONS=true 2024-08-20T21:36:48.5903964Z SHA1=40ec5f6ddd9787aca0449b24128343ff4c4a88b3 2024-08-20T21:36:48.5904455Z GITHUB_SHA=f2fb9405c2fa9f9502a76363091cce6fd8179736 2024-08-20T21:36:48.5905170Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/pull/133712/merge 2024-08-20T21:36:48.5906012Z UCC_HOME=/usr 2024-08-20T21:36:48.5906281Z VERBOSE_TEST_LOGS=False 2024-08-20T21:36:48.5906614Z GITHUB_REF=refs/pull/133712/merge 2024-08-20T21:36:48.5906969Z SHARD_NUMBER=1 2024-08-20T21:36:48.5907242Z GITHUB_REF_PROTECTED=false 2024-08-20T21:36:48.5907573Z HOME=/var/lib/jenkins 2024-08-20T21:36:48.5907915Z GITHUB_API_URL=https://api.github.com 2024-08-20T21:36:48.5908325Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2024-08-20T21:36:48.5908697Z UCX_COMMIT= 2024-08-20T21:36:48.5908967Z SCCACHE_S3_KEY_PREFIX=pull 2024-08-20T21:36:48.5909282Z NUM_TEST_SHARDS=4 2024-08-20T21:36:48.5909557Z UCX_HOME=/usr 2024-08-20T21:36:48.5910425Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_856aaf90-5051-4db3-9d9e-3b80e87b9cd2 2024-08-20T21:36:48.5911591Z JOB_NAME=linux-focal-py3.12-clang10 / test (default, 1, 4, amz2023.linux.2xlarge) 2024-08-20T21:36:48.5912777Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_856aaf90-5051-4db3-9d9e-3b80e87b9cd2 2024-08-20T21:36:48.5913953Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json 2024-08-20T21:36:48.5914623Z GITHUB_EVENT_NAME=pull_request 2024-08-20T21:36:48.5914959Z DASHBOARD_TAG= 2024-08-20T21:36:48.5915240Z GITHUB_RUN_ID=10479309237 2024-08-20T21:36:48.5916203Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_856aaf90-5051-4db3-9d9e-3b80e87b9cd2 2024-08-20T21:36:48.5917095Z GITHUB_ACTOR=pytorchmergebot 2024-08-20T21:36:48.5917442Z PR_NUMBER=133712 2024-08-20T21:36:48.5917719Z DESIRED_CUDA= 2024-08-20T21:36:48.5917977Z GITHUB_RUN_ATTEMPT=1 2024-08-20T21:36:48.5918273Z VALGRIND=ON 2024-08-20T21:36:48.5918639Z ANACONDA_PYTHON_VERSION=3.12 2024-08-20T21:36:48.5919054Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2024-08-20T21:36:48.5919501Z TERM=vt100 2024-08-20T21:36:48.5919762Z INSTALLED_VISION=yes 2024-08-20T21:36:48.5920050Z BRANCH=pull/133712 2024-08-20T21:36:48.5920360Z OPENSSL_ROOT_DIR=/opt/openssl 2024-08-20T21:36:48.5920712Z CUDA_PATH=/usr/local/cuda 2024-08-20T21:36:48.5921475Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux 2024-08-20T21:36:48.5922243Z GITHUB_SERVER_URL=https://github.com 2024-08-20T21:36:48.5922627Z UCC_COMMIT= 2024-08-20T21:36:48.5922872Z REENABLED_ISSUES= 2024-08-20T21:36:48.5923140Z DOCS= 2024-08-20T21:36:48.5923384Z INSTALLED_ANDROID= 2024-08-20T21:36:48.5923647Z SHLVL=1 2024-08-20T21:36:48.5923885Z MAX_JOBS=6 2024-08-20T21:36:48.5924134Z GITHUB_ACTOR_ID=97764156 2024-08-20T21:36:48.5924585Z GITHUB_WORKFLOW_SHA=f2fb9405c2fa9f9502a76363091cce6fd8179736 2024-08-20T21:36:48.5925104Z GITHUB_REF_NAME=133712/merge 2024-08-20T21:36:48.5925714Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla 2024-08-20T21:36:48.5926401Z GITHUB_JOB=test 2024-08-20T21:36:48.5926688Z NO_TEST_TIMEOUT=False 2024-08-20T21:36:48.5926993Z TD_DISTRIBUTED=False 2024-08-20T21:36:48.5927306Z GITHUB_REPOSITORY=pytorch/pytorch 2024-08-20T21:36:48.5927700Z GITHUB_RETENTION_DAYS=90 2024-08-20T21:36:48.5928015Z OPENSSL_DIR=/opt/openssl 2024-08-20T21:36:48.5928341Z GITHUB_ACTION_REPOSITORY= 2024-08-20T21:36:48.5929419Z PATH=/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.12/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2024-08-20T21:36:48.5930559Z GITHUB_BASE_REF=gh/XuehaiPan/146/base 2024-08-20T21:36:48.5930939Z INSTALLED_ACL= 2024-08-20T21:36:48.5931198Z CI=true 2024-08-20T21:36:48.5931461Z GITHUB_REPOSITORY_OWNER=pytorch 2024-08-20T21:36:48.5931804Z JOB_ID=29025267541 2024-08-20T21:36:48.5932092Z INSTALLED_PROTOBUF=yes 2024-08-20T21:36:48.5932429Z GITHUB_HEAD_REF=gh/XuehaiPan/146/head 2024-08-20T21:36:48.5932806Z GITHUB_ACTION_REF= 2024-08-20T21:36:48.5933242Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2 2024-08-20T21:36:48.5933696Z TEST_SHOWLOCALS=False 2024-08-20T21:36:48.5933988Z GITHUB_WORKFLOW=pull 2024-08-20T21:36:48.5934308Z DEBIAN_FRONTEND=noninteractive 2024-08-20T21:36:48.5935331Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_856aaf90-5051-4db3-9d9e-3b80e87b9cd2 2024-08-20T21:36:48.5936155Z NO_TD=False 2024-08-20T21:36:48.5936437Z SKIP_SCCACHE_INITIALIZATION=1 2024-08-20T21:36:48.5936786Z _=/usr/bin/env 2024-08-20T21:36:48.5937091Z + echo 'Testing pytorch' 2024-08-20T21:36:48.5937412Z Testing pytorch 2024-08-20T21:36:48.5937707Z + export LANG=C.UTF-8 2024-08-20T21:36:48.5938007Z + LANG=C.UTF-8 2024-08-20T21:36:48.5961618Z + PR_NUMBER=133712 2024-08-20T21:36:48.5962164Z + [[ default == \d\e\f\a\u\l\t ]] 2024-08-20T21:36:48.5962782Z + export CUDA_VISIBLE_DEVICES=0 2024-08-20T21:36:48.5963323Z + CUDA_VISIBLE_DEVICES=0 2024-08-20T21:36:48.5963776Z + export HIP_VISIBLE_DEVICES=0 2024-08-20T21:36:48.5964338Z + HIP_VISIBLE_DEVICES=0 2024-08-20T21:36:48.5964757Z + [[ default == \d\i\s\t\r\i\b\u\t\e\d ]] 2024-08-20T21:36:48.5965283Z + [[ default == \s\l\o\w ]] 2024-08-20T21:36:48.5965887Z + [[ linux-focal-py3.12-clang10 == *slow-gradcheck* ]] 2024-08-20T21:36:48.5966622Z + [[ linux-focal-py3.12-clang10 == *cuda* ]] 2024-08-20T21:36:48.5967148Z + [[ linux-focal-py3.12-clang10 == *rocm* ]] 2024-08-20T21:36:48.5967651Z + [[ linux-focal-py3.12-clang10 == *xpu* ]] 2024-08-20T21:36:48.5968081Z + [[ default == *crossref* ]] 2024-08-20T21:36:48.5968526Z + [[ linux-focal-py3.12-clang10 == *rocm* ]] 2024-08-20T21:36:48.5969027Z + [[ linux-focal-py3.12-clang10 == *xpu* ]] 2024-08-20T21:36:48.5969557Z + [[ linux-focal-py3.12-clang10 != *-bazel-* ]] 2024-08-20T21:36:48.5970057Z + pip_install --user ninja==1.10.2 2024-08-20T21:36:48.5970569Z + pip install --progress-bar off --user ninja==1.10.2 2024-08-20T21:36:49.0316751Z Collecting ninja==1.10.2 2024-08-20T21:36:49.0495019Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl.metadata (5.0 kB) 2024-08-20T21:36:49.0588653Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (108 kB) 2024-08-20T21:36:49.2231212Z Installing collected packages: ninja 2024-08-20T21:36:49.2313217Z  WARNING: The script ninja is installed in '/var/lib/jenkins/.local/bin' which is not on PATH. 2024-08-20T21:36:49.2314550Z Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. 2024-08-20T21:36:49.2362243Z Successfully installed ninja-1.10.2 2024-08-20T21:36:49.3210291Z + export PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.12/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2024-08-20T21:36:49.3212741Z + PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.12/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2024-08-20T21:36:49.3214415Z + [[ linux-focal-py3.12-clang10 == *aarch64* ]] 2024-08-20T21:36:49.3214918Z + install_tlparse 2024-08-20T21:36:49.3215286Z + pip_install --user tlparse==0.3.25 2024-08-20T21:36:49.3215894Z + pip install --progress-bar off --user tlparse==0.3.25 2024-08-20T21:36:49.7043994Z Collecting tlparse==0.3.25 2024-08-20T21:36:49.7190629Z Downloading tlparse-0.3.25-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.7 kB) 2024-08-20T21:36:49.7275851Z Downloading tlparse-0.3.25-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.2 MB) 2024-08-20T21:36:49.9062360Z Installing collected packages: tlparse 2024-08-20T21:36:49.9727805Z Successfully installed tlparse-0.3.25 2024-08-20T21:36:50.0463307Z ++ python -m site --user-base 2024-08-20T21:36:50.0639915Z + PATH=/var/lib/jenkins/.local/bin:/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.12/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2024-08-20T21:36:50.0642220Z + [[ linux-focal-py3.12-clang10 == *asan* ]] 2024-08-20T21:36:50.0642775Z + [[ linux-focal-py3.12-clang10 == *-debug* ]] 2024-08-20T21:36:50.0643746Z + [[ linux-focal-py3.12-clang10 != *-bazel-* ]] 2024-08-20T21:36:50.0644787Z + echo 'We are not in debug mode: linux-focal-py3.12-clang10. Expect the assertion to pass' 2024-08-20T21:36:50.0645823Z We are not in debug mode: linux-focal-py3.12-clang10. Expect the assertion to pass 2024-08-20T21:36:50.0646558Z + cd test 2024-08-20T21:36:50.0647243Z + python -c 'import torch; torch._C._crash_if_debug_asserts_fail(424242)' 2024-08-20T21:36:51.8300040Z + [[ default == \n\o\g\p\u\_\N\O\_\A\V\X\2 ]] 2024-08-20T21:36:51.8300562Z + [[ default == \n\o\g\p\u\_\A\V\X\5\1\2 ]] 2024-08-20T21:36:51.8303632Z + DYNAMO_BENCHMARK_FLAGS=() 2024-08-20T21:36:51.8304932Z + [[ default == *pr_time_benchmarks* ]] 2024-08-20T21:36:51.8305657Z + [[ default == *dynamo_eager* ]] 2024-08-20T21:36:51.8306390Z + [[ default == *aot_eager* ]] 2024-08-20T21:36:51.8306896Z + [[ default == *aot_inductor* ]] 2024-08-20T21:36:51.8307271Z + [[ default == *inductor* ]] 2024-08-20T21:36:51.8307623Z + [[ default == *dynamic* ]] 2024-08-20T21:36:51.8307966Z + [[ default == *cpu* ]] 2024-08-20T21:36:51.8308528Z + DYNAMO_BENCHMARK_FLAGS+=(--device cuda) 2024-08-20T21:36:51.8340667Z + [[ linux-focal-py3.12-clang10 == *libtorch* ]] 2024-08-20T21:36:51.8341353Z + [[ linux-focal-py3.12-clang10 == *-bazel-* ]] 2024-08-20T21:36:51.8343814Z + cd test 2024-08-20T21:36:51.8344681Z + python -c 'import torch; print(torch.__config__.show())' 2024-08-20T21:36:53.0862491Z PyTorch built with: 2024-08-20T21:36:53.0863411Z - GCC 4.2 2024-08-20T21:36:53.0863713Z - C++ Version: 201703 2024-08-20T21:36:53.0864080Z - clang 10.0.0 2024-08-20T21:36:53.0864929Z - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications 2024-08-20T21:36:53.0866356Z - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) 2024-08-20T21:36:53.0867026Z - OpenMP 201511 (a.k.a. OpenMP 4.5) 2024-08-20T21:36:53.0867529Z - LAPACK is enabled (usually provided by MKL) 2024-08-20T21:36:53.0868003Z - NNPACK is enabled 2024-08-20T21:36:53.0868380Z - CPU capability usage: AVX512 2024-08-20T21:36:53.0876784Z - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CXX_COMPILER=/opt/cache/bin/clang++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=1 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DLIBKINETO_NOROCTRACER -DLIBKINETO_NOXPUPTI=ON -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=braced-scalar-init -Werror=range-loop-construct -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-strict-overflow -Wno-strict-aliasing -Wvla-extension -Wnewline-eof -Winconsistent-missing-override -Winconsistent-missing-destructor-override -Wno-pass-failed -Wno-error=old-style-cast -Wconstant-conversion -Wno-missing-braces -Qunused-arguments -fcolor-diagnostics -faligned-new -Werror -fno-math-errno -fno-trapping-math -Werror=format, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.5.0, USE_CUDA=OFF, USE_CUDNN=OFF, USE_CUSPARSELT=OFF, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, 2024-08-20T21:36:53.0884184Z 2024-08-20T21:36:53.3543860Z + cd test 2024-08-20T21:36:53.3544664Z + python -c 'import torch; print(torch.__config__.parallel_info())' 2024-08-20T21:36:54.5626406Z ATen/Parallel: 2024-08-20T21:36:54.5627042Z at::get_num_threads() : 4 2024-08-20T21:36:54.5627414Z at::get_num_interop_threads() : 4 2024-08-20T21:36:54.5627817Z OpenMP 201511 (a.k.a. OpenMP 4.5) 2024-08-20T21:36:54.5628214Z omp_get_max_threads() : 4 2024-08-20T21:36:54.5629267Z Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications 2024-08-20T21:36:54.5630075Z mkl_get_max_threads() : 4 2024-08-20T21:36:54.5631009Z Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) 2024-08-20T21:36:54.5631632Z std::thread::hardware_concurrency() : 8 2024-08-20T21:36:54.5632027Z Environment variables: 2024-08-20T21:36:54.5632358Z OMP_NUM_THREADS : [not set] 2024-08-20T21:36:54.5632723Z MKL_NUM_THREADS : [not set] 2024-08-20T21:36:54.5633067Z ATen parallel backend: OpenMP 2024-08-20T21:36:54.5633320Z 2024-08-20T21:36:54.8017884Z + [[ linux-focal-py3.12-clang10 == *aarch64* ]] 2024-08-20T21:36:54.8018795Z + [[ default == *backward* ]] 2024-08-20T21:36:54.8019241Z + [[ default == *xla* ]] 2024-08-20T21:36:54.8019609Z + [[ default == *executorch* ]] 2024-08-20T21:36:54.8020058Z + [[ default == \j\i\t\_\l\e\g\a\c\y ]] 2024-08-20T21:36:54.8020687Z + [[ linux-focal-py3.12-clang10 == *libtorch* ]] 2024-08-20T21:36:54.8021140Z + [[ default == distributed ]] 2024-08-20T21:36:54.8021591Z + [[ default == *inductor_distributed* ]] 2024-08-20T21:36:54.8022098Z + [[ default == *inductor-halide* ]] 2024-08-20T21:36:54.8022637Z + [[ default == *inductor-micro-benchmark* ]] 2024-08-20T21:36:54.8023141Z + [[ default == *huggingface* ]] 2024-08-20T21:36:54.8023506Z + [[ default == *timm* ]] 2024-08-20T21:36:54.8023888Z + [[ default == *torchbench* ]] 2024-08-20T21:36:54.8024349Z + [[ default == *inductor_cpp_wrapper_abi_compatible* ]] 2024-08-20T21:36:54.8024882Z + [[ default == *inductor* ]] 2024-08-20T21:36:54.8025279Z + [[ default == *dynamo* ]] 2024-08-20T21:36:54.8025726Z + [[ linux-focal-py3.12-clang10 == *rocm* ]] 2024-08-20T21:36:54.8026196Z + [[ 1 == 1 ]] 2024-08-20T21:36:54.8026487Z + [[ 4 -gt 1 ]] 2024-08-20T21:36:54.8026819Z + test_without_numpy 2024-08-20T21:36:54.8027141Z ++ dirname .ci/pytorch/test.sh 2024-08-20T21:36:54.8033728Z + pushd .ci/pytorch 2024-08-20T21:36:54.8034427Z ~/workspace/.ci/pytorch ~/workspace 2024-08-20T21:36:54.8035958Z + python -c 'import sys;sys.path.insert(0, '\''fake_numpy'\'');from unittest import TestCase;import torch;x=torch.randn(3,3);TestCase().assertRaises(RuntimeError, lambda: x.numpy())' 2024-08-20T21:36:55.5322307Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_subclasses/functional_tensor.py:271: UserWarning: Failed to initialize NumPy: Sorry PyTorch, but our NumPy is in the other folder (Triggered internally at /var/lib/jenkins/workspace/torch/csrc/utils/tensor_numpy.cpp:84.) 2024-08-20T21:36:56.1139669Z cpu = _conversion_method_template(device=torch.device("cpu")) 2024-08-20T21:36:56.1141154Z + python -c 'import sys;sys.path.insert(0, '\''fake_numpy'\'');import torch;print(torch.tensor([torch.tensor(0.), torch.tensor(1.)]))' 2024-08-20T21:36:56.8519069Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_subclasses/functional_tensor.py:271: UserWarning: Failed to initialize NumPy: Sorry PyTorch, but our NumPy is in the other folder (Triggered internally at /var/lib/jenkins/workspace/torch/csrc/utils/tensor_numpy.cpp:84.) 2024-08-20T21:36:56.8521100Z cpu = _conversion_method_template(device=torch.device("cpu")) 2024-08-20T21:36:57.1982154Z tensor([0., 1.]) 2024-08-20T21:36:57.4239661Z + [[ default == *dynamo* ]] 2024-08-20T21:36:57.4240231Z + popd 2024-08-20T21:36:57.4240468Z ~/workspace 2024-08-20T21:36:57.4240745Z + install_torchvision 2024-08-20T21:36:57.4241062Z + local orig_preload 2024-08-20T21:36:57.4241351Z + local commit 2024-08-20T21:36:57.4243237Z ++ get_pinned_commit vision 2024-08-20T21:36:57.4243635Z ++ cat .github/ci_commit_pins/vision.txt 2024-08-20T21:36:57.4257689Z + commit=d23a6e1664d20707c11781299611436e1f0c104f 2024-08-20T21:36:57.4258404Z + orig_preload= 2024-08-20T21:36:57.4259159Z + '[' -n '' ']' 2024-08-20T21:36:57.4260039Z + pip_install --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2024-08-20T21:36:57.4261585Z + pip install --progress-bar off --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2024-08-20T21:36:57.7595574Z Collecting git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2024-08-20T21:36:57.7600916Z Cloning https://github.com/pytorch/vision.git (to revision d23a6e1664d20707c11781299611436e1f0c104f) to /tmp/pip-req-build-pvrb2dj1 2024-08-20T21:36:57.7620579Z Running command git clone --filter=blob:none --quiet https://github.com/pytorch/vision.git /tmp/pip-req-build-pvrb2dj1 2024-08-20T21:36:59.1047790Z Running command git rev-parse -q --verify 'sha^d23a6e1664d20707c11781299611436e1f0c104f' 2024-08-20T21:36:59.1066172Z Running command git fetch -q https://github.com/pytorch/vision.git d23a6e1664d20707c11781299611436e1f0c104f 2024-08-20T21:37:00.4486798Z Running command git checkout -q d23a6e1664d20707c11781299611436e1f0c104f 2024-08-20T21:37:00.7412722Z Resolved https://github.com/pytorch/vision.git to commit d23a6e1664d20707c11781299611436e1f0c104f 2024-08-20T21:37:02.9364912Z Preparing metadata (setup.py) ... [?25l- \ done 2024-08-20T21:37:02.9397596Z [?25hRequirement already satisfied: numpy in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torchvision==0.19.0a0+d23a6e1) (1.26.0) 2024-08-20T21:37:02.9401489Z Requirement already satisfied: torch in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torchvision==0.19.0a0+d23a6e1) (2.5.0a0+git40ec5f6) 2024-08-20T21:37:02.9407601Z Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torchvision==0.19.0a0+d23a6e1) (10.3.0) 2024-08-20T21:37:02.9455527Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (3.13.1) 2024-08-20T21:37:02.9459965Z Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (4.12.2) 2024-08-20T21:37:02.9462610Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (2.8.8) 2024-08-20T21:37:02.9465281Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (3.1.4) 2024-08-20T21:37:02.9467957Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (2024.6.1) 2024-08-20T21:37:02.9478367Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (72.1.0) 2024-08-20T21:37:02.9483269Z Requirement already satisfied: sympy==1.13.1 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (1.13.1) 2024-08-20T21:37:02.9496400Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from sympy==1.13.1->torch->torchvision==0.19.0a0+d23a6e1) (1.3.0) 2024-08-20T21:37:02.9604599Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from jinja2->torch->torchvision==0.19.0a0+d23a6e1) (2.1.5) 2024-08-20T21:37:02.9699339Z Building wheels for collected packages: torchvision 2024-08-20T21:38:09.3510487Z Building wheel for torchvision (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | done 2024-08-20T21:38:09.3545937Z [?25h Created wheel for torchvision: filename=torchvision-0.19.0a0+d23a6e1-cp312-cp312-linux_x86_64.whl size=1116927 sha256=e25972fdd7227e5aff4ef205a66a78a5485856d052fff0bfb0ec624dea5378ac 2024-08-20T21:38:09.3548086Z Stored in directory: /var/lib/jenkins/.cache/pip/wheels/b9/aa/81/39d3509ec629531316195ffac7a7b05ff7603f393064d63ec9 2024-08-20T21:38:09.3581439Z Successfully built torchvision 2024-08-20T21:38:09.4842154Z Installing collected packages: torchvision 2024-08-20T21:38:09.9272138Z Successfully installed torchvision-0.19.0a0+d23a6e1 2024-08-20T21:38:10.0333168Z + '[' -n '' ']' 2024-08-20T21:38:10.0333551Z + test_python_shard 1 2024-08-20T21:38:10.0335057Z + [[ -z 4 ]] 2024-08-20T21:38:10.0336146Z + python test/run_test.py --exclude-jit-executor --exclude-distributed-tests --shard 1 4 --verbose 2024-08-20T21:38:10.1387021Z /var/lib/jenkins/workspace/test/run_test.py:21: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html 2024-08-20T21:38:10.1388207Z import pkg_resources 2024-08-20T21:38:12.8944444Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:38:13.8960248Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2024-08-20T21:38:13.9775857Z Ignoring disabled issues: [''] 2024-08-20T21:38:13.9891559Z Found test times from artifacts 2024-08-20T21:38:14.0325088Z Found test times from artifacts 2024-08-20T21:38:14.0339546Z Running 25% of tests based on TD 2024-08-20T21:38:14.0549242Z Running parallel tests on 3 processes 2024-08-20T21:38:14.0551465Z Name: tests to run (est. time: 49.92min) 2024-08-20T21:38:14.0552229Z Serial tests (6): 2024-08-20T21:38:14.0552626Z test_ci_sanity_check_fail 1/1 2024-08-20T21:38:14.0552979Z doctests 1/1 2024-08-20T21:38:14.0553488Z inductor/test_max_autotune 1/1 2024-08-20T21:38:14.0554132Z inductor/test_distributed_patterns 1/1 2024-08-20T21:38:14.0554759Z test_utils 1/1 2024-08-20T21:38:14.0555098Z test_nn 1/1 2024-08-20T21:38:14.0555550Z Parallel tests (29): 2024-08-20T21:38:14.0556153Z inductor/test_torchinductor 3/4 2024-08-20T21:38:14.0556712Z inductor/test_torchinductor_opinfo 9/39 2024-08-20T21:38:14.0557410Z inductor/test_torchinductor_opinfo 10/39 2024-08-20T21:38:14.0559183Z inductor/test_torchinductor_opinfo 11/39 2024-08-20T21:38:14.0560014Z inductor/test_torchinductor_opinfo 21/39 2024-08-20T21:38:14.0560658Z inductor/test_torchinductor_opinfo 22/39 2024-08-20T21:38:14.0561499Z inductor/test_torchinductor_opinfo 23/39 2024-08-20T21:38:14.0562198Z inductor/test_torchinductor_opinfo 33/39 2024-08-20T21:38:14.0563013Z inductor/test_torchinductor_opinfo 34/39 2024-08-20T21:38:14.0563670Z inductor/test_torchinductor_opinfo 35/39 2024-08-20T21:38:14.0564487Z inductor/test_torchinductor_codegen_dynamic_shapes 2/3 2024-08-20T21:38:14.0565461Z inductor/test_torchinductor_codegen_dynamic_shapes 3/3 2024-08-20T21:38:14.0566109Z inductor/test_mmdecomp 1/1 2024-08-20T21:38:14.0566714Z dynamo/test_interop 1/1 2024-08-20T21:38:14.0567353Z dynamo/test_logging 1/1 2024-08-20T21:38:14.0567977Z dynamo/test_exc 1/1 2024-08-20T21:38:14.0568532Z dynamo/test_global 1/1 2024-08-20T21:38:14.0569132Z dynamo/test_unspec 1/1 2024-08-20T21:38:14.0569681Z inductor/test_cudagraph_trees 1/1 2024-08-20T21:38:14.0570086Z dynamo/test_ctx_manager 1/1 2024-08-20T21:38:14.0570458Z dynamo/test_subgraphs 1/1 2024-08-20T21:38:14.0570818Z dynamo/test_autograd_function 1/1 2024-08-20T21:38:14.0571266Z dynamo/test_activation_checkpointing 1/1 2024-08-20T21:38:14.0571718Z inductor/test_inductor_freezing 1/1 2024-08-20T21:38:14.0572150Z inductor/test_mkldnn_pattern_matcher 1/2 2024-08-20T21:38:14.0572587Z inductor/test_cuda_repro 1/1 2024-08-20T21:38:14.0573005Z inductor/test_kernel_benchmark 1/1 2024-08-20T21:38:14.0573427Z inductor/test_triton_heuristics 1/1 2024-08-20T21:38:14.0573833Z inductor/test_cudacodecache 1/1 2024-08-20T21:38:14.0574235Z Name: excluded (est. time: 62.64min) 2024-08-20T21:38:14.0574623Z Serial tests (38): 2024-08-20T21:38:14.0574908Z test_fx 1/1 2024-08-20T21:38:14.0575191Z test_reductions 1/1 2024-08-20T21:38:14.0575519Z test_multiprocessing 1/1 2024-08-20T21:38:14.0575862Z test_tensorexpr 1/1 2024-08-20T21:38:14.0576213Z distributions/test_distributions 1/1 2024-08-20T21:38:14.0576618Z nn/test_pooling 1/1 2024-08-20T21:38:14.0576930Z test_cpp_extensions_jit 1/1 2024-08-20T21:38:14.0577289Z test_dispatch 1/1 2024-08-20T21:38:14.0577804Z test_multiprocessing_spawn 1/1 2024-08-20T21:38:14.0578177Z nn/test_convolution 1/1 2024-08-20T21:38:14.0578532Z test_tensor_creation_ops 1/1 2024-08-20T21:38:14.0578888Z test_torch 1/1 2024-08-20T21:38:14.0579248Z test_cpp_extensions_open_device_registration 1/1 2024-08-20T21:38:14.0579731Z inductor/test_benchmark_fusion 1/1 2024-08-20T21:38:14.0580127Z test_spectral_ops 1/1 2024-08-20T21:38:14.0580446Z test_cpp_api_parity 1/1 2024-08-20T21:38:14.0580795Z test_mobile_optimizer 1/1 2024-08-20T21:38:14.0581148Z test_sort_and_select 1/1 2024-08-20T21:38:14.0581473Z test_fake_tensor 1/1 2024-08-20T21:38:14.0581793Z test_overrides 1/1 2024-08-20T21:38:14.0582118Z test_namedtuple_return_api 1/1 2024-08-20T21:38:14.0582480Z test_autocast 1/1 2024-08-20T21:38:14.0582795Z test_python_dispatch 1/1 2024-08-20T21:38:14.0583165Z test_cpp_extensions_mtia_backend 1/1 2024-08-20T21:38:14.0583599Z test_cpp_extensions_stream_and_event 1/1 2024-08-20T21:38:14.0584031Z test_jit_disabled 1/1 2024-08-20T21:38:14.0584353Z test_native_mha 1/1 2024-08-20T21:38:14.0584666Z test_autograd_fallback 1/1 2024-08-20T21:38:14.0585018Z test_show_pickle 1/1 2024-08-20T21:38:14.0585366Z test_cpp_extensions_aot_no_ninja 1/1 2024-08-20T21:38:14.0585778Z test_cpp_extensions_aot_ninja 1/1 2024-08-20T21:38:14.0586184Z inductor/test_flex_attention 1/1 2024-08-20T21:38:14.0586588Z inductor/test_cutlass_backend 1/1 2024-08-20T21:38:14.0586961Z test_cuda_trace 1/1 2024-08-20T21:38:14.0587284Z test_cuda_nvml_based_avail 1/1 2024-08-20T21:38:14.0587662Z test_cuda_primary_ctx 1/1 2024-08-20T21:38:14.0588002Z test_autoload_disable 1/1 2024-08-20T21:38:14.0588441Z test_autoload_enable 1/1 2024-08-20T21:38:14.0588794Z Parallel tests (29): 2024-08-20T21:38:14.0589126Z inductor/test_cpu_cpp_wrapper 13/14 2024-08-20T21:38:14.0589639Z inductor/test_cpu_select_algorithm 6/13 2024-08-20T21:38:14.0590098Z inductor/test_cpu_select_algorithm 7/13 2024-08-20T21:38:14.0590542Z inductor/test_cpu_select_algorithm 8/13 2024-08-20T21:38:14.0591016Z test_ops 5/5 2024-08-20T21:38:14.0591310Z test_ops_fwd_gradients 1/1 2024-08-20T21:38:14.0591645Z test_decomp 1/10 2024-08-20T21:38:14.0591952Z test_transformers 1/2 2024-08-20T21:38:14.0592283Z test_transformers 2/2 2024-08-20T21:38:14.0592599Z test_quantization 1/4 2024-08-20T21:38:14.0592935Z test_modules 1/1 2024-08-20T21:38:14.0593296Z functorch/test_aotdispatch 1/5 2024-08-20T21:38:14.0593678Z functorch/test_aotdispatch 2/5 2024-08-20T21:38:14.0594054Z test_proxy_tensor 1/2 2024-08-20T21:38:14.0594380Z test_proxy_tensor 2/2 2024-08-20T21:38:14.0594695Z functorch/test_vmap 1/2 2024-08-20T21:38:14.0595038Z functorch/test_vmap 2/2 2024-08-20T21:38:14.0595372Z test_schema_check 1/1 2024-08-20T21:38:14.0595702Z inductor/test_inductor_utils 1/1 2024-08-20T21:38:14.0596094Z inductor/test_pad_mm 1/1 2024-08-20T21:38:14.0596447Z inductor/test_metrics 1/1 2024-08-20T21:38:14.0596853Z inductor/test_ck_backend 1/1 2024-08-20T21:38:14.0597223Z inductor/test_halide 1/1 2024-08-20T21:38:14.0597582Z inductor/test_loop_ordering 1/1 2024-08-20T21:38:14.0597958Z dynamo/test_torchrec 1/1 2024-08-20T21:38:14.0598300Z test_jiterator 1/1 2024-08-20T21:38:14.0598620Z lazy/test_meta_kernel 1/1 2024-08-20T21:38:14.0598981Z lazy/test_extract_compiled_graph 1/1 2024-08-20T21:38:14.0599387Z lazy/test_bindings 1/1 2024-08-20T21:38:14.0709432Z Running test_ci_sanity_check_fail 1/1 ... [2024-08-20 21:38:14.070396] 2024-08-20T21:38:14.0711085Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:38:14.0714360Z 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'] ... [2024-08-20 21:38:14.070880] 2024-08-20T21:38:16.2134930Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:38:16.2573587Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:38:16.2597295Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:38:22.0640612Z Running doctests 1/1 ... [2024-08-20 21:38:22.063701] 2024-08-20T21:38:22.1876956Z Start doctest_module('/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch') 2024-08-20T21:38:22.1877635Z Listing tests 2024-08-20T21:38:22.4293290Z msg = Cannot scrape callname=meshgrid in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=426. 2024-08-20T21:38:22.4295470Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:22.4297188Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors. 2024-08-20T21:38:22.4299112Z 2024-08-20T21:38:22.4299692Z This is helpful when you want to visualize data over some 2024-08-20T21:38:22.4301120Z range of inputs. See below for a plotting example. 2024-08-20T21:38:22.4301985Z 2024-08-20T21:38:22.4302706Z Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as 2024-08-20T21:38:22.4303869Z inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`, 2024-08-20T21:38:22.4304862Z this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots 2024-08-20T21:38:22.4305922Z G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where 2024-08-20T21:38:22.4306579Z the output :math:`G_i` is constructed by expanding :math:`T_i` 2024-08-20T21:38:22.4307120Z to the result shape. 2024-08-20T21:38:22.4307636Z 2024-08-20T21:38:22.4307757Z .. note:: 2024-08-20T21:38:22.4308154Z 0D inputs are treated equivalently to 1D inputs of a 2024-08-20T21:38:22.4308644Z single element. 2024-08-20T21:38:22.4308872Z 2024-08-20T21:38:22.4308976Z .. warning:: 2024-08-20T21:38:22.4309412Z `torch.meshgrid(*tensors)` currently has the same behavior 2024-08-20T21:38:22.4310100Z as calling `numpy.meshgrid(*arrays, indexing='ij')`. 2024-08-20T21:38:22.4310471Z 2024-08-20T21:38:22.4310684Z In the future `torch.meshgrid` will transition to 2024-08-20T21:38:22.4311228Z `indexing='xy'` as the default. 2024-08-20T21:38:22.4311538Z 2024-08-20T21:38:22.4311775Z https://github.com/pytorch/pytorch/issues/50276 tracks 2024-08-20T21:38:22.4312477Z this issue with the goal of migrating to NumPy's behavior. 2024-08-20T21:38:22.4312879Z 2024-08-20T21:38:22.4312981Z .. seealso:: 2024-08-20T21:38:22.4313184Z 2024-08-20T21:38:22.4313407Z :func:`torch.cartesian_prod` has the same effect but it 2024-08-20T21:38:22.4314167Z collects the data in a tensor of vectors. 2024-08-20T21:38:22.4314577Z 2024-08-20T21:38:22.4314733Z Args: 2024-08-20T21:38:22.4315500Z tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be 2024-08-20T21:38:22.4316829Z treated as tensors of size :math:`(1,)` automatically 2024-08-20T21:38:22.4317552Z 2024-08-20T21:38:22.4317918Z indexing: (str, optional): the indexing mode, either "xy" 2024-08-20T21:38:22.4318929Z or "ij", defaults to "ij". See warning for future changes. 2024-08-20T21:38:22.4319638Z 2024-08-20T21:38:22.4320032Z If "xy" is selected, the first dimension corresponds 2024-08-20T21:38:22.4321086Z to the cardinality of the second input and the second 2024-08-20T21:38:22.4322129Z dimension corresponds to the cardinality of the first 2024-08-20T21:38:22.4323045Z input. 2024-08-20T21:38:22.4323392Z 2024-08-20T21:38:22.4323779Z If "ij" is selected, the dimensions are in the same 2024-08-20T21:38:22.4324685Z order as the cardinality of the inputs. 2024-08-20T21:38:22.4325204Z 2024-08-20T21:38:22.4325303Z Returns: 2024-08-20T21:38:22.4325697Z seq (sequence of Tensors): If the input has :math:`N` 2024-08-20T21:38:22.4326404Z tensors of size :math:`S_0 \ldots S_{N-1}``, then the 2024-08-20T21:38:22.4327009Z output will also have :math:`N` tensors, where each tensor 2024-08-20T21:38:22.4327759Z is of shape :math:`(S_0, ..., S_{N-1})`. 2024-08-20T21:38:22.4328079Z 2024-08-20T21:38:22.4328202Z Example:: 2024-08-20T21:38:22.4328376Z 2024-08-20T21:38:22.4328512Z >>> x = torch.tensor([1, 2, 3]) 2024-08-20T21:38:22.4328947Z >>> y = torch.tensor([4, 5, 6]) 2024-08-20T21:38:22.4329235Z 2024-08-20T21:38:22.4329733Z Observe the element-wise pairings across the grid, (1, 4), 2024-08-20T21:38:22.4330676Z (1, 5), ..., (3, 6). This is the same thing as the 2024-08-20T21:38:22.4331387Z cartesian product. 2024-08-20T21:38:22.4332304Z >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij') 2024-08-20T21:38:22.4333190Z >>> grid_x 2024-08-20T21:38:22.4333721Z tensor([[1, 1, 1], 2024-08-20T21:38:22.4334332Z [2, 2, 2], 2024-08-20T21:38:22.4334854Z [3, 3, 3]]) 2024-08-20T21:38:22.4335426Z >>> grid_y 2024-08-20T21:38:22.4335968Z tensor([[4, 5, 6], 2024-08-20T21:38:22.4336567Z [4, 5, 6], 2024-08-20T21:38:22.4337146Z [4, 5, 6]]) 2024-08-20T21:38:22.4337430Z 2024-08-20T21:38:22.4337647Z This correspondence can be seen when these grids are 2024-08-20T21:38:22.4338151Z stacked properly. 2024-08-20T21:38:22.4338753Z >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))), 2024-08-20T21:38:22.4339364Z ... torch.cartesian_prod(x, y)) 2024-08-20T21:38:22.4339806Z True 2024-08-20T21:38:22.4340054Z 2024-08-20T21:38:22.4340291Z `torch.meshgrid` is commonly used to produce a grid for 2024-08-20T21:38:22.4340794Z plotting. 2024-08-20T21:38:22.4341154Z >>> # xdoctest: +REQUIRES(module:matplotlib) 2024-08-20T21:38:22.4341663Z >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW) 2024-08-20T21:38:22.4342158Z >>> import matplotlib.pyplot as plt 2024-08-20T21:38:22.4342717Z >>> xs = torch.linspace(-5, 5, steps=100) 2024-08-20T21:38:22.4343270Z >>> ys = torch.linspace(-5, 5, steps=100) 2024-08-20T21:38:22.4343845Z >>> x, y = torch.meshgrid(xs, ys, indexing='xy') 2024-08-20T21:38:22.4344351Z >>> z = torch.sin(torch.sqrt(x * x + y * y)) 2024-08-20T21:38:22.4344888Z >>> ax = plt.axes(projection='3d') 2024-08-20T21:38:22.4345394Z >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy()) 2024-08-20T21:38:22.4345853Z >>> plt.show() 2024-08-20T21:38:22.4346079Z 2024-08-20T21:38:22.4346235Z .. image:: ../_static/img/meshgrid.png 2024-08-20T21:38:22.4346838Z :width: 512 2024-08-20T21:38:22.4347040Z 2024-08-20T21:38:22.4347133Z 2024-08-20T21:38:22.4347691Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:22.4348170Z 2024-08-20T21:38:22.4348968Z msg = Cannot scrape callname=_unique_impl in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=815. 2024-08-20T21:38:22.4350194Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:22.4351284Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> Tuple[Tensor, Tensor, Tensor] 2024-08-20T21:38:22.4351957Z 2024-08-20T21:38:22.4352153Z Returns the unique elements of the input tensor. 2024-08-20T21:38:22.4352502Z 2024-08-20T21:38:22.4352921Z .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that 2024-08-20T21:38:22.4353829Z this function also eliminates non-consecutive duplicate values. 2024-08-20T21:38:22.4354403Z 2024-08-20T21:38:22.4354715Z .. note:: Currently in the CUDA implementation and the CPU implementation, 2024-08-20T21:38:22.4355589Z `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument. 2024-08-20T21:38:22.4356566Z Sorting could be slow, so if your input tensor is already sorted, it is recommended to use 2024-08-20T21:38:22.4357357Z :func:`torch.unique_consecutive` which avoids the sorting. 2024-08-20T21:38:22.4357765Z 2024-08-20T21:38:22.4357860Z Args: 2024-08-20T21:38:22.4358149Z input (Tensor): the input tensor 2024-08-20T21:38:22.4358727Z sorted (bool): Whether to sort the unique elements in ascending order 2024-08-20T21:38:22.4359319Z before returning as output. 2024-08-20T21:38:22.4359881Z return_inverse (bool): Whether to also return the indices for where 2024-08-20T21:38:22.4360624Z elements in the original input ended up in the returned unique list. 2024-08-20T21:38:22.4361381Z return_counts (bool): Whether to also return the counts for each unique 2024-08-20T21:38:22.4361948Z element. 2024-08-20T21:38:22.4362420Z dim (int, optional): the dimension to operate upon. If ``None``, the 2024-08-20T21:38:22.4363145Z unique of the flattened input is returned. Otherwise, each of the 2024-08-20T21:38:22.4363865Z tensors indexed by the given dimension is treated as one of the 2024-08-20T21:38:22.4364590Z elements to apply the unique operation upon. See examples for more 2024-08-20T21:38:22.4365161Z details. Default: ``None`` 2024-08-20T21:38:22.4365446Z 2024-08-20T21:38:22.4365541Z Returns: 2024-08-20T21:38:22.4366173Z (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing 2024-08-20T21:38:22.4366721Z 2024-08-20T21:38:22.4367061Z - **output** (*Tensor*): the output list of unique scalar elements. 2024-08-20T21:38:22.4367828Z - **inverse_indices** (*Tensor*): (optional) if 2024-08-20T21:38:22.4368416Z :attr:`return_inverse` is True, there will be an additional 2024-08-20T21:38:22.4369091Z returned tensor (same shape as input) representing the indices 2024-08-20T21:38:22.4369783Z for where elements in the original input map to in the output; 2024-08-20T21:38:22.4370462Z otherwise, this function will only return a single tensor. 2024-08-20T21:38:22.4371081Z - **counts** (*Tensor*): (optional) if 2024-08-20T21:38:22.4371621Z :attr:`return_counts` is True, there will be an additional 2024-08-20T21:38:22.4372275Z returned tensor (same shape as output or output.size(dim), 2024-08-20T21:38:22.4372943Z if dim was specified) representing the number of occurrences 2024-08-20T21:38:22.4373498Z for each unique value or tensor. 2024-08-20T21:38:22.4373811Z 2024-08-20T21:38:22.4373920Z Example:: 2024-08-20T21:38:22.4374091Z 2024-08-20T21:38:22.4374376Z >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long)) 2024-08-20T21:38:22.4374920Z >>> output 2024-08-20T21:38:22.4396734Z tensor([1, 2, 3]) 2024-08-20T21:38:22.4397100Z 2024-08-20T21:38:22.4397291Z >>> output, inverse_indices = torch.unique( 2024-08-20T21:38:22.4397956Z ... torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True) 2024-08-20T21:38:22.4398541Z >>> output 2024-08-20T21:38:22.4398804Z tensor([1, 2, 3]) 2024-08-20T21:38:22.4399116Z >>> inverse_indices 2024-08-20T21:38:22.4399454Z tensor([0, 2, 1, 2]) 2024-08-20T21:38:22.4399676Z 2024-08-20T21:38:22.4399863Z >>> output, inverse_indices = torch.unique( 2024-08-20T21:38:22.4400520Z ... torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True) 2024-08-20T21:38:22.4401127Z >>> output 2024-08-20T21:38:22.4401557Z tensor([1, 2, 3]) 2024-08-20T21:38:22.4401869Z >>> inverse_indices 2024-08-20T21:38:22.4402198Z tensor([[0, 2], 2024-08-20T21:38:22.4402505Z [1, 2]]) 2024-08-20T21:38:22.4402703Z 2024-08-20T21:38:22.4402822Z >>> a = torch.tensor([ 2024-08-20T21:38:22.4403162Z ... [ 2024-08-20T21:38:22.4403445Z ... [1, 1, 0, 0], 2024-08-20T21:38:22.4403784Z ... [1, 1, 0, 0], 2024-08-20T21:38:22.4404135Z ... [0, 0, 1, 1], 2024-08-20T21:38:22.4404472Z ... ], 2024-08-20T21:38:22.4404731Z ... [ 2024-08-20T21:38:22.4405013Z ... [0, 0, 1, 1], 2024-08-20T21:38:22.4405361Z ... [0, 0, 1, 1], 2024-08-20T21:38:22.4405698Z ... [1, 1, 1, 1], 2024-08-20T21:38:22.4406035Z ... ], 2024-08-20T21:38:22.4406303Z ... [ 2024-08-20T21:38:22.4406562Z ... [1, 1, 0, 0], 2024-08-20T21:38:22.4406908Z ... [1, 1, 0, 0], 2024-08-20T21:38:22.4407256Z ... [0, 0, 1, 1], 2024-08-20T21:38:22.4407685Z ... ], 2024-08-20T21:38:22.4407957Z ... ]) 2024-08-20T21:38:22.4408118Z 2024-08-20T21:38:22.4408439Z >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]` 2024-08-20T21:38:22.4409199Z >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match 2024-08-20T21:38:22.4409842Z >>> # each other, so one of them will be removed. 2024-08-20T21:38:22.4410321Z >>> (a[0, :, :] == a[2, :, :]).all() 2024-08-20T21:38:22.4410702Z tensor(True) 2024-08-20T21:38:22.4411050Z >>> a_unique_dim0 = torch.unique(a, dim=0) 2024-08-20T21:38:22.4411469Z >>> a_unique_dim0 2024-08-20T21:38:22.4411891Z tensor([[[0, 0, 1, 1], 2024-08-20T21:38:22.4412235Z [0, 0, 1, 1], 2024-08-20T21:38:22.4412554Z [1, 1, 1, 1]], 2024-08-20T21:38:22.4412897Z [[1, 1, 0, 0], 2024-08-20T21:38:22.4413236Z [1, 1, 0, 0], 2024-08-20T21:38:22.4413556Z [0, 0, 1, 1]]]) 2024-08-20T21:38:22.4413804Z 2024-08-20T21:38:22.4414237Z >>> # Notice which sub-tensors from `a` match with the sub-tensors from 2024-08-20T21:38:22.4414810Z >>> # `a_unique_dim0`: 2024-08-20T21:38:22.4415198Z >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all() 2024-08-20T21:38:22.4415630Z tensor(True) 2024-08-20T21:38:22.4415986Z >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all() 2024-08-20T21:38:22.4416402Z tensor(True) 2024-08-20T21:38:22.4416598Z 2024-08-20T21:38:22.4416886Z >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are 2024-08-20T21:38:22.4417603Z >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of 2024-08-20T21:38:22.4418155Z >>> # them will be removed. 2024-08-20T21:38:22.4418547Z >>> (a[:, 0, :] == a[:, 1, :]).all() 2024-08-20T21:38:22.4418944Z tensor(True) 2024-08-20T21:38:22.4419259Z >>> torch.unique(a, dim=1) 2024-08-20T21:38:22.4419616Z tensor([[[0, 0, 1, 1], 2024-08-20T21:38:22.4419954Z [1, 1, 0, 0]], 2024-08-20T21:38:22.4420294Z [[1, 1, 1, 1], 2024-08-20T21:38:22.4420607Z [0, 0, 1, 1]], 2024-08-20T21:38:22.4420945Z [[0, 0, 1, 1], 2024-08-20T21:38:22.4421277Z [1, 1, 0, 0]]]) 2024-08-20T21:38:22.4421510Z 2024-08-20T21:38:22.4421806Z >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared. 2024-08-20T21:38:22.4422510Z >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and 2024-08-20T21:38:22.4423176Z >>> # `a[:, :, 3]` match each other as well. So in this case, two of the 2024-08-20T21:38:22.4423775Z >>> # sub-tensors will be removed. 2024-08-20T21:38:22.4424213Z >>> (a[:, :, 0] == a[:, :, 1]).all() 2024-08-20T21:38:22.4424604Z tensor(True) 2024-08-20T21:38:22.4424988Z >>> (a[:, :, 2] == a[:, :, 3]).all() 2024-08-20T21:38:22.4425389Z tensor(True) 2024-08-20T21:38:22.4425699Z >>> torch.unique(a, dim=2) 2024-08-20T21:38:22.4426050Z tensor([[[0, 1], 2024-08-20T21:38:22.4426359Z [0, 1], 2024-08-20T21:38:22.4426658Z [1, 0]], 2024-08-20T21:38:22.4426952Z [[1, 0], 2024-08-20T21:38:22.4427253Z [1, 0], 2024-08-20T21:38:22.4427551Z [1, 1]], 2024-08-20T21:38:22.4427841Z [[0, 1], 2024-08-20T21:38:22.4428139Z [0, 1], 2024-08-20T21:38:22.4428437Z [1, 0]]]) 2024-08-20T21:38:22.4428725Z 2024-08-20T21:38:22.4429269Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:22.4429754Z 2024-08-20T21:38:22.4494815Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=560. 2024-08-20T21:38:22.4495972Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:22.4496503Z 2024-08-20T21:38:22.4496752Z Load a model from a github repo or a local directory. 2024-08-20T21:38:22.4497113Z 2024-08-20T21:38:22.4497424Z Note: Loading a model is the typical use case, but this can also be used to 2024-08-20T21:38:22.4498186Z for loading other objects such as tokenizers, loss functions, etc. 2024-08-20T21:38:22.4498620Z 2024-08-20T21:38:22.4498917Z If ``source`` is 'github', ``repo_or_dir`` is expected to be 2024-08-20T21:38:22.4499513Z of the form ``repo_owner/repo_name[:ref]`` with an optional 2024-08-20T21:38:22.4500145Z ref (a tag or a branch). 2024-08-20T21:38:22.4500379Z 2024-08-20T21:38:22.4500879Z If ``source`` is 'local', ``repo_or_dir`` is expected to be a 2024-08-20T21:38:22.4501493Z path to a local directory. 2024-08-20T21:38:22.4501733Z 2024-08-20T21:38:22.4501821Z Args: 2024-08-20T21:38:22.4502190Z repo_or_dir (str): If ``source`` is 'github', 2024-08-20T21:38:22.4502897Z this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with 2024-08-20T21:38:22.4503993Z an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified, 2024-08-20T21:38:22.4504955Z the default branch is assumed to be ``main`` if it exists, and otherwise ``master``. 2024-08-20T21:38:22.4506048Z If ``source`` is 'local' then it should be a path to a local directory. 2024-08-20T21:38:22.4506877Z model (str): the name of a callable (entrypoint) defined in the 2024-08-20T21:38:22.4507535Z repo/dir's ``hubconf.py``. 2024-08-20T21:38:22.4508122Z *args (optional): the corresponding args for callable ``model``. 2024-08-20T21:38:22.4508895Z source (str, optional): 'github' or 'local'. Specifies how 2024-08-20T21:38:22.4509651Z ``repo_or_dir`` is to be interpreted. Default is 'github'. 2024-08-20T21:38:22.4510405Z trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``. 2024-08-20T21:38:22.4511225Z This parameter was introduced in v1.12 and helps ensuring that users 2024-08-20T21:38:22.4511918Z only run code from repos that they trust. 2024-08-20T21:38:22.4512255Z 2024-08-20T21:38:22.4512648Z - If ``False``, a prompt will ask the user whether the repo should 2024-08-20T21:38:22.4513199Z be trusted. 2024-08-20T21:38:22.4513757Z - If ``True``, the repo will be added to the trusted list and loaded 2024-08-20T21:38:22.4514418Z without requiring explicit confirmation. 2024-08-20T21:38:22.4515106Z - If ``"check"``, the repo will be checked against the list of 2024-08-20T21:38:22.4515825Z trusted repos in the cache. If it is not present in that list, the 2024-08-20T21:38:22.4516591Z behaviour will fall back onto the ``trust_repo=False`` option. 2024-08-20T21:38:22.4517395Z - If ``None``: this will raise a warning, inviting the user to set 2024-08-20T21:38:22.4518115Z ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This 2024-08-20T21:38:22.4519030Z is only present for backward compatibility and will be removed in 2024-08-20T21:38:22.4519641Z v2.0. 2024-08-20T21:38:22.4519806Z 2024-08-20T21:38:22.4520134Z Default is ``None`` and will eventually change to ``"check"`` in v2.0. 2024-08-20T21:38:22.4520931Z force_reload (bool, optional): whether to force a fresh download of 2024-08-20T21:38:22.4521681Z the github repo unconditionally. Does not have any effect if 2024-08-20T21:38:22.4522346Z ``source = 'local'``. Default is ``False``. 2024-08-20T21:38:22.4522981Z verbose (bool, optional): If ``False``, mute messages about hitting 2024-08-20T21:38:22.4523769Z local caches. Note that the message about first download cannot be 2024-08-20T21:38:22.4524567Z muted. Does not have any effect if ``source = 'local'``. 2024-08-20T21:38:22.4525121Z Default is ``True``. 2024-08-20T21:38:22.4525803Z skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit 2024-08-20T21:38:22.4526814Z specified by the ``github`` argument properly belongs to the repo owner. This will make 2024-08-20T21:38:22.4528028Z requests to the GitHub API; you can specify a non-default GitHub token by setting the 2024-08-20T21:38:22.4528884Z ``GITHUB_TOKEN`` environment variable. Default is ``False``. 2024-08-20T21:38:22.4529636Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2024-08-20T21:38:22.4530133Z 2024-08-20T21:38:22.4530243Z Returns: 2024-08-20T21:38:22.4530637Z The output of the ``model`` callable when called with the given 2024-08-20T21:38:22.4531235Z ``*args`` and ``**kwargs``. 2024-08-20T21:38:22.4531476Z 2024-08-20T21:38:22.4531661Z Example: 2024-08-20T21:38:22.4532042Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-08-20T21:38:22.4532499Z >>> # from a github repo 2024-08-20T21:38:22.4532852Z >>> repo = "pytorch/vision" 2024-08-20T21:38:22.4533277Z >>> model = torch.hub.load( 2024-08-20T21:38:22.4533779Z ... repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1" 2024-08-20T21:38:22.4534280Z ... ) 2024-08-20T21:38:22.4534537Z >>> # from a local directory 2024-08-20T21:38:22.4535008Z >>> path = "/some/local/path/pytorch/vision" 2024-08-20T21:38:22.4535505Z >>> # xdoctest: +SKIP 2024-08-20T21:38:22.4536039Z >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT") 2024-08-20T21:38:22.4536542Z 2024-08-20T21:38:22.4536950Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:22.4537433Z 2024-08-20T21:38:22.4538263Z msg = Cannot scrape callname=download_url_to_file in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=687. 2024-08-20T21:38:22.4539550Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:22.4540226Z Download object at the given URL to a local path. 2024-08-20T21:38:22.4540586Z 2024-08-20T21:38:22.4540680Z Args: 2024-08-20T21:38:22.4540993Z url (str): URL of the object to download 2024-08-20T21:38:22.4541634Z dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file`` 2024-08-20T21:38:22.4542588Z hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``. 2024-08-20T21:38:22.4543322Z Default: None 2024-08-20T21:38:22.4543856Z progress (bool, optional): whether or not to display a progress bar to stderr 2024-08-20T21:38:22.4544470Z Default: True 2024-08-20T21:38:22.4544690Z 2024-08-20T21:38:22.4544787Z Example: 2024-08-20T21:38:22.4545129Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-08-20T21:38:22.4545602Z >>> # xdoctest: +REQUIRES(POSIX) 2024-08-20T21:38:22.4546062Z >>> torch.hub.download_url_to_file( 2024-08-20T21:38:22.4546962Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth", 2024-08-20T21:38:22.4547683Z ... "/tmp/temporary_file", 2024-08-20T21:38:22.4548060Z ... ) 2024-08-20T21:38:22.4548215Z 2024-08-20T21:38:22.4548316Z 2024-08-20T21:38:22.4548844Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:22.4549344Z 2024-08-20T21:38:22.4550129Z msg = Cannot scrape callname=load_state_dict_from_url in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=812. 2024-08-20T21:38:22.4551372Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:22.4552052Z Loads the Torch serialized object at the given URL. 2024-08-20T21:38:22.4552420Z 2024-08-20T21:38:22.4552664Z If downloaded file is a zip file, it will be automatically 2024-08-20T21:38:22.4553176Z decompressed. 2024-08-20T21:38:22.4553352Z 2024-08-20T21:38:22.4553730Z If the object is already present in `model_dir`, it's deserialized and 2024-08-20T21:38:22.4554272Z returned. 2024-08-20T21:38:22.4554723Z The default value of ``model_dir`` is ``/checkpoints`` where 2024-08-20T21:38:22.4555442Z ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`. 2024-08-20T21:38:22.4555873Z 2024-08-20T21:38:22.4555967Z Args: 2024-08-20T21:38:22.4556273Z url (str): URL of the object to download 2024-08-20T21:38:22.4556858Z model_dir (str, optional): directory in which to save the object 2024-08-20T21:38:22.4557756Z map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load) 2024-08-20T21:38:22.4558743Z progress (bool, optional): whether or not to display a progress bar to stderr. 2024-08-20T21:38:22.4559363Z Default: True 2024-08-20T21:38:22.4560194Z check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention 2024-08-20T21:38:22.4561202Z ``filename-.ext`` where ```` is the first eight or more 2024-08-20T21:38:22.4561992Z digits of the SHA256 hash of the contents of the file. The hash is used to 2024-08-20T21:38:22.4562738Z ensure unique names and to verify the contents of the file. 2024-08-20T21:38:22.4563251Z Default: False 2024-08-20T21:38:22.4563933Z file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set. 2024-08-20T21:38:22.4565001Z weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects. 2024-08-20T21:38:22.4565926Z Recommended for untrusted sources. See :func:`~torch.load` for more details. 2024-08-20T21:38:22.4566442Z 2024-08-20T21:38:22.4566539Z Example: 2024-08-20T21:38:22.4566883Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-08-20T21:38:22.4567489Z >>> state_dict = torch.hub.load_state_dict_from_url( 2024-08-20T21:38:22.4568236Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth" 2024-08-20T21:38:22.4568792Z ... ) 2024-08-20T21:38:22.4568951Z 2024-08-20T21:38:22.4569050Z 2024-08-20T21:38:22.4569573Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:22.4570066Z 2024-08-20T21:38:22.4576289Z msg = Cannot scrape callname=Library.fallback in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=334. 2024-08-20T21:38:22.4577527Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-08-20T21:38:22.4578337Z Registers the function implementation as the fallback for the given key. 2024-08-20T21:38:22.4578806Z 2024-08-20T21:38:22.4579091Z This function only works for a library with global namespace ("_"). 2024-08-20T21:38:22.4579556Z 2024-08-20T21:38:22.4579658Z Args: 2024-08-20T21:38:22.4580214Z fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel` 2024-08-20T21:38:22.4580898Z to register a fallthrough. 2024-08-20T21:38:22.4581773Z dispatch_key: dispatch key that the input function should be registered for. By default, it uses 2024-08-20T21:38:22.4582609Z the dispatch key that the library was created with. 2024-08-20T21:38:22.4583486Z with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument 2024-08-20T21:38:22.4584627Z to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls. 2024-08-20T21:38:22.4585269Z 2024-08-20T21:38:22.4585388Z Example:: 2024-08-20T21:38:22.4585781Z >>> my_lib = Library("_", "IMPL") 2024-08-20T21:38:22.4586247Z >>> def fallback_kernel(op, *args, **kwargs): 2024-08-20T21:38:22.4586769Z >>> # Handle all autocast ops generically 2024-08-20T21:38:22.4587209Z >>> # ... 2024-08-20T21:38:22.4587589Z >>> my_lib.fallback(fallback_kernel, "Autocast") 2024-08-20T21:38:22.4588041Z 2024-08-20T21:38:22.4589191Z Original Error: IndentationError('expected an indented block after function definition on line 2', ('', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7)) 2024-08-20T21:38:22.4590183Z 2024-08-20T21:38:22.4590343Z my_lib.fallback(fallback_kernel, "Autocast") 2024-08-20T21:38:22.4590753Z ^ 2024-08-20T21:38:22.4639333Z msg = Cannot scrape callname=register_fake in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=692. 2024-08-20T21:38:22.4640565Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-08-20T21:38:22.4641380Z Register a FakeTensor implementation ("fake impl") for this operator. 2024-08-20T21:38:22.4641838Z 2024-08-20T21:38:22.4642165Z Also sometimes known as a "meta kernel", "abstract impl". 2024-08-20T21:38:22.4642569Z 2024-08-20T21:38:22.4642891Z An "FakeTensor implementation" specifies the behavior of this operator on 2024-08-20T21:38:22.4643688Z Tensors that carry no data ("FakeTensor"). Given some input Tensors with 2024-08-20T21:38:22.4644461Z certain properties (sizes/strides/storage_offset/device), it specifies 2024-08-20T21:38:22.4645110Z what the properties of the output Tensors are. 2024-08-20T21:38:22.4645450Z 2024-08-20T21:38:22.4645760Z The FakeTensor implementation has the same signature as the operator. 2024-08-20T21:38:22.4646524Z It is run for both FakeTensors and meta tensors. To write a FakeTensor 2024-08-20T21:38:22.4647803Z implementation, assume that all Tensor inputs to the operator are 2024-08-20T21:38:22.4648820Z regular CPU/CUDA/Meta tensors, but they do not have storage, and 2024-08-20T21:38:22.4649733Z you are trying to return regular CPU/CUDA/Meta tensor(s) as output. 2024-08-20T21:38:22.4650977Z The FakeTensor implementation must consist of only PyTorch operations 2024-08-20T21:38:22.4651712Z (and may not directly access the storage or data of any input or 2024-08-20T21:38:22.4652270Z intermediate Tensors). 2024-08-20T21:38:22.4652492Z 2024-08-20T21:38:22.4652696Z This API may be used as a decorator (see examples). 2024-08-20T21:38:22.4653068Z 2024-08-20T21:38:22.4653245Z For a detailed guide on custom ops, please see 2024-08-20T21:38:22.4653893Z https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html 2024-08-20T21:38:22.4654348Z 2024-08-20T21:38:22.4654461Z Examples: 2024-08-20T21:38:22.4654723Z >>> import torch 2024-08-20T21:38:22.4655051Z >>> import numpy as np 2024-08-20T21:38:22.4655421Z >>> from torch import Tensor 2024-08-20T21:38:22.4655777Z >>> 2024-08-20T21:38:22.4656292Z >>> # Example 1: an operator without data-dependent output shape 2024-08-20T21:38:22.4657005Z >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=()) 2024-08-20T21:38:22.4657812Z >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2024-08-20T21:38:22.4658509Z >>> raise NotImplementedError("Implementation goes here") 2024-08-20T21:38:22.4659159Z >>> 2024-08-20T21:38:22.4659527Z >>> @torch.library.register_fake("mylib::custom_linear") 2024-08-20T21:38:22.4660032Z >>> def _(x, weight, bias): 2024-08-20T21:38:22.4660419Z >>> assert x.dim() == 2 2024-08-20T21:38:22.4660803Z >>> assert weight.dim() == 2 2024-08-20T21:38:22.4661219Z >>> assert bias.dim() == 1 2024-08-20T21:38:22.4661661Z >>> assert x.shape[1] == weight.shape[1] 2024-08-20T21:38:22.4662143Z >>> assert weight.shape[0] == bias.shape[0] 2024-08-20T21:38:22.4662630Z >>> assert x.device == weight.device 2024-08-20T21:38:22.4663041Z >>> 2024-08-20T21:38:22.4663333Z >>> return (x @ weight.t()) + bias 2024-08-20T21:38:22.4663732Z >>> 2024-08-20T21:38:22.4664107Z >>> with torch._subclasses.fake_tensor.FakeTensorMode(): 2024-08-20T21:38:22.4664610Z >>> x = torch.randn(2, 3) 2024-08-20T21:38:22.4665011Z >>> w = torch.randn(3, 3) 2024-08-20T21:38:22.4665397Z >>> b = torch.randn(3) 2024-08-20T21:38:22.4665830Z >>> y = torch.ops.mylib.custom_linear(x, w, b) 2024-08-20T21:38:22.4666266Z >>> 2024-08-20T21:38:22.4666543Z >>> assert y.shape == (2, 3) 2024-08-20T21:38:22.4666893Z >>> 2024-08-20T21:38:22.4667355Z >>> # Example 2: an operator with data-dependent output shape 2024-08-20T21:38:22.4668043Z >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=()) 2024-08-20T21:38:22.4668707Z >>> def custom_nonzero(x: Tensor) -> Tensor: 2024-08-20T21:38:22.4669177Z >>> x_np = x.numpy(force=True) 2024-08-20T21:38:22.4669725Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2024-08-20T21:38:22.4670247Z >>> return torch.tensor(res, device=x.device) 2024-08-20T21:38:22.4670669Z >>> 2024-08-20T21:38:22.4671047Z >>> @torch.library.register_fake("mylib::custom_nonzero") 2024-08-20T21:38:22.4671542Z >>> def _(x): 2024-08-20T21:38:22.4671983Z >>> # Number of nonzero-elements is data-dependent. 2024-08-20T21:38:22.4672549Z >>> # Since we cannot peek at the data in an fake impl, 2024-08-20T21:38:22.4673140Z >>> # we use the ctx object to construct a new symint that 2024-08-20T21:38:22.4673729Z >>> # represents the data-dependent size. 2024-08-20T21:38:22.4674204Z >>> ctx = torch.library.get_ctx() 2024-08-20T21:38:22.4674653Z >>> nnz = ctx.new_dynamic_size() 2024-08-20T21:38:22.4675066Z >>> shape = [nnz, x.dim()] 2024-08-20T21:38:22.4675541Z >>> result = x.new_empty(shape, dtype=torch.int64) 2024-08-20T21:38:22.4676012Z >>> return result 2024-08-20T21:38:22.4676329Z >>> 2024-08-20T21:38:22.4676723Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2024-08-20T21:38:22.4677216Z >>> 2024-08-20T21:38:22.4677501Z >>> x = torch.tensor([0, 1, 2, 3, 4, 0]) 2024-08-20T21:38:22.4678148Z >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x) 2024-08-20T21:38:22.4678777Z >>> trace.print_readable() 2024-08-20T21:38:22.4679119Z >>> 2024-08-20T21:38:22.4679565Z >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x)) 2024-08-20T21:38:22.4680023Z 2024-08-20T21:38:22.4680121Z 2024-08-20T21:38:22.4681077Z Original Error: IndentationError('expected an indented block after function definition on line 37', ('', 38, 1, '_._ = None\n', 38, 2)) 2024-08-20T21:38:22.4681944Z 2024-08-20T21:38:22.4682037Z _._ = None 2024-08-20T21:38:22.4682276Z ^ 2024-08-20T21:38:22.4683244Z msg = Cannot scrape callname=register_autograd in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=813. 2024-08-20T21:38:22.4684483Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:22.4685154Z Register a backward formula for this custom op. 2024-08-20T21:38:22.4685632Z 2024-08-20T21:38:22.4685919Z In order for an operator to work with autograd, you need to register 2024-08-20T21:38:22.4686490Z a backward formula: 2024-08-20T21:38:22.4686982Z 1. You must tell us how to compute gradients during the backward pass 2024-08-20T21:38:22.4687704Z by providing us a "backward" function. 2024-08-20T21:38:22.4688320Z 2. If you need any values from the forward to compute gradients, you can 2024-08-20T21:38:22.4688961Z use `setup_context` to save values for backward. 2024-08-20T21:38:22.4689318Z 2024-08-20T21:38:22.4689612Z ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``: 2024-08-20T21:38:22.4690427Z - ``grads`` is one or more gradients. The number of gradients matches 2024-08-20T21:38:22.4691003Z the number of outputs of the operator. 2024-08-20T21:38:22.4691615Z The ``ctx`` object is `the same ctx object `_ used by 2024-08-20T21:38:22.4692446Z :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the 2024-08-20T21:38:22.4693407Z same as :meth:`torch.autograd.Function.backward`. 2024-08-20T21:38:22.4694002Z 2024-08-20T21:38:22.4694529Z ``setup_context(ctx, inputs, output)`` runs during the forward pass. 2024-08-20T21:38:22.4695294Z Please save quantities needed for backward onto the ``ctx`` object via 2024-08-20T21:38:22.4696076Z either :meth:`torch.autograd.function.FunctionCtx.save_for_backward` 2024-08-20T21:38:22.4696814Z or assigning them as attributes of ``ctx``. If your custom op has 2024-08-20T21:38:22.4697615Z kwarg-only arguments, we expect the signature of ``setup_context`` 2024-08-20T21:38:22.4698320Z to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``. 2024-08-20T21:38:22.4698828Z 2024-08-20T21:38:22.4699141Z Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is, 2024-08-20T21:38:22.4699904Z they may not directly access :meth:`torch.Tensor.data_ptr` and they must 2024-08-20T21:38:22.4700802Z not depend on or mutate global state. If you need a non-traceable backward, 2024-08-20T21:38:22.4701614Z you can make it a separate custom_op that you call inside ``backward_fn``. 2024-08-20T21:38:22.4702092Z 2024-08-20T21:38:22.4702191Z Examples: 2024-08-20T21:38:22.4702465Z >>> import torch 2024-08-20T21:38:22.4702794Z >>> import numpy as np 2024-08-20T21:38:22.4703151Z >>> from torch import Tensor 2024-08-20T21:38:22.4703521Z >>> 2024-08-20T21:38:22.4703945Z >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=()) 2024-08-20T21:38:22.4704571Z >>> def numpy_sin(x: Tensor) -> Tensor: 2024-08-20T21:38:22.4705009Z >>> x_np = x.cpu().numpy() 2024-08-20T21:38:22.4705407Z >>> y_np = np.sin(x_np) 2024-08-20T21:38:22.4705860Z >>> return torch.from_numpy(y_np).to(device=x.device) 2024-08-20T21:38:22.4706329Z >>> 2024-08-20T21:38:22.4706747Z >>> def setup_context(ctx, inputs, output) -> Tensor: 2024-08-20T21:38:22.4707209Z >>> x, = inputs 2024-08-20T21:38:22.4707563Z >>> ctx.save_for_backward(x) 2024-08-20T21:38:22.4707941Z >>> 2024-08-20T21:38:22.4708202Z >>> def backward(ctx, grad): 2024-08-20T21:38:22.4708601Z >>> x, = ctx.saved_tensors 2024-08-20T21:38:22.4709000Z >>> return grad * x.cos() 2024-08-20T21:38:22.4709352Z >>> 2024-08-20T21:38:22.4709659Z >>> torch.library.register_autograd( 2024-08-20T21:38:22.4710215Z ... "mylib::numpy_sin", backward, setup_context=setup_context 2024-08-20T21:38:22.4710700Z ... ) 2024-08-20T21:38:22.4710953Z >>> 2024-08-20T21:38:22.4711264Z >>> x = torch.randn(3, requires_grad=True) 2024-08-20T21:38:22.4711698Z >>> y = numpy_sin(x) 2024-08-20T21:38:22.4712165Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2024-08-20T21:38:22.4712720Z >>> assert torch.allclose(grad_x, x.cos()) 2024-08-20T21:38:22.4713195Z >>> 2024-08-20T21:38:22.4713555Z >>> # Example with a keyword-only arg 2024-08-20T21:38:22.4714128Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2024-08-20T21:38:22.4714806Z >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor: 2024-08-20T21:38:22.4715295Z >>> x_np = x.cpu().numpy() 2024-08-20T21:38:22.4715690Z >>> y_np = x_np * val 2024-08-20T21:38:22.4716146Z >>> return torch.from_numpy(y_np).to(device=x.device) 2024-08-20T21:38:22.4716595Z >>> 2024-08-20T21:38:22.4717123Z >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor: 2024-08-20T21:38:22.4717751Z >>> ctx.val = keyword_only_inputs["val"] 2024-08-20T21:38:22.4718161Z >>> 2024-08-20T21:38:22.4718434Z >>> def backward(ctx, grad): 2024-08-20T21:38:22.4718832Z >>> return grad * ctx.val 2024-08-20T21:38:22.4719183Z >>> 2024-08-20T21:38:22.4719486Z >>> torch.library.register_autograd( 2024-08-20T21:38:22.4720039Z ... "mylib::numpy_mul", backward, setup_context=setup_context 2024-08-20T21:38:22.4720522Z ... ) 2024-08-20T21:38:22.4720776Z >>> 2024-08-20T21:38:22.4721086Z >>> x = torch.randn(3, requires_grad=True) 2024-08-20T21:38:22.4721527Z >>> y = numpy_mul(x, val=3.14) 2024-08-20T21:38:22.4722032Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2024-08-20T21:38:22.4722651Z >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14)) 2024-08-20T21:38:22.4723040Z 2024-08-20T21:38:22.4723129Z 2024-08-20T21:38:22.4723665Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:22.4724151Z 2024-08-20T21:38:22.4725007Z msg = Cannot scrape callname=opcheck in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=1221. 2024-08-20T21:38:22.4726200Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:22.4726987Z Given an operator and some sample arguments, tests if the operator is 2024-08-20T21:38:22.4727704Z registered correctly. 2024-08-20T21:38:22.4727934Z 2024-08-20T21:38:22.4728233Z That is, when you use the torch.library/TORCH_LIBRARY APIs to create a 2024-08-20T21:38:22.4729032Z custom op, you specified metadata (e.g. mutability info) about the custom op 2024-08-20T21:38:22.4729824Z and these APIs require that the functions you pass them satisfy certain 2024-08-20T21:38:22.4730606Z properties (e.g. no data pointer access in the fake/meta/abstract kernel) 2024-08-20T21:38:22.4731264Z ``opcheck`` tests these metadata and properties. 2024-08-20T21:38:22.4731608Z 2024-08-20T21:38:22.4731748Z Concretely, we test the following: 2024-08-20T21:38:22.4732294Z - test_schema: if the operator's schema is correct. 2024-08-20T21:38:22.4732991Z - test_autograd_registration: if autograd was registered correctly. 2024-08-20T21:38:22.4733707Z - test_faketensor: If the operator has a FakeTensor kernel 2024-08-20T21:38:22.4734345Z (and if it is correct). The FakeTensor kernel is necessary ( 2024-08-20T21:38:22.4735050Z but not sufficient) for the operator to work with PyTorch compilation 2024-08-20T21:38:22.4735637Z APIs (torch.compile/export/FX). 2024-08-20T21:38:22.4736242Z - test_aot_dispatch_dynamic: If the operator has correct behavior 2024-08-20T21:38:22.4736903Z with PyTorch compilation APIs (torch.compile/export/FX). 2024-08-20T21:38:22.4737583Z This checks that the outputs (and gradients, if applicable) are the 2024-08-20T21:38:22.4738269Z same under eager-mode PyTorch and torch.compile. 2024-08-20T21:38:22.4738798Z This test is a superset of ``test_faketensor``. 2024-08-20T21:38:22.4739140Z 2024-08-20T21:38:22.4739412Z For best results, please call ``opcheck`` multiple times with a 2024-08-20T21:38:22.4740052Z representative set of inputs. If your operator supports 2024-08-20T21:38:22.4740752Z autograd, please use ``opcheck`` with inputs with ``requires_grad = True``; 2024-08-20T21:38:22.4741638Z if your operator supports multiple devices (e.g. CPU and CUDA), please 2024-08-20T21:38:22.4742294Z use ``opcheck`` with inputs on all supported devices. 2024-08-20T21:38:22.4742667Z 2024-08-20T21:38:22.4742761Z Args: 2024-08-20T21:38:22.4743155Z op: The operator. Must either be a function decorated with 2024-08-20T21:38:22.4743834Z :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket 2024-08-20T21:38:22.4744651Z found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo) 2024-08-20T21:38:22.4745252Z args: The args to the operator 2024-08-20T21:38:22.4745680Z kwargs: The kwargs to the operator 2024-08-20T21:38:22.4746217Z test_utils: Tests that we should run. Default: all of them. 2024-08-20T21:38:22.4746994Z Example: ("test_schema", "test_faketensor") 2024-08-20T21:38:22.4747593Z raise_exception: If we should raise an exception on the first 2024-08-20T21:38:22.4748230Z error. If False, we will return a dict with information 2024-08-20T21:38:22.4748756Z on if each test passed or not. 2024-08-20T21:38:22.4749051Z 2024-08-20T21:38:22.4749169Z .. warning:: 2024-08-20T21:38:22.4749341Z 2024-08-20T21:38:22.4749636Z opcheck and :func:`torch.autograd.gradcheck` test different things; 2024-08-20T21:38:22.4750379Z opcheck tests if your usage of torch.library APIs is correct while 2024-08-20T21:38:22.4751116Z :func:`torch.autograd.gradcheck` tests if your autograd formula is 2024-08-20T21:38:22.4751850Z mathematically correct. Use both to test custom ops that support 2024-08-20T21:38:22.4752400Z gradient computation. 2024-08-20T21:38:22.4752765Z 2024-08-20T21:38:22.4752864Z Example: 2024-08-20T21:38:22.4753022Z 2024-08-20T21:38:22.4753219Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-20T21:38:22.4753818Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2024-08-20T21:38:22.4754517Z >>> def numpy_add(x: Tensor, y: float) -> Tensor: 2024-08-20T21:38:22.4754998Z >>> x_np = x.numpy(force=True) 2024-08-20T21:38:22.4755401Z >>> z_np = x_np + y 2024-08-20T21:38:22.4755831Z >>> return torch.from_numpy(z_np).to(x.device) 2024-08-20T21:38:22.4756275Z >>> 2024-08-20T21:38:22.4756541Z >>> @numpy_sin.register_fake 2024-08-20T21:38:22.4756922Z >>> def _(x, y): 2024-08-20T21:38:22.4757269Z >>> return torch.empty_like(x) 2024-08-20T21:38:22.4757646Z >>> 2024-08-20T21:38:22.4757960Z >>> def setup_context(ctx, inputs, output): 2024-08-20T21:38:22.4758398Z >>> y, = inputs 2024-08-20T21:38:22.4758715Z >>> ctx.y = y 2024-08-20T21:38:22.4759017Z >>> 2024-08-20T21:38:22.4759297Z >>> def backward(ctx, grad): 2024-08-20T21:38:22.4759692Z >>> return grad * ctx.y, None 2024-08-20T21:38:22.4760079Z >>> 2024-08-20T21:38:22.4760516Z >>> numpy_sin.register_autograd(backward, setup_context=setup_context) 2024-08-20T21:38:22.4761053Z >>> 2024-08-20T21:38:22.4761319Z >>> sample_inputs = [ 2024-08-20T21:38:22.4761682Z >>> (torch.randn(3), 3.14), 2024-08-20T21:38:22.4762191Z >>> (torch.randn(2, 3, device='cuda'), 2.718), 2024-08-20T21:38:22.4762723Z >>> (torch.randn(1, 10, requires_grad=True), 1.234), 2024-08-20T21:38:22.4763428Z >>> (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18), 2024-08-20T21:38:22.4763943Z >>> ] 2024-08-20T21:38:22.4764198Z >>> 2024-08-20T21:38:22.4764479Z >>> for args in sample_inputs: 2024-08-20T21:38:22.4764915Z >>> torch.library.opcheck(foo, args) 2024-08-20T21:38:22.4765249Z 2024-08-20T21:38:22.4765338Z 2024-08-20T21:38:22.4765869Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:22.4766347Z 2024-08-20T21:38:22.5112713Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py line=1042. 2024-08-20T21:38:22.5113939Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:22.5114934Z load(f, map_location=None, pickle_module=pickle, *, weights_only=False, mmap=None, **pickle_load_args) 2024-08-20T21:38:22.5115552Z 2024-08-20T21:38:22.5115786Z Loads an object saved with :func:`torch.save` from a file. 2024-08-20T21:38:22.5116182Z 2024-08-20T21:38:22.5116592Z :func:`torch.load` uses Python's unpickling facilities but treats storages, 2024-08-20T21:38:22.5117364Z which underlie tensors, specially. They are first deserialized on the 2024-08-20T21:38:22.5118141Z CPU and are then moved to the device they were saved from. If this fails 2024-08-20T21:38:22.5119019Z (e.g. because the run time system doesn't have certain devices), an exception 2024-08-20T21:38:22.5119836Z is raised. However, storages can be dynamically remapped to an alternative 2024-08-20T21:38:22.5120534Z set of devices using the :attr:`map_location` argument. 2024-08-20T21:38:22.5120922Z 2024-08-20T21:38:22.5121259Z If :attr:`map_location` is a callable, it will be called once for each serialized 2024-08-20T21:38:22.5122068Z storage with two arguments: storage and location. The storage argument 2024-08-20T21:38:22.5122842Z will be the initial deserialization of the storage, residing on the CPU. 2024-08-20T21:38:22.5123614Z Each serialized storage has a location tag associated with it which 2024-08-20T21:38:22.5124354Z identifies the device it was saved from, and this tag is the second 2024-08-20T21:38:22.5125406Z argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'`` 2024-08-20T21:38:22.5126322Z for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors. 2024-08-20T21:38:22.5127079Z :attr:`map_location` should return either ``None`` or a storage. If 2024-08-20T21:38:22.5127967Z :attr:`map_location` returns a storage, it will be used as the final deserialized 2024-08-20T21:38:22.5128799Z object, already moved to the right device. Otherwise, :func:`torch.load` will 2024-08-20T21:38:22.5129726Z fall back to the default behavior, as if :attr:`map_location` wasn't specified. 2024-08-20T21:38:22.5130228Z 2024-08-20T21:38:22.5130575Z If :attr:`map_location` is a :class:`torch.device` object or a string containing 2024-08-20T21:38:22.5131386Z a device tag, it indicates the location where all tensors should be loaded. 2024-08-20T21:38:22.5131886Z 2024-08-20T21:38:22.5132237Z Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags 2024-08-20T21:38:22.5133050Z appearing in the file (keys), to ones that specify where to put the 2024-08-20T21:38:22.5133607Z storages (values). 2024-08-20T21:38:22.5133806Z 2024-08-20T21:38:22.5134094Z User extensions can register their own location tags and tagging and 2024-08-20T21:38:22.5134899Z deserialization methods using :func:`torch.serialization.register_package`. 2024-08-20T21:38:22.5135401Z 2024-08-20T21:38:22.5135507Z Args: 2024-08-20T21:38:22.5136204Z f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`), 2024-08-20T21:38:22.5137064Z or a string or os.PathLike object containing a file name 2024-08-20T21:38:22.5137906Z map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage 2024-08-20T21:38:22.5138594Z locations 2024-08-20T21:38:22.5139092Z pickle_module: module used for unpickling metadata and objects (has to 2024-08-20T21:38:22.5139796Z match the :attr:`pickle_module` used to serialize file) 2024-08-20T21:38:22.5140467Z weights_only: Indicates whether unpickler should be restricted to 2024-08-20T21:38:22.5141114Z loading only tensors, primitive types, dictionaries 2024-08-20T21:38:22.5141923Z and any types added via :func:`torch.serialization.add_safe_globals`. 2024-08-20T21:38:22.5142863Z mmap: Indicates whether the file should be mmaped rather than loading all the storages into memory. 2024-08-20T21:38:22.5143949Z Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they 2024-08-20T21:38:22.5145069Z are moved to the location that they were tagged with when saving, or specified by ``map_location``. This 2024-08-20T21:38:22.5146337Z second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the 2024-08-20T21:38:22.5147529Z tensor storages from disk to CPU memory in the first step, ``f`` is mmaped. 2024-08-20T21:38:22.5148335Z pickle_load_args: (Python 3 only) optional keyword arguments passed over to 2024-08-20T21:38:22.5149124Z :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g., 2024-08-20T21:38:22.5149705Z :attr:`errors=...`. 2024-08-20T21:38:22.5149948Z 2024-08-20T21:38:22.5150068Z .. warning:: 2024-08-20T21:38:22.5150535Z :func:`torch.load()` unless `weights_only` parameter is set to `True`, 2024-08-20T21:38:22.5151260Z uses ``pickle`` module implicitly, which is known to be insecure. 2024-08-20T21:38:22.5152060Z It is possible to construct malicious pickle data which will execute arbitrary code 2024-08-20T21:38:22.5152923Z during unpickling. Never load data that could have come from an untrusted 2024-08-20T21:38:22.5153839Z source in an unsafe mode, or that could have been tampered with. **Only load data you trust**. 2024-08-20T21:38:22.5154424Z 2024-08-20T21:38:22.5154536Z .. note:: 2024-08-20T21:38:22.5155172Z When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors 2024-08-20T21:38:22.5156208Z will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')`` 2024-08-20T21:38:22.5157141Z and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint. 2024-08-20T21:38:22.5157689Z 2024-08-20T21:38:22.5157786Z .. note:: 2024-08-20T21:38:22.5158391Z By default, we decode byte strings as ``utf-8``. This is to avoid a common error 2024-08-20T21:38:22.5159271Z case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...`` 2024-08-20T21:38:22.5159989Z when loading files saved by Python 2 in Python 3. If this default 2024-08-20T21:38:22.5160804Z is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how 2024-08-20T21:38:22.5161750Z these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them 2024-08-20T21:38:22.5162653Z to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them 2024-08-20T21:38:22.5163457Z as byte arrays which can be decoded later with ``byte_array.decode(...)``. 2024-08-20T21:38:22.5163955Z 2024-08-20T21:38:22.5164059Z Example: 2024-08-20T21:38:22.5164396Z >>> # xdoctest: +SKIP("undefined filepaths") 2024-08-20T21:38:22.5164902Z >>> torch.load("tensors.pt", weights_only=True) 2024-08-20T21:38:22.5165384Z # Load all tensors onto the CPU 2024-08-20T21:38:22.5166009Z >>> torch.load("tensors.pt", map_location=torch.device("cpu"), weights_only=True) 2024-08-20T21:38:22.5166694Z # Load all tensors onto the CPU, using a function 2024-08-20T21:38:22.5167159Z >>> torch.load( 2024-08-20T21:38:22.5167784Z ... "tensors.pt", map_location=lambda storage, loc: storage, weights_only=True 2024-08-20T21:38:22.5168359Z ... ) 2024-08-20T21:38:22.5168656Z # Load all tensors onto GPU 1 2024-08-20T21:38:22.5169047Z >>> torch.load( 2024-08-20T21:38:22.5169350Z ... "tensors.pt", 2024-08-20T21:38:22.5169799Z ... map_location=lambda storage, loc: storage.cuda(1), 2024-08-20T21:38:22.5170294Z ... weights_only=True, 2024-08-20T21:38:22.5170841Z ... ) # type: ignore[attr-defined] 2024-08-20T21:38:22.5171287Z # Map tensors from GPU 1 to GPU 0 2024-08-20T21:38:22.5171913Z >>> torch.load("tensors.pt", map_location={"cuda:1": "cuda:0"}, weights_only=True) 2024-08-20T21:38:22.5172565Z # Load tensor from io.BytesIO object 2024-08-20T21:38:22.5173197Z # Loading from a buffer setting weights_only=False, warning this can be unsafe 2024-08-20T21:38:22.5173857Z >>> with open("tensor.pt", "rb") as f: 2024-08-20T21:38:22.5174312Z ... buffer = io.BytesIO(f.read()) 2024-08-20T21:38:22.5174766Z >>> torch.load(buffer, weights_only=False) 2024-08-20T21:38:22.5175367Z # Load a module with 'ascii' encoding for unpickling 2024-08-20T21:38:22.5176078Z # Loading from a module setting weights_only=False, warning this can be unsafe 2024-08-20T21:38:22.5176824Z >>> torch.load("module.pt", encoding="ascii", weights_only=False) 2024-08-20T21:38:22.5177348Z 2024-08-20T21:38:22.5177882Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:22.5178368Z 2024-08-20T21:38:22.7463662Z msg = Cannot scrape callname=cudart in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/__init__.py line=341. 2024-08-20T21:38:22.7464900Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-08-20T21:38:22.7465533Z Retrieves the CUDA runtime API module. 2024-08-20T21:38:22.7465855Z 2024-08-20T21:38:22.7465872Z 2024-08-20T21:38:22.7466206Z This function initializes the CUDA runtime environment if it is not already 2024-08-20T21:38:22.7467017Z initialized and returns the CUDA runtime API module (_cudart). The CUDA 2024-08-20T21:38:22.7468050Z runtime API module provides access to various CUDA runtime functions. 2024-08-20T21:38:22.7468526Z 2024-08-20T21:38:22.7468620Z Args: 2024-08-20T21:38:22.7468871Z ``None`` 2024-08-20T21:38:22.7469038Z 2024-08-20T21:38:22.7469152Z Returns: 2024-08-20T21:38:22.7469498Z module: The CUDA runtime API module (_cudart). 2024-08-20T21:38:22.7469843Z 2024-08-20T21:38:22.7469948Z Raises: 2024-08-20T21:38:22.7470476Z RuntimeError: If CUDA cannot be re-initialized in a forked subprocess. 2024-08-20T21:38:22.7471440Z AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable. 2024-08-20T21:38:22.7472089Z 2024-08-20T21:38:22.7472270Z Example of CUDA operations with profiling: 2024-08-20T21:38:22.7472695Z >>> import torch 2024-08-20T21:38:22.7473086Z >>> from torch.cuda import cudart, check_error 2024-08-20T21:38:22.7473533Z >>> import os 2024-08-20T21:38:22.7473811Z >>> 2024-08-20T21:38:22.7474167Z >>> os.environ['CUDA_PROFILE'] = '1' 2024-08-20T21:38:22.7474570Z >>> 2024-08-20T21:38:22.7474886Z >>> def perform_cuda_operations_with_streams(): 2024-08-20T21:38:22.7475376Z >>> stream = torch.cuda.Stream() 2024-08-20T21:38:22.7475841Z >>> with torch.cuda.stream(stream): 2024-08-20T21:38:22.7476387Z >>> x = torch.randn(100, 100, device='cuda') 2024-08-20T21:38:22.7476944Z >>> y = torch.randn(100, 100, device='cuda') 2024-08-20T21:38:22.7477397Z >>> z = torch.mul(x, y) 2024-08-20T21:38:22.7477778Z >>> return z 2024-08-20T21:38:22.7478065Z >>> 2024-08-20T21:38:22.7478347Z >>> torch.cuda.synchronize() 2024-08-20T21:38:22.7478801Z >>> print("====== Start nsys profiling ======") 2024-08-20T21:38:22.7479299Z >>> check_error(cudart().cudaProfilerStart()) 2024-08-20T21:38:22.7479821Z >>> with torch.autograd.profiler.emit_nvtx(): 2024-08-20T21:38:22.7480374Z >>> result = perform_cuda_operations_with_streams() 2024-08-20T21:38:22.7480892Z >>> print("CUDA operations completed.") 2024-08-20T21:38:22.7481438Z >>> check_error(torch.cuda.cudart().cudaProfilerStop()) 2024-08-20T21:38:22.7482112Z >>> print("====== End nsys profiling ======") 2024-08-20T21:38:22.7482434Z 2024-08-20T21:38:22.7482702Z To run this example and save the profiling information, execute: 2024-08-20T21:38:22.7483724Z >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2024-08-20T21:38:22.7484357Z 2024-08-20T21:38:22.7484700Z This command profiles the CUDA operations in the provided script and saves 2024-08-20T21:38:22.7485444Z the profiling information to a file named `trace_name.prof`. 2024-08-20T21:38:22.7486229Z The `--profile-from-start off` option ensures that profiling starts only 2024-08-20T21:38:22.7486907Z after the `cudaProfilerStart` call in the script. 2024-08-20T21:38:22.7487767Z The `--csv` and `--print-summary` options format the profiling output as a 2024-08-20T21:38:22.7488397Z CSV file and print a summary, respectively. 2024-08-20T21:38:22.7489138Z The `-o` option specifies the output file name, and the `-f` option forces the 2024-08-20T21:38:22.7489837Z overwrite of the output file if it already exists. 2024-08-20T21:38:22.7490274Z 2024-08-20T21:38:22.7491429Z Original Error: SyntaxError('invalid syntax', ('', 1, 1, '$ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py\n', 1, 2)) 2024-08-20T21:38:22.7492430Z 2024-08-20T21:38:22.7492989Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2024-08-20T21:38:22.7493705Z ^ 2024-08-20T21:38:22.7616135Z msg = Cannot scrape callname=Future.then in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py line=101. 2024-08-20T21:38:22.7617650Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:22.7618158Z 2024-08-20T21:38:22.7618510Z Append the given callback function to this ``Future``, which will be run 2024-08-20T21:38:22.7619272Z when the ``Future`` is completed. Multiple callbacks can be added to 2024-08-20T21:38:22.7620009Z the same ``Future``, but the order in which they will be executed cannot 2024-08-20T21:38:22.7620705Z be guaranteed (to enforce a certain order consider chaining: 2024-08-20T21:38:22.7621396Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which 2024-08-20T21:38:22.7622124Z is the reference to this ``Future``. The callback function can use the 2024-08-20T21:38:22.7622859Z :meth:`value` method to get the value. Note that if this ``Future`` is 2024-08-20T21:38:22.7623594Z already completed, the given callback will be run immediately inline. 2024-08-20T21:38:22.7624045Z 2024-08-20T21:38:22.7624389Z If the ``Future``'s value contains tensors that reside on GPUs, the 2024-08-20T21:38:22.7625135Z callback might be invoked while the async kernels that are populating 2024-08-20T21:38:22.7626383Z those tensors haven't yet finished executing on the device. However, the 2024-08-20T21:38:22.7627507Z callback will be invoked with some dedicated streams set as current 2024-08-20T21:38:22.7628224Z (fetched from a global pool) which will be synchronized with those 2024-08-20T21:38:22.7628964Z kernels. Hence any operation performed by the callback on these tensors 2024-08-20T21:38:22.7629719Z will be scheduled on the device after the kernels complete. In other 2024-08-20T21:38:22.7630514Z words, as long as the callback doesn't switch streams, it can safely 2024-08-20T21:38:22.7631256Z manipulate the result without any additional synchronization. This is 2024-08-20T21:38:22.7631984Z similar to the non-blocking behavior of :meth:`wait`. 2024-08-20T21:38:22.7632340Z 2024-08-20T21:38:22.7632626Z Similarly, if the callback returns a value that contains tensors that 2024-08-20T21:38:22.7633363Z reside on a GPU, it can do so even if the kernels that are producing 2024-08-20T21:38:22.7634094Z these tensors are still running on the device, as long as the callback 2024-08-20T21:38:22.7634876Z didn't change streams during its execution. If one wants to change 2024-08-20T21:38:22.7635810Z streams, one must be careful to re-synchronize them with the original 2024-08-20T21:38:22.7636561Z streams, that is, those that were current when the callback was invoked. 2024-08-20T21:38:22.7637016Z 2024-08-20T21:38:22.7637119Z Args: 2024-08-20T21:38:22.7637517Z callback(``Callable``): a ``Callable`` that takes this ``Future`` as 2024-08-20T21:38:22.7638097Z the only argument. 2024-08-20T21:38:22.7638393Z 2024-08-20T21:38:22.7638500Z Returns: 2024-08-20T21:38:22.7638865Z A new ``Future`` object that holds the return value of the 2024-08-20T21:38:22.7639487Z ``callback`` and will be marked as completed when the given 2024-08-20T21:38:22.7639997Z ``callback`` finishes. 2024-08-20T21:38:22.7640219Z 2024-08-20T21:38:22.7640463Z .. note:: Note that if the callback function throws, either 2024-08-20T21:38:22.7641129Z through the original future being completed with an exception and 2024-08-20T21:38:22.7641837Z calling ``fut.wait()``, or through other code in the callback, the 2024-08-20T21:38:22.7642530Z future returned by ``then`` will be marked appropriately with the 2024-08-20T21:38:22.7643274Z encountered error. However, if this callback later completes 2024-08-20T21:38:22.7643965Z additional futures, those futures are not marked as completed with 2024-08-20T21:38:22.7644701Z an error and the user is responsible for handling completion/waiting 2024-08-20T21:38:22.7645285Z on those futures independently. 2024-08-20T21:38:22.7645571Z 2024-08-20T21:38:22.7645669Z Example:: 2024-08-20T21:38:22.7646010Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2024-08-20T21:38:22.7646462Z >>> def callback(fut): 2024-08-20T21:38:22.7647152Z ... print(f"RPC return value is {fut.wait()}.") 2024-08-20T21:38:22.7647737Z >>> fut = torch.futures.Future() 2024-08-20T21:38:22.7648231Z >>> # The inserted callback will print the return value when 2024-08-20T21:38:22.7648781Z >>> # receiving the response from "worker1" 2024-08-20T21:38:22.7649230Z >>> cb_fut = fut.then(callback) 2024-08-20T21:38:22.7649606Z >>> chain_cb_fut = cb_fut.then( 2024-08-20T21:38:22.7650072Z ... lambda x : print(f"Chained cb done. {x.wait()}") 2024-08-20T21:38:22.7650529Z ... ) 2024-08-20T21:38:22.7650776Z >>> fut.set_result(5) 2024-08-20T21:38:22.7651103Z RPC return value is 5. 2024-08-20T21:38:22.7651437Z Chained cb done. None 2024-08-20T21:38:22.7651649Z 2024-08-20T21:38:22.7652077Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:22.7652572Z 2024-08-20T21:38:22.7653449Z msg = Cannot scrape callname=Future.set_result in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py line=209. 2024-08-20T21:38:22.7654793Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:22.7655294Z 2024-08-20T21:38:22.7655596Z Set the result for this ``Future``, which will mark this ``Future`` as 2024-08-20T21:38:22.7656335Z completed and trigger all attached callbacks. Note that a ``Future`` 2024-08-20T21:38:22.7656923Z cannot be marked completed twice. 2024-08-20T21:38:22.7657200Z 2024-08-20T21:38:22.7657490Z If the result contains tensors that reside on GPUs, this method can be 2024-08-20T21:38:22.7658225Z called even if the asynchronous kernels that are populating those 2024-08-20T21:38:22.7659123Z tensors haven't yet completed running on the device, provided that the 2024-08-20T21:38:22.7660237Z streams on which those kernels were enqueued are set as the current ones 2024-08-20T21:38:22.7661164Z when this method is called. Put simply, it's safe to call this method 2024-08-20T21:38:22.7661929Z immediately after launching those kernels, without any additional 2024-08-20T21:38:22.7662749Z synchronization, as long as one doesn't change streams in between. This 2024-08-20T21:38:22.7663645Z method will record events on all the relevant current streams and will 2024-08-20T21:38:22.7664521Z use them to ensure proper scheduling for all the consumers of this 2024-08-20T21:38:22.7665046Z ``Future``. 2024-08-20T21:38:22.7665210Z 2024-08-20T21:38:22.7665299Z Args: 2024-08-20T21:38:22.7665647Z result (object): the result object of this ``Future``. 2024-08-20T21:38:22.7666010Z 2024-08-20T21:38:22.7666110Z Example:: 2024-08-20T21:38:22.7666455Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2024-08-20T21:38:22.7666918Z >>> import threading 2024-08-20T21:38:22.7667221Z >>> import time 2024-08-20T21:38:22.7667541Z >>> def slow_set_future(fut, value): 2024-08-20T21:38:22.7667938Z ... time.sleep(0.5) 2024-08-20T21:38:22.7668273Z ... fut.set_result(value) 2024-08-20T21:38:22.7668661Z >>> fut = torch.futures.Future() 2024-08-20T21:38:22.7669067Z >>> t = threading.Thread( 2024-08-20T21:38:22.7669415Z ... target=slow_set_future, 2024-08-20T21:38:22.7669810Z ... args=(fut, torch.ones(2) * 3) 2024-08-20T21:38:22.7670237Z ... ) 2024-08-20T21:38:22.7670474Z >>> t.start() 2024-08-20T21:38:22.7670827Z >>> print(fut.wait()) 2024-08-20T21:38:22.7671145Z tensor([3., 3.]) 2024-08-20T21:38:22.7671421Z >>> t.join() 2024-08-20T21:38:22.7671599Z 2024-08-20T21:38:22.7672001Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:22.7672483Z 2024-08-20T21:38:22.7872946Z msg = Cannot scrape callname=sum in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py line=201. 2024-08-20T21:38:22.7874798Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:22.7875548Z Return the sum of each row of the given sparse tensor. 2024-08-20T21:38:22.7875931Z 2024-08-20T21:38:22.7876440Z Returns the sum of each row of the sparse tensor :attr:`input` in the given 2024-08-20T21:38:22.7877194Z dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions, 2024-08-20T21:38:22.7877896Z reduce over all of them. When sum over all ``sparse_dim``, this method 2024-08-20T21:38:22.7878566Z returns a dense tensor instead of a sparse tensor. 2024-08-20T21:38:22.7878934Z 2024-08-20T21:38:22.7879293Z All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output 2024-08-20T21:38:22.7880078Z tensor having :attr:`dim` fewer dimensions than :attr:`input`. 2024-08-20T21:38:22.7880488Z 2024-08-20T21:38:22.7880777Z During backward, only gradients at ``nnz`` locations of :attr:`input` 2024-08-20T21:38:22.7881556Z will propagate back. Note that the gradients of :attr:`input` is coalesced. 2024-08-20T21:38:22.7882045Z 2024-08-20T21:38:22.7882150Z Args: 2024-08-20T21:38:22.7882445Z input (Tensor): the input sparse tensor 2024-08-20T21:38:22.7883164Z dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce 2024-08-20T21:38:22.7883836Z over all dims. 2024-08-20T21:38:22.7884390Z dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. 2024-08-20T21:38:22.7885055Z Default: dtype of :attr:`input`. 2024-08-20T21:38:22.7885367Z 2024-08-20T21:38:22.7885477Z Example:: 2024-08-20T21:38:22.7885637Z 2024-08-20T21:38:22.7885745Z >>> nnz = 3 2024-08-20T21:38:22.7886024Z >>> dims = [5, 5, 2, 3] 2024-08-20T21:38:22.7886479Z >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), 2024-08-20T21:38:22.7887110Z torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) 2024-08-20T21:38:22.7887777Z >>> V = torch.randn(nnz, dims[2], dims[3]) 2024-08-20T21:38:22.7888225Z >>> size = torch.Size(dims) 2024-08-20T21:38:22.7888758Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-08-20T21:38:22.7889265Z >>> S = torch.sparse_coo_tensor(I, V, size) 2024-08-20T21:38:22.7889691Z >>> S 2024-08-20T21:38:22.7889989Z tensor(indices=tensor([[2, 0, 3], 2024-08-20T21:38:22.7890383Z [2, 4, 1]]), 2024-08-20T21:38:22.7891014Z values=tensor([[[-0.6438, -1.6467, 1.4004], 2024-08-20T21:38:22.7891563Z [ 0.3411, 0.0918, -0.2312]], 2024-08-20T21:38:22.7891875Z 2024-08-20T21:38:22.7892090Z [[ 0.5348, 0.0634, -2.0494], 2024-08-20T21:38:22.7892618Z [-0.7125, -1.0646, 2.1844]], 2024-08-20T21:38:22.7892929Z 2024-08-20T21:38:22.7893154Z [[ 0.1276, 0.1874, -0.6334], 2024-08-20T21:38:22.7893674Z [-1.9682, -0.5340, 0.7483]]]), 2024-08-20T21:38:22.7894187Z size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo) 2024-08-20T21:38:22.7894552Z 2024-08-20T21:38:22.7894822Z # when sum over only part of sparse_dims, return a sparse tensor 2024-08-20T21:38:22.7895382Z >>> torch.sparse.sum(S, [1, 3]) 2024-08-20T21:38:22.7895798Z tensor(indices=tensor([[0, 2, 3]]), 2024-08-20T21:38:22.7896301Z values=tensor([[-1.4512, 0.4073], 2024-08-20T21:38:22.7896799Z [-0.8901, 0.2017], 2024-08-20T21:38:22.7897269Z [-0.3183, -1.7539]]), 2024-08-20T21:38:22.7897749Z size=(5, 2), nnz=3, layout=torch.sparse_coo) 2024-08-20T21:38:22.7898082Z 2024-08-20T21:38:22.7898299Z # when sum over all sparse dim, return a dense tensor 2024-08-20T21:38:22.7898780Z # with summed dims squeezed 2024-08-20T21:38:22.7899186Z >>> torch.sparse.sum(S, [0, 1, 3]) 2024-08-20T21:38:22.7899645Z tensor([-2.6596, -1.1450]) 2024-08-20T21:38:22.7899973Z 2024-08-20T21:38:22.7900505Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:22.7900989Z 2024-08-20T21:38:23.2826614Z msg = Cannot scrape callname=vmap in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/apis.py line=40. 2024-08-20T21:38:23.2827903Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:23.2828421Z 2024-08-20T21:38:23.2828802Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that 2024-08-20T21:38:23.2829536Z maps ``func`` over some dimension of the inputs. Semantically, vmap 2024-08-20T21:38:23.2830272Z pushes the map into PyTorch operations called by ``func``, effectively 2024-08-20T21:38:23.2830852Z vectorizing those operations. 2024-08-20T21:38:23.2831106Z 2024-08-20T21:38:23.2831402Z vmap is useful for handling batch dimensions: one can write a function 2024-08-20T21:38:23.2832143Z ``func`` that runs on examples and then lift it to a function that can 2024-08-20T21:38:23.2832860Z take batches of examples with ``vmap(func)``. vmap can also be used to 2024-08-20T21:38:23.2833529Z compute batched gradients when composed with autograd. 2024-08-20T21:38:23.2833897Z 2024-08-20T21:38:23.2834021Z .. note:: 2024-08-20T21:38:23.2834403Z :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for 2024-08-20T21:38:23.2835039Z convenience. Use whichever one you'd like. 2024-08-20T21:38:23.2835365Z 2024-08-20T21:38:23.2835467Z Args: 2024-08-20T21:38:23.2835880Z func (function): A Python function that takes one or more arguments. 2024-08-20T21:38:23.2836471Z Must return one or more Tensors. 2024-08-20T21:38:23.2837047Z in_dims (int or nested structure): Specifies which dimension of the 2024-08-20T21:38:23.2837714Z inputs should be mapped over. ``in_dims`` should have a 2024-08-20T21:38:23.2838344Z structure like the inputs. If the ``in_dim`` for a particular 2024-08-20T21:38:23.2839016Z input is None, then that indicates there is no map dimension. 2024-08-20T21:38:23.2839545Z Default: 0. 2024-08-20T21:38:23.2839992Z out_dims (int or Tuple[int]): Specifies where the mapped dimension 2024-08-20T21:38:23.2840690Z should appear in the outputs. If ``out_dims`` is a Tuple, then 2024-08-20T21:38:23.2841308Z it should have one element per output. Default: 0. 2024-08-20T21:38:23.2841891Z randomness (str): Specifies whether the randomness in this 2024-08-20T21:38:23.2842798Z vmap should be the same or different across batches. If 'different', 2024-08-20T21:38:23.2843596Z the randomness for each batch will be different. If 'same', the 2024-08-20T21:38:23.2844381Z randomness will be the same across batches. If 'error', any calls to 2024-08-20T21:38:23.2845194Z random functions will error. Default: 'error'. WARNING: this flag 2024-08-20T21:38:23.2845910Z only applies to random PyTorch operations and does not apply to 2024-08-20T21:38:23.2846565Z Python's random module or numpy randomness. 2024-08-20T21:38:23.2847555Z chunk_size (None or int): If None (default), apply a single vmap over inputs. 2024-08-20T21:38:23.2848368Z If not None, then compute the vmap :attr:`chunk_size` samples at a time. 2024-08-20T21:38:23.2849299Z Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop. 2024-08-20T21:38:23.2850269Z If you run into memory issues computing the vmap, please try a non-None chunk_size. 2024-08-20T21:38:23.2850824Z 2024-08-20T21:38:23.2850918Z Returns: 2024-08-20T21:38:23.2851322Z Returns a new "batched" function. It takes the same inputs as 2024-08-20T21:38:23.2851970Z ``func``, except each input has an extra dimension at the index 2024-08-20T21:38:23.2852631Z specified by ``in_dims``. It takes returns the same outputs as 2024-08-20T21:38:23.2853295Z ``func``, except each output has an extra dimension at the index 2024-08-20T21:38:23.2853819Z specified by ``out_dims``. 2024-08-20T21:38:23.2854067Z 2024-08-20T21:38:23.2854165Z .. warning: 2024-08-20T21:38:23.2854662Z :func:`vmap` works best with functional-style code. Please do not 2024-08-20T21:38:23.2855507Z perform any side-effects in ``func``, with the exception of 2024-08-20T21:38:23.2856295Z in-place PyTorch operations. Examples of side-effects include mutating 2024-08-20T21:38:23.2857068Z Python data structures and assigning values to variables not captured 2024-08-20T21:38:23.2857640Z in ``func``. 2024-08-20T21:38:23.2857811Z 2024-08-20T21:38:23.2858136Z One example of using :func:`vmap` is to compute batched dot products. PyTorch 2024-08-20T21:38:23.2858997Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully 2024-08-20T21:38:23.2859748Z rummaging through docs, use :func:`vmap` to construct a new function. 2024-08-20T21:38:23.2860198Z 2024-08-20T21:38:23.2860458Z >>> torch.dot # [D], [D] -> [] 2024-08-20T21:38:23.2861163Z >>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N] 2024-08-20T21:38:23.2861768Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2024-08-20T21:38:23.2862207Z >>> batched_dot(x, y) 2024-08-20T21:38:23.2862437Z 2024-08-20T21:38:23.2862756Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler 2024-08-20T21:38:23.2863367Z model authoring experience. 2024-08-20T21:38:23.2863598Z 2024-08-20T21:38:23.2863750Z >>> batch_size, feature_size = 3, 5 2024-08-20T21:38:23.2864246Z >>> weights = torch.randn(feature_size, requires_grad=True) 2024-08-20T21:38:23.2864727Z >>> 2024-08-20T21:38:23.2864990Z >>> def model(feature_vec): 2024-08-20T21:38:23.2865404Z >>> # Very simple linear model with activation 2024-08-20T21:38:23.2865910Z >>> return feature_vec.dot(weights).relu() 2024-08-20T21:38:23.2866333Z >>> 2024-08-20T21:38:23.2866655Z >>> examples = torch.randn(batch_size, feature_size) 2024-08-20T21:38:23.2867165Z >>> result = torch.vmap(model)(examples) 2024-08-20T21:38:23.2867470Z 2024-08-20T21:38:23.2867829Z :func:`vmap` can also help vectorize computations that were previously difficult 2024-08-20T21:38:23.2868717Z or impossible to batch. One example is higher-order gradient computation. 2024-08-20T21:38:23.2869559Z The PyTorch autograd engine computes vjps (vector-Jacobian products). 2024-08-20T21:38:23.2870389Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually 2024-08-20T21:38:23.2871300Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`, 2024-08-20T21:38:23.2872128Z we can vectorize the whole computation, computing the Jacobian in a single 2024-08-20T21:38:23.2872736Z call to ``autograd.grad``. 2024-08-20T21:38:23.2872958Z 2024-08-20T21:38:23.2873069Z >>> # Setup 2024-08-20T21:38:23.2873325Z >>> N = 5 2024-08-20T21:38:23.2873607Z >>> f = lambda x: x ** 2 2024-08-20T21:38:23.2873993Z >>> x = torch.randn(N, requires_grad=True) 2024-08-20T21:38:23.2874396Z >>> y = f(x) 2024-08-20T21:38:23.2874679Z >>> I_N = torch.eye(N) 2024-08-20T21:38:23.2874978Z >>> 2024-08-20T21:38:23.2875240Z >>> # Sequential approach 2024-08-20T21:38:23.2875767Z >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0] 2024-08-20T21:38:23.2876362Z >>> for v in I_N.unbind()] 2024-08-20T21:38:23.2876806Z >>> jacobian = torch.stack(jacobian_rows) 2024-08-20T21:38:23.2877214Z >>> 2024-08-20T21:38:23.2877497Z >>> # vectorized gradient computation 2024-08-20T21:38:23.2877889Z >>> def get_vjp(v): 2024-08-20T21:38:23.2878256Z >>> return torch.autograd.grad(y, x, v) 2024-08-20T21:38:23.2878720Z >>> jacobian = torch.vmap(get_vjp)(I_N) 2024-08-20T21:38:23.2879021Z 2024-08-20T21:38:23.2879387Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions 2024-08-20T21:38:23.2879927Z 2024-08-20T21:38:23.2880186Z >>> torch.dot # [D], [D] -> [] 2024-08-20T21:38:23.2881013Z >>> batched_dot = torch.vmap(torch.vmap(torch.dot)) # [N1, N0, D], [N1, N0, D] -> [N1, N0] 2024-08-20T21:38:23.2881731Z >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5) 2024-08-20T21:38:23.2882323Z >>> batched_dot(x, y) # tensor of size [2, 3] 2024-08-20T21:38:23.2882644Z 2024-08-20T21:38:23.2882986Z If the inputs are not batched along the first dimension, ``in_dims`` specifies 2024-08-20T21:38:23.2904019Z the dimension that each inputs are batched along as 2024-08-20T21:38:23.2904435Z 2024-08-20T21:38:23.2904797Z >>> torch.dot # [N], [N] -> [] 2024-08-20T21:38:23.2905551Z >>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D] 2024-08-20T21:38:23.2906176Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2024-08-20T21:38:23.2906856Z >>> batched_dot(x, y) # output is [5] instead of [2] if batched along the 0th dimension 2024-08-20T21:38:23.2907363Z 2024-08-20T21:38:23.2907729Z If there are multiple inputs each of which is batched along different dimensions, 2024-08-20T21:38:23.2908521Z ``in_dims`` must be a tuple with the batch dimension for each input as 2024-08-20T21:38:23.2908971Z 2024-08-20T21:38:23.2909235Z >>> torch.dot # [D], [D] -> [] 2024-08-20T21:38:23.2909997Z >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N] 2024-08-20T21:38:23.2910648Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2024-08-20T21:38:23.2911397Z >>> batched_dot(x, y) # second arg doesn't have a batch dim because in_dim[1] was None 2024-08-20T21:38:23.2911915Z 2024-08-20T21:38:23.2912252Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct 2024-08-20T21:38:23.2912889Z matching the shape of the input: 2024-08-20T21:38:23.2913145Z 2024-08-20T21:38:23.2913388Z >>> f = lambda dict: torch.dot(dict['x'], dict['y']) 2024-08-20T21:38:23.2913890Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2024-08-20T21:38:23.2914368Z >>> input = {'x': x, 'y': y} 2024-08-20T21:38:23.2914907Z >>> batched_dot = torch.vmap(f, in_dims=({'x': 0, 'y': None},)) 2024-08-20T21:38:23.2915425Z >>> batched_dot(input) 2024-08-20T21:38:23.2915642Z 2024-08-20T21:38:23.2916040Z By default, the output is batched along the first dimension. However, it can be batched 2024-08-20T21:38:23.2916726Z along any dimension by using ``out_dims`` 2024-08-20T21:38:23.2917039Z 2024-08-20T21:38:23.2917152Z >>> f = lambda x: x ** 2 2024-08-20T21:38:23.2917674Z >>> x = torch.randn(2, 5) 2024-08-20T21:38:23.2918074Z >>> batched_pow = torch.vmap(f, out_dims=1) 2024-08-20T21:38:23.2918499Z >>> batched_pow(x) # [5, 2] 2024-08-20T21:38:23.2918753Z 2024-08-20T21:38:23.2919156Z For any function that uses kwargs, the returned function will not batch the kwargs but will 2024-08-20T21:38:23.2919833Z accept kwargs 2024-08-20T21:38:23.2919998Z 2024-08-20T21:38:23.2920117Z >>> x = torch.randn([2, 5]) 2024-08-20T21:38:23.2920482Z >>> def fn(x, scale=4.): 2024-08-20T21:38:23.2920824Z >>> return x * scale 2024-08-20T21:38:23.2921124Z >>> 2024-08-20T21:38:23.2921394Z >>> batched_pow = torch.vmap(fn) 2024-08-20T21:38:23.2921856Z >>> assert torch.allclose(batched_pow(x), x * 4) 2024-08-20T21:38:23.2922498Z >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5] 2024-08-20T21:38:23.2922975Z 2024-08-20T21:38:23.2923101Z .. note:: 2024-08-20T21:38:23.2923617Z vmap does not provide general autobatching or handle variable-length 2024-08-20T21:38:23.2924205Z sequences out of the box. 2024-08-20T21:38:23.2924440Z 2024-08-20T21:38:23.2924845Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:23.2925325Z 2024-08-20T21:38:24.4038875Z msg = Cannot scrape callname=triton_op in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/triton.py line=17. 2024-08-20T21:38:24.4040178Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:24.4041017Z Create a custom operator whose implementation is backed by 1+ triton kernels. 2024-08-20T21:38:24.4041576Z 2024-08-20T21:38:24.4042196Z Use this instead of :func:`torch.library.custom_op` when the implementation 2024-08-20T21:38:24.4043000Z consists of 1+ triton kernels. :func:`torch.library.custom_op` treats 2024-08-20T21:38:24.4043684Z custom operators as opaque (:func:`torch.compile` and 2024-08-20T21:38:24.4044738Z :func:`torch.export.export` will never trace into them), but ``triton_op`` 2024-08-20T21:38:24.4045734Z makes the implementation visible to these subsystems, allowing them 2024-08-20T21:38:24.4046935Z to optimize the triton kernel(s). 2024-08-20T21:38:24.4047224Z 2024-08-20T21:38:24.4047715Z Note that ``fn`` must only consist of calls to PyTorch-understood 2024-08-20T21:38:24.4048442Z operators and triton kernels. Any triton kernels called inside ``fn`` 2024-08-20T21:38:24.4049200Z must be wrapped in a call to :func:`torch._library.capture_triton``. 2024-08-20T21:38:24.4049652Z 2024-08-20T21:38:24.4049764Z Args: 2024-08-20T21:38:24.4050242Z name (str): A name for the custom op that looks like "{namespace}::{name}", 2024-08-20T21:38:24.4051122Z e.g. "mylib::my_linear". The name is used as the op's stable identifier 2024-08-20T21:38:24.4051815Z in PyTorch subsystems (e.g. torch.export, FX graphs). 2024-08-20T21:38:24.4052529Z To avoid name collisions, please use your project name as the namespace; 2024-08-20T21:38:24.4053308Z e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace. 2024-08-20T21:38:24.4054143Z mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates. 2024-08-20T21:38:24.4055014Z This MUST be accurate, otherwise, the behavior is undefined. If "unknown", 2024-08-20T21:38:24.4055849Z it pessimistically assumes that all inputs to the operator are being mutated. 2024-08-20T21:38:24.4056626Z schema (None | str): A schema string for the operator. If None 2024-08-20T21:38:24.4057401Z (recommended) we'll infer a schema for the operator from its type 2024-08-20T21:38:24.4058107Z annotations. We recommend letting us infer a schema unless you 2024-08-20T21:38:24.4058678Z have a specific reason not to. 2024-08-20T21:38:24.4059239Z Example: "(Tensor x, int y) -> (Tensor, Tensor)". 2024-08-20T21:38:24.4059779Z 2024-08-20T21:38:24.4059900Z Example:: 2024-08-20T21:38:24.4060062Z 2024-08-20T21:38:24.4060245Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-20T21:38:24.4060701Z >>> import torch 2024-08-20T21:38:24.4061131Z >>> from torch._library import triton_op, capture_triton 2024-08-20T21:38:24.4061592Z >>> 2024-08-20T21:38:24.4061852Z >>> import triton 2024-08-20T21:38:24.4062214Z >>> from triton import language as tl 2024-08-20T21:38:24.4062603Z >>> 2024-08-20T21:38:24.4062855Z >>> @triton.jit 2024-08-20T21:38:24.4063163Z >>> def add_kernel( 2024-08-20T21:38:24.4063474Z >>> in_ptr0, 2024-08-20T21:38:24.4063780Z >>> in_ptr1, 2024-08-20T21:38:24.4064091Z >>> out_ptr, 2024-08-20T21:38:24.4064383Z >>> n_elements, 2024-08-20T21:38:24.4064741Z >>> BLOCK_SIZE: "tl.constexpr", 2024-08-20T21:38:24.4065131Z >>> ): 2024-08-20T21:38:24.4065420Z >>> pid = tl.program_id(axis=0) 2024-08-20T21:38:24.4065872Z >>> block_start = pid * BLOCK_SIZE 2024-08-20T21:38:24.4066373Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2024-08-20T21:38:24.4066855Z >>> mask = offsets < n_elements 2024-08-20T21:38:24.4067322Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2024-08-20T21:38:24.4067826Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2024-08-20T21:38:24.4068261Z >>> output = x + y 2024-08-20T21:38:24.4068687Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2024-08-20T21:38:24.4069140Z >>> 2024-08-20T21:38:24.4069449Z >>> @triton_op("mylib::add", mutates_args={}) 2024-08-20T21:38:24.4070200Z >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: 2024-08-20T21:38:24.4070768Z >>> output = torch.empty_like(x) 2024-08-20T21:38:24.4071202Z >>> n_elements = output.numel() 2024-08-20T21:38:24.4071598Z >>> 2024-08-20T21:38:24.4071867Z >>> def grid(meta): 2024-08-20T21:38:24.4072324Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2024-08-20T21:38:24.4072808Z >>> 2024-08-20T21:38:24.4073241Z >>> # NB: we need to wrap the triton kernel in a call to capture_triton 2024-08-20T21:38:24.4073948Z >>> capture_triton(add_kernel)[grid](x, y, output, n_elements, 16) 2024-08-20T21:38:24.4074491Z >>> return output 2024-08-20T21:38:24.4074818Z >>> 2024-08-20T21:38:24.4075067Z >>> @torch.compile 2024-08-20T21:38:24.4075390Z >>> def f(x, y): 2024-08-20T21:38:24.4075713Z >>> return add(x, y) 2024-08-20T21:38:24.4076037Z >>> 2024-08-20T21:38:24.4076336Z >>> x = torch.randn(3, device="cuda") 2024-08-20T21:38:24.4076784Z >>> y = torch.randn(3, device="cuda") 2024-08-20T21:38:24.4077166Z >>> 2024-08-20T21:38:24.4077414Z >>> z = f(x, y) 2024-08-20T21:38:24.4077758Z >>> assert torch.allclose(z, x + y) 2024-08-20T21:38:24.4078064Z 2024-08-20T21:38:24.4078151Z 2024-08-20T21:38:24.4078692Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:24.4079175Z 2024-08-20T21:38:24.4080061Z msg = Cannot scrape callname=capture_triton in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/triton.py line=163. 2024-08-20T21:38:24.4081335Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:24.4082073Z Allows capture of a triton kernel into a graph via make_fx or 2024-08-20T21:38:24.4082661Z non-strict export (coming soon). 2024-08-20T21:38:24.4082939Z 2024-08-20T21:38:24.4083244Z These technologies perform Dispatcher-based tracing (via 2024-08-20T21:38:24.4083889Z ``__torch_dispatch__``) and cannot see calls to raw triton kernels. 2024-08-20T21:38:24.4084582Z The ``capture_triton`` API returns a new callable that can actually 2024-08-20T21:38:24.4085139Z be traced into a graph. 2024-08-20T21:38:24.4085454Z 2024-08-20T21:38:24.4085566Z Examples: 2024-08-20T21:38:24.4085727Z 2024-08-20T21:38:24.4085840Z >>> # xdoctest: +SKIP 2024-08-20T21:38:24.4086185Z >>> import torch 2024-08-20T21:38:24.4086505Z >>> import triton 2024-08-20T21:38:24.4086853Z >>> from triton import language as tl 2024-08-20T21:38:24.4087514Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2024-08-20T21:38:24.4088227Z >>> from torch._higher_order_ops.triton_kernel_wrap import capture_triton 2024-08-20T21:38:24.4088775Z >>> 2024-08-20T21:38:24.4089033Z >>> @triton.jit 2024-08-20T21:38:24.4089344Z >>> def add_kernel( 2024-08-20T21:38:24.4089659Z >>> in_ptr0, 2024-08-20T21:38:24.4089973Z >>> in_ptr1, 2024-08-20T21:38:24.4090285Z >>> out_ptr, 2024-08-20T21:38:24.4090581Z >>> n_elements, 2024-08-20T21:38:24.4090946Z >>> BLOCK_SIZE: "tl.constexpr", 2024-08-20T21:38:24.4091352Z >>> ): 2024-08-20T21:38:24.4091644Z >>> pid = tl.program_id(axis=0) 2024-08-20T21:38:24.4092099Z >>> block_start = pid * BLOCK_SIZE 2024-08-20T21:38:24.4092614Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2024-08-20T21:38:24.4093103Z >>> mask = offsets < n_elements 2024-08-20T21:38:24.4093578Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2024-08-20T21:38:24.4094092Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2024-08-20T21:38:24.4094530Z >>> output = x + y 2024-08-20T21:38:24.4094967Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2024-08-20T21:38:24.4095424Z >>> 2024-08-20T21:38:24.4095678Z >>> def add(x, y): 2024-08-20T21:38:24.4096114Z >>> output = torch.empty_like(x) 2024-08-20T21:38:24.4096566Z >>> n_elements = output.numel() 2024-08-20T21:38:24.4096955Z >>> 2024-08-20T21:38:24.4097225Z >>> def grid_fn(meta): 2024-08-20T21:38:24.4097718Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2024-08-20T21:38:24.4098195Z >>> 2024-08-20T21:38:24.4098626Z >>> capture_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16) 2024-08-20T21:38:24.4099178Z >>> return output 2024-08-20T21:38:24.4099492Z >>> 2024-08-20T21:38:24.4099784Z >>> x = torch.randn(3, device="cuda") 2024-08-20T21:38:24.4100233Z >>> y = torch.randn(3, device="cuda") 2024-08-20T21:38:24.4100640Z >>> gm = make_fx(add)(x, y) 2024-08-20T21:38:24.4101017Z >>> print(gm.code) 2024-08-20T21:38:24.4101370Z >>> # def forward(self, x_1, y_1): 2024-08-20T21:38:24.4101975Z >>> # empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False) 2024-08-20T21:38:24.4102768Z >>> # triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation( 2024-08-20T21:38:24.4103419Z >>> # kernel_idx = 0, constant_args_idx = 0, 2024-08-20T21:38:24.4103902Z >>> # grid = [(1, 1, 1)], kwargs = { 2024-08-20T21:38:24.4104544Z >>> # 'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like, 2024-08-20T21:38:24.4105166Z >>> # 'n_elements': 3, 'BLOCK_SIZE': 16 2024-08-20T21:38:24.4105603Z >>> # }) 2024-08-20T21:38:24.4105917Z >>> # return empty_like 2024-08-20T21:38:24.4106190Z 2024-08-20T21:38:24.4106277Z 2024-08-20T21:38:24.4106810Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:24.4107291Z 2024-08-20T21:38:24.5732679Z msg = Cannot scrape callname=assert_almost_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=330. 2024-08-20T21:38:24.5734386Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:24.5735298Z 2024-08-20T21:38:24.5735680Z Raises an AssertionError if two items are not equal up to desired 2024-08-20T21:38:24.5736865Z precision. 2024-08-20T21:38:24.5737017Z 2024-08-20T21:38:24.5737281Z .. note:: It is recommended to use one of `assert_allclose`, 2024-08-20T21:38:24.5737894Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-08-20T21:38:24.5738861Z instead of this function for more consistent floating point 2024-08-20T21:38:24.5739661Z comparisons. 2024-08-20T21:38:24.5739954Z 2024-08-20T21:38:24.5740468Z The test verifies that the elements of `actual` and `desired` satisfy. 2024-08-20T21:38:24.5741200Z 2024-08-20T21:38:24.5741512Z ``abs(desired-actual) < float64(1.5 * 10**(-decimal))`` 2024-08-20T21:38:24.5741890Z 2024-08-20T21:38:24.5742204Z That is a looser test than originally documented, but agrees with what the 2024-08-20T21:38:24.5742999Z actual implementation in `assert_array_almost_equal` did up to rounding 2024-08-20T21:38:24.5744055Z vagaries. An exception is raised at conflicting values. For ndarrays this 2024-08-20T21:38:24.5745066Z delegates to assert_array_almost_equal 2024-08-20T21:38:24.5745642Z 2024-08-20T21:38:24.5745817Z Parameters 2024-08-20T21:38:24.5746126Z ---------- 2024-08-20T21:38:24.5746378Z actual : array_like 2024-08-20T21:38:24.5746879Z The object to check. 2024-08-20T21:38:24.5747192Z desired : array_like 2024-08-20T21:38:24.5747620Z The expected object. 2024-08-20T21:38:24.5747944Z decimal : int, optional 2024-08-20T21:38:24.5748272Z Desired precision, default is 7. 2024-08-20T21:38:24.5748660Z err_msg : str, optional 2024-08-20T21:38:24.5749064Z The error message to be printed in case of failure. 2024-08-20T21:38:24.5749538Z verbose : bool, optional 2024-08-20T21:38:24.5750014Z If True, the conflicting values are appended to the error message. 2024-08-20T21:38:24.5750635Z 2024-08-20T21:38:24.5750724Z Raises 2024-08-20T21:38:24.5750994Z ------ 2024-08-20T21:38:24.5751221Z AssertionError 2024-08-20T21:38:24.5751661Z If actual and desired are not equal up to specified precision. 2024-08-20T21:38:24.5752091Z 2024-08-20T21:38:24.5752198Z See Also 2024-08-20T21:38:24.5752446Z -------- 2024-08-20T21:38:24.5752894Z assert_allclose: Compare two array_like objects for equality with desired 2024-08-20T21:38:24.5753529Z relative and/or absolute precision. 2024-08-20T21:38:24.5754106Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-08-20T21:38:24.5754542Z 2024-08-20T21:38:24.5754636Z Examples 2024-08-20T21:38:24.5754898Z -------- 2024-08-20T21:38:24.5755234Z >>> from torch._numpy.testing import assert_almost_equal 2024-08-20T21:38:24.5755763Z >>> assert_almost_equal(2.3333333333333, 2.33333334) 2024-08-20T21:38:24.5756306Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) 2024-08-20T21:38:24.5756814Z Traceback (most recent call last): 2024-08-20T21:38:24.5757172Z ... 2024-08-20T21:38:24.5757419Z AssertionError: 2024-08-20T21:38:24.5757734Z Arrays are not almost equal to 10 decimals 2024-08-20T21:38:24.5758147Z ACTUAL: 2.3333333333333 2024-08-20T21:38:24.5758465Z DESIRED: 2.33333334 2024-08-20T21:38:24.5758650Z 2024-08-20T21:38:24.5758832Z >>> assert_almost_equal(np.array([1.0,2.3333333333333]), 2024-08-20T21:38:24.5759341Z ... np.array([1.0,2.33333334]), decimal=9) 2024-08-20T21:38:24.5759804Z Traceback (most recent call last): 2024-08-20T21:38:24.5760149Z ... 2024-08-20T21:38:24.5760391Z AssertionError: 2024-08-20T21:38:24.5760711Z Arrays are not almost equal to 9 decimals 2024-08-20T21:38:24.5761097Z 2024-08-20T21:38:24.5761373Z Mismatched elements: 1 / 2 (50%) 2024-08-20T21:38:24.5761858Z Max absolute difference: 6.666699636781459e-09 2024-08-20T21:38:24.5762387Z Max relative difference: 2.8571569790287484e-09 2024-08-20T21:38:24.5762891Z x: torch.ndarray([1.0000, 2.3333], dtype=float64) 2024-08-20T21:38:24.5763399Z y: torch.ndarray([1.0000, 2.3333], dtype=float64) 2024-08-20T21:38:24.5763730Z 2024-08-20T21:38:24.5763735Z 2024-08-20T21:38:24.5764132Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:24.5764740Z 2024-08-20T21:38:24.5765625Z msg = Cannot scrape callname=assert_approx_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=455. 2024-08-20T21:38:24.5766946Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:24.5767544Z 2024-08-20T21:38:24.5767854Z Raises an AssertionError if two items are not equal up to significant 2024-08-20T21:38:24.5768402Z digits. 2024-08-20T21:38:24.5768554Z 2024-08-20T21:38:24.5768793Z .. note:: It is recommended to use one of `assert_allclose`, 2024-08-20T21:38:24.5769412Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-08-20T21:38:24.5770056Z instead of this function for more consistent floating point 2024-08-20T21:38:24.5770581Z comparisons. 2024-08-20T21:38:24.5770790Z 2024-08-20T21:38:24.5771027Z Given two numbers, check that they are approximately equal. 2024-08-20T21:38:24.5771708Z Approximately equal is defined as the number of significant digits 2024-08-20T21:38:24.5772248Z that agree. 2024-08-20T21:38:24.5772415Z 2024-08-20T21:38:24.5772514Z Parameters 2024-08-20T21:38:24.5772794Z ---------- 2024-08-20T21:38:24.5773032Z actual : scalar 2024-08-20T21:38:24.5773322Z The object to check. 2024-08-20T21:38:24.5773642Z desired : scalar 2024-08-20T21:38:24.5773920Z The expected object. 2024-08-20T21:38:24.5774253Z significant : int, optional 2024-08-20T21:38:24.5774615Z Desired precision, default is 7. 2024-08-20T21:38:24.5774993Z err_msg : str, optional 2024-08-20T21:38:24.5775400Z The error message to be printed in case of failure. 2024-08-20T21:38:24.5775873Z verbose : bool, optional 2024-08-20T21:38:24.5776427Z If True, the conflicting values are appended to the error message. 2024-08-20T21:38:24.5776875Z 2024-08-20T21:38:24.5776962Z Raises 2024-08-20T21:38:24.5777218Z ------ 2024-08-20T21:38:24.5777443Z AssertionError 2024-08-20T21:38:24.5777883Z If actual and desired are not equal up to specified precision. 2024-08-20T21:38:24.5778304Z 2024-08-20T21:38:24.5778408Z See Also 2024-08-20T21:38:24.5778654Z -------- 2024-08-20T21:38:24.5779099Z assert_allclose: Compare two array_like objects for equality with desired 2024-08-20T21:38:24.5779735Z relative and/or absolute precision. 2024-08-20T21:38:24.5780307Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-08-20T21:38:24.5780737Z 2024-08-20T21:38:24.5780829Z Examples 2024-08-20T21:38:24.5781088Z -------- 2024-08-20T21:38:24.5781665Z >>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20) # doctest: +SKIP 2024-08-20T21:38:24.5782610Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20, # doctest: +SKIP 2024-08-20T21:38:24.5783258Z ... significant=8) 2024-08-20T21:38:24.5783963Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20, # doctest: +SKIP 2024-08-20T21:38:24.5784605Z ... significant=8) 2024-08-20T21:38:24.5785048Z Traceback (most recent call last): 2024-08-20T21:38:24.5785395Z ... 2024-08-20T21:38:24.5785639Z AssertionError: 2024-08-20T21:38:24.5785974Z Items are not equal to 8 significant digits: 2024-08-20T21:38:24.5786424Z ACTUAL: 1.234567e-21 2024-08-20T21:38:24.5786761Z DESIRED: 1.2345672e-21 2024-08-20T21:38:24.5786959Z 2024-08-20T21:38:24.5787167Z the evaluated condition that raises the exception is 2024-08-20T21:38:24.5787519Z 2024-08-20T21:38:24.5787819Z >>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1) 2024-08-20T21:38:24.5788294Z True 2024-08-20T21:38:24.5788436Z 2024-08-20T21:38:24.5788441Z 2024-08-20T21:38:24.5788833Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:24.5789317Z 2024-08-20T21:38:24.5790205Z msg = Cannot scrape callname=assert_array_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=734. 2024-08-20T21:38:24.5791499Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:24.5792091Z 2024-08-20T21:38:24.5792360Z Raises an AssertionError if two array_like objects are not equal. 2024-08-20T21:38:24.5792802Z 2024-08-20T21:38:24.5793073Z Given two array_like objects, check that the shape is equal and all 2024-08-20T21:38:24.5793803Z elements of these objects are equal (but see the Notes for the special 2024-08-20T21:38:24.5794522Z handling of a scalar). An exception is raised at shape mismatch or 2024-08-20T21:38:24.5795242Z conflicting values. In contrast to the standard usage in numpy, NaNs 2024-08-20T21:38:24.5795992Z are compared like numbers, no assertion is raised if both objects have 2024-08-20T21:38:24.5796626Z NaNs in the same positions. 2024-08-20T21:38:24.5796957Z 2024-08-20T21:38:24.5797394Z The usual caution for verifying equality with floating point numbers is 2024-08-20T21:38:24.5798061Z advised. 2024-08-20T21:38:24.5798258Z 2024-08-20T21:38:24.5798414Z Parameters 2024-08-20T21:38:24.5798979Z ---------- 2024-08-20T21:38:24.5799290Z x : array_like 2024-08-20T21:38:24.5799569Z The actual object to check. 2024-08-20T21:38:24.5799921Z y : array_like 2024-08-20T21:38:24.5800212Z The desired, expected object. 2024-08-20T21:38:24.5800573Z err_msg : str, optional 2024-08-20T21:38:24.5800978Z The error message to be printed in case of failure. 2024-08-20T21:38:24.5801450Z verbose : bool, optional 2024-08-20T21:38:24.5801930Z If True, the conflicting values are appended to the error message. 2024-08-20T21:38:24.5802480Z strict : bool, optional 2024-08-20T21:38:24.5802961Z If True, raise an AssertionError when either the shape or the data 2024-08-20T21:38:24.5803620Z type of the array_like objects does not match. The special 2024-08-20T21:38:24.5804387Z handling for scalars mentioned in the Notes section is disabled. 2024-08-20T21:38:24.5804820Z 2024-08-20T21:38:24.5804923Z Raises 2024-08-20T21:38:24.5805165Z ------ 2024-08-20T21:38:24.5805407Z AssertionError 2024-08-20T21:38:24.5805752Z If actual and desired objects are not equal. 2024-08-20T21:38:24.5806077Z 2024-08-20T21:38:24.5806168Z See Also 2024-08-20T21:38:24.5806427Z -------- 2024-08-20T21:38:24.5806873Z assert_allclose: Compare two array_like objects for equality with desired 2024-08-20T21:38:24.5807600Z relative and/or absolute precision. 2024-08-20T21:38:24.5808188Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-08-20T21:38:24.5808619Z 2024-08-20T21:38:24.5808708Z Notes 2024-08-20T21:38:24.5808958Z ----- 2024-08-20T21:38:24.5809350Z When one of `x` and `y` is a scalar and the other is array_like, the 2024-08-20T21:38:24.5810175Z function checks that each element of the array_like object is equal to 2024-08-20T21:38:24.5810944Z the scalar. This behaviour can be disabled with the `strict` parameter. 2024-08-20T21:38:24.5811402Z 2024-08-20T21:38:24.5811494Z Examples 2024-08-20T21:38:24.5811758Z -------- 2024-08-20T21:38:24.5812072Z The first assert does not raise an exception: 2024-08-20T21:38:24.5812402Z 2024-08-20T21:38:24.5812599Z >>> np.testing.assert_array_equal([1.0,2.33333,np.nan], 2024-08-20T21:38:24.5813128Z ... [np.exp(0),2.33333, np.nan]) 2024-08-20T21:38:24.5813455Z 2024-08-20T21:38:24.5813776Z Use `assert_allclose` or one of the nulp (number of floating point values) 2024-08-20T21:38:24.5814367Z functions for these cases instead: 2024-08-20T21:38:24.5814645Z 2024-08-20T21:38:24.5814824Z >>> np.testing.assert_allclose([1.0,np.pi,np.nan], 2024-08-20T21:38:24.5815343Z ... [1, np.sqrt(np.pi)**2, np.nan], 2024-08-20T21:38:24.5815888Z ... rtol=1e-10, atol=0) 2024-08-20T21:38:24.5816194Z 2024-08-20T21:38:24.5816472Z As mentioned in the Notes section, `assert_array_equal` has special 2024-08-20T21:38:24.5817215Z handling for scalars. Here the test checks that each value in `x` is 3: 2024-08-20T21:38:24.5817677Z 2024-08-20T21:38:24.5817822Z >>> x = np.full((2, 5), fill_value=3) 2024-08-20T21:38:24.5818336Z >>> np.testing.assert_array_equal(x, 3) 2024-08-20T21:38:24.5818641Z 2024-08-20T21:38:24.5818935Z Use `strict` to raise an AssertionError when comparing a scalar with an 2024-08-20T21:38:24.5819491Z array: 2024-08-20T21:38:24.5819629Z 2024-08-20T21:38:24.5819818Z >>> np.testing.assert_array_equal(x, 3, strict=True) 2024-08-20T21:38:24.5820305Z Traceback (most recent call last): 2024-08-20T21:38:24.5820675Z ... 2024-08-20T21:38:24.5820914Z AssertionError: 2024-08-20T21:38:24.5821201Z Arrays are not equal 2024-08-20T21:38:24.5821493Z 2024-08-20T21:38:24.5821755Z (shapes (2, 5), () mismatch) 2024-08-20T21:38:24.5822112Z x: torch.ndarray([[3, 3, 3, 3, 3], 2024-08-20T21:38:24.5822481Z [3, 3, 3, 3, 3]]) 2024-08-20T21:38:24.5822787Z y: torch.ndarray(3) 2024-08-20T21:38:24.5822986Z 2024-08-20T21:38:24.5823262Z The `strict` parameter also ensures that the array data types match: 2024-08-20T21:38:24.5823701Z 2024-08-20T21:38:24.5823824Z >>> x = np.array([2, 2, 2]) 2024-08-20T21:38:24.5824203Z >>> y = np.array([2., 2., 2.], dtype=np.float32) 2024-08-20T21:38:24.5824714Z >>> np.testing.assert_array_equal(x, y, strict=True) 2024-08-20T21:38:24.5825193Z Traceback (most recent call last): 2024-08-20T21:38:24.5825543Z ... 2024-08-20T21:38:24.5825789Z AssertionError: 2024-08-20T21:38:24.5826072Z Arrays are not equal 2024-08-20T21:38:24.5826350Z 2024-08-20T21:38:24.5826679Z (dtypes dtype("int64"), dtype("float32") mismatch) 2024-08-20T21:38:24.5827141Z x: torch.ndarray([2, 2, 2]) 2024-08-20T21:38:24.5827481Z y: torch.ndarray([2., 2., 2.]) 2024-08-20T21:38:24.5827739Z 2024-08-20T21:38:24.5828142Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:24.5828633Z 2024-08-20T21:38:24.5829615Z msg = Cannot scrape callname=assert_array_almost_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=840. 2024-08-20T21:38:24.5830951Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:24.5831451Z 2024-08-20T21:38:24.5831728Z Raises an AssertionError if two objects are not equal up to desired 2024-08-20T21:38:24.5832279Z precision. 2024-08-20T21:38:24.5832428Z 2024-08-20T21:38:24.5832681Z .. note:: It is recommended to use one of `assert_allclose`, 2024-08-20T21:38:24.5833289Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-08-20T21:38:24.5833936Z instead of this function for more consistent floating point 2024-08-20T21:38:24.5834458Z comparisons. 2024-08-20T21:38:24.5834652Z 2024-08-20T21:38:24.5834976Z The test verifies identical shapes and that the elements of ``actual`` and 2024-08-20T21:38:24.5835548Z ``desired`` satisfy. 2024-08-20T21:38:24.5835751Z 2024-08-20T21:38:24.5835967Z ``abs(desired-actual) < 1.5 * 10**(-decimal)`` 2024-08-20T21:38:24.5836288Z 2024-08-20T21:38:24.5836614Z That is a looser test than originally documented, but agrees with what the 2024-08-20T21:38:24.5837403Z actual implementation did up to rounding vagaries. An exception is raised 2024-08-20T21:38:24.5838197Z at shape mismatch or conflicting values. In contrast to the standard usage 2024-08-20T21:38:24.5838976Z in numpy, NaNs are compared like numbers, no assertion is raised if both 2024-08-20T21:38:24.5839578Z objects have NaNs in the same positions. 2024-08-20T21:38:24.5839887Z 2024-08-20T21:38:24.5839983Z Parameters 2024-08-20T21:38:24.5840253Z ---------- 2024-08-20T21:38:24.5840489Z x : array_like 2024-08-20T21:38:24.5840778Z The actual object to check. 2024-08-20T21:38:24.5841130Z y : array_like 2024-08-20T21:38:24.5841412Z The desired, expected object. 2024-08-20T21:38:24.5841785Z decimal : int, optional 2024-08-20T21:38:24.5842128Z Desired precision, default is 6. 2024-08-20T21:38:24.5842501Z err_msg : str, optional 2024-08-20T21:38:24.5842902Z The error message to be printed in case of failure. 2024-08-20T21:38:24.5843371Z verbose : bool, optional 2024-08-20T21:38:24.5843845Z If True, the conflicting values are appended to the error message. 2024-08-20T21:38:24.5844365Z 2024-08-20T21:38:24.5844452Z Raises 2024-08-20T21:38:24.5844704Z ------ 2024-08-20T21:38:24.5844929Z AssertionError 2024-08-20T21:38:24.5845362Z If actual and desired are not equal up to specified precision. 2024-08-20T21:38:24.5845801Z 2024-08-20T21:38:24.5845895Z See Also 2024-08-20T21:38:24.5846142Z -------- 2024-08-20T21:38:24.5846587Z assert_allclose: Compare two array_like objects for equality with desired 2024-08-20T21:38:24.5847668Z relative and/or absolute precision. 2024-08-20T21:38:24.5848255Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-08-20T21:38:24.5848673Z 2024-08-20T21:38:24.5848766Z Examples 2024-08-20T21:38:24.5849049Z -------- 2024-08-20T21:38:24.5849349Z the first assert does not raise an exception 2024-08-20T21:38:24.5849664Z 2024-08-20T21:38:24.5849883Z >>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan], 2024-08-20T21:38:24.5850423Z ... [1.0,2.333,np.nan]) 2024-08-20T21:38:24.5850744Z 2024-08-20T21:38:24.5850982Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2024-08-20T21:38:24.5851533Z ... [1.0,2.33339,np.nan], decimal=5) 2024-08-20T21:38:24.5852010Z Traceback (most recent call last): 2024-08-20T21:38:24.5852370Z ... 2024-08-20T21:38:24.5852606Z AssertionError: 2024-08-20T21:38:24.5852928Z Arrays are not almost equal to 5 decimals 2024-08-20T21:38:24.5853324Z 2024-08-20T21:38:24.5853596Z Mismatched elements: 1 / 3 (33.3%) 2024-08-20T21:38:24.5854081Z Max absolute difference: 5.999999999994898e-05 2024-08-20T21:38:24.5854614Z Max relative difference: 2.5713661239633743e-05 2024-08-20T21:38:24.5855267Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2024-08-20T21:38:24.5855849Z y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64) 2024-08-20T21:38:24.5856211Z 2024-08-20T21:38:24.5856448Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2024-08-20T21:38:24.5856995Z ... [1.0,2.33333, 5], decimal=5) 2024-08-20T21:38:24.5857465Z Traceback (most recent call last): 2024-08-20T21:38:24.5857825Z ... 2024-08-20T21:38:24.5858060Z AssertionError: 2024-08-20T21:38:24.5858388Z Arrays are not almost equal to 5 decimals 2024-08-20T21:38:24.5858794Z 2024-08-20T21:38:24.5859063Z x and y nan location mismatch: 2024-08-20T21:38:24.5859525Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2024-08-20T21:38:24.5860101Z y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64) 2024-08-20T21:38:24.5860460Z 2024-08-20T21:38:24.5860465Z 2024-08-20T21:38:24.5860884Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:24.5861366Z 2024-08-20T21:38:24.5862334Z msg = Cannot scrape callname=clear_and_catch_warnings in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=1790. 2024-08-20T21:38:24.5863674Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:24.5864455Z Context manager that resets warning registry for catching warnings 2024-08-20T21:38:24.5864886Z 2024-08-20T21:38:24.5865225Z Warnings can be slippery, because, whenever a warning is triggered, Python 2024-08-20T21:38:24.5866005Z adds a ``__warningregistry__`` member to the *calling* module. This makes 2024-08-20T21:38:24.5866794Z it impossible to retrigger the warning in this module, whatever you put in 2024-08-20T21:38:24.5867608Z the warnings filters. This context manager accepts a sequence of `modules` 2024-08-20T21:38:24.5868265Z as a keyword argument to its constructor and: 2024-08-20T21:38:24.5868612Z 2024-08-20T21:38:24.5868920Z * stores and removes any ``__warningregistry__`` entries in given `modules` 2024-08-20T21:38:24.5869500Z on entry; 2024-08-20T21:38:24.5869912Z * resets ``__warningregistry__`` to its previous state on exit. 2024-08-20T21:38:24.5870425Z 2024-08-20T21:38:24.5870726Z This makes it possible to trigger any warning afresh inside the context 2024-08-20T21:38:24.5871433Z manager without disturbing the state of warnings outside. 2024-08-20T21:38:24.5871818Z 2024-08-20T21:38:24.5872141Z For compatibility with Python 3.0, please consider all arguments to be 2024-08-20T21:38:24.5872733Z keyword-only. 2024-08-20T21:38:24.5872925Z 2024-08-20T21:38:24.5873022Z Parameters 2024-08-20T21:38:24.5873320Z ---------- 2024-08-20T21:38:24.5873584Z record : bool, optional 2024-08-20T21:38:24.5874054Z Specifies whether warnings should be captured by a custom 2024-08-20T21:38:24.5874767Z implementation of ``warnings.showwarning()`` and be appended to a list 2024-08-20T21:38:24.5875514Z returned by the context manager. Otherwise None is returned by the 2024-08-20T21:38:24.5876263Z context manager. The objects appended to the list are arguments whose 2024-08-20T21:38:24.5876943Z attributes mirror the arguments to ``showwarning()``. 2024-08-20T21:38:24.5877452Z modules : sequence, optional 2024-08-20T21:38:24.5877996Z Sequence of modules for which to reset warnings registry on entry and 2024-08-20T21:38:24.5878800Z restore on exit. To work correctly, all 'ignore' filters should 2024-08-20T21:38:24.5879365Z filter by one of these modules. 2024-08-20T21:38:24.5879646Z 2024-08-20T21:38:24.5879741Z Examples 2024-08-20T21:38:24.5880020Z -------- 2024-08-20T21:38:24.5880288Z >>> import warnings 2024-08-20T21:38:24.5880733Z >>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP 2024-08-20T21:38:24.5881306Z ... modules=[np.core.fromnumeric]): 2024-08-20T21:38:24.5881886Z ... warnings.simplefilter('always') 2024-08-20T21:38:24.5882544Z ... warnings.filterwarnings('ignore', module='np.core.fromnumeric') 2024-08-20T21:38:24.5883240Z ... # do something that raises a warning but ignore those in 2024-08-20T21:38:24.5883771Z ... # np.core.fromnumeric 2024-08-20T21:38:24.5884105Z 2024-08-20T21:38:24.5884636Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:24.5885123Z 2024-08-20T21:38:24.7635413Z msg = Cannot scrape callname=Conv1d in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py line=355. 2024-08-20T21:38:24.7636755Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:24.7637529Z Applies a 1D convolution over a quantized input signal composed of 2024-08-20T21:38:24.7638125Z several quantized input planes. 2024-08-20T21:38:24.7638410Z 2024-08-20T21:38:24.7638710Z For details on input arguments, parameters, and implementation see 2024-08-20T21:38:24.7639280Z :class:`~torch.nn.Conv1d`. 2024-08-20T21:38:24.7639520Z 2024-08-20T21:38:24.7639636Z .. note:: 2024-08-20T21:38:24.7640068Z Only `zeros` is supported for the :attr:`padding_mode` argument. 2024-08-20T21:38:24.7640500Z 2024-08-20T21:38:24.7640611Z .. note:: 2024-08-20T21:38:24.7640991Z Only `torch.quint8` is supported for the input data type. 2024-08-20T21:38:24.7641396Z 2024-08-20T21:38:24.7641401Z 2024-08-20T21:38:24.7641500Z Attributes: 2024-08-20T21:38:24.7641952Z weight (Tensor): packed tensor derived from the learnable weight 2024-08-20T21:38:24.7642501Z parameter. 2024-08-20T21:38:24.7642962Z scale (Tensor): scalar for the output scale 2024-08-20T21:38:24.7643518Z zero_point (Tensor): scalar for the output zero point 2024-08-20T21:38:24.7643886Z 2024-08-20T21:38:24.7644096Z See :class:`~torch.nn.Conv1d` for other attributes. 2024-08-20T21:38:24.7644448Z 2024-08-20T21:38:24.7644554Z Examples:: 2024-08-20T21:38:24.7644729Z 2024-08-20T21:38:24.7644925Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) 2024-08-20T21:38:24.7645462Z >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2) 2024-08-20T21:38:24.7646154Z >>> input = torch.randn(20, 16, 100) 2024-08-20T21:38:24.7646595Z >>> # quantize input to quint8 2024-08-20T21:38:24.7647199Z >>> # xdoctest: +SKIP 2024-08-20T21:38:24.7647763Z >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, 2024-08-20T21:38:24.7648385Z ... dtype=torch.quint8) 2024-08-20T21:38:24.7648846Z >>> output = m(q_input) 2024-08-20T21:38:24.7649089Z 2024-08-20T21:38:24.7649175Z 2024-08-20T21:38:24.7649735Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:24.7650215Z 2024-08-20T21:38:24.7858152Z msg = Cannot scrape callname=LSTM in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/rnn.py line=11. 2024-08-20T21:38:24.7859509Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:24.7860224Z A quantized long short-term memory (LSTM). 2024-08-20T21:38:24.7860549Z 2024-08-20T21:38:24.7860940Z For the description and the argument types, please, refer to :class:`~torch.nn.LSTM` 2024-08-20T21:38:24.7861485Z 2024-08-20T21:38:24.7861585Z Attributes: 2024-08-20T21:38:24.7861916Z layers : instances of the `_LSTMLayer` 2024-08-20T21:38:24.7862229Z 2024-08-20T21:38:24.7862357Z .. note:: 2024-08-20T21:38:24.7862800Z To access the weights and biases, you need to access them per layer. 2024-08-20T21:38:24.7863492Z See examples in :class:`~torch.ao.nn.quantizable.LSTM` 2024-08-20T21:38:24.7863888Z 2024-08-20T21:38:24.7863990Z Examples:: 2024-08-20T21:38:24.7864274Z >>> # xdoctest: +SKIP 2024-08-20T21:38:24.7864620Z >>> custom_module_config = { 2024-08-20T21:38:24.7865325Z ... 'float_to_observed_custom_module_class': { 2024-08-20T21:38:24.7865836Z ... nn.LSTM: nn.quantizable.LSTM, 2024-08-20T21:38:24.7866248Z ... }, 2024-08-20T21:38:24.7866693Z ... 'observed_to_quantized_custom_module_class': { 2024-08-20T21:38:24.7867259Z ... nn.quantizable.LSTM: nn.quantized.LSTM, 2024-08-20T21:38:24.7867700Z ... } 2024-08-20T21:38:24.7867969Z ... } 2024-08-20T21:38:24.7868419Z >>> tq.prepare(model, prepare_custom_module_class=custom_module_config) 2024-08-20T21:38:24.7869145Z >>> tq.convert(model, convert_custom_module_class=custom_module_config) 2024-08-20T21:38:24.7869690Z 2024-08-20T21:38:24.7870226Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:24.7870710Z 2024-08-20T21:38:24.9033914Z msg = Cannot scrape callname=BaseSparsifier.squash_mask in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py line=227. 2024-08-20T21:38:24.9035473Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:24.9036193Z Squashes the sparse masks into the appropriate tensors. 2024-08-20T21:38:24.9036578Z 2024-08-20T21:38:24.9036881Z If either the `params_to_keep` or `params_to_keep_per_layer` is set, 2024-08-20T21:38:24.9037573Z the module will have a `sparse_params` dict attached to it. 2024-08-20T21:38:24.9037995Z 2024-08-20T21:38:24.9038089Z Args: 2024-08-20T21:38:24.9038505Z params_to_keep: List of keys to save in the module or a dict 2024-08-20T21:38:24.9039142Z representing the modules and keys that will have 2024-08-20T21:38:24.9039683Z sparsity parameters saved 2024-08-20T21:38:24.9040296Z params_to_keep_per_layer: Dict to specify the params that should be 2024-08-20T21:38:24.9040966Z saved for specific layers. The keys in the dict 2024-08-20T21:38:24.9041551Z should be the module fqn, while the values should 2024-08-20T21:38:24.9042168Z be a list of strings with the names of the variables 2024-08-20T21:38:24.9042924Z to save in the `sparse_params` 2024-08-20T21:38:24.9043249Z 2024-08-20T21:38:24.9043349Z Examples: 2024-08-20T21:38:24.9043705Z >>> # xdoctest: +SKIP("locals are undefined") 2024-08-20T21:38:24.9044258Z >>> # Don't save any sparse params 2024-08-20T21:38:24.9044693Z >>> sparsifier.squash_mask() 2024-08-20T21:38:24.9045237Z >>> hasattr(model.submodule1, 'sparse_params') 2024-08-20T21:38:24.9045676Z False 2024-08-20T21:38:24.9045844Z 2024-08-20T21:38:24.9045993Z >>> # Keep sparse params per layer 2024-08-20T21:38:24.9046434Z >>> sparsifier.squash_mask( 2024-08-20T21:38:24.9047068Z ... params_to_keep_per_layer={ 2024-08-20T21:38:24.9047695Z ... 'submodule1.linear1': ('foo', 'bar'), 2024-08-20T21:38:24.9048256Z ... 'submodule2.linear42': ('baz',) 2024-08-20T21:38:24.9048688Z ... }) 2024-08-20T21:38:24.9049094Z >>> print(model.submodule1.linear1.sparse_params) 2024-08-20T21:38:24.9049619Z {'foo': 42, 'bar': 24} 2024-08-20T21:38:24.9050080Z >>> print(model.submodule2.linear42.sparse_params) 2024-08-20T21:38:24.9050589Z {'baz': 0.1} 2024-08-20T21:38:24.9050790Z 2024-08-20T21:38:24.9050949Z >>> # Keep sparse params for all layers 2024-08-20T21:38:24.9051568Z >>> sparsifier.squash_mask(params_to_keep=('foo', 'bar')) 2024-08-20T21:38:24.9052162Z >>> print(model.submodule1.linear1.sparse_params) 2024-08-20T21:38:24.9052677Z {'foo': 42, 'bar': 24} 2024-08-20T21:38:24.9053138Z >>> print(model.submodule2.linear42.sparse_params) 2024-08-20T21:38:24.9053784Z {'foo': 42, 'bar': 24} 2024-08-20T21:38:24.9054035Z 2024-08-20T21:38:24.9054306Z >>> # Keep some sparse params for all layers, and specific ones for 2024-08-20T21:38:24.9054863Z >>> # some other layers 2024-08-20T21:38:24.9055267Z >>> sparsifier.squash_mask( 2024-08-20T21:38:24.9055747Z ... params_to_keep=('foo', 'bar'), 2024-08-20T21:38:24.9056207Z ... params_to_keep_per_layer={ 2024-08-20T21:38:24.9056738Z ... 'submodule2.linear42': ('baz',) 2024-08-20T21:38:24.9057159Z ... }) 2024-08-20T21:38:24.9057554Z >>> print(model.submodule1.linear1.sparse_params) 2024-08-20T21:38:24.9058079Z {'foo': 42, 'bar': 24} 2024-08-20T21:38:24.9058537Z >>> print(model.submodule2.linear42.sparse_params) 2024-08-20T21:38:24.9059088Z {'foo': 42, 'bar': 24, 'baz': 0.1} 2024-08-20T21:38:24.9059483Z 2024-08-20T21:38:24.9060027Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:24.9060514Z 2024-08-20T21:38:25.0027260Z msg = Cannot scrape callname=DTypeConfig in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/backend_config/backend_config.py line=181. 2024-08-20T21:38:25.0028779Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:25.0029284Z 2024-08-20T21:38:25.0029618Z Config object that specifies the supported data types passed as arguments to 2024-08-20T21:38:25.0030451Z quantize ops in the reference model spec, for input and output activations, 2024-08-20T21:38:25.0031049Z weights, and biases. 2024-08-20T21:38:25.0031244Z 2024-08-20T21:38:25.0031451Z For example, consider the following reference model: 2024-08-20T21:38:25.0031808Z 2024-08-20T21:38:25.0032071Z quant1 - [dequant1 - fp32_linear - quant2] - dequant2 2024-08-20T21:38:25.0032439Z 2024-08-20T21:38:25.0032730Z The pattern in the square brackets refers to the reference pattern of 2024-08-20T21:38:25.0033496Z statically quantized linear. Setting the input dtype as `torch.quint8` 2024-08-20T21:38:25.0034259Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument 2024-08-20T21:38:25.0035034Z to the first quantize op (quant1). Similarly, setting the output dtype as 2024-08-20T21:38:25.0036044Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to 2024-08-20T21:38:25.0036620Z the second quantize op (quant2). 2024-08-20T21:38:25.0036887Z 2024-08-20T21:38:25.0037210Z Note that the dtype here does not refer to the interface dtypes of the 2024-08-20T21:38:25.0037946Z op. For example, the "input dtype" here is not the dtype of the input 2024-08-20T21:38:25.0038669Z tensor passed to the quantized linear op. Though it can still be the 2024-08-20T21:38:25.0039383Z same as the interface dtype, this is not always the case, e.g. the 2024-08-20T21:38:25.0040110Z interface dtype is fp32 in dynamic quantization but the "input dtype" 2024-08-20T21:38:25.0040860Z specified in the DTypeConfig would still be quint8. The semantics of 2024-08-20T21:38:25.0041589Z dtypes here are the same as the semantics of the dtypes specified in 2024-08-20T21:38:25.0042146Z the observers. 2024-08-20T21:38:25.0042325Z 2024-08-20T21:38:25.0042640Z These dtypes are matched against the ones specified in the user's 2024-08-20T21:38:25.0043370Z QConfig. If there is a match, and the QConfig satisfies the constraints 2024-08-20T21:38:25.0044110Z specified in the DTypeConfig (if any), then we will quantize the given 2024-08-20T21:38:25.0044861Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and 2024-08-20T21:38:25.0045452Z the pattern will not be quantized. 2024-08-20T21:38:25.0045718Z 2024-08-20T21:38:25.0045841Z Example usage:: 2024-08-20T21:38:25.0046023Z 2024-08-20T21:38:25.0046145Z >>> # xdoctest: +SKIP(failing) 2024-08-20T21:38:25.0046537Z >>> dtype_config1 = DTypeConfig( 2024-08-20T21:38:25.0047153Z ... input_dtype=torch.quint8, 2024-08-20T21:38:25.0047778Z ... output_dtype=torch.quint8, 2024-08-20T21:38:25.0048190Z ... weight_dtype=torch.qint8, 2024-08-20T21:38:25.0048576Z ... bias_dtype=torch.float) 2024-08-20T21:38:25.0048853Z 2024-08-20T21:38:25.0048979Z >>> dtype_config2 = DTypeConfig( 2024-08-20T21:38:25.0049430Z ... input_dtype=DTypeWithConstraints( 2024-08-20T21:38:25.0049857Z ... dtype=torch.quint8, 2024-08-20T21:38:25.0050248Z ... quant_min_lower_bound=0, 2024-08-20T21:38:25.0050667Z ... quant_max_upper_bound=255, 2024-08-20T21:38:25.0051044Z ... ), 2024-08-20T21:38:25.0051369Z ... output_dtype=DTypeWithConstraints( 2024-08-20T21:38:25.0051809Z ... dtype=torch.quint8, 2024-08-20T21:38:25.0052191Z ... quant_min_lower_bound=0, 2024-08-20T21:38:25.0052607Z ... quant_max_upper_bound=255, 2024-08-20T21:38:25.0052997Z ... ), 2024-08-20T21:38:25.0053312Z ... weight_dtype=DTypeWithConstraints( 2024-08-20T21:38:25.0053756Z ... dtype=torch.qint8, 2024-08-20T21:38:25.0054249Z ... quant_min_lower_bound=-128, 2024-08-20T21:38:25.0054664Z ... quant_max_upper_bound=127, 2024-08-20T21:38:25.0055052Z ... ), 2024-08-20T21:38:25.0055347Z ... bias_dtype=torch.float) 2024-08-20T21:38:25.0055608Z 2024-08-20T21:38:25.0055731Z >>> dtype_config1.input_dtype 2024-08-20T21:38:25.0056094Z torch.quint8 2024-08-20T21:38:25.0056277Z 2024-08-20T21:38:25.0056399Z >>> dtype_config2.input_dtype 2024-08-20T21:38:25.0056761Z torch.quint8 2024-08-20T21:38:25.0056931Z 2024-08-20T21:38:25.0057099Z >>> dtype_config2.input_dtype_with_constraints 2024-08-20T21:38:25.0058134Z DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None) 2024-08-20T21:38:25.0058981Z 2024-08-20T21:38:25.0059396Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:25.0059882Z 2024-08-20T21:38:25.1354493Z msg = Cannot scrape callname=ModelReportVisualizer.generate_filtered_tables in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=301. 2024-08-20T21:38:25.1356732Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:25.1357253Z 2024-08-20T21:38:25.1357665Z Takes in optional filter values and generates two tables with desired information. 2024-08-20T21:38:25.1358207Z 2024-08-20T21:38:25.1358554Z The generated tables are presented in both a list-of-lists format 2024-08-20T21:38:25.1359091Z 2024-08-20T21:38:25.1359438Z The reason for the two tables are that they handle different things: 2024-08-20T21:38:25.1360221Z 1.) the first table handles all tensor level information 2024-08-20T21:38:25.1360960Z 2.) the second table handles and displays all channel based information 2024-08-20T21:38:25.1361488Z 2024-08-20T21:38:25.1362033Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2024-08-20T21:38:25.1363488Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2024-08-20T21:38:25.1364699Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2024-08-20T21:38:25.1365428Z 2024-08-20T21:38:25.1365539Z Tensor table columns: 2024-08-20T21:38:25.1366062Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2024-08-20T21:38:25.1366797Z ---- --------- --------- --------- --------- --------- 2024-08-20T21:38:25.1367166Z 2024-08-20T21:38:25.1367460Z Per-Channel table columns: 2024-08-20T21:38:25.1367997Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2024-08-20T21:38:25.1368799Z ---- --------- ------- --------- --------- --------- --------- 2024-08-20T21:38:25.1369243Z 2024-08-20T21:38:25.1369332Z Args: 2024-08-20T21:38:25.1370046Z feature_filter (str, optional): Filters the features presented to only those that 2024-08-20T21:38:25.1370773Z contain this filter substring 2024-08-20T21:38:25.1371314Z Default = "", results in all the features being printed 2024-08-20T21:38:25.1372104Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-08-20T21:38:25.1373025Z Default = "", results in all the modules in the reports to be visible in the table 2024-08-20T21:38:25.1373586Z 2024-08-20T21:38:25.1373721Z Returns a dictionary with two keys: 2024-08-20T21:38:25.1374270Z (Dict[str, Tuple[List, List]]) A dict containing two keys: 2024-08-20T21:38:25.1374825Z "tensor_level_info", "channel_level_info" 2024-08-20T21:38:25.1375317Z Each key maps to a tuple with: 2024-08-20T21:38:25.1375798Z A list of the headers of each table 2024-08-20T21:38:25.1376375Z A list of lists containing the table information row by row 2024-08-20T21:38:25.1377078Z The 0th index row will contain the headers of the columns 2024-08-20T21:38:25.1377704Z The rest of the rows will contain data 2024-08-20T21:38:25.1378064Z 2024-08-20T21:38:25.1378187Z Example Use: 2024-08-20T21:38:25.1378516Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:38:25.1379088Z >>> mod_report_visualizer.generate_filtered_tables( 2024-08-20T21:38:25.1379704Z ... feature_filter = "per_channel_min", 2024-08-20T21:38:25.1380153Z ... module_fqn_filter = "block1" 2024-08-20T21:38:25.1380847Z ... ) # generates table with per_channel_min info for all modules in block 1 of the model 2024-08-20T21:38:25.1381448Z 2024-08-20T21:38:25.1381898Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:25.1382402Z 2024-08-20T21:38:25.1383886Z msg = Cannot scrape callname=ModelReportVisualizer.generate_table_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=400. 2024-08-20T21:38:25.1385786Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:25.1386290Z 2024-08-20T21:38:25.1386713Z Takes in optional filter values and prints out formatted tables of the information. 2024-08-20T21:38:25.1387414Z 2024-08-20T21:38:25.1387896Z The reason for the two tables printed out instead of one large one are that they handle different things: 2024-08-20T21:38:25.1388877Z 1.) the first table handles all tensor level information 2024-08-20T21:38:25.1389589Z 2.) the second table handles and displays all channel based information 2024-08-20T21:38:25.1390061Z 2024-08-20T21:38:25.1390581Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2024-08-20T21:38:25.1391873Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2024-08-20T21:38:25.1393042Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2024-08-20T21:38:25.1393745Z 2024-08-20T21:38:25.1393855Z Tensor table columns: 2024-08-20T21:38:25.1394374Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2024-08-20T21:38:25.1395057Z ---- --------- --------- --------- --------- --------- 2024-08-20T21:38:25.1395406Z 2024-08-20T21:38:25.1395562Z Per-Channel table columns: 2024-08-20T21:38:25.1395857Z 2024-08-20T21:38:25.1396154Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2024-08-20T21:38:25.1396893Z ---- --------- ------- --------- --------- --------- --------- 2024-08-20T21:38:25.1397263Z 2024-08-20T21:38:25.1397365Z Args: 2024-08-20T21:38:25.1397899Z feature_filter (str, optional): Filters the features presented to only those that 2024-08-20T21:38:25.1398555Z contain this filter substring 2024-08-20T21:38:25.1399048Z Default = "", results in all the features being printed 2024-08-20T21:38:25.1399838Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-08-20T21:38:25.1400703Z Default = "", results in all the modules in the reports to be visible in the table 2024-08-20T21:38:25.1401206Z 2024-08-20T21:38:25.1401324Z Example Use: 2024-08-20T21:38:25.1401699Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:38:25.1402239Z >>> mod_report_visualizer.generate_table_visualization( 2024-08-20T21:38:25.1402771Z ... feature_filter = "per_channel_min", 2024-08-20T21:38:25.1403207Z ... module_fqn_filter = "block1" 2024-08-20T21:38:25.1403585Z ... ) 2024-08-20T21:38:25.1403986Z >>> # prints out neatly formatted table with per_channel_min info 2024-08-20T21:38:25.1404548Z >>> # for all modules in block 1 of the model 2024-08-20T21:38:25.1404880Z 2024-08-20T21:38:25.1405333Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:25.1405830Z 2024-08-20T21:38:25.1407218Z msg = Cannot scrape callname=ModelReportVisualizer.generate_plot_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=565. 2024-08-20T21:38:25.1409101Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:25.1409612Z 2024-08-20T21:38:25.1409950Z Takes in a feature and optional module_filter and plots of the desired data. 2024-08-20T21:38:25.1410438Z 2024-08-20T21:38:25.1410806Z For per channel features, it averages the value across the channels and plots a point 2024-08-20T21:38:25.1411713Z per module. The reason for this is that for models with hundreds of channels, it can 2024-08-20T21:38:25.1412626Z be hard to differentiate one channel line from another, and so the point of generating 2024-08-20T21:38:25.1413545Z a single average point per module is to give a sense of general trends that encourage 2024-08-20T21:38:25.1414184Z further deep dives. 2024-08-20T21:38:25.1414383Z 2024-08-20T21:38:25.1414480Z Note: 2024-08-20T21:38:25.1414991Z Only features in the report that have tensor value data are plottable by this class 2024-08-20T21:38:25.1415731Z When the tensor information is plotted, it will plot: 2024-08-20T21:38:25.1416422Z idx as the x val, feature value as the y_val 2024-08-20T21:38:25.1416982Z When the channel information is plotted, it will plot: 2024-08-20T21:38:25.1417743Z the first idx of each module as the x val, feature value as the y_val [for each channel] 2024-08-20T21:38:25.1418627Z The reason for this is that we want to be able to compare values across the 2024-08-20T21:38:25.1419457Z channels for same layer, and it will be hard if values are staggered by idx 2024-08-20T21:38:25.1420188Z This means each module is represented by only 1 x value 2024-08-20T21:38:25.1420653Z Args: 2024-08-20T21:38:25.1421093Z feature_filter (str): Filters the features presented to only those that 2024-08-20T21:38:25.1421696Z contain this filter substring 2024-08-20T21:38:25.1422370Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-08-20T21:38:25.1423233Z Default = "", results in all the modules in the reports to be visible in the table 2024-08-20T21:38:25.1423744Z 2024-08-20T21:38:25.1423856Z Example Use: 2024-08-20T21:38:25.1424172Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:38:25.1424707Z >>> mod_report_visualizer.generate_plot_visualization( 2024-08-20T21:38:25.1425237Z ... feature_filter = "per_channel_min", 2024-08-20T21:38:25.1425670Z ... module_fqn_filter = "block1" 2024-08-20T21:38:25.1426047Z ... ) 2024-08-20T21:38:25.1426435Z >>> # outputs line plot of per_channel_min information for all 2024-08-20T21:38:25.1427333Z >>> # modules in block1 of model each channel gets it's own line, 2024-08-20T21:38:25.1428059Z >>> # and it's plotted across the in-order modules on the x-axis 2024-08-20T21:38:25.1428465Z 2024-08-20T21:38:25.1428961Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:25.1429445Z 2024-08-20T21:38:25.1430841Z msg = Cannot scrape callname=ModelReportVisualizer.generate_histogram_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=645. 2024-08-20T21:38:25.1432589Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:25.1433104Z 2024-08-20T21:38:25.1433484Z Takes in a feature and optional module_filter and plots the histogram of desired data. 2024-08-20T21:38:25.1434045Z 2024-08-20T21:38:25.1434134Z Note: 2024-08-20T21:38:25.1434632Z Only features in the report that have tensor value data can be viewed as a histogram 2024-08-20T21:38:25.1435598Z If you want to plot a histogram from all the channel values of a specific feature for 2024-08-20T21:38:25.1436496Z a specific model, make sure to specify both the model and the feature properly 2024-08-20T21:38:25.1437360Z in the filters and you should be able to see a distribution of the channel data 2024-08-20T21:38:25.1437872Z 2024-08-20T21:38:25.1437960Z Args: 2024-08-20T21:38:25.1438446Z feature_filter (str, optional): Filters the features presented to only those that 2024-08-20T21:38:25.1439098Z contain this filter substring 2024-08-20T21:38:25.1439582Z Default = "", results in all the features being printed 2024-08-20T21:38:25.1440306Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-08-20T21:38:25.1441163Z Default = "", results in all the modules in the reports to be visible in the table 2024-08-20T21:38:25.1441970Z num_bins (int, optional): The number of bins to create the histogram with 2024-08-20T21:38:25.1442698Z Default = 10, the values will be split into 10 equal sized bins 2024-08-20T21:38:25.1443124Z 2024-08-20T21:38:25.1443222Z Example Use: 2024-08-20T21:38:25.1443504Z >>> # xdoctest: +SKIP 2024-08-20T21:38:25.1444091Z >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization( 2024-08-20T21:38:25.1444803Z ... feature_filter = "per_channel_min", 2024-08-20T21:38:25.1445398Z ... module_fqn_filter = "block1" 2024-08-20T21:38:25.1445766Z ... ) 2024-08-20T21:38:25.1446282Z # outputs histogram of per_channel_min information for all modules in block1 of model 2024-08-20T21:38:25.1447454Z information is gathered across all channels for all modules in block 1 for the 2024-08-20T21:38:25.1448332Z per_channel_min and is displayed in a histogram of equally sized bins 2024-08-20T21:38:25.1448808Z 2024-08-20T21:38:25.1449231Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:25.1449712Z 2024-08-20T21:38:25.4270722Z msg = Cannot scrape callname=DeviceMesh.__getitem__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py line=473. 2024-08-20T21:38:25.4272116Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-08-20T21:38:25.4272605Z 2024-08-20T21:38:25.4273001Z Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh. 2024-08-20T21:38:25.4273912Z The submesh created consists of the dimensions and the communicators indicated by 2024-08-20T21:38:25.4274539Z ``mesh_dim_names`` 2024-08-20T21:38:25.4274717Z 2024-08-20T21:38:25.4274817Z Args: 2024-08-20T21:38:25.4275270Z mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the 2024-08-20T21:38:25.4276000Z mesh dimension of the DeviceMesh to create the submesh for. 2024-08-20T21:38:25.4276502Z Returns: 2024-08-20T21:38:25.4276759Z A :class:`DeviceMesh` object 2024-08-20T21:38:25.4277021Z 2024-08-20T21:38:25.4277405Z The following program runs on each process/rank in an SPMD manner in a world size of 8. 2024-08-20T21:38:25.4278077Z In the first example: 2024-08-20T21:38:25.4278883Z Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]). 2024-08-20T21:38:25.4279799Z Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of DeviceMesh:([4, 5, 6, 7]). 2024-08-20T21:38:25.4280670Z Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of DeviceMesh:([0, 4]). 2024-08-20T21:38:25.4281478Z Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of DeviceMesh:([1, 5]). 2024-08-20T21:38:25.4282298Z Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of DeviceMesh:([2, 6]). 2024-08-20T21:38:25.4283109Z Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of DeviceMesh:([3, 7]). 2024-08-20T21:38:25.4283597Z 2024-08-20T21:38:25.4283719Z In the second example: 2024-08-20T21:38:25.4284323Z Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]). 2024-08-20T21:38:25.4285300Z Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]). 2024-08-20T21:38:25.4286277Z Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]). 2024-08-20T21:38:25.4287234Z Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]). 2024-08-20T21:38:25.4287921Z 2024-08-20T21:38:25.4288047Z Example:: 2024-08-20T21:38:25.4288327Z >>> # xdoctest: +SKIP("no rank") 2024-08-20T21:38:25.4288816Z >>> from torch.distributed.device_mesh import DeviceMesh 2024-08-20T21:38:25.4289296Z >>> 2024-08-20T21:38:25.4289711Z >>> # Initialize a 2D device mesh as (2, 4) to represent the topology 2024-08-20T21:38:25.4290428Z >>> # of cross-host(dim 0), and within-host (dim 1). 2024-08-20T21:38:25.4291104Z >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2024-08-20T21:38:25.4291730Z >>> tp_mesh = mesh_2d["tp"] 2024-08-20T21:38:25.4292100Z >>> dp_mesh = mesh_2d["dp"] 2024-08-20T21:38:25.4292427Z >>> 2024-08-20T21:38:25.4292696Z >>> # Initialize a 3D mesh. 2024-08-20T21:38:25.4293324Z >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp")) 2024-08-20T21:38:25.4294277Z >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh. 2024-08-20T21:38:25.4295165Z >>> dp_cp_mesh = mesh_3d["dp", "cp"] 2024-08-20T21:38:25.4295594Z >>> cp_dp_mesh = mesh_3d["cp", "dp"] 2024-08-20T21:38:25.4295875Z 2024-08-20T21:38:25.4296940Z Original Error: SyntaxError('positional argument follows keyword argument', ('', 6, 82, 'mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))\n', 6, 83)) 2024-08-20T21:38:25.4297990Z 2024-08-20T21:38:25.4298316Z mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2024-08-20T21:38:25.4298985Z ^ 2024-08-20T21:38:25.4626434Z msg = Cannot scrape callname=gather_object in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py line=2745. 2024-08-20T21:38:25.4627852Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:25.4628362Z 2024-08-20T21:38:25.4628650Z Gathers picklable objects from the whole group in a single process. 2024-08-20T21:38:25.4629113Z 2024-08-20T21:38:25.4629432Z Similar to :func:`gather`, but Python objects can be passed in. Note that the 2024-08-20T21:38:25.4630109Z object must be picklable in order to be gathered. 2024-08-20T21:38:25.4630449Z 2024-08-20T21:38:25.4630537Z Args: 2024-08-20T21:38:25.4630844Z obj (Any): Input object. Must be picklable. 2024-08-20T21:38:25.4631446Z object_gather_list (list[Any]): Output list. On the ``dst`` rank, it 2024-08-20T21:38:25.4632126Z should be correctly sized as the size of the group for this 2024-08-20T21:38:25.4632904Z collective and will contain the output. Must be ``None`` on non-dst 2024-08-20T21:38:25.4633682Z ranks. (default is ``None``) 2024-08-20T21:38:25.4634481Z dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). (default is 0) 2024-08-20T21:38:25.4635452Z group: (ProcessGroup, optional): The process group to work on. If None, 2024-08-20T21:38:25.4636179Z the default process group will be used. Default is ``None``. 2024-08-20T21:38:25.4636587Z 2024-08-20T21:38:25.4636692Z Returns: 2024-08-20T21:38:25.4637090Z None. On the ``dst`` rank, ``object_gather_list`` will contain the 2024-08-20T21:38:25.4637639Z output of the collective. 2024-08-20T21:38:25.4637874Z 2024-08-20T21:38:25.4638192Z .. note:: Note that this API differs slightly from the gather collective 2024-08-20T21:38:25.4638947Z since it does not provide an async_op handle and thus will be a blocking 2024-08-20T21:38:25.4639521Z call. 2024-08-20T21:38:25.4639666Z 2024-08-20T21:38:25.4640068Z .. note:: For NCCL-based processed groups, internal tensor representations 2024-08-20T21:38:25.4640849Z of objects must be moved to the GPU device before communication takes 2024-08-20T21:38:25.4641490Z place. In this case, the device used is given by 2024-08-20T21:38:25.4642208Z ``torch.cuda.current_device()`` and it is the user's responsiblity to 2024-08-20T21:38:25.4642958Z ensure that this is set so that each rank has an individual GPU, via 2024-08-20T21:38:25.4643524Z ``torch.cuda.set_device()``. 2024-08-20T21:38:25.4643784Z 2024-08-20T21:38:25.4643883Z .. warning:: 2024-08-20T21:38:25.4644313Z :func:`gather_object` uses ``pickle`` module implicitly, which is 2024-08-20T21:38:25.4645042Z known to be insecure. It is possible to construct malicious pickle data 2024-08-20T21:38:25.4645812Z which will execute arbitrary code during unpickling. Only call this 2024-08-20T21:38:25.4646397Z function with data you trust. 2024-08-20T21:38:25.4646864Z 2024-08-20T21:38:25.4646972Z .. warning:: 2024-08-20T21:38:25.4647498Z Calling :func:`gather_object` with GPU tensors is not well supported 2024-08-20T21:38:25.4648345Z and inefficient as it incurs GPU -> CPU transfer since tensors would be 2024-08-20T21:38:25.4649029Z pickled. Please consider using :func:`gather` instead. 2024-08-20T21:38:25.4649551Z 2024-08-20T21:38:25.4649652Z Example:: 2024-08-20T21:38:25.4649990Z >>> # xdoctest: +SKIP("need process group init") 2024-08-20T21:38:25.4650574Z >>> # Note: Process group initialization omitted on each rank. 2024-08-20T21:38:25.4651114Z >>> import torch.distributed as dist 2024-08-20T21:38:25.4651546Z >>> # Assumes world_size of 3. 2024-08-20T21:38:25.4670754Z >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object 2024-08-20T21:38:25.4671345Z >>> output = [None for _ in gather_objects] 2024-08-20T21:38:25.4671775Z >>> dist.gather_object( 2024-08-20T21:38:25.4672163Z ... gather_objects[dist.get_rank()], 2024-08-20T21:38:25.4672648Z ... output if dist.get_rank() == 0 else None, 2024-08-20T21:38:25.4673086Z ... dst=0 2024-08-20T21:38:25.4673356Z ... ) 2024-08-20T21:38:25.4673604Z >>> # On rank 0 2024-08-20T21:38:25.4673874Z >>> output 2024-08-20T21:38:25.4674262Z ['foo', 12, {1: 2}] 2024-08-20T21:38:25.4674466Z 2024-08-20T21:38:25.4674894Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:25.4675379Z 2024-08-20T21:38:25.4802712Z msg = Cannot scrape callname=__doc__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/launch.py line=2. 2024-08-20T21:38:25.4804785Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:25.4805518Z 2024-08-20T21:38:25.4805814Z Module ``torch.distributed.launch``. 2024-08-20T21:38:25.4806251Z 2024-08-20T21:38:25.4806876Z ``torch.distributed.launch`` is a module that spawns up multiple distributed 2024-08-20T21:38:25.4807984Z training processes on each of the training nodes. 2024-08-20T21:38:25.4808606Z 2024-08-20T21:38:25.4809569Z .. warning:: 2024-08-20T21:38:25.4809877Z 2024-08-20T21:38:25.4810753Z This module is going to be deprecated in favor of :ref:`torchrun `. 2024-08-20T21:38:25.4811649Z 2024-08-20T21:38:25.4812423Z The utility can be used for single-node distributed training, in which one or 2024-08-20T21:38:25.4813583Z more processes per node will be spawned. The utility can be used for either 2024-08-20T21:38:25.4814378Z CPU training or GPU training. If the utility is used for GPU training, 2024-08-20T21:38:25.4815178Z each distributed process will be operating on a single GPU. This can achieve 2024-08-20T21:38:25.4816048Z well-improved single-node training performance. It can also be used in 2024-08-20T21:38:25.4816942Z multi-node distributed training, by spawning up multiple processes on each node 2024-08-20T21:38:25.4817828Z for well-improved multi-node distributed training performance as well. 2024-08-20T21:38:25.4818604Z This will especially be beneficial for systems with multiple Infiniband 2024-08-20T21:38:25.4819537Z interfaces that have direct-GPU support, since all of them can be utilized for 2024-08-20T21:38:25.4820186Z aggregated communication bandwidth. 2024-08-20T21:38:25.4820462Z 2024-08-20T21:38:25.4820870Z In both cases of single-node distributed training or multi-node distributed 2024-08-20T21:38:25.4821668Z training, this utility will launch the given number of processes per node 2024-08-20T21:38:25.4822541Z (``--nproc-per-node``). If used for GPU training, this number needs to be less 2024-08-20T21:38:25.4823322Z or equal to the number of GPUs on the current system (``nproc_per_node``), 2024-08-20T21:38:25.4824075Z and each process will be operating on a single GPU from *GPU 0 to 2024-08-20T21:38:25.4824714Z GPU (nproc_per_node - 1)*. 2024-08-20T21:38:25.4824938Z 2024-08-20T21:38:25.4825054Z **How to use this module:** 2024-08-20T21:38:25.4825294Z 2024-08-20T21:38:25.4825533Z 1. Single-Node multi-process distributed training 2024-08-20T21:38:25.4825877Z 2024-08-20T21:38:25.4825987Z :: 2024-08-20T21:38:25.4826122Z 2024-08-20T21:38:25.4826509Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-08-20T21:38:25.4827293Z YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other 2024-08-20T21:38:25.4828014Z arguments of your training script) 2024-08-20T21:38:25.4828330Z 2024-08-20T21:38:25.4828687Z 2. Multi-Node multi-process distributed training: (e.g. two nodes) 2024-08-20T21:38:25.4829125Z 2024-08-20T21:38:25.4829129Z 2024-08-20T21:38:25.4829317Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)* 2024-08-20T21:38:25.4829677Z 2024-08-20T21:38:25.4829768Z :: 2024-08-20T21:38:25.4829895Z 2024-08-20T21:38:25.4830284Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-08-20T21:38:25.4831023Z --nnodes=2 --node-rank=0 --master-addr="192.168.1.1" 2024-08-20T21:38:25.4831726Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2024-08-20T21:38:25.4832370Z and all other arguments of your training script) 2024-08-20T21:38:25.4832732Z 2024-08-20T21:38:25.4832837Z Node 2: 2024-08-20T21:38:25.4832973Z 2024-08-20T21:38:25.4833062Z :: 2024-08-20T21:38:25.4833202Z 2024-08-20T21:38:25.4833576Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-08-20T21:38:25.4834328Z --nnodes=2 --node-rank=1 --master-addr="192.168.1.1" 2024-08-20T21:38:25.4835028Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2024-08-20T21:38:25.4835661Z and all other arguments of your training script) 2024-08-20T21:38:25.4836026Z 2024-08-20T21:38:25.4836261Z 3. To look up what optional arguments this module offers: 2024-08-20T21:38:25.4836642Z 2024-08-20T21:38:25.4836744Z :: 2024-08-20T21:38:25.4836871Z 2024-08-20T21:38:25.4837097Z python -m torch.distributed.launch --help 2024-08-20T21:38:25.4837437Z 2024-08-20T21:38:25.4837441Z 2024-08-20T21:38:25.4837551Z **Important Notices:** 2024-08-20T21:38:25.4837826Z 2024-08-20T21:38:25.4838147Z 1. This utility and multi-process distributed (single-node or 2024-08-20T21:38:25.4838936Z multi-node) GPU training currently only achieves the best performance using 2024-08-20T21:38:25.4839764Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to 2024-08-20T21:38:25.4840384Z use for GPU training. 2024-08-20T21:38:25.4840579Z 2024-08-20T21:38:25.4840941Z 2. In your training program, you must parse the command-line argument: 2024-08-20T21:38:25.4841777Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module. 2024-08-20T21:38:25.4842557Z If your training program uses GPUs, you should ensure that your code only 2024-08-20T21:38:25.4843353Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by: 2024-08-20T21:38:25.4843784Z 2024-08-20T21:38:25.4843908Z Parsing the local_rank argument 2024-08-20T21:38:25.4844172Z 2024-08-20T21:38:25.4844261Z :: 2024-08-20T21:38:25.4844386Z 2024-08-20T21:38:25.4844514Z >>> # xdoctest: +SKIP 2024-08-20T21:38:25.4844829Z >>> import argparse 2024-08-20T21:38:25.4845193Z >>> parser = argparse.ArgumentParser() 2024-08-20T21:38:25.4845833Z >>> parser.add_argument("--local-rank", "--local_rank", type=int) 2024-08-20T21:38:25.4846367Z >>> args = parser.parse_args() 2024-08-20T21:38:25.4846836Z 2024-08-20T21:38:25.4846998Z Set your device to local rank using either 2024-08-20T21:38:25.4847366Z 2024-08-20T21:38:25.4847475Z :: 2024-08-20T21:38:25.4847602Z 2024-08-20T21:38:25.4847884Z >>> torch.cuda.set_device(args.local_rank) # before your code runs 2024-08-20T21:38:25.4848317Z 2024-08-20T21:38:25.4848404Z or 2024-08-20T21:38:25.4848543Z 2024-08-20T21:38:25.4848631Z :: 2024-08-20T21:38:25.4848756Z 2024-08-20T21:38:25.4848969Z >>> with torch.cuda.device(args.local_rank): 2024-08-20T21:38:25.4849400Z >>> # your code to run 2024-08-20T21:38:25.4849731Z >>> ... 2024-08-20T21:38:25.4849890Z 2024-08-20T21:38:25.4850019Z .. versionchanged:: 2.0.0 2024-08-20T21:38:25.4850241Z 2024-08-20T21:38:25.4850668Z The launcher will passes the ``--local-rank=`` argument to your script. 2024-08-20T21:38:25.4851558Z From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the 2024-08-20T21:38:25.4852417Z previously used underscored ``--local_rank``. 2024-08-20T21:38:25.4852745Z 2024-08-20T21:38:25.4853075Z For backward compatibility, it may be necessary for users to handle both 2024-08-20T21:38:25.4853989Z cases in their argument parsing code. This means including both ``"--local-rank"`` 2024-08-20T21:38:25.4854890Z and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is 2024-08-20T21:38:25.4855693Z provided, the launcher will trigger an error: "error: unrecognized arguments: 2024-08-20T21:38:25.4856566Z --local-rank=". For training code that only supports PyTorch 2.0.0+, 2024-08-20T21:38:25.4857289Z including ``"--local-rank"`` should be sufficient. 2024-08-20T21:38:25.4857648Z 2024-08-20T21:38:25.4857977Z 3. In your training program, you are supposed to call the following function 2024-08-20T21:38:25.4858799Z at the beginning to start the distributed backend. It is strongly recommended 2024-08-20T21:38:25.4859590Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work, 2024-08-20T21:38:25.4860324Z but ``env://`` is the one that is officially supported by this module. 2024-08-20T21:38:25.4860752Z 2024-08-20T21:38:25.4860853Z :: 2024-08-20T21:38:25.4860980Z 2024-08-20T21:38:25.4861309Z >>> torch.distributed.init_process_group(backend='YOUR BACKEND', 2024-08-20T21:38:25.4861990Z >>> init_method='env://') 2024-08-20T21:38:25.4862326Z 2024-08-20T21:38:25.4862659Z 4. In your training program, you can either use regular distributed functions 2024-08-20T21:38:25.4863481Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your 2024-08-20T21:38:25.4864337Z training program uses GPUs for training and you would like to use 2024-08-20T21:38:25.4865029Z :func:`torch.nn.parallel.DistributedDataParallel` module, 2024-08-20T21:38:25.4865566Z here is how to configure it. 2024-08-20T21:38:25.4865801Z 2024-08-20T21:38:25.4865889Z :: 2024-08-20T21:38:25.4866033Z 2024-08-20T21:38:25.4866292Z >>> model = torch.nn.parallel.DistributedDataParallel(model, 2024-08-20T21:38:25.4866911Z >>> device_ids=[args.local_rank], 2024-08-20T21:38:25.4867466Z >>> output_device=args.local_rank) 2024-08-20T21:38:25.4867847Z 2024-08-20T21:38:25.4868169Z Please ensure that ``device_ids`` argument is set to be the only GPU device id 2024-08-20T21:38:25.4868986Z that your code will be operating on. This is generally the local rank of the 2024-08-20T21:38:25.4869799Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``, 2024-08-20T21:38:25.4870575Z and ``output_device`` needs to be ``args.local_rank`` in order to use this 2024-08-20T21:38:25.4871128Z utility 2024-08-20T21:38:25.4871264Z 2024-08-20T21:38:25.4871670Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable 2024-08-20T21:38:25.4872457Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with 2024-08-20T21:38:25.4873301Z ``--use-env=True``. You must adjust the subprocess example above to replace 2024-08-20T21:38:25.4874094Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher 2024-08-20T21:38:25.4874786Z will not pass ``--local-rank`` when you specify this flag. 2024-08-20T21:38:25.4875179Z 2024-08-20T21:38:25.4875283Z .. warning:: 2024-08-20T21:38:25.4875441Z 2024-08-20T21:38:25.4875728Z ``local_rank`` is NOT globally unique: it is only unique per process 2024-08-20T21:38:25.4876506Z on a machine. Thus, don't use it to decide if you should, e.g., 2024-08-20T21:38:25.4877067Z write to a networked filesystem. See 2024-08-20T21:38:25.4877661Z https://github.com/pytorch/pytorch/issues/12042 for an example of 2024-08-20T21:38:25.4878389Z how things can go wrong if you don't do this correctly. 2024-08-20T21:38:25.4878766Z 2024-08-20T21:38:25.4878771Z 2024-08-20T21:38:25.4878776Z 2024-08-20T21:38:25.4878781Z 2024-08-20T21:38:25.4879248Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:25.4879744Z 2024-08-20T21:38:25.5731832Z msg = Cannot scrape callname=init_from_local_shards in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py line=361. 2024-08-20T21:38:25.5733349Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:25.5733894Z 2024-08-20T21:38:25.5734216Z Creates an :class:`ShardedTensor` from local shards and the global metadata. 2024-08-20T21:38:25.5734896Z Needs to be called on all ranks in an SPMD fashion. 2024-08-20T21:38:25.5735251Z 2024-08-20T21:38:25.5735339Z Args: 2024-08-20T21:38:25.5735855Z local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list 2024-08-20T21:38:25.5736620Z of shards that represent the local shards on this rank. 2024-08-20T21:38:25.5737310Z global_size (int...): a list, tuple, or `torch.Size` of integers defining the 2024-08-20T21:38:25.5737949Z shape of the overall sharded tensor. 2024-08-20T21:38:25.5738258Z 2024-08-20T21:38:25.5738370Z Keyword args: 2024-08-20T21:38:25.5738868Z process_group (ProcessGroup, optional): The process group to work on. If None, 2024-08-20T21:38:25.5739548Z the default process group will be used. 2024-08-20T21:38:25.5740093Z init_rrefs (bool, optional): Whether or not to initialize 2024-08-20T21:38:25.5740747Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2024-08-20T21:38:25.5741454Z Need to initialize the RPC Framework if specified as ``True``. 2024-08-20T21:38:25.5741994Z Default: ``False``. 2024-08-20T21:38:25.5742215Z 2024-08-20T21:38:25.5742321Z Returns: 2024-08-20T21:38:25.5742872Z A :class:`ShardedTensor` object handle on this rank 2024-08-20T21:38:25.5743246Z 2024-08-20T21:38:25.5743250Z 2024-08-20T21:38:25.5743345Z Examples: 2024-08-20T21:38:25.5743846Z Suppose we want construct a sharded tensor on two ranks, global size = (10, 5), 2024-08-20T21:38:25.5744610Z each shard have a (5, 5) local tensor, we can do it like below: 2024-08-20T21:38:25.5745031Z 2024-08-20T21:38:25.5745130Z on rank 0: 2024-08-20T21:38:25.5745454Z >>> # xdoctest: +SKIP("not distributed") 2024-08-20T21:38:25.5745911Z >>> local_shard_metadata = ShardMetadata( 2024-08-20T21:38:25.5746353Z >>> shard_offsets=[0, 0], 2024-08-20T21:38:25.5747076Z >>> shard_lengths=[5, 5], 2024-08-20T21:38:25.5747453Z >>> placement="rank:0/cuda:0" 2024-08-20T21:38:25.5747821Z >>> ) 2024-08-20T21:38:25.5748223Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2024-08-20T21:38:25.5748890Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2024-08-20T21:38:25.5749315Z 2024-08-20T21:38:25.5749415Z on rank 1: 2024-08-20T21:38:25.5749736Z >>> # xdoctest: +SKIP("not distributed") 2024-08-20T21:38:25.5750186Z >>> local_shard_metadata = ShardMetadata( 2024-08-20T21:38:25.5750624Z >>> shard_offsets=[5, 0], 2024-08-20T21:38:25.5750989Z >>> shard_lengths=[5, 5], 2024-08-20T21:38:25.5751350Z >>> placement="rank:1/cuda:1" 2024-08-20T21:38:25.5751716Z >>> ) 2024-08-20T21:38:25.5752113Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2024-08-20T21:38:25.5752825Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2024-08-20T21:38:25.5753234Z 2024-08-20T21:38:25.5753669Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:25.5754167Z 2024-08-20T21:38:25.5868345Z msg = Cannot scrape callname=ShardedTensor._init_from_local_tensor in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=784. 2024-08-20T21:38:25.5870453Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:25.5870956Z 2024-08-20T21:38:25.5871334Z Initialize a ShardedTensor given only one local tensor, global sharded tensor 2024-08-20T21:38:25.5872218Z size and sharding spec on each rank. 2024-08-20T21:38:25.5872511Z 2024-08-20T21:38:25.5872599Z Args: 2024-08-20T21:38:25.5873045Z local_tensor (Tensor): Single tensor of local shard stored in each rank. 2024-08-20T21:38:25.5873841Z sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): 2024-08-20T21:38:25.5874570Z The specification describing how to shard the Tensor. 2024-08-20T21:38:25.5875173Z global_size (Sequence[int]): Size of the sharded tensor. 2024-08-20T21:38:25.5876029Z process_group (ProcessGroup, optional): The process group to aggregate on. 2024-08-20T21:38:25.5876723Z Default: None 2024-08-20T21:38:25.5877279Z init_rrefs (bool, optional): Whether or not to initialize 2024-08-20T21:38:25.5878335Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2024-08-20T21:38:25.5879555Z Need to initialize the RPC Framework if specified as ``True``. 2024-08-20T21:38:25.5880543Z Default: ``False``. 2024-08-20T21:38:25.5880953Z 2024-08-20T21:38:25.5881138Z Returns: 2024-08-20T21:38:25.5881670Z A :class:`ShardedTensor` sharded based on the given sharding_spec with local 2024-08-20T21:38:25.5882319Z tensor stored in the current rank. 2024-08-20T21:38:25.5882619Z 2024-08-20T21:38:25.5882725Z Examples: 2024-08-20T21:38:25.5882978Z >>> # xdoctest: +SKIP 2024-08-20T21:38:25.5883378Z >>> # All tensors below are of torch.int64 type. 2024-08-20T21:38:25.5883869Z >>> # We have 2 process groups, 2 ranks. 2024-08-20T21:38:25.5884409Z >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank 2024-08-20T21:38:25.5885076Z >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2])) 2024-08-20T21:38:25.5885819Z >>> local_tensor 2024-08-20T21:38:25.5886120Z tensor([[1, 2, 3, 4]]) # Rank 0 2024-08-20T21:38:25.5886499Z tensor([[3, 4, 5, 6]]) # Rank 1 2024-08-20T21:38:25.5886873Z >>> sharding_dim = 0 2024-08-20T21:38:25.5887228Z >>> sharding_spec = ChunkShardingSpec( 2024-08-20T21:38:25.5887739Z dim=sharding_dim, 2024-08-20T21:38:25.5888084Z placements=[ 2024-08-20T21:38:25.5888393Z "rank:0/cuda:0", 2024-08-20T21:38:25.5888747Z "rank:1/cuda:1", 2024-08-20T21:38:25.5889084Z ], 2024-08-20T21:38:25.5889328Z ) 2024-08-20T21:38:25.5889809Z >>> st = ShardedTensor._init_from_local_tensor(local_tensor, sharding_spec, [2, 4]) 2024-08-20T21:38:25.5890411Z >>> st 2024-08-20T21:38:25.5890653Z ShardedTensor( 2024-08-20T21:38:25.5890967Z ShardedTensorMetadata( 2024-08-20T21:38:25.5891331Z shards_metadata=[ 2024-08-20T21:38:25.5891904Z ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0), 2024-08-20T21:38:25.5892806Z ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1), 2024-08-20T21:38:25.5893418Z ], 2024-08-20T21:38:25.5893698Z size=torch.Size([2, 4]) 2024-08-20T21:38:25.5894053Z ) 2024-08-20T21:38:25.5894300Z >>> st.local_tensor() 2024-08-20T21:38:25.5894620Z tensor([1, 2, 3, 4]) # Rank 0 2024-08-20T21:38:25.5894987Z tensor([3, 4, 5, 6]) # Rank 1 2024-08-20T21:38:25.5895246Z 2024-08-20T21:38:25.5895613Z Warning: This API is experimental and subject to change. It lacks of a fully across 2024-08-20T21:38:25.5896480Z rank validations, and we only validate the local shard on the current rank. 2024-08-20T21:38:25.5897285Z We fully rely on the user to ensure local tensor is sharded based on the 2024-08-20T21:38:25.5897888Z sharding spec. 2024-08-20T21:38:25.5898115Z 2024-08-20T21:38:25.5898594Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:25.5899081Z 2024-08-20T21:38:25.5900187Z msg = Cannot scrape callname=ShardedTensor.reshard in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=1023. 2024-08-20T21:38:25.5901745Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:25.5902262Z 2024-08-20T21:38:25.5902604Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support 2024-08-20T21:38:25.5903228Z single local shard. 2024-08-20T21:38:25.5903413Z 2024-08-20T21:38:25.5903795Z If ``resharding_spec`` is same as the original one, this becomes a no-op. 2024-08-20T21:38:25.5904574Z If only ``resharding_spec`` shares the same sharding dim with the original one, 2024-08-20T21:38:25.5905196Z we swap local shards directly. 2024-08-20T21:38:25.5905795Z For more generic cases, we merge different shards across different ranks and split 2024-08-20T21:38:25.5906657Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API. 2024-08-20T21:38:25.5907182Z 2024-08-20T21:38:25.5907271Z Args: 2024-08-20T21:38:25.5907791Z resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The 2024-08-20T21:38:25.5908550Z specification describing how the tensor is sharded. 2024-08-20T21:38:25.5908933Z 2024-08-20T21:38:25.5909029Z Returns: 2024-08-20T21:38:25.5909449Z A :class:`ShardedTensor` object whose local shards are resharded. 2024-08-20T21:38:25.5909877Z 2024-08-20T21:38:25.5909985Z Examples: 2024-08-20T21:38:25.5910236Z >>> # xdoctest: +SKIP 2024-08-20T21:38:25.5910611Z >>> # We have 2 process groups, 2 ranks. 2024-08-20T21:38:25.5911165Z >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank 2024-08-20T21:38:25.5911714Z >>> tensor = torch.stack([tensor, tensor]) 2024-08-20T21:38:25.5912170Z >>> tensor 2024-08-20T21:38:25.5912490Z tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0 2024-08-20T21:38:25.5913021Z tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1 2024-08-20T21:38:25.5913485Z tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2 2024-08-20T21:38:25.5913952Z tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3 2024-08-20T21:38:25.5914374Z >>> sharding_dim = 0 2024-08-20T21:38:25.5914727Z >>> spec = ChunkShardingSpec( 2024-08-20T21:38:25.5915107Z dim=sharding_dim, 2024-08-20T21:38:25.5915437Z placements=[ 2024-08-20T21:38:25.5915763Z "rank:0/cuda:0", 2024-08-20T21:38:25.5916117Z "rank:1/cuda:1", 2024-08-20T21:38:25.5916452Z "rank:2/cuda:2", 2024-08-20T21:38:25.5916802Z "rank:3/cuda:3", 2024-08-20T21:38:25.5917134Z ], 2024-08-20T21:38:25.5917378Z ) 2024-08-20T21:38:25.5917650Z >>> current_offsets = [0] * 2 2024-08-20T21:38:25.5918035Z >>> current_offsets[0] = rank * 2 2024-08-20T21:38:25.5918436Z >>> shard_metadata = ShardMetadata( 2024-08-20T21:38:25.5918919Z shard_offsets=copy.deepcopy(current_offsets), 2024-08-20T21:38:25.5919402Z shard_sizes=tensor.size(), 2024-08-20T21:38:25.5919821Z placement=spec.placements[rank], 2024-08-20T21:38:25.5920223Z ) 2024-08-20T21:38:25.5920486Z >>> local_shards = [ 2024-08-20T21:38:25.5920782Z Shard( 2024-08-20T21:38:25.5921068Z tensor=tensor, 2024-08-20T21:38:25.5921434Z metadata=shard_metadata, 2024-08-20T21:38:25.5921804Z ) 2024-08-20T21:38:25.5922055Z ] 2024-08-20T21:38:25.5922505Z >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size()) 2024-08-20T21:38:25.5923071Z >>> sharding_dim = 1 2024-08-20T21:38:25.5923443Z >>> resharding_spec = ChunkShardingSpec( 2024-08-20T21:38:25.5923876Z dim=sharding_dim, 2024-08-20T21:38:25.5924204Z placements=[ 2024-08-20T21:38:25.5924524Z "rank:0/cuda:0", 2024-08-20T21:38:25.5924875Z "rank:1/cuda:1", 2024-08-20T21:38:25.5925213Z "rank:2/cuda:2", 2024-08-20T21:38:25.5925560Z "rank:3/cuda:3", 2024-08-20T21:38:25.5925890Z ], 2024-08-20T21:38:25.5926135Z ) 2024-08-20T21:38:25.5926413Z >>> st.reshard(resharding_spec) 2024-08-20T21:38:25.5926958Z >>> tensor = st.local_shards()[0].tensor 2024-08-20T21:38:25.5927430Z >>> tensor 2024-08-20T21:38:25.5927790Z tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0 2024-08-20T21:38:25.5928330Z tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1 2024-08-20T21:38:25.5928858Z tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2 2024-08-20T21:38:25.5929404Z tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3 2024-08-20T21:38:25.5929758Z 2024-08-20T21:38:25.5930205Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:25.5930691Z 2024-08-20T21:38:25.6082693Z msg = Cannot scrape callname=ShardingPlan in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharding_plan/api.py line=12. 2024-08-20T21:38:25.6085179Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:25.6086137Z 2024-08-20T21:38:25.6086562Z Representation of a sharding plan, describes how to shard a module 2024-08-20T21:38:25.6087456Z across hosts. `plan` is used to shard module parameters according to the spec provided, 2024-08-20T21:38:25.6088432Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output 2024-08-20T21:38:25.6089317Z layout of a module with a spec, and when to convert back to data parallel fashion. 2024-08-20T21:38:25.6089850Z 2024-08-20T21:38:25.6089939Z Args: 2024-08-20T21:38:25.6090450Z plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`, 2024-08-20T21:38:25.6091193Z :class:`torch.distributed._shard.sharder.Sharder`]): 2024-08-20T21:38:25.6092284Z a dict describes how to shard a module, there're currently two ways to shard a module: 2024-08-20T21:38:25.6093194Z 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of 2024-08-20T21:38:25.6093860Z a parameter to a `ShardingSpec`. 2024-08-20T21:38:25.6094551Z 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module 2024-08-20T21:38:25.6095202Z to a `Sharder` object. 2024-08-20T21:38:25.6095907Z output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional): 2024-08-20T21:38:25.6096968Z a dict specifies the layout of a module's output which produces a ShardedTensor, 2024-08-20T21:38:25.6097843Z keyed by the name of module to ShardingSpec("" in key means the root module). 2024-08-20T21:38:25.6098465Z Default: `None` 2024-08-20T21:38:25.6099005Z return_local_tensor (List[str], optional): a list of string, each element enables 2024-08-20T21:38:25.6099946Z a module's sharded output to be returned as a Tensor from its local shards to 2024-08-20T21:38:25.6100788Z ensure further processing in a data parallel fashion. ("" in list means the 2024-08-20T21:38:25.6101383Z root module). 2024-08-20T21:38:25.6101685Z Default: None 2024-08-20T21:38:25.6101982Z Example: 2024-08-20T21:38:25.6102521Z Suppose we want to shard a module with two linear layers and then run it with DDP, we also 2024-08-20T21:38:25.6103504Z want to convert the output of the second linear layer back to DDP, we can do it as follows: 2024-08-20T21:38:25.6104073Z 2024-08-20T21:38:25.6104305Z >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d) 2024-08-20T21:38:25.6104811Z >>> class MyModule(nn.Module): 2024-08-20T21:38:25.6105260Z >>> def __init__(self) -> None: 2024-08-20T21:38:25.6105657Z >>> super().__init__() 2024-08-20T21:38:25.6106020Z >>> self.fc1 = nn.Linear() 2024-08-20T21:38:25.6106414Z >>> self.gelu = nn.GELU() 2024-08-20T21:38:25.6106812Z >>> self.fc2 = nn.Linear() 2024-08-20T21:38:25.6107197Z >>> self.relu = nn.Linear() 2024-08-20T21:38:25.6107572Z >>> 2024-08-20T21:38:25.6107837Z >>> def forward(self, input): 2024-08-20T21:38:25.6108325Z >>> return self.relu(self.fc2(self.gelu(self.fc1(input)))) 2024-08-20T21:38:25.6108874Z 2024-08-20T21:38:25.6108878Z 2024-08-20T21:38:25.6109045Z >>> # xdoctest: +SKIP("Undefined spec1, spec2) 2024-08-20T21:38:25.6109519Z >>> sharding_plan = ShardingPlan( 2024-08-20T21:38:25.6109905Z >>> plan={ 2024-08-20T21:38:25.6110188Z >>> "fc1.weight": spec1, 2024-08-20T21:38:25.6110575Z >>> "fc2.weight": spec2 2024-08-20T21:38:25.6110929Z >>> }, 2024-08-20T21:38:25.6111190Z >>> output_plan={ 2024-08-20T21:38:25.6111522Z >>> "fc2": output_spec 2024-08-20T21:38:25.6111866Z >>> }, 2024-08-20T21:38:25.6112141Z >>> return_local_tensor=["fc2"] 2024-08-20T21:38:25.6112523Z >>> ) 2024-08-20T21:38:25.6112665Z 2024-08-20T21:38:25.6113142Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:25.6113626Z 2024-08-20T21:38:25.6996265Z msg = Cannot scrape callname=local_map in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_tensor/experimental/func_map.py line=33. 2024-08-20T21:38:25.6998796Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:25.6999313Z 2024-08-20T21:38:25.6999711Z ``local_map`` is an experimental API that allows users to apply on :class:`DTensors` 2024-08-20T21:38:25.7000519Z a function that is written to be applied on :class:`~torch.Tensors`. 2024-08-20T21:38:25.7000963Z 2024-08-20T21:38:25.7001066Z Args: 2024-08-20T21:38:25.7001472Z func (Callable): the function to be applied on each local shard of 2024-08-20T21:38:25.7002032Z :class:`DTensor`s. 2024-08-20T21:38:25.7002545Z out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]): 2024-08-20T21:38:25.7003659Z the desired placements of the :class:`DTensor`s in ``func``'s flattened output. 2024-08-20T21:38:25.7004515Z If the flattened ``output`` is a single value, the ``out_placements`` should be 2024-08-20T21:38:25.7005350Z of type `PlacementType`. Otherwise if the flattened ``output`` has multiple 2024-08-20T21:38:25.7006172Z values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1 2024-08-20T21:38:25.7006827Z mapping to the flattened ``output``. 2024-08-20T21:38:25.7007581Z Besides, for :class:`Tensor` output, we use `PlacementType` as its 2024-08-20T21:38:25.7008416Z placements (a `Tuple[Placement]` value). For non-:class:`Tensor` output, 2024-08-20T21:38:25.7009030Z the `PlacementType` should be `None`. 2024-08-20T21:38:25.7009672Z Note that the only exception is when no :class:`DTensor` argument is passed 2024-08-20T21:38:25.7010499Z in. In this case, even if `out_placements` is not `None`, the result function 2024-08-20T21:38:25.7011288Z should ignore the desired placements because the application is not on 2024-08-20T21:38:25.7011880Z :class:`DTensors`. 2024-08-20T21:38:25.7012310Z in_placements (Tuple[`PlacementType`, ...], optional): 2024-08-20T21:38:25.7013210Z the required placements of the :class:`DTensor`s in ``func``'s flattened input. 2024-08-20T21:38:25.7014096Z If ``in_placements`` is specified, ``local_map`` would examine whether the 2024-08-20T21:38:25.7015331Z placements of each :class:`DTensor` argument is the same as the required 2024-08-20T21:38:25.7016567Z placements or not. If the placements are not the same and 2024-08-20T21:38:25.7017914Z ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if 2024-08-20T21:38:25.7018992Z ``redistribute_inputs`` is `True`, the argument will be first redistributed to 2024-08-20T21:38:25.7019894Z the required sharding placements before passing its local tensor to ``func``. 2024-08-20T21:38:25.7020700Z The only exception is when required placements are not ``None`` and the 2024-08-20T21:38:25.7021504Z argument is a :class:`torch.Tensor`. In this case, the placements examination 2024-08-20T21:38:25.7022469Z will be skipped and the argument will be directly passed to ``func``. 2024-08-20T21:38:25.7023239Z If ``in_placements`` is ``None``, no placements examination will be performed. 2024-08-20T21:38:25.7023831Z Default: None 2024-08-20T21:38:25.7024197Z device_mesh (:class:`DeviceMesh`, optional): 2024-08-20T21:38:25.7024807Z the device mesh that all the :class:`DTensor`s are placed on. If not 2024-08-20T21:38:25.7025692Z specified, this will be inferred from the input :class:`DTensor`s' device 2024-08-20T21:38:25.7026493Z mesh. `local_map` requires every :class:`DTensor`s to be placed on the same 2024-08-20T21:38:25.7027105Z device mesh. Default: None. 2024-08-20T21:38:25.7027515Z redistribute_inputs (bool, optional): 2024-08-20T21:38:25.7028159Z the bool value indicating whether to reshard the input :class:`DTensor`s when 2024-08-20T21:38:25.7028999Z their placements are different from the required input placements. If this 2024-08-20T21:38:25.7029805Z value is ``False`` and some :class:`DTensor` input has a different placement, 2024-08-20T21:38:25.7030470Z an exception will be raised. Default: False. 2024-08-20T21:38:25.7030805Z 2024-08-20T21:38:25.7030913Z Returns: 2024-08-20T21:38:25.7031406Z A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor` 2024-08-20T21:38:25.7032253Z and returns a :class:`DTensor` constructed from the return value of ``func``. 2024-08-20T21:38:25.7032744Z 2024-08-20T21:38:25.7032849Z Raises: 2024-08-20T21:38:25.7033330Z AssertionError: If the input :class:`DTensor`s are not placed on the same device 2024-08-20T21:38:25.7034253Z mesh, or if they are placed on a different device mesh than the ``device_mesh`` 2024-08-20T21:38:25.7034880Z argument passed in. 2024-08-20T21:38:25.7035086Z 2024-08-20T21:38:25.7035536Z AssertionError: For any non-:class:`DTensor` output, we require its corresponding 2024-08-20T21:38:25.7036398Z output placement in ``out_placements`` be None. An AssertionError will be raised 2024-08-20T21:38:25.7037031Z if this is not the case. 2024-08-20T21:38:25.7037261Z 2024-08-20T21:38:25.7037613Z ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs 2024-08-20T21:38:25.7038306Z a redistribution according to ``in_placements``. 2024-08-20T21:38:25.7038668Z 2024-08-20T21:38:25.7038762Z Example: 2024-08-20T21:38:25.7039045Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:38:25.7039503Z >>> def mm_allreduce_forward(device_mesh, W, X): 2024-08-20T21:38:25.7040000Z >>> partial_sum_tensor = torch.mm(W, X) 2024-08-20T21:38:25.7040634Z >>> reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh) 2024-08-20T21:38:25.7041232Z >>> return reduced_tensor 2024-08-20T21:38:25.7041581Z >>> 2024-08-20T21:38:25.7041885Z >>> W = torch.randn(12, 8, requires_grad=False) 2024-08-20T21:38:25.7042363Z >>> X = torch.randn(8, 16, requires_grad=False) 2024-08-20T21:38:25.7042813Z >>> Y = torch.mm(W, X) 2024-08-20T21:38:25.7043373Z >>> row_wise = [Shard(0)] # row-wise sharding placements on 1-d mesh 2024-08-20T21:38:25.7044125Z >>> col_wise = [Shard(1)] # col-wise sharding placements on 1-d mesh 2024-08-20T21:38:25.7044627Z >>> 2024-08-20T21:38:25.7045131Z >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor convertion 2024-08-20T21:38:25.7045821Z >>> local_mm_allreduce_forward = local_map( 2024-08-20T21:38:25.7046247Z >>> mm_allreduce_forward, 2024-08-20T21:38:25.7046636Z >>> out_placements=[Replicate()], 2024-08-20T21:38:25.7047299Z >>> in_placements=[col_wise, row_wise], 2024-08-20T21:38:25.7047810Z >>> device_mesh=device_mesh, 2024-08-20T21:38:25.7048179Z >>> ) 2024-08-20T21:38:25.7048421Z >>> 2024-08-20T21:38:25.7048997Z >>> W_dt = distribute_tensor(W, device_mesh, (col_wise)) # col-wisely sharded W tensor 2024-08-20T21:38:25.7050081Z >>> X_dt = distribute_tensor(X, device_mesh, (row_wise)) # row-wisely sharded X tensor 2024-08-20T21:38:25.7051066Z >>> Y_dt = local_mm_allreduce_forward(device_mesh, W_dt, X_dt) # apply local_mm_allreduce_forward to DTensors 2024-08-20T21:38:25.7051686Z 2024-08-20T21:38:25.7051940Z NOTE: This API is currently experimental and subject to change 2024-08-20T21:38:25.7052366Z 2024-08-20T21:38:25.7052760Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:25.7053253Z 2024-08-20T21:38:25.7054367Z msg = Cannot scrape callname=register_sharding in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_tensor/experimental/register_sharding.py line=25. 2024-08-20T21:38:25.7055857Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:25.7056358Z 2024-08-20T21:38:25.7056722Z ``register_sharding`` is an experimental API that allows users to register sharding 2024-08-20T21:38:25.7057592Z strategies for an operator when the tensor inputs and outputs are :class:`DTensor`s. 2024-08-20T21:38:25.7058576Z It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``, 2024-08-20T21:38:25.7059461Z e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2) 2024-08-20T21:38:25.7060340Z when users would like to overwrite default sharding strategies of existing operators. 2024-08-20T21:38:25.7060892Z 2024-08-20T21:38:25.7060981Z Args: 2024-08-20T21:38:25.7061285Z op (Union[OpOverload, List[OpOverload]]): 2024-08-20T21:38:25.7061881Z An op or a list of ops to register the customized sharding function. 2024-08-20T21:38:25.7062402Z 2024-08-20T21:38:25.7062496Z Returns: 2024-08-20T21:38:25.7063116Z A function decorator which can be used to wrap a function that defines the sharding 2024-08-20T21:38:25.7064029Z strategy for the operator specified in ``op``. The defined sharding strategy will be 2024-08-20T21:38:25.7064929Z registered to DTensor and will override the default sharding strategy if DTensor has 2024-08-20T21:38:25.7065881Z already implemented the operator. The customized sharding function takes the same inputs 2024-08-20T21:38:25.7066795Z as the original op (except that if an arg is a :class:`torch.Tensor`, it will be 2024-08-20T21:38:25.7067755Z replaced by a tensor-like object that DTensor uses internally). The function should 2024-08-20T21:38:25.7068751Z return a sequence of 2-tuples, each specifying acceptable output placements and its 2024-08-20T21:38:25.7069425Z corresponding intput placements. 2024-08-20T21:38:25.7069707Z 2024-08-20T21:38:25.7069799Z Example: 2024-08-20T21:38:25.7070085Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:38:25.7070550Z >>> @register_sharding(aten._softmax.default) 2024-08-20T21:38:25.7071080Z >>> def custom_softmax_sharding(x, dim, half_to_float): 2024-08-20T21:38:25.7071624Z >>> softmax_dim = dim if dim >= 0 else dim + x.ndim 2024-08-20T21:38:25.7072117Z >>> acceptable_shardings = [] 2024-08-20T21:38:25.7072487Z >>> 2024-08-20T21:38:25.7072851Z >>> all_replicate = ([Replicate()], [Replicate(), None, None]) 2024-08-20T21:38:25.7073436Z >>> acceptable_shardings.append(all_replicate) 2024-08-20T21:38:25.7073875Z >>> 2024-08-20T21:38:25.7074153Z >>> for sharding_dim in range(x.ndim): 2024-08-20T21:38:25.7074614Z >>> if sharding_dim != softmax_dim: 2024-08-20T21:38:25.7075043Z >>> all_sharded = ( 2024-08-20T21:38:25.7075430Z >>> [Shard(sharding_dim)], 2024-08-20T21:38:25.7075902Z >>> [Shard(sharding_dim), None, None], 2024-08-20T21:38:25.7076331Z >>> ) 2024-08-20T21:38:25.7076700Z >>> acceptable_shardings.append(all_sharded) 2024-08-20T21:38:25.7077142Z >>> 2024-08-20T21:38:25.7077415Z >>> return acceptable_shardings 2024-08-20T21:38:25.7077698Z 2024-08-20T21:38:25.7078097Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:25.7078687Z 2024-08-20T21:38:25.8042249Z msg = Cannot scrape callname=post_localSGD_hook in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/post_localSGD_hook.py line=72. 2024-08-20T21:38:25.8044479Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:25.8045053Z 2024-08-20T21:38:25.8045255Z Run post-localSGD algorithm. 2024-08-20T21:38:25.8045513Z 2024-08-20T21:38:25.8045893Z This DDP communication hook is used for running post-localSGD algorithm, 2024-08-20T21:38:25.8046574Z by combining with a model averaging component (e.g., 2024-08-20T21:38:25.8047632Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`) 2024-08-20T21:38:25.8048383Z that runs after the optimizer step. 2024-08-20T21:38:25.8048662Z 2024-08-20T21:38:25.8048767Z Args: 2024-08-20T21:38:25.8049262Z state (PostLocalSGDState): State information to run post-localSGD. 2024-08-20T21:38:25.8050103Z Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD. 2024-08-20T21:38:25.8051332Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2024-08-20T21:38:25.8052409Z Note that since DDP comm hook only supports single process single device mode, 2024-08-20T21:38:25.8053113Z only exactly one tensor is stored in this bucket. 2024-08-20T21:38:25.8053481Z 2024-08-20T21:38:25.8053572Z Returns: 2024-08-20T21:38:25.8054049Z Future handler of the communication, which updates the gradients in place. 2024-08-20T21:38:25.8054539Z 2024-08-20T21:38:25.8054655Z Example:: 2024-08-20T21:38:25.8055125Z >>> # xdoctest: +SKIP 2024-08-20T21:38:25.8055673Z >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup, 2024-08-20T21:38:25.8056311Z start_localSGD_iter=10) 2024-08-20T21:38:25.8056878Z >>> ddp_model.register_comm_hook(state, post_localSGD_hook) 2024-08-20T21:38:25.8057745Z >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``. 2024-08-20T21:38:25.8058852Z >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module. 2024-08-20T21:38:25.8059512Z 2024-08-20T21:38:25.8059915Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:25.8060411Z 2024-08-20T21:38:25.8096385Z msg = Cannot scrape callname=powerSGD_hook in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py line=342. 2024-08-20T21:38:25.8098953Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:25.8099858Z 2024-08-20T21:38:25.8100156Z Implement PowerSGD algorithm. 2024-08-20T21:38:25.8100597Z 2024-08-20T21:38:25.8101091Z This DDP communication hook implements PowerSGD gradient compression 2024-08-20T21:38:25.8101921Z algorithm described in the `paper `_. 2024-08-20T21:38:25.8102717Z Once gradient tensors are aggregated across all workers, this hook applies 2024-08-20T21:38:25.8103306Z compression as follows: 2024-08-20T21:38:25.8103525Z 2024-08-20T21:38:25.8104263Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups: 2024-08-20T21:38:25.8105043Z 2024-08-20T21:38:25.8105622Z 1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth. 2024-08-20T21:38:25.8106356Z 2024-08-20T21:38:25.8106990Z 1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases). 2024-08-20T21:38:25.8107722Z 2024-08-20T21:38:25.8107850Z 2. Handles uncompressed tensors: 2024-08-20T21:38:25.8108120Z 2024-08-20T21:38:25.8108810Z 2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression; 2024-08-20T21:38:25.8109857Z 2024-08-20T21:38:25.8110316Z 2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor. 2024-08-20T21:38:25.8110937Z 2024-08-20T21:38:25.8111260Z 3. Handles the tensors that should be compressed by PowerSGD compression: 2024-08-20T21:38:25.8111732Z 2024-08-20T21:38:25.8112167Z 3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M, 2024-08-20T21:38:25.8113139Z such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized; 2024-08-20T21:38:25.8113755Z 2024-08-20T21:38:25.8113949Z 3.2. Computes each P in Ps, which is equal to MQ; 2024-08-20T21:38:25.8114302Z 2024-08-20T21:38:25.8114439Z 3.3. Allreduces Ps as a batch; 2024-08-20T21:38:25.8114701Z 2024-08-20T21:38:25.8114836Z 3.4. Orthogonalizes each P in Ps; 2024-08-20T21:38:25.8115128Z 2024-08-20T21:38:25.8115402Z 3.5. Computes each Q in Qs, which is approximately equal to M^TP; 2024-08-20T21:38:25.8115833Z 2024-08-20T21:38:25.8115970Z 3.6. Allreduces Qs as a batch; 2024-08-20T21:38:25.8116226Z 2024-08-20T21:38:25.8116656Z 3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T. 2024-08-20T21:38:25.8117234Z 2024-08-20T21:38:25.8117784Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations. 2024-08-20T21:38:25.8118895Z This not only gives the user more control over the tradeoff between speedup and accuracy, 2024-08-20T21:38:25.8120050Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers. 2024-08-20T21:38:25.8120874Z 2024-08-20T21:38:25.8120964Z Args: 2024-08-20T21:38:25.8121678Z state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc. 2024-08-20T21:38:25.8122910Z To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter`` 2024-08-20T21:38:25.8123697Z and ``min_compression_rate``. 2024-08-20T21:38:25.8124656Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2024-08-20T21:38:25.8125742Z Note that since DDP comm hook only supports single process single device mode, 2024-08-20T21:38:25.8126467Z only exactly one tensor is stored in this bucket. 2024-08-20T21:38:25.8126824Z 2024-08-20T21:38:25.8126919Z Returns: 2024-08-20T21:38:25.8127513Z Future handler of the communication, which updates the gradients in place. 2024-08-20T21:38:25.8128004Z 2024-08-20T21:38:25.8128139Z Example:: 2024-08-20T21:38:25.8128396Z >>> # xdoctest: +SKIP 2024-08-20T21:38:25.8128972Z >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, 2024-08-20T21:38:25.8129715Z start_powerSGD_iter=10, min_compression_rate=0.5) 2024-08-20T21:38:25.8130363Z >>> ddp_model.register_comm_hook(state, powerSGD_hook) 2024-08-20T21:38:25.8130730Z 2024-08-20T21:38:25.8131140Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:25.8131640Z 2024-08-20T21:38:25.8147241Z msg = Cannot scrape callname=PeriodicModelAverager in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/model_averaging/averagers.py line=36. 2024-08-20T21:38:25.8150189Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:25.8150698Z 2024-08-20T21:38:25.8151007Z Averages parameters periodically after the warm-up stage. 2024-08-20T21:38:25.8151446Z 2024-08-20T21:38:25.8151898Z This can be used for running `post-local SGD `_, 2024-08-20T21:38:25.8152673Z by running :class:`~torch.nn.DistributedDataParallel` (DDP) 2024-08-20T21:38:25.8153403Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`. 2024-08-20T21:38:25.8154055Z 2024-08-20T21:38:25.8154159Z Args: 2024-08-20T21:38:25.8154502Z period (int): The number of steps per model averaging. 2024-08-20T21:38:25.8155250Z Usually the period should be greater than ``1`` to reduce the communication cost. 2024-08-20T21:38:25.8155963Z Otherwise, only DDP needs to be used. 2024-08-20T21:38:25.8156643Z warmup_steps (int): The number of warm-up steps. During this stage, 2024-08-20T21:38:25.8157239Z model averaging is skipped. 2024-08-20T21:38:25.8157867Z process_group: The process group to be used for all-reduce. 2024-08-20T21:38:25.8158453Z If ``None``, the default process group, which 2024-08-20T21:38:25.8159079Z is created by :func:`torch.distributed.init_process_group`, 2024-08-20T21:38:25.8159662Z will be used. (default: ``None``) 2024-08-20T21:38:25.8159980Z 2024-08-20T21:38:25.8160090Z Example:: 2024-08-20T21:38:25.8160250Z 2024-08-20T21:38:25.8160409Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:38:25.8160838Z >>> import torch 2024-08-20T21:38:25.8161171Z >>> import torch.distributed as dist 2024-08-20T21:38:25.8161872Z >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD 2024-08-20T21:38:25.8162804Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2024-08-20T21:38:25.8163444Z >>> import torch.nn as nn 2024-08-20T21:38:25.8163763Z >>> 2024-08-20T21:38:25.8164131Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2024-08-20T21:38:25.8164649Z >>> torch.cuda.set_device(rank) 2024-08-20T21:38:25.8165169Z >>> module = nn.Linear(1, 1, bias=False).cuda() 2024-08-20T21:38:25.8165956Z >>> model = nn.parallel.DistributedDataParallel( 2024-08-20T21:38:25.8166664Z >>> module, device_ids=[rank], output_device=rank 2024-08-20T21:38:25.8167468Z >>> ) 2024-08-20T21:38:25.8168155Z >>> # Register a post-localSGD communication hook. 2024-08-20T21:38:25.8169330Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2024-08-20T21:38:25.8170705Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-08-20T21:38:25.8171556Z >>> 2024-08-20T21:38:25.8172466Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2024-08-20T21:38:25.8173216Z >>> # After 100 steps, run model averaging every 4 steps. 2024-08-20T21:38:25.8174075Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-08-20T21:38:25.8175019Z >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2024-08-20T21:38:25.8175636Z >>> for step in range(0, 200): 2024-08-20T21:38:25.8176008Z >>> optimizer.zero_grad() 2024-08-20T21:38:25.8176397Z >>> loss = loss_fn(output, labels) 2024-08-20T21:38:25.8176803Z >>> loss.backward() 2024-08-20T21:38:25.8177132Z >>> optimizer.step() 2024-08-20T21:38:25.8177619Z >>> # Will average model parameters globally every 4 steps. Thus, 2024-08-20T21:38:25.8178403Z >>> # inter-node communication only occurs every 4 iterations after 2024-08-20T21:38:25.8178980Z >>> # the initial ``warmup_steps`` period. 2024-08-20T21:38:25.8179502Z >>> averager.average_parameters(model.parameters()) 2024-08-20T21:38:25.8179863Z 2024-08-20T21:38:25.8180272Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:25.8180756Z 2024-08-20T21:38:25.8182079Z msg = Cannot scrape callname=HierarchicalModelAverager in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/model_averaging/hierarchical_model_averager.py line=18. 2024-08-20T21:38:25.8183733Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:25.8184251Z 2024-08-20T21:38:25.8184682Z Runs hierarchical model averaging (`hierarchical SGD `_). 2024-08-20T21:38:25.8185398Z 2024-08-20T21:38:25.8185850Z Process groups of different sizes are organized in a hierarchy, and they average parameters 2024-08-20T21:38:25.8187226Z by using different periods concurrently after the warm-up stage. 2024-08-20T21:38:25.8188259Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager` 2024-08-20T21:38:25.8189603Z that supports `post-local SGD `_, which essentially only supports 2024-08-20T21:38:25.8190730Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine 2024-08-20T21:38:25.8191802Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`. 2024-08-20T21:38:25.8192912Z Similarly, the process groups within this class do not have such an intra-machine process 2024-08-20T21:38:25.8193923Z subgroup, which should be embedded by the post-local SGD communication hook instead. 2024-08-20T21:38:25.8194461Z 2024-08-20T21:38:25.8194561Z Args: 2024-08-20T21:38:25.8195036Z period_group_size_dict: An ordered dict mapping keys of model averaging period to 2024-08-20T21:38:25.8195825Z process group size, used for initializing process groups of 2024-08-20T21:38:25.8196568Z different sizes in a hierarchy to average parameters concurrently. 2024-08-20T21:38:25.8197313Z Particularly, at each iteration, there will be at most a single 2024-08-20T21:38:25.8198173Z process group that runs averaging -- the period of such group should 2024-08-20T21:38:25.8199113Z have the largest period which the current step can be divided by. 2024-08-20T21:38:25.8199812Z For example, if the dict has three keys: 2, 4, and 8, 2024-08-20T21:38:25.8200488Z then this means totally three process groups will be created to 2024-08-20T21:38:25.8201230Z average parameters every 2, 4, and 8 iterations, respectively. 2024-08-20T21:38:25.8201955Z At the 4th iteration, only the second process group will run 2024-08-20T21:38:25.8202647Z averaging, because the first process group should be a 2024-08-20T21:38:25.8203383Z subset of the second process group, and no need to execute the first 2024-08-20T21:38:25.8204021Z process group redundantly. 2024-08-20T21:38:25.8204640Z On the other hand, the third process group can only be triggered 2024-08-20T21:38:25.8205404Z every 8 iterations, so it will not be triggered at the 4th iteration. 2024-08-20T21:38:25.8206408Z warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped. 2024-08-20T21:38:25.8207672Z process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging. 2024-08-20T21:38:25.8208677Z If ``None``, the default process group, which is created 2024-08-20T21:38:25.8209402Z by :func:`torch.distributed.init_process_group`, will be used. 2024-08-20T21:38:25.8210030Z (default: ``None``) 2024-08-20T21:38:25.8210363Z 2024-08-20T21:38:25.8210482Z Example:: 2024-08-20T21:38:25.8210833Z >>> # xdoctest: +SKIP('undefined rank') 2024-08-20T21:38:25.8211301Z >>> from collections import OrderedDict 2024-08-20T21:38:25.8211721Z >>> import torch 2024-08-20T21:38:25.8212055Z >>> import torch.distributed as dist 2024-08-20T21:38:25.8212717Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2024-08-20T21:38:25.8213382Z >>> PostLocalSGDState, 2024-08-20T21:38:25.8213806Z >>> post_localSGD_hook, 2024-08-20T21:38:25.8214152Z >>> ) 2024-08-20T21:38:25.8214834Z >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD 2024-08-20T21:38:25.8215603Z >>> import torch.nn as nn 2024-08-20T21:38:25.8215941Z >>> 2024-08-20T21:38:25.8216311Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2024-08-20T21:38:25.8216823Z >>> torch.cuda.set_device(rank) 2024-08-20T21:38:25.8217268Z >>> module = nn.Linear(1, 1, bias=False).to(rank) 2024-08-20T21:38:25.8217806Z >>> model = nn.parallel.DistributedDataParallel( 2024-08-20T21:38:25.8218343Z >>> module, device_ids=[rank], output_device=rank 2024-08-20T21:38:25.8218785Z >>> ) 2024-08-20T21:38:25.8219188Z >>> # Register a post-localSGD communication hook. 2024-08-20T21:38:25.8220010Z >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4. 2024-08-20T21:38:25.8220707Z >>> subgroup, _ = dist.new_subgroups() 2024-08-20T21:38:25.8221420Z >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100) 2024-08-20T21:38:25.8222181Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-08-20T21:38:25.8222642Z >>> 2024-08-20T21:38:25.8223170Z >>> # Average parameters among each group of 8 processes every 4 iterations, and among all 2024-08-20T21:38:25.8223912Z >>> # the 16 processes every 16 iterations. 2024-08-20T21:38:25.8224479Z >>> averager = hierarchicalSGD.HierarchicalModelAverager( 2024-08-20T21:38:25.8225202Z >>> period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100) 2024-08-20T21:38:25.8226238Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-08-20T21:38:25.8227211Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2024-08-20T21:38:25.8227962Z >>> # After 100 steps, run model averaging at two levels. 2024-08-20T21:38:25.8228467Z >>> for step in range(0, 200): 2024-08-20T21:38:25.8228843Z >>> optimizer.zero_grad() 2024-08-20T21:38:25.8229235Z >>> loss = loss_fn(output, labels) 2024-08-20T21:38:25.8229642Z >>> loss.backward() 2024-08-20T21:38:25.8229967Z >>> optimizer.step() 2024-08-20T21:38:25.8230395Z >>> # Average parameters after ``optimizer.step()``. 2024-08-20T21:38:25.8231238Z >>> # Thus, the inter-node communication only occurs periodically after ``warmup_steps``. 2024-08-20T21:38:25.8231983Z >>> averager.average_parameters(model.parameters()) 2024-08-20T21:38:25.8232360Z 2024-08-20T21:38:25.8232465Z .. warning :: 2024-08-20T21:38:25.8233000Z The last group size in the dict must be the size of the provided ``process_group``, 2024-08-20T21:38:25.8233837Z which indicates model averaging at the highest level of the hierarchy. 2024-08-20T21:38:25.8234719Z If ``process_group`` is not provided, then the last group size should be equal to the world size. 2024-08-20T21:38:25.8235319Z 2024-08-20T21:38:25.8235420Z .. warning :: 2024-08-20T21:38:25.8235882Z `HierarchicalModelAverager` is experimental and subject to change. 2024-08-20T21:38:25.8236333Z 2024-08-20T21:38:25.8236729Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:25.8237225Z 2024-08-20T21:38:25.8435829Z msg = Cannot scrape callname=BroadcastingTorchSaveReader in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/format_utils.py line=40. 2024-08-20T21:38:25.8438654Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:25.8439619Z 2024-08-20T21:38:25.8440406Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint 2024-08-20T21:38:25.8442086Z on the coordinator rank, and then broadcast and shard each tensor to all ranks. 2024-08-20T21:38:25.8443035Z 2024-08-20T21:38:25.8443369Z . N.B. Intended to be used with DynamicMetaLoadPlanner 2024-08-20T21:38:25.8444012Z 2024-08-20T21:38:25.8444127Z .. warning:: 2024-08-20T21:38:25.8444515Z Current implementation only supports loading Tensors. 2024-08-20T21:38:25.8444891Z 2024-08-20T21:38:25.8445026Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:38:25.8445432Z >>> sd = {"mode": model} 2024-08-20T21:38:25.8445742Z >>> dcp.load( 2024-08-20T21:38:25.8445989Z >>> sd, 2024-08-20T21:38:25.8446334Z >>> storage_reader=BroadcastingTorchSaveReader(), 2024-08-20T21:38:25.8447073Z >>> planner=DynamicMetaLoadPlanner(), 2024-08-20T21:38:25.8447577Z >>> checkpoint_id="path_to_model.pt" 2024-08-20T21:38:25.8448075Z >>> ) 2024-08-20T21:38:25.8448297Z 2024-08-20T21:38:25.8449094Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:25.8450011Z 2024-08-20T21:38:25.8452050Z msg = Cannot scrape callname=DynamicMetaLoadPlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/format_utils.py line=151. 2024-08-20T21:38:25.8454834Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:25.8455809Z 2024-08-20T21:38:25.8456698Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, 2024-08-20T21:38:25.8458904Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a 2024-08-20T21:38:25.8460270Z metadata file, like Torch Save files. 2024-08-20T21:38:25.8460803Z 2024-08-20T21:38:25.8461223Z . N.B. Intended to be used with BroadcastingTorchSaveReader 2024-08-20T21:38:25.8461705Z 2024-08-20T21:38:25.8461814Z .. warning:: 2024-08-20T21:38:25.8462202Z Current implementation only supports loading Tensors. 2024-08-20T21:38:25.8462752Z 2024-08-20T21:38:25.8462891Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:38:25.8463301Z >>> sd = {"mode": model} 2024-08-20T21:38:25.8463617Z >>> dcp.load( 2024-08-20T21:38:25.8463866Z >>> sd, 2024-08-20T21:38:25.8464221Z >>> storage_reader=BroadcastingTorchSaveReader(), 2024-08-20T21:38:25.8464958Z >>> planner=DynamicMetaLoadPlanner(), 2024-08-20T21:38:25.8465718Z >>> checkpoint_id="path_to_model.pt" 2024-08-20T21:38:25.8466396Z >>> ) 2024-08-20T21:38:25.8466622Z 2024-08-20T21:38:25.8467411Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:25.8468055Z 2024-08-20T21:38:25.8537674Z msg = Cannot scrape callname=load_sharded_optimizer_state_dict in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/optimizer.py line=220. 2024-08-20T21:38:25.8539224Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:25.8539741Z 2024-08-20T21:38:25.8540029Z Load a state_dict in conjunction with FSDP sharded optimizer state. 2024-08-20T21:38:25.8540469Z 2024-08-20T21:38:25.8540697Z This is the current recommended way to checkpoint FSDP. 2024-08-20T21:38:25.8541177Z >>> # xdoctest: +SKIP 2024-08-20T21:38:25.8541575Z >>> import torch.distributed.checkpoint as dist_cp 2024-08-20T21:38:25.8542290Z >>> # Save 2024-08-20T21:38:25.8542727Z >>> model: torch.nn.Model 2024-08-20T21:38:25.8543366Z >>> optim_params = model.parameters() 2024-08-20T21:38:25.8544185Z >>> optim = torch.optim.SGD(optim_params, lr=0.01) 2024-08-20T21:38:25.8544958Z >>> # Save 2024-08-20T21:38:25.8545756Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT): 2024-08-20T21:38:25.8547025Z >>> state_dict = { 2024-08-20T21:38:25.8547714Z >>> "optimizer": FSDP.optim_state_dict(model, optim), 2024-08-20T21:38:25.8548639Z >>> "model": model.state_dict() 2024-08-20T21:38:25.8549331Z >>> } 2024-08-20T21:38:25.8549798Z >>> dist_cp.save_state_dict( 2024-08-20T21:38:25.8550475Z >>> state_dict=optim_state, 2024-08-20T21:38:25.8551376Z >>> storage_writer=dist_cp.FileSystemWriter("checkpoint"), 2024-08-20T21:38:25.8552404Z >>> planner=dist_cp.DefaultSavePlanner(), 2024-08-20T21:38:25.8553403Z >>> ) 2024-08-20T21:38:25.8553813Z >>> 2024-08-20T21:38:25.8554221Z >>> # Load 2024-08-20T21:38:25.8555042Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT): 2024-08-20T21:38:25.8556190Z >>> model_state_dict = model_tp.state_dict() 2024-08-20T21:38:25.8556974Z >>> checkpoint = { 2024-08-20T21:38:25.8557589Z >>> "model": model_state_dict 2024-08-20T21:38:25.8558274Z >>> } 2024-08-20T21:38:25.8558753Z >>> dist_cp.load_state_dict( 2024-08-20T21:38:25.8559403Z >>> state_dict=checkpoint, 2024-08-20T21:38:25.8560279Z >>> storage_reader=dist_cp.FileSystemReader(checkpoint_file), 2024-08-20T21:38:25.8561312Z >>> planner=dist_cp.DefaultLoadPlanner(), 2024-08-20T21:38:25.8562080Z >>> ) 2024-08-20T21:38:25.8562700Z >>> model.load_state_dict(checkpoint["model_state"]) 2024-08-20T21:38:25.8563505Z >>> 2024-08-20T21:38:25.8564143Z >>> optim_state = dist_cp.load_sharded_optimizer_state_dict( 2024-08-20T21:38:25.8565057Z >>> model_state_dict, 2024-08-20T21:38:25.8565718Z >>> optimizer_key="optimizer", 2024-08-20T21:38:25.8566641Z >>> storage_reader=dist_cp.FileSystemReader("checkpoint"), 2024-08-20T21:38:25.8567646Z >>> ) 2024-08-20T21:38:25.8568064Z >>> 2024-08-20T21:38:25.8568612Z >>> flattened_osd = FSDP.optim_state_dict_to_load( 2024-08-20T21:38:25.8569534Z >>> model, optim, optim_state["optimizer"] 2024-08-20T21:38:25.8570279Z >>> ) 2024-08-20T21:38:25.8570670Z >>> 2024-08-20T21:38:25.8571159Z >>> optim.load_state_dict(flattened_osd) 2024-08-20T21:38:25.8571711Z 2024-08-20T21:38:25.8572529Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:25.8573487Z 2024-08-20T21:38:25.8581900Z msg = Cannot scrape callname=SavePlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/planner.py line=110. 2024-08-20T21:38:25.8584574Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:25.8585652Z 2024-08-20T21:38:25.8586389Z Abstract class defining the protocol used by save_state_dict to plan the save process. 2024-08-20T21:38:25.8587451Z 2024-08-20T21:38:25.8588181Z SavePlanners are stateful objects that can be used to customize the whole save process. 2024-08-20T21:38:25.8589270Z 2024-08-20T21:38:25.8590025Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it 2024-08-20T21:38:25.8591205Z will be visible to the whole process. 2024-08-20T21:38:25.8591676Z 2024-08-20T21:38:25.8592305Z A planner subclass can expect the following sequence of calls during save_state_dict: 2024-08-20T21:38:25.8593312Z 2024-08-20T21:38:25.8593734Z 1) set_up_planner - called on all ranks. 2024-08-20T21:38:25.8594852Z Signals the start of a checkpoint save. 2024-08-20T21:38:25.8595441Z 2024-08-20T21:38:25.8595838Z 2) create_local_plan - called on all ranks. 2024-08-20T21:38:25.8597157Z Process the state_dict and produces a `SavePlan` that will be sent for global planning. 2024-08-20T21:38:25.8598234Z 2024-08-20T21:38:25.8598825Z 3) create_global_plan - called on the coordinator rank only. 2024-08-20T21:38:25.8600028Z Takes the SavePlan from all ranks and make any global decision. 2024-08-20T21:38:25.8600814Z 2024-08-20T21:38:25.8601133Z 4) finish_plan - called on all ranks. 2024-08-20T21:38:25.8601734Z This gives each rank a chance to adjust to global planning decisions. 2024-08-20T21:38:25.8602193Z 2024-08-20T21:38:25.8602446Z 5) resolve_data - called multiple times on each rank 2024-08-20T21:38:25.8603084Z Lookups a value on the `state_dict` for the storage layer to write. 2024-08-20T21:38:25.8603526Z 2024-08-20T21:38:25.8603949Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as 2024-08-20T21:38:25.8604772Z most changes can be expressed by changes in a single method. 2024-08-20T21:38:25.8605171Z 2024-08-20T21:38:25.8605317Z There are 3 usual patterns of extension: 2024-08-20T21:38:25.8605625Z 2024-08-20T21:38:25.8606099Z Rewriting state_dict. This is the simplest way to extend the save process as it 2024-08-20T21:38:25.8607755Z doesn't requite understanding the intrincacies of how SavePlan works: 2024-08-20T21:38:25.8608575Z 2024-08-20T21:38:25.8608823Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:38:25.8609642Z >>> class RenamePlanner(DefaultSavePlanner): 2024-08-20T21:38:25.8610454Z >>> def set_up_planner( 2024-08-20T21:38:25.8611034Z >>> self, 2024-08-20T21:38:25.8611575Z >>> state_dict: STATE_DICT_TYPE, 2024-08-20T21:38:25.8612372Z >>> storage_meta: Optional[StorageMeta], 2024-08-20T21:38:25.8613164Z >>> is_coordinator: bool, 2024-08-20T21:38:25.8613895Z >>> ) -> None: 2024-08-20T21:38:25.8614487Z >>> # prefix all keys with `foo_`` 2024-08-20T21:38:25.8615754Z >>> super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator) 2024-08-20T21:38:25.8616851Z 2024-08-20T21:38:25.8617715Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted 2024-08-20T21:38:25.8618922Z 2024-08-20T21:38:25.8619169Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:38:25.8619981Z >>> class FP16Planner(DefaultSavePlanner): 2024-08-20T21:38:25.8620773Z >>> def create_local_plan(self): 2024-08-20T21:38:25.8621556Z >>> plan = super().create_local_plan() 2024-08-20T21:38:25.8622330Z >>> for p in plan: 2024-08-20T21:38:25.8622974Z >>> if p.tensor_data is not None: 2024-08-20T21:38:25.8623907Z >>> p.tensor_data.properties.dtype = torch.float16 2024-08-20T21:38:25.8624782Z >>> return plan 2024-08-20T21:38:25.8625297Z >>> 2024-08-20T21:38:25.8625973Z >>> def resolve_data(self, write_item): 2024-08-20T21:38:25.8626827Z >>> item = super().resolve_data(write_item) 2024-08-20T21:38:25.8628129Z >>> return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16) 2024-08-20T21:38:25.8629168Z 2024-08-20T21:38:25.8630273Z Using the global planning step to make central decisions that can't be made individually by each rank 2024-08-20T21:38:25.8631431Z 2024-08-20T21:38:25.8631710Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:38:25.8632432Z >>> from itertools import islice 2024-08-20T21:38:25.8633111Z >>> from dataclasses import replace 2024-08-20T21:38:25.8634037Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner): 2024-08-20T21:38:25.8635636Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0 2024-08-20T21:38:25.8637046Z >>> # This sample doesn't handle ShardedTensors 2024-08-20T21:38:25.8637958Z >>> def create_global_plan(self, all_plans): 2024-08-20T21:38:25.8638743Z >>> def chunk(it, size): 2024-08-20T21:38:25.8639382Z >>> it = iter(it) 2024-08-20T21:38:25.8640184Z >>> return list(iter(lambda: tuple(islice(it, size)), ())) 2024-08-20T21:38:25.8641083Z >>> all_plans = [ 2024-08-20T21:38:25.8641795Z >>> replace(plan, items=items) for plan, items in 2024-08-20T21:38:25.8642903Z >>> zip(all_plans, chunk(all_plans[0].items, len(all_plans))) 2024-08-20T21:38:25.8643845Z >>> ] 2024-08-20T21:38:25.8644426Z >>> return super().create_global_plan(all_plans) 2024-08-20T21:38:25.8645035Z 2024-08-20T21:38:25.8645473Z Finally, some planners need to save additional metadata in the checkpoint, this is 2024-08-20T21:38:25.8646386Z accomplished by having each rank contribute their data items in the local plan and 2024-08-20T21:38:25.8647447Z the global planner aggregate them: 2024-08-20T21:38:25.8647735Z 2024-08-20T21:38:25.8647872Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:38:25.8648375Z >>> class SaveExtraDataPlanner(DefaultSavePlanner): 2024-08-20T21:38:25.8648999Z >>> def create_local_plan(self) -> SavePlan: 2024-08-20T21:38:25.8649485Z >>> plan = super().create_local_plan() 2024-08-20T21:38:25.8650163Z >>> return replace(plan, planner_data="per-rank-data") 2024-08-20T21:38:25.8651186Z >>> 2024-08-20T21:38:25.8652351Z >>> def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]: 2024-08-20T21:38:25.8653865Z >>> global_plan, metadata = super().create_global_plan(all_plans) 2024-08-20T21:38:25.8654970Z >>> merged_data = [p.planner_data for p in global_plan] 2024-08-20T21:38:25.8656018Z >>> metadata = replace(metadata, planner_data=merged_data) 2024-08-20T21:38:25.8656942Z >>> return global_plan, metadata 2024-08-20T21:38:25.8657469Z 2024-08-20T21:38:25.8658253Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:25.8659127Z 2024-08-20T21:38:25.8660196Z msg = Cannot scrape callname=LoadPlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/planner.py line=270. 2024-08-20T21:38:25.8661553Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:25.8662072Z 2024-08-20T21:38:25.8662453Z Abstract class defining the protocol used by load_state_dict to plan the load process. 2024-08-20T21:38:25.8663015Z 2024-08-20T21:38:25.8663604Z LoadPlanner are stateful objects that can be used to customize the whole load process. 2024-08-20T21:38:25.8664597Z 2024-08-20T21:38:25.8665328Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it 2024-08-20T21:38:25.8666619Z will be visible to the whole process. 2024-08-20T21:38:25.8667155Z 2024-08-20T21:38:25.8667843Z A planner subclass can expect the following sequence of calls during load_state_dict: 2024-08-20T21:38:25.8668836Z 2024-08-20T21:38:25.8669203Z 1) set_up_planner - called on all ranks. 2024-08-20T21:38:25.8670237Z Signals the start of loading a checkpoint. 2024-08-20T21:38:25.8670831Z 2024-08-20T21:38:25.8671223Z 2) create_local_plan - called on all ranks. 2024-08-20T21:38:25.8672522Z Process the state_dict and produces a `LoadPlan` that will be sent for global planning. 2024-08-20T21:38:25.8673617Z 2024-08-20T21:38:25.8674187Z 3) create_global_plan - called on the coordinator rank only. 2024-08-20T21:38:25.8675409Z Takes the LoadPlan from all ranks and make any global decision. 2024-08-20T21:38:25.8676205Z 2024-08-20T21:38:25.8676651Z 4) load_bytes - called multiple times on each rank 2024-08-20T21:38:25.8677814Z This is called once per non-tensor value in state_dict. 2024-08-20T21:38:25.8678535Z 2024-08-20T21:38:25.8679241Z 5) resolve_tensor and commit_tensor - called multiple times on each rank 2024-08-20T21:38:25.8680563Z They are called in pair for each Tensor value in state_dict. 2024-08-20T21:38:25.8681343Z 2024-08-20T21:38:25.8682104Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as 2024-08-20T21:38:25.8683546Z most changes can be expressed by changes in a single method. 2024-08-20T21:38:25.8683967Z 2024-08-20T21:38:25.8684128Z There are two usual patterns of extension: 2024-08-20T21:38:25.8684455Z 2024-08-20T21:38:25.8684801Z Rewriting state_dict. This is the simplest way to extend the load process as it 2024-08-20T21:38:25.8685753Z doesn't requite understanding the intrincacies of how LoadPlan works. We need 2024-08-20T21:38:25.8686561Z to keep a reference to the original state_dict as load happens in place so 2024-08-20T21:38:25.8687201Z we need to be able to perform it in place 2024-08-20T21:38:25.8687654Z 2024-08-20T21:38:25.8687792Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:38:25.8688257Z >>> class RenamePlanner(DefaultLoadPlanner): 2024-08-20T21:38:25.8688687Z >>> def set_up_planner( 2024-08-20T21:38:25.8689012Z >>> self, 2024-08-20T21:38:25.8718044Z >>> state_dict: STATE_DICT_TYPE, 2024-08-20T21:38:25.8718803Z >>> metadata: Metadata, 2024-08-20T21:38:25.8719475Z >>> is_coordinator: bool, 2024-08-20T21:38:25.8720242Z >>> ) -> None: 2024-08-20T21:38:25.8720826Z >>> self.original_state_dict = state_dict 2024-08-20T21:38:25.8721822Z >>> state_dict = {"foo_" + k: v for k, v in state_dict.items()} 2024-08-20T21:38:25.8722858Z >>> 2024-08-20T21:38:25.8723351Z >>> if self.flatten_sharded_tensors: 2024-08-20T21:38:25.8724246Z >>> state_dict = _flatten_sharded_tensors(state_dict) 2024-08-20T21:38:25.8725080Z >>> 2024-08-20T21:38:25.8725541Z >>> if self.flatten_state_dict: 2024-08-20T21:38:25.8726506Z >>> state_dict, self.mappings = flatten_state_dict(state_dict) 2024-08-20T21:38:25.8727517Z >>> 2024-08-20T21:38:25.8727978Z >>> self.state_dict = state_dict 2024-08-20T21:38:25.8728713Z >>> self.metadata = metadata 2024-08-20T21:38:25.8729496Z >>> self.is_coordinator = is_coordinator 2024-08-20T21:38:25.8730230Z >>> 2024-08-20T21:38:25.8730773Z >>> def load_bytes(self, read_item, value): 2024-08-20T21:38:25.8731580Z >>> # Remove the "foo_" prefix 2024-08-20T21:38:25.8732657Z >>> self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False) 2024-08-20T21:38:25.8733460Z 2024-08-20T21:38:25.8733468Z 2024-08-20T21:38:25.8734066Z Modifying resolve_tensor and commit_tensor to handle load time transformation. 2024-08-20T21:38:25.8735010Z 2024-08-20T21:38:25.8735262Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:38:25.8736135Z >>> class MetaModelMaterialize(DefaultSavePlanner): 2024-08-20T21:38:25.8737029Z >>> def resolve_tensor(self, read_item): 2024-08-20T21:38:25.8737893Z >>> tensor = super().resolve_tensor(read_item) 2024-08-20T21:38:25.8738856Z >>> return torch.empty_like(tensor, device="cpu") 2024-08-20T21:38:25.8739645Z >>> 2024-08-20T21:38:25.8740193Z >>> def commit_tensor(self, read_item, tensor): 2024-08-20T21:38:25.8741285Z >>> self.state_dict[read_item.dest_index.fqn] = tensor 2024-08-20T21:38:25.8741967Z 2024-08-20T21:38:25.8742742Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:25.8743625Z 2024-08-20T21:38:25.8857357Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_loader.py line=61. 2024-08-20T21:38:25.8859608Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:25.8860118Z 2024-08-20T21:38:25.8860314Z Load a distributed ``state_dict`` in SPMD style. 2024-08-20T21:38:25.8860655Z 2024-08-20T21:38:25.8860900Z Each rank will try to read the least amount of data necessary 2024-08-20T21:38:25.8861627Z to fullfill the requested `state_dict`. When loading :class:`ShardedTensor` 2024-08-20T21:38:25.8862441Z or :class:`DTensor` instances, each rank only reads data for their local shards. 2024-08-20T21:38:25.8862937Z 2024-08-20T21:38:25.8863298Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2024-08-20T21:38:25.8864169Z load will first call ``state_dict`` before attempting deserialization, followed by 2024-08-20T21:38:25.8865330Z ``load_state_dict`` once the deserialization is complete. 2024-08-20T21:38:25.8865991Z 2024-08-20T21:38:25.8866189Z .. warning:: 2024-08-20T21:38:25.8866841Z All tensors in ``state_dict`` must be allocated on their 2024-08-20T21:38:25.8867913Z destination device *prior to* calling this function. 2024-08-20T21:38:25.8868610Z 2024-08-20T21:38:25.8869375Z All non-tensor data is loaded using `torch.load()` and modified in place 2024-08-20T21:38:25.8870430Z on state_dict. 2024-08-20T21:38:25.8870763Z 2024-08-20T21:38:25.8870939Z .. warning:: 2024-08-20T21:38:25.8871729Z Users must call `load_state_dict` on the root module to ensure load 2024-08-20T21:38:25.8873109Z pos-processing and non-tensor data properly propagates. 2024-08-20T21:38:25.8873852Z 2024-08-20T21:38:25.8874020Z .. note: 2024-08-20T21:38:25.8874831Z If no process group is initialized, this function will assume the intent 2024-08-20T21:38:25.8876291Z is to load a checkpoint into the local process. This can be useful in the 2024-08-20T21:38:25.8877827Z case of local inference, and when using regular Tensors (as opposed to DTensor 2024-08-20T21:38:25.8879265Z or ShardedTensor) 2024-08-20T21:38:25.8879638Z 2024-08-20T21:38:25.8879818Z .. note: 2024-08-20T21:38:25.8880377Z Rank 0 is assumed to be the coordinator rank. 2024-08-20T21:38:25.8881000Z 2024-08-20T21:38:25.8881151Z Args: 2024-08-20T21:38:25.8881765Z state_dict (Dict[str, Any]): The state_dict to save. 2024-08-20T21:38:25.8882735Z checkpoint_id (Union[str, os.PathLike, None]): 2024-08-20T21:38:25.8883848Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-08-20T21:38:25.8885160Z depends on the storage. It can be a path to a folder or to a file. 2024-08-20T21:38:25.8886594Z It can also be a key if the storage is a key-value store. 2024-08-20T21:38:25.8887644Z (Default: ``None``) 2024-08-20T21:38:25.8888327Z storage_reader (Optional[StorageReader]): 2024-08-20T21:38:25.8889344Z Instance of StorageWriter used to perform reads. If this is not 2024-08-20T21:38:25.8890724Z specified, DCP will automatically infer the reader based on the 2024-08-20T21:38:25.8892059Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-08-20T21:38:25.8893098Z be raised. (Default: ``None``) 2024-08-20T21:38:25.8893882Z planner (Optional[LoadPlanner]): 2024-08-20T21:38:25.8895201Z Instance of LoadPlanner. If this is not specificed, the default 2024-08-20T21:38:25.8896286Z planner will be used. (Default: ``None``) 2024-08-20T21:38:25.8897128Z process_group (Optional[ProcessGroup]): 2024-08-20T21:38:25.8898350Z ProcessGroup to be used for cross-rank synchronization. 2024-08-20T21:38:25.8899295Z (Default: ``None``) 2024-08-20T21:38:25.8899712Z 2024-08-20T21:38:25.8900112Z Returns: 2024-08-20T21:38:25.8900509Z None. 2024-08-20T21:38:25.8900736Z 2024-08-20T21:38:25.8900886Z Examples 2024-08-20T21:38:25.8901258Z >>> # xdoctest: +SKIP 2024-08-20T21:38:25.8901802Z >>> my_model = MyModule() 2024-08-20T21:38:25.8902578Z >>> optimizer = Adagrad(my_model.parameters()) 2024-08-20T21:38:25.8903482Z >>> model_state_dict = my_model.state_dict() 2024-08-20T21:38:25.8904828Z >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader("/checkpoint/1") 2024-08-20T21:38:25.8905916Z 2024-08-20T21:38:25.8906301Z >>> torch.distributed.checkpoint.load_state_dict( 2024-08-20T21:38:25.8907166Z >>> state_dict=model_state_dict, 2024-08-20T21:38:25.8907929Z >>> storage_reader=fs_storage_reader, 2024-08-20T21:38:25.8908668Z >>> ) 2024-08-20T21:38:25.8908924Z 2024-08-20T21:38:25.8909386Z >>> # module.load_state_dict() function might have customized steps 2024-08-20T21:38:25.8910503Z >>> # to flush the state_dict, must call it to 2024-08-20T21:38:25.8911353Z >>> # ensure correct behavior. 2024-08-20T21:38:25.8912041Z >>> my_model.load_state_dict(model_state_dict) 2024-08-20T21:38:25.8912600Z 2024-08-20T21:38:25.8912781Z .. note:: 2024-08-20T21:38:25.8913525Z load_state_dict uses collectives to coordinate reads across ranks. 2024-08-20T21:38:25.8914838Z For NCCL-based process groups, internal tensor representations of 2024-08-20T21:38:25.8915628Z objects must be moved to the GPU device before communication takes place. 2024-08-20T21:38:25.8916425Z In this case, the device used is given by ``torch.cuda.current_device()`` 2024-08-20T21:38:25.8917316Z and it is the user's responsibility to ensure that this is set so that each 2024-08-20T21:38:25.8918052Z rank has an individual GPU, via ``torch.cuda.set_device()``. 2024-08-20T21:38:25.8918478Z 2024-08-20T21:38:25.8918875Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:25.8919359Z 2024-08-20T21:38:25.8920929Z msg = Cannot scrape callname=save in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=67. 2024-08-20T21:38:25.8923454Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:25.8924526Z 2024-08-20T21:38:25.8924811Z Save a distributed model in SPMD style. 2024-08-20T21:38:25.8925364Z 2024-08-20T21:38:25.8925814Z This function is different from ``torch.save()`` as it handles 2024-08-20T21:38:25.8927219Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards. 2024-08-20T21:38:25.8928258Z 2024-08-20T21:38:25.8928837Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2024-08-20T21:38:25.8929551Z save will call ``state_dict`` before serialization. 2024-08-20T21:38:25.8929918Z 2024-08-20T21:38:25.8930028Z .. warning:: 2024-08-20T21:38:25.8930518Z There is no guarantees of Backwards Compatibility across PyTorch versions 2024-08-20T21:38:25.8931117Z for saved state_dicts. 2024-08-20T21:38:25.8931351Z 2024-08-20T21:38:25.8931448Z .. warning:: 2024-08-20T21:38:25.8931904Z If using the `process_group` argument, make sure that only its ranks 2024-08-20T21:38:25.8932645Z call `save_state_dict` and that all data in state_dict belong to it. 2024-08-20T21:38:25.8933182Z 2024-08-20T21:38:25.8933354Z .. note:: 2024-08-20T21:38:25.8934415Z When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of 2024-08-20T21:38:25.8936035Z the shard_group should be calling `save_state_dict` and the corresponding process 2024-08-20T21:38:25.8937212Z group needs to be passed in. 2024-08-20T21:38:25.8937694Z 2024-08-20T21:38:25.8937873Z .. note:: 2024-08-20T21:38:25.8938819Z If no process group is available, this function assumes the intention is to save the 2024-08-20T21:38:25.8940071Z state_dict in the local process. 2024-08-20T21:38:25.8940603Z 2024-08-20T21:38:25.8940768Z .. note: 2024-08-20T21:38:25.8941519Z Rank 0 is assumed to be the coordinator rank. 2024-08-20T21:38:25.8942126Z 2024-08-20T21:38:25.8942135Z 2024-08-20T21:38:25.8942295Z Args: 2024-08-20T21:38:25.8942856Z state_dict (Dict[str, Any]): The state_dict to save. 2024-08-20T21:38:25.8943788Z checkpoint_id (Union[str, os.PathLike, None]): 2024-08-20T21:38:25.8944947Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-08-20T21:38:25.8946309Z depends on the storage. It can be a path to a folder or to a file. 2024-08-20T21:38:25.8947976Z It can also be a key if the storage is a key-value store. 2024-08-20T21:38:25.8948916Z (Default: ``None``) 2024-08-20T21:38:25.8949593Z storage_writer (Optional[StorageWriter]): 2024-08-20T21:38:25.8950678Z Instance of StorageWriter used to perform writes. If this is not 2024-08-20T21:38:25.8951996Z specified, DCP will automatically infer the writer based on the 2024-08-20T21:38:25.8953300Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-08-20T21:38:25.8954339Z be raised. (Default: ``None``) 2024-08-20T21:38:25.8955082Z planner (Optional[SavePlanner]): 2024-08-20T21:38:25.8956087Z Instance of SavePlanner. If this is not specificed, the default 2024-08-20T21:38:25.8957197Z planner will be used. (Default: ``None``) 2024-08-20T21:38:25.8958095Z process_group (Optional[ProcessGroup]): 2024-08-20T21:38:25.8959263Z ProcessGroup to be used for cross-rank synchronization. 2024-08-20T21:38:25.8960208Z (Default: ``None``) 2024-08-20T21:38:25.8960614Z 2024-08-20T21:38:25.8960802Z Returns: 2024-08-20T21:38:25.8961422Z Metadata: Metadata object for the saved checkpoint. 2024-08-20T21:38:25.8962113Z 2024-08-20T21:38:25.8962287Z Example: 2024-08-20T21:38:25.8962757Z >>> # xdoctest: +SKIP 2024-08-20T21:38:25.8963354Z >>> my_model = MyModule() 2024-08-20T21:38:25.8963799Z 2024-08-20T21:38:25.8964038Z >>> state_dict = {"model": my_model} 2024-08-20T21:38:25.8964601Z 2024-08-20T21:38:25.8965355Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2024-08-20T21:38:25.8966734Z >>> torch.distributed.checkpoint.save( 2024-08-20T21:38:25.8967867Z >>> state_dict=state_dict, 2024-08-20T21:38:25.8968601Z >>> storage_writer=fs_storage_writer, 2024-08-20T21:38:25.8969335Z >>> ) 2024-08-20T21:38:25.8969598Z 2024-08-20T21:38:25.8969780Z .. note:: 2024-08-20T21:38:25.8970574Z save_state_dict uses collectives to coordinate writes across ranks. 2024-08-20T21:38:25.8972133Z For NCCL-based process groups, internal tensor representations of 2024-08-20T21:38:25.8973572Z objects must be moved to the GPU device before communication takes place. 2024-08-20T21:38:25.8975070Z In this case, the device used is given by ``torch.cuda.current_device()`` 2024-08-20T21:38:25.8976687Z and it is the user's responsibility to ensure that this is set so that 2024-08-20T21:38:25.8978083Z each rank has an individual GPU, via ``torch.cuda.set_device()``. 2024-08-20T21:38:25.8978923Z 2024-08-20T21:38:25.8979676Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:25.8980625Z 2024-08-20T21:38:25.8982502Z msg = Cannot scrape callname=async_save in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=170. 2024-08-20T21:38:25.8984617Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:25.8985554Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the 2024-08-20T21:38:25.8986480Z staging storage (defaults to CPU memory), and then calls the `save` in a separate thread. 2024-08-20T21:38:25.8987039Z 2024-08-20T21:38:25.8987159Z .. warning:: 2024-08-20T21:38:25.8987548Z This feature is experimental and subject to change. 2024-08-20T21:38:25.8987913Z 2024-08-20T21:38:25.8988006Z Args: 2024-08-20T21:38:25.8988518Z state_dict (Dict[str, Any]): The state_dict to save. 2024-08-20T21:38:25.8989371Z checkpoint_id (Union[str, os.PathLike, None]): 2024-08-20T21:38:25.8990454Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-08-20T21:38:25.8991828Z depends on the storage. It can be a path to a folder or to a file. 2024-08-20T21:38:25.8993260Z It can also be a key if the storage is a key-value store. 2024-08-20T21:38:25.8994513Z (Default: ``None``) 2024-08-20T21:38:25.8995273Z storage_writer (Optional[StorageWriter]): 2024-08-20T21:38:25.8996564Z Instance of StorageWriter used to perform 'stage' and 'save'. If 2024-08-20T21:38:25.8998003Z this is not specified, DCP will automatically infer the writer based on the 2024-08-20T21:38:25.8999346Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-08-20T21:38:25.9000416Z be raised. (Default: ``None``) 2024-08-20T21:38:25.9001212Z planner (Optional[SavePlanner]): 2024-08-20T21:38:25.9002263Z Instance of SavePlanner. If this is not specificed, the default 2024-08-20T21:38:25.9003347Z planner will be used. (Default: ``None``) 2024-08-20T21:38:25.9004250Z process_group (Optional[ProcessGroup]): 2024-08-20T21:38:25.9005430Z ProcessGroup to be used for cross-rank synchronization. 2024-08-20T21:38:25.9006364Z (Default: ``None``) 2024-08-20T21:38:25.9006767Z 2024-08-20T21:38:25.9006909Z Returns: 2024-08-20T21:38:25.9007572Z Future: A future holding the resultant Metadata object from `save`. 2024-08-20T21:38:25.9008412Z 2024-08-20T21:38:25.9008587Z Example: 2024-08-20T21:38:25.9009057Z >>> # xdoctest: +SKIP 2024-08-20T21:38:25.9009675Z >>> my_model = MyModule() 2024-08-20T21:38:25.9010122Z 2024-08-20T21:38:25.9010366Z >>> state_dict = {"model": my_model} 2024-08-20T21:38:25.9010890Z 2024-08-20T21:38:25.9011645Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2024-08-20T21:38:25.9013165Z >>> checkpoint_future = torch.distributed.checkpoint.async_save( 2024-08-20T21:38:25.9014190Z >>> state_dict=state_dict, 2024-08-20T21:38:25.9015160Z >>> storage_writer=fs_storage_writer, 2024-08-20T21:38:25.9015896Z >>> ) 2024-08-20T21:38:25.9016320Z >>> 2024-08-20T21:38:25.9016782Z >>> # ... do some work ... 2024-08-20T21:38:25.9017383Z >>> 2024-08-20T21:38:25.9017869Z >>> checkpoint_future.result() 2024-08-20T21:38:25.9018382Z 2024-08-20T21:38:25.9018548Z 2024-08-20T21:38:25.9019511Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:25.9020422Z 2024-08-20T21:38:25.9071376Z msg = Cannot scrape callname=construct_and_record_rdzv_event in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/elastic/events/__init__.py line=91. 2024-08-20T21:38:25.9072931Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:25.9073448Z 2024-08-20T21:38:25.9073697Z Initialize rendezvous event object and record its operations. 2024-08-20T21:38:25.9074117Z 2024-08-20T21:38:25.9074278Z Args: 2024-08-20T21:38:25.9074781Z run_id (str): The run id of the rendezvous. 2024-08-20T21:38:25.9075694Z message (str): The message describing the event. 2024-08-20T21:38:25.9076893Z node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED). 2024-08-20T21:38:25.9078289Z name (str): Event name. (E.g. Current action being performed). 2024-08-20T21:38:25.9079345Z hostname (str): Hostname of the node. 2024-08-20T21:38:25.9080234Z pid (Optional[int]): The process id of the node. 2024-08-20T21:38:25.9081532Z master_endpoint (str): The master endpoint for the rendezvous store, if known. 2024-08-20T21:38:25.9083179Z local_id (Optional[int]): The local_id of the node, if defined in dynamic_rendezvous.py 2024-08-20T21:38:25.9084786Z rank (Optional[int]): The rank of the node, if known. 2024-08-20T21:38:25.9085648Z Returns: 2024-08-20T21:38:25.9086082Z None 2024-08-20T21:38:25.9086522Z Example: 2024-08-20T21:38:25.9087078Z >>> # See DynamicRendezvousHandler class 2024-08-20T21:38:25.9087981Z >>> def _record( 2024-08-20T21:38:25.9088506Z ... self, 2024-08-20T21:38:25.9088984Z ... message: str, 2024-08-20T21:38:25.9089669Z ... node_state: NodeState = NodeState.RUNNING, 2024-08-20T21:38:25.9090477Z ... rank: Optional[int] = None, 2024-08-20T21:38:25.9090954Z ... ) -> None: 2024-08-20T21:38:25.9091290Z ... construct_and_record_rdzv_event( 2024-08-20T21:38:25.9091818Z ... name=f"{self.__class__.__name__}.{get_method_name()}", 2024-08-20T21:38:25.9092339Z ... run_id=self._settings.run_id, 2024-08-20T21:38:25.9092763Z ... message=message, 2024-08-20T21:38:25.9093132Z ... node_state=node_state, 2024-08-20T21:38:25.9093550Z ... hostname=self._this_node.addr, 2024-08-20T21:38:25.9093989Z ... pid=self._this_node.pid, 2024-08-20T21:38:25.9094708Z ... local_id=self._this_node.local_id, 2024-08-20T21:38:25.9095396Z ... rank=rank, 2024-08-20T21:38:25.9095943Z ... ) 2024-08-20T21:38:25.9096214Z 2024-08-20T21:38:25.9096974Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:25.9097901Z 2024-08-20T21:38:26.1959819Z msg = Cannot scrape callname=MixedPrecision in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/api.py line=113. 2024-08-20T21:38:26.1961267Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:26.1961775Z 2024-08-20T21:38:26.1962057Z This configures FSDP-native mixed precision training. 2024-08-20T21:38:26.1962422Z 2024-08-20T21:38:26.1962525Z Attributes: 2024-08-20T21:38:26.1963016Z param_dtype (Optional[torch.dtype]): This specifies the dtype for model 2024-08-20T21:38:26.1963756Z parameters during forward and backward and thus the dtype for 2024-08-20T21:38:26.1964471Z forward and backward computation. Outside forward and backward, the 2024-08-20T21:38:26.1965458Z *sharded* parameters are kept in full precision (e.g. for the 2024-08-20T21:38:26.1966160Z optimizer step), and for model checkpointing, the parameters are 2024-08-20T21:38:26.1966810Z always saved in full precision. (Default: ``None``) 2024-08-20T21:38:26.1967548Z reduce_dtype (Optional[torch.dtype]): This specifies the dtype for 2024-08-20T21:38:26.1968377Z gradient reduction (i.e. reduce-scatter or all-reduce). If this is 2024-08-20T21:38:26.1969082Z ``None`` but ``param_dtype`` is not ``None``, then this takes on 2024-08-20T21:38:26.1969757Z the ``param_dtype`` value, still running gradient reduction in low 2024-08-20T21:38:26.1970475Z precision. This is permitted to differ from ``param_dtype``, e.g. 2024-08-20T21:38:26.1971199Z to force gradient reduction to run in full precision. (Default: 2024-08-20T21:38:26.1971715Z ``None``) 2024-08-20T21:38:26.1972170Z buffer_dtype (Optional[torch.dtype]): This specifies the dtype for 2024-08-20T21:38:26.1972892Z buffers. FSDP does not shard buffers. Rather, FSDP casts them to 2024-08-20T21:38:26.1973583Z ``buffer_dtype`` in the first forward pass and keeps them in that 2024-08-20T21:38:26.1974293Z dtype thereafter. For model checkpointing, the buffers are saved 2024-08-20T21:38:26.1974978Z in full precision except for ``LOCAL_STATE_DICT``. (Default: 2024-08-20T21:38:26.1975475Z ``None``) 2024-08-20T21:38:26.1975903Z keep_low_precision_grads (bool): If ``False``, then FSDP upcasts 2024-08-20T21:38:26.1976608Z gradients to full precision after the backward pass in preparation 2024-08-20T21:38:26.1977343Z for the optimizer step. If ``True``, then FSDP keeps the gradients 2024-08-20T21:38:26.1978174Z in the dtype used for gradient reduction, which can save memory if 2024-08-20T21:38:26.1978898Z using a custom optimizer that supports running in low precision. 2024-08-20T21:38:26.1979453Z (Default: ``False``) 2024-08-20T21:38:26.1979953Z cast_forward_inputs (bool): If ``True``, then this FSDP module casts 2024-08-20T21:38:26.1980666Z its forward args and kwargs to ``param_dtype``. This is to ensure 2024-08-20T21:38:26.1981389Z that parameter and input dtypes match for forward computation, as 2024-08-20T21:38:26.1982109Z required by many ops. This may need to be set to ``True`` when only 2024-08-20T21:38:26.1982851Z applying mixed precision to some but not all FSDP modules, in which 2024-08-20T21:38:26.1983671Z case a mixed-precision FSDP submodule needs to recast its inputs. 2024-08-20T21:38:26.1984222Z (Default: ``False``) 2024-08-20T21:38:26.1984741Z cast_root_forward_inputs (bool): If ``True``, then the root FSDP module 2024-08-20T21:38:26.1985464Z casts its forward args and kwargs to ``param_dtype``, overriding 2024-08-20T21:38:26.1986231Z the value of ``cast_forward_inputs``. For non-root FSDP modules, 2024-08-20T21:38:26.1986825Z this does not do anything. (Default: ``True``) 2024-08-20T21:38:26.1987458Z _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies 2024-08-20T21:38:26.1988146Z module classes to ignore for mixed precision when using an 2024-08-20T21:38:26.1988778Z ``auto_wrap_policy``: Modules of these classes will have FSDP 2024-08-20T21:38:26.1989460Z applied to them separately with mixed precision disabled (meaning 2024-08-20T21:38:26.1990179Z that the final FSDP construction would deviate from the specified 2024-08-20T21:38:26.1990869Z policy). If ``auto_wrap_policy`` is not specified, then this does 2024-08-20T21:38:26.1991571Z not do anything. This API is experimental and subject to change. 2024-08-20T21:38:26.1992207Z (Default: ``(_BatchNorm,)``) 2024-08-20T21:38:26.1992477Z 2024-08-20T21:38:26.1992708Z .. note:: This API is experimental and subject to change. 2024-08-20T21:38:26.1993090Z 2024-08-20T21:38:26.1993383Z .. note:: Only floating point tensors are cast to their specified dtypes. 2024-08-20T21:38:26.1993943Z 2024-08-20T21:38:26.1994187Z .. note:: In ``summon_full_params``, parameters are forced to full 2024-08-20T21:38:26.1994732Z precision, but buffers are not. 2024-08-20T21:38:26.1995002Z 2024-08-20T21:38:26.1995277Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when 2024-08-20T21:38:26.1996010Z their inputs are in a low precision like ``float16`` or ``bfloat16``. 2024-08-20T21:38:26.1996856Z Disabling FSDP's mixed precision for those norm modules only means that 2024-08-20T21:38:26.1997603Z the affine parameters are kept in ``float32``. However, this incurs 2024-08-20T21:38:26.1998439Z separate all-gathers and reduce-scatters for those norm modules, which 2024-08-20T21:38:26.1999215Z may be inefficient, so if the workload permits, the user should prefer 2024-08-20T21:38:26.1999875Z to still apply mixed precision to those modules. 2024-08-20T21:38:26.2000220Z 2024-08-20T21:38:26.2000514Z .. note:: By default, if the user passes a model with any ``_BatchNorm`` 2024-08-20T21:38:26.2001246Z modules and specifies an ``auto_wrap_policy``, then the batch norm 2024-08-20T21:38:26.2001986Z modules will have FSDP applied to them separately with mixed precision 2024-08-20T21:38:26.2002667Z disabled. See the ``_module_classes_to_ignore`` argument. 2024-08-20T21:38:26.2003065Z 2024-08-20T21:38:26.2003332Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and 2024-08-20T21:38:26.2004045Z ``cast_forward_inputs=False`` by default. For the root FSDP instance, 2024-08-20T21:38:26.2004700Z its ``cast_root_forward_inputs`` takes precedence over its 2024-08-20T21:38:26.2005448Z ``cast_forward_inputs``. For non-root FSDP instances, their 2024-08-20T21:38:26.2006128Z ``cast_root_forward_inputs`` values are ignored. The default setting is 2024-08-20T21:38:26.2006875Z sufficient for the typical case where each FSDP instance has the same 2024-08-20T21:38:26.2007716Z ``MixedPrecision`` configuration and only needs to cast inputs to the 2024-08-20T21:38:26.2008492Z ``param_dtype`` at the beginning of the model's forward pass. 2024-08-20T21:38:26.2008893Z 2024-08-20T21:38:26.2009191Z .. note:: For nested FSDP instances with different ``MixedPrecision`` 2024-08-20T21:38:26.2009930Z configurations, we recommend setting individual ``cast_forward_inputs`` 2024-08-20T21:38:26.2010716Z values to configure casting inputs or not before each instance's 2024-08-20T21:38:26.2011420Z forward. In such a case, since the casts happen before each FSDP 2024-08-20T21:38:26.2012190Z instance's forward, a parent FSDP instance should have its non-FSDP 2024-08-20T21:38:26.2012945Z submodules run before its FSDP submodules to avoid the activation dtype 2024-08-20T21:38:26.2013709Z being changed due to a different ``MixedPrecision`` configuration. 2024-08-20T21:38:26.2014151Z 2024-08-20T21:38:26.2014266Z Example:: 2024-08-20T21:38:26.2014428Z 2024-08-20T21:38:26.2014602Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:38:26.2015165Z >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3)) 2024-08-20T21:38:26.2015666Z >>> model[1] = FSDP( 2024-08-20T21:38:26.2015985Z >>> model[1], 2024-08-20T21:38:26.2016580Z >>> mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True), 2024-08-20T21:38:26.2017226Z >>> ) 2024-08-20T21:38:26.2017482Z >>> model = FSDP( 2024-08-20T21:38:26.2017795Z >>> model, 2024-08-20T21:38:26.2018386Z >>> mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), 2024-08-20T21:38:26.2019028Z >>> ) 2024-08-20T21:38:26.2019198Z 2024-08-20T21:38:26.2019499Z The above shows a working example. On the other hand, if ``model[1]`` 2024-08-20T21:38:26.2020230Z were replaced with ``model[0]``, meaning that the submodule using 2024-08-20T21:38:26.2020933Z different ``MixedPrecision`` ran its forward first, then ``model[1]`` 2024-08-20T21:38:26.2021760Z would incorrectly see ``float16`` activations instead of ``bfloat16`` 2024-08-20T21:38:26.2022307Z ones. 2024-08-20T21:38:26.2022449Z 2024-08-20T21:38:26.2022453Z 2024-08-20T21:38:26.2022874Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:26.2023356Z 2024-08-20T21:38:26.2117674Z msg = Cannot scrape callname=FullyShardedDataParallel.set_state_dict_type in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=648. 2024-08-20T21:38:26.2120327Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:26.2121218Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2024-08-20T21:38:26.2121806Z 2024-08-20T21:38:26.2122319Z Also takes (optional) configuration for the model's and optimizer's state dict. 2024-08-20T21:38:26.2123633Z The target module does not have to be a FSDP module. If the target 2024-08-20T21:38:26.2124908Z module is a FSDP module, its ``state_dict_type`` will also be changed. 2024-08-20T21:38:26.2125457Z 2024-08-20T21:38:26.2125821Z .. note:: This API should be called for only the top-level (root) 2024-08-20T21:38:26.2126423Z module. 2024-08-20T21:38:26.2126600Z 2024-08-20T21:38:26.2126949Z .. note:: This API enables users to transparently use the conventional 2024-08-20T21:38:26.2127803Z ``state_dict`` API to take model checkpoints in cases where the 2024-08-20T21:38:26.2128578Z root FSDP module is wrapped by another ``nn.Module``. For example, 2024-08-20T21:38:26.2129679Z the following will ensure ``state_dict`` is called on all non-FSDP 2024-08-20T21:38:26.2130508Z instances, while dispatching into `sharded_state_dict` implementation 2024-08-20T21:38:26.2131130Z for FSDP: 2024-08-20T21:38:26.2131369Z 2024-08-20T21:38:26.2131498Z Example:: 2024-08-20T21:38:26.2131670Z 2024-08-20T21:38:26.2131844Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:38:26.2132363Z >>> model = DDP(FSDP(...)) 2024-08-20T21:38:26.2132774Z >>> FSDP.set_state_dict_type( 2024-08-20T21:38:26.2133211Z >>> model, 2024-08-20T21:38:26.2133633Z >>> StateDictType.SHARDED_STATE_DICT, 2024-08-20T21:38:26.2134269Z >>> state_dict_config = ShardedStateDictConfig(offload_to_cpu=True), 2024-08-20T21:38:26.2135076Z >>> optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True), 2024-08-20T21:38:26.2135702Z >>> ) 2024-08-20T21:38:26.2136100Z >>> param_state_dict = model.state_dict() 2024-08-20T21:38:26.2136670Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2024-08-20T21:38:26.2137097Z 2024-08-20T21:38:26.2137192Z Args: 2024-08-20T21:38:26.2137564Z module (torch.nn.Module): Root module. 2024-08-20T21:38:26.2138238Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2024-08-20T21:38:26.2139095Z state_dict_config (Optional[StateDictConfig]): the configuration for the 2024-08-20T21:38:26.2139742Z target ``state_dict_type``. 2024-08-20T21:38:26.2140384Z optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration 2024-08-20T21:38:26.2141095Z for the optimizer state dict. 2024-08-20T21:38:26.2141468Z 2024-08-20T21:38:26.2141568Z Returns: 2024-08-20T21:38:26.2142075Z A StateDictSettings that include the previous state_dict type and 2024-08-20T21:38:26.2142666Z configuration for the module. 2024-08-20T21:38:26.2143122Z 2024-08-20T21:38:26.2143748Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:26.2144234Z 2024-08-20T21:38:26.2145603Z msg = Cannot scrape callname=FullyShardedDataParallel.state_dict_type in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=804. 2024-08-20T21:38:26.2147710Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:26.2148616Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2024-08-20T21:38:26.2149187Z 2024-08-20T21:38:26.2149647Z This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of 2024-08-20T21:38:26.2150485Z :meth:`set_state_dict_type` for the detail. 2024-08-20T21:38:26.2150836Z 2024-08-20T21:38:26.2150943Z Example:: 2024-08-20T21:38:26.2151114Z 2024-08-20T21:38:26.2151358Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:38:26.2151812Z >>> model = DDP(FSDP(...)) 2024-08-20T21:38:26.2152289Z >>> with FSDP.state_dict_type( 2024-08-20T21:38:26.2152691Z >>> model, 2024-08-20T21:38:26.2153118Z >>> StateDictType.SHARDED_STATE_DICT, 2024-08-20T21:38:26.2153560Z >>> ): 2024-08-20T21:38:26.2153892Z >>> checkpoint = model.state_dict() 2024-08-20T21:38:26.2154209Z 2024-08-20T21:38:26.2154370Z Args: 2024-08-20T21:38:26.2154682Z module (torch.nn.Module): Root module. 2024-08-20T21:38:26.2155316Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2024-08-20T21:38:26.2156101Z state_dict_config (Optional[StateDictConfig]): the model ``state_dict`` 2024-08-20T21:38:26.2156836Z configuration for the target ``state_dict_type``. 2024-08-20T21:38:26.2157520Z optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer 2024-08-20T21:38:26.2158376Z ``state_dict`` configuration for the target ``state_dict_type``. 2024-08-20T21:38:26.2158897Z 2024-08-20T21:38:26.2159458Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:26.2159949Z 2024-08-20T21:38:26.2179207Z msg = Cannot scrape callname=FullyShardedDataParallel.optim_state_dict in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1801. 2024-08-20T21:38:26.2181494Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:26.2182031Z 2024-08-20T21:38:26.2182441Z Transform the state-dict of an optimizer corresponding to a sharded model. 2024-08-20T21:38:26.2183048Z 2024-08-20T21:38:26.2183402Z The given state-dict can be transformed to one of three types: 2024-08-20T21:38:26.2184406Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict. 2024-08-20T21:38:26.2185361Z 2024-08-20T21:38:26.2185891Z For full optimizer state_dict, all states are unflattened and not sharded. 2024-08-20T21:38:26.2187269Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to 2024-08-20T21:38:26.2187901Z avoid OOM. 2024-08-20T21:38:26.2188071Z 2024-08-20T21:38:26.2188378Z For sharded optimizer state_dict, all states are unflattened but sharded. 2024-08-20T21:38:26.2189148Z CPU only can be specified via :meth:`state_dict_type` to further save 2024-08-20T21:38:26.2189684Z memory. 2024-08-20T21:38:26.2189836Z 2024-08-20T21:38:26.2190130Z For local state_dict, no transformation will be performed. But a state 2024-08-20T21:38:26.2190917Z will be converted from nn.Tensor to ShardedTensor to represent its sharding 2024-08-20T21:38:26.2191533Z nature (this is not supported yet). 2024-08-20T21:38:26.2191819Z 2024-08-20T21:38:26.2191923Z Example:: 2024-08-20T21:38:26.2192069Z 2024-08-20T21:38:26.2192242Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:38:26.2192876Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2024-08-20T21:38:26.2193567Z >>> from torch.distributed.fsdp import StateDictType 2024-08-20T21:38:26.2194174Z >>> from torch.distributed.fsdp import FullStateDictConfig 2024-08-20T21:38:26.2195005Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2024-08-20T21:38:26.2195543Z >>> # Save a checkpoint 2024-08-20T21:38:26.2195892Z >>> model, optim = ... 2024-08-20T21:38:26.2196243Z >>> FSDP.set_state_dict_type( 2024-08-20T21:38:26.2196592Z >>> model, 2024-08-20T21:38:26.2196915Z >>> StateDictType.FULL_STATE_DICT, 2024-08-20T21:38:26.2197388Z >>> FullStateDictConfig(rank0_only=False), 2024-08-20T21:38:26.2197929Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-08-20T21:38:26.2198410Z >>> ) 2024-08-20T21:38:26.2198692Z >>> state_dict = model.state_dict() 2024-08-20T21:38:26.2199185Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2024-08-20T21:38:26.2199751Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2024-08-20T21:38:26.2200215Z >>> # Load a checkpoint 2024-08-20T21:38:26.2200543Z >>> model, optim = ... 2024-08-20T21:38:26.2200959Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2024-08-20T21:38:26.2201444Z >>> FSDP.set_state_dict_type( 2024-08-20T21:38:26.2201788Z >>> model, 2024-08-20T21:38:26.2202108Z >>> StateDictType.FULL_STATE_DICT, 2024-08-20T21:38:26.2202578Z >>> FullStateDictConfig(rank0_only=False), 2024-08-20T21:38:26.2203089Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-08-20T21:38:26.2203540Z >>> ) 2024-08-20T21:38:26.2203823Z >>> model.load_state_dict(state_dict) 2024-08-20T21:38:26.2204298Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2024-08-20T21:38:26.2204792Z >>> model, optim, optim_state_dict 2024-08-20T21:38:26.2205181Z >>> ) 2024-08-20T21:38:26.2205472Z >>> optim.load_state_dict(optim_state_dict) 2024-08-20T21:38:26.2205867Z 2024-08-20T21:38:26.2205958Z Args: 2024-08-20T21:38:26.2206363Z model (torch.nn.Module): Root module (which may or may not be a 2024-08-20T21:38:26.2207116Z :class:`FullyShardedDataParallel` instance) whose parameters 2024-08-20T21:38:26.2207826Z were passed into the optimizer ``optim``. 2024-08-20T21:38:26.2208471Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2024-08-20T21:38:26.2208964Z parameters. 2024-08-20T21:38:26.2209432Z optim_state_dict (Dict[str, Any]): the target optimizer state_dict to 2024-08-20T21:38:26.2210177Z transform. If the value is None, optim.state_dict() will be used. ( 2024-08-20T21:38:26.2210730Z Default: ``None``) 2024-08-20T21:38:26.2211350Z group (dist.ProcessGroup): Model's process group across which parameters 2024-08-20T21:38:26.2212085Z are sharded or ``None`` if using the default process group. ( 2024-08-20T21:38:26.2212611Z Default: ``None``) 2024-08-20T21:38:26.2212827Z 2024-08-20T21:38:26.2212924Z Returns: 2024-08-20T21:38:26.2213344Z Dict[str, Any]: A :class:`dict` containing the optimizer state for 2024-08-20T21:38:26.2214001Z ``model``. The sharding of the optimizer state is based on 2024-08-20T21:38:26.2214487Z ``state_dict_type``. 2024-08-20T21:38:26.2214711Z 2024-08-20T21:38:26.2215107Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:26.2215593Z 2024-08-20T21:38:26.2216917Z msg = Cannot scrape callname=FullyShardedDataParallel.optim_state_dict_to_load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1899. 2024-08-20T21:38:26.2218571Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:26.2219074Z 2024-08-20T21:38:26.2219682Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model. 2024-08-20T21:38:26.2220346Z 2024-08-20T21:38:26.2220561Z Given a ``optim_state_dict`` that is transformed through 2024-08-20T21:38:26.2221219Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer 2024-08-20T21:38:26.2221948Z state_dict that can be loaded to ``optim`` which is the optimizer for 2024-08-20T21:38:26.2222724Z ``model``. ``model`` must be sharded by FullyShardedDataParallel. 2024-08-20T21:38:26.2223149Z 2024-08-20T21:38:26.2223310Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:38:26.2223955Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2024-08-20T21:38:26.2224631Z >>> from torch.distributed.fsdp import StateDictType 2024-08-20T21:38:26.2225235Z >>> from torch.distributed.fsdp import FullStateDictConfig 2024-08-20T21:38:26.2225903Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2024-08-20T21:38:26.2226451Z >>> # Save a checkpoint 2024-08-20T21:38:26.2226779Z >>> model, optim = ... 2024-08-20T21:38:26.2227122Z >>> FSDP.set_state_dict_type( 2024-08-20T21:38:26.2227472Z >>> model, 2024-08-20T21:38:26.2227793Z >>> StateDictType.FULL_STATE_DICT, 2024-08-20T21:38:26.2228263Z >>> FullStateDictConfig(rank0_only=False), 2024-08-20T21:38:26.2228785Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-08-20T21:38:26.2229229Z >>> ) 2024-08-20T21:38:26.2229510Z >>> state_dict = model.state_dict() 2024-08-20T21:38:26.2229940Z >>> original_osd = optim.state_dict() 2024-08-20T21:38:26.2230388Z >>> optim_state_dict = FSDP.optim_state_dict( 2024-08-20T21:38:26.2230810Z >>> model, 2024-08-20T21:38:26.2231084Z >>> optim, 2024-08-20T21:38:26.2231384Z >>> optim_state_dict=original_osd 2024-08-20T21:38:26.2231767Z >>> ) 2024-08-20T21:38:26.2232095Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2024-08-20T21:38:26.2232607Z >>> # Load a checkpoint 2024-08-20T21:38:26.2232949Z >>> model, optim = ... 2024-08-20T21:38:26.2233365Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2024-08-20T21:38:26.2233900Z >>> FSDP.set_state_dict_type( 2024-08-20T21:38:26.2234261Z >>> model, 2024-08-20T21:38:26.2234581Z >>> StateDictType.FULL_STATE_DICT, 2024-08-20T21:38:26.2235036Z >>> FullStateDictConfig(rank0_only=False), 2024-08-20T21:38:26.2235566Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-08-20T21:38:26.2236014Z >>> ) 2024-08-20T21:38:26.2236283Z >>> model.load_state_dict(state_dict) 2024-08-20T21:38:26.2236769Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2024-08-20T21:38:26.2237263Z >>> model, optim, optim_state_dict 2024-08-20T21:38:26.2237642Z >>> ) 2024-08-20T21:38:26.2237945Z >>> optim.load_state_dict(optim_state_dict) 2024-08-20T21:38:26.2238256Z 2024-08-20T21:38:26.2238359Z Args: 2024-08-20T21:38:26.2238750Z model (torch.nn.Module): Root module (which may or may not be a 2024-08-20T21:38:26.2239443Z :class:`FullyShardedDataParallel` instance) whose parameters 2024-08-20T21:38:26.2240046Z were passed into the optimizer ``optim``. 2024-08-20T21:38:26.2240654Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2024-08-20T21:38:26.2241159Z parameters. 2024-08-20T21:38:26.2241629Z optim_state_dict (Dict[str, Any]): The optimizer states to be loaded. 2024-08-20T21:38:26.2242334Z is_named_optimizer (bool): Is this optimizer a NamedOptimizer or 2024-08-20T21:38:26.2243060Z KeyedOptimizer. Only set to True if ``optim`` is TorchRec's 2024-08-20T21:38:26.2243766Z KeyedOptimizer or torch.distributed's NamedOptimizer. 2024-08-20T21:38:26.2244406Z load_directly (bool): If this is set to True, this API will also 2024-08-20T21:38:26.2245098Z call optim.load_state_dict(result) before returning the result. 2024-08-20T21:38:26.2245817Z Otherwise, users are responsible to call ``optim.load_state_dict()`` 2024-08-20T21:38:26.2246386Z (Default: ``False``) 2024-08-20T21:38:26.2247233Z group (dist.ProcessGroup): Model's process group across which parameters 2024-08-20T21:38:26.2248059Z are sharded or ``None`` if using the default process group. ( 2024-08-20T21:38:26.2248590Z Default: ``None``) 2024-08-20T21:38:26.2248807Z 2024-08-20T21:38:26.2249208Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:26.2249856Z 2024-08-20T21:38:26.2403010Z msg = Cannot scrape callname=_RemoteModule.__init__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=137. 2024-08-20T21:38:26.2405624Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:26.2406433Z 2024-08-20T21:38:26.2406958Z RemoteModule instance can only be created after RPC initialization. 2024-08-20T21:38:26.2407827Z 2024-08-20T21:38:26.2408393Z It creates a user-specified module on a specified remote node. 2024-08-20T21:38:26.2409741Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2024-08-20T21:38:26.2411007Z executed on the remote node. 2024-08-20T21:38:26.2411882Z It takes care of autograd recording to ensure the backward pass propagates 2024-08-20T21:38:26.2412550Z gradients back to the corresponding remote module. 2024-08-20T21:38:26.2413383Z It can be shared across processors using `RPC framework `__, 2024-08-20T21:38:26.2414406Z without incurring any overheads of copying the actual module, 2024-08-20T21:38:26.2415232Z which is equivalent to an :class:`~torch.distributed.rpc.RRef` 2024-08-20T21:38:26.2416158Z pointing to the remote module. 2024-08-20T21:38:26.2416551Z 2024-08-20T21:38:26.2416972Z The arguments of ``forward_async`` and ``forward`` are the same as 2024-08-20T21:38:26.2418204Z the ``forward`` method of the module returned by the ``module_cls``. 2024-08-20T21:38:26.2419022Z 2024-08-20T21:38:26.2419796Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now. 2024-08-20T21:38:26.2420537Z 2024-08-20T21:38:26.2421066Z Particularly, to create a hybrid model, typically the local modules should be 2024-08-20T21:38:26.2422088Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``). 2024-08-20T21:38:26.2422864Z Hybrid Example: 2024-08-20T21:38:26.2423186Z >>> class HybridModel(nn.Module): 2024-08-20T21:38:26.2423713Z >>> def __init__(self) -> None: 2024-08-20T21:38:26.2424141Z >>> nn.Module.__init__(self) 2024-08-20T21:38:26.2424628Z >>> self.remote_embedding = RemoteModule(...) 2024-08-20T21:38:26.2425352Z >>> self.local_linear = nn.Linear(...) 2024-08-20T21:38:26.2425757Z 2024-08-20T21:38:26.2426116Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2024-08-20T21:38:26.2427620Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``, 2024-08-20T21:38:26.2429009Z the generated ``RemoteModule`` will have 2 methods in signature of 2024-08-20T21:38:26.2430090Z ``def forward(input: Tensor) -> Tensor:`` and 2024-08-20T21:38:26.2431171Z ``def forward_async(input: Tensor) -> Future[Tensor]:``. 2024-08-20T21:38:26.2431856Z 2024-08-20T21:38:26.2432034Z .. note:: 2024-08-20T21:38:26.2432660Z If the remote module is placed on a cuda device, 2024-08-20T21:38:26.2433343Z any input CPU tensors will be automatically moved to the same cuda device, 2024-08-20T21:38:26.2434403Z and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend. 2024-08-20T21:38:26.2435125Z 2024-08-20T21:38:26.2435225Z Args: 2024-08-20T21:38:26.2435852Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-08-20T21:38:26.2436840Z The device can be a local device or a remote device specified by one of the following remote 2024-08-20T21:38:26.2437522Z formats: 2024-08-20T21:38:26.2437692Z 2024-08-20T21:38:26.2437875Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2024-08-20T21:38:26.2438423Z 2. "/" (ex: "trainer0/cuda:0"). 2024-08-20T21:38:26.2438792Z 2024-08-20T21:38:26.2439132Z In addition, the device field can be optional and the default value is "cpu". 2024-08-20T21:38:26.2439930Z module_cls (nn.Module): For example, 2024-08-20T21:38:26.2440341Z >>> class MyModule(nn.Module): 2024-08-20T21:38:26.2440750Z >>> def forward(input): 2024-08-20T21:38:26.2441131Z >>> return input + 1 2024-08-20T21:38:26.2441477Z >>> 2024-08-20T21:38:26.2441747Z >>> module_cls = MyModule 2024-08-20T21:38:26.2442256Z args (Sequence, optional): args to be passed to ``module_cls``. 2024-08-20T21:38:26.2442921Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2024-08-20T21:38:26.2443703Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2024-08-20T21:38:26.2444575Z to be created. The type object should be decorated by @torch.jit.interface. 2024-08-20T21:38:26.2445449Z If not provided, the generated RemoteModule is not torchscript-able. 2024-08-20T21:38:26.2446238Z Warning, this is an experimental API and susceptible to frequent changes. 2024-08-20T21:38:26.2447017Z 2024-08-20T21:38:26.2447117Z Returns: 2024-08-20T21:38:26.2447662Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-08-20T21:38:26.2448552Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2024-08-20T21:38:26.2449360Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-08-20T21:38:26.2450120Z on the user-provided module on the remote side. 2024-08-20T21:38:26.2450461Z 2024-08-20T21:38:26.2450578Z Example:: 2024-08-20T21:38:26.2450917Z Run the following code in two different processes: 2024-08-20T21:38:26.2451284Z 2024-08-20T21:38:26.2451422Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:38:26.2451818Z >>> # On worker 0: 2024-08-20T21:38:26.2452250Z >>> import torch 2024-08-20T21:38:26.2452593Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:38:26.2453039Z >>> from torch import nn, Tensor 2024-08-20T21:38:26.2453608Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-08-20T21:38:26.2454154Z >>> 2024-08-20T21:38:26.2454471Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-08-20T21:38:26.2454961Z >>> remote_linear_module = RemoteModule( 2024-08-20T21:38:26.2455427Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-08-20T21:38:26.2455846Z >>> ) 2024-08-20T21:38:26.2456118Z >>> input = torch.randn(128, 20) 2024-08-20T21:38:26.2456582Z >>> ret_fut = remote_linear_module.forward_async(input) 2024-08-20T21:38:26.2457062Z >>> ret = ret_fut.wait() 2024-08-20T21:38:26.2457401Z >>> rpc.shutdown() 2024-08-20T21:38:26.2457598Z 2024-08-20T21:38:26.2457700Z >>> # On worker 1: 2024-08-20T21:38:26.2458001Z >>> import torch 2024-08-20T21:38:26.2458347Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:38:26.2458739Z >>> 2024-08-20T21:38:26.2459052Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-08-20T21:38:26.2459497Z >>> rpc.shutdown() 2024-08-20T21:38:26.2459695Z 2024-08-20T21:38:26.2460104Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:26.2460609Z 2024-08-20T21:38:26.2461754Z msg = Cannot scrape callname=_RemoteModule.init_from_module_rref in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=514. 2024-08-20T21:38:26.2463251Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:26.2463751Z 2024-08-20T21:38:26.2464183Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef. 2024-08-20T21:38:26.2464771Z 2024-08-20T21:38:26.2465193Z This alternate initialization method can be particularly useful if we want to create multiple 2024-08-20T21:38:26.2466204Z RemoteModule instances that share the same underlying module and reduce memory consumption. 2024-08-20T21:38:26.2466788Z 2024-08-20T21:38:26.2467162Z Moreover, this also provides a workaround for passing script RemoteModule over RPC, 2024-08-20T21:38:26.2467996Z which is not supported. The recommended way is as follows: 2024-08-20T21:38:26.2468399Z 2024-08-20T21:38:26.2468543Z 1. the sender creates a RemoteModule; 2024-08-20T21:38:26.2469036Z 2. the sender sends its ``module_rref`` over RPC; 2024-08-20T21:38:26.2469847Z 3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``. 2024-08-20T21:38:26.2470468Z 2024-08-20T21:38:26.2470566Z Example:: 2024-08-20T21:38:26.2470915Z Run the following code in two different processes: 2024-08-20T21:38:26.2471271Z 2024-08-20T21:38:26.2471421Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:38:26.2471807Z >>> # On worker 0: 2024-08-20T21:38:26.2472110Z >>> import torch 2024-08-20T21:38:26.2472462Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:38:26.2472893Z >>> from torch import nn, Tensor 2024-08-20T21:38:26.2473463Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-08-20T21:38:26.2474014Z >>> 2024-08-20T21:38:26.2474320Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-08-20T21:38:26.2474791Z >>> remote_module = RemoteModule( 2024-08-20T21:38:26.2475238Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-08-20T21:38:26.2475645Z >>> ) 2024-08-20T21:38:26.2475884Z >>> 2024-08-20T21:38:26.2476158Z >>> remote_module1 = rpc.rpc_sync( 2024-08-20T21:38:26.2476545Z >>> "worker1/cpu", 2024-08-20T21:38:26.2476920Z >>> RemoteModule.init_from_module_rref, 2024-08-20T21:38:26.2477440Z >>> ("worker1/cpu", remote_module1.get_module_rref()), 2024-08-20T21:38:26.2477884Z >>> ) 2024-08-20T21:38:26.2478139Z >>> rpc.shutdown() 2024-08-20T21:38:26.2478335Z 2024-08-20T21:38:26.2478508Z >>> # On worker 1: 2024-08-20T21:38:26.2478800Z >>> import torch 2024-08-20T21:38:26.2479146Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:38:26.2479552Z >>> 2024-08-20T21:38:26.2479858Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-08-20T21:38:26.2479986Z >>> rpc.shutdown() 2024-08-20T21:38:26.2479992Z 2024-08-20T21:38:26.2480080Z Args: 2024-08-20T21:38:26.2480587Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-08-20T21:38:26.2481023Z The device can be a local device or a remote device specified by one of the following remote 2024-08-20T21:38:26.2481118Z formats: 2024-08-20T21:38:26.2481124Z 2024-08-20T21:38:26.2481319Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2024-08-20T21:38:26.2481513Z 2. "/" (ex: "trainer0/cuda:0"). 2024-08-20T21:38:26.2481518Z 2024-08-20T21:38:26.2481859Z In addition, the device field can be optional and the default value is "cpu". 2024-08-20T21:38:26.2482215Z module_rref (RRef[nn.Module]): The module reference shared by both the caller and 2024-08-20T21:38:26.2482339Z the created remote module. 2024-08-20T21:38:26.2482699Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2024-08-20T21:38:26.2483051Z to be created. The type object should be decorated by @torch.jit.interface. 2024-08-20T21:38:26.2483425Z If not provided, the generated RemoteModule is not torchscript-able. 2024-08-20T21:38:26.2483760Z Warning, this is an experimental API and susceptible to frequent changes. 2024-08-20T21:38:26.2483765Z 2024-08-20T21:38:26.2483859Z Returns: 2024-08-20T21:38:26.2484178Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-08-20T21:38:26.2484570Z user-provided ``module_rref``, it has a blocking ``forward`` method and an 2024-08-20T21:38:26.2484922Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-08-20T21:38:26.2485154Z on the user-provided module on the remote side. 2024-08-20T21:38:26.2485160Z 2024-08-20T21:38:26.2485566Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:26.2485628Z 2024-08-20T21:38:26.2486572Z msg = Cannot scrape callname=RemoteModule in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=606. 2024-08-20T21:38:26.2486999Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:26.2487004Z 2024-08-20T21:38:26.2487300Z A RemoteModule instance can only be created after RPC initialization. 2024-08-20T21:38:26.2487306Z 2024-08-20T21:38:26.2487744Z It creates a user-specified module on a specified remote node. 2024-08-20T21:38:26.2488080Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2024-08-20T21:38:26.2488205Z executed on the remote node. 2024-08-20T21:38:26.2488542Z It takes care of autograd recording to ensure the backward pass propagates 2024-08-20T21:38:26.2488740Z gradients back to the corresponding remote module. 2024-08-20T21:38:26.2488746Z 2024-08-20T21:38:26.2489036Z It generates two methods ``forward_async`` and ``forward`` based on the 2024-08-20T21:38:26.2489348Z signature of the ``forward`` method of ``module_cls``. ``forward_async`` 2024-08-20T21:38:26.2489676Z runs asynchronously and returns a Future. The arguments of ``forward_async`` 2024-08-20T21:38:26.2489947Z and ``forward`` are the same as the ``forward`` method of the module 2024-08-20T21:38:26.2490086Z returned by the ``module_cls``. 2024-08-20T21:38:26.2490092Z 2024-08-20T21:38:26.2490362Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2024-08-20T21:38:26.2490792Z that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``, 2024-08-20T21:38:26.2491089Z the generated ``RemoteModule`` will have 2 methods with the signatures: 2024-08-20T21:38:26.2491151Z 2024-08-20T21:38:26.2491364Z | ``def forward(input: Tensor) -> Tensor:`` 2024-08-20T21:38:26.2491645Z | ``def forward_async(input: Tensor) -> Future[Tensor]:`` 2024-08-20T21:38:26.2491651Z 2024-08-20T21:38:26.2491744Z Args: 2024-08-20T21:38:26.2492231Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-08-20T21:38:26.2492707Z The format should be "/", where the device field can be parsed as torch.device type. 2024-08-20T21:38:26.2492890Z E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0". 2024-08-20T21:38:26.2493236Z In addition, the device field can be optional and the default value is "cpu". 2024-08-20T21:38:26.2493581Z module_cls (nn.Module): Class for the module to be created remotely. For example, 2024-08-20T21:38:26.2493586Z 2024-08-20T21:38:26.2493714Z >>> class MyModule(nn.Module): 2024-08-20T21:38:26.2493843Z >>> def forward(input): 2024-08-20T21:38:26.2493964Z >>> return input + 1 2024-08-20T21:38:26.2494055Z >>> 2024-08-20T21:38:26.2494183Z >>> module_cls = MyModule 2024-08-20T21:38:26.2494189Z 2024-08-20T21:38:26.2494443Z args (Sequence, optional): args to be passed to ``module_cls``. 2024-08-20T21:38:26.2494695Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2024-08-20T21:38:26.2494712Z 2024-08-20T21:38:26.2494805Z Returns: 2024-08-20T21:38:26.2495125Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-08-20T21:38:26.2495508Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2024-08-20T21:38:26.2495857Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-08-20T21:38:26.2496090Z on the user-provided module on the remote side. 2024-08-20T21:38:26.2496095Z 2024-08-20T21:38:26.2496211Z Example:: 2024-08-20T21:38:26.2496403Z Run the following code in two different processes: 2024-08-20T21:38:26.2496413Z 2024-08-20T21:38:26.2496550Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:38:26.2496669Z >>> # On worker 0: 2024-08-20T21:38:26.2496770Z >>> import torch 2024-08-20T21:38:26.2496935Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:38:26.2497111Z >>> from torch import nn, Tensor 2024-08-20T21:38:26.2497400Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-08-20T21:38:26.2497505Z >>> 2024-08-20T21:38:26.2497678Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-08-20T21:38:26.2497826Z >>> remote_linear_module = RemoteModule( 2024-08-20T21:38:26.2498000Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-08-20T21:38:26.2498090Z >>> ) 2024-08-20T21:38:26.2498213Z >>> input = torch.randn(128, 20) 2024-08-20T21:38:26.2498424Z >>> ret_fut = remote_linear_module.forward_async(input) 2024-08-20T21:38:26.2498537Z >>> ret = ret_fut.wait() 2024-08-20T21:38:26.2498644Z >>> rpc.shutdown() 2024-08-20T21:38:26.2498652Z 2024-08-20T21:38:26.2498766Z >>> # On worker 1: 2024-08-20T21:38:26.2498867Z >>> import torch 2024-08-20T21:38:26.2499019Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:38:26.2499120Z >>> 2024-08-20T21:38:26.2499297Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-08-20T21:38:26.2499401Z >>> rpc.shutdown() 2024-08-20T21:38:26.2499420Z 2024-08-20T21:38:26.2499661Z Furthermore, a more practical example that is combined with 2024-08-20T21:38:26.2500284Z `DistributedDataParallel `__ (DDP) 2024-08-20T21:38:26.2500742Z can be found in this `tutorial `__. 2024-08-20T21:38:26.2500748Z 2024-08-20T21:38:26.2501143Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:26.2501149Z 2024-08-20T21:38:26.2640581Z msg = Cannot scrape callname=DistributedOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/optimizer.py line=130. 2024-08-20T21:38:26.2641366Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:26.2641377Z 2024-08-20T21:38:26.2641974Z DistributedOptimizer takes remote references to parameters scattered 2024-08-20T21:38:26.2642567Z across workers and applies the given optimizer locally for each parameter. 2024-08-20T21:38:26.2642577Z 2024-08-20T21:38:26.2643181Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order 2024-08-20T21:38:26.2643411Z to retrieve the gradients for specific parameters. 2024-08-20T21:38:26.2643417Z 2024-08-20T21:38:26.2643523Z Concurrent calls to 2024-08-20T21:38:26.2643800Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`, 2024-08-20T21:38:26.2643984Z either from the same or different clients, will 2024-08-20T21:38:26.2644362Z be serialized on each worker -- as each worker's optimizer can only work 2024-08-20T21:38:26.2644657Z on one set of gradients at a time. However, there is no guarantee that 2024-08-20T21:38:26.2645062Z the full forward-backward-optimizer sequence will execute for one client 2024-08-20T21:38:26.2645369Z at a time. This means that the gradients being applied may not correspond 2024-08-20T21:38:26.2645683Z to the latest forward pass executed on a given worker. Also, there is no 2024-08-20T21:38:26.2645818Z guaranteed ordering across workers. 2024-08-20T21:38:26.2645823Z 2024-08-20T21:38:26.2646148Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled 2024-08-20T21:38:26.2646477Z by default, so that optimizer updates are not blocked by the Python Global 2024-08-20T21:38:26.2646994Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed 2024-08-20T21:38:26.2647375Z Model Parallel). This feature is currently enabled for most optimizers. You 2024-08-20T21:38:26.2647737Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support 2024-08-20T21:38:26.2647866Z for your own custom optimizers. 2024-08-20T21:38:26.2647872Z 2024-08-20T21:38:26.2647973Z Args: 2024-08-20T21:38:26.2648225Z optimizer_class (optim.Optimizer): the class of optimizer to 2024-08-20T21:38:26.2648355Z instantiate on each worker. 2024-08-20T21:38:26.2648773Z params_rref (list[RRef]): list of RRefs to local or remote parameters 2024-08-20T21:38:26.2648876Z to optimize. 2024-08-20T21:38:26.2649164Z args: arguments to pass to the optimizer constructor on each worker. 2024-08-20T21:38:26.2649474Z kwargs: arguments to pass to the optimizer constructor on each worker. 2024-08-20T21:38:26.2649479Z 2024-08-20T21:38:26.2649588Z Example:: 2024-08-20T21:38:26.2649726Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:38:26.2649955Z >>> import torch.distributed.autograd as dist_autograd 2024-08-20T21:38:26.2650109Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:38:26.2650230Z >>> from torch import optim 2024-08-20T21:38:26.2650494Z >>> from torch.distributed.optim import DistributedOptimizer 2024-08-20T21:38:26.2650585Z >>> 2024-08-20T21:38:26.2650768Z >>> with dist_autograd.context() as context_id: 2024-08-20T21:38:26.2650878Z >>> # Forward pass. 2024-08-20T21:38:26.2651150Z >>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3)) 2024-08-20T21:38:26.2651426Z >>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1)) 2024-08-20T21:38:26.2651583Z >>> loss = rref1.to_here() + rref2.to_here() 2024-08-20T21:38:26.2651673Z >>> 2024-08-20T21:38:26.2651800Z >>> # Backward pass. 2024-08-20T21:38:26.2651993Z >>> dist_autograd.backward(context_id, [loss.sum()]) 2024-08-20T21:38:26.2652083Z >>> 2024-08-20T21:38:26.2652198Z >>> # Optimizer. 2024-08-20T21:38:26.2652354Z >>> dist_optim = DistributedOptimizer( 2024-08-20T21:38:26.2652457Z >>> optim.SGD, 2024-08-20T21:38:26.2652575Z >>> [rref1, rref2], 2024-08-20T21:38:26.2652736Z >>> lr=0.05, 2024-08-20T21:38:26.2652829Z >>> ) 2024-08-20T21:38:26.2652968Z >>> dist_optim.step(context_id) 2024-08-20T21:38:26.2652973Z 2024-08-20T21:38:26.2653159Z __ https://github.com/pytorch/tutorials/pull/1465 2024-08-20T21:38:26.2653170Z 2024-08-20T21:38:26.2653600Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:26.2653605Z 2024-08-20T21:38:26.2659992Z msg = Cannot scrape callname=PostLocalSGDOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/post_localSGD_optimizer.py line=9. 2024-08-20T21:38:26.2660833Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:26.2660843Z 2024-08-20T21:38:26.2661988Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD `_, 2024-08-20T21:38:26.2662348Z This optimizer runs local optimizer at every step. 2024-08-20T21:38:26.2663103Z After the warm-up stage, it averages parameters periodically afer the local optimizer is applied. 2024-08-20T21:38:26.2663109Z 2024-08-20T21:38:26.2663199Z Args: 2024-08-20T21:38:26.2663328Z optim: The local optimizer. 2024-08-20T21:38:26.2663705Z averager: A model averager instance to run post-localSGD algorithm. 2024-08-20T21:38:26.2663716Z 2024-08-20T21:38:26.2663820Z Example:: 2024-08-20T21:38:26.2663825Z 2024-08-20T21:38:26.2663999Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:38:26.2664103Z >>> import torch 2024-08-20T21:38:26.2664245Z >>> import torch.distributed as dist 2024-08-20T21:38:26.2664619Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2024-08-20T21:38:26.2664735Z >>> import torch.nn as nn 2024-08-20T21:38:26.2664988Z >>> from torch.distributed.optim import PostLocalSGDOptimizer 2024-08-20T21:38:26.2665356Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2024-08-20T21:38:26.2665475Z >>> PostLocalSGDState, 2024-08-20T21:38:26.2665592Z >>> post_localSGD_hook, 2024-08-20T21:38:26.2665695Z >>> ) 2024-08-20T21:38:26.2665784Z >>> 2024-08-20T21:38:26.2665986Z >>> model = nn.parallel.DistributedDataParallel( 2024-08-20T21:38:26.2666180Z >>> module, device_ids=[rank], output_device=rank 2024-08-20T21:38:26.2666387Z >>> ) 2024-08-20T21:38:26.2666488Z >>> 2024-08-20T21:38:26.2666736Z >>> # Register a post-localSGD communication hook. 2024-08-20T21:38:26.2667116Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2024-08-20T21:38:26.2667332Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-08-20T21:38:26.2667423Z >>> 2024-08-20T21:38:26.2667765Z >>> # Create a post-localSGD optimizer that wraps a local optimizer. 2024-08-20T21:38:26.2668125Z >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as 2024-08-20T21:38:26.2668338Z >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-08-20T21:38:26.2668615Z >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01) 2024-08-20T21:38:26.2668764Z >>> opt = PostLocalSGDOptimizer( 2024-08-20T21:38:26.2668879Z >>> optim=local_optim, 2024-08-20T21:38:26.2669205Z >>> averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2024-08-20T21:38:26.2669308Z >>> ) 2024-08-20T21:38:26.2669397Z >>> 2024-08-20T21:38:26.2669725Z >>> # In the first 100 steps, DDP runs global gradient averaging at every step. 2024-08-20T21:38:26.2670234Z >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default), 2024-08-20T21:38:26.2670843Z >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer. 2024-08-20T21:38:26.2670977Z >>> for step in range(0, 200): 2024-08-20T21:38:26.2671086Z >>> opt.zero_grad() 2024-08-20T21:38:26.2671220Z >>> loss = loss_fn(output, labels) 2024-08-20T21:38:26.2671395Z >>> loss.backward() 2024-08-20T21:38:26.2671498Z >>> opt.step() 2024-08-20T21:38:26.2671504Z 2024-08-20T21:38:26.2671900Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:26.2671918Z 2024-08-20T21:38:26.2779736Z msg = Cannot scrape callname=ZeroRedundancyOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/zero_redundancy_optimizer.py line=282. 2024-08-20T21:38:26.2782619Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:26.2783556Z 2024-08-20T21:38:26.2784568Z Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group. 2024-08-20T21:38:26.2785904Z 2024-08-20T21:38:26.2786192Z The sharing is done as described by ZeRO_. 2024-08-20T21:38:26.2786764Z 2024-08-20T21:38:26.2787113Z The local optimizer instance in each rank is only 2024-08-20T21:38:26.2788318Z responsible for updating approximately ``1 / world_size`` parameters and 2024-08-20T21:38:26.2789697Z hence only needs to keep ``1 / world_size`` optimizer states. After 2024-08-20T21:38:26.2791109Z parameters are updated locally, each rank will broadcast its parameters to 2024-08-20T21:38:26.2792487Z all other peers to keep all model replicas in the same state. 2024-08-20T21:38:26.2793678Z ``ZeroRedundancyOptimizer`` can be used in conjunction with 2024-08-20T21:38:26.2795213Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak 2024-08-20T21:38:26.2796360Z memory consumption. 2024-08-20T21:38:26.2796700Z 2024-08-20T21:38:26.2797435Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number 2024-08-20T21:38:26.2798934Z of parameters at each rank. Each parameter belongs to a single rank and is 2024-08-20T21:38:26.2800431Z not divided among ranks. The partition is arbitrary and might not match the 2024-08-20T21:38:26.2801621Z the parameter registration or usage order. 2024-08-20T21:38:26.2802214Z 2024-08-20T21:38:26.2802394Z Arguments: 2024-08-20T21:38:26.2803130Z params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s 2024-08-20T21:38:26.2804349Z or :class:`dict` s giving all parameters, which will be sharded 2024-08-20T21:38:26.2805592Z across ranks. 2024-08-20T21:38:26.2805939Z 2024-08-20T21:38:26.2806133Z Keyword Args: 2024-08-20T21:38:26.2806943Z optimizer_class (:class:`torch.nn.Optimizer`): the class of the local 2024-08-20T21:38:26.2808044Z optimizer. 2024-08-20T21:38:26.2808869Z process_group (``ProcessGroup``, optional): ``torch.distributed`` 2024-08-20T21:38:26.2810147Z ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by 2024-08-20T21:38:26.2811243Z :meth:`torch.distributed.init_process_group`). 2024-08-20T21:38:26.2812433Z parameters_as_bucket_view (bool, optional): if ``True``, parameters are 2024-08-20T21:38:26.2813821Z packed into buckets to speed up communication, and ``param.data`` 2024-08-20T21:38:26.2815146Z fields point to bucket views at different offsets; if ``False``, 2024-08-20T21:38:26.2816453Z each individual parameter is communicated separately, and each 2024-08-20T21:38:26.2817609Z ``params.data`` stays intact (default: ``False``). 2024-08-20T21:38:26.2818723Z overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is 2024-08-20T21:38:26.2820134Z overlapped with :class:`DistributedDataParallel` 's gradient 2024-08-20T21:38:26.2821422Z synchronization; this requires (1) either a functional optimizer 2024-08-20T21:38:26.2822672Z for the ``optimizer_class`` argument or one with a functional 2024-08-20T21:38:26.2823840Z equivalent and (2) registering a DDP communication hook 2024-08-20T21:38:26.2825041Z constructed from one of the functions in ``ddp_zero_hook.py``; 2024-08-20T21:38:26.2826156Z parameters are packed into buckets matching those in 2024-08-20T21:38:26.2827110Z :class:`DistributedDataParallel`, meaning that the 2024-08-20T21:38:26.2828354Z ``parameters_as_bucket_view`` argument is ignored. 2024-08-20T21:38:26.2829495Z If ``False``, :meth:`step` runs disjointly after the backward pass 2024-08-20T21:38:26.2830442Z (per normal). 2024-08-20T21:38:26.2830801Z (default: ``False``) 2024-08-20T21:38:26.2831313Z **defaults: any trailing arguments, which are forwarded to the local 2024-08-20T21:38:26.2831851Z optimizer. 2024-08-20T21:38:26.2832039Z 2024-08-20T21:38:26.2832149Z Example:: 2024-08-20T21:38:26.2832296Z 2024-08-20T21:38:26.2832418Z >>> # xdoctest: +SKIP 2024-08-20T21:38:26.2832742Z >>> import torch.nn as nn 2024-08-20T21:38:26.2833247Z >>> from torch.distributed.optim import ZeroRedundancyOptimizer 2024-08-20T21:38:26.2833950Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2024-08-20T21:38:26.2834685Z >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)]) 2024-08-20T21:38:26.2835311Z >>> ddp = DDP(model, device_ids=[rank]) 2024-08-20T21:38:26.2835763Z >>> opt = ZeroRedundancyOptimizer( 2024-08-20T21:38:26.2836176Z >>> ddp.parameters(), 2024-08-20T21:38:26.2836556Z >>> optimizer_class=torch.optim.Adam, 2024-08-20T21:38:26.2836970Z >>> lr=0.01 2024-08-20T21:38:26.2837241Z >>> ) 2024-08-20T21:38:26.2837497Z >>> ddp(inputs).sum().backward() 2024-08-20T21:38:26.2837873Z >>> opt.step() 2024-08-20T21:38:26.2838048Z 2024-08-20T21:38:26.2838158Z .. warning:: 2024-08-20T21:38:26.2838574Z Currently, ``ZeroRedundancyOptimizer`` requires that all of the 2024-08-20T21:38:26.2839271Z passed-in parameters are the same dense type. 2024-08-20T21:38:26.2839607Z 2024-08-20T21:38:26.2839718Z .. warning:: 2024-08-20T21:38:26.2840159Z If you pass ``overlap_with_ddp=True``, be wary of the following: Given 2024-08-20T21:38:26.2840885Z the way that overlapping :class:`DistributedDataParallel` with 2024-08-20T21:38:26.2841623Z :class:`ZeroRedundancyOptimizer` is currently implemented, the first 2024-08-20T21:38:26.2842373Z two or three training iterations do not perform parameter updates in 2024-08-20T21:38:26.2843082Z the optimizer step, depending on if ``static_graph=False`` or 2024-08-20T21:38:26.2843832Z ``static_graph=True``, respectively. This is because it needs 2024-08-20T21:38:26.2844456Z information about the gradient bucketing strategy used by 2024-08-20T21:38:26.2845134Z :class:`DistributedDataParallel`, which is not finalized until the 2024-08-20T21:38:26.2845849Z second forward pass if ``static_graph=False`` or until the third 2024-08-20T21:38:26.2846558Z forward pass if ``static_graph=True``. To adjust for this, one option 2024-08-20T21:38:26.2847409Z is to prepend dummy inputs. 2024-08-20T21:38:26.2847680Z 2024-08-20T21:38:26.2848005Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change. 2024-08-20T21:38:26.2848486Z 2024-08-20T21:38:26.2848657Z .. _ZeRO: https://arxiv.org/abs/1910.02054 2024-08-20T21:38:26.2848966Z 2024-08-20T21:38:26.2848971Z 2024-08-20T21:38:26.2849386Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:26.2849883Z 2024-08-20T21:38:26.3010119Z msg = Cannot scrape callname=_CustomReducer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/pipelining/microbatch.py line=28. 2024-08-20T21:38:26.3011546Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:26.3012045Z 2024-08-20T21:38:26.3012377Z Custom reducer class that can be used to specify a custom operation that 2024-08-20T21:38:26.3013055Z reduces losses of multiple microbatches into one value. 2024-08-20T21:38:26.3013441Z 2024-08-20T21:38:26.3013538Z Example: 2024-08-20T21:38:26.3013795Z >>> # xdoctest: +SKIP 2024-08-20T21:38:26.3014121Z >>> sum_reducer = _CustomReducer( 2024-08-20T21:38:26.3014483Z >>> torch.tensor(0.0), 2024-08-20T21:38:26.3014820Z >>> lambda a, b: a + b 2024-08-20T21:38:26.3015126Z >>> ) 2024-08-20T21:38:26.3015502Z 2024-08-20T21:38:26.3015906Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:26.3016403Z 2024-08-20T21:38:26.3489183Z msg = Cannot scrape callname=async_execution in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/functions.py line=6. 2024-08-20T21:38:26.3491331Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:26.3492227Z 2024-08-20T21:38:26.3492893Z A decorator for a function indicating that the return value of the function 2024-08-20T21:38:26.3494291Z is guaranteed to be a :class:`~torch.futures.Future` object and this 2024-08-20T21:38:26.3495690Z function can run asynchronously on the RPC callee. More specifically, the 2024-08-20T21:38:26.3496678Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped 2024-08-20T21:38:26.3497470Z function and installs subsequent processing steps as a callback to that 2024-08-20T21:38:26.3498261Z :class:`~torch.futures.Future`. The installed callback will read the value 2024-08-20T21:38:26.3499005Z from the :class:`~torch.futures.Future` when completed and send the 2024-08-20T21:38:26.3499692Z value back as the RPC response. That also means the returned 2024-08-20T21:38:26.3500408Z :class:`~torch.futures.Future` only exists on the callee side and is never 2024-08-20T21:38:26.3501206Z sent through RPC. This decorator is useful when the wrapped function's 2024-08-20T21:38:26.3501929Z (``fn``) execution needs to pause and resume due to, e.g., containing 2024-08-20T21:38:26.3502653Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals. 2024-08-20T21:38:26.3503111Z 2024-08-20T21:38:26.3503425Z .. note:: To enable asynchronous execution, applications must pass the 2024-08-20T21:38:26.3504177Z function object returned by this decorator to RPC APIs. If RPC detected 2024-08-20T21:38:26.3504949Z attributes installed by this decorator, it knows that this function 2024-08-20T21:38:26.3505640Z returns a ``Future`` object and will handle that accordingly. 2024-08-20T21:38:26.3506338Z However, this does not mean this decorator has to be outmost one when 2024-08-20T21:38:26.3507092Z defining a function. For example, when combined with ``@staticmethod`` 2024-08-20T21:38:26.3508063Z or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the 2024-08-20T21:38:26.3508801Z inner decorator to allow the target function be recognized as a static 2024-08-20T21:38:26.3509575Z or class function. This target function can still execute asynchronously 2024-08-20T21:38:26.3510357Z because, when accessed, the static or class method preserves attributes 2024-08-20T21:38:26.3511006Z installed by ``@rpc.functions.async_execution``. 2024-08-20T21:38:26.3534531Z 2024-08-20T21:38:26.3534537Z 2024-08-20T21:38:26.3534699Z Example:: 2024-08-20T21:38:26.3535172Z The returned :class:`~torch.futures.Future` object can come from 2024-08-20T21:38:26.3535791Z :meth:`~torch.distributed.rpc.rpc_async`, 2024-08-20T21:38:26.3536418Z :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future` 2024-08-20T21:38:26.3537116Z constructor. The example below shows directly using the 2024-08-20T21:38:26.3537662Z :class:`~torch.futures.Future` returned by 2024-08-20T21:38:26.3538148Z :meth:`~torch.futures.Future.then`. 2024-08-20T21:38:26.3538441Z 2024-08-20T21:38:26.3538604Z >>> from torch.distributed import rpc 2024-08-20T21:38:26.3539012Z >>> 2024-08-20T21:38:26.3539349Z >>> # omitting setup and shutdown RPC 2024-08-20T21:38:26.3539812Z >>> 2024-08-20T21:38:26.3540095Z >>> # On all workers 2024-08-20T21:38:26.3540548Z >>> @rpc.functions.async_execution 2024-08-20T21:38:26.3541214Z >>> def async_add_chained(to, x, y, z): 2024-08-20T21:38:26.3542195Z >>> # This function runs on "worker1" and returns immediately when 2024-08-20T21:38:26.3543289Z >>> # the callback is installed through the `then(cb)` API. In the 2024-08-20T21:38:26.3544803Z >>> # mean time, the `rpc_async` to "worker2" can run concurrently. 2024-08-20T21:38:26.3545588Z >>> # When the return value of that `rpc_async` arrives at 2024-08-20T21:38:26.3546224Z >>> # "worker1", "worker1" will run the lambda function accordingly 2024-08-20T21:38:26.3547122Z >>> # and set the value for the previously returned `Future`, which 2024-08-20T21:38:26.3547819Z >>> # will then trigger RPC to send the result back to "worker0". 2024-08-20T21:38:26.3548451Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-08-20T21:38:26.3548982Z >>> lambda fut: fut.wait() + z 2024-08-20T21:38:26.3549370Z >>> ) 2024-08-20T21:38:26.3549624Z >>> 2024-08-20T21:38:26.3549867Z >>> # On worker0 2024-08-20T21:38:26.3550158Z >>> # xdoctest: +SKIP 2024-08-20T21:38:26.3550480Z >>> ret = rpc.rpc_sync( 2024-08-20T21:38:26.3550802Z >>> "worker1", 2024-08-20T21:38:26.3551098Z >>> async_add_chained, 2024-08-20T21:38:26.3551492Z >>> args=("worker2", torch.ones(2), 1, 1) 2024-08-20T21:38:26.3551899Z >>> ) 2024-08-20T21:38:26.3552183Z >>> print(ret) # prints tensor([3., 3.]) 2024-08-20T21:38:26.3552502Z 2024-08-20T21:38:26.3552811Z When combined with TorchScript decorators, this decorator must be the 2024-08-20T21:38:26.3553386Z outmost one. 2024-08-20T21:38:26.3553558Z 2024-08-20T21:38:26.3553681Z >>> from torch import Tensor 2024-08-20T21:38:26.3554077Z >>> from torch.futures import Future 2024-08-20T21:38:26.3554519Z >>> from torch.distributed import rpc 2024-08-20T21:38:26.3554900Z >>> 2024-08-20T21:38:26.3555180Z >>> # omitting setup and shutdown RPC 2024-08-20T21:38:26.3555565Z >>> 2024-08-20T21:38:26.3555799Z >>> # On all workers 2024-08-20T21:38:26.3556121Z >>> @torch.jit.script 2024-08-20T21:38:26.3556681Z >>> def script_add(x: Tensor, y: Tensor) -> Tensor: 2024-08-20T21:38:26.3557121Z >>> return x + y 2024-08-20T21:38:26.3557427Z >>> 2024-08-20T21:38:26.3557704Z >>> @rpc.functions.async_execution 2024-08-20T21:38:26.3558093Z >>> @torch.jit.script 2024-08-20T21:38:26.3558641Z >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]: 2024-08-20T21:38:26.3559392Z >>> return rpc.rpc_async(to, script_add, (x, y)) 2024-08-20T21:38:26.3559813Z >>> 2024-08-20T21:38:26.3560061Z >>> # On worker0 2024-08-20T21:38:26.3560365Z >>> ret = rpc.rpc_sync( 2024-08-20T21:38:26.3560682Z >>> "worker1", 2024-08-20T21:38:26.3560980Z >>> async_add, 2024-08-20T21:38:26.3561323Z >>> args=("worker2", torch.ones(2), 1) 2024-08-20T21:38:26.3561711Z >>> ) 2024-08-20T21:38:26.3562006Z >>> print(ret) # prints tensor([2., 2.]) 2024-08-20T21:38:26.3562310Z 2024-08-20T21:38:26.3562617Z When combined with static or class method, this decorator must be the 2024-08-20T21:38:26.3563186Z inner one. 2024-08-20T21:38:26.3563348Z 2024-08-20T21:38:26.3563497Z >>> from torch.distributed import rpc 2024-08-20T21:38:26.3563889Z >>> 2024-08-20T21:38:26.3564172Z >>> # omitting setup and shutdown RPC 2024-08-20T21:38:26.3564551Z >>> 2024-08-20T21:38:26.3564799Z >>> # On all workers 2024-08-20T21:38:26.3565142Z >>> class AsyncExecutionClass: 2024-08-20T21:38:26.3565495Z >>> 2024-08-20T21:38:26.3565740Z >>> @staticmethod 2024-08-20T21:38:26.3566081Z >>> @rpc.functions.async_execution 2024-08-20T21:38:26.3566536Z >>> def static_async_add(to, x, y, z): 2024-08-20T21:38:26.3567069Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-08-20T21:38:26.3567717Z >>> lambda fut: fut.wait() + z 2024-08-20T21:38:26.3568108Z >>> ) 2024-08-20T21:38:26.3568374Z >>> 2024-08-20T21:38:26.3568619Z >>> @classmethod 2024-08-20T21:38:26.3568953Z >>> @rpc.functions.async_execution 2024-08-20T21:38:26.3569398Z >>> def class_async_add(cls, to, x, y, z): 2024-08-20T21:38:26.3569970Z >>> ret_fut = torch.futures.Future() 2024-08-20T21:38:26.3570476Z >>> rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-08-20T21:38:26.3571040Z >>> lambda fut: ret_fut.set_result(fut.wait() + z) 2024-08-20T21:38:26.3571509Z >>> ) 2024-08-20T21:38:26.3571779Z >>> return ret_fut 2024-08-20T21:38:26.3572107Z >>> 2024-08-20T21:38:26.3572389Z >>> @rpc.functions.async_execution 2024-08-20T21:38:26.3572835Z >>> def bound_async_add(self, to, x, y, z): 2024-08-20T21:38:26.3573385Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-08-20T21:38:26.3573918Z >>> lambda fut: fut.wait() + z 2024-08-20T21:38:26.3574307Z >>> ) 2024-08-20T21:38:26.3574567Z >>> 2024-08-20T21:38:26.3574808Z >>> # On worker0 2024-08-20T21:38:26.3575097Z >>> ret = rpc.rpc_sync( 2024-08-20T21:38:26.3575428Z >>> "worker1", 2024-08-20T21:38:26.3575790Z >>> AsyncExecutionClass.static_async_add, 2024-08-20T21:38:26.3576266Z >>> args=("worker2", torch.ones(2), 1, 2) 2024-08-20T21:38:26.3576671Z >>> ) 2024-08-20T21:38:26.3576969Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-20T21:38:26.3577359Z >>> 2024-08-20T21:38:26.3577615Z >>> ret = rpc.rpc_sync( 2024-08-20T21:38:26.3577941Z >>> "worker1", 2024-08-20T21:38:26.3578289Z >>> AsyncExecutionClass.class_async_add, 2024-08-20T21:38:26.3578766Z >>> args=("worker2", torch.ones(2), 1, 2) 2024-08-20T21:38:26.3579169Z >>> ) 2024-08-20T21:38:26.3579449Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-20T21:38:26.3579763Z 2024-08-20T21:38:26.3579970Z This decorator also works with RRef helpers, i.e., . 2024-08-20T21:38:26.3580517Z :meth:`torch.distributed.rpc.RRef.rpc_sync`, 2024-08-20T21:38:26.3581051Z :meth:`torch.distributed.rpc.RRef.rpc_async`, and 2024-08-20T21:38:26.3581597Z :meth:`torch.distributed.rpc.RRef.remote`. 2024-08-20T21:38:26.3581929Z 2024-08-20T21:38:26.3582091Z >>> from torch.distributed import rpc 2024-08-20T21:38:26.3582471Z >>> 2024-08-20T21:38:26.3582788Z >>> # reuse the AsyncExecutionClass class above 2024-08-20T21:38:26.3583332Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-08-20T21:38:26.3584041Z >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2) 2024-08-20T21:38:26.3584640Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-20T21:38:26.3585043Z >>> 2024-08-20T21:38:26.3585371Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-08-20T21:38:26.3586055Z >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait() 2024-08-20T21:38:26.3586696Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-20T21:38:26.3587084Z >>> 2024-08-20T21:38:26.3587422Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-08-20T21:38:26.3588099Z >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here() 2024-08-20T21:38:26.3588727Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-20T21:38:26.3589043Z 2024-08-20T21:38:26.3589485Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:26.3589986Z 2024-08-20T21:38:26.3591111Z msg = Cannot scrape callname=TensorPipeRpcBackendOptions.set_device_map in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/options.py line=108. 2024-08-20T21:38:26.3592616Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:26.3593113Z 2024-08-20T21:38:26.3593390Z Set device mapping between each RPC caller and callee pair. This 2024-08-20T21:38:26.3594042Z function can be called multiple times to incrementally add 2024-08-20T21:38:26.3594574Z device placement configurations. 2024-08-20T21:38:26.3594833Z 2024-08-20T21:38:26.3594936Z Args: 2024-08-20T21:38:26.3595177Z to (str): Callee name. 2024-08-20T21:38:26.3595661Z device_map (Dict of int, str, or torch.device): Device placement 2024-08-20T21:38:26.3596392Z mappings from this worker to the callee. This map must be 2024-08-20T21:38:26.3596885Z invertible. 2024-08-20T21:38:26.3597077Z 2024-08-20T21:38:26.3597172Z Example: 2024-08-20T21:38:26.3597461Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:38:26.3597854Z >>> # both workers 2024-08-20T21:38:26.3598163Z >>> def add(x, y): 2024-08-20T21:38:26.3598605Z >>> print(x) # tensor([1., 1.], device='cuda:1') 2024-08-20T21:38:26.3599061Z >>> return x + y, (x + y).to(2) 2024-08-20T21:38:26.3599438Z >>> 2024-08-20T21:38:26.3599682Z >>> # on worker 0 2024-08-20T21:38:26.3600042Z >>> options = TensorPipeRpcBackendOptions( 2024-08-20T21:38:26.3600493Z >>> num_worker_threads=8, 2024-08-20T21:38:26.3600882Z >>> device_maps={"worker1": {0: 1}} 2024-08-20T21:38:26.3601402Z >>> # maps worker0's cuda:0 to worker1's cuda:1 2024-08-20T21:38:26.3601830Z >>> ) 2024-08-20T21:38:26.3602139Z >>> options.set_device_map("worker1", {1: 2}) 2024-08-20T21:38:26.3602676Z >>> # maps worker0's cuda:1 to worker1's cuda:2 2024-08-20T21:38:26.3603100Z >>> 2024-08-20T21:38:26.3603344Z >>> rpc.init_rpc( 2024-08-20T21:38:26.3603633Z >>> "worker0", 2024-08-20T21:38:26.3603930Z >>> rank=0, 2024-08-20T21:38:26.3604213Z >>> world_size=2, 2024-08-20T21:38:26.3604577Z >>> backend=rpc.BackendType.TENSORPIPE, 2024-08-20T21:38:26.3605036Z >>> rpc_backend_options=options 2024-08-20T21:38:26.3605410Z >>> ) 2024-08-20T21:38:26.3605633Z >>> 2024-08-20T21:38:26.3605880Z >>> x = torch.ones(2) 2024-08-20T21:38:26.3606303Z >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1)) 2024-08-20T21:38:26.3606918Z >>> # The first argument will be moved to cuda:1 on worker1. When 2024-08-20T21:38:26.3607703Z >>> # sending the return value back, it will follow the invert of 2024-08-20T21:38:26.3608365Z >>> # the device map, and hence will be moved back to cuda:0 and 2024-08-20T21:38:26.3608879Z >>> # cuda:1 on worker0 2024-08-20T21:38:26.3609370Z >>> print(rets[0]) # tensor([2., 2.], device='cuda:0') 2024-08-20T21:38:26.3609977Z >>> print(rets[1]) # tensor([2., 2.], device='cuda:1') 2024-08-20T21:38:26.3610325Z 2024-08-20T21:38:26.3610835Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:26.3611322Z 2024-08-20T21:38:26.3860146Z msg = Cannot scrape callname=PrepareModuleInput in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py line=378. 2024-08-20T21:38:26.3862783Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:26.3863609Z 2024-08-20T21:38:26.3864293Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to 2024-08-20T21:38:26.3865679Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``. 2024-08-20T21:38:26.3866624Z 2024-08-20T21:38:26.3866802Z Keyword Args: 2024-08-20T21:38:26.3867480Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2024-08-20T21:38:26.3869105Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2024-08-20T21:38:26.3870489Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2024-08-20T21:38:26.3871297Z as a placeholder. default: None. 2024-08-20T21:38:26.3871884Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2024-08-20T21:38:26.3872876Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2024-08-20T21:38:26.3874110Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2024-08-20T21:38:26.3874979Z input_kwarg_layouts (Dict[str, Placement]): 2024-08-20T21:38:26.3876026Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2024-08-20T21:38:26.3876838Z default: None 2024-08-20T21:38:26.3877226Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2024-08-20T21:38:26.3878128Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2024-08-20T21:38:26.3879015Z have the desired DTensor layouts. default: None. 2024-08-20T21:38:26.3879495Z use_local_output (bool, optional): 2024-08-20T21:38:26.3880277Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2024-08-20T21:38:26.3881030Z Returns: 2024-08-20T21:38:26.3881707Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs. 2024-08-20T21:38:26.3882306Z 2024-08-20T21:38:26.3882424Z Example:: 2024-08-20T21:38:26.3882700Z >>> # xdoctest: +SKIP(failing) 2024-08-20T21:38:26.3883370Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput 2024-08-20T21:38:26.3884208Z >>> from torch.distributed.device_mesh import init_device_mesh 2024-08-20T21:38:26.3884716Z >>> ... 2024-08-20T21:38:26.3885278Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2024-08-20T21:38:26.3886022Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2024-08-20T21:38:26.3886433Z >>> 2024-08-20T21:38:26.3887037Z >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor 2024-08-20T21:38:26.3887946Z >>> # and then redistributed to Replicated DTensor. 2024-08-20T21:38:26.3888416Z >>> parallelize_module( 2024-08-20T21:38:26.3888823Z >>> block, # this can be a submodule or module 2024-08-20T21:38:26.3889255Z >>> tp_mesh, 2024-08-20T21:38:26.3889565Z >>> parallelize_plan={ 2024-08-20T21:38:26.3889951Z >>> "attn": PrepareModuleInput( 2024-08-20T21:38:26.3890436Z >>> input_layouts=(Shard(0), None, None, ...), 2024-08-20T21:38:26.3891002Z >>> desired_input_layouts=(Replicate(), None, None, ...) 2024-08-20T21:38:26.3891489Z >>> ), 2024-08-20T21:38:26.3891865Z >>> } 2024-08-20T21:38:26.3892120Z >>> ) 2024-08-20T21:38:26.3892260Z 2024-08-20T21:38:26.3892688Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:26.3893177Z 2024-08-20T21:38:26.3894228Z msg = Cannot scrape callname=PrepareModuleOutput in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py line=533. 2024-08-20T21:38:26.3895652Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:26.3896163Z 2024-08-20T21:38:26.3896811Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to 2024-08-20T21:38:26.3897950Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``. 2024-08-20T21:38:26.3898534Z 2024-08-20T21:38:26.3898647Z Keyword Args: 2024-08-20T21:38:26.3899008Z output_layouts (Union[Placement, Tuple[Placement]]): 2024-08-20T21:38:26.3899852Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2024-08-20T21:38:26.3901034Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2024-08-20T21:38:26.3901903Z ``None`` need to be specified as a placeholder. 2024-08-20T21:38:26.3902496Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2024-08-20T21:38:26.3903456Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2024-08-20T21:38:26.3904300Z have the desired DTensor layouts. 2024-08-20T21:38:26.3904729Z use_local_output (bool, optional): 2024-08-20T21:38:26.3905583Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2024-08-20T21:38:26.3906335Z Returns: 2024-08-20T21:38:26.3906958Z A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs. 2024-08-20T21:38:26.3907522Z 2024-08-20T21:38:26.3907621Z Example:: 2024-08-20T21:38:26.3907895Z >>> # xdoctest: +SKIP(failing) 2024-08-20T21:38:26.3908563Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput 2024-08-20T21:38:26.3909400Z >>> from torch.distributed.device_mesh import init_device_mesh 2024-08-20T21:38:26.3909910Z >>> ... 2024-08-20T21:38:26.3910471Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2024-08-20T21:38:26.3911213Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2024-08-20T21:38:26.3911626Z >>> 2024-08-20T21:38:26.3912318Z >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor 2024-08-20T21:38:26.3913212Z >>> # and then redistributed to Sharded DTensor. 2024-08-20T21:38:26.3913668Z >>> parallelize_module( 2024-08-20T21:38:26.3914065Z >>> block, # this can be a submodule or module 2024-08-20T21:38:26.3914512Z >>> tp_mesh, 2024-08-20T21:38:26.3914874Z >>> parallelize_plan = PrepareModuleOutput( 2024-08-20T21:38:26.3915344Z >>> output_layouts=Replicate(), 2024-08-20T21:38:26.3915793Z >>> desired_output_layouts=Shard(0) 2024-08-20T21:38:26.3916199Z >>> ) 2024-08-20T21:38:26.3916438Z >>> ) 2024-08-20T21:38:26.3916590Z 2024-08-20T21:38:26.3916991Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:26.3917473Z 2024-08-20T21:38:26.4577752Z msg = Cannot scrape callname=MixtureSameFamily in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/mixture_same_family.py line=13. 2024-08-20T21:38:26.4580218Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:26.4580723Z 2024-08-20T21:38:26.4581055Z The `MixtureSameFamily` distribution implements a (batch of) mixture 2024-08-20T21:38:26.4581811Z distribution where all component are from different parameterizations of 2024-08-20T21:38:26.4582971Z the same distribution type. It is parameterized by a `Categorical` 2024-08-20T21:38:26.4583932Z "selecting distribution" (over `k` component) and a component 2024-08-20T21:38:26.4584598Z distribution, i.e., a `Distribution` with a rightmost batch shape 2024-08-20T21:38:26.4585244Z (equal to `[k]`) which indexes each (batch of) component. 2024-08-20T21:38:26.4585613Z 2024-08-20T21:38:26.4585739Z Examples:: 2024-08-20T21:38:26.4585890Z 2024-08-20T21:38:26.4586036Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:38:26.4586612Z >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally 2024-08-20T21:38:26.4587182Z >>> # weighted normal distributions 2024-08-20T21:38:26.4587614Z >>> mix = D.Categorical(torch.ones(5,)) 2024-08-20T21:38:26.4588113Z >>> comp = D.Normal(torch.randn(5,), torch.rand(5,)) 2024-08-20T21:38:26.4588613Z >>> gmm = MixtureSameFamily(mix, comp) 2024-08-20T21:38:26.4588915Z 2024-08-20T21:38:26.4589197Z >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally 2024-08-20T21:38:26.4589781Z >>> # weighted bivariate normal distributions 2024-08-20T21:38:26.4590253Z >>> mix = D.Categorical(torch.ones(5,)) 2024-08-20T21:38:26.4590691Z >>> comp = D.Independent(D.Normal( 2024-08-20T21:38:26.4591132Z ... torch.randn(5,2), torch.rand(5,2)), 1) 2024-08-20T21:38:26.4591615Z >>> gmm = MixtureSameFamily(mix, comp) 2024-08-20T21:38:26.4591914Z 2024-08-20T21:38:26.4592163Z >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each 2024-08-20T21:38:26.4592824Z >>> # consisting of 5 random weighted bivariate normal distributions 2024-08-20T21:38:26.4593411Z >>> mix = D.Categorical(torch.rand(3,5)) 2024-08-20T21:38:26.4593965Z >>> comp = D.Independent(D.Normal( 2024-08-20T21:38:26.4594421Z ... torch.randn(3,5,2), torch.rand(3,5,2)), 1) 2024-08-20T21:38:26.4594914Z >>> gmm = MixtureSameFamily(mix, comp) 2024-08-20T21:38:26.4595221Z 2024-08-20T21:38:26.4595326Z Args: 2024-08-20T21:38:26.4595813Z mixture_distribution: `torch.distributions.Categorical`-like 2024-08-20T21:38:26.4596481Z instance. Manages the probability of selecting component. 2024-08-20T21:38:26.4597112Z The number of categories must match the rightmost batch 2024-08-20T21:38:26.4597736Z dimension of the `component_distribution`. Must have either 2024-08-20T21:38:26.4598326Z scalar `batch_shape` or `batch_shape` matching 2024-08-20T21:38:26.4598898Z `component_distribution.batch_shape[:-1]` 2024-08-20T21:38:26.4599576Z component_distribution: `torch.distributions.Distribution`-like 2024-08-20T21:38:26.4600301Z instance. Right-most batch dimension indexes component. 2024-08-20T21:38:26.4600708Z 2024-08-20T21:38:26.4601099Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:26.4601585Z 2024-08-20T21:38:26.4732693Z msg = Cannot scrape callname=RelaxedBernoulli in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/relaxed_bernoulli.py line=111. 2024-08-20T21:38:26.4734100Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:26.4734614Z 2024-08-20T21:38:26.4734864Z Creates a RelaxedBernoulli distribution, parametrized by 2024-08-20T21:38:26.4735503Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits` 2024-08-20T21:38:26.4736214Z (but not both). This is a relaxed version of the `Bernoulli` distribution, 2024-08-20T21:38:26.4736918Z so the values are in (0, 1), and has reparametrizable samples. 2024-08-20T21:38:26.4737336Z 2024-08-20T21:38:26.4737444Z Example:: 2024-08-20T21:38:26.4737592Z 2024-08-20T21:38:26.4737851Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-08-20T21:38:26.4738366Z >>> m = RelaxedBernoulli(torch.tensor([2.2]), 2024-08-20T21:38:26.4738858Z ... torch.tensor([0.1, 0.2, 0.3, 0.99])) 2024-08-20T21:38:26.4739299Z >>> m.sample() 2024-08-20T21:38:26.4739801Z tensor([ 0.2951, 0.3442, 0.8918, 0.9021]) 2024-08-20T21:38:26.4740106Z 2024-08-20T21:38:26.4740194Z Args: 2024-08-20T21:38:26.4740504Z temperature (Tensor): relaxation temperature 2024-08-20T21:38:26.4741057Z probs (Number, Tensor): the probability of sampling `1` 2024-08-20T21:38:26.4741703Z logits (Number, Tensor): the log-odds of sampling `1` 2024-08-20T21:38:26.4742078Z 2024-08-20T21:38:26.4742470Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:26.4742955Z 2024-08-20T21:38:26.4750996Z msg = Cannot scrape callname=RelaxedOneHotCategorical in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/relaxed_categorical.py line=99. 2024-08-20T21:38:26.4753189Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:26.4753708Z 2024-08-20T21:38:26.4753982Z Creates a RelaxedOneHotCategorical distribution parametrized by 2024-08-20T21:38:26.4754678Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`. 2024-08-20T21:38:26.4755429Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so 2024-08-20T21:38:26.4756119Z its samples are on simplex, and are reparametrizable. 2024-08-20T21:38:26.4756492Z 2024-08-20T21:38:26.4756597Z Example:: 2024-08-20T21:38:26.4756756Z 2024-08-20T21:38:26.4756996Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-08-20T21:38:26.4757546Z >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]), 2024-08-20T21:38:26.4758084Z ... torch.tensor([0.1, 0.2, 0.3, 0.4])) 2024-08-20T21:38:26.4758538Z >>> m.sample() 2024-08-20T21:38:26.4758865Z tensor([ 0.1294, 0.2324, 0.3859, 0.2523]) 2024-08-20T21:38:26.4759172Z 2024-08-20T21:38:26.4759402Z Args: 2024-08-20T21:38:26.4759719Z temperature (Tensor): relaxation temperature 2024-08-20T21:38:26.4760201Z probs (Tensor): event probabilities 2024-08-20T21:38:26.4760730Z logits (Tensor): unnormalized log probability for each event 2024-08-20T21:38:26.4761151Z 2024-08-20T21:38:26.4761549Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:26.4762028Z 2024-08-20T21:38:26.8132091Z msg = Cannot scrape callname=assoc_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=230. 2024-08-20T21:38:26.8134401Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:26.8135569Z Return a new dict with new, potentially nested, key value pair 2024-08-20T21:38:26.8136238Z 2024-08-20T21:38:26.8136515Z >>> purchase = {'name': 'Alice', 2024-08-20T21:38:26.8137190Z ... 'order': {'items': ['Apple', 'Orange'], 2024-08-20T21:38:26.8138074Z ... 'costs': [0.50, 1.25]}, 2024-08-20T21:38:26.8138845Z ... 'credit card': '5555-1234-1234-1234'} 2024-08-20T21:38:26.8139600Z >>> assoc_in(purchase, ['order', 'costs'], [0.25, 1.00]) # doctest: +SKIP 2024-08-20T21:38:26.8140352Z {'credit card': '5555-1234-1234-1234', 2024-08-20T21:38:26.8140907Z 'name': 'Alice', 2024-08-20T21:38:26.8141507Z 'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}} 2024-08-20T21:38:26.8142261Z 2024-08-20T21:38:26.8143243Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:26.8143775Z 2024-08-20T21:38:26.8144965Z msg = Cannot scrape callname=update_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=245. 2024-08-20T21:38:26.8146521Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:26.8147423Z Update value in a (potentially) nested dictionary 2024-08-20T21:38:26.8147843Z 2024-08-20T21:38:26.8147941Z inputs: 2024-08-20T21:38:26.8148312Z d - dictionary on which to operate 2024-08-20T21:38:26.8148985Z keys - list or tuple giving the location of the value to be changed in d 2024-08-20T21:38:26.8150060Z func - function to operate on that value 2024-08-20T21:38:26.8150368Z 2024-08-20T21:38:26.8150714Z If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the 2024-08-20T21:38:26.8151519Z original dictionary with v replaced by func(v), but does not mutate the 2024-08-20T21:38:26.8152138Z original dictionary. 2024-08-20T21:38:26.8152363Z 2024-08-20T21:38:26.8152691Z If k0 is not a key in d, update_in creates nested dictionaries to the depth 2024-08-20T21:38:26.8153519Z specified by the keys, with the innermost value set to func(default). 2024-08-20T21:38:26.8154042Z 2024-08-20T21:38:26.8154159Z >>> inc = lambda x: x + 1 2024-08-20T21:38:26.8154629Z >>> update_in({'a': 0}, ['a'], inc) 2024-08-20T21:38:26.8155025Z {'a': 1} 2024-08-20T21:38:26.8155240Z 2024-08-20T21:38:26.8155421Z >>> transaction = {'name': 'Alice', 2024-08-20T21:38:26.8156008Z ... 'purchase': {'items': ['Apple', 'Orange'], 2024-08-20T21:38:26.8156596Z ... 'costs': [0.50, 1.25]}, 2024-08-20T21:38:26.8157195Z ... 'credit card': '5555-1234-1234-1234'} 2024-08-20T21:38:26.8157930Z >>> update_in(transaction, ['purchase', 'costs'], sum) # doctest: +SKIP 2024-08-20T21:38:26.8158619Z {'credit card': '5555-1234-1234-1234', 2024-08-20T21:38:26.8159080Z 'name': 'Alice', 2024-08-20T21:38:26.8159554Z 'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}} 2024-08-20T21:38:26.8159985Z 2024-08-20T21:38:26.8160152Z >>> # updating a value when k0 is not in d 2024-08-20T21:38:26.8160666Z >>> update_in({}, [1, 2, 3], str, default="bar") 2024-08-20T21:38:26.8161140Z {1: {2: {3: 'bar'}}} 2024-08-20T21:38:26.8161735Z >>> update_in({1: 'foo'}, [2, 3, 4], inc, 0) 2024-08-20T21:38:26.8162252Z {1: 'foo', 2: {3: {4: 1}}} 2024-08-20T21:38:26.8162572Z 2024-08-20T21:38:26.8163160Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:26.8163715Z 2024-08-20T21:38:26.8164835Z msg = Cannot scrape callname=get_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=303. 2024-08-20T21:38:26.8166378Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:26.8167161Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys. 2024-08-20T21:38:26.8167659Z 2024-08-20T21:38:26.8167935Z If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless 2024-08-20T21:38:26.8168676Z ``no_default`` is specified, then it raises KeyError or IndexError. 2024-08-20T21:38:26.8169172Z 2024-08-20T21:38:26.8169456Z ``get_in`` is a generalization of ``operator.getitem`` for nested data 2024-08-20T21:38:26.8170138Z structures such as dictionaries and lists. 2024-08-20T21:38:26.8170455Z 2024-08-20T21:38:26.8170675Z >>> transaction = {'name': 'Alice', 2024-08-20T21:38:26.8171233Z ... 'purchase': {'items': ['Apple', 'Orange'], 2024-08-20T21:38:26.8171812Z ... 'costs': [0.50, 1.25]}, 2024-08-20T21:38:26.8172421Z ... 'credit card': '5555-1234-1234-1234'} 2024-08-20T21:38:26.8172969Z >>> get_in(['purchase', 'items', 0], transaction) 2024-08-20T21:38:26.8173429Z 'Apple' 2024-08-20T21:38:26.8173753Z >>> get_in(['name'], transaction) 2024-08-20T21:38:26.8174134Z 'Alice' 2024-08-20T21:38:26.8174559Z >>> get_in(['purchase', 'total'], transaction) 2024-08-20T21:38:26.8175139Z >>> get_in(['purchase', 'items', 'apple'], transaction) 2024-08-20T21:38:26.8175713Z >>> get_in(['purchase', 'items', 10], transaction) 2024-08-20T21:38:26.8176278Z >>> get_in(['purchase', 'total'], transaction, 0) 2024-08-20T21:38:26.8176705Z 0 2024-08-20T21:38:26.8177014Z >>> get_in(['y'], {}, no_default=True) 2024-08-20T21:38:26.8177436Z Traceback (most recent call last): 2024-08-20T21:38:26.8177813Z ... 2024-08-20T21:38:26.8178141Z KeyError: 'y' 2024-08-20T21:38:26.8178327Z 2024-08-20T21:38:26.8178525Z See Also: 2024-08-20T21:38:26.8178795Z itertoolz.get 2024-08-20T21:38:26.8179088Z operator.getitem 2024-08-20T21:38:26.8179387Z 2024-08-20T21:38:26.8179923Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:26.8180410Z 2024-08-20T21:38:26.8181425Z msg = Cannot scrape callname=groupby in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=355. 2024-08-20T21:38:26.8182862Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:26.8183498Z Group a collection by a key function 2024-08-20T21:38:26.8183776Z 2024-08-20T21:38:26.8184088Z >>> names = ['Alice', 'Bob', 'Charlie', 'Dan', 'Edith', 'Frank'] 2024-08-20T21:38:26.8184627Z >>> groupby(len, names) # doctest: +SKIP 2024-08-20T21:38:26.8185316Z {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} 2024-08-20T21:38:26.8185710Z 2024-08-20T21:38:26.8185854Z >>> iseven = lambda x: x % 2 == 0 2024-08-20T21:38:26.8186346Z >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP 2024-08-20T21:38:26.8186883Z {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} 2024-08-20T21:38:26.8187181Z 2024-08-20T21:38:26.8187419Z Non-callable keys imply grouping on a member. 2024-08-20T21:38:26.8187753Z 2024-08-20T21:38:26.8188017Z >>> groupby('gender', [{'name': 'Alice', 'gender': 'F'}, 2024-08-20T21:38:26.8188608Z ... {'name': 'Bob', 'gender': 'M'}, 2024-08-20T21:38:26.8189227Z ... {'name': 'Charlie', 'gender': 'M'}]) # doctest:+SKIP 2024-08-20T21:38:26.8189796Z {'F': [{'gender': 'F', 'name': 'Alice'}], 2024-08-20T21:38:26.8190367Z 'M': [{'gender': 'M', 'name': 'Bob'}, 2024-08-20T21:38:26.8190851Z {'gender': 'M', 'name': 'Charlie'}]} 2024-08-20T21:38:26.8191150Z 2024-08-20T21:38:26.8191340Z Not to be confused with ``itertools.groupby`` 2024-08-20T21:38:26.8191676Z 2024-08-20T21:38:26.8191779Z See Also: 2024-08-20T21:38:26.8192038Z countby 2024-08-20T21:38:26.8192289Z 2024-08-20T21:38:26.8192807Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:26.8193300Z 2024-08-20T21:38:27.1675729Z msg = Cannot scrape callname=SyncBatchNorm in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py line=601. 2024-08-20T21:38:27.1677611Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:27.1678405Z Applies Batch Normalization over a N-Dimensional input. 2024-08-20T21:38:27.1679034Z 2024-08-20T21:38:27.1680012Z The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper 2024-08-20T21:38:27.1680987Z `Batch Normalization: Accelerating Deep Network Training by Reducing 2024-08-20T21:38:27.1681725Z Internal Covariate Shift `__ . 2024-08-20T21:38:27.1682157Z 2024-08-20T21:38:27.1682302Z .. math:: 2024-08-20T21:38:27.1682460Z 2024-08-20T21:38:27.1682882Z y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta 2024-08-20T21:38:27.1683392Z 2024-08-20T21:38:27.1683767Z The mean and standard-deviation are calculated per-dimension over all 2024-08-20T21:38:27.1684628Z mini-batches of the same process groups. :math:`\gamma` and :math:`\beta` 2024-08-20T21:38:27.1685434Z are learnable parameter vectors of size `C` (where `C` is the input size). 2024-08-20T21:38:27.1686148Z By default, the elements of :math:`\gamma` are sampled from 2024-08-20T21:38:27.1686840Z :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. 2024-08-20T21:38:27.1687838Z The standard-deviation is calculated via the biased estimator, equivalent to 2024-08-20T21:38:27.1688472Z `torch.var(input, unbiased=False)`. 2024-08-20T21:38:27.1688775Z 2024-08-20T21:38:27.1689093Z Also by default, during training this layer keeps running estimates of its 2024-08-20T21:38:27.1690165Z computed mean and variance, which are then used for normalization during 2024-08-20T21:38:27.1690969Z evaluation. The running estimates are kept with a default :attr:`momentum` 2024-08-20T21:38:27.1691538Z of 0.1. 2024-08-20T21:38:27.1691701Z 2024-08-20T21:38:27.1692011Z If :attr:`track_running_stats` is set to ``False``, this layer then does not 2024-08-20T21:38:27.1692775Z keep running estimates, and batch statistics are instead used during 2024-08-20T21:38:27.1693336Z evaluation time as well. 2024-08-20T21:38:27.1693582Z 2024-08-20T21:38:27.1693684Z .. note:: 2024-08-20T21:38:27.1694143Z This :attr:`momentum` argument is different from one used in optimizer 2024-08-20T21:38:27.1694902Z classes and the conventional notion of momentum. Mathematically, the 2024-08-20T21:38:27.1695537Z update rule for running statistics here is 2024-08-20T21:38:27.1696376Z :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, 2024-08-20T21:38:27.1697221Z where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the 2024-08-20T21:38:27.1697793Z new observed value. 2024-08-20T21:38:27.1698028Z 2024-08-20T21:38:27.1698438Z Because the Batch Normalization is done for each channel in the ``C`` dimension, computing 2024-08-20T21:38:27.1699445Z statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch 2024-08-20T21:38:27.1700229Z Normalization or Spatio-temporal Batch Normalization. 2024-08-20T21:38:27.1700620Z 2024-08-20T21:38:27.1700797Z Currently :class:`SyncBatchNorm` only supports 2024-08-20T21:38:27.1701631Z :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use 2024-08-20T21:38:27.1702447Z :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert 2024-08-20T21:38:27.1703169Z :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping 2024-08-20T21:38:27.1703721Z Network with DDP. 2024-08-20T21:38:27.1703915Z 2024-08-20T21:38:27.1704008Z Args: 2024-08-20T21:38:27.1704371Z num_features: :math:`C` from an expected input of size 2024-08-20T21:38:27.1704860Z :math:`(N, C, +)` 2024-08-20T21:38:27.1705341Z eps: a value added to the denominator for numerical stability. 2024-08-20T21:38:27.1705926Z Default: ``1e-5`` 2024-08-20T21:38:27.1706411Z momentum: the value used for the running_mean and running_var 2024-08-20T21:38:27.1707107Z computation. Can be set to ``None`` for cumulative moving average 2024-08-20T21:38:27.1707694Z (i.e. simple average). Default: 0.1 2024-08-20T21:38:27.1708286Z affine: a boolean value that when set to ``True``, this module has 2024-08-20T21:38:27.1708918Z learnable affine parameters. Default: ``True`` 2024-08-20T21:38:27.1709550Z track_running_stats: a boolean value that when set to ``True``, this 2024-08-20T21:38:27.1710311Z module tracks the running mean and variance, and when set to ``False``, 2024-08-20T21:38:27.1711099Z this module does not track such statistics, and initializes statistics 2024-08-20T21:38:27.1711829Z buffers :attr:`running_mean` and :attr:`running_var` as ``None``. 2024-08-20T21:38:27.1712584Z When these buffers are ``None``, this module always uses batch statistics. 2024-08-20T21:38:27.1713282Z in both training and eval modes. Default: ``True`` 2024-08-20T21:38:27.1713952Z process_group: synchronization of stats happen within each process group 2024-08-20T21:38:27.1714728Z individually. Default behavior is synchronization across the whole 2024-08-20T21:38:27.1715286Z world 2024-08-20T21:38:27.1715461Z 2024-08-20T21:38:27.1715572Z Shape: 2024-08-20T21:38:27.1715882Z - Input: :math:`(N, C, +)` 2024-08-20T21:38:27.1716393Z - Output: :math:`(N, C, +)` (same shape as input) 2024-08-20T21:38:27.1716736Z 2024-08-20T21:38:27.1716919Z .. note:: 2024-08-20T21:38:27.1717394Z Synchronization of batchnorm statistics occurs only while training, i.e. 2024-08-20T21:38:27.1718151Z synchronization is disabled when ``model.eval()`` is set or if 2024-08-20T21:38:27.1718740Z ``self.training`` is otherwise ``False``. 2024-08-20T21:38:27.1719061Z 2024-08-20T21:38:27.1719164Z Examples:: 2024-08-20T21:38:27.1719341Z 2024-08-20T21:38:27.1719456Z >>> # xdoctest: +SKIP 2024-08-20T21:38:27.1719827Z >>> # With Learnable Parameters 2024-08-20T21:38:27.1720235Z >>> m = nn.SyncBatchNorm(100) 2024-08-20T21:38:27.1720670Z >>> # creating process group (optional) 2024-08-20T21:38:27.1721178Z >>> # ranks is a list of int identifying rank ids. 2024-08-20T21:38:27.1721637Z >>> ranks = list(range(8)) 2024-08-20T21:38:27.1722024Z >>> r1, r2 = ranks[:4], ranks[4:] 2024-08-20T21:38:27.1722506Z >>> # Note: every rank calls into new_group for every 2024-08-20T21:38:27.1723068Z >>> # process group created, even if that rank is not 2024-08-20T21:38:27.1723542Z >>> # part of the group. 2024-08-20T21:38:27.1724121Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2024-08-20T21:38:27.1724880Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2024-08-20T21:38:27.1725441Z >>> # Without Learnable Parameters 2024-08-20T21:38:27.1726023Z >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group) 2024-08-20T21:38:27.1726632Z >>> input = torch.randn(20, 100, 35, 45, 10) 2024-08-20T21:38:27.1727063Z >>> output = m(input) 2024-08-20T21:38:27.1727302Z 2024-08-20T21:38:27.1727612Z >>> # network is nn.BatchNorm layer 2024-08-20T21:38:27.1728291Z >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group) 2024-08-20T21:38:27.1729028Z >>> # only single gpu per process is currently supported 2024-08-20T21:38:27.1729714Z >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel( 2024-08-20T21:38:27.1730327Z >>> sync_bn_network, 2024-08-20T21:38:27.1730798Z >>> device_ids=[args.local_rank], 2024-08-20T21:38:27.1731315Z >>> output_device=args.local_rank) 2024-08-20T21:38:27.1731746Z 2024-08-20T21:38:27.1732299Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:27.1732792Z 2024-08-20T21:38:27.1733811Z msg = Cannot scrape callname=SyncBatchNorm.convert_sync_batchnorm in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py line=824. 2024-08-20T21:38:27.1735231Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:27.1736154Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers. 2024-08-20T21:38:27.1736735Z 2024-08-20T21:38:27.1736830Z Args: 2024-08-20T21:38:27.1737324Z module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers 2024-08-20T21:38:27.1738108Z process_group (optional): process group to scope synchronization, 2024-08-20T21:38:27.1738696Z default is the whole world 2024-08-20T21:38:27.1738988Z 2024-08-20T21:38:27.1739087Z Returns: 2024-08-20T21:38:27.1739609Z The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm` 2024-08-20T21:38:27.1740424Z layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer, 2024-08-20T21:38:27.1741163Z a new :class:`torch.nn.SyncBatchNorm` layer object will be returned 2024-08-20T21:38:27.1741716Z instead. 2024-08-20T21:38:27.1741902Z 2024-08-20T21:38:27.1742017Z Example:: 2024-08-20T21:38:27.1742188Z 2024-08-20T21:38:27.1742340Z >>> # Network with nn.BatchNorm layer 2024-08-20T21:38:27.1742845Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-20T21:38:27.1743440Z >>> module = torch.nn.Sequential( 2024-08-20T21:38:27.1743894Z >>> torch.nn.Linear(20, 100), 2024-08-20T21:38:27.1744376Z >>> torch.nn.BatchNorm1d(100), 2024-08-20T21:38:27.1744811Z >>> ).cuda() 2024-08-20T21:38:27.1745202Z >>> # creating process group (optional) 2024-08-20T21:38:27.1745720Z >>> # ranks is a list of int identifying rank ids. 2024-08-20T21:38:27.1746202Z >>> ranks = list(range(8)) 2024-08-20T21:38:27.1746603Z >>> r1, r2 = ranks[:4], ranks[4:] 2024-08-20T21:38:27.1747341Z >>> # Note: every rank calls into new_group for every 2024-08-20T21:38:27.1747917Z >>> # process group created, even if that rank is not 2024-08-20T21:38:27.1748405Z >>> # part of the group. 2024-08-20T21:38:27.1748816Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:38:27.1749465Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2024-08-20T21:38:27.1750239Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2024-08-20T21:38:27.1751068Z >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group) 2024-08-20T21:38:27.1751638Z 2024-08-20T21:38:27.1751731Z 2024-08-20T21:38:27.1752312Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:27.1752795Z 2024-08-20T21:38:27.1927834Z msg = Cannot scrape callname=Unflatten in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py line=60. 2024-08-20T21:38:27.1929345Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:27.1929864Z 2024-08-20T21:38:27.1930316Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`. 2024-08-20T21:38:27.1930923Z 2024-08-20T21:38:27.1931294Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can 2024-08-20T21:38:27.1932170Z be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively. 2024-08-20T21:38:27.1932661Z 2024-08-20T21:38:27.1933097Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be 2024-08-20T21:38:27.1934078Z a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input; a `NamedShape` 2024-08-20T21:38:27.1934843Z (tuple of `(name, size)` tuples) for `NamedTensor` input. 2024-08-20T21:38:27.1935217Z 2024-08-20T21:38:27.1935306Z Shape: 2024-08-20T21:38:27.1935856Z - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at 2024-08-20T21:38:27.1936696Z dimension :attr:`dim` and :math:`*` means any number of dimensions including none. 2024-08-20T21:38:27.1937597Z - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and 2024-08-20T21:38:27.1938220Z :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`. 2024-08-20T21:38:27.1938560Z 2024-08-20T21:38:27.1938648Z Args: 2024-08-20T21:38:27.1938983Z dim (Union[int, str]): Dimension to be unflattened 2024-08-20T21:38:27.1939782Z unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension 2024-08-20T21:38:27.1940415Z 2024-08-20T21:38:27.1940512Z Examples: 2024-08-20T21:38:27.1940787Z >>> input = torch.randn(2, 50) 2024-08-20T21:38:27.1941154Z >>> # With tuple of ints 2024-08-20T21:38:27.1941505Z >>> m = nn.Sequential( 2024-08-20T21:38:27.1941840Z >>> nn.Linear(50, 50), 2024-08-20T21:38:27.1942184Z >>> nn.Unflatten(1, (2, 5, 5)) 2024-08-20T21:38:27.1942553Z >>> ) 2024-08-20T21:38:27.1942814Z >>> output = m(input) 2024-08-20T21:38:27.1943125Z >>> output.size() 2024-08-20T21:38:27.1943436Z torch.Size([2, 2, 5, 5]) 2024-08-20T21:38:27.1943773Z >>> # With torch.Size 2024-08-20T21:38:27.1944088Z >>> m = nn.Sequential( 2024-08-20T21:38:27.1944534Z >>> nn.Linear(50, 50), 2024-08-20T21:38:27.1944923Z >>> nn.Unflatten(1, torch.Size([2, 5, 5])) 2024-08-20T21:38:27.1945322Z >>> ) 2024-08-20T21:38:27.1945584Z >>> output = m(input) 2024-08-20T21:38:27.1945902Z >>> output.size() 2024-08-20T21:38:27.1946198Z torch.Size([2, 2, 5, 5]) 2024-08-20T21:38:27.1946573Z >>> # With namedshape (tuple of tuples) 2024-08-20T21:38:27.1947369Z >>> input = torch.randn(2, 50, names=('N', 'features')) 2024-08-20T21:38:27.1948069Z >>> unflatten = nn.Unflatten('features', (('C', 2), ('H', 5), ('W', 5))) 2024-08-20T21:38:27.1948639Z >>> output = unflatten(input) 2024-08-20T21:38:27.1949007Z >>> output.size() 2024-08-20T21:38:27.1949306Z torch.Size([2, 2, 5, 5]) 2024-08-20T21:38:27.1949550Z 2024-08-20T21:38:27.1949943Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:27.1950431Z 2024-08-20T21:38:27.2271037Z msg = Cannot scrape callname=TripletMarginWithDistanceLoss in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py line=1696. 2024-08-20T21:38:27.2272445Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:27.2273184Z Creates a criterion that measures the triplet loss given input 2024-08-20T21:38:27.2273903Z tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor, 2024-08-20T21:38:27.2274614Z positive, and negative examples, respectively), and a nonnegative, 2024-08-20T21:38:27.2275463Z real-valued function ("distance function") used to compute the relationship 2024-08-20T21:38:27.2276235Z between the anchor and positive example ("positive distance") and the 2024-08-20T21:38:27.2277088Z anchor and negative example ("negative distance"). 2024-08-20T21:38:27.2277452Z 2024-08-20T21:38:27.2277818Z The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``) 2024-08-20T21:38:27.2278369Z can be described as: 2024-08-20T21:38:27.2278595Z 2024-08-20T21:38:27.2278721Z .. math:: 2024-08-20T21:38:27.2279079Z \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad 2024-08-20T21:38:27.2279729Z l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} 2024-08-20T21:38:27.2280112Z 2024-08-20T21:38:27.2280544Z where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function 2024-08-20T21:38:27.2281456Z quantifying the closeness of two tensors, referred to as the :attr:`distance_function`; 2024-08-20T21:38:27.2282343Z and :math:`margin` is a nonnegative margin representing the minimum difference 2024-08-20T21:38:27.2283174Z between the positive and negative distances that is required for the loss to 2024-08-20T21:38:27.2284005Z be 0. The input tensors have :math:`N` elements each and can be of any shape 2024-08-20T21:38:27.2284641Z that the distance function can handle. 2024-08-20T21:38:27.2284942Z 2024-08-20T21:38:27.2285144Z If :attr:`reduction` is not ``'none'`` 2024-08-20T21:38:27.2285594Z (default ``'mean'``), then: 2024-08-20T21:38:27.2285857Z 2024-08-20T21:38:27.2285955Z .. math:: 2024-08-20T21:38:27.2286216Z \ell(x, y) = 2024-08-20T21:38:27.2286494Z \begin{cases} 2024-08-20T21:38:27.2287038Z \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ 2024-08-20T21:38:27.2287934Z \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} 2024-08-20T21:38:27.2288443Z \end{cases} 2024-08-20T21:38:27.2288632Z 2024-08-20T21:38:27.2288952Z See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet 2024-08-20T21:38:27.2289781Z loss for input tensors using the :math:`l_p` distance as the distance function. 2024-08-20T21:38:27.2290287Z 2024-08-20T21:38:27.2290380Z Args: 2024-08-20T21:38:27.2290970Z distance_function (Callable, optional): A nonnegative, real-valued function that 2024-08-20T21:38:27.2291736Z quantifies the closeness of two tensors. If not specified, 2024-08-20T21:38:27.2292364Z `nn.PairwiseDistance` will be used. Default: ``None`` 2024-08-20T21:38:27.2293231Z margin (float, optional): A nonnegative margin representing the minimum difference 2024-08-20T21:38:27.2294125Z between the positive and negative distances required for the loss to be 0. Larger 2024-08-20T21:38:27.2295037Z margins penalize cases where the negative examples are not distant enough from the 2024-08-20T21:38:27.2295795Z anchors, relative to the positives. Default: :math:`1`. 2024-08-20T21:38:27.2296528Z swap (bool, optional): Whether to use the distance swap described in the paper 2024-08-20T21:38:27.2297370Z `Learning shallow convolutional feature descriptors with triplet losses` by 2024-08-20T21:38:27.2298218Z V. Balntas, E. Riba et al. If True, and if the positive example is closer to the 2024-08-20T21:38:27.2299111Z negative example than the anchor is, swaps the positive example and the anchor in 2024-08-20T21:38:27.2299818Z the loss computation. Default: ``False``. 2024-08-20T21:38:27.2300513Z reduction (str, optional): Specifies the (optional) reduction to apply to the output: 2024-08-20T21:38:27.2301406Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2024-08-20T21:38:27.2302179Z ``'mean'``: the sum of the output will be divided by the number of 2024-08-20T21:38:27.2303018Z elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'`` 2024-08-20T21:38:27.2303515Z 2024-08-20T21:38:27.2303520Z 2024-08-20T21:38:27.2303616Z Shape: 2024-08-20T21:38:27.2304192Z - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions 2024-08-20T21:38:27.2304919Z as supported by the distance function. 2024-08-20T21:38:27.2305673Z - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar 2024-08-20T21:38:27.2306301Z otherwise. 2024-08-20T21:38:27.2306503Z 2024-08-20T21:38:27.2306607Z Examples:: 2024-08-20T21:38:27.2306769Z 2024-08-20T21:38:27.2306899Z >>> # Initialize embeddings 2024-08-20T21:38:27.2307283Z >>> embedding = nn.Embedding(1000, 128) 2024-08-20T21:38:27.2307746Z >>> anchor_ids = torch.randint(0, 1000, (1,)) 2024-08-20T21:38:27.2308232Z >>> positive_ids = torch.randint(0, 1000, (1,)) 2024-08-20T21:38:27.2308712Z >>> negative_ids = torch.randint(0, 1000, (1,)) 2024-08-20T21:38:27.2309174Z >>> anchor = embedding(anchor_ids) 2024-08-20T21:38:27.2309600Z >>> positive = embedding(positive_ids) 2024-08-20T21:38:27.2310031Z >>> negative = embedding(negative_ids) 2024-08-20T21:38:27.2310425Z >>> 2024-08-20T21:38:27.2310743Z >>> # Built-in Distance Function 2024-08-20T21:38:27.2311119Z >>> triplet_loss = \ 2024-08-20T21:38:27.2311690Z >>> nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance()) 2024-08-20T21:38:27.2312412Z >>> output = triplet_loss(anchor, positive, negative) 2024-08-20T21:38:27.2312880Z >>> output.backward() 2024-08-20T21:38:27.2313190Z >>> 2024-08-20T21:38:27.2313454Z >>> # Custom Distance Function 2024-08-20T21:38:27.2313823Z >>> def l_infinity(x1, x2): 2024-08-20T21:38:27.2314338Z >>> return torch.max(torch.abs(x1 - x2), dim=1).values 2024-08-20T21:38:27.2314795Z >>> 2024-08-20T21:38:27.2315176Z >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time") 2024-08-20T21:38:27.2315710Z >>> triplet_loss = ( 2024-08-20T21:38:27.2316273Z >>> nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5)) 2024-08-20T21:38:27.2316981Z >>> output = triplet_loss(anchor, positive, negative) 2024-08-20T21:38:27.2317455Z >>> output.backward() 2024-08-20T21:38:27.2317770Z >>> 2024-08-20T21:38:27.2318043Z >>> # Custom Distance Function (Lambda) 2024-08-20T21:38:27.2318454Z >>> triplet_loss = ( 2024-08-20T21:38:27.2318836Z >>> nn.TripletMarginWithDistanceLoss( 2024-08-20T21:38:27.2319577Z >>> distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y))) 2024-08-20T21:38:27.2320215Z >>> output = triplet_loss(anchor, positive, negative) 2024-08-20T21:38:27.2320684Z >>> output.backward() 2024-08-20T21:38:27.2320896Z 2024-08-20T21:38:27.2320995Z Reference: 2024-08-20T21:38:27.2321572Z V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses: 2024-08-20T21:38:27.2322389Z http://www.bmva.org/bmvc/2016/papers/paper119/index.html 2024-08-20T21:38:27.2322866Z 2024-08-20T21:38:27.2323407Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17)) 2024-08-20T21:38:27.2323908Z 2024-08-20T21:38:27.2848257Z msg = Cannot scrape callname=MaxUnpool2d in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py line=395. 2024-08-20T21:38:27.2849588Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:27.2850286Z Computes a partial inverse of :class:`MaxPool2d`. 2024-08-20T21:38:27.2850699Z 2024-08-20T21:38:27.2851139Z :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost. 2024-08-20T21:38:27.2851658Z 2024-08-20T21:38:27.2851969Z :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d` 2024-08-20T21:38:27.2852753Z including the indices of the maximal values and computes a partial inverse 2024-08-20T21:38:27.2853493Z in which all non-maximal values are set to zero. 2024-08-20T21:38:27.2853833Z 2024-08-20T21:38:27.2853937Z Note: 2024-08-20T21:38:27.2854485Z This operation may behave nondeterministically when the input indices has repeat values. 2024-08-20T21:38:27.2855815Z See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. 2024-08-20T21:38:27.2856494Z 2024-08-20T21:38:27.2856816Z .. note:: :class:`MaxPool2d` can map several input sizes to the same output 2024-08-20T21:38:27.2857518Z sizes. Hence, the inversion process can get ambiguous. 2024-08-20T21:38:27.2858166Z To accommodate this, you can provide the needed output size 2024-08-20T21:38:27.2858873Z as an additional argument :attr:`output_size` in the forward call. 2024-08-20T21:38:27.2859482Z See the Inputs and Example below. 2024-08-20T21:38:27.2859888Z 2024-08-20T21:38:27.2859982Z Args: 2024-08-20T21:38:27.2860380Z kernel_size (int or tuple): Size of the max pooling window. 2024-08-20T21:38:27.2861015Z stride (int or tuple): Stride of the max pooling window. 2024-08-20T21:38:27.2861589Z It is set to :attr:`kernel_size` by default. 2024-08-20T21:38:27.2862177Z padding (int or tuple): Padding that was added to the input 2024-08-20T21:38:27.2862601Z 2024-08-20T21:38:27.2862698Z Inputs: 2024-08-20T21:38:27.2863072Z - `input`: the input Tensor to invert 2024-08-20T21:38:27.2863719Z - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d` 2024-08-20T21:38:27.2864432Z - `output_size` (optional): the targeted output size 2024-08-20T21:38:27.2864794Z 2024-08-20T21:38:27.2864905Z Shape: 2024-08-20T21:38:27.2865387Z - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. 2024-08-20T21:38:27.2866206Z - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where 2024-08-20T21:38:27.2866679Z 2024-08-20T21:38:27.2866797Z .. math:: 2024-08-20T21:38:27.2867489Z H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} 2024-08-20T21:38:27.2868068Z 2024-08-20T21:38:27.2868166Z .. math:: 2024-08-20T21:38:27.2868860Z W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} 2024-08-20T21:38:27.2869418Z 2024-08-20T21:38:27.2869654Z or as given by :attr:`output_size` in the call operator 2024-08-20T21:38:27.2870037Z 2024-08-20T21:38:27.2870270Z Example:: 2024-08-20T21:38:27.2870445Z 2024-08-20T21:38:27.2870663Z >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True) 2024-08-20T21:38:27.2871205Z >>> unpool = nn.MaxUnpool2d(2, stride=2) 2024-08-20T21:38:27.2871687Z >>> input = torch.tensor([[[[ 1., 2., 3., 4.], 2024-08-20T21:38:27.2872177Z [ 5., 6., 7., 8.], 2024-08-20T21:38:27.2872639Z [ 9., 10., 11., 12.], 2024-08-20T21:38:27.2873088Z [13., 14., 15., 16.]]]]) 2024-08-20T21:38:27.2873540Z >>> output, indices = pool(input) 2024-08-20T21:38:27.2873963Z >>> unpool(output, indices) 2024-08-20T21:38:27.2874362Z tensor([[[[ 0., 0., 0., 0.], 2024-08-20T21:38:27.2874761Z [ 0., 6., 0., 8.], 2024-08-20T21:38:27.2875156Z [ 0., 0., 0., 0.], 2024-08-20T21:38:27.2875567Z [ 0., 14., 0., 16.]]]]) 2024-08-20T21:38:27.2876133Z >>> # Now using output_size to resolve an ambiguous size for the inverse 2024-08-20T21:38:27.2876780Z >>> input = torch.tensor([[[[ 1., 2., 3., 4., 5.], 2024-08-20T21:38:27.2877290Z [ 6., 7., 8., 9., 10.], 2024-08-20T21:38:27.2877748Z [11., 12., 13., 14., 15.], 2024-08-20T21:38:27.2878224Z [16., 17., 18., 19., 20.]]]]) 2024-08-20T21:38:27.2878691Z >>> output, indices = pool(input) 2024-08-20T21:38:27.2879199Z >>> # This call will not work without specifying output_size 2024-08-20T21:38:27.2879798Z >>> unpool(output, indices, output_size=input.size()) 2024-08-20T21:38:27.2880419Z tensor([[[[ 0., 0., 0., 0., 0.], 2024-08-20T21:38:27.2880830Z [ 0., 7., 0., 9., 0.], 2024-08-20T21:38:27.2881241Z [ 0., 0., 0., 0., 0.], 2024-08-20T21:38:27.2881657Z [ 0., 17., 0., 19., 0.]]]]) 2024-08-20T21:38:27.2881953Z 2024-08-20T21:38:27.2881958Z 2024-08-20T21:38:27.2882045Z 2024-08-20T21:38:27.2882589Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:27.2883086Z 2024-08-20T21:38:27.3129608Z msg = Cannot scrape callname=EmbeddingBag in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py line=270. 2024-08-20T21:38:27.3132684Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:27.3134104Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings. 2024-08-20T21:38:27.3134758Z 2024-08-20T21:38:27.3135212Z For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`, 2024-08-20T21:38:27.3135955Z and with 2D inputs, this class 2024-08-20T21:38:27.3136228Z 2024-08-20T21:38:27.3136658Z * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``, 2024-08-20T21:38:27.3137705Z * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``, 2024-08-20T21:38:27.3138728Z * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``. 2024-08-20T21:38:27.3139322Z 2024-08-20T21:38:27.3139811Z However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these 2024-08-20T21:38:27.3140567Z operations. 2024-08-20T21:38:27.3140733Z 2024-08-20T21:38:27.3141145Z EmbeddingBag also supports per-sample weights as an argument to the forward 2024-08-20T21:38:27.3141980Z pass. This scales the output of the Embedding before performing a weighted 2024-08-20T21:38:27.3142812Z reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the 2024-08-20T21:38:27.3143643Z only supported ``mode`` is ``"sum"``, which computes a weighted sum according to 2024-08-20T21:38:27.3144247Z :attr:`per_sample_weights`. 2024-08-20T21:38:27.3144727Z 2024-08-20T21:38:27.3144819Z Args: 2024-08-20T21:38:27.3145211Z num_embeddings (int): size of the dictionary of embeddings 2024-08-20T21:38:27.3145819Z embedding_dim (int): the size of each embedding vector 2024-08-20T21:38:27.3146635Z max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm` 2024-08-20T21:38:27.3147617Z is renormalized to have norm :attr:`max_norm`. 2024-08-20T21:38:27.3148609Z norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``. 2024-08-20T21:38:27.3149715Z scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of 2024-08-20T21:38:27.3150660Z the words in the mini-batch. Default ``False``. 2024-08-20T21:38:27.3151322Z Note: this option is not supported when ``mode="max"``. 2024-08-20T21:38:27.3152095Z mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag. 2024-08-20T21:38:27.3152923Z ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights` 2024-08-20T21:38:27.3153686Z into consideration. ``"mean"`` computes the average of the values 2024-08-20T21:38:27.3154398Z in the bag, ``"max"`` computes the max value over each bag. 2024-08-20T21:38:27.3154967Z Default: ``"mean"`` 2024-08-20T21:38:27.3155840Z sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See 2024-08-20T21:38:27.3156820Z Notes for more details regarding sparse gradients. Note: this option is not 2024-08-20T21:38:27.3157495Z supported when ``mode="max"``. 2024-08-20T21:38:27.3158358Z include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element 2024-08-20T21:38:27.3159348Z is equivalent to the size of `indices`. This matches the CSR format. 2024-08-20T21:38:27.3160272Z padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the 2024-08-20T21:38:27.3161261Z gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated 2024-08-20T21:38:27.3162141Z during training, i.e. it remains as a fixed "pad". For a newly constructed 2024-08-20T21:38:27.3163019Z EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all 2024-08-20T21:38:27.3163886Z zeros, but can be updated to another value to be used as the padding vector. 2024-08-20T21:38:27.3164755Z Note that the embedding vector at :attr:`padding_idx` is excluded from the 2024-08-20T21:38:27.3165403Z reduction. 2024-08-20T21:38:27.3165695Z 2024-08-20T21:38:27.3165807Z Attributes: 2024-08-20T21:38:27.3166399Z weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)` 2024-08-20T21:38:27.3167177Z initialized from :math:`\mathcal{N}(0, 1)`. 2024-08-20T21:38:27.3167599Z 2024-08-20T21:38:27.3167740Z Examples:: 2024-08-20T21:38:27.3167909Z 2024-08-20T21:38:27.3168142Z >>> # an EmbeddingBag module containing 10 tensors of size 3 2024-08-20T21:38:27.3168835Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum') 2024-08-20T21:38:27.3169379Z >>> # a batch of 2 samples of 4 indices each 2024-08-20T21:38:27.3169960Z >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long) 2024-08-20T21:38:27.3170583Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2024-08-20T21:38:27.3171292Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-08-20T21:38:27.3171767Z >>> embedding_sum(input, offsets) 2024-08-20T21:38:27.3172255Z tensor([[-0.8861, -5.4350, -0.0523], 2024-08-20T21:38:27.3172738Z [ 1.1306, -2.5798, -1.0044]]) 2024-08-20T21:38:27.3173023Z 2024-08-20T21:38:27.3173168Z >>> # Example with padding_idx 2024-08-20T21:38:27.3173795Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2) 2024-08-20T21:38:27.3174499Z >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long) 2024-08-20T21:38:27.3175116Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2024-08-20T21:38:27.3175598Z >>> embedding_sum(input, offsets) 2024-08-20T21:38:27.3176026Z tensor([[ 0.0000, 0.0000, 0.0000], 2024-08-20T21:38:27.3176507Z [-0.7082, 3.2145, -2.6251]]) 2024-08-20T21:38:27.3176788Z 2024-08-20T21:38:27.3177028Z >>> # An EmbeddingBag can be loaded from an Embedding like so 2024-08-20T21:38:27.3177627Z >>> embedding = nn.Embedding(10, 3, padding_idx=2) 2024-08-20T21:38:27.3178187Z >>> embedding_sum = nn.EmbeddingBag.from_pretrained( 2024-08-20T21:38:27.3178666Z embedding.weight, 2024-08-20T21:38:27.3179080Z padding_idx=embedding.padding_idx, 2024-08-20T21:38:27.3179553Z mode='sum') 2024-08-20T21:38:27.3179851Z 2024-08-20T21:38:27.3180380Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:27.3180863Z 2024-08-20T21:38:27.3484026Z msg = Cannot scrape callname=DistributedDataParallel.join in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py line=1748. 2024-08-20T21:38:27.3486530Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:27.3487498Z 2024-08-20T21:38:27.3488043Z Context manager for training with uneven inputs across processes in DDP. 2024-08-20T21:38:27.3488587Z 2024-08-20T21:38:27.3488960Z This context manager will keep track of already-joined DDP processes, 2024-08-20T21:38:27.3489704Z and "shadow" the forward and backward passes by inserting collective 2024-08-20T21:38:27.3490516Z communication operations to match with the ones created by non-joined 2024-08-20T21:38:27.3491267Z DDP processes. This will ensure each collective call has a corresponding 2024-08-20T21:38:27.3492106Z call by already-joined DDP processes, preventing hangs or errors that 2024-08-20T21:38:27.3492814Z would otherwise happen when training with uneven inputs across 2024-08-20T21:38:27.3493528Z processes. Alternatively, if the flag ``throw_on_early_termination`` is 2024-08-20T21:38:27.3494274Z specified to be ``True``, all trainers will throw an error once one rank 2024-08-20T21:38:27.3494998Z runs out of inputs, allowing these errors to be caught and handled 2024-08-20T21:38:27.3495559Z according to application logic. 2024-08-20T21:38:27.3495818Z 2024-08-20T21:38:27.3496108Z Once all DDP processes have joined, the context manager will broadcast 2024-08-20T21:38:27.3496864Z the model corresponding to the last joined process to all processes to 2024-08-20T21:38:27.3497502Z ensure the model is the same across all processes 2024-08-20T21:38:27.3497961Z (which is guaranteed by DDP). 2024-08-20T21:38:27.3498210Z 2024-08-20T21:38:27.3498485Z To use this to enable training with uneven inputs across processes, 2024-08-20T21:38:27.3499215Z simply wrap this context manager around your training loop. No further 2024-08-20T21:38:27.3499870Z modifications to the model or data loading is required. 2024-08-20T21:38:27.3500256Z 2024-08-20T21:38:27.3500368Z .. warning:: 2024-08-20T21:38:27.3500827Z If the model or training loop this context manager is wrapped around 2024-08-20T21:38:27.3501518Z has additional distributed collective operations, such as 2024-08-20T21:38:27.3502209Z ``SyncBatchNorm`` in the model's forward pass, then the flag 2024-08-20T21:38:27.3503016Z ``throw_on_early_termination`` must be enabled. This is because this 2024-08-20T21:38:27.3503799Z context manager is not aware of non-DDP collective communication. 2024-08-20T21:38:27.3504456Z This flag will cause all ranks to throw when any one rank 2024-08-20T21:38:27.3505117Z exhausts inputs, allowing these errors to be caught and recovered 2024-08-20T21:38:27.3505676Z from across all ranks. 2024-08-20T21:38:27.3505897Z 2024-08-20T21:38:27.3505990Z Args: 2024-08-20T21:38:27.3506363Z divide_by_initial_world_size (bool): If ``True``, will divide 2024-08-20T21:38:27.3507033Z gradients by the initial ``world_size`` DDP training was launched 2024-08-20T21:38:27.3535624Z with. If ``False``, will compute the effective world size 2024-08-20T21:38:27.3536765Z (number of ranks that have not depleted their inputs yet) and 2024-08-20T21:38:27.3537775Z divide gradients by that during allreduce. Set 2024-08-20T21:38:27.3538769Z ``divide_by_initial_world_size=True`` to ensure every input 2024-08-20T21:38:27.3539981Z sample including the uneven inputs have equal weight in terms of 2024-08-20T21:38:27.3541180Z how much they contribute to the global gradient. This is 2024-08-20T21:38:27.3542184Z achieved by always dividing the gradient by the initial 2024-08-20T21:38:27.3542810Z ``world_size`` even when we encounter uneven inputs. If you set 2024-08-20T21:38:27.3543430Z this to ``False``, we divide the gradient by the remaining 2024-08-20T21:38:27.3544079Z number of nodes. This ensures parity with training on a smaller 2024-08-20T21:38:27.3544735Z ``world_size`` although it also means the uneven inputs would 2024-08-20T21:38:27.3545509Z contribute more towards the global gradient. Typically, you 2024-08-20T21:38:27.3546164Z would want to set this to ``True`` for cases where the last few 2024-08-20T21:38:27.3547180Z inputs of your training job are uneven. In extreme cases, where 2024-08-20T21:38:27.3548341Z there is a large discrepancy in the number of inputs, setting 2024-08-20T21:38:27.3549434Z this to ``False`` might provide better results. 2024-08-20T21:38:27.3550554Z enable (bool): Whether to enable uneven input detection or not. Pass 2024-08-20T21:38:27.3551803Z in ``enable=False`` to disable in cases where you know that 2024-08-20T21:38:27.3552984Z inputs are even across participating processes. Default is 2024-08-20T21:38:27.3553484Z ``True``. 2024-08-20T21:38:27.3553887Z throw_on_early_termination (bool): Whether to throw an error 2024-08-20T21:38:27.3554496Z or continue training when at least one rank has exhausted 2024-08-20T21:38:27.3555137Z inputs. If ``True``, will throw upon the first rank reaching end 2024-08-20T21:38:27.3555781Z of data. If ``False``, will continue training with a smaller 2024-08-20T21:38:27.3556418Z effective world size until all ranks are joined. Note that if 2024-08-20T21:38:27.3556985Z this flag is specified, then the flag 2024-08-20T21:38:27.3557502Z ``divide_by_initial_world_size`` would be ignored. Default 2024-08-20T21:38:27.3557971Z is ``False``. 2024-08-20T21:38:27.3558154Z 2024-08-20T21:38:27.3558159Z 2024-08-20T21:38:27.3558258Z Example:: 2024-08-20T21:38:27.3558407Z 2024-08-20T21:38:27.3558539Z >>> # xdoctest: +SKIP("Distributed") 2024-08-20T21:38:27.3558917Z >>> import torch 2024-08-20T21:38:27.3559225Z >>> import torch.distributed as dist 2024-08-20T21:38:27.3559597Z >>> import os 2024-08-20T21:38:27.3559906Z >>> import torch.multiprocessing as mp 2024-08-20T21:38:27.3560315Z >>> import torch.nn as nn 2024-08-20T21:38:27.3560677Z >>> # On each spawned worker 2024-08-20T21:38:27.3561037Z >>> def worker(rank): 2024-08-20T21:38:27.3561467Z >>> dist.init_process_group("nccl", rank=rank, world_size=2) 2024-08-20T21:38:27.3561993Z >>> torch.cuda.set_device(rank) 2024-08-20T21:38:27.3562598Z >>> model = nn.Linear(1, 1, bias=False).to(rank) 2024-08-20T21:38:27.3563161Z >>> model = torch.nn.parallel.DistributedDataParallel( 2024-08-20T21:38:27.3563750Z >>> model, device_ids=[rank], output_device=rank 2024-08-20T21:38:27.3564180Z >>> ) 2024-08-20T21:38:27.3564497Z >>> # Rank 1 gets one more input than rank 0. 2024-08-20T21:38:27.3565061Z >>> inputs = [torch.tensor([1]).float() for _ in range(10 + rank)] 2024-08-20T21:38:27.3565583Z >>> with model.join(): 2024-08-20T21:38:27.3565925Z >>> for _ in range(5): 2024-08-20T21:38:27.3566303Z >>> for inp in inputs: 2024-08-20T21:38:27.3566721Z >>> loss = model(inp).sum() 2024-08-20T21:38:27.3567142Z >>> loss.backward() 2024-08-20T21:38:27.3567780Z >>> # Without the join() API, the below synchronization will hang 2024-08-20T21:38:27.3568554Z >>> # blocking for rank 1's allreduce to complete. 2024-08-20T21:38:27.3569056Z >>> torch.cuda.synchronize(device=rank) 2024-08-20T21:38:27.3569387Z 2024-08-20T21:38:27.3569782Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:27.3570277Z 2024-08-20T21:38:27.3571402Z msg = Cannot scrape callname=DistributedDataParallel._register_fused_optim in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py line=2039. 2024-08-20T21:38:27.3572891Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:27.3573469Z 2024-08-20T21:38:27.3573901Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction. 2024-08-20T21:38:27.3574480Z 2024-08-20T21:38:27.3574841Z Registers an optimizer with DDP such that the optimization for a 2024-08-20T21:38:27.3575606Z parameter will run immediately when that parameter's gradient is 2024-08-20T21:38:27.3576292Z finished with reduction, instead of waiting for all parameters' 2024-08-20T21:38:27.3576992Z gradients to finish reduction. This can result in a training speedup 2024-08-20T21:38:27.3577732Z depending on your workload since the optimizer can run while gradient 2024-08-20T21:38:27.3578495Z reduction for other parameters are still ongoing. In addition, this has 2024-08-20T21:38:27.3579249Z the potential to reduce peak memory consumption during training, as it 2024-08-20T21:38:27.3580051Z only needs to load the per-parameter optimizer states of a single 2024-08-20T21:38:27.3580832Z parameter at a time, instead of loading all per-parameter optimizer 2024-08-20T21:38:27.3581379Z states at once. 2024-08-20T21:38:27.3581548Z 2024-08-20T21:38:27.3581636Z Args: 2024-08-20T21:38:27.3582045Z optim (Type): a ``torch.optim.Optimizer`` class to be registered 2024-08-20T21:38:27.3582591Z as a fused optimizer. 2024-08-20T21:38:27.3583098Z *args (Sequence[Any]): Arguments to forward to `optim`. 2024-08-20T21:38:27.3583749Z optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters 2024-08-20T21:38:27.3584488Z to optimize, similar to `params` argument of traditional `torch.optim` 2024-08-20T21:38:27.3585205Z Optimizers. If this is omitted, all DDP model parameters will be 2024-08-20T21:38:27.3585735Z optimized. 2024-08-20T21:38:27.3586169Z **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`. 2024-08-20T21:38:27.3586599Z 2024-08-20T21:38:27.3586704Z .. warning :: 2024-08-20T21:38:27.3587146Z _register_fused_optim should only be called once on a DDP instance, 2024-08-20T21:38:27.3587856Z and registering multiple fused optimizers for the same DDP model 2024-08-20T21:38:27.3588434Z is not currently supported. Please ping 2024-08-20T21:38:27.3589062Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-08-20T21:38:27.3589633Z for your use case. 2024-08-20T21:38:27.3589830Z 2024-08-20T21:38:27.3589929Z .. warning :: 2024-08-20T21:38:27.3590336Z _register_fused_optim and register_comm_hook currently do not 2024-08-20T21:38:27.3591093Z compose together, meaning that custom DDP communication hooks are 2024-08-20T21:38:27.3591737Z not supported with overlapped optimizers. Please ping 2024-08-20T21:38:27.3592411Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-08-20T21:38:27.3592979Z for your use case. 2024-08-20T21:38:27.3593177Z 2024-08-20T21:38:27.3593289Z .. warning :: 2024-08-20T21:38:27.3593730Z Gradient accumulation and DDP `no_sync` are currently not supported 2024-08-20T21:38:27.3594337Z with overlapped optimizer. Please ping 2024-08-20T21:38:27.3594942Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-08-20T21:38:27.3595497Z for your use case. 2024-08-20T21:38:27.3595713Z 2024-08-20T21:38:27.3595808Z Example:: 2024-08-20T21:38:27.3595958Z 2024-08-20T21:38:27.3596135Z >>> # xdoctest: +SKIP("No rendezvous handler") 2024-08-20T21:38:27.3596946Z >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...') 2024-08-20T21:38:27.3597774Z >>> net = torch.nn.parallel.DistributedDataParallel(model, pg) 2024-08-20T21:38:27.3598330Z >>> lr = 1e-2 2024-08-20T21:38:27.3598605Z >>> betas = (0.9, 0.99) 2024-08-20T21:38:27.3598945Z >>> eps = 1e-6 2024-08-20T21:38:27.3599418Z >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps) 2024-08-20T21:38:27.3600010Z >>> # Example with subset of parameters 2024-08-20T21:38:27.3600486Z >>> params_to_opt = [list(net.parameters())[0]] 2024-08-20T21:38:27.3600944Z >>> net._register_fused_optim( 2024-08-20T21:38:27.3601504Z ... torch.optim.Adam, lr, optim_params=params_to_opt, betas=betas, eps=eps 2024-08-20T21:38:27.3602079Z ... ) 2024-08-20T21:38:27.3602276Z 2024-08-20T21:38:27.3602687Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:27.3603170Z 2024-08-20T21:38:27.3768124Z msg = Cannot scrape callname=convert_conv2d_weight_memory_format in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/memory_format.py line=6. 2024-08-20T21:38:27.3770734Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:27.3772090Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``. 2024-08-20T21:38:27.3772996Z 2024-08-20T21:38:27.3773467Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2024-08-20T21:38:27.3774509Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2024-08-20T21:38:27.3775549Z This function is used to facilitate the computation to adopt NHWC kernels, which 2024-08-20T21:38:27.3777069Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2024-08-20T21:38:27.3778123Z 2024-08-20T21:38:27.3778306Z .. note:: 2024-08-20T21:38:27.3779088Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2024-08-20T21:38:27.3780522Z than the utility function ``convert_conv2d_weight_memory_format``. Any 2024-08-20T21:38:27.3781897Z layer with 4d weight will be affected by ``model.to``, which does not 2024-08-20T21:38:27.3782648Z necessarily benefit from conversion to specified ``memory_format``. 2024-08-20T21:38:27.3783407Z One place we are confident in is that NHWC(channels_last) conversion for 2024-08-20T21:38:27.3784188Z convolution in cuDNN, as it is beneficial to run convolution in NHWC, 2024-08-20T21:38:27.3784931Z even in cases where we have to apply permutation to input tensors. 2024-08-20T21:38:27.3785371Z 2024-08-20T21:38:27.3785676Z Hence our strategy here is to convert only the weight of convolution to 2024-08-20T21:38:27.3786298Z channels_last. This ensures that; 2024-08-20T21:38:27.3786885Z 1. Fast convolution kernels will be used, the benefit of which could 2024-08-20T21:38:27.3787642Z outweigh overhead of permutation (if input is not in the same format). 2024-08-20T21:38:27.3788588Z 2. No unnecessary permutations are applied on layers that do not benefit 2024-08-20T21:38:27.3789201Z from memory_format conversion. 2024-08-20T21:38:27.3789479Z 2024-08-20T21:38:27.3789801Z The optimal case is that, layers between convolution layers are channels 2024-08-20T21:38:27.3790577Z last compatible. Input tensor would be permuted to channels last when it 2024-08-20T21:38:27.3791363Z encounters the first convolution layer and stay in that memory format. 2024-08-20T21:38:27.3792148Z Hence following convolutions will not need to permute its input tensor. 2024-08-20T21:38:27.3792622Z 2024-08-20T21:38:27.3792933Z In case where a channels last incompatible layer is between convolution 2024-08-20T21:38:27.3793703Z layers, we need to permute the input tensor back to contiguous format 2024-08-20T21:38:27.3794474Z for that layer. The input tensor will go through the remaining layers in 2024-08-20T21:38:27.3795258Z contiguous format and be permuted to channels last when it encounters 2024-08-20T21:38:27.3796090Z another convolution layer. There's no point in propagating that 2024-08-20T21:38:27.3796820Z permutation to an earlier layer, as most layers are quite agnostic to 2024-08-20T21:38:27.3797390Z ``memory_format``. 2024-08-20T21:38:27.3797601Z 2024-08-20T21:38:27.3797913Z This claim might change when PyTorch supports fusion of permutation, as 2024-08-20T21:38:27.3798701Z there might have been a better spot to fuse the permutation other than 2024-08-20T21:38:27.3799316Z immediately before a convolution. 2024-08-20T21:38:27.3799607Z 2024-08-20T21:38:27.3799700Z Args: 2024-08-20T21:38:27.3800224Z module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container 2024-08-20T21:38:27.3800807Z ``nn.Module`` 2024-08-20T21:38:27.3801275Z memory_format: user specified ``memory_format``, 2024-08-20T21:38:27.3801858Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2024-08-20T21:38:27.3802276Z 2024-08-20T21:38:27.3802371Z Returns: 2024-08-20T21:38:27.3802719Z The original module with updated ``nn.Conv2d`` 2024-08-20T21:38:27.3803065Z 2024-08-20T21:38:27.3803160Z Example: 2024-08-20T21:38:27.3803507Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-20T21:38:27.3804050Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2024-08-20T21:38:27.3804724Z >>> input = torch.randint(1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda") 2024-08-20T21:38:27.3805343Z >>> model = nn.Sequential( 2024-08-20T21:38:27.3805750Z >>> nn.Conv2d(8, 4, 3)).cuda().half() 2024-08-20T21:38:27.3806175Z >>> # This is identical to: 2024-08-20T21:38:27.3806761Z >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2024-08-20T21:38:27.3807714Z >>> model = nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2024-08-20T21:38:27.3808347Z >>> out = model(input) 2024-08-20T21:38:27.3808674Z 2024-08-20T21:38:27.3809222Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:27.3809703Z 2024-08-20T21:38:27.3810737Z msg = Cannot scrape callname=convert_conv3d_weight_memory_format in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/memory_format.py line=81. 2024-08-20T21:38:27.3812120Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:27.3812890Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format`` 2024-08-20T21:38:27.3813690Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2024-08-20T21:38:27.3814600Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2024-08-20T21:38:27.3815488Z This function is used to facilitate the computation to adopt NHWC kernels, which 2024-08-20T21:38:27.3816493Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2024-08-20T21:38:27.3817078Z 2024-08-20T21:38:27.3817194Z .. note:: 2024-08-20T21:38:27.3817676Z Calling ``model.to(memory_format=torch.channels_last_3d)`` is more aggressive 2024-08-20T21:38:27.3818472Z than the utility function ``convert_conv3d_weight_memory_format``. Any 2024-08-20T21:38:27.3819232Z layer with 4d weight will be affected by ``model.to``, which does not 2024-08-20T21:38:27.3819974Z necessarily benefit from conversion to specified ``memory_format``. 2024-08-20T21:38:27.3820761Z One place we are confident in is that NDHWC(channels_last_3d) conversion for 2024-08-20T21:38:27.3821572Z convolution in cuDNN, as it is beneficial to run convolution in NDHWC, 2024-08-20T21:38:27.3822320Z even in cases where we have to apply permutation to input tensors. 2024-08-20T21:38:27.3822763Z 2024-08-20T21:38:27.3823071Z Hence our strategy here is to convert only the weight of convolution to 2024-08-20T21:38:27.3823703Z channels_last_3d. This ensures that; 2024-08-20T21:38:27.3824303Z 1. Fast convolution kernels will be used, the benefit of which could 2024-08-20T21:38:27.3825048Z outweigh overhead of permutation (if input is not in the same format). 2024-08-20T21:38:27.3825828Z 2. No unnecessary permutations are applied on layers that do not benefit 2024-08-20T21:38:27.3826440Z from memory_format conversion. 2024-08-20T21:38:27.3826718Z 2024-08-20T21:38:27.3827039Z The optimal case is that, layers between convolution layers are channels 2024-08-20T21:38:27.3827896Z last compatible. Input tensor would be permuted to channels last when it 2024-08-20T21:38:27.3828680Z encounters the first convolution layer and stay in that memory format. 2024-08-20T21:38:27.3829460Z Hence following convolutions will not need to permute its input tensor. 2024-08-20T21:38:27.3829940Z 2024-08-20T21:38:27.3830246Z In case where a channels last incompatible layer is between convolution 2024-08-20T21:38:27.3831016Z layers, we need to permute the input tensor back to contiguous format 2024-08-20T21:38:27.3831787Z for that layer. The input tensor will go through the remaining layers in 2024-08-20T21:38:27.3832558Z contiguous format and be permuted to channels last when it encounters 2024-08-20T21:38:27.3833382Z another convolution layer. There's no point in propagating that 2024-08-20T21:38:27.3834115Z permutation to an earlier layer, as most layers are quite agnostic to 2024-08-20T21:38:27.3834689Z ``memory_format``. 2024-08-20T21:38:27.3834901Z 2024-08-20T21:38:27.3835218Z This claim might change when PyTorch supports fusion of permutation, as 2024-08-20T21:38:27.3835999Z there might have been a better spot to fuse the permutation other than 2024-08-20T21:38:27.3836615Z immediately before a convolution. 2024-08-20T21:38:27.3836914Z 2024-08-20T21:38:27.3837006Z Args: 2024-08-20T21:38:27.3837449Z module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container 2024-08-20T21:38:27.3838029Z ``nn.Module`` 2024-08-20T21:38:27.3838483Z memory_format: user specified ``memory_format``, 2024-08-20T21:38:27.3839078Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2024-08-20T21:38:27.3839480Z 2024-08-20T21:38:27.3839587Z Returns: 2024-08-20T21:38:27.3839916Z The original module with updated ``nn.Conv3d`` 2024-08-20T21:38:27.3840270Z 2024-08-20T21:38:27.3840364Z Example: 2024-08-20T21:38:27.3840709Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-20T21:38:27.3841256Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2024-08-20T21:38:27.3841944Z >>> input = torch.randint(1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda") 2024-08-20T21:38:27.3842563Z >>> model = nn.Sequential( 2024-08-20T21:38:27.3843032Z >>> nn.Conv3d(8, 4, 3)).cuda().half() 2024-08-20T21:38:27.3843450Z >>> # This is identical to: 2024-08-20T21:38:27.3844050Z >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2024-08-20T21:38:27.3844918Z >>> model = nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2024-08-20T21:38:27.3845551Z >>> out = model(input) 2024-08-20T21:38:27.3845875Z 2024-08-20T21:38:27.3846402Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:27.3847086Z 2024-08-20T21:38:27.3993146Z msg = Cannot scrape callname=random_structured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=936. 2024-08-20T21:38:27.3995525Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:27.3996674Z Prune tensor by removing random channels along the specified dimension. 2024-08-20T21:38:27.3997280Z 2024-08-20T21:38:27.3997644Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2024-08-20T21:38:27.3998747Z by removing the specified ``amount`` of (currently unpruned) channels 2024-08-20T21:38:27.3999806Z along the specified ``dim`` selected at random. 2024-08-20T21:38:27.4000916Z Modifies module in place (and also return the modified module) 2024-08-20T21:38:27.4001836Z by: 2024-08-20T21:38:27.4002102Z 2024-08-20T21:38:27.4002652Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-08-20T21:38:27.4003408Z binary mask applied to the parameter ``name`` by the pruning method. 2024-08-20T21:38:27.4004155Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-08-20T21:38:27.4005162Z original (unpruned) parameter is stored in a new parameter named 2024-08-20T21:38:27.4005888Z ``name+'_orig'``. 2024-08-20T21:38:27.4006155Z 2024-08-20T21:38:27.4006310Z Args: 2024-08-20T21:38:27.4006944Z module (nn.Module): module containing the tensor to prune 2024-08-20T21:38:27.4008144Z name (str): parameter name within ``module`` on which pruning 2024-08-20T21:38:27.4009096Z will act. 2024-08-20T21:38:27.4009846Z amount (int or float): quantity of parameters to prune. 2024-08-20T21:38:27.4010478Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-08-20T21:38:27.4011153Z fraction of parameters to prune. If ``int``, it represents the 2024-08-20T21:38:27.4011919Z absolute number of parameters to prune. 2024-08-20T21:38:27.4012558Z dim (int): index of the dim along which we define channels to prune. 2024-08-20T21:38:27.4013344Z 2024-08-20T21:38:27.4013509Z Returns: 2024-08-20T21:38:27.4014353Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-08-20T21:38:27.4015205Z 2024-08-20T21:38:27.4015375Z Examples: 2024-08-20T21:38:27.4015883Z >>> # xdoctest: +SKIP 2024-08-20T21:38:27.4016556Z >>> m = prune.random_structured( 2024-08-20T21:38:27.4017126Z ... nn.Linear(5, 3), 'weight', amount=3, dim=1 2024-08-20T21:38:27.4017562Z ... ) 2024-08-20T21:38:27.4017964Z >>> columns_pruned = int(sum(torch.sum(m.weight, dim=0) == 0)) 2024-08-20T21:38:27.4018474Z >>> print(columns_pruned) 2024-08-20T21:38:27.4018820Z 3 2024-08-20T21:38:27.4019056Z 2024-08-20T21:38:27.4019576Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:27.4020077Z 2024-08-20T21:38:27.4020953Z msg = Cannot scrape callname=ln_structured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=977. 2024-08-20T21:38:27.4022192Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:27.4023206Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension. 2024-08-20T21:38:27.4023790Z 2024-08-20T21:38:27.4024261Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2024-08-20T21:38:27.4025010Z by removing the specified ``amount`` of (currently unpruned) channels 2024-08-20T21:38:27.4025763Z along the specified ``dim`` with the lowest L\ ``n``-norm. 2024-08-20T21:38:27.4026420Z Modifies module in place (and also return the modified module) 2024-08-20T21:38:27.4026938Z by: 2024-08-20T21:38:27.4027076Z 2024-08-20T21:38:27.4027435Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-08-20T21:38:27.4028176Z binary mask applied to the parameter ``name`` by the pruning method. 2024-08-20T21:38:27.4028914Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-08-20T21:38:27.4029627Z original (unpruned) parameter is stored in a new parameter named 2024-08-20T21:38:27.4030210Z ``name+'_orig'``. 2024-08-20T21:38:27.4030415Z 2024-08-20T21:38:27.4030519Z Args: 2024-08-20T21:38:27.4030890Z module (nn.Module): module containing the tensor to prune 2024-08-20T21:38:27.4031538Z name (str): parameter name within ``module`` on which pruning 2024-08-20T21:38:27.4032056Z will act. 2024-08-20T21:38:27.4032478Z amount (int or float): quantity of parameters to prune. 2024-08-20T21:38:27.4033109Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-08-20T21:38:27.4033781Z fraction of parameters to prune. If ``int``, it represents the 2024-08-20T21:38:27.4034377Z absolute number of parameters to prune. 2024-08-20T21:38:27.4035038Z n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid 2024-08-20T21:38:27.4035677Z entries for argument ``p`` in :func:`torch.norm`. 2024-08-20T21:38:27.4036392Z dim (int): index of the dim along which we define channels to prune. 2024-08-20T21:38:27.4037134Z importance_scores (torch.Tensor): tensor of importance scores (of same 2024-08-20T21:38:27.4037867Z shape as module parameter) used to compute mask for pruning. 2024-08-20T21:38:27.4038599Z The values in this tensor indicate the importance of the corresponding 2024-08-20T21:38:27.4039228Z elements in the parameter being pruned. 2024-08-20T21:38:27.4039874Z If unspecified or None, the module parameter will be used in its place. 2024-08-20T21:38:27.4040354Z 2024-08-20T21:38:27.4040468Z Returns: 2024-08-20T21:38:27.4040917Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-08-20T21:38:27.4041394Z 2024-08-20T21:38:27.4041491Z Examples: 2024-08-20T21:38:27.4041808Z >>> from torch.nn.utils import prune 2024-08-20T21:38:27.4042246Z >>> m = prune.ln_structured( 2024-08-20T21:38:27.4042854Z ... nn.Conv2d(5, 3, 2), 'weight', amount=0.3, dim=1, n=float('-inf') 2024-08-20T21:38:27.4043373Z ... ) 2024-08-20T21:38:27.4043624Z 2024-08-20T21:38:27.4044139Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:27.4044640Z 2024-08-20T21:38:27.4045500Z msg = Cannot scrape callname=global_unstructured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=1024. 2024-08-20T21:38:27.4046995Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:27.4047589Z 2024-08-20T21:38:27.4048148Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``. 2024-08-20T21:38:27.4048876Z 2024-08-20T21:38:27.4048999Z Modifies modules in place by: 2024-08-20T21:38:27.4049254Z 2024-08-20T21:38:27.4049614Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-08-20T21:38:27.4050356Z binary mask applied to the parameter ``name`` by the pruning method. 2024-08-20T21:38:27.4051074Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-08-20T21:38:27.4051786Z original (unpruned) parameter is stored in a new parameter named 2024-08-20T21:38:27.4052478Z ``name+'_orig'``. 2024-08-20T21:38:27.4052666Z 2024-08-20T21:38:27.4052756Z Args: 2024-08-20T21:38:27.4053155Z parameters (Iterable of (module, name) tuples): parameters of 2024-08-20T21:38:27.4053838Z the model to prune in a global fashion, i.e. by aggregating all 2024-08-20T21:38:27.4054531Z weights prior to deciding which ones to prune. module must be of 2024-08-20T21:38:27.4055167Z type :class:`nn.Module`, and name must be a string. 2024-08-20T21:38:27.4055819Z pruning_method (function): a valid pruning function from this module, 2024-08-20T21:38:27.4056499Z or a custom one implemented by the user that satisfies the 2024-08-20T21:38:27.4057279Z implementation guidelines and has ``PRUNING_TYPE='unstructured'``. 2024-08-20T21:38:27.4058033Z importance_scores (dict): a dictionary mapping (module, name) tuples to 2024-08-20T21:38:27.4058857Z the corresponding parameter's importance scores tensor. The tensor 2024-08-20T21:38:27.4059599Z should be the same shape as the parameter, and is used for computing 2024-08-20T21:38:27.4060174Z mask for pruning. 2024-08-20T21:38:27.4060674Z If unspecified or None, the parameter will be used in place of its 2024-08-20T21:38:27.4061225Z importance scores. 2024-08-20T21:38:27.4061602Z kwargs: other keyword arguments such as: 2024-08-20T21:38:27.4062188Z amount (int or float): quantity of parameters to prune across the 2024-08-20T21:38:27.4062735Z specified parameters. 2024-08-20T21:38:27.4063213Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-08-20T21:38:27.4063879Z fraction of parameters to prune. If ``int``, it represents the 2024-08-20T21:38:27.4064538Z absolute number of parameters to prune. 2024-08-20T21:38:27.4064873Z 2024-08-20T21:38:27.4064963Z Raises: 2024-08-20T21:38:27.4065360Z TypeError: if ``PRUNING_TYPE != 'unstructured'`` 2024-08-20T21:38:27.4065714Z 2024-08-20T21:38:27.4065802Z Note: 2024-08-20T21:38:27.4066290Z Since global structured pruning doesn't make much sense unless the 2024-08-20T21:38:27.4067008Z norm is normalized by the size of the parameter, we now limit the 2024-08-20T21:38:27.4067640Z scope of global pruning to unstructured methods. 2024-08-20T21:38:27.4067988Z 2024-08-20T21:38:27.4068083Z Examples: 2024-08-20T21:38:27.4068378Z >>> from torch.nn.utils import prune 2024-08-20T21:38:27.4068824Z >>> from collections import OrderedDict 2024-08-20T21:38:27.4069260Z >>> net = nn.Sequential(OrderedDict([ 2024-08-20T21:38:27.4069736Z ... ('first', nn.Linear(10, 4)), 2024-08-20T21:38:27.4070193Z ... ('second', nn.Linear(4, 1)), 2024-08-20T21:38:27.4070555Z ... ])) 2024-08-20T21:38:27.4070827Z >>> parameters_to_prune = ( 2024-08-20T21:38:27.4071240Z ... (net.first, 'weight'), 2024-08-20T21:38:27.4071646Z ... (net.second, 'weight'), 2024-08-20T21:38:27.4072000Z ... ) 2024-08-20T21:38:27.4072276Z >>> prune.global_unstructured( 2024-08-20T21:38:27.4072647Z ... parameters_to_prune, 2024-08-20T21:38:27.4073062Z ... pruning_method=prune.L1Unstructured, 2024-08-20T21:38:27.4073489Z ... amount=10, 2024-08-20T21:38:27.4073761Z ... ) 2024-08-20T21:38:27.4074198Z >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0)) 2024-08-20T21:38:27.4074759Z tensor(10) 2024-08-20T21:38:27.4074920Z 2024-08-20T21:38:27.4074924Z 2024-08-20T21:38:27.4075314Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:27.4075804Z 2024-08-20T21:38:27.4076639Z msg = Cannot scrape callname=custom_from_mask in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=1143. 2024-08-20T21:38:27.4077906Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:27.4079025Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``. 2024-08-20T21:38:27.4079804Z 2024-08-20T21:38:27.4080080Z Modifies module in place (and also return the modified module) by: 2024-08-20T21:38:27.4080534Z 2024-08-20T21:38:27.4080893Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-08-20T21:38:27.4081641Z binary mask applied to the parameter ``name`` by the pruning method. 2024-08-20T21:38:27.4082370Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-08-20T21:38:27.4083088Z original (unpruned) parameter is stored in a new parameter named 2024-08-20T21:38:27.4083663Z ``name+'_orig'``. 2024-08-20T21:38:27.4083863Z 2024-08-20T21:38:27.4083955Z Args: 2024-08-20T21:38:27.4084344Z module (nn.Module): module containing the tensor to prune 2024-08-20T21:38:27.4084988Z name (str): parameter name within ``module`` on which pruning 2024-08-20T21:38:27.4085489Z will act. 2024-08-20T21:38:27.4085915Z mask (Tensor): binary mask to be applied to the parameter. 2024-08-20T21:38:27.4086433Z 2024-08-20T21:38:27.4086530Z Returns: 2024-08-20T21:38:27.4086997Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-08-20T21:38:27.4087544Z 2024-08-20T21:38:27.4087643Z Examples: 2024-08-20T21:38:27.4087961Z >>> from torch.nn.utils import prune 2024-08-20T21:38:27.4088408Z >>> m = prune.custom_from_mask( 2024-08-20T21:38:27.4089012Z ... nn.Linear(5, 3), name='bias', mask=torch.tensor([0, 1, 0]) 2024-08-20T21:38:27.4089514Z ... ) 2024-08-20T21:38:27.4089788Z >>> print(m.bias_mask) 2024-08-20T21:38:27.4090124Z tensor([0., 1., 0.]) 2024-08-20T21:38:27.4090357Z 2024-08-20T21:38:27.4090444Z 2024-08-20T21:38:27.4091053Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:27.4091540Z 2024-08-20T21:38:27.6044203Z msg = Cannot scrape callname=AveragedModel in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py line=106. 2024-08-20T21:38:27.6046104Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:27.6047695Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA). 2024-08-20T21:38:27.6048792Z 2024-08-20T21:38:27.6049325Z Stochastic Weight Averaging was proposed in `Averaging Weights Leads to 2024-08-20T21:38:27.6050598Z Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii 2024-08-20T21:38:27.6051733Z Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson 2024-08-20T21:38:27.6052381Z (UAI 2018). 2024-08-20T21:38:27.6052550Z 2024-08-20T21:38:27.6052853Z Exponential Moving Average is a variation of `Polyak averaging`_, 2024-08-20T21:38:27.6053671Z but using exponential weights instead of equal weights across iterations. 2024-08-20T21:38:27.6054229Z 2024-08-20T21:38:27.6054591Z AveragedModel class creates a copy of the provided module :attr:`model` 2024-08-20T21:38:27.6055508Z on the device :attr:`device` and allows to compute running averages of the 2024-08-20T21:38:27.6056373Z parameters of the :attr:`model`. 2024-08-20T21:38:27.6056847Z 2024-08-20T21:38:27.6057007Z Args: 2024-08-20T21:38:27.6057654Z model (torch.nn.Module): model to use with SWA/EMA 2024-08-20T21:38:27.6058808Z device (torch.device, optional): if provided, the averaged model will be 2024-08-20T21:38:27.6059752Z stored on the :attr:`device` 2024-08-20T21:38:27.6060385Z avg_fn (function, optional): the averaging function used to update 2024-08-20T21:38:27.6061171Z parameters; the function must take in the current value of the 2024-08-20T21:38:27.6061960Z :class:`AveragedModel` parameter, the current value of :attr:`model` 2024-08-20T21:38:27.6062747Z parameter, and the number of models already averaged; if None, 2024-08-20T21:38:27.6063447Z an equally weighted average is used (default: None) 2024-08-20T21:38:27.6064425Z multi_avg_fn (function, optional): the averaging function used to update 2024-08-20T21:38:27.6065296Z parameters inplace; the function must take in the current values of the 2024-08-20T21:38:27.6066209Z :class:`AveragedModel` parameters as a list, the current values of :attr:`model` 2024-08-20T21:38:27.6067098Z parameters as a list, and the number of models already averaged; if None, 2024-08-20T21:38:27.6067857Z an equally weighted average is used (default: None) 2024-08-20T21:38:27.6068570Z use_buffers (bool): if ``True``, it will compute running averages for 2024-08-20T21:38:27.6069385Z both the parameters and the buffers of the model. (default: ``False``) 2024-08-20T21:38:27.6069886Z 2024-08-20T21:38:27.6070001Z Example: 2024-08-20T21:38:27.6070339Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:38:27.6070891Z >>> loader, optimizer, model, loss_fn = ... 2024-08-20T21:38:27.6071500Z >>> swa_model = torch.optim.swa_utils.AveragedModel(model) 2024-08-20T21:38:27.6072244Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 2024-08-20T21:38:27.6072859Z >>> T_max=300) 2024-08-20T21:38:27.6073332Z >>> swa_start = 160 2024-08-20T21:38:27.6073798Z >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05) 2024-08-20T21:38:27.6074263Z >>> for i in range(300): 2024-08-20T21:38:27.6074702Z >>> for input, target in loader: 2024-08-20T21:38:27.6075143Z >>> optimizer.zero_grad() 2024-08-20T21:38:27.6075667Z >>> loss_fn(model(input), target).backward() 2024-08-20T21:38:27.6076291Z >>> optimizer.step() 2024-08-20T21:38:27.6076686Z >>> if i > swa_start: 2024-08-20T21:38:27.6077162Z >>> swa_model.update_parameters(model) 2024-08-20T21:38:27.6077673Z >>> swa_scheduler.step() 2024-08-20T21:38:27.6078065Z >>> else: 2024-08-20T21:38:27.6078435Z >>> scheduler.step() 2024-08-20T21:38:27.6078787Z >>> 2024-08-20T21:38:27.6079146Z >>> # Update bn statistics for the swa_model at the end 2024-08-20T21:38:27.6079785Z >>> torch.optim.swa_utils.update_bn(loader, swa_model) 2024-08-20T21:38:27.6080155Z 2024-08-20T21:38:27.6080600Z You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters. 2024-08-20T21:38:27.6081451Z If no averaging function is provided, the default is to compute 2024-08-20T21:38:27.6082235Z equally-weighted average of the weights (SWA). 2024-08-20T21:38:27.6082594Z 2024-08-20T21:38:27.6082691Z Example: 2024-08-20T21:38:27.6083089Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:38:27.6083678Z >>> # Compute exponential moving averages of the weights and buffers 2024-08-20T21:38:27.6084346Z >>> ema_model = torch.optim.swa_utils.AveragedModel(model, 2024-08-20T21:38:27.6085036Z >>> torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True) 2024-08-20T21:38:27.6085490Z 2024-08-20T21:38:27.6085606Z .. note:: 2024-08-20T21:38:27.6086074Z When using SWA/EMA with models containing Batch Normalization you may 2024-08-20T21:38:27.6086881Z need to update the activation statistics for Batch Normalization. 2024-08-20T21:38:27.6087736Z This can be done either by using the :meth:`torch.optim.swa_utils.update_bn` 2024-08-20T21:38:27.6088560Z or by setting :attr:`use_buffers` to `True`. The first approach updates the 2024-08-20T21:38:27.6089459Z statistics in a post-training step by passing data through the model. The 2024-08-20T21:38:27.6090281Z second does it during the parameter update phase by averaging all buffers. 2024-08-20T21:38:27.6091091Z Empirical evidence has shown that updating the statistics in normalization 2024-08-20T21:38:27.6091890Z layers increases accuracy, but you may wish to empirically test which 2024-08-20T21:38:27.6092645Z approach yields the best results in your problem. 2024-08-20T21:38:27.6093001Z 2024-08-20T21:38:27.6093101Z .. note:: 2024-08-20T21:38:27.6093693Z :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model. 2024-08-20T21:38:27.6094221Z 2024-08-20T21:38:27.6094328Z .. note:: 2024-08-20T21:38:27.6094745Z When :meth:`update_parameters` is called for the first time (i.e. 2024-08-20T21:38:27.6095428Z :attr:`n_averaged` is `0`) the parameters of `model` are copied 2024-08-20T21:38:27.6096116Z to the parameters of :class:`AveragedModel`. For every subsequent 2024-08-20T21:38:27.6096822Z call of :meth:`update_parameters` the function `avg_fn` is used 2024-08-20T21:38:27.6097355Z to update the parameters. 2024-08-20T21:38:27.6097621Z 2024-08-20T21:38:27.6097912Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2024-08-20T21:38:27.6098521Z https://arxiv.org/abs/1803.05407 2024-08-20T21:38:27.6099121Z .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should 2024-08-20T21:38:27.6099704Z Average: 2024-08-20T21:38:27.6100015Z https://arxiv.org/abs/1806.05594 2024-08-20T21:38:27.6100754Z .. _SWALP: Stochastic Weight Averaging in Low-Precision Training: 2024-08-20T21:38:27.6101321Z https://arxiv.org/abs/1904.11943 2024-08-20T21:38:27.6101981Z .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That 2024-08-20T21:38:27.6102542Z Generalizes Well: 2024-08-20T21:38:27.6102896Z https://arxiv.org/abs/2001.02312 2024-08-20T21:38:27.6103302Z .. _Polyak averaging: 2024-08-20T21:38:27.6103871Z https://paperswithcode.com/method/polyak-averaging 2024-08-20T21:38:27.6104344Z 2024-08-20T21:38:27.6104877Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:27.6105360Z 2024-08-20T21:38:27.6106138Z msg = Cannot scrape callname=SWALR in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py line=357. 2024-08-20T21:38:27.6107434Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:27.6108217Z Anneals the learning rate in each parameter group to a fixed value. 2024-08-20T21:38:27.6108657Z 2024-08-20T21:38:27.6108975Z This learning rate scheduler is meant to be used with Stochastic Weight 2024-08-20T21:38:27.6109720Z Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`). 2024-08-20T21:38:27.6110176Z 2024-08-20T21:38:27.6110269Z Args: 2024-08-20T21:38:27.6110640Z optimizer (torch.optim.Optimizer): wrapped optimizer 2024-08-20T21:38:27.6111303Z swa_lrs (float or list): the learning rate value for all param groups 2024-08-20T21:38:27.6111922Z together or separately for each group. 2024-08-20T21:38:27.6112512Z annealing_epochs (int): number of epochs in the annealing phase 2024-08-20T21:38:27.6113044Z (default: 10) 2024-08-20T21:38:27.6113539Z annealing_strategy (str): "cos" or "linear"; specifies the annealing 2024-08-20T21:38:27.6114267Z strategy: "cos" for cosine annealing, "linear" for linear annealing 2024-08-20T21:38:27.6114830Z (default: "cos") 2024-08-20T21:38:27.6115353Z last_epoch (int): the index of the last epoch (default: -1) 2024-08-20T21:38:27.6115763Z 2024-08-20T21:38:27.6116002Z The :class:`SWALR` scheduler can be used together with other 2024-08-20T21:38:27.6116705Z schedulers to switch to a constant learning rate late in the training 2024-08-20T21:38:27.6117271Z as in the example below. 2024-08-20T21:38:27.6117512Z 2024-08-20T21:38:27.6117610Z Example: 2024-08-20T21:38:27.6117940Z >>> # xdoctest: +SKIP("Undefined variables") 2024-08-20T21:38:27.6118404Z >>> loader, optimizer, model = ... 2024-08-20T21:38:27.6118845Z >>> lr_lambda = lambda epoch: 0.9 2024-08-20T21:38:27.6119508Z >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, 2024-08-20T21:38:27.6120075Z >>> lr_lambda=lr_lambda) 2024-08-20T21:38:27.6120573Z >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, 2024-08-20T21:38:27.6121191Z >>> anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05) 2024-08-20T21:38:27.6121687Z >>> swa_start = 160 2024-08-20T21:38:27.6122029Z >>> for i in range(300): 2024-08-20T21:38:27.6122422Z >>> for input, target in loader: 2024-08-20T21:38:27.6122848Z >>> optimizer.zero_grad() 2024-08-20T21:38:27.6123320Z >>> loss_fn(model(input), target).backward() 2024-08-20T21:38:27.6123788Z >>> optimizer.step() 2024-08-20T21:38:27.6124162Z >>> if i > swa_start: 2024-08-20T21:38:27.6124549Z >>> swa_scheduler.step() 2024-08-20T21:38:27.6124933Z >>> else: 2024-08-20T21:38:27.6125234Z >>> scheduler.step() 2024-08-20T21:38:27.6125517Z 2024-08-20T21:38:27.6125806Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2024-08-20T21:38:27.6126401Z https://arxiv.org/abs/1803.05407 2024-08-20T21:38:27.6126781Z 2024-08-20T21:38:27.6127306Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:27.6127905Z 2024-08-20T21:38:27.9958324Z msg = Cannot scrape callname=assert_close in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_comparison.py line=1274. 2024-08-20T21:38:27.9961627Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:27.9962828Z Asserts that ``actual`` and ``expected`` are close. 2024-08-20T21:38:27.9963791Z 2024-08-20T21:38:27.9964930Z If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if 2024-08-20T21:38:27.9966143Z 2024-08-20T21:38:27.9966331Z .. math:: 2024-08-20T21:38:27.9966625Z 2024-08-20T21:38:27.9967768Z \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert 2024-08-20T21:38:27.9969005Z 2024-08-20T21:38:27.9970004Z Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are 2024-08-20T21:38:27.9971575Z only considered equal to each other if ``equal_nan`` is ``True``. 2024-08-20T21:38:27.9972284Z 2024-08-20T21:38:27.9972785Z In addition, they are only considered close if they have the same 2024-08-20T21:38:27.9973590Z 2024-08-20T21:38:27.9974189Z - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``), 2024-08-20T21:38:27.9975363Z - ``dtype`` (if ``check_dtype`` is ``True``), 2024-08-20T21:38:27.9976068Z - ``layout`` (if ``check_layout`` is ``True``), and 2024-08-20T21:38:27.9976604Z - stride (if ``check_stride`` is ``True``). 2024-08-20T21:38:27.9976929Z 2024-08-20T21:38:27.9977358Z If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed. 2024-08-20T21:38:27.9977960Z 2024-08-20T21:38:27.9978482Z If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are 2024-08-20T21:38:27.9979660Z checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR, 2024-08-20T21:38:27.9980618Z or ``ccol_indices`` and ``row_indices`` for CSC and BSC layouts, respectively, 2024-08-20T21:38:27.9981630Z are always checked for equality whereas the values are checked for closeness according to the definition above. 2024-08-20T21:38:27.9982320Z 2024-08-20T21:38:27.9982733Z If ``actual`` and ``expected`` are quantized, they are considered close if they have the same 2024-08-20T21:38:27.9983784Z :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the 2024-08-20T21:38:27.9984536Z definition above. 2024-08-20T21:38:27.9984938Z 2024-08-20T21:38:27.9985470Z ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which 2024-08-20T21:38:27.9986698Z :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types 2024-08-20T21:38:27.9987940Z have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s 2024-08-20T21:38:27.9989250Z or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all 2024-08-20T21:38:27.9990247Z their elements are considered close according to the above definition. 2024-08-20T21:38:27.9990712Z 2024-08-20T21:38:27.9990827Z .. note:: 2024-08-20T21:38:27.9990988Z 2024-08-20T21:38:27.9991447Z Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e. 2024-08-20T21:38:27.9992625Z :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus, 2024-08-20T21:38:27.9993619Z Python scalars of different types can be checked, but require ``check_dtype=False``. 2024-08-20T21:38:27.9994159Z 2024-08-20T21:38:27.9994252Z Args: 2024-08-20T21:38:27.9994525Z actual (Any): Actual input. 2024-08-20T21:38:27.9994927Z expected (Any): Expected input. 2024-08-20T21:38:27.9995703Z allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types 2024-08-20T21:38:27.9996543Z are allowed. Otherwise type equality is required. 2024-08-20T21:38:27.9997404Z rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default 2024-08-20T21:38:27.9998496Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2024-08-20T21:38:27.9999515Z atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default 2024-08-20T21:38:28.0000545Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2024-08-20T21:38:28.0001429Z equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal. 2024-08-20T21:38:28.0002352Z check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same 2024-08-20T21:38:28.0003244Z :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different 2024-08-20T21:38:28.0004156Z :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared. 2024-08-20T21:38:28.0005124Z check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this 2024-08-20T21:38:28.0006332Z check is disabled, tensors with different ``dtype``'s are promoted to a common ``dtype`` (according to 2024-08-20T21:38:28.0007179Z :func:`torch.promote_types`) before being compared. 2024-08-20T21:38:28.0008169Z check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this 2024-08-20T21:38:28.0009384Z check is disabled, tensors with different ``layout``'s are converted to strided tensors before being 2024-08-20T21:38:28.0010096Z compared. 2024-08-20T21:38:28.0010788Z check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride. 2024-08-20T21:38:28.0011933Z msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during 2024-08-20T21:38:28.0013067Z the comparison. Can also passed as callable in which case it will be called with the generated message and 2024-08-20T21:38:28.0013870Z should return the new message. 2024-08-20T21:38:28.0014163Z 2024-08-20T21:38:28.0014267Z Raises: 2024-08-20T21:38:28.0014720Z ValueError: If no :class:`torch.Tensor` can be constructed from an input. 2024-08-20T21:38:28.0015492Z ValueError: If only ``rtol`` or ``atol`` is specified. 2024-08-20T21:38:28.0016298Z AssertionError: If corresponding inputs are not Python scalars and are not directly related. 2024-08-20T21:38:28.0017381Z AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have 2024-08-20T21:38:28.0018136Z different types. 2024-08-20T21:38:28.0018958Z AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match. 2024-08-20T21:38:28.0020214Z AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match. 2024-08-20T21:38:28.0021286Z AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`. 2024-08-20T21:38:28.0022271Z AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same 2024-08-20T21:38:28.0022979Z :attr:`~torch.Tensor.layout`. 2024-08-20T21:38:28.0023566Z AssertionError: If only one of corresponding tensors is quantized. 2024-08-20T21:38:28.0024620Z AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s. 2024-08-20T21:38:28.0025682Z AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same 2024-08-20T21:38:28.0026390Z :attr:`~torch.Tensor.device`. 2024-08-20T21:38:28.0027125Z AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``. 2024-08-20T21:38:28.0028226Z AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride. 2024-08-20T21:38:28.0029420Z AssertionError: If the values of corresponding tensors are not close according to the definition above. 2024-08-20T21:38:28.0030065Z 2024-08-20T21:38:28.0030681Z The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching 2024-08-20T21:38:28.0031593Z ``dtype``'s, the maximum of both tolerances is used. 2024-08-20T21:38:28.0031963Z 2024-08-20T21:38:28.0032176Z +---------------------------+------------+----------+ 2024-08-20T21:38:28.0032666Z | ``dtype`` | ``rtol`` | ``atol`` | 2024-08-20T21:38:28.0033131Z +===========================+============+==========+ 2024-08-20T21:38:28.0033690Z | :attr:`~torch.float16` | ``1e-3`` | ``1e-5`` | 2024-08-20T21:38:28.0034239Z +---------------------------+------------+----------+ 2024-08-20T21:38:28.0034785Z | :attr:`~torch.bfloat16` | ``1.6e-2`` | ``1e-5`` | 2024-08-20T21:38:28.0035339Z +---------------------------+------------+----------+ 2024-08-20T21:38:28.0035891Z | :attr:`~torch.float32` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:38:28.0036427Z +---------------------------+------------+----------+ 2024-08-20T21:38:28.0036978Z | :attr:`~torch.float64` | ``1e-7`` | ``1e-7`` | 2024-08-20T21:38:28.0037606Z +---------------------------+------------+----------+ 2024-08-20T21:38:28.0038163Z | :attr:`~torch.complex32` | ``1e-3`` | ``1e-5`` | 2024-08-20T21:38:28.0038718Z +---------------------------+------------+----------+ 2024-08-20T21:38:28.0039262Z | :attr:`~torch.complex64` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:38:28.0039816Z +---------------------------+------------+----------+ 2024-08-20T21:38:28.0040378Z | :attr:`~torch.complex128` | ``1e-7`` | ``1e-7`` | 2024-08-20T21:38:28.0040917Z +---------------------------+------------+----------+ 2024-08-20T21:38:28.0041465Z | :attr:`~torch.quint8` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:38:28.0042013Z +---------------------------+------------+----------+ 2024-08-20T21:38:28.0042570Z | :attr:`~torch.quint2x4` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:38:28.0043106Z +---------------------------+------------+----------+ 2024-08-20T21:38:28.0043659Z | :attr:`~torch.quint4x2` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:38:28.0044280Z +---------------------------+------------+----------+ 2024-08-20T21:38:28.0044818Z | :attr:`~torch.qint8` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:38:28.0045368Z +---------------------------+------------+----------+ 2024-08-20T21:38:28.0045921Z | :attr:`~torch.qint32` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:38:28.0046455Z +---------------------------+------------+----------+ 2024-08-20T21:38:28.0047183Z | other | ``0.0`` | ``0.0`` | 2024-08-20T21:38:28.0047803Z +---------------------------+------------+----------+ 2024-08-20T21:38:28.0048116Z 2024-08-20T21:38:28.0048220Z .. note:: 2024-08-20T21:38:28.0048391Z 2024-08-20T21:38:28.0048915Z :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged 2024-08-20T21:38:28.0050112Z to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might 2024-08-20T21:38:28.0051144Z define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default: 2024-08-20T21:38:28.0051663Z 2024-08-20T21:38:28.0051776Z >>> import functools 2024-08-20T21:38:28.0052344Z >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0) 2024-08-20T21:38:28.0053031Z >>> assert_equal(1e-9, 1e-10) 2024-08-20T21:38:28.0053435Z Traceback (most recent call last): 2024-08-20T21:38:28.0053831Z ... 2024-08-20T21:38:28.0054141Z AssertionError: Scalars are not equal! 2024-08-20T21:38:28.0054549Z 2024-08-20T21:38:28.0054907Z Expected 1e-10 but got 1e-09. 2024-08-20T21:38:28.0055528Z Absolute difference: 9.000000000000001e-10 2024-08-20T21:38:28.0055964Z Relative difference: 9.0 2024-08-20T21:38:28.0056227Z 2024-08-20T21:38:28.0056326Z Examples: 2024-08-20T21:38:28.0056632Z >>> # tensor to tensor comparison 2024-08-20T21:38:28.0057150Z >>> expected = torch.tensor([1e0, 1e-1, 1e-2]) 2024-08-20T21:38:28.0057659Z >>> actual = torch.acos(torch.cos(expected)) 2024-08-20T21:38:28.0058178Z >>> torch.testing.assert_close(actual, expected) 2024-08-20T21:38:28.0058522Z 2024-08-20T21:38:28.0058672Z >>> # scalar to scalar comparison 2024-08-20T21:38:28.0059058Z >>> import math 2024-08-20T21:38:28.0059387Z >>> expected = math.sqrt(2.0) 2024-08-20T21:38:28.0059791Z >>> actual = 2.0 / math.sqrt(2.0) 2024-08-20T21:38:28.0060252Z >>> torch.testing.assert_close(actual, expected) 2024-08-20T21:38:28.0060606Z 2024-08-20T21:38:28.0060765Z >>> # numpy array to numpy array comparison 2024-08-20T21:38:28.0061215Z >>> import numpy as np 2024-08-20T21:38:28.0061658Z >>> expected = np.array([1e0, 1e-1, 1e-2]) 2024-08-20T21:38:28.0062131Z >>> actual = np.arccos(np.cos(expected)) 2024-08-20T21:38:28.0062637Z >>> torch.testing.assert_close(actual, expected) 2024-08-20T21:38:28.0062988Z 2024-08-20T21:38:28.0063138Z >>> # sequence to sequence comparison 2024-08-20T21:38:28.0063567Z >>> import numpy as np 2024-08-20T21:38:28.0064169Z >>> # The types of the sequences do not have to match. They only have to have the same 2024-08-20T21:38:28.0064855Z >>> # length and their elements have to match. 2024-08-20T21:38:28.0065396Z >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)] 2024-08-20T21:38:28.0065891Z >>> actual = tuple(expected) 2024-08-20T21:38:28.0066333Z >>> torch.testing.assert_close(actual, expected) 2024-08-20T21:38:28.0066684Z 2024-08-20T21:38:28.0066825Z >>> # mapping to mapping comparison 2024-08-20T21:38:28.0067287Z >>> from collections import OrderedDict 2024-08-20T21:38:28.0067709Z >>> import numpy as np 2024-08-20T21:38:28.0068070Z >>> foo = torch.tensor(1.0) 2024-08-20T21:38:28.0068434Z >>> bar = 2.0 2024-08-20T21:38:28.0068827Z >>> baz = np.array(3.0) 2024-08-20T21:38:28.0069423Z >>> # The types and a possible ordering of mappings do not have to match. They only 2024-08-20T21:38:28.0070237Z >>> # have to have the same set of keys and their elements have to match. 2024-08-20T21:38:28.0070966Z >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)]) 2024-08-20T21:38:28.0071569Z >>> actual = {"baz": baz, "bar": bar, "foo": foo} 2024-08-20T21:38:28.0072100Z >>> torch.testing.assert_close(actual, expected) 2024-08-20T21:38:28.0072446Z 2024-08-20T21:38:28.0072619Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2024-08-20T21:38:28.0073063Z >>> actual = expected.clone() 2024-08-20T21:38:28.0073574Z >>> # By default, directly related instances can be compared 2024-08-20T21:38:28.0074255Z >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected) 2024-08-20T21:38:28.0074958Z >>> # This check can be made more strict with allow_subclasses=False 2024-08-20T21:38:28.0075529Z >>> torch.testing.assert_close( 2024-08-20T21:38:28.0076078Z ... torch.nn.Parameter(actual), expected, allow_subclasses=False 2024-08-20T21:38:28.0076595Z ... ) 2024-08-20T21:38:28.0076898Z Traceback (most recent call last): 2024-08-20T21:38:28.0077289Z ... 2024-08-20T21:38:28.0077694Z TypeError: No comparison pair was able to handle inputs of type 2024-08-20T21:38:28.0078504Z and . 2024-08-20T21:38:28.0079275Z >>> # If the inputs are not directly related, they are never considered close 2024-08-20T21:38:28.0079980Z >>> torch.testing.assert_close(actual.numpy(), expected) 2024-08-20T21:38:28.0080583Z Traceback (most recent call last): 2024-08-20T21:38:28.0080987Z ... 2024-08-20T21:38:28.0081630Z TypeError: No comparison pair was able to handle inputs of type 2024-08-20T21:38:28.0082353Z and . 2024-08-20T21:38:28.0082989Z >>> # Exceptions to these rules are Python scalars. They can be checked regardless of 2024-08-20T21:38:28.0083666Z >>> # their type if check_dtype=False. 2024-08-20T21:38:28.0084185Z >>> torch.testing.assert_close(1.0, 1, check_dtype=False) 2024-08-20T21:38:28.0084576Z 2024-08-20T21:38:28.0084696Z >>> # NaN != NaN by default. 2024-08-20T21:38:28.0085123Z >>> expected = torch.tensor(float("Nan")) 2024-08-20T21:38:28.0085560Z >>> actual = expected.clone() 2024-08-20T21:38:28.0086025Z >>> torch.testing.assert_close(actual, expected) 2024-08-20T21:38:28.0086512Z Traceback (most recent call last): 2024-08-20T21:38:28.0086895Z ... 2024-08-20T21:38:28.0087209Z AssertionError: Scalars are not close! 2024-08-20T21:38:28.0087748Z 2024-08-20T21:38:28.0088044Z Expected nan but got nan. 2024-08-20T21:38:28.0088556Z Absolute difference: nan (up to 1e-05 allowed) 2024-08-20T21:38:28.0089165Z Relative difference: nan (up to 1.3e-06 allowed) 2024-08-20T21:38:28.0089765Z >>> torch.testing.assert_close(actual, expected, equal_nan=True) 2024-08-20T21:38:28.0090197Z 2024-08-20T21:38:28.0090358Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2024-08-20T21:38:28.0090837Z >>> actual = torch.tensor([1.0, 4.0, 5.0]) 2024-08-20T21:38:28.0091348Z >>> # The default error message can be overwritten. 2024-08-20T21:38:28.0092076Z >>> torch.testing.assert_close(actual, expected, msg="Argh, the tensors are not close!") 2024-08-20T21:38:28.0092769Z Traceback (most recent call last): 2024-08-20T21:38:28.0093168Z ... 2024-08-20T21:38:28.0093510Z AssertionError: Argh, the tensors are not close! 2024-08-20T21:38:28.0094199Z >>> # If msg is a callable, it can be used to augment the generated message with 2024-08-20T21:38:28.0094817Z >>> # extra information 2024-08-20T21:38:28.0095184Z >>> torch.testing.assert_close( 2024-08-20T21:38:28.0095825Z ... actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter" 2024-08-20T21:38:28.0096359Z ... ) 2024-08-20T21:38:28.0096651Z Traceback (most recent call last): 2024-08-20T21:38:28.0097051Z ... 2024-08-20T21:38:28.0097329Z AssertionError: Header 2024-08-20T21:38:28.0097662Z 2024-08-20T21:38:28.0098020Z Tensor-likes are not close! 2024-08-20T21:38:28.0098390Z 2024-08-20T21:38:28.0098699Z Mismatched elements: 2 / 3 (66.7%) 2024-08-20T21:38:28.0099364Z Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed) 2024-08-20T21:38:28.0100204Z Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed) 2024-08-20T21:38:28.0100756Z 2024-08-20T21:38:28.0101032Z Footer 2024-08-20T21:38:28.0101283Z 2024-08-20T21:38:28.0101797Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:28.0102298Z 2024-08-20T21:38:29.1318244Z msg = Cannot scrape callname=register_pytree_node in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py line=111. 2024-08-20T21:38:29.1319587Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:29.1320329Z Register a container-like type as pytree node. 2024-08-20T21:38:29.1320699Z 2024-08-20T21:38:29.1320797Z Args: 2024-08-20T21:38:29.1321211Z cls (type): A Python type to treat as an internal pytree node. 2024-08-20T21:38:29.1321995Z flatten_fn (callable): A function to be used during flattening, taking an instance of 2024-08-20T21:38:29.1323177Z ``cls`` and returning a pair, with (1) an iterable for the children to be flattened 2024-08-20T21:38:29.1324102Z recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be 2024-08-20T21:38:29.1324796Z passed to the ``unflatten_fn``. 2024-08-20T21:38:29.1325492Z unflatten_fn (callable): A function taking two arguments: the auxiliary data that was 2024-08-20T21:38:29.1326406Z returned by ``flatten_fn`` and stored in the treespec, and the unflattened children. 2024-08-20T21:38:29.1327152Z The function should return an instance of ``cls``. 2024-08-20T21:38:29.1327966Z serialized_type_name (str, optional): A keyword argument used to specify the fully 2024-08-20T21:38:29.1328716Z qualified name used when serializing the tree spec. 2024-08-20T21:38:29.1329513Z to_dumpable_context (callable, optional): An optional keyword argument to custom specify how 2024-08-20T21:38:29.1330478Z to convert the context of the pytree to a custom json dumpable representation. This is 2024-08-20T21:38:29.1331410Z used for json serialization, which is being used in :mod:`torch.export` right now. 2024-08-20T21:38:29.1332348Z from_dumpable_context (callable, optional): An optional keyword argument to custom specify 2024-08-20T21:38:29.1333279Z how to convert the custom json dumpable representation of the context back to the 2024-08-20T21:38:29.1334172Z original context. This is used for json deserialization, which is being used in 2024-08-20T21:38:29.1334841Z :mod:`torch.export` right now. 2024-08-20T21:38:29.1335140Z 2024-08-20T21:38:29.1335278Z Example:: 2024-08-20T21:38:29.1335441Z 2024-08-20T21:38:29.1335556Z >>> # xdoctest: +SKIP 2024-08-20T21:38:29.1335990Z >>> # Registry a Python type with lambda functions 2024-08-20T21:38:29.1336476Z >>> register_pytree_node( 2024-08-20T21:38:29.1336819Z ... set, 2024-08-20T21:38:29.1337170Z ... lambda s: (sorted(s), None, None), 2024-08-20T21:38:29.1337658Z ... lambda children, _: set(children), 2024-08-20T21:38:29.1338065Z ... ) 2024-08-20T21:38:29.1338310Z 2024-08-20T21:38:29.1338872Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:29.1339492Z 2024-08-20T21:38:29.1820332Z msg = Cannot scrape callname=SelectiveCheckpointContext in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py line=1201. 2024-08-20T21:38:29.1821757Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:29.1822275Z 2024-08-20T21:38:29.1822544Z Context passed to policy function during selective checkpointing. 2024-08-20T21:38:29.1822976Z 2024-08-20T21:38:29.1823305Z This class is used to pass relevant metadata to the policy function during 2024-08-20T21:38:29.1824114Z selective checkpointing. The metadata includes whether the current invocation 2024-08-20T21:38:29.1824888Z of the policy function is during recomputation or not. 2024-08-20T21:38:29.1825335Z 2024-08-20T21:38:29.1825430Z Example: 2024-08-20T21:38:29.1825833Z >>> # xdoctest: +SKIP(stub) 2024-08-20T21:38:29.1826335Z >>> 2024-08-20T21:38:29.1826828Z >>> def policy_fn(ctx, op, *args, **kwargs): 2024-08-20T21:38:29.1827662Z >>> print(ctx.is_recompute) 2024-08-20T21:38:29.1828003Z >>> 2024-08-20T21:38:29.1828490Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2024-08-20T21:38:29.1829101Z >>> 2024-08-20T21:38:29.1829412Z >>> out = torch.utils.checkpoint.checkpoint( 2024-08-20T21:38:29.1829862Z >>> fn, x, y, 2024-08-20T21:38:29.1830173Z >>> use_reentrant=False, 2024-08-20T21:38:29.1830529Z >>> context_fn=context_fn, 2024-08-20T21:38:29.1830879Z >>> ) 2024-08-20T21:38:29.1831022Z 2024-08-20T21:38:29.1831460Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:29.1831943Z 2024-08-20T21:38:29.1833136Z msg = Cannot scrape callname=create_selective_checkpoint_contexts in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py line=1335. 2024-08-20T21:38:29.1834537Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:29.1835063Z 2024-08-20T21:38:29.1835374Z Helper to avoid recomputing certain ops during activation checkpointing. 2024-08-20T21:38:29.1835851Z 2024-08-20T21:38:29.1836156Z Use this with `torch.utils.checkpoint.checkpoint` to control which 2024-08-20T21:38:29.1836795Z operations are recomputed during the backward pass. 2024-08-20T21:38:29.1837166Z 2024-08-20T21:38:29.1837255Z Args: 2024-08-20T21:38:29.1837542Z policy_fn_or_list (Callable or List): 2024-08-20T21:38:29.1838110Z - If a policy function is provided, it should accept a 2024-08-20T21:38:29.1838799Z :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and 2024-08-20T21:38:29.1839576Z kwargs to the op, and return a :class:`CheckpointPolicy` enum value 2024-08-20T21:38:29.1840344Z indicating whether the execution of the op should be recomputed or not. 2024-08-20T21:38:29.1841164Z - If a list of operations is provided, it is equivalent to a policy 2024-08-20T21:38:29.1841856Z returning `CheckpointPolicy.MUST_SAVE` for the specified 2024-08-20T21:38:29.1842547Z operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other 2024-08-20T21:38:29.1843092Z operations. 2024-08-20T21:38:29.1843560Z allow_cache_entry_mutation (bool, optional): By default, an error is 2024-08-20T21:38:29.1844301Z raised if any tensors cached by selective activation checkpoint are 2024-08-20T21:38:29.1845036Z mutated in order to ensure correctness. If set to `True`, this check 2024-08-20T21:38:29.1845598Z is disabled. 2024-08-20T21:38:29.1845886Z Returns: 2024-08-20T21:38:29.1846150Z A tuple of two context managers. 2024-08-20T21:38:29.1846438Z 2024-08-20T21:38:29.1846529Z Example: 2024-08-20T21:38:29.1847019Z >>> # xdoctest: +REQUIRES(LINUX) 2024-08-20T21:38:29.1847493Z >>> import functools 2024-08-20T21:38:29.1847803Z >>> 2024-08-20T21:38:29.1848107Z >>> x = torch.rand(10, 10, requires_grad=True) 2024-08-20T21:38:29.1848709Z >>> y = torch.rand(10, 10, requires_grad=True) 2024-08-20T21:38:29.1849125Z >>> 2024-08-20T21:38:29.1849371Z >>> ops_to_save = [ 2024-08-20T21:38:29.1849700Z >>> torch.ops.aten.mm.default, 2024-08-20T21:38:29.1850083Z >>> ] 2024-08-20T21:38:29.1850324Z >>> 2024-08-20T21:38:29.1850610Z >>> def policy_fn(ctx, op, *args, **kwargs): 2024-08-20T21:38:29.1851054Z >>> if op in ops_to_save: 2024-08-20T21:38:29.1851470Z >>> return CheckpointPolicy.MUST_SAVE 2024-08-20T21:38:29.1851879Z >>> else: 2024-08-20T21:38:29.1852242Z >>> return CheckpointPolicy.PREFER_RECOMPUTE 2024-08-20T21:38:29.1852688Z >>> 2024-08-20T21:38:29.1853169Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2024-08-20T21:38:29.1853786Z >>> 2024-08-20T21:38:29.1854040Z >>> # or equivalently 2024-08-20T21:38:29.1854597Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save) 2024-08-20T21:38:29.1855218Z >>> 2024-08-20T21:38:29.1855466Z >>> def fn(x, y): 2024-08-20T21:38:29.1855996Z >>> return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y 2024-08-20T21:38:29.1856515Z >>> 2024-08-20T21:38:29.1856847Z >>> out = torch.utils.checkpoint.checkpoint( 2024-08-20T21:38:29.1857289Z >>> fn, x, y, 2024-08-20T21:38:29.1857604Z >>> use_reentrant=False, 2024-08-20T21:38:29.1857980Z >>> context_fn=context_fn, 2024-08-20T21:38:29.1858323Z >>> ) 2024-08-20T21:38:29.1858478Z 2024-08-20T21:38:29.1858916Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:29.1859403Z 2024-08-20T21:38:29.2030365Z msg = Cannot scrape callname=CppExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=925. 2024-08-20T21:38:29.2031685Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:29.2032208Z 2024-08-20T21:38:29.2032396Z Create a :class:`setuptools.Extension` for C++. 2024-08-20T21:38:29.2032747Z 2024-08-20T21:38:29.2033056Z Convenience method that creates a :class:`setuptools.Extension` with the 2024-08-20T21:38:29.2033828Z bare minimum (but often sufficient) arguments to build a C++ extension. 2024-08-20T21:38:29.2034282Z 2024-08-20T21:38:29.2034683Z All arguments are forwarded to the :class:`setuptools.Extension` 2024-08-20T21:38:29.2045104Z constructor. Full list arguments can be found at 2024-08-20T21:38:29.2046868Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2024-08-20T21:38:29.2047793Z 2024-08-20T21:38:29.2047890Z Example: 2024-08-20T21:38:29.2048158Z >>> # xdoctest: +SKIP 2024-08-20T21:38:29.2048570Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-08-20T21:38:29.2049060Z >>> from setuptools import setup 2024-08-20T21:38:29.2049640Z >>> from torch.utils.cpp_extension import BuildExtension, CppExtension 2024-08-20T21:38:29.2050186Z >>> setup( 2024-08-20T21:38:29.2050537Z ... name='extension', 2024-08-20T21:38:29.2050879Z ... ext_modules=[ 2024-08-20T21:38:29.2051191Z ... CppExtension( 2024-08-20T21:38:29.2051591Z ... name='extension', 2024-08-20T21:38:29.2052061Z ... sources=['extension.cpp'], 2024-08-20T21:38:29.2052555Z ... extra_compile_args=['-g'], 2024-08-20T21:38:29.2053123Z ... extra_link_flags=['-Wl,--no-as-needed', '-lm']) 2024-08-20T21:38:29.2053591Z ... ], 2024-08-20T21:38:29.2053950Z ... cmdclass={ 2024-08-20T21:38:29.2054484Z ... 'build_ext': BuildExtension 2024-08-20T21:38:29.2055025Z ... }) 2024-08-20T21:38:29.2055300Z 2024-08-20T21:38:29.2056079Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:29.2056678Z 2024-08-20T21:38:29.2057652Z msg = Cannot scrape callname=CUDAExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=976. 2024-08-20T21:38:29.2059146Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:29.2059649Z 2024-08-20T21:38:29.2059866Z Create a :class:`setuptools.Extension` for CUDA/C++. 2024-08-20T21:38:29.2060229Z 2024-08-20T21:38:29.2060539Z Convenience method that creates a :class:`setuptools.Extension` with the 2024-08-20T21:38:29.2061283Z bare minimum (but often sufficient) arguments to build a CUDA/C++ 2024-08-20T21:38:29.2062019Z extension. This includes the CUDA include path, library path and runtime 2024-08-20T21:38:29.2062575Z library. 2024-08-20T21:38:29.2062733Z 2024-08-20T21:38:29.2063000Z All arguments are forwarded to the :class:`setuptools.Extension` 2024-08-20T21:38:29.2063619Z constructor. Full list arguments can be found at 2024-08-20T21:38:29.2064459Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2024-08-20T21:38:29.2065061Z 2024-08-20T21:38:29.2065155Z Example: 2024-08-20T21:38:29.2065421Z >>> # xdoctest: +SKIP 2024-08-20T21:38:29.2065817Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-08-20T21:38:29.2066309Z >>> from setuptools import setup 2024-08-20T21:38:29.2066897Z >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension 2024-08-20T21:38:29.2067466Z >>> setup( 2024-08-20T21:38:29.2067786Z ... name='cuda_extension', 2024-08-20T21:38:29.2068154Z ... ext_modules=[ 2024-08-20T21:38:29.2068470Z ... CUDAExtension( 2024-08-20T21:38:29.2068905Z ... name='cuda_extension', 2024-08-20T21:38:29.2069493Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-08-20T21:38:29.2070106Z ... extra_compile_args={'cxx': ['-g'], 2024-08-20T21:38:29.2070774Z ... 'nvcc': ['-O2']}, 2024-08-20T21:38:29.2071390Z ... extra_link_flags=['-Wl,--no-as-needed', '-lcuda']) 2024-08-20T21:38:29.2071868Z ... ], 2024-08-20T21:38:29.2072131Z ... cmdclass={ 2024-08-20T21:38:29.2072519Z ... 'build_ext': BuildExtension 2024-08-20T21:38:29.2072917Z ... }) 2024-08-20T21:38:29.2073076Z 2024-08-20T21:38:29.2073188Z Compute capabilities: 2024-08-20T21:38:29.2073400Z 2024-08-20T21:38:29.2073821Z By default the extension will be compiled to run on all archs of the cards visible during the 2024-08-20T21:38:29.2074805Z building process of the extension, plus PTX. If down the road a new card is installed the 2024-08-20T21:38:29.2075814Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's 2024-08-20T21:38:29.2076898Z newer than the newest version for which your nvcc can build fully-compiled binaries, Pytorch 2024-08-20T21:38:29.2077889Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does 2024-08-20T21:38:29.2078614Z support (see below for details on PTX). 2024-08-20T21:38:29.2078907Z 2024-08-20T21:38:29.2079330Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which 2024-08-20T21:38:29.2080069Z CCs you want the extension to support: 2024-08-20T21:38:29.2080356Z 2024-08-20T21:38:29.2080610Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py`` 2024-08-20T21:38:29.2081342Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py`` 2024-08-20T21:38:29.2081866Z 2024-08-20T21:38:29.2082294Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified 2024-08-20T21:38:29.2083414Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >= 2024-08-20T21:38:29.2084530Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with 2024-08-20T21:38:29.2085611Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to 2024-08-20T21:38:29.2086693Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on 2024-08-20T21:38:29.2087969Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better 2024-08-20T21:38:29.2088968Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6, 2024-08-20T21:38:29.2090094Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but 2024-08-20T21:38:29.2090800Z "8.0 8.6" would be better. 2024-08-20T21:38:29.2091029Z 2024-08-20T21:38:29.2091527Z Note that while it's possible to include all supported archs, the more archs get included the 2024-08-20T21:38:29.2092517Z slower the building process will be, as it will build a separate kernel image for each arch. 2024-08-20T21:38:29.2093092Z 2024-08-20T21:38:29.2093656Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows. 2024-08-20T21:38:29.2094560Z To workaround the issue, move python binding logic to pure C++ file. 2024-08-20T21:38:29.2095020Z 2024-08-20T21:38:29.2095124Z Example use: 2024-08-20T21:38:29.2095402Z #include 2024-08-20T21:38:29.2095827Z at::Tensor SigmoidAlphaBlendForwardCuda(....) 2024-08-20T21:38:29.2096185Z 2024-08-20T21:38:29.2096283Z Instead of: 2024-08-20T21:38:29.2096565Z #include 2024-08-20T21:38:29.2097004Z torch::Tensor SigmoidAlphaBlendForwardCuda(...) 2024-08-20T21:38:29.2097368Z 2024-08-20T21:38:29.2097728Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460 2024-08-20T21:38:29.2098910Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48 2024-08-20T21:38:29.2099718Z 2024-08-20T21:38:29.2099857Z Relocatable device code linking: 2024-08-20T21:38:29.2100175Z 2024-08-20T21:38:29.2100555Z If you want to reference device symbols across compilation units (across object files), 2024-08-20T21:38:29.2101547Z the object files need to be built with `relocatable device code` (-rdc=true or -dc). 2024-08-20T21:38:29.2102586Z An exception to this rule is "dynamic parallelism" (nested kernel launches) which is not used a lot anymore. 2024-08-20T21:38:29.2103695Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it. 2024-08-20T21:38:29.2104835Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step 2024-08-20T21:38:29.2105720Z help reduce the protentional perf degradation of `-rdc`. 2024-08-20T21:38:29.2106326Z Note that it needs to be used at both steps to be useful. 2024-08-20T21:38:29.2106707Z 2024-08-20T21:38:29.2107344Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step. 2024-08-20T21:38:29.2108445Z There is also a case where `-dlink` is used without `-rdc`: 2024-08-20T21:38:29.2109257Z when an extension is linked against a static lib containing rdc-compiled objects 2024-08-20T21:38:29.2110035Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem). 2024-08-20T21:38:29.2110491Z 2024-08-20T21:38:29.2110764Z Note: Ninja is required to build a CUDA Extension with RDC linking. 2024-08-20T21:38:29.2111202Z 2024-08-20T21:38:29.2111307Z Example: 2024-08-20T21:38:29.2111555Z >>> # xdoctest: +SKIP 2024-08-20T21:38:29.2111961Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-08-20T21:38:29.2112421Z >>> CUDAExtension( 2024-08-20T21:38:29.2112785Z ... name='cuda_extension', 2024-08-20T21:38:29.2113327Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-08-20T21:38:29.2113805Z ... dlink=True, 2024-08-20T21:38:29.2114164Z ... dlink_libraries=["dlink_lib"], 2024-08-20T21:38:29.2114663Z ... extra_compile_args={'cxx': ['-g'], 2024-08-20T21:38:29.2115219Z ... 'nvcc': ['-O2', '-rdc=true']}) 2024-08-20T21:38:29.2115549Z 2024-08-20T21:38:29.2115959Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:29.2116577Z 2024-08-20T21:38:29.2117376Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1234. 2024-08-20T21:38:29.2118616Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:29.2119115Z 2024-08-20T21:38:29.2119360Z Load a PyTorch C++ extension just-in-time (JIT). 2024-08-20T21:38:29.2119698Z 2024-08-20T21:38:29.2119991Z To load an extension, a Ninja build file is emitted, which is used to 2024-08-20T21:38:29.2120691Z compile the given sources into a dynamic library. This library is 2024-08-20T21:38:29.2121401Z subsequently loaded into the current Python process as a module and 2024-08-20T21:38:29.2122007Z returned from this function, ready for use. 2024-08-20T21:38:29.2122319Z 2024-08-20T21:38:29.2122596Z By default, the directory to which the build file is emitted and the 2024-08-20T21:38:29.2123343Z resulting library compiled to is ``/torch_extensions/``, where 2024-08-20T21:38:29.2124096Z ```` is the temporary folder on the current platform and ```` 2024-08-20T21:38:29.2124821Z the name of the extension. This location can be overridden in two ways. 2024-08-20T21:38:29.2125568Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it 2024-08-20T21:38:29.2126294Z replaces ``/torch_extensions`` and all extensions will be compiled 2024-08-20T21:38:29.2127014Z into subfolders of this directory. Second, if the ``build_directory`` 2024-08-20T21:38:29.2127869Z argument to this function is supplied, it overrides the entire path, i.e. 2024-08-20T21:38:29.2128565Z the library will be compiled into that folder directly. 2024-08-20T21:38:29.2128938Z 2024-08-20T21:38:29.2129341Z To compile the sources, the default system compiler (``c++``) is used, 2024-08-20T21:38:29.2130112Z which can be overridden by setting the ``CXX`` environment variable. To pass 2024-08-20T21:38:29.2130891Z additional arguments to the compilation process, ``extra_cflags`` or 2024-08-20T21:38:29.2131642Z ``extra_ldflags`` can be provided. For example, to compile your extension 2024-08-20T21:38:29.2132433Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use 2024-08-20T21:38:29.2133062Z ``extra_cflags`` to pass further include directories. 2024-08-20T21:38:29.2133413Z 2024-08-20T21:38:29.2133735Z CUDA support with mixed compilation is provided. Simply pass CUDA source 2024-08-20T21:38:29.2134457Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be 2024-08-20T21:38:29.2135206Z detected and compiled with nvcc rather than the C++ compiler. This includes 2024-08-20T21:38:29.2135979Z passing the CUDA lib64 directory as a library directory, and linking 2024-08-20T21:38:29.2136623Z ``cudart``. You can pass additional flags to nvcc via 2024-08-20T21:38:29.2137241Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various 2024-08-20T21:38:29.2137988Z heuristics for finding the CUDA install directory are used, which usually 2024-08-20T21:38:29.2138760Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the 2024-08-20T21:38:29.2139306Z safest option. 2024-08-20T21:38:29.2139482Z 2024-08-20T21:38:29.2139570Z Args: 2024-08-20T21:38:29.2140000Z name: The name of the extension to build. This MUST be the same as the 2024-08-20T21:38:29.2140578Z name of the pybind11 module! 2024-08-20T21:38:29.2141127Z sources: A list of relative or absolute paths to C++ source files. 2024-08-20T21:38:29.2141857Z extra_cflags: optional list of compiler flags to forward to the build. 2024-08-20T21:38:29.2142591Z extra_cuda_cflags: optional list of compiler flags to forward to nvcc 2024-08-20T21:38:29.2143168Z when building CUDA sources. 2024-08-20T21:38:29.2143734Z extra_ldflags: optional list of linker flags to forward to the build. 2024-08-20T21:38:29.2144465Z extra_include_paths: optional list of include directories to forward 2024-08-20T21:38:29.2145004Z to the build. 2024-08-20T21:38:29.2145427Z build_directory: optional path to use as build workspace. 2024-08-20T21:38:29.2146121Z verbose: If ``True``, turns on verbose logging of load steps. 2024-08-20T21:38:29.2146979Z with_cuda: Determines whether CUDA headers and libraries are added to 2024-08-20T21:38:29.2147652Z the build. If set to ``None`` (default), this value is 2024-08-20T21:38:29.2148290Z automatically determined based on the existence of ``.cu`` or 2024-08-20T21:38:29.2148938Z ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers 2024-08-20T21:38:29.2149478Z and libraries to be included. 2024-08-20T21:38:29.2150030Z is_python_module: If ``True`` (default), imports the produced shared 2024-08-20T21:38:29.2150713Z library as a Python module. If ``False``, behavior depends on 2024-08-20T21:38:29.2151242Z ``is_standalone``. 2024-08-20T21:38:29.2151740Z is_standalone: If ``False`` (default) loads the constructed extension 2024-08-20T21:38:29.2152448Z into the process as a plain dynamic library. If ``True``, build a 2024-08-20T21:38:29.2153012Z standalone executable. 2024-08-20T21:38:29.2153249Z 2024-08-20T21:38:29.2153353Z Returns: 2024-08-20T21:38:29.2153623Z If ``is_python_module`` is ``True``: 2024-08-20T21:38:29.2154143Z Returns the loaded PyTorch extension as a Python module. 2024-08-20T21:38:29.2154547Z 2024-08-20T21:38:29.2154824Z If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``: 2024-08-20T21:38:29.2155547Z Returns nothing. (The shared library is loaded into the process as 2024-08-20T21:38:29.2156088Z a side effect.) 2024-08-20T21:38:29.2156301Z 2024-08-20T21:38:29.2156428Z If ``is_standalone`` is ``True``. 2024-08-20T21:38:29.2157097Z Return the path to the executable. (On Windows, TORCH_LIB_PATH is 2024-08-20T21:38:29.2157763Z added to the PATH environment variable as a side effect.) 2024-08-20T21:38:29.2158174Z 2024-08-20T21:38:29.2158267Z Example: 2024-08-20T21:38:29.2158531Z >>> # xdoctest: +SKIP 2024-08-20T21:38:29.2158907Z >>> from torch.utils.cpp_extension import load 2024-08-20T21:38:29.2159351Z >>> module = load( 2024-08-20T21:38:29.2159734Z ... name='extension', 2024-08-20T21:38:29.2160222Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-08-20T21:38:29.2160752Z ... extra_cflags=['-O2'], 2024-08-20T21:38:29.2161112Z ... verbose=True) 2024-08-20T21:38:29.2161315Z 2024-08-20T21:38:29.2161705Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:29.2162204Z 2024-08-20T21:38:29.2163042Z msg = Cannot scrape callname=load_inline in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1523. 2024-08-20T21:38:29.2164322Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:29.2164820Z 2024-08-20T21:38:29.2165177Z Load a PyTorch C++ extension just-in-time (JIT) from string sources. 2024-08-20T21:38:29.2165624Z 2024-08-20T21:38:29.2165931Z This function behaves exactly like :func:`load`, but takes its sources as 2024-08-20T21:38:29.2166703Z strings rather than filenames. These strings are stored to files in the 2024-08-20T21:38:29.2167551Z build directory, after which the behavior of :func:`load_inline` is 2024-08-20T21:38:29.2168094Z identical to :func:`load`. 2024-08-20T21:38:29.2168329Z 2024-08-20T21:38:29.2168420Z See `the 2024-08-20T21:38:29.2168985Z tests `_ 2024-08-20T21:38:29.2169720Z for good examples of using this function. 2024-08-20T21:38:29.2170019Z 2024-08-20T21:38:29.2170413Z Sources may omit two required parts of a typical non-inline C++ extension: 2024-08-20T21:38:29.2171219Z the necessary header includes, as well as the (pybind11) binding code. More 2024-08-20T21:38:29.2172019Z precisely, strings passed to ``cpp_sources`` are first concatenated into a 2024-08-20T21:38:29.2172735Z single ``.cpp`` file. This file is then prepended with ``#include 2024-08-20T21:38:29.2173377Z ``. 2024-08-20T21:38:29.2173576Z 2024-08-20T21:38:29.2173884Z Furthermore, if the ``functions`` argument is supplied, bindings will be 2024-08-20T21:38:29.2174640Z automatically generated for each function specified. ``functions`` can 2024-08-20T21:38:29.2175414Z either be a list of function names, or a dictionary mapping from function 2024-08-20T21:38:29.2176202Z names to docstrings. If a list is given, the name of each function is used 2024-08-20T21:38:29.2176785Z as its docstring. 2024-08-20T21:38:29.2176960Z 2024-08-20T21:38:29.2177253Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu`` 2024-08-20T21:38:29.2177934Z file and prepended with ``torch/types.h``, ``cuda.h`` and 2024-08-20T21:38:29.2178598Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled 2024-08-20T21:38:29.2179319Z separately, but ultimately linked into a single library. Note that no 2024-08-20T21:38:29.2180083Z bindings are generated for functions in ``cuda_sources`` per se. To bind 2024-08-20T21:38:29.2180863Z to a CUDA kernel, you must create a C++ function that calls it, and either 2024-08-20T21:38:29.2181611Z declare or define this C++ function in one of the ``cpp_sources`` (and 2024-08-20T21:38:29.2182195Z include its name in ``functions``). 2024-08-20T21:38:29.2182467Z 2024-08-20T21:38:29.2182720Z See :func:`load` for a description of arguments omitted below. 2024-08-20T21:38:29.2183121Z 2024-08-20T21:38:29.2183223Z Args: 2024-08-20T21:38:29.2183643Z cpp_sources: A string, or list of strings, containing C++ source code. 2024-08-20T21:38:29.2184398Z cuda_sources: A string, or list of strings, containing CUDA source code. 2024-08-20T21:38:29.2185199Z functions: A list of function names for which to generate function 2024-08-20T21:38:29.2185915Z bindings. If a dictionary is given, it should map function names to 2024-08-20T21:38:29.2186602Z docstrings (which are otherwise just the function names). 2024-08-20T21:38:29.2187293Z with_cuda: Determines whether CUDA headers and libraries are added to 2024-08-20T21:38:29.2187948Z the build. If set to ``None`` (default), this value is 2024-08-20T21:38:29.2188583Z automatically determined based on whether ``cuda_sources`` is 2024-08-20T21:38:29.2189211Z provided. Set it to ``True`` to force CUDA headers 2024-08-20T21:38:29.2189693Z and libraries to be included. 2024-08-20T21:38:29.2190236Z with_pytorch_error_handling: Determines whether pytorch error and 2024-08-20T21:38:29.2190928Z warning macros are handled by pytorch instead of pybind. To do 2024-08-20T21:38:29.2191645Z this, each function ``foo`` is called via an intermediary ``_safe_foo`` 2024-08-20T21:38:29.2192373Z function. This redirection might cause issues in obscure cases 2024-08-20T21:38:29.2193055Z of cpp. This flag should be set to ``False`` when this redirect 2024-08-20T21:38:29.2193582Z causes issues. 2024-08-20T21:38:29.2193781Z 2024-08-20T21:38:29.2193873Z Example: 2024-08-20T21:38:29.2194213Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-08-20T21:38:29.2194768Z >>> from torch.utils.cpp_extension import load_inline 2024-08-20T21:38:29.2195225Z >>> source = """ 2024-08-20T21:38:29.2195611Z at::Tensor sin_add(at::Tensor x, at::Tensor y) { 2024-08-20T21:38:29.2196070Z return x.sin() + y.sin(); 2024-08-20T21:38:29.2196394Z } 2024-08-20T21:38:29.2196620Z """ 2024-08-20T21:38:29.2197017Z >>> module = load_inline(name='inline_extension', 2024-08-20T21:38:29.2197499Z ... cpp_sources=[source], 2024-08-20T21:38:29.2198020Z ... functions=['sin_add']) 2024-08-20T21:38:29.2198338Z 2024-08-20T21:38:29.2198447Z .. note:: 2024-08-20T21:38:29.2198852Z By default, the Ninja backend uses #CPUS + 2 workers to build the 2024-08-20T21:38:29.2199563Z extension. This may use up too many resources on some systems. One 2024-08-20T21:38:29.2200375Z can control the number of workers by setting the `MAX_JOBS` environment 2024-08-20T21:38:29.2201018Z variable to a non-negative number. 2024-08-20T21:38:29.2201317Z 2024-08-20T21:38:29.2201709Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:29.2202206Z 2024-08-20T21:38:29.2232585Z msg = Cannot scrape callname=ThroughputBenchmark in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/throughput_benchmark.py line=61. 2024-08-20T21:38:29.2233964Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:29.2234483Z 2024-08-20T21:38:29.2234903Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark. 2024-08-20T21:38:29.2235473Z 2024-08-20T21:38:29.2235858Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible 2024-08-20T21:38:29.2236744Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference 2024-08-20T21:38:29.2237573Z server like load. It can emulate multiple calling threads to a single module 2024-08-20T21:38:29.2238391Z provided. In the future we plan to enhance this component to support inter and 2024-08-20T21:38:29.2239298Z intra-op parallelism as well as multiple models running in a single process. 2024-08-20T21:38:29.2239801Z 2024-08-20T21:38:29.2240142Z Please note that even though nn.Module is supported, it might incur an overhead 2024-08-20T21:38:29.2240963Z from the need to hold GIL every time we execute Python code or pass around 2024-08-20T21:38:29.2241750Z inputs as Python objects. As soon as you have a ScriptModule version of your 2024-08-20T21:38:29.2242549Z model for inference deployment it is better to switch to using it in this 2024-08-20T21:38:29.2243225Z benchmark. 2024-08-20T21:38:29.2243378Z 2024-08-20T21:38:29.2243473Z Example:: 2024-08-20T21:38:29.2243629Z 2024-08-20T21:38:29.2243777Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:38:29.2244272Z >>> from torch.utils import ThroughputBenchmark 2024-08-20T21:38:29.2244766Z >>> bench = ThroughputBenchmark(my_module) 2024-08-20T21:38:29.2245364Z >>> # Pre-populate benchmark's data set with the inputs 2024-08-20T21:38:29.2245853Z >>> for input in inputs: 2024-08-20T21:38:29.2246380Z ... # Both args and kwargs work, same as any PyTorch Module / ScriptModule 2024-08-20T21:38:29.2247199Z ... bench.add_input(input[0], x2=input[1]) 2024-08-20T21:38:29.2247884Z >>> # Inputs supplied above are randomly used during the execution 2024-08-20T21:38:29.2248426Z >>> stats = bench.benchmark( 2024-08-20T21:38:29.2248807Z ... num_calling_threads=4, 2024-08-20T21:38:29.2249186Z ... num_warmup_iters = 100, 2024-08-20T21:38:29.2249554Z ... num_iters = 1000, 2024-08-20T21:38:29.2249878Z ... ) 2024-08-20T21:38:29.2250258Z >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms)) 2024-08-20T21:38:29.2250873Z >>> print("Number of iterations: {}".format(stats.num_iters)) 2024-08-20T21:38:29.2251281Z 2024-08-20T21:38:29.2251690Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:29.2252185Z 2024-08-20T21:38:29.4085102Z msg = Cannot scrape callname=DistributedSampler in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/distributed.py line=17. 2024-08-20T21:38:29.4087621Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:29.4088691Z Sampler that restricts data loading to a subset of the dataset. 2024-08-20T21:38:29.4089118Z 2024-08-20T21:38:29.4089290Z It is especially useful in conjunction with 2024-08-20T21:38:29.4089984Z :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each 2024-08-20T21:38:29.4090857Z process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a 2024-08-20T21:38:29.4091680Z :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the 2024-08-20T21:38:29.4092581Z original dataset that is exclusive to it. 2024-08-20T21:38:29.4092898Z 2024-08-20T21:38:29.4093026Z .. note:: 2024-08-20T21:38:29.4093508Z Dataset is assumed to be of constant size and that any instance of it always 2024-08-20T21:38:29.4094193Z returns the same elements in the same order. 2024-08-20T21:38:29.4094539Z 2024-08-20T21:38:29.4094633Z Args: 2024-08-20T21:38:29.4094925Z dataset: Dataset used for sampling. 2024-08-20T21:38:29.4095501Z num_replicas (int, optional): Number of processes participating in 2024-08-20T21:38:29.4096273Z distributed training. By default, :attr:`world_size` is retrieved from the 2024-08-20T21:38:29.4096903Z current distributed group. 2024-08-20T21:38:29.4097510Z rank (int, optional): Rank of the current process within :attr:`num_replicas`. 2024-08-20T21:38:29.4098287Z By default, :attr:`rank` is retrieved from the current distributed 2024-08-20T21:38:29.4098824Z group. 2024-08-20T21:38:29.4099296Z shuffle (bool, optional): If ``True`` (default), sampler will shuffle the 2024-08-20T21:38:29.4099871Z indices. 2024-08-20T21:38:29.4100321Z seed (int, optional): random seed used to shuffle the sampler if 2024-08-20T21:38:29.4101006Z :attr:`shuffle=True`. This number should be identical across all 2024-08-20T21:38:29.4101649Z processes in the distributed group. Default: ``0``. 2024-08-20T21:38:29.4102321Z drop_last (bool, optional): if ``True``, then the sampler will drop the 2024-08-20T21:38:29.4103047Z tail of the data to make it evenly divisible across the number of 2024-08-20T21:38:29.4103869Z replicas. If ``False``, the sampler will add extra indices to make 2024-08-20T21:38:29.4104598Z the data evenly divisible across the replicas. Default: ``False``. 2024-08-20T21:38:29.4105043Z 2024-08-20T21:38:29.4105159Z .. warning:: 2024-08-20T21:38:29.4105574Z In distributed mode, calling the :meth:`set_epoch` method at 2024-08-20T21:38:29.4106343Z the beginning of each epoch **before** creating the :class:`DataLoader` iterator 2024-08-20T21:38:29.4107217Z is necessary to make shuffling work properly across multiple epochs. Otherwise, 2024-08-20T21:38:29.4107880Z the same ordering will be always used. 2024-08-20T21:38:29.4108206Z 2024-08-20T21:38:29.4108306Z Example:: 2024-08-20T21:38:29.4108513Z 2024-08-20T21:38:29.4108698Z >>> # xdoctest: +SKIP 2024-08-20T21:38:29.4109212Z >>> sampler = DistributedSampler(dataset) if is_distributed else None 2024-08-20T21:38:29.4109911Z >>> loader = DataLoader(dataset, shuffle=(sampler is None), 2024-08-20T21:38:29.4110456Z ... sampler=sampler) 2024-08-20T21:38:29.4110940Z >>> for epoch in range(start_epoch, n_epochs): 2024-08-20T21:38:29.4111391Z ... if is_distributed: 2024-08-20T21:38:29.4111792Z ... sampler.set_epoch(epoch) 2024-08-20T21:38:29.4112210Z ... train(loader) 2024-08-20T21:38:29.4112512Z 2024-08-20T21:38:29.4113113Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:29.4113596Z 2024-08-20T21:38:29.5974581Z gathering tests 2024-08-20T21:38:29.5986658Z running 695 test(s) 2024-08-20T21:38:29.6002010Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::typename:0, line 980 <- wrt source file 2024-08-20T21:38:29.6028657Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::typename:0 2024-08-20T21:38:29.6030195Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::is_tensor:0, line 1016 <- wrt source file 2024-08-20T21:38:29.6034046Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::is_tensor:0 2024-08-20T21:38:29.6035777Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_device:0, line 1085 <- wrt source file 2024-08-20T21:38:29.6037943Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_device:0 2024-08-20T21:38:29.6039994Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_tensor_type:0, line 1134 <- wrt source file 2024-08-20T21:38:29.6041904Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_tensor_type:0 2024-08-20T21:38:29.6043841Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_dtype:0, line 1171 <- wrt source file 2024-08-20T21:38:29.6045439Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_dtype:0 2024-08-20T21:38:29.6047637Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::use_deterministic_algorithms:0, line 1326 <- wrt source file 2024-08-20T21:38:29.6049945Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::use_deterministic_algorithms:0 2024-08-20T21:38:29.6051568Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::compile:0, line 2412 <- wrt source file 2024-08-20T21:38:29.6053019Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::compile:0 2024-08-20T21:38:29.6054778Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0, line 2661 <- wrt source file 2024-08-20T21:38:29.6056707Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0 2024-08-20T21:38:29.6058538Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_C.cpython-312-x86_64-linux-gnu.so::Generator:0, line 15 <- wrt source file 2024-08-20T21:38:29.6060333Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_C.cpython-312-x86_64-linux-gnu.so::Generator:0 2024-08-20T21:38:29.6062132Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_C.cpython-312-x86_64-linux-gnu.so::_LinAlgError:0, line 5 <- wrt source file 2024-08-20T21:38:29.6063999Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_C.cpython-312-x86_64-linux-gnu.so::_LinAlgError:0 2024-08-20T21:38:29.6065716Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::custom_op:0, line 55 <- wrt source file 2024-08-20T21:38:29.6067219Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::custom_op:0 2024-08-20T21:38:29.6068720Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl:0, line 137 <- wrt source file 2024-08-20T21:38:29.6070161Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl:0 2024-08-20T21:38:29.6071696Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl_abstract:0, line 206 <- wrt source file 2024-08-20T21:38:29.6314904Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl_abstract:0 2024-08-20T21:38:29.6316661Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_namedtensor_internals.py::update_names:0, line 118 <- wrt source file 2024-08-20T21:38:29.6318369Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_namedtensor_internals.py::update_names:0 2024-08-20T21:38:29.6320037Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_hook:0, line 546 <- wrt source file 2024-08-20T21:38:29.6374383Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_hook:0 2024-08-20T21:38:29.6376143Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0, line 603 <- wrt source file 2024-08-20T21:38:29.6499102Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0 2024-08-20T21:38:29.6500841Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.refine_names:0, line 1232 <- wrt source file 2024-08-20T21:38:29.6629971Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.refine_names:0 2024-08-20T21:38:29.6632835Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.align_to:0, line 1277 <- wrt source file 2024-08-20T21:38:29.6637585Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.align_to:0 2024-08-20T21:38:29.6639254Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.rename:0, line 1350 <- wrt source file 2024-08-20T21:38:29.6644598Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.rename:0 2024-08-20T21:38:29.6646195Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0, line 1380 <- wrt source file 2024-08-20T21:38:29.6686008Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0 2024-08-20T21:38:29.6687698Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.dim_order:0, line 1403 <- wrt source file 2024-08-20T21:38:29.6689999Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.dim_order:0 2024-08-20T21:38:29.6691609Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor_str.py::set_printoptions:0, line 53 <- wrt source file 2024-08-20T21:38:29.6712550Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor_str.py::set_printoptions:0 2024-08-20T21:38:29.6714196Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_tensors:0, line 63 <- wrt source file 2024-08-20T21:38:29.6719029Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_tensors:0 2024-08-20T21:38:29.6720665Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_shapes:0, line 91 <- wrt source file 2024-08-20T21:38:29.6722458Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_shapes:0 2024-08-20T21:38:29.6724026Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::split:0, line 178 <- wrt source file 2024-08-20T21:38:29.6734062Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::split:0 2024-08-20T21:38:29.6735570Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::einsum:0, line 287 <- wrt source file 2024-08-20T21:38:29.6944228Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::einsum:0 2024-08-20T21:38:29.6945870Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_unique_consecutive_impl:0, line 1014 <- wrt source file 2024-08-20T21:38:29.6955956Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_unique_consecutive_impl:0 2024-08-20T21:38:29.6957839Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::tensordot:0, line 1289 <- wrt source file 2024-08-20T21:38:29.6966284Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::tensordot:0 2024-08-20T21:38:29.6968011Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cartesian_prod:0, line 1373 <- wrt source file 2024-08-20T21:38:29.6980273Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cartesian_prod:0 2024-08-20T21:38:29.6981937Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::block_diag:0, line 1407 <- wrt source file 2024-08-20T21:38:29.6988719Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::block_diag:0 2024-08-20T21:38:29.6990258Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cdist:0, line 1458 <- wrt source file 2024-08-20T21:38:29.7002120Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cdist:0 2024-08-20T21:38:29.7003657Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_1d:0, line 1499 <- wrt source file 2024-08-20T21:38:29.7017920Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_1d:0 2024-08-20T21:38:29.7019480Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_2d:0, line 1535 <- wrt source file 2024-08-20T21:38:29.7034247Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_2d:0 2024-08-20T21:38:29.7035881Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_3d:0, line 1573 <- wrt source file 2024-08-20T21:38:29.7054793Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_3d:0 2024-08-20T21:38:29.7056320Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::norm:0, line 1746 <- wrt source file 2024-08-20T21:38:29.7085537Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::norm:0 2024-08-20T21:38:29.7087147Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::unravel_index:0, line 1913 <- wrt source file 2024-08-20T21:38:29.7111726Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::unravel_index:0 2024-08-20T21:38:29.7113636Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::chain_matmul:0, line 2013 <- wrt source file 2024-08-20T21:38:29.7116323Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::chain_matmul:0 2024-08-20T21:38:29.7117943Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_lu_impl:0, line 2113 <- wrt source file 2024-08-20T21:38:29.7119444Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_lu_impl:0 2024-08-20T21:38:29.7121212Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::list:0, line 468 <- wrt source file 2024-08-20T21:38:29.7122575Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::list:0 2024-08-20T21:38:29.7123967Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::help:0, line 528 <- wrt source file 2024-08-20T21:38:29.7125337Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::help:0 2024-08-20T21:38:29.7126753Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::_load_local:0, line 667 <- wrt source file 2024-08-20T21:38:29.7128454Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::_load_local:0 2024-08-20T21:38:29.7129980Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.define:0, line 129 <- wrt source file 2024-08-20T21:38:29.7131512Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.define:0 2024-08-20T21:38:29.7133170Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library._impl_with_aoti_compile:0, line 217 <- wrt source file 2024-08-20T21:38:29.7206436Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library._impl_with_aoti_compile:0 2024-08-20T21:38:29.7208188Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.impl:0, line 268 <- wrt source file 2024-08-20T21:38:29.7211225Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.impl:0 2024-08-20T21:38:29.7212736Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::define:0, line 452 <- wrt source file 2024-08-20T21:38:29.7316720Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::define:0 2024-08-20T21:38:29.7318178Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::impl:0, line 519 <- wrt source file 2024-08-20T21:38:29.7326199Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::impl:0 2024-08-20T21:38:29.7327888Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_kernel:0, line 641 <- wrt source file 2024-08-20T21:38:29.7329434Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_kernel:0 2024-08-20T21:38:29.7331058Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_torch_dispatch:0, line 958 <- wrt source file 2024-08-20T21:38:29.7419947Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_torch_dispatch:0 2024-08-20T21:38:29.7421569Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_vmap:0, line 1047 <- wrt source file 2024-08-20T21:38:29.7553274Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_vmap:0 2024-08-20T21:38:29.7554909Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_ignored_functions:0, line 111 <- wrt source file 2024-08-20T21:38:29.7559831Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_ignored_functions:0 2024-08-20T21:38:29.7561532Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_testing_overrides:0, line 418 <- wrt source file 2024-08-20T21:38:29.7595123Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_testing_overrides:0 2024-08-20T21:38:29.7596803Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::wrap_torch_function:0, line 1564 <- wrt source file 2024-08-20T21:38:29.7598769Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::wrap_torch_function:0 2024-08-20T21:38:29.7600520Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::handle_torch_function:0, line 1699 <- wrt source file 2024-08-20T21:38:29.7602350Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::handle_torch_function:0 2024-08-20T21:38:29.7604306Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_method_or_property:0, line 1947 <- wrt source file 2024-08-20T21:38:29.7629653Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_method_or_property:0 2024-08-20T21:38:29.7631325Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_like:0, line 1966 <- wrt source file 2024-08-20T21:38:29.7636631Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_like:0 2024-08-20T21:38:29.7638770Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/quasirandom.py::SobolEngine:0, line 39 <- wrt source file 2024-08-20T21:38:29.7640454Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/quasirandom.py::SobolEngine:0 2024-08-20T21:38:29.7642096Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::add_safe_globals:0, line 216 <- wrt source file 2024-08-20T21:38:29.7644110Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::add_safe_globals:0 2024-08-20T21:38:29.7646280Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::safe_globals:0, line 241 <- wrt source file 2024-08-20T21:38:29.7648188Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::safe_globals:0 2024-08-20T21:38:29.7650258Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::register_package:0, line 304 <- wrt source file 2024-08-20T21:38:29.7652110Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::register_package:0 2024-08-20T21:38:29.7653772Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::save:0, line 765 <- wrt source file 2024-08-20T21:38:29.7655334Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::save:0 2024-08-20T21:38:29.7657239Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/torch_version.py::TorchVersion:0, line 18 <- wrt source file 2024-08-20T21:38:29.7658894Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/torch_version.py::TorchVersion:0 2024-08-20T21:38:29.7660540Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_mode_options:0, line 134 <- wrt source file 2024-08-20T21:38:29.7662213Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_mode_options:0 2024-08-20T21:38:29.7663892Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_options:0, line 164 <- wrt source file 2024-08-20T21:38:29.7665517Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_options:0 2024-08-20T21:38:29.7667286Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims_common/__init__.py::compute_required_storage_length:0, line 1747 <- wrt source file 2024-08-20T21:38:29.7669163Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims_common/__init__.py::compute_required_storage_length:0 2024-08-20T21:38:29.7670950Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::allow_in_graph:0, line 103 <- wrt source file 2024-08-20T21:38:29.7672590Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::allow_in_graph:0 2024-08-20T21:38:29.7674405Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::substitute_in_graph:0, line 146 <- wrt source file 2024-08-20T21:38:30.5914800Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::substitute_in_graph:0 2024-08-20T21:38:30.5916740Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::wrap_numpy:0, line 255 <- wrt source file 2024-08-20T21:38:30.5918483Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::wrap_numpy:0 2024-08-20T21:38:30.5920556Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_compiling:0, line 286 <- wrt source file 2024-08-20T21:38:30.5922691Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_compiling:0 2024-08-20T21:38:30.5924799Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0, line 307 <- wrt source file 2024-08-20T21:38:30.5926856Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0 2024-08-20T21:38:30.5929007Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::save:0, line 214 <- wrt source file 2024-08-20T21:38:30.5931034Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::save:0 2024-08-20T21:38:30.5932819Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::load:0, line 298 <- wrt source file 2024-08-20T21:38:30.5934322Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::load:0 2024-08-20T21:38:30.5935933Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::register_dataclass:0, line 396 <- wrt source file 2024-08-20T21:38:30.5937616Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::register_dataclass:0 2024-08-20T21:38:30.5939351Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::Future.add_done_callback:0, line 196 <- wrt source file 2024-08-20T21:38:30.5941117Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::Future.add_done_callback:0 2024-08-20T21:38:30.5942875Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::Future.set_exception:0, line 258 <- wrt source file 2024-08-20T21:38:30.5944573Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::Future.set_exception:0 2024-08-20T21:38:30.5946352Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::collect_all:0, line 289 <- wrt source file 2024-08-20T21:38:30.5948175Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::collect_all:0 2024-08-20T21:38:30.5949760Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/__init__.py::annotate:0, line 146 <- wrt source file 2024-08-20T21:38:30.5951266Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/__init__.py::annotate:0 2024-08-20T21:38:30.5952941Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/monitor/__init__.py::TensorboardEventHandler:0, line 22 <- wrt source file 2024-08-20T21:38:30.5954706Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/monitor/__init__.py::TensorboardEventHandler:0 2024-08-20T21:38:30.5956440Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::as_nested_tensor:0, line 56 <- wrt source file 2024-08-20T21:38:30.6007904Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::as_nested_tensor:0 2024-08-20T21:38:30.6011142Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor:0, line 215 <- wrt source file 2024-08-20T21:38:30.6014039Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor:0 2024-08-20T21:38:30.6016907Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::narrow:0, line 277 <- wrt source file 2024-08-20T21:38:30.6085006Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::narrow:0 2024-08-20T21:38:30.6088082Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor_from_jagged:0, line 361 <- wrt source file 2024-08-20T21:38:30.6106066Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor_from_jagged:0 2024-08-20T21:38:30.6109343Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::check_sparse_tensor_invariants:0, line 454 <- wrt source file 2024-08-20T21:38:30.6136265Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::check_sparse_tensor_invariants:0 2024-08-20T21:38:30.6139750Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::as_sparse_gradcheck:0, line 540 <- wrt source file 2024-08-20T21:38:30.6237499Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::as_sparse_gradcheck:0 2024-08-20T21:38:30.6240637Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/decorators.py::substitute_in_graph:0, line 190 <- wrt source file 2024-08-20T21:38:30.6243774Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/decorators.py::substitute_in_graph:0 2024-08-20T21:38:30.6247308Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/variables/base.py::VariableTracker.python_type:0, line 203 <- wrt source file 2024-08-20T21:38:30.6250733Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/variables/base.py::VariableTracker.python_type:0 2024-08-20T21:38:30.6254015Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/aot_autograd.py::aot_function:0, line 825 <- wrt source file 2024-08-20T21:38:30.6503344Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/aot_autograd.py::aot_function:0 2024-08-20T21:38:30.6506479Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/apis.py::grad:0, line 324 <- wrt source file 2024-08-20T21:38:30.6509191Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/apis.py::grad:0 2024-08-20T21:38:30.6512271Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/benchmark_utils.py::benchmark_utilization:0, line 184 <- wrt source file 2024-08-20T21:38:30.6515590Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/benchmark_utils.py::benchmark_utilization:0 2024-08-20T21:38:30.6518786Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::vjp:0, line 271 <- wrt source file 2024-08-20T21:38:30.6541946Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::vjp:0 2024-08-20T21:38:30.6545384Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacrev:0, line 510 <- wrt source file 2024-08-20T21:38:30.6619385Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacrev:0 2024-08-20T21:38:30.6622522Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jvp:0, line 1064 <- wrt source file 2024-08-20T21:38:30.7594941Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jvp:0 2024-08-20T21:38:30.7598045Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0, line 1219 <- wrt source file 2024-08-20T21:38:30.7653455Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0 2024-08-20T21:38:30.7656637Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::hessian:0, line 1384 <- wrt source file 2024-08-20T21:38:30.7672098Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::hessian:0 2024-08-20T21:38:30.7675302Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::functionalize:0, line 1548 <- wrt source file 2024-08-20T21:38:30.7678529Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::functionalize:0 2024-08-20T21:38:30.7681955Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::linearize:0, line 1748 <- wrt source file 2024-08-20T21:38:30.7841850Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::linearize:0 2024-08-20T21:38:30.7845078Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/functional_call.py::functional_call:0, line 36 <- wrt source file 2024-08-20T21:38:30.7848515Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/functional_call.py::functional_call:0 2024-08-20T21:38:30.7851649Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/fx_minifier.py::minifier:0, line 194 <- wrt source file 2024-08-20T21:38:30.7854614Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/fx_minifier.py::minifier:0 2024-08-20T21:38:30.7858097Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py::CompilerWrapper.post_compile:0, line 110 <- wrt source file 2024-08-20T21:38:30.7861965Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py::CompilerWrapper.post_compile:0 2024-08-20T21:38:30.7865617Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/associative_scan.py::associative_scan:0, line 69 <- wrt source file 2024-08-20T21:38:30.7869003Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/associative_scan.py::associative_scan:0 2024-08-20T21:38:30.7872134Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/cond.py::cond:0, line 81 <- wrt source file 2024-08-20T21:38:30.7874997Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/cond.py::cond:0 2024-08-20T21:38:30.7878018Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/while_loop.py::while_loop:0, line 90 <- wrt source file 2024-08-20T21:38:30.7881140Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/while_loop.py::while_loop:0 2024-08-20T21:38:30.7884780Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/cpp_builder.py::get_name_and_dir_from_output_file_path:0, line 1166 <- wrt source file 2024-08-20T21:38:30.7888403Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/cpp_builder.py::get_name_and_dir_from_output_file_path:0 2024-08-20T21:38:30.7891667Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::custom_op:0, line 83 <- wrt source file 2024-08-20T21:38:30.8218391Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::custom_op:0 2024-08-20T21:38:30.8220202Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.set_kernel_enabled:0, line 212 <- wrt source file 2024-08-20T21:38:30.8287238Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.set_kernel_enabled:0 2024-08-20T21:38:30.8289315Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0, line 281 <- wrt source file 2024-08-20T21:38:30.8291182Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0 2024-08-20T21:38:30.8293042Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_fake:0, line 401 <- wrt source file 2024-08-20T21:38:30.8349960Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_fake:0 2024-08-20T21:38:30.8351852Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0, line 524 <- wrt source file 2024-08-20T21:38:30.8480221Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0 2024-08-20T21:38:30.8482121Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_vmap:0, line 698 <- wrt source file 2024-08-20T21:38:30.8609584Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_vmap:0 2024-08-20T21:38:30.8611500Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_class_registry.py::register_fake_class:0, line 184 <- wrt source file 2024-08-20T21:38:30.8613348Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_class_registry.py::register_fake_class:0 2024-08-20T21:38:30.8615222Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0, line 159 <- wrt source file 2024-08-20T21:38:30.8666221Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0 2024-08-20T21:38:30.8667990Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/infer_schema.py::infer_schema:0, line 45 <- wrt source file 2024-08-20T21:38:30.8672175Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/infer_schema.py::infer_schema:0 2024-08-20T21:38:30.8674079Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_logging/_internal.py::set_logs:0, line 417 <- wrt source file 2024-08-20T21:38:30.8675683Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_logging/_internal.py::set_logs:0 2024-08-20T21:38:30.8677575Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_equal:0, line 170 <- wrt source file 2024-08-20T21:38:30.8712943Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_equal:0 2024-08-20T21:38:30.8714758Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::print_assert_equal:0, line 305 <- wrt source file 2024-08-20T21:38:30.8716506Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::print_assert_equal:0 2024-08-20T21:38:30.8718285Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0, line 996 <- wrt source file 2024-08-20T21:38:30.8762151Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0 2024-08-20T21:38:30.8764001Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0, line 1061 <- wrt source file 2024-08-20T21:38:30.8765762Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0 2024-08-20T21:38:30.8767606Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0, line 1282 <- wrt source file 2024-08-20T21:38:30.8829836Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0 2024-08-20T21:38:30.8831916Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0, line 1348 <- wrt source file 2024-08-20T21:38:30.8834012Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0 2024-08-20T21:38:30.8835874Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0, line 1411 <- wrt source file 2024-08-20T21:38:30.8838073Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0 2024-08-20T21:38:30.8839904Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0, line 1456 <- wrt source file 2024-08-20T21:38:30.8841551Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0 2024-08-20T21:38:30.8843244Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_warns:0, line 1566 <- wrt source file 2024-08-20T21:38:30.8845640Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_warns:0 2024-08-20T21:38:30.8848074Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims/context.py::TorchRefsMode:0, line 85 <- wrt source file 2024-08-20T21:38:30.8850266Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims/context.py::TorchRefsMode:0 2024-08-20T21:38:30.8852178Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/amp/grad_scaler.py::GradScaler:0, line 60 <- wrt source file 2024-08-20T21:38:30.8853776Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/amp/grad_scaler.py::GradScaler:0 2024-08-20T21:38:30.8855608Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0, line 23 <- wrt source file 2024-08-20T21:38:30.8857593Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0 2024-08-20T21:38:30.8859902Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0, line 22 <- wrt source file 2024-08-20T21:38:30.8862110Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0 2024-08-20T21:38:30.8864267Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0, line 25 <- wrt source file 2024-08-20T21:38:30.8866353Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0 2024-08-20T21:38:30.8868487Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0, line 66 <- wrt source file 2024-08-20T21:38:30.8870634Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0 2024-08-20T21:38:30.8872753Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0, line 140 <- wrt source file 2024-08-20T21:38:30.8874825Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0 2024-08-20T21:38:30.8876875Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0, line 25 <- wrt source file 2024-08-20T21:38:30.8915505Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0 2024-08-20T21:38:30.8917764Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0, line 318 <- wrt source file 2024-08-20T21:38:30.8947233Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0 2024-08-20T21:38:30.8950186Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0, line 210 <- wrt source file 2024-08-20T21:38:30.8952585Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0 2024-08-20T21:38:30.8954369Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0, line 282 <- wrt source file 2024-08-20T21:38:30.8956087Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0 2024-08-20T21:38:30.8958068Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0, line 358 <- wrt source file 2024-08-20T21:38:30.8959809Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0 2024-08-20T21:38:30.8961875Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/__init__.py::Quantize:0, line 95 <- wrt source file 2024-08-20T21:38:30.8964363Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/__init__.py::Quantize:0 2024-08-20T21:38:30.8966696Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/__init__.py::DeQuantize:0, line 145 <- wrt source file 2024-08-20T21:38:30.8969628Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/__init__.py::DeQuantize:0 2024-08-20T21:38:30.8972817Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0, line 42 <- wrt source file 2024-08-20T21:38:30.8974738Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0 2024-08-20T21:38:30.8977365Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0, line 123 <- wrt source file 2024-08-20T21:38:30.8980039Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0 2024-08-20T21:38:30.8982185Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0, line 207 <- wrt source file 2024-08-20T21:38:30.8984092Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0 2024-08-20T21:38:30.8986097Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0, line 293 <- wrt source file 2024-08-20T21:38:30.8988144Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0 2024-08-20T21:38:30.8990213Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0, line 375 <- wrt source file 2024-08-20T21:38:30.8992403Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0 2024-08-20T21:38:30.8994490Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0, line 457 <- wrt source file 2024-08-20T21:38:30.8996545Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0 2024-08-20T21:38:30.8998572Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0, line 30 <- wrt source file 2024-08-20T21:38:30.9001114Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0 2024-08-20T21:38:30.9003056Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0, line 516 <- wrt source file 2024-08-20T21:38:30.9004966Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0 2024-08-20T21:38:30.9006850Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRU:0, line 801 <- wrt source file 2024-08-20T21:38:30.9008869Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRU:0 2024-08-20T21:38:30.9011436Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0, line 1203 <- wrt source file 2024-08-20T21:38:30.9013593Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0 2024-08-20T21:38:30.9015599Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0, line 1269 <- wrt source file 2024-08-20T21:38:30.9018067Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0 2024-08-20T21:38:30.9020326Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0, line 1322 <- wrt source file 2024-08-20T21:38:30.9023833Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0 2024-08-20T21:38:30.9026537Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/activation.py::ReLU6:0, line 36 <- wrt source file 2024-08-20T21:38:30.9028406Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/activation.py::ReLU6:0 2024-08-20T21:38:30.9030230Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0, line 506 <- wrt source file 2024-08-20T21:38:30.9031987Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0 2024-08-20T21:38:30.9033771Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0, line 635 <- wrt source file 2024-08-20T21:38:30.9035543Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0 2024-08-20T21:38:30.9037396Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0, line 892 <- wrt source file 2024-08-20T21:38:30.9039294Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0 2024-08-20T21:38:30.9041343Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0, line 1014 <- wrt source file 2024-08-20T21:38:30.9043253Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0 2024-08-20T21:38:30.9045172Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0, line 1140 <- wrt source file 2024-08-20T21:38:30.9047312Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0 2024-08-20T21:38:30.9049273Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0, line 112 <- wrt source file 2024-08-20T21:38:30.9051223Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0 2024-08-20T21:38:30.9053208Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0, line 276 <- wrt source file 2024-08-20T21:38:30.9055225Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0 2024-08-20T21:38:30.9057296Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0, line 24 <- wrt source file 2024-08-20T21:38:30.9059706Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0 2024-08-20T21:38:30.9062856Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0, line 177 <- wrt source file 2024-08-20T21:38:30.9065046Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0 2024-08-20T21:38:30.9068457Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/linear.py::Linear:0, line 138 <- wrt source file 2024-08-20T21:38:30.9070782Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/linear.py::Linear:0 2024-08-20T21:38:30.9072918Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py::ActivationSparsifier:0, line 62 <- wrt source file 2024-08-20T21:38:30.9075327Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py::ActivationSparsifier:0 2024-08-20T21:38:30.9078311Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py::BaseDataScheduler.get_schedule_param:0, line 98 <- wrt source file 2024-08-20T21:38:30.9080823Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py::BaseDataScheduler.get_schedule_param:0 2024-08-20T21:38:30.9083276Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0, line 55 <- wrt source file 2024-08-20T21:38:30.9086198Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0 2024-08-20T21:38:30.9088384Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0, line 22 <- wrt source file 2024-08-20T21:38:30.9090942Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0 2024-08-20T21:38:30.9092949Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0, line 47 <- wrt source file 2024-08-20T21:38:30.9094957Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0 2024-08-20T21:38:30.9097260Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0, line 178 <- wrt source file 2024-08-20T21:38:30.9100435Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0 2024-08-20T21:38:30.9103000Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0, line 31 <- wrt source file 2024-08-20T21:38:30.9105967Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0 2024-08-20T21:38:30.9109014Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0, line 76 <- wrt source file 2024-08-20T21:38:30.9112633Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0 2024-08-20T21:38:30.9116268Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0, line 130 <- wrt source file 2024-08-20T21:38:30.9119745Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0 2024-08-20T21:38:30.9122177Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0, line 163 <- wrt source file 2024-08-20T21:38:30.9125389Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0 2024-08-20T21:38:30.9127557Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/observer.py::_with_args:0, line 93 <- wrt source file 2024-08-20T21:38:30.9129989Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/observer.py::_with_args:0 2024-08-20T21:38:30.9133324Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0, line 115 <- wrt source file 2024-08-20T21:38:30.9135348Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0 2024-08-20T21:38:30.9137390Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::fuse_fx:0, line 218 <- wrt source file 2024-08-20T21:38:30.9139767Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::fuse_fx:0 2024-08-20T21:38:30.9142197Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0, line 286 <- wrt source file 2024-08-20T21:38:30.9144943Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0 2024-08-20T21:38:30.9146931Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0, line 424 <- wrt source file 2024-08-20T21:38:30.9148831Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0 2024-08-20T21:38:30.9151213Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0, line 595 <- wrt source file 2024-08-20T21:38:30.9153793Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0 2024-08-20T21:38:30.9155925Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0, line 654 <- wrt source file 2024-08-20T21:38:30.9158254Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0 2024-08-20T21:38:30.9160925Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::_convert_to_reference_decomposed_fx:0, line 706 <- wrt source file 2024-08-20T21:38:30.9163309Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::_convert_to_reference_decomposed_fx:0 2024-08-20T21:38:30.9165608Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_pt2e:0, line 49 <- wrt source file 2024-08-20T21:38:30.9168159Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_pt2e:0 2024-08-20T21:38:30.9170676Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0, line 123 <- wrt source file 2024-08-20T21:38:30.9172892Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0 2024-08-20T21:38:30.9175027Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0, line 215 <- wrt source file 2024-08-20T21:38:30.9177139Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0 2024-08-20T21:38:30.9178983Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0, line 145 <- wrt source file 2024-08-20T21:38:30.9180888Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0 2024-08-20T21:38:30.9182929Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0, line 512 <- wrt source file 2024-08-20T21:38:30.9184846Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0 2024-08-20T21:38:30.9186691Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0, line 534 <- wrt source file 2024-08-20T21:38:30.9188502Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0 2024-08-20T21:38:30.9190339Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0, line 548 <- wrt source file 2024-08-20T21:38:30.9192145Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0 2024-08-20T21:38:30.9193948Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0, line 570 <- wrt source file 2024-08-20T21:38:30.9195708Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0 2024-08-20T21:38:30.9197596Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0, line 696 <- wrt source file 2024-08-20T21:38:30.9199430Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0 2024-08-20T21:38:30.9202007Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/backend_config/onednn.py::_fuse_linear_bn_leaky_relu:0, line 85 <- wrt source file 2024-08-20T21:38:30.9204080Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/backend_config/onednn.py::_fuse_linear_bn_leaky_relu:0 2024-08-20T21:38:30.9206153Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report.py::ModelReport:0, line 84 <- wrt source file 2024-08-20T21:38:30.9208234Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report.py::ModelReport:0 2024-08-20T21:38:30.9210281Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/pt2e/prepare.py::_get_edge_or_node_to_group_id:0, line 185 <- wrt source file 2024-08-20T21:38:30.9212382Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/pt2e/prepare.py::_get_edge_or_node_to_group_id:0 2024-08-20T21:38:30.9214535Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/pt2e/utils.py::_replace_literals_with_new_placeholders:0, line 439 <- wrt source file 2024-08-20T21:38:30.9217542Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/pt2e/utils.py::_replace_literals_with_new_placeholders:0 2024-08-20T21:38:30.9220689Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/anomaly_mode.py::detect_anomaly:0, line 27 <- wrt source file 2024-08-20T21:38:30.9222439Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/anomaly_mode.py::detect_anomaly:0 2024-08-20T21:38:30.9224122Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::make_dual:0, line 83 <- wrt source file 2024-08-20T21:38:30.9225895Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::make_dual:0 2024-08-20T21:38:30.9227561Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::unpack_dual:0, line 153 <- wrt source file 2024-08-20T21:38:30.9229222Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::unpack_dual:0 2024-08-20T21:38:30.9231134Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::dual_level:0, line 189 <- wrt source file 2024-08-20T21:38:30.9232897Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::dual_level:0 2024-08-20T21:38:30.9234687Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.save_for_backward:0, line 66 <- wrt source file 2024-08-20T21:38:30.9236576Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.save_for_backward:0 2024-08-20T21:38:30.9238453Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.save_for_forward:0, line 109 <- wrt source file 2024-08-20T21:38:30.9240309Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.save_for_forward:0 2024-08-20T21:38:30.9242160Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.mark_dirty:0, line 160 <- wrt source file 2024-08-20T21:38:30.9244039Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.mark_dirty:0 2024-08-20T21:38:30.9245931Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.mark_non_differentiable:0, line 207 <- wrt source file 2024-08-20T21:38:30.9248527Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.mark_non_differentiable:0 2024-08-20T21:38:30.9251252Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.set_materialize_grads:0, line 236 <- wrt source file 2024-08-20T21:38:30.9254586Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.set_materialize_grads:0 2024-08-20T21:38:30.9257643Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::Function:0, line 479 <- wrt source file 2024-08-20T21:38:30.9260546Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::Function:0 2024-08-20T21:38:30.9262729Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::vjp:0, line 294 <- wrt source file 2024-08-20T21:38:30.9264717Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::vjp:0 2024-08-20T21:38:30.9266904Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::jvp:0, line 396 <- wrt source file 2024-08-20T21:38:30.9268764Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::jvp:0 2024-08-20T21:38:30.9270824Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::jacobian:0, line 631 <- wrt source file 2024-08-20T21:38:30.9273480Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::jacobian:0 2024-08-20T21:38:30.9275194Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::hessian:0, line 885 <- wrt source file 2024-08-20T21:38:30.9277721Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::hessian:0 2024-08-20T21:38:30.9280491Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::vhp:0, line 1001 <- wrt source file 2024-08-20T21:38:30.9282883Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::vhp:0 2024-08-20T21:38:30.9284928Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::hvp:0, line 1100 <- wrt source file 2024-08-20T21:38:30.9286795Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::hvp:0 2024-08-20T21:38:30.9288833Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::no_grad:0, line 50 <- wrt source file 2024-08-20T21:38:30.9290584Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::no_grad:0 2024-08-20T21:38:30.9292959Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::enable_grad:0, line 108 <- wrt source file 2024-08-20T21:38:30.9295816Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::enable_grad:0 2024-08-20T21:38:30.9298414Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::set_grad_enabled:0, line 166 <- wrt source file 2024-08-20T21:38:30.9300301Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::set_grad_enabled:0 2024-08-20T21:38:30.9302028Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::inference_mode:0, line 232 <- wrt source file 2024-08-20T21:38:30.9303723Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::inference_mode:0 2024-08-20T21:38:30.9305366Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.name:0, line 62 <- wrt source file 2024-08-20T21:38:30.9306929Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.name:0 2024-08-20T21:38:30.9308565Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.register_hook:0, line 112 <- wrt source file 2024-08-20T21:38:30.9310256Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.register_hook:0 2024-08-20T21:38:30.9312052Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.register_prehook:0, line 149 <- wrt source file 2024-08-20T21:38:30.9313781Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.register_prehook:0 2024-08-20T21:38:30.9315500Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::saved_tensors_hooks:0, line 272 <- wrt source file 2024-08-20T21:38:30.9317192Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::saved_tensors_hooks:0 2024-08-20T21:38:30.9319417Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::save_on_cpu:0, line 337 <- wrt source file 2024-08-20T21:38:30.9322209Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::save_on_cpu:0 2024-08-20T21:38:30.9325089Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::disable_saved_tensors_hooks:0, line 394 <- wrt source file 2024-08-20T21:38:30.9328095Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::disable_saved_tensors_hooks:0 2024-08-20T21:38:30.9329921Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::register_multi_grad_hook:0, line 471 <- wrt source file 2024-08-20T21:38:30.9332729Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::register_multi_grad_hook:0 2024-08-20T21:38:30.9335782Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::allow_mutation_on_saved_tensors:0, line 727 <- wrt source file 2024-08-20T21:38:30.9338515Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::allow_mutation_on_saved_tensors:0 2024-08-20T21:38:30.9340596Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::profile:0, line 176 <- wrt source file 2024-08-20T21:38:30.9343363Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::profile:0 2024-08-20T21:38:30.9349081Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::record_function:0, line 695 <- wrt source file 2024-08-20T21:38:30.9352001Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::record_function:0 2024-08-20T21:38:30.9354231Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::emit_itt:0, line 829 <- wrt source file 2024-08-20T21:38:30.9357204Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::emit_itt:0 2024-08-20T21:38:30.9360170Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::emit_nvtx:0, line 902 <- wrt source file 2024-08-20T21:38:30.9363046Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::emit_nvtx:0 2024-08-20T21:38:30.9364962Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_jit_fn:0, line 114 <- wrt source file 2024-08-20T21:38:30.9367287Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_jit_fn:0 2024-08-20T21:38:30.9369750Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_jit_fn:1, line 125 <- wrt source file 2024-08-20T21:38:30.9372077Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_jit_fn:1 2024-08-20T21:38:30.9374793Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_jit_fn:2, line 138 <- wrt source file 2024-08-20T21:38:30.9377386Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_jit_fn:2 2024-08-20T21:38:30.9379577Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_multi_output_jit_fn:0, line 171 <- wrt source file 2024-08-20T21:38:30.9381361Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_multi_output_jit_fn:0 2024-08-20T21:38:30.9383079Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/profiler.py::profile:0, line 75 <- wrt source file 2024-08-20T21:38:30.9385050Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/profiler.py::profile:0 2024-08-20T21:38:30.9386708Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::DeviceMesh:0, line 265 <- wrt source file 2024-08-20T21:38:30.9388493Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::DeviceMesh:0 2024-08-20T21:38:30.9391091Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::DeviceMesh.get_local_rank:0, line 682 <- wrt source file 2024-08-20T21:38:30.9393080Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::DeviceMesh.get_local_rank:0 2024-08-20T21:38:30.9394949Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::init_device_mesh:0, line 755 <- wrt source file 2024-08-20T21:38:30.9396728Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::init_device_mesh:0 2024-08-20T21:38:30.9399346Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::_coalescing_manager:0, line 2251 <- wrt source file 2024-08-20T21:38:30.9401260Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::_coalescing_manager:0 2024-08-20T21:38:30.9403166Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::batch_isend_irecv:0, line 2343 <- wrt source file 2024-08-20T21:38:30.9405011Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::batch_isend_irecv:0 2024-08-20T21:38:30.9406850Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_reduce:0, line 2452 <- wrt source file 2024-08-20T21:38:30.9408827Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_reduce:0 2024-08-20T21:38:30.9410690Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_object:0, line 2696 <- wrt source file 2024-08-20T21:38:30.9412603Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_object:0 2024-08-20T21:38:30.9414496Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::send_object_list:0, line 2896 <- wrt source file 2024-08-20T21:38:30.9416341Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::send_object_list:0 2024-08-20T21:38:30.9418220Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::recv_object_list:0, line 2989 <- wrt source file 2024-08-20T21:38:30.9420058Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::recv_object_list:0 2024-08-20T21:38:30.9421957Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::broadcast_object_list:0, line 3091 <- wrt source file 2024-08-20T21:38:30.9423940Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::broadcast_object_list:0 2024-08-20T21:38:30.9426546Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::scatter_object_list:0, line 3200 <- wrt source file 2024-08-20T21:38:30.9428447Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::scatter_object_list:0 2024-08-20T21:38:30.9430309Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather:0, line 3295 <- wrt source file 2024-08-20T21:38:30.9432084Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather:0 2024-08-20T21:38:30.9434062Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_into_tensor:0, line 3377 <- wrt source file 2024-08-20T21:38:30.9435982Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_into_tensor:0 2024-08-20T21:38:30.9437906Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_coalesced:0, line 3502 <- wrt source file 2024-08-20T21:38:30.9439807Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_coalesced:0 2024-08-20T21:38:30.9441662Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::scatter:0, line 3655 <- wrt source file 2024-08-20T21:38:30.9443404Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::scatter:0 2024-08-20T21:38:30.9445562Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::reduce_scatter_tensor:0, line 3790 <- wrt source file 2024-08-20T21:38:30.9448193Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::reduce_scatter_tensor:0 2024-08-20T21:38:30.9450163Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_to_all_single:0, line 3917 <- wrt source file 2024-08-20T21:38:30.9452809Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_to_all_single:0 2024-08-20T21:38:30.9464896Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_to_all:0, line 4029 <- wrt source file 2024-08-20T21:38:30.9467707Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_to_all:0 2024-08-20T21:38:30.9469652Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::monitored_barrier:0, line 4203 <- wrt source file 2024-08-20T21:38:30.9472021Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::monitored_barrier:0 2024-08-20T21:38:30.9473890Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::new_subgroups:0, line 4754 <- wrt source file 2024-08-20T21:38:30.9476512Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::new_subgroups:0 2024-08-20T21:38:30.9478470Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::new_subgroups_by_enumeration:0, line 4856 <- wrt source file 2024-08-20T21:38:30.9480511Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::new_subgroups_by_enumeration:0 2024-08-20T21:38:30.9483015Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/run.py::__doc__:0, line 87 <- wrt source file 2024-08-20T21:38:30.9484568Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/run.py::__doc__:0 2024-08-20T21:38:30.9486879Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/autograd/__init__.py::context:0, line 39 <- wrt source file 2024-08-20T21:38:30.9488826Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/autograd/__init__.py::context:0 2024-08-20T21:38:30.9490943Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/checkpoint_activation.py::checkpoint:0, line 48 <- wrt source file 2024-08-20T21:38:30.9493501Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/checkpoint_activation.py::checkpoint:0 2024-08-20T21:38:30.9495416Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/contract.py::contract:0, line 46 <- wrt source file 2024-08-20T21:38:30.9497215Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/contract.py::contract:0 2024-08-20T21:38:30.9499758Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/replicate.py::replicate:0, line 190 <- wrt source file 2024-08-20T21:38:30.9501597Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/replicate.py::replicate:0 2024-08-20T21:38:30.9503746Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_optim/__init__.py::named_params_with_sharded_tensor:0, line 30 <- wrt source file 2024-08-20T21:38:30.9506422Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_optim/__init__.py::named_params_with_sharded_tensor:0 2024-08-20T21:38:30.9508536Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py::custom_sharded_op_impl:0, line 457 <- wrt source file 2024-08-20T21:38:30.9511549Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py::custom_sharded_op_impl:0 2024-08-20T21:38:30.9513719Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/_ops/_common.py::_sharded_op_common:0, line 18 <- wrt source file 2024-08-20T21:38:30.9515790Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/_ops/_common.py::_sharded_op_common:0 2024-08-20T21:38:30.9518897Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_tensor/random.py::OffsetBasedRNGTracker._set_pre_op_offset:0, line 231 <- wrt source file 2024-08-20T21:38:30.9521400Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_tensor/random.py::OffsetBasedRNGTracker._set_pre_op_offset:0 2024-08-20T21:38:30.9524045Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_tensor/ops/_common_rules.py::pointwise_rule:0, line 234 <- wrt source file 2024-08-20T21:38:30.9526140Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_tensor/ops/_common_rules.py::pointwise_rule:0 2024-08-20T21:38:30.9528144Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_tools/memory_tracker.py::MemoryTracker:0, line 54 <- wrt source file 2024-08-20T21:38:30.9530444Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_tools/memory_tracker.py::MemoryTracker:0 2024-08-20T21:38:30.9532682Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/join.py::Join:0, line 141 <- wrt source file 2024-08-20T21:38:30.9534401Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/join.py::Join:0 2024-08-20T21:38:30.9537121Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/__init__.py::register_ddp_comm_hook:0, line 107 <- wrt source file 2024-08-20T21:38:30.9539262Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/__init__.py::register_ddp_comm_hook:0 2024-08-20T21:38:30.9541516Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/debugging_hooks.py::noop_hook:0, line 23 <- wrt source file 2024-08-20T21:38:30.9543605Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/debugging_hooks.py::noop_hook:0 2024-08-20T21:38:30.9545725Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::allreduce_hook:0, line 49 <- wrt source file 2024-08-20T21:38:30.9548021Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::allreduce_hook:0 2024-08-20T21:38:30.9550214Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::fp16_compress_hook:0, line 69 <- wrt source file 2024-08-20T21:38:30.9552381Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::fp16_compress_hook:0 2024-08-20T21:38:30.9554625Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::bf16_compress_hook:0, line 118 <- wrt source file 2024-08-20T21:38:30.9557557Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::bf16_compress_hook:0 2024-08-20T21:38:30.9559904Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::fp16_compress_wrapper:0, line 163 <- wrt source file 2024-08-20T21:38:30.9562806Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::fp16_compress_wrapper:0 2024-08-20T21:38:30.9565046Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::bf16_compress_wrapper:0, line 202 <- wrt source file 2024-08-20T21:38:30.9567348Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::bf16_compress_wrapper:0 2024-08-20T21:38:30.9569978Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py::batched_powerSGD_hook:0, line 705 <- wrt source file 2024-08-20T21:38:30.9573756Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py::batched_powerSGD_hook:0 2024-08-20T21:38:30.9576671Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/quantization_hooks.py::quantization_pertensor_hook:0, line 64 <- wrt source file 2024-08-20T21:38:30.9579118Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/quantization_hooks.py::quantization_pertensor_hook:0 2024-08-20T21:38:30.9581920Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/quantization_hooks.py::quantization_perchannel_hook:0, line 145 <- wrt source file 2024-08-20T21:38:30.9584363Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/quantization_hooks.py::quantization_perchannel_hook:0 2024-08-20T21:38:30.9586968Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict.py::get_state_dict:0, line 1077 <- wrt source file 2024-08-20T21:38:30.9588888Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict.py::get_state_dict:0 2024-08-20T21:38:30.9591095Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict.py::_patch_model_state_dict:0, line 1318 <- wrt source file 2024-08-20T21:38:30.9593561Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict.py::_patch_model_state_dict:0 2024-08-20T21:38:30.9596430Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict.py::_patch_optimizer_state_dict:0, line 1377 <- wrt source file 2024-08-20T21:38:30.9598590Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict.py::_patch_optimizer_state_dict:0 2024-08-20T21:38:30.9600866Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/elastic/rendezvous/api.py::RendezvousHandler.shutdown:0, line 213 <- wrt source file 2024-08-20T21:38:30.9603335Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/elastic/rendezvous/api.py::RendezvousHandler.shutdown:0 2024-08-20T21:38:30.9606591Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/elastic/utils/distributed.py::get_free_port:0, line 136 <- wrt source file 2024-08-20T21:38:30.9608749Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/elastic/utils/distributed.py::get_free_port:0 2024-08-20T21:38:30.9610726Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/api.py::StateDictType:0, line 260 <- wrt source file 2024-08-20T21:38:30.9612447Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/api.py::StateDictType:0 2024-08-20T21:38:30.9614314Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/api.py::FullStateDictConfig:0, line 301 <- wrt source file 2024-08-20T21:38:30.9616109Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/api.py::FullStateDictConfig:0 2024-08-20T21:38:30.9618231Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel:0, line 140 <- wrt source file 2024-08-20T21:38:30.9620859Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel:0 2024-08-20T21:38:30.9623240Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel.shard_full_optim_state_dict:0, line 1486 <- wrt source file 2024-08-20T21:38:30.9625732Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel.shard_full_optim_state_dict:0 2024-08-20T21:38:30.9628288Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel.scatter_full_optim_state_dict:0, line 1606 <- wrt source file 2024-08-20T21:38:30.9630825Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel.scatter_full_optim_state_dict:0 2024-08-20T21:38:30.9633344Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel.rekey_optim_state_dict:0, line 1691 <- wrt source file 2024-08-20T21:38:30.9635823Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel.rekey_optim_state_dict:0 2024-08-20T21:38:30.9638698Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/sharded_grad_scaler.py::ShardedGradScaler:0, line 51 <- wrt source file 2024-08-20T21:38:30.9640693Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/sharded_grad_scaler.py::ShardedGradScaler:0 2024-08-20T21:38:30.9642753Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/wrap.py::CustomPolicy:0, line 236 <- wrt source file 2024-08-20T21:38:30.9645215Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/wrap.py::CustomPolicy:0 2024-08-20T21:38:30.9648218Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/functional.py::_all_gather_base:0, line 134 <- wrt source file 2024-08-20T21:38:30.9651146Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/functional.py::_all_gather_base:0 2024-08-20T21:38:30.9654463Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py::_apply_optimizer_in_backward:0, line 42 <- wrt source file 2024-08-20T21:38:30.9658185Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py::_apply_optimizer_in_backward:0 2024-08-20T21:38:30.9661926Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py::_get_in_backward_optimizers:0, line 113 <- wrt source file 2024-08-20T21:38:30.9665676Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py::_get_in_backward_optimizers:0 2024-08-20T21:38:30.9668761Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/named_optimizer.py::_NamedOptimizer:0, line 53 <- wrt source file 2024-08-20T21:38:30.9670699Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/named_optimizer.py::_NamedOptimizer:0 2024-08-20T21:38:30.9672633Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/utils.py::register_functional_optim:0, line 38 <- wrt source file 2024-08-20T21:38:30.9674520Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/utils.py::register_functional_optim:0 2024-08-20T21:38:30.9676381Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/pipelining/_IR.py::pipe_split:0, line 335 <- wrt source file 2024-08-20T21:38:30.9678136Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/pipelining/_IR.py::pipe_split:0 2024-08-20T21:38:30.9680084Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/pipelining/microbatch.py::TensorChunkSpec.from_tuple:0, line 82 <- wrt source file 2024-08-20T21:38:30.9682165Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/pipelining/microbatch.py::TensorChunkSpec.from_tuple:0 2024-08-20T21:38:30.9684268Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/pipelining/microbatch.py::TensorChunkSpec.from_dict:0, line 101 <- wrt source file 2024-08-20T21:38:30.9686339Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/pipelining/microbatch.py::TensorChunkSpec.from_dict:0 2024-08-20T21:38:30.9688328Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/api.py::_wait_all:0, line 175 <- wrt source file 2024-08-20T21:38:30.9689936Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/api.py::_wait_all:0 2024-08-20T21:38:30.9691724Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/api.py::shutdown:0, line 346 <- wrt source file 2024-08-20T21:38:30.9693352Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/api.py::shutdown:0 2024-08-20T21:38:30.9694991Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/api.py::remote:0, line 605 <- wrt source file 2024-08-20T21:38:30.9696576Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/api.py::remote:0 2024-08-20T21:38:30.9698215Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/api.py::rpc_sync:0, line 785 <- wrt source file 2024-08-20T21:38:30.9699835Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/api.py::rpc_sync:0 2024-08-20T21:38:30.9701496Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/api.py::rpc_async:0, line 877 <- wrt source file 2024-08-20T21:38:30.9703110Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/api.py::rpc_async:0 2024-08-20T21:38:30.9705068Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/server_process_global_profiler.py::_server_process_global_profile:0, line 61 <- wrt source file 2024-08-20T21:38:30.9707337Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/server_process_global_profiler.py::_server_process_global_profile:0 2024-08-20T21:38:30.9709462Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/api.py::parallelize_module:0, line 54 <- wrt source file 2024-08-20T21:38:30.9712282Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/api.py::parallelize_module:0 2024-08-20T21:38:30.9715424Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/ddp.py::_pre_dp_module_transform:0, line 88 <- wrt source file 2024-08-20T21:38:30.9717727Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/ddp.py::_pre_dp_module_transform:0 2024-08-20T21:38:30.9720286Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/loss.py::loss_parallel:0, line 54 <- wrt source file 2024-08-20T21:38:30.9722913Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/loss.py::loss_parallel:0 2024-08-20T21:38:30.9725007Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py::ColwiseParallel:0, line 62 <- wrt source file 2024-08-20T21:38:30.9727282Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py::ColwiseParallel:0 2024-08-20T21:38:30.9729268Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py::RowwiseParallel:0, line 180 <- wrt source file 2024-08-20T21:38:30.9731924Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py::RowwiseParallel:0 2024-08-20T21:38:30.9733935Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py::SequenceParallel:0, line 308 <- wrt source file 2024-08-20T21:38:30.9736511Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py::SequenceParallel:0 2024-08-20T21:38:30.9738461Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/bernoulli.py::Bernoulli:0, line 29 <- wrt source file 2024-08-20T21:38:30.9740552Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/bernoulli.py::Bernoulli:0 2024-08-20T21:38:30.9743242Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/beta.py::Beta:0, line 20 <- wrt source file 2024-08-20T21:38:30.9745936Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/beta.py::Beta:0 2024-08-20T21:38:30.9748125Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/binomial.py::Binomial:0, line 28 <- wrt source file 2024-08-20T21:38:30.9750324Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/binomial.py::Binomial:0 2024-08-20T21:38:30.9752518Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/categorical.py::Categorical:0, line 40 <- wrt source file 2024-08-20T21:38:30.9754587Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/categorical.py::Categorical:0 2024-08-20T21:38:30.9756490Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/cauchy.py::Cauchy:0, line 23 <- wrt source file 2024-08-20T21:38:30.9759277Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/cauchy.py::Cauchy:0 2024-08-20T21:38:30.9762076Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/chi2.py::Chi2:0, line 15 <- wrt source file 2024-08-20T21:38:30.9763898Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/chi2.py::Chi2:0 2024-08-20T21:38:30.9766294Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/constraints.py::is_dependent:0, line 160 <- wrt source file 2024-08-20T21:38:30.9769232Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/constraints.py::is_dependent:0 2024-08-20T21:38:30.9772478Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/constraints.py::_DependentProperty:0, line 181 <- wrt source file 2024-08-20T21:38:30.9775864Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/constraints.py::_DependentProperty:0 2024-08-20T21:38:30.9779340Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/continuous_bernoulli.py::ContinuousBernoulli:0, line 34 <- wrt source file 2024-08-20T21:38:30.9782689Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/continuous_bernoulli.py::ContinuousBernoulli:0 2024-08-20T21:38:30.9785411Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/dirichlet.py::Dirichlet:0, line 39 <- wrt source file 2024-08-20T21:38:30.9788280Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/dirichlet.py::Dirichlet:0 2024-08-20T21:38:30.9791351Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/exponential.py::Exponential:0, line 19 <- wrt source file 2024-08-20T21:38:30.9793928Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/exponential.py::Exponential:0 2024-08-20T21:38:30.9796416Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/fishersnedecor.py::FisherSnedecor:0, line 21 <- wrt source file 2024-08-20T21:38:30.9799497Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/fishersnedecor.py::FisherSnedecor:0 2024-08-20T21:38:30.9802873Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/gamma.py::Gamma:0, line 23 <- wrt source file 2024-08-20T21:38:30.9804480Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/gamma.py::Gamma:0 2024-08-20T21:38:30.9806135Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/geometric.py::Geometric:0, line 34 <- wrt source file 2024-08-20T21:38:30.9807916Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/geometric.py::Geometric:0 2024-08-20T21:38:30.9809600Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/gumbel.py::Gumbel:0, line 21 <- wrt source file 2024-08-20T21:38:30.9811210Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/gumbel.py::Gumbel:0 2024-08-20T21:38:30.9812896Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/half_cauchy.py::HalfCauchy:0, line 23 <- wrt source file 2024-08-20T21:38:30.9814705Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/half_cauchy.py::HalfCauchy:0 2024-08-20T21:38:30.9816521Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/half_normal.py::HalfNormal:0, line 23 <- wrt source file 2024-08-20T21:38:30.9818245Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/half_normal.py::HalfNormal:0 2024-08-20T21:38:30.9820109Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/independent.py::Independent:0, line 23 <- wrt source file 2024-08-20T21:38:30.9821875Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/independent.py::Independent:0 2024-08-20T21:38:30.9823673Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/inverse_gamma.py::InverseGamma:0, line 21 <- wrt source file 2024-08-20T21:38:30.9825465Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/inverse_gamma.py::InverseGamma:0 2024-08-20T21:38:30.9827337Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/kumaraswamy.py::Kumaraswamy:0, line 28 <- wrt source file 2024-08-20T21:38:30.9829097Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/kumaraswamy.py::Kumaraswamy:0 2024-08-20T21:38:30.9830824Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/laplace.py::Laplace:0, line 19 <- wrt source file 2024-08-20T21:38:30.9832460Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/laplace.py::Laplace:0 2024-08-20T21:38:30.9834168Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/lkj_cholesky.py::LKJCholesky:0, line 41 <- wrt source file 2024-08-20T21:38:30.9835923Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/lkj_cholesky.py::LKJCholesky:0 2024-08-20T21:38:30.9837672Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/log_normal.py::LogNormal:0, line 20 <- wrt source file 2024-08-20T21:38:30.9839564Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/log_normal.py::LogNormal:0 2024-08-20T21:38:30.9842499Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/logistic_normal.py::LogisticNormal:0, line 25 <- wrt source file 2024-08-20T21:38:30.9845463Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/logistic_normal.py::LogisticNormal:0 2024-08-20T21:38:30.9848765Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/lowrank_multivariate_normal.py::LowRankMultivariateNormal:0, line 61 <- wrt source file 2024-08-20T21:38:30.9851909Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/lowrank_multivariate_normal.py::LowRankMultivariateNormal:0 2024-08-20T21:38:30.9853892Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/multinomial.py::Multinomial:0, line 36 <- wrt source file 2024-08-20T21:38:30.9855666Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/multinomial.py::Multinomial:0 2024-08-20T21:38:30.9857560Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/multivariate_normal.py::MultivariateNormal:0, line 101 <- wrt source file 2024-08-20T21:38:30.9859534Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/multivariate_normal.py::MultivariateNormal:0 2024-08-20T21:38:30.9861330Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/normal.py::Normal:0, line 21 <- wrt source file 2024-08-20T21:38:30.9862933Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/normal.py::Normal:0 2024-08-20T21:38:30.9864732Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/one_hot_categorical.py::OneHotCategorical:0, line 31 <- wrt source file 2024-08-20T21:38:30.9866791Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/one_hot_categorical.py::OneHotCategorical:0 2024-08-20T21:38:30.9868575Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/pareto.py::Pareto:0, line 17 <- wrt source file 2024-08-20T21:38:30.9870481Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/pareto.py::Pareto:0 2024-08-20T21:38:30.9873296Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/poisson.py::Poisson:0, line 23 <- wrt source file 2024-08-20T21:38:30.9874965Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/poisson.py::Poisson:0 2024-08-20T21:38:30.9876645Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/studentT.py::StudentT:0, line 21 <- wrt source file 2024-08-20T21:38:30.9878327Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/studentT.py::StudentT:0 2024-08-20T21:38:30.9880077Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/transforms.py::CatTransform:0, line 1033 <- wrt source file 2024-08-20T21:38:30.9881844Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/transforms.py::CatTransform:0 2024-08-20T21:38:30.9883634Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/transforms.py::StackTransform:0, line 1139 <- wrt source file 2024-08-20T21:38:30.9885437Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/transforms.py::StackTransform:0 2024-08-20T21:38:30.9887456Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/transforms.py::CumulativeDistributionTransform:0, line 1213 <- wrt source file 2024-08-20T21:38:30.9889519Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/transforms.py::CumulativeDistributionTransform:0 2024-08-20T21:38:30.9891357Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/uniform.py::Uniform:0, line 21 <- wrt source file 2024-08-20T21:38:30.9893123Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/uniform.py::Uniform:0 2024-08-20T21:38:30.9894791Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/utils.py::clamp_probs:0, line 107 <- wrt source file 2024-08-20T21:38:30.9896452Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/utils.py::clamp_probs:0 2024-08-20T21:38:30.9898133Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/von_mises.py::VonMises:0, line 115 <- wrt source file 2024-08-20T21:38:30.9899821Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/von_mises.py::VonMises:0 2024-08-20T21:38:30.9901495Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/weibull.py::Weibull:0, line 19 <- wrt source file 2024-08-20T21:38:30.9903138Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/weibull.py::Weibull:0 2024-08-20T21:38:30.9904783Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/wishart.py::Wishart:0, line 40 <- wrt source file 2024-08-20T21:38:30.9906415Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/wishart.py::Wishart:0 2024-08-20T21:38:30.9908132Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:0, line 720 <- wrt source file 2024-08-20T21:38:30.9909954Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:0 2024-08-20T21:38:30.9911592Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/graph.py::_snake_case:0, line 84 <- wrt source file 2024-08-20T21:38:30.9913098Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/graph.py::_snake_case:0 2024-08-20T21:38:30.9914725Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/graph.py::Graph.eliminate_dead_code:0, line 1603 <- wrt source file 2024-08-20T21:38:30.9916399Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/graph.py::Graph.eliminate_dead_code:0 2024-08-20T21:38:30.9918095Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/graph.py::Graph.on_generate_code:0, line 1677 <- wrt source file 2024-08-20T21:38:30.9919750Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/graph.py::Graph.on_generate_code:0 2024-08-20T21:38:30.9921501Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/interpreter.py::Interpreter:0, line 41 <- wrt source file 2024-08-20T21:38:30.9923080Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/interpreter.py::Interpreter:0 2024-08-20T21:38:30.9924710Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/interpreter.py::Transformer:0, line 407 <- wrt source file 2024-08-20T21:38:30.9926310Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/interpreter.py::Transformer:0 2024-08-20T21:38:30.9928079Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/subgraph_rewriter.py::replace_pattern:0, line 114 <- wrt source file 2024-08-20T21:38:30.9929791Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/subgraph_rewriter.py::replace_pattern:0 2024-08-20T21:38:30.9931471Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/tensor_type.py::TensorType:0, line 12 <- wrt source file 2024-08-20T21:38:30.9933037Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/tensor_type.py::TensorType:0 2024-08-20T21:38:30.9934715Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/tensor_type.py::is_consistent:0, line 63 <- wrt source file 2024-08-20T21:38:30.9936302Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/tensor_type.py::is_consistent:0 2024-08-20T21:38:30.9937940Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/tensor_type.py::is_more_precise:0, line 89 <- wrt source file 2024-08-20T21:38:30.9939565Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/tensor_type.py::is_more_precise:0 2024-08-20T21:38:30.9941396Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/rewriter.py::AST_Rewriter.visit_AnnAssign:0, line 94 <- wrt source file 2024-08-20T21:38:30.9943353Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/rewriter.py::AST_Rewriter.visit_AnnAssign:0 2024-08-20T21:38:30.9945276Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/core.py::reify:0, line 43 <- wrt source file 2024-08-20T21:38:30.9948082Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/core.py::reify:0 2024-08-20T21:38:30.9949988Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/match.py::VarDispatcher:0, line 43 <- wrt source file 2024-08-20T21:38:30.9952030Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/match.py::VarDispatcher:0 2024-08-20T21:38:30.9953951Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/more.py::unifiable:0, line 11 <- wrt source file 2024-08-20T21:38:30.9955815Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/more.py::unifiable:0 2024-08-20T21:38:30.9957707Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/more.py::reify_object:0, line 37 <- wrt source file 2024-08-20T21:38:30.9959567Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/more.py::reify_object:0 2024-08-20T21:38:30.9961470Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/more.py::unify_object:0, line 92 <- wrt source file 2024-08-20T21:38:30.9963359Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/more.py::unify_object:0 2024-08-20T21:38:30.9965325Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::merge:0, line 22 <- wrt source file 2024-08-20T21:38:30.9967392Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::merge:0 2024-08-20T21:38:30.9969432Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::merge_with:0, line 49 <- wrt source file 2024-08-20T21:38:30.9971472Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::merge_with:0 2024-08-20T21:38:30.9973521Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::valmap:0, line 75 <- wrt source file 2024-08-20T21:38:30.9975505Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::valmap:0 2024-08-20T21:38:30.9977526Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::keymap:0, line 91 <- wrt source file 2024-08-20T21:38:30.9979635Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::keymap:0 2024-08-20T21:38:30.9981670Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::itemmap:0, line 107 <- wrt source file 2024-08-20T21:38:30.9983684Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::itemmap:0 2024-08-20T21:38:30.9985727Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::valfilter:0, line 123 <- wrt source file 2024-08-20T21:38:30.9987769Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::valfilter:0 2024-08-20T21:38:30.9989828Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::keyfilter:0, line 143 <- wrt source file 2024-08-20T21:38:30.9991868Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::keyfilter:0 2024-08-20T21:38:30.9993926Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::itemfilter:0, line 163 <- wrt source file 2024-08-20T21:38:30.9996022Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::itemfilter:0 2024-08-20T21:38:30.9998059Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::assoc:0, line 189 <- wrt source file 2024-08-20T21:38:31.0000043Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::assoc:0 2024-08-20T21:38:31.0002049Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::dissoc:0, line 206 <- wrt source file 2024-08-20T21:38:31.0004102Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::dissoc:0 2024-08-20T21:38:31.0006111Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::first:0, line 393 <- wrt source file 2024-08-20T21:38:31.0008164Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::first:0 2024-08-20T21:38:31.0010131Z * DOCTEST : /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 2024-08-20T21:38:31.0012047Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/utils.py::transitive_get:0 2024-08-20T21:38:31.0013974Z * 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 2024-08-20T21:38:31.0015838Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/utils.py::_toposort:0 2024-08-20T21:38:31.0017753Z * 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 2024-08-20T21:38:31.0019645Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/utils.py::reverse_dict:0 2024-08-20T21:38:31.0021749Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/utils.py::freeze:0, line 93 <- wrt source file 2024-08-20T21:38:31.0023584Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/utils.py::freeze:0 2024-08-20T21:38:31.0025475Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/variable.py::variables:0, line 63 <- wrt source file 2024-08-20T21:38:31.0027380Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/variable.py::variables:0 2024-08-20T21:38:31.0029428Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/core.py::dispatch:0, line 19 <- wrt source file 2024-08-20T21:38:31.0031540Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/core.py::dispatch:0 2024-08-20T21:38:31.0033742Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher:0, line 99 <- wrt source file 2024-08-20T21:38:31.0035970Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher:0 2024-08-20T21:38:31.0038289Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.register:0, line 123 <- wrt source file 2024-08-20T21:38:31.0040699Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.register:0 2024-08-20T21:38:31.0043052Z * 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 2024-08-20T21:38:31.0045312Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.add:0 2024-08-20T21:38:31.0047873Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.dispatch:0, line 284 <- wrt source file 2024-08-20T21:38:31.0050246Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.dispatch:0 2024-08-20T21:38:31.0052586Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::str_signature:0, line 411 <- wrt source file 2024-08-20T21:38:31.0054841Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::str_signature:0 2024-08-20T21:38:31.0057064Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::expand_tuples:0, line 16 <- wrt source file 2024-08-20T21:38:31.0059238Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::expand_tuples:0 2024-08-20T21:38:31.0061407Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::_toposort:0, line 39 <- wrt source file 2024-08-20T21:38:31.0063535Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::_toposort:0 2024-08-20T21:38:31.0065686Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::reverse_dict:0, line 67 <- wrt source file 2024-08-20T21:38:31.0067964Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::reverse_dict:0 2024-08-20T21:38:31.0070113Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::groupby:0, line 86 <- wrt source file 2024-08-20T21:38:31.0072204Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::groupby:0 2024-08-20T21:38:31.0074329Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::typename:0, line 116 <- wrt source file 2024-08-20T21:38:31.0076448Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::typename:0 2024-08-20T21:38:31.0078619Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::isvariadic:0, line 46 <- wrt source file 2024-08-20T21:38:31.0080808Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::isvariadic:0 2024-08-20T21:38:31.0083002Z * 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 2024-08-20T21:38:31.0085247Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::Variadic:0 2024-08-20T21:38:31.0087387Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/graph_drawer.py::FxGraphDrawer.get_dot_graph:0, line 116 <- wrt source file 2024-08-20T21:38:31.0089318Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/graph_drawer.py::FxGraphDrawer.get_dot_graph:0 2024-08-20T21:38:31.0091126Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/shape_prop.py::ShapeProp:0, line 81 <- wrt source file 2024-08-20T21:38:31.0092768Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/shape_prop.py::ShapeProp:0 2024-08-20T21:38:31.0094468Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/split_module.py::split_module:0, line 76 <- wrt source file 2024-08-20T21:38:31.0096173Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/split_module.py::split_module:0 2024-08-20T21:38:31.0098219Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/utils/matcher_with_name_node_map_utils.py::SubgraphMatcherWithNameNodeMap:0, line 53 <- wrt source file 2024-08-20T21:38:31.0100518Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/utils/matcher_with_name_node_map_utils.py::SubgraphMatcherWithNameNodeMap:0 2024-08-20T21:38:31.0102585Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/_check.py::AttributeTypeIsSupportedChecker:0, line 36 <- wrt source file 2024-08-20T21:38:31.0104402Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/_check.py::AttributeTypeIsSupportedChecker:0 2024-08-20T21:38:31.0106237Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/mobile/__init__.py::_load_for_lite_interpreter:0, line 22 <- wrt source file 2024-08-20T21:38:31.0108037Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/mobile/__init__.py::_load_for_lite_interpreter:0 2024-08-20T21:38:31.0109919Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/mobile/__init__.py::_get_mobile_model_contained_types:0, line 122 <- wrt source file 2024-08-20T21:38:31.0111891Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/mobile/__init__.py::_get_mobile_model_contained_types:0 2024-08-20T21:38:31.0113739Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/mobile/__init__.py::_get_model_ops_and_info:0, line 214 <- wrt source file 2024-08-20T21:38:31.0115496Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/mobile/__init__.py::_get_model_ops_and_info:0 2024-08-20T21:38:31.0117188Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/masked/_ops.py::logaddexp:0, line 1511 <- wrt source file 2024-08-20T21:38:31.0118710Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/masked/_ops.py::logaddexp:0 2024-08-20T21:38:31.0120402Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/masked/maskedtensor/core.py::is_masked_tensor:0, line 25 <- wrt source file 2024-08-20T21:38:31.0122185Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/masked/maskedtensor/core.py::is_masked_tensor:0 2024-08-20T21:38:31.0124034Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::fractional_max_pool2d_with_indices:0, line 467 <- wrt source file 2024-08-20T21:38:31.0125896Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::fractional_max_pool2d_with_indices:0 2024-08-20T21:38:31.0127919Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::fractional_max_pool3d_with_indices:0, line 586 <- wrt source file 2024-08-20T21:38:31.0980906Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::fractional_max_pool3d_with_indices:0 2024-08-20T21:38:31.0994563Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::gumbel_softmax:0, line 2181 <- wrt source file 2024-08-20T21:38:31.1003874Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::gumbel_softmax:0 2024-08-20T21:38:31.1007378Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::embedding:0, line 2487 <- wrt source file 2024-08-20T21:38:31.1040233Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::embedding:0 2024-08-20T21:38:31.1043149Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::embedding_bag:0, line 2627 <- wrt source file 2024-08-20T21:38:31.1050237Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::embedding_bag:0 2024-08-20T21:38:31.1053131Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::ctc_loss:0, line 3049 <- wrt source file 2024-08-20T21:38:31.1071719Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::ctc_loss:0 2024-08-20T21:38:31.1074528Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::nll_loss:0, line 3126 <- wrt source file 2024-08-20T21:38:31.1091225Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::nll_loss:0 2024-08-20T21:38:31.1094189Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::cross_entropy:0, line 3451 <- wrt source file 2024-08-20T21:38:31.1102153Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::cross_entropy:0 2024-08-20T21:38:31.1105489Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::binary_cross_entropy:0, line 3523 <- wrt source file 2024-08-20T21:38:31.1110149Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::binary_cross_entropy:0 2024-08-20T21:38:31.1113927Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0, line 3600 <- wrt source file 2024-08-20T21:38:31.1118456Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0 2024-08-20T21:38:31.1121986Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::pad:0, line 5065 <- wrt source file 2024-08-20T21:38:31.1130409Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::pad:0 2024-08-20T21:38:31.1133158Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_input:0, line 32 <- wrt source file 2024-08-20T21:38:31.1173964Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_input:0 2024-08-20T21:38:31.1177068Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_weight:0, line 79 <- wrt source file 2024-08-20T21:38:31.1180735Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_weight:0 2024-08-20T21:38:31.1184088Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_input:0, line 130 <- wrt source file 2024-08-20T21:38:31.1190921Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_input:0 2024-08-20T21:38:31.1194058Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_weight:0, line 177 <- wrt source file 2024-08-20T21:38:31.1197774Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_weight:0 2024-08-20T21:38:31.1201121Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_input:0, line 228 <- wrt source file 2024-08-20T21:38:31.1233949Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_input:0 2024-08-20T21:38:31.1237418Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_weight:0, line 275 <- wrt source file 2024-08-20T21:38:31.1256433Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_weight:0 2024-08-20T21:38:31.1260086Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::calculate_gain:0, line 102 <- wrt source file 2024-08-20T21:38:31.1263127Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::calculate_gain:0 2024-08-20T21:38:31.1265931Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::uniform_:0, line 159 <- wrt source file 2024-08-20T21:38:31.1268964Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::uniform_:0 2024-08-20T21:38:31.1271502Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::normal_:0, line 186 <- wrt source file 2024-08-20T21:38:31.1273232Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::normal_:0 2024-08-20T21:38:31.1274996Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::trunc_normal_:0, line 221 <- wrt source file 2024-08-20T21:38:31.1289696Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::trunc_normal_:0 2024-08-20T21:38:31.1294300Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::constant_:0, line 235 <- wrt source file 2024-08-20T21:38:31.1297458Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::constant_:0 2024-08-20T21:38:31.1300319Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::ones_:0, line 252 <- wrt source file 2024-08-20T21:38:31.1303203Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::ones_:0 2024-08-20T21:38:31.1305905Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::zeros_:0, line 265 <- wrt source file 2024-08-20T21:38:31.1308827Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::zeros_:0 2024-08-20T21:38:31.1311494Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::eye_:0, line 281 <- wrt source file 2024-08-20T21:38:31.1314247Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::eye_:0 2024-08-20T21:38:31.1316945Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::dirac_:0, line 303 <- wrt source file 2024-08-20T21:38:31.1319834Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::dirac_:0 2024-08-20T21:38:31.1322814Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_uniform_:0, line 389 <- wrt source file 2024-08-20T21:38:31.1325977Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_uniform_:0 2024-08-20T21:38:31.1329173Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_normal_:0, line 429 <- wrt source file 2024-08-20T21:38:31.1332221Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_normal_:0 2024-08-20T21:38:31.1335384Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_uniform_:0, line 488 <- wrt source file 2024-08-20T21:38:31.1338003Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_uniform_:0 2024-08-20T21:38:31.1341370Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_normal_:0, line 553 <- wrt source file 2024-08-20T21:38:31.1344666Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_normal_:0 2024-08-20T21:38:31.1347531Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::orthogonal_:0, line 592 <- wrt source file 2024-08-20T21:38:31.1350536Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::orthogonal_:0 2024-08-20T21:38:31.1353193Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::sparse_:0, line 645 <- wrt source file 2024-08-20T21:38:31.1355761Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::sparse_:0 2024-08-20T21:38:31.1358621Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0, line 81 <- wrt source file 2024-08-20T21:38:31.1361506Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0 2024-08-20T21:38:31.1364609Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/bias.py::CausalBias:0, line 94 <- wrt source file 2024-08-20T21:38:31.1367523Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/bias.py::CausalBias:0 2024-08-20T21:38:31.1370934Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Threshold:0, line 70 <- wrt source file 2024-08-20T21:38:31.1373879Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Threshold:0 2024-08-20T21:38:31.1376950Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU:0, line 112 <- wrt source file 2024-08-20T21:38:31.1380390Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU:0 2024-08-20T21:38:31.1383696Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::RReLU:0, line 171 <- wrt source file 2024-08-20T21:38:31.1386847Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::RReLU:0 2024-08-20T21:38:31.1390169Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardtanh:0, line 227 <- wrt source file 2024-08-20T21:38:31.1393668Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardtanh:0 2024-08-20T21:38:31.1397184Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU6:0, line 292 <- wrt source file 2024-08-20T21:38:31.1400471Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU6:0 2024-08-20T21:38:31.1403947Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Sigmoid:0, line 320 <- wrt source file 2024-08-20T21:38:31.1407275Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Sigmoid:0 2024-08-20T21:38:31.1410955Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0, line 352 <- wrt source file 2024-08-20T21:38:31.1414587Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0 2024-08-20T21:38:31.1417972Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanh:0, line 385 <- wrt source file 2024-08-20T21:38:31.1421483Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanh:0 2024-08-20T21:38:31.1424843Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SiLU:0, line 418 <- wrt source file 2024-08-20T21:38:31.1428324Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SiLU:0 2024-08-20T21:38:31.1431672Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Mish:0, line 457 <- wrt source file 2024-08-20T21:38:31.1435011Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Mish:0 2024-08-20T21:38:31.1438100Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardswish:0, line 502 <- wrt source file 2024-08-20T21:38:31.1441442Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardswish:0 2024-08-20T21:38:31.1444866Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ELU:0, line 545 <- wrt source file 2024-08-20T21:38:31.1448316Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ELU:0 2024-08-20T21:38:31.1451111Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::CELU:0, line 587 <- wrt source file 2024-08-20T21:38:31.1454351Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::CELU:0 2024-08-20T21:38:31.1457495Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SELU:0, line 640 <- wrt source file 2024-08-20T21:38:31.1460989Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SELU:0 2024-08-20T21:38:31.1464361Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GLU:0, line 678 <- wrt source file 2024-08-20T21:38:31.1467646Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GLU:0 2024-08-20T21:38:31.1470854Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GELU:0, line 720 <- wrt source file 2024-08-20T21:38:31.1473676Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GELU:0 2024-08-20T21:38:31.1476610Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardshrink:0, line 763 <- wrt source file 2024-08-20T21:38:31.1479542Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardshrink:0 2024-08-20T21:38:31.1482703Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LeakyReLU:0, line 812 <- wrt source file 2024-08-20T21:38:31.1486190Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LeakyReLU:0 2024-08-20T21:38:31.1490058Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSigmoid:0, line 848 <- wrt source file 2024-08-20T21:38:31.1493521Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSigmoid:0 2024-08-20T21:38:31.1497029Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softplus:0, line 881 <- wrt source file 2024-08-20T21:38:31.1500613Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softplus:0 2024-08-20T21:38:31.1504070Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softshrink:0, line 924 <- wrt source file 2024-08-20T21:38:31.1507765Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softshrink:0 2024-08-20T21:38:31.1511430Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0, line 1026 <- wrt source file 2024-08-20T21:38:31.1515288Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0 2024-08-20T21:38:31.1518552Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::PReLU:0, line 1489 <- wrt source file 2024-08-20T21:38:31.1520771Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::PReLU:0 2024-08-20T21:38:31.1522728Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softsign:0, line 1531 <- wrt source file 2024-08-20T21:38:31.1524782Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softsign:0 2024-08-20T21:38:31.1526725Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanhshrink:0, line 1554 <- wrt source file 2024-08-20T21:38:31.1528557Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanhshrink:0 2024-08-20T21:38:31.1530390Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmin:0, line 1589 <- wrt source file 2024-08-20T21:38:31.1532042Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmin:0 2024-08-20T21:38:31.1533712Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax:0, line 1647 <- wrt source file 2024-08-20T21:38:31.1535340Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax:0 2024-08-20T21:38:31.1537028Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax2d:0, line 1688 <- wrt source file 2024-08-20T21:38:31.1538708Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax2d:0 2024-08-20T21:38:31.1540416Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSoftmax:0, line 1724 <- wrt source file 2024-08-20T21:38:31.1542101Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSoftmax:0 2024-08-20T21:38:31.1543816Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0, line 330 <- wrt source file 2024-08-20T21:38:31.1560174Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0 2024-08-20T21:38:31.1563530Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0, line 441 <- wrt source file 2024-08-20T21:38:31.1807190Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0 2024-08-20T21:38:31.1810202Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0, line 552 <- wrt source file 2024-08-20T21:38:31.4368025Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0 2024-08-20T21:38:31.4452590Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0, line 21 <- wrt source file 2024-08-20T21:38:31.4473487Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0 2024-08-20T21:38:31.4476712Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential:0, line 86 <- wrt source file 2024-08-20T21:38:31.4479720Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential:0 2024-08-20T21:38:31.4482803Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleList:0, line 292 <- wrt source file 2024-08-20T21:38:31.4485813Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleList:0 2024-08-20T21:38:31.4488950Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleDict:0, line 474 <- wrt source file 2024-08-20T21:38:31.4491968Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleDict:0 2024-08-20T21:38:31.4495074Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterList:0, line 606 <- wrt source file 2024-08-20T21:38:31.4498147Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterList:0 2024-08-20T21:38:31.4501656Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterDict:0, line 758 <- wrt source file 2024-08-20T21:38:31.4504900Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterDict:0 2024-08-20T21:38:31.4508118Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0, line 38 <- wrt source file 2024-08-20T21:38:31.4511291Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0 2024-08-20T21:38:31.4514470Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0, line 77 <- wrt source file 2024-08-20T21:38:31.4517592Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0 2024-08-20T21:38:31.4520619Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout:0, line 60 <- wrt source file 2024-08-20T21:38:31.4523455Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout:0 2024-08-20T21:38:31.4526399Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout1d:0, line 105 <- wrt source file 2024-08-20T21:38:31.4529409Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout1d:0 2024-08-20T21:38:31.4532854Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout2d:0, line 157 <- wrt source file 2024-08-20T21:38:31.4536263Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout2d:0 2024-08-20T21:38:31.4539648Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout3d:0, line 202 <- wrt source file 2024-08-20T21:38:31.4615309Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout3d:0 2024-08-20T21:38:31.4618765Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0, line 245 <- wrt source file 2024-08-20T21:38:31.4621761Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0 2024-08-20T21:38:31.4624932Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0, line 294 <- wrt source file 2024-08-20T21:38:31.4701021Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0 2024-08-20T21:38:31.4704528Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py::Flatten:0, line 30 <- wrt source file 2024-08-20T21:38:31.4707491Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py::Flatten:0 2024-08-20T21:38:31.4710320Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Fold:0, line 111 <- wrt source file 2024-08-20T21:38:31.4714625Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Fold:0 2024-08-20T21:38:31.4717440Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Unfold:0, line 261 <- wrt source file 2024-08-20T21:38:31.4730098Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Unfold:0 2024-08-20T21:38:31.4733152Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0, line 187 <- wrt source file 2024-08-20T21:38:31.4743971Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0 2024-08-20T21:38:31.4747411Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0, line 303 <- wrt source file 2024-08-20T21:38:31.4942246Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0 2024-08-20T21:38:31.4945675Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0, line 419 <- wrt source file 2024-08-20T21:38:31.7502334Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0 2024-08-20T21:38:31.7590425Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0, line 87 <- wrt source file 2024-08-20T21:38:31.7593456Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0 2024-08-20T21:38:31.7596438Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Identity:0, line 34 <- wrt source file 2024-08-20T21:38:31.7600284Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Identity:0 2024-08-20T21:38:31.7603510Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Linear:0, line 80 <- wrt source file 2024-08-20T21:38:31.7609760Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Linear:0 2024-08-20T21:38:31.7612678Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Bilinear:0, line 179 <- wrt source file 2024-08-20T21:38:31.7634775Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Bilinear:0 2024-08-20T21:38:31.7637649Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::L1Loss:0, line 115 <- wrt source file 2024-08-20T21:38:31.7643198Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::L1Loss:0 2024-08-20T21:38:31.7646037Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::NLLLoss:0, line 211 <- wrt source file 2024-08-20T21:38:31.7670088Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::NLLLoss:0 2024-08-20T21:38:31.7673378Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0, line 321 <- wrt source file 2024-08-20T21:38:31.7677445Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0 2024-08-20T21:38:31.7680578Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0, line 406 <- wrt source file 2024-08-20T21:38:31.7693107Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0 2024-08-20T21:38:31.7696109Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::KLDivLoss:0, line 517 <- wrt source file 2024-08-20T21:38:31.7702657Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::KLDivLoss:0 2024-08-20T21:38:31.7706126Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MSELoss:0, line 595 <- wrt source file 2024-08-20T21:38:31.7709244Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MSELoss:0 2024-08-20T21:38:31.7712113Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCELoss:0, line 677 <- wrt source file 2024-08-20T21:38:31.7715583Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCELoss:0 2024-08-20T21:38:31.7718572Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0, line 748 <- wrt source file 2024-08-20T21:38:31.7728129Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0 2024-08-20T21:38:31.7731332Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0, line 941 <- wrt source file 2024-08-20T21:38:31.7736525Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0 2024-08-20T21:38:31.7739701Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0, line 1261 <- wrt source file 2024-08-20T21:38:31.7745951Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0 2024-08-20T21:38:31.7749847Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0, line 1401 <- wrt source file 2024-08-20T21:38:31.7755972Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0 2024-08-20T21:38:31.7759220Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0, line 1466 <- wrt source file 2024-08-20T21:38:31.7763755Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0 2024-08-20T21:38:31.7766889Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0, line 1545 <- wrt source file 2024-08-20T21:38:31.7772963Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0 2024-08-20T21:38:31.7776066Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0, line 1645 <- wrt source file 2024-08-20T21:38:31.7785256Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0 2024-08-20T21:38:31.7788496Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CTCLoss:0, line 1886 <- wrt source file 2024-08-20T21:38:31.7817170Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CTCLoss:0 2024-08-20T21:38:31.7820291Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.register_buffer:0, line 545 <- wrt source file 2024-08-20T21:38:31.7823560Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.register_buffer:0 2024-08-20T21:38:31.7826752Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.apply:0, line 1005 <- wrt source file 2024-08-20T21:38:31.7833321Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.apply:0 2024-08-20T21:38:31.7836349Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.to:0, line 1259 <- wrt source file 2024-08-20T21:38:31.7840021Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.to:0 2024-08-20T21:38:31.7843108Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.state_dict:0, line 2167 <- wrt source file 2024-08-20T21:38:31.7846248Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.state_dict:0 2024-08-20T21:38:31.7849696Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.parameters:0, line 2609 <- wrt source file 2024-08-20T21:38:31.7852855Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.parameters:0 2024-08-20T21:38:31.7856122Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_parameters:0, line 2637 <- wrt source file 2024-08-20T21:38:31.7859487Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_parameters:0 2024-08-20T21:38:31.7862722Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.buffers:0, line 2664 <- wrt source file 2024-08-20T21:38:31.7865767Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.buffers:0 2024-08-20T21:38:31.7868941Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_buffers:0, line 2691 <- wrt source file 2024-08-20T21:38:31.7872308Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_buffers:0 2024-08-20T21:38:31.7875606Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_children:0, line 2722 <- wrt source file 2024-08-20T21:38:31.7878736Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_children:0 2024-08-20T21:38:31.7882558Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.modules:0, line 2746 <- wrt source file 2024-08-20T21:38:31.7886300Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.modules:0 2024-08-20T21:38:31.7891072Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_modules:0, line 2784 <- wrt source file 2024-08-20T21:38:31.7894802Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_modules:0 2024-08-20T21:38:31.7898836Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0, line 38 <- wrt source file 2024-08-20T21:38:31.7903340Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0 2024-08-20T21:38:31.7906850Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LayerNorm:0, line 151 <- wrt source file 2024-08-20T21:38:31.7911061Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LayerNorm:0 2024-08-20T21:38:31.7914841Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::GroupNorm:0, line 262 <- wrt source file 2024-08-20T21:38:31.7920145Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::GroupNorm:0 2024-08-20T21:38:31.7923892Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::RMSNorm:0, line 355 <- wrt source file 2024-08-20T21:38:31.7927397Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::RMSNorm:0 2024-08-20T21:38:31.7930492Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad1d:0, line 69 <- wrt source file 2024-08-20T21:38:31.7933570Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad1d:0 2024-08-20T21:38:31.7936698Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad2d:0, line 120 <- wrt source file 2024-08-20T21:38:31.7953461Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad2d:0 2024-08-20T21:38:31.7956567Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad3d:0, line 184 <- wrt source file 2024-08-20T21:38:32.4464115Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad3d:0 2024-08-20T21:38:32.4646517Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0, line 238 <- wrt source file 2024-08-20T21:38:32.4656349Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0 2024-08-20T21:38:32.4659459Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0, line 291 <- wrt source file 2024-08-20T21:38:32.4663801Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0 2024-08-20T21:38:32.4666890Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0, line 347 <- wrt source file 2024-08-20T21:38:32.4689466Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0 2024-08-20T21:38:32.4692615Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0, line 391 <- wrt source file 2024-08-20T21:38:32.4697065Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0 2024-08-20T21:38:32.4700211Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0, line 435 <- wrt source file 2024-08-20T21:38:32.4704340Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0 2024-08-20T21:38:32.4707476Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0, line 492 <- wrt source file 2024-08-20T21:38:32.4710674Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0 2024-08-20T21:38:32.4713829Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0, line 550 <- wrt source file 2024-08-20T21:38:32.4716964Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0 2024-08-20T21:38:32.4720100Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0, line 593 <- wrt source file 2024-08-20T21:38:32.4723227Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0 2024-08-20T21:38:32.4726380Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0, line 650 <- wrt source file 2024-08-20T21:38:33.0065177Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0 2024-08-20T21:38:33.0251898Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0, line 684 <- wrt source file 2024-08-20T21:38:33.0261844Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0 2024-08-20T21:38:33.0264737Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0, line 739 <- wrt source file 2024-08-20T21:38:33.0270455Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0 2024-08-20T21:38:33.0273478Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0, line 798 <- wrt source file 2024-08-20T21:38:33.0295817Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0 2024-08-20T21:38:33.0298950Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0, line 40 <- wrt source file 2024-08-20T21:38:33.0302389Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0 2024-08-20T21:38:33.0305639Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0, line 93 <- wrt source file 2024-08-20T21:38:33.0309212Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0 2024-08-20T21:38:33.0312352Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0, line 118 <- wrt source file 2024-08-20T21:38:33.0315303Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0 2024-08-20T21:38:33.0318269Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0, line 195 <- wrt source file 2024-08-20T21:38:33.0370015Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0 2024-08-20T21:38:33.0373019Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0, line 278 <- wrt source file 2024-08-20T21:38:33.2692684Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0 2024-08-20T21:38:33.2726337Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0, line 352 <- wrt source file 2024-08-20T21:38:33.2737830Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0 2024-08-20T21:38:33.2740894Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0, line 534 <- wrt source file 2024-08-20T21:38:33.3507765Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0 2024-08-20T21:38:33.3535366Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0, line 622 <- wrt source file 2024-08-20T21:38:33.3546538Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0 2024-08-20T21:38:33.3549740Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0, line 714 <- wrt source file 2024-08-20T21:38:33.3590813Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0 2024-08-20T21:38:33.3594205Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0, line 827 <- wrt source file 2024-08-20T21:38:33.5314270Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0 2024-08-20T21:38:33.5347544Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0, line 917 <- wrt source file 2024-08-20T21:38:33.5401565Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0 2024-08-20T21:38:33.5404901Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0, line 1003 <- wrt source file 2024-08-20T21:38:33.6191194Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0 2024-08-20T21:38:33.6194395Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool1d:0, line 1117 <- wrt source file 2024-08-20T21:38:33.6200504Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool1d:0 2024-08-20T21:38:33.6204048Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool2d:0, line 1168 <- wrt source file 2024-08-20T21:38:33.6257306Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool2d:0 2024-08-20T21:38:33.6261349Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool3d:0, line 1227 <- wrt source file 2024-08-20T21:38:33.8521189Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool3d:0 2024-08-20T21:38:33.8585850Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0, line 1282 <- wrt source file 2024-08-20T21:38:33.8592207Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0 2024-08-20T21:38:33.8595420Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0, line 1316 <- wrt source file 2024-08-20T21:38:33.8603636Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0 2024-08-20T21:38:33.8606868Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0, line 1359 <- wrt source file 2024-08-20T21:38:33.8636464Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0 2024-08-20T21:38:33.8639711Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0, line 1406 <- wrt source file 2024-08-20T21:38:33.8642891Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0 2024-08-20T21:38:33.8646116Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0, line 1437 <- wrt source file 2024-08-20T21:38:33.8651175Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0 2024-08-20T21:38:33.8654406Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0, line 1476 <- wrt source file 2024-08-20T21:38:33.8683458Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0 2024-08-20T21:38:33.8686794Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNN:0, line 589 <- wrt source file 2024-08-20T21:38:33.8695830Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNN:0 2024-08-20T21:38:33.8698622Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTM:0, line 946 <- wrt source file 2024-08-20T21:38:33.9020015Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTM:0 2024-08-20T21:38:33.9022824Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRU:0, line 1284 <- wrt source file 2024-08-20T21:38:33.9039382Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRU:0 2024-08-20T21:38:33.9042280Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNNCell:0, line 1535 <- wrt source file 2024-08-20T21:38:33.9051578Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNNCell:0 2024-08-20T21:38:33.9054465Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTMCell:0, line 1657 <- wrt source file 2024-08-20T21:38:33.9061784Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTMCell:0 2024-08-20T21:38:33.9065030Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRUCell:0, line 1771 <- wrt source file 2024-08-20T21:38:33.9075142Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRUCell:0 2024-08-20T21:38:33.9078066Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding:0, line 69 <- wrt source file 2024-08-20T21:38:33.9089091Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding:0 2024-08-20T21:38:33.9092283Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0, line 241 <- wrt source file 2024-08-20T21:38:33.9095613Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0 2024-08-20T21:38:33.9099026Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0, line 519 <- wrt source file 2024-08-20T21:38:33.9102424Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0 2024-08-20T21:38:33.9105720Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer:0, line 90 <- wrt source file 2024-08-20T21:38:34.5352678Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer:0 2024-08-20T21:38:34.5369956Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer.forward:0, line 258 <- wrt source file 2024-08-20T21:38:34.5373369Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer.forward:0 2024-08-20T21:38:34.5376785Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0, line 323 <- wrt source file 2024-08-20T21:38:34.7239471Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0 2024-08-20T21:38:34.7244357Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0, line 536 <- wrt source file 2024-08-20T21:38:34.8495360Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0 2024-08-20T21:38:34.8504172Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0, line 657 <- wrt source file 2024-08-20T21:38:34.8723765Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0 2024-08-20T21:38:34.8727448Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0, line 961 <- wrt source file 2024-08-20T21:38:34.9105987Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0 2024-08-20T21:38:34.9109705Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::Upsample:0, line 77 <- wrt source file 2024-08-20T21:38:34.9138079Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::Upsample:0 2024-08-20T21:38:34.9141318Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0, line 223 <- wrt source file 2024-08-20T21:38:34.9149772Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0 2024-08-20T21:38:34.9153482Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0, line 273 <- wrt source file 2024-08-20T21:38:34.9157902Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0 2024-08-20T21:38:34.9161247Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0, line 126 <- wrt source file 2024-08-20T21:38:34.9164455Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0 2024-08-20T21:38:34.9167921Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0, line 619 <- wrt source file 2024-08-20T21:38:34.9171397Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0 2024-08-20T21:38:34.9175026Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0, line 1418 <- wrt source file 2024-08-20T21:38:34.9178724Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0 2024-08-20T21:38:34.9182582Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0, line 1981 <- wrt source file 2024-08-20T21:38:34.9186522Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0 2024-08-20T21:38:34.9190511Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1, line 1991 <- wrt source file 2024-08-20T21:38:34.9194440Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1 2024-08-20T21:38:34.9198506Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0, line 2026 <- wrt source file 2024-08-20T21:38:34.9202813Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0 2024-08-20T21:38:34.9206571Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_per_sample_grad.py::call_for_per_sample_grads:0, line 35 <- wrt source file 2024-08-20T21:38:34.9209997Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_per_sample_grad.py::call_for_per_sample_grads:0 2024-08-20T21:38:34.9213310Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/init.py::skip_init:0, line 33 <- wrt source file 2024-08-20T21:38:34.9216126Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/init.py::skip_init:0 2024-08-20T21:38:34.9219185Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0, line 265 <- wrt source file 2024-08-20T21:38:34.9222367Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0 2024-08-20T21:38:34.9225608Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::weight_norm:0, line 360 <- wrt source file 2024-08-20T21:38:34.9228822Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::weight_norm:0 2024-08-20T21:38:34.9232201Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0, line 591 <- wrt source file 2024-08-20T21:38:34.9235501Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0 2024-08-20T21:38:34.9238886Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrize.py::register_parametrization:0, line 506 <- wrt source file 2024-08-20T21:38:34.9242272Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrize.py::register_parametrization:0 2024-08-20T21:38:34.9245408Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::identity:0, line 845 <- wrt source file 2024-08-20T21:38:34.9248482Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::identity:0 2024-08-20T21:38:34.9251461Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::random_unstructured:0, line 881 <- wrt source file 2024-08-20T21:38:34.9254530Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::random_unstructured:0 2024-08-20T21:38:34.9257595Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::l1_unstructured:0, line 924 <- wrt source file 2024-08-20T21:38:34.9260546Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::l1_unstructured:0 2024-08-20T21:38:34.9263459Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::remove:0, line 1191 <- wrt source file 2024-08-20T21:38:34.9266253Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::remove:0 2024-08-20T21:38:34.9269070Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::is_pruned:0, line 1219 <- wrt source file 2024-08-20T21:38:34.9271873Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::is_pruned:0 2024-08-20T21:38:34.9275012Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pad_packed_sequence:0, line 357 <- wrt source file 2024-08-20T21:38:34.9277987Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pad_packed_sequence:0 2024-08-20T21:38:34.9280929Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pad_sequence:0, line 435 <- wrt source file 2024-08-20T21:38:34.9283750Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pad_sequence:0 2024-08-20T21:38:34.9286686Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0, line 493 <- wrt source file 2024-08-20T21:38:34.9289665Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0 2024-08-20T21:38:34.9292563Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pack_sequence:0, line 549 <- wrt source file 2024-08-20T21:38:34.9295432Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pack_sequence:0 2024-08-20T21:38:34.9298355Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0, line 577 <- wrt source file 2024-08-20T21:38:34.9301247Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0 2024-08-20T21:38:34.9304403Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0, line 313 <- wrt source file 2024-08-20T21:38:34.9307508Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0 2024-08-20T21:38:34.9310752Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0, line 345 <- wrt source file 2024-08-20T21:38:34.9314029Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0 2024-08-20T21:38:34.9317257Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/stateless.py::functional_call:0, line 214 <- wrt source file 2024-08-20T21:38:34.9320344Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/stateless.py::functional_call:0 2024-08-20T21:38:34.9323449Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0, line 133 <- wrt source file 2024-08-20T21:38:34.9326445Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0 2024-08-20T21:38:34.9329648Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0, line 155 <- wrt source file 2024-08-20T21:38:34.9332832Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0 2024-08-20T21:38:34.9336126Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0, line 317 <- wrt source file 2024-08-20T21:38:34.9339474Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0 2024-08-20T21:38:34.9343238Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_expanded_weights/expanded_weights_utils.py::sum_over_all_but_batch_and_last_n:0, line 178 <- wrt source file 2024-08-20T21:38:34.9347493Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_expanded_weights/expanded_weights_utils.py::sum_over_all_but_batch_and_last_n:0 2024-08-20T21:38:34.9351159Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0, line 308 <- wrt source file 2024-08-20T21:38:34.9354044Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0 2024-08-20T21:38:34.9357064Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0, line 410 <- wrt source file 2024-08-20T21:38:34.9360150Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0 2024-08-20T21:38:34.9363133Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::StepLR:0, line 510 <- wrt source file 2024-08-20T21:38:34.9365971Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::StepLR:0 2024-08-20T21:38:34.9368972Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0, line 570 <- wrt source file 2024-08-20T21:38:34.9371930Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0 2024-08-20T21:38:34.9374915Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0, line 635 <- wrt source file 2024-08-20T21:38:34.9377881Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0 2024-08-20T21:38:34.9380973Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LinearLR:0, line 713 <- wrt source file 2024-08-20T21:38:34.9383825Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LinearLR:0 2024-08-20T21:38:34.9386801Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::SequentialLR:0, line 842 <- wrt source file 2024-08-20T21:38:34.9389773Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::SequentialLR:0 2024-08-20T21:38:34.9392801Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0, line 979 <- wrt source file 2024-08-20T21:38:34.9395756Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0 2024-08-20T21:38:34.9398851Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0, line 1135 <- wrt source file 2024-08-20T21:38:34.9401956Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0 2024-08-20T21:38:34.9405122Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ReduceLROnPlateau:0, line 1278 <- wrt source file 2024-08-20T21:38:34.9408268Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ReduceLROnPlateau:0 2024-08-20T21:38:34.9411294Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CyclicLR:0, line 1510 <- wrt source file 2024-08-20T21:38:34.9414164Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CyclicLR:0 2024-08-20T21:38:34.9417413Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0, line 1780 <- wrt source file 2024-08-20T21:38:34.9420916Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0 2024-08-20T21:38:34.9424567Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1, line 1796 <- wrt source file 2024-08-20T21:38:34.9428068Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1 2024-08-20T21:38:34.9431311Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::OneCycleLR:0, line 1941 <- wrt source file 2024-08-20T21:38:34.9434223Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::OneCycleLR:0 2024-08-20T21:38:34.9437121Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py::update_bn:0, line 319 <- wrt source file 2024-08-20T21:38:34.9439914Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py::update_bn:0 2024-08-20T21:38:34.9442838Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/package/glob_group.py::GlobGroup:0, line 21 <- wrt source file 2024-08-20T21:38:34.9445724Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/package/glob_group.py::GlobGroup:0 2024-08-20T21:38:34.9448849Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/profiler/profiler.py::profile:0, line 534 <- wrt source file 2024-08-20T21:38:34.9451702Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/profiler/profiler.py::profile:0 2024-08-20T21:38:34.9454960Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/semi_structured.py::to_sparse_semi_structured:0, line 337 <- wrt source file 2024-08-20T21:38:34.9458326Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/semi_structured.py::to_sparse_semi_structured:0 2024-08-20T21:38:34.9461511Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_creation.py::make_tensor:0, line 113 <- wrt source file 2024-08-20T21:38:34.9464416Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_creation.py::make_tensor:0 2024-08-20T21:38:34.9467522Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::parametrize:0, line 591 <- wrt source file 2024-08-20T21:38:34.9470738Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::parametrize:0 2024-08-20T21:38:34.9473989Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::decorateIf:0, line 746 <- wrt source file 2024-08-20T21:38:34.9477197Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::decorateIf:0 2024-08-20T21:38:34.9480625Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_symmetric_psd_matrix:0, line 4346 <- wrt source file 2024-08-20T21:38:34.9484182Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_symmetric_psd_matrix:0 2024-08-20T21:38:34.9487844Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_psd_matrix:0, line 4360 <- wrt source file 2024-08-20T21:38:34.9491404Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_psd_matrix:0 2024-08-20T21:38:34.9495000Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_pd_matrix:0, line 4390 <- wrt source file 2024-08-20T21:38:34.9498643Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_pd_matrix:0 2024-08-20T21:38:34.9502084Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/logging_utils.py::logs_to_string:0, line 192 <- wrt source file 2024-08-20T21:38:34.9505378Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/logging_utils.py::logs_to_string:0 2024-08-20T21:38:34.9520473Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/distributed/_tensor/common_dtensor.py::skip_unless_torch_gpu:0, line 288 <- wrt source file 2024-08-20T21:38:34.9524399Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/distributed/_tensor/common_dtensor.py::skip_unless_torch_gpu:0 2024-08-20T21:38:34.9528597Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/optests/autograd_registration.py::autograd_registration_check:0, line 29 <- wrt source file 2024-08-20T21:38:34.9532641Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/optests/autograd_registration.py::autograd_registration_check:0 2024-08-20T21:38:34.9536110Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_flatten:0, line 257 <- wrt source file 2024-08-20T21:38:34.9539132Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_flatten:0 2024-08-20T21:38:34.9542268Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0, line 299 <- wrt source file 2024-08-20T21:38:34.9545246Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0 2024-08-20T21:38:34.9548340Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0, line 329 <- wrt source file 2024-08-20T21:38:34.9551195Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0 2024-08-20T21:38:34.9554093Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0, line 364 <- wrt source file 2024-08-20T21:38:34.9556962Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0 2024-08-20T21:38:34.9559935Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0, line 399 <- wrt source file 2024-08-20T21:38:34.9562908Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0 2024-08-20T21:38:34.9565840Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_map:0, line 436 <- wrt source file 2024-08-20T21:38:34.9568734Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_map:0 2024-08-20T21:38:34.9571687Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0, line 812 <- wrt source file 2024-08-20T21:38:34.9574689Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0 2024-08-20T21:38:34.9577605Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::tree_map:0, line 933 <- wrt source file 2024-08-20T21:38:34.9580331Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::tree_map:0 2024-08-20T21:38:34.9583837Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::rename_privateuse1_backend:0, line 69 <- wrt source file 2024-08-20T21:38:34.9587332Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::rename_privateuse1_backend:0 2024-08-20T21:38:34.9591007Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::generate_methods_for_privateuse1_backend:0, line 322 <- wrt source file 2024-08-20T21:38:34.9594905Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::generate_methods_for_privateuse1_backend:0 2024-08-20T21:38:34.9598504Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::_get_custom_mod_func:0, line 354 <- wrt source file 2024-08-20T21:38:34.9601843Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::_get_custom_mod_func:0 2024-08-20T21:38:34.9605153Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::checkpoint_sequential:0, line 548 <- wrt source file 2024-08-20T21:38:34.9608403Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::checkpoint_sequential:0 2024-08-20T21:38:34.9611640Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::set_checkpoint_early_stop:0, line 750 <- wrt source file 2024-08-20T21:38:34.9615011Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::set_checkpoint_early_stop:0 2024-08-20T21:38:34.9618070Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/dlpack.py::from_dlpack:0, line 72 <- wrt source file 2024-08-20T21:38:34.9620882Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/dlpack.py::from_dlpack:0 2024-08-20T21:38:34.9623873Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::IterableDataset:0, line 98 <- wrt source file 2024-08-20T21:38:34.9626949Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::IterableDataset:0 2024-08-20T21:38:34.9630038Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::StackDataset:0, line 223 <- wrt source file 2024-08-20T21:38:34.9633051Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::StackDataset:0 2024-08-20T21:38:34.9636096Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::random_split:0, line 445 <- wrt source file 2024-08-20T21:38:34.9639106Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::random_split:0 2024-08-20T21:38:34.9642095Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::Sampler:0, line 42 <- wrt source file 2024-08-20T21:38:34.9644947Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::Sampler:0 2024-08-20T21:38:34.9648434Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::WeightedRandomSampler:0, line 240 <- wrt source file 2024-08-20T21:38:34.9651734Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::WeightedRandomSampler:0 2024-08-20T21:38:34.9654921Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::BatchSampler:0, line 303 <- wrt source file 2024-08-20T21:38:34.9658089Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::BatchSampler:0 2024-08-20T21:38:34.9661792Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::default_convert:0, line 39 <- wrt source file 2024-08-20T21:38:34.9665001Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::default_convert:0 2024-08-20T21:38:34.9668172Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::collate:0, line 137 <- wrt source file 2024-08-20T21:38:34.9671667Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::collate:0 2024-08-20T21:38:34.9674833Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::default_collate:0, line 364 <- wrt source file 2024-08-20T21:38:34.9678050Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::default_collate:0 2024-08-20T21:38:34.9681393Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/datapipe.py::IterDataPipe:0, line 96 <- wrt source file 2024-08-20T21:38:34.9684717Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/datapipe.py::IterDataPipe:0 2024-08-20T21:38:34.9688165Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/datapipe.py::MapDataPipe:0, line 263 <- wrt source file 2024-08-20T21:38:34.9691641Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/datapipe.py::MapDataPipe:0 2024-08-20T21:38:34.9695204Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/callable.py::MapperIterDataPipe:0, line 51 <- wrt source file 2024-08-20T21:38:34.9698866Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/callable.py::MapperIterDataPipe:0 2024-08-20T21:38:34.9702608Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/callable.py::CollatorIterDataPipe:0, line 197 <- wrt source file 2024-08-20T21:38:34.9706337Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/callable.py::CollatorIterDataPipe:0 2024-08-20T21:38:34.9710194Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combinatorics.py::ShufflerIterDataPipe:0, line 87 <- wrt source file 2024-08-20T21:38:34.9714058Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combinatorics.py::ShufflerIterDataPipe:0 2024-08-20T21:38:34.9717925Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0, line 48 <- wrt source file 2024-08-20T21:38:34.9721667Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0 2024-08-20T21:38:34.9725428Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ForkerIterDataPipe:0, line 98 <- wrt source file 2024-08-20T21:38:34.9729188Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ForkerIterDataPipe:0 2024-08-20T21:38:34.9732863Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::_ChildDataPipe:0, line 317 <- wrt source file 2024-08-20T21:38:34.9736461Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::_ChildDataPipe:0 2024-08-20T21:38:34.9740383Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::DemultiplexerIterDataPipe:0, line 403 <- wrt source file 2024-08-20T21:38:34.9744292Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::DemultiplexerIterDataPipe:0 2024-08-20T21:38:34.9748378Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::MultiplexerIterDataPipe:0, line 613 <- wrt source file 2024-08-20T21:38:34.9752251Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::MultiplexerIterDataPipe:0 2024-08-20T21:38:34.9756087Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ZipperIterDataPipe:0, line 681 <- wrt source file 2024-08-20T21:38:34.9759807Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ZipperIterDataPipe:0 2024-08-20T21:38:34.9763616Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/filelister.py::FileListerIterDataPipe:0, line 30 <- wrt source file 2024-08-20T21:38:34.9767504Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/filelister.py::FileListerIterDataPipe:0 2024-08-20T21:38:34.9771523Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/fileopener.py::FileOpenerIterDataPipe:0, line 34 <- wrt source file 2024-08-20T21:38:34.9775391Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/fileopener.py::FileOpenerIterDataPipe:0 2024-08-20T21:38:34.9779210Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::BatcherIterDataPipe:0, line 62 <- wrt source file 2024-08-20T21:38:34.9782915Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::BatcherIterDataPipe:0 2024-08-20T21:38:34.9786806Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::UnBatcherIterDataPipe:0, line 122 <- wrt source file 2024-08-20T21:38:34.9790571Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::UnBatcherIterDataPipe:0 2024-08-20T21:38:34.9794340Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::GrouperIterDataPipe:0, line 189 <- wrt source file 2024-08-20T21:38:34.9798045Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::GrouperIterDataPipe:0 2024-08-20T21:38:34.9801778Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/selecting.py::FilterIterDataPipe:0, line 36 <- wrt source file 2024-08-20T21:38:34.9810072Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/selecting.py::FilterIterDataPipe:0 2024-08-20T21:38:34.9813961Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/streamreader.py::StreamReaderIterDataPipe:0, line 24 <- wrt source file 2024-08-20T21:38:34.9817919Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/streamreader.py::StreamReaderIterDataPipe:0 2024-08-20T21:38:34.9821841Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0, line 26 <- wrt source file 2024-08-20T21:38:34.9825858Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0 2024-08-20T21:38:34.9829603Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/callable.py::MapperMapDataPipe:0, line 35 <- wrt source file 2024-08-20T21:38:34.9833204Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/callable.py::MapperMapDataPipe:0 2024-08-20T21:38:34.9836996Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combinatorics.py::ShufflerIterDataPipe:0, line 33 <- wrt source file 2024-08-20T21:38:34.9840850Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combinatorics.py::ShufflerIterDataPipe:0 2024-08-20T21:38:34.9844666Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0, line 28 <- wrt source file 2024-08-20T21:38:34.9848538Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0 2024-08-20T21:38:34.9852254Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0, line 72 <- wrt source file 2024-08-20T21:38:34.9855916Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0 2024-08-20T21:38:34.9859709Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0, line 28 <- wrt source file 2024-08-20T21:38:34.9863360Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0 2024-08-20T21:38:34.9867126Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0, line 26 <- wrt source file 2024-08-20T21:38:34.9870885Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0 2024-08-20T21:38:34.9874597Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/utils/common.py::validate_input_col:0, line 36 <- wrt source file 2024-08-20T21:38:34.9878195Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/utils/common.py::validate_input_col:0 2024-08-20T21:38:34.9881797Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/utils/decoder.py::basichandlers:0, line 47 <- wrt source file 2024-08-20T21:38:34.9885318Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/utils/decoder.py::basichandlers:0 2024-08-20T21:38:34.9888843Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0, line 433 <- wrt source file 2024-08-20T21:38:34.9892182Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0 2024-08-20T21:38:34.9895745Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0, line 529 <- wrt source file 2024-08-20T21:38:34.9899303Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0 2024-08-20T21:38:34.9902767Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0, line 216 <- wrt source file 2024-08-20T21:38:34.9906329Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0 2024-08-20T21:38:34.9909821Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0, line 314 <- wrt source file 2024-08-20T21:38:34.9913329Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0 2024-08-20T21:38:34.9916893Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0, line 362 <- wrt source file 2024-08-20T21:38:34.9920382Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0 2024-08-20T21:38:34.9923921Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0, line 394 <- wrt source file 2024-08-20T21:38:34.9927474Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0 2024-08-20T21:38:34.9931010Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_tensor:0, line 441 <- wrt source file 2024-08-20T21:38:34.9934577Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_tensor:0 2024-08-20T21:38:34.9938129Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram:0, line 480 <- wrt source file 2024-08-20T21:38:34.9941684Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram:0 2024-08-20T21:38:34.9945328Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram_raw:0, line 533 <- wrt source file 2024-08-20T21:38:34.9949116Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram_raw:0 2024-08-20T21:38:34.9952700Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0, line 599 <- wrt source file 2024-08-20T21:38:34.9956162Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0 2024-08-20T21:38:34.9959662Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_images:0, line 648 <- wrt source file 2024-08-20T21:38:34.9963149Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_images:0 2024-08-20T21:38:34.9966622Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0, line 811 <- wrt source file 2024-08-20T21:38:34.9970127Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0 2024-08-20T21:38:34.9973675Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0, line 878 <- wrt source file 2024-08-20T21:38:34.9977232Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0 2024-08-20T21:38:34.9980952Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0, line 989 <- wrt source file 2024-08-20T21:38:34.9984463Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0 2024-08-20T21:38:34.9988259Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0, line 1063 <- wrt source file 2024-08-20T21:38:34.9992257Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0 2024-08-20T21:38:34.9996250Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_marginchart:0, line 1084 <- wrt source file 2024-08-20T21:38:35.0000166Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_marginchart:0 2024-08-20T21:38:35.0004008Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars:0, line 1108 <- wrt source file 2024-08-20T21:38:35.0007695Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars:0 2024-08-20T21:38:35.0011310Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_mesh:0, line 1154 <- wrt source file 2024-08-20T21:38:35.0014882Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_mesh:0 2024-08-20T21:38:35.0016652Z ============ 2024-08-20T21:38:35.0017224Z Finished doctests 2024-08-20T21:38:35.0017701Z 335 / 695 passed 2024-08-20T21:38:35.0018189Z  2024-08-20T21:38:35.0018801Z === Found 101 parse-time warnings === 2024-08-20T21:38:35.0019715Z --- Parse Warning: 1 / 101 --- 2024-08-20T21:38:35.0022649Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=meshgrid in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=426. 2024-08-20T21:38:35.0025733Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.0027219Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors. 2024-08-20T21:38:35.0028233Z 2024-08-20T21:38:35.0028882Z This is helpful when you want to visualize data over some 2024-08-20T21:38:35.0029960Z range of inputs. See below for a plotting example. 2024-08-20T21:38:35.0030751Z 2024-08-20T21:38:35.0031490Z Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as 2024-08-20T21:38:35.0032748Z inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`, 2024-08-20T21:38:35.0034047Z this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots 2024-08-20T21:38:35.0035306Z G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where 2024-08-20T21:38:35.0036440Z the output :math:`G_i` is constructed by expanding :math:`T_i` 2024-08-20T21:38:35.0037387Z to the result shape. 2024-08-20T21:38:35.0037973Z 2024-08-20T21:38:35.0038388Z .. note:: 2024-08-20T21:38:35.0039075Z 0D inputs are treated equivalently to 1D inputs of a 2024-08-20T21:38:35.0039946Z single element. 2024-08-20T21:38:35.0040519Z 2024-08-20T21:38:35.0040925Z .. warning:: 2024-08-20T21:38:35.0041707Z `torch.meshgrid(*tensors)` currently has the same behavior 2024-08-20T21:38:35.0042932Z as calling `numpy.meshgrid(*arrays, indexing='ij')`. 2024-08-20T21:38:35.0043855Z 2024-08-20T21:38:35.0044440Z In the future `torch.meshgrid` will transition to 2024-08-20T21:38:35.0045438Z `indexing='xy'` as the default. 2024-08-20T21:38:35.0046120Z 2024-08-20T21:38:35.0046904Z https://github.com/pytorch/pytorch/issues/50276 tracks 2024-08-20T21:38:35.0048237Z this issue with the goal of migrating to NumPy's behavior. 2024-08-20T21:38:35.0049112Z 2024-08-20T21:38:35.0049523Z .. seealso:: 2024-08-20T21:38:35.0050031Z 2024-08-20T21:38:35.0050639Z :func:`torch.cartesian_prod` has the same effect but it 2024-08-20T21:38:35.0051632Z collects the data in a tensor of vectors. 2024-08-20T21:38:35.0052391Z 2024-08-20T21:38:35.0052771Z Args: 2024-08-20T21:38:35.0053714Z tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be 2024-08-20T21:38:35.0055040Z treated as tensors of size :math:`(1,)` automatically 2024-08-20T21:38:35.0055887Z 2024-08-20T21:38:35.0056517Z indexing: (str, optional): the indexing mode, either "xy" 2024-08-20T21:38:35.0057643Z or "ij", defaults to "ij". See warning for future changes. 2024-08-20T21:38:35.0058522Z 2024-08-20T21:38:35.0059109Z If "xy" is selected, the first dimension corresponds 2024-08-20T21:38:35.0060174Z to the cardinality of the second input and the second 2024-08-20T21:38:35.0061269Z dimension corresponds to the cardinality of the first 2024-08-20T21:38:35.0062134Z input. 2024-08-20T21:38:35.0062646Z 2024-08-20T21:38:35.0063389Z If "ij" is selected, the dimensions are in the same 2024-08-20T21:38:35.0064348Z order as the cardinality of the inputs. 2024-08-20T21:38:35.0065103Z 2024-08-20T21:38:35.0065500Z Returns: 2024-08-20T21:38:35.0066194Z seq (sequence of Tensors): If the input has :math:`N` 2024-08-20T21:38:35.0067382Z tensors of size :math:`S_0 \ldots S_{N-1}``, then the 2024-08-20T21:38:35.0068471Z output will also have :math:`N` tensors, where each tensor 2024-08-20T21:38:35.0069579Z is of shape :math:`(S_0, ..., S_{N-1})`. 2024-08-20T21:38:35.0070308Z 2024-08-20T21:38:35.0070714Z Example:: 2024-08-20T21:38:35.0071174Z 2024-08-20T21:38:35.0071647Z >>> x = torch.tensor([1, 2, 3]) 2024-08-20T21:38:35.0072417Z >>> y = torch.tensor([4, 5, 6]) 2024-08-20T21:38:35.0073081Z 2024-08-20T21:38:35.0073875Z Observe the element-wise pairings across the grid, (1, 4), 2024-08-20T21:38:35.0074940Z (1, 5), ..., (3, 6). This is the same thing as the 2024-08-20T21:38:35.0075756Z cartesian product. 2024-08-20T21:38:35.0076704Z >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij') 2024-08-20T21:38:35.0077558Z >>> grid_x 2024-08-20T21:38:35.0078092Z tensor([[1, 1, 1], 2024-08-20T21:38:35.0078709Z [2, 2, 2], 2024-08-20T21:38:35.0079322Z [3, 3, 3]]) 2024-08-20T21:38:35.0079917Z >>> grid_y 2024-08-20T21:38:35.0080468Z tensor([[4, 5, 6], 2024-08-20T21:38:35.0081078Z [4, 5, 6], 2024-08-20T21:38:35.0081666Z [4, 5, 6]]) 2024-08-20T21:38:35.0082253Z 2024-08-20T21:38:35.0082863Z This correspondence can be seen when these grids are 2024-08-20T21:38:35.0083735Z stacked properly. 2024-08-20T21:38:35.0084647Z >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))), 2024-08-20T21:38:35.0085711Z ... torch.cartesian_prod(x, y)) 2024-08-20T21:38:35.0086449Z True 2024-08-20T21:38:35.0086998Z 2024-08-20T21:38:35.0087784Z `torch.meshgrid` is commonly used to produce a grid for 2024-08-20T21:38:35.0088646Z plotting. 2024-08-20T21:38:35.0089306Z >>> # xdoctest: +REQUIRES(module:matplotlib) 2024-08-20T21:38:35.0090200Z >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW) 2024-08-20T21:38:35.0091069Z >>> import matplotlib.pyplot as plt 2024-08-20T21:38:35.0092049Z >>> xs = torch.linspace(-5, 5, steps=100) 2024-08-20T21:38:35.0093022Z >>> ys = torch.linspace(-5, 5, steps=100) 2024-08-20T21:38:35.0094036Z >>> x, y = torch.meshgrid(xs, ys, indexing='xy') 2024-08-20T21:38:35.0094933Z >>> z = torch.sin(torch.sqrt(x * x + y * y)) 2024-08-20T21:38:35.0095884Z >>> ax = plt.axes(projection='3d') 2024-08-20T21:38:35.0096793Z >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy()) 2024-08-20T21:38:35.0097614Z >>> plt.show() 2024-08-20T21:38:35.0098164Z 2024-08-20T21:38:35.0098672Z .. image:: ../_static/img/meshgrid.png 2024-08-20T21:38:35.0099395Z :width: 512 2024-08-20T21:38:35.0099922Z 2024-08-20T21:38:35.0100311Z 2024-08-20T21:38:35.0101285Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.0102314Z 2024-08-20T21:38:35.0102731Z warnings.warn(msg) 2024-08-20T21:38:35.0103233Z 2024-08-20T21:38:35.0103816Z --- Parse Warning: 2 / 101 --- 2024-08-20T21:38:35.0106768Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_unique_impl in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=815. 2024-08-20T21:38:35.0109948Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.0111951Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> Tuple[Tensor, Tensor, Tensor] 2024-08-20T21:38:35.0113307Z 2024-08-20T21:38:35.0113863Z Returns the unique elements of the input tensor. 2024-08-20T21:38:35.0114646Z 2024-08-20T21:38:35.0115583Z .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that 2024-08-20T21:38:35.0117200Z this function also eliminates non-consecutive duplicate values. 2024-08-20T21:38:35.0118126Z 2024-08-20T21:38:35.0118905Z .. note:: Currently in the CUDA implementation and the CPU implementation, 2024-08-20T21:38:35.0120469Z `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument. 2024-08-20T21:38:35.0122200Z Sorting could be slow, so if your input tensor is already sorted, it is recommended to use 2024-08-20T21:38:35.0123637Z :func:`torch.unique_consecutive` which avoids the sorting. 2024-08-20T21:38:35.0124501Z 2024-08-20T21:38:35.0124876Z Args: 2024-08-20T21:38:35.0125398Z input (Tensor): the input tensor 2024-08-20T21:38:35.0126455Z sorted (bool): Whether to sort the unique elements in ascending order 2024-08-20T21:38:35.0127571Z before returning as output. 2024-08-20T21:38:35.0128587Z return_inverse (bool): Whether to also return the indices for where 2024-08-20T21:38:35.0129915Z elements in the original input ended up in the returned unique list. 2024-08-20T21:38:35.0131270Z return_counts (bool): Whether to also return the counts for each unique 2024-08-20T21:38:35.0132276Z element. 2024-08-20T21:38:35.0133118Z dim (int, optional): the dimension to operate upon. If ``None``, the 2024-08-20T21:38:35.0134425Z unique of the flattened input is returned. Otherwise, each of the 2024-08-20T21:38:35.0135686Z tensors indexed by the given dimension is treated as one of the 2024-08-20T21:38:35.0136986Z elements to apply the unique operation upon. See examples for more 2024-08-20T21:38:35.0138126Z details. Default: ``None`` 2024-08-20T21:38:35.0138782Z 2024-08-20T21:38:35.0139182Z Returns: 2024-08-20T21:38:35.0140153Z (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing 2024-08-20T21:38:35.0141276Z 2024-08-20T21:38:35.0142114Z - **output** (*Tensor*): the output list of unique scalar elements. 2024-08-20T21:38:35.0143306Z - **inverse_indices** (*Tensor*): (optional) if 2024-08-20T21:38:35.0144330Z :attr:`return_inverse` is True, there will be an additional 2024-08-20T21:38:35.0145518Z returned tensor (same shape as input) representing the indices 2024-08-20T21:38:35.0146914Z for where elements in the original input map to in the output; 2024-08-20T21:38:35.0148117Z otherwise, this function will only return a single tensor. 2024-08-20T21:38:35.0149238Z - **counts** (*Tensor*): (optional) if 2024-08-20T21:38:35.0150229Z :attr:`return_counts` is True, there will be an additional 2024-08-20T21:38:35.0151382Z returned tensor (same shape as output or output.size(dim), 2024-08-20T21:38:35.0152557Z if dim was specified) representing the number of occurrences 2024-08-20T21:38:35.0153568Z for each unique value or tensor. 2024-08-20T21:38:35.0154278Z 2024-08-20T21:38:35.0154668Z Example:: 2024-08-20T21:38:35.0155117Z 2024-08-20T21:38:35.0155853Z >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long)) 2024-08-20T21:38:35.0156819Z >>> output 2024-08-20T21:38:35.0157332Z tensor([1, 2, 3]) 2024-08-20T21:38:35.0158048Z 2024-08-20T21:38:35.0158554Z >>> output, inverse_indices = torch.unique( 2024-08-20T21:38:35.0159726Z ... torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True) 2024-08-20T21:38:35.0160790Z >>> output 2024-08-20T21:38:35.0161294Z tensor([1, 2, 3]) 2024-08-20T21:38:35.0161884Z >>> inverse_indices 2024-08-20T21:38:35.0162499Z tensor([0, 2, 1, 2]) 2024-08-20T21:38:35.0163051Z 2024-08-20T21:38:35.0163567Z >>> output, inverse_indices = torch.unique( 2024-08-20T21:38:35.0164751Z ... torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True) 2024-08-20T21:38:35.0165794Z >>> output 2024-08-20T21:38:35.0166313Z tensor([1, 2, 3]) 2024-08-20T21:38:35.0166896Z >>> inverse_indices 2024-08-20T21:38:35.0167552Z tensor([[0, 2], 2024-08-20T21:38:35.0168111Z [1, 2]]) 2024-08-20T21:38:35.0168651Z 2024-08-20T21:38:35.0169067Z >>> a = torch.tensor([ 2024-08-20T21:38:35.0169673Z ... [ 2024-08-20T21:38:35.0170188Z ... [1, 1, 0, 0], 2024-08-20T21:38:35.0170819Z ... [1, 1, 0, 0], 2024-08-20T21:38:35.0171469Z ... [0, 0, 1, 1], 2024-08-20T21:38:35.0172086Z ... ], 2024-08-20T21:38:35.0172563Z ... [ 2024-08-20T21:38:35.0173077Z ... [0, 0, 1, 1], 2024-08-20T21:38:35.0173710Z ... [0, 0, 1, 1], 2024-08-20T21:38:35.0174334Z ... [1, 1, 1, 1], 2024-08-20T21:38:35.0174947Z ... ], 2024-08-20T21:38:35.0175443Z ... [ 2024-08-20T21:38:35.0175939Z ... [1, 1, 0, 0], 2024-08-20T21:38:35.0176570Z ... [1, 1, 0, 0], 2024-08-20T21:38:35.0177210Z ... [0, 0, 1, 1], 2024-08-20T21:38:35.0177804Z ... ], 2024-08-20T21:38:35.0178300Z ... ]) 2024-08-20T21:38:35.0178749Z 2024-08-20T21:38:35.0179511Z >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]` 2024-08-20T21:38:35.0180868Z >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match 2024-08-20T21:38:35.0182157Z >>> # each other, so one of them will be removed. 2024-08-20T21:38:35.0182986Z >>> (a[0, :, :] == a[2, :, :]).all() 2024-08-20T21:38:35.0183694Z tensor(True) 2024-08-20T21:38:35.0184328Z >>> a_unique_dim0 = torch.unique(a, dim=0) 2024-08-20T21:38:35.0185077Z >>> a_unique_dim0 2024-08-20T21:38:35.0185672Z tensor([[[0, 0, 1, 1], 2024-08-20T21:38:35.0186292Z [0, 0, 1, 1], 2024-08-20T21:38:35.0186892Z [1, 1, 1, 1]], 2024-08-20T21:38:35.0187516Z [[1, 1, 0, 0], 2024-08-20T21:38:35.0188123Z [1, 1, 0, 0], 2024-08-20T21:38:35.0188736Z [0, 0, 1, 1]]]) 2024-08-20T21:38:35.0189337Z 2024-08-20T21:38:35.0190248Z >>> # Notice which sub-tensors from `a` match with the sub-tensors from 2024-08-20T21:38:35.0191243Z >>> # `a_unique_dim0`: 2024-08-20T21:38:35.0191975Z >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all() 2024-08-20T21:38:35.0192743Z tensor(True) 2024-08-20T21:38:35.0193365Z >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all() 2024-08-20T21:38:35.0194129Z tensor(True) 2024-08-20T21:38:35.0194631Z 2024-08-20T21:38:35.0195354Z >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are 2024-08-20T21:38:35.0196622Z >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of 2024-08-20T21:38:35.0197593Z >>> # them will be removed. 2024-08-20T21:38:35.0198306Z >>> (a[:, 0, :] == a[:, 1, :]).all() 2024-08-20T21:38:35.0199015Z tensor(True) 2024-08-20T21:38:35.0199669Z >>> torch.unique(a, dim=1) 2024-08-20T21:38:35.0200338Z tensor([[[0, 0, 1, 1], 2024-08-20T21:38:35.0200957Z [1, 1, 0, 0]], 2024-08-20T21:38:35.0201584Z [[1, 1, 1, 1], 2024-08-20T21:38:35.0202186Z [0, 0, 1, 1]], 2024-08-20T21:38:35.0202808Z [[0, 0, 1, 1], 2024-08-20T21:38:35.0203427Z [1, 1, 0, 0]]]) 2024-08-20T21:38:35.0204010Z 2024-08-20T21:38:35.0204759Z >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared. 2024-08-20T21:38:35.0206002Z >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and 2024-08-20T21:38:35.0207256Z >>> # `a[:, :, 3]` match each other as well. So in this case, two of the 2024-08-20T21:38:35.0208346Z >>> # sub-tensors will be removed. 2024-08-20T21:38:35.0209115Z >>> (a[:, :, 0] == a[:, :, 1]).all() 2024-08-20T21:38:35.0209816Z tensor(True) 2024-08-20T21:38:35.0210391Z >>> (a[:, :, 2] == a[:, :, 3]).all() 2024-08-20T21:38:35.0211089Z tensor(True) 2024-08-20T21:38:35.0211656Z >>> torch.unique(a, dim=2) 2024-08-20T21:38:35.0212312Z tensor([[[0, 1], 2024-08-20T21:38:35.0212877Z [0, 1], 2024-08-20T21:38:35.0213426Z [1, 0]], 2024-08-20T21:38:35.0213967Z [[1, 0], 2024-08-20T21:38:35.0214514Z [1, 0], 2024-08-20T21:38:35.0215061Z [1, 1]], 2024-08-20T21:38:35.0215604Z [[0, 1], 2024-08-20T21:38:35.0216146Z [0, 1], 2024-08-20T21:38:35.0216696Z [1, 0]]]) 2024-08-20T21:38:35.0217225Z 2024-08-20T21:38:35.0218193Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.0219222Z 2024-08-20T21:38:35.0219629Z warnings.warn(msg) 2024-08-20T21:38:35.0220148Z 2024-08-20T21:38:35.0220739Z --- Parse Warning: 3 / 101 --- 2024-08-20T21:38:35.0223524Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=560. 2024-08-20T21:38:35.0226575Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.0227635Z 2024-08-20T21:38:35.0228212Z Load a model from a github repo or a local directory. 2024-08-20T21:38:35.0229017Z 2024-08-20T21:38:35.0229799Z Note: Loading a model is the typical use case, but this can also be used to 2024-08-20T21:38:35.0231144Z for loading other objects such as tokenizers, loss functions, etc. 2024-08-20T21:38:35.0232082Z 2024-08-20T21:38:35.0232820Z If ``source`` is 'github', ``repo_or_dir`` is expected to be 2024-08-20T21:38:35.0233895Z of the form ``repo_owner/repo_name[:ref]`` with an optional 2024-08-20T21:38:35.0234785Z ref (a tag or a branch). 2024-08-20T21:38:35.0235346Z 2024-08-20T21:38:35.0236078Z If ``source`` is 'local', ``repo_or_dir`` is expected to be a 2024-08-20T21:38:35.0236978Z path to a local directory. 2024-08-20T21:38:35.0237559Z 2024-08-20T21:38:35.0237938Z Args: 2024-08-20T21:38:35.0238584Z repo_or_dir (str): If ``source`` is 'github', 2024-08-20T21:38:35.0239868Z this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with 2024-08-20T21:38:35.0241790Z an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified, 2024-08-20T21:38:35.0243508Z the default branch is assumed to be ``main`` if it exists, and otherwise ``master``. 2024-08-20T21:38:35.0245130Z If ``source`` is 'local' then it should be a path to a local directory. 2024-08-20T21:38:35.0246400Z model (str): the name of a callable (entrypoint) defined in the 2024-08-20T21:38:35.0247640Z repo/dir's ``hubconf.py``. 2024-08-20T21:38:35.0248743Z *args (optional): the corresponding args for callable ``model``. 2024-08-20T21:38:35.0250029Z source (str, optional): 'github' or 'local'. Specifies how 2024-08-20T21:38:35.0251258Z ``repo_or_dir`` is to be interpreted. Default is 'github'. 2024-08-20T21:38:35.0252500Z trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``. 2024-08-20T21:38:35.0253870Z This parameter was introduced in v1.12 and helps ensuring that users 2024-08-20T21:38:35.0254980Z only run code from repos that they trust. 2024-08-20T21:38:35.0255724Z 2024-08-20T21:38:35.0256544Z - If ``False``, a prompt will ask the user whether the repo should 2024-08-20T21:38:35.0257475Z be trusted. 2024-08-20T21:38:35.0258434Z - If ``True``, the repo will be added to the trusted list and loaded 2024-08-20T21:38:35.0259510Z without requiring explicit confirmation. 2024-08-20T21:38:35.0260646Z - If ``"check"``, the repo will be checked against the list of 2024-08-20T21:38:35.0261834Z trusted repos in the cache. If it is not present in that list, the 2024-08-20T21:38:35.0263100Z behaviour will fall back onto the ``trust_repo=False`` option. 2024-08-20T21:38:35.0264451Z - If ``None``: this will raise a warning, inviting the user to set 2024-08-20T21:38:35.0265625Z ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This 2024-08-20T21:38:35.0266863Z is only present for backward compatibility and will be removed in 2024-08-20T21:38:35.0267830Z v2.0. 2024-08-20T21:38:35.0268270Z 2024-08-20T21:38:35.0268998Z Default is ``None`` and will eventually change to ``"check"`` in v2.0. 2024-08-20T21:38:35.0270299Z force_reload (bool, optional): whether to force a fresh download of 2024-08-20T21:38:35.0271528Z the github repo unconditionally. Does not have any effect if 2024-08-20T21:38:35.0272679Z ``source = 'local'``. Default is ``False``. 2024-08-20T21:38:35.0273736Z verbose (bool, optional): If ``False``, mute messages about hitting 2024-08-20T21:38:35.0274994Z local caches. Note that the message about first download cannot be 2024-08-20T21:38:35.0276438Z muted. Does not have any effect if ``source = 'local'``. 2024-08-20T21:38:35.0277335Z Default is ``True``. 2024-08-20T21:38:35.0278467Z skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit 2024-08-20T21:38:35.0280141Z specified by the ``github`` argument properly belongs to the repo owner. This will make 2024-08-20T21:38:35.0281960Z requests to the GitHub API; you can specify a non-default GitHub token by setting the 2024-08-20T21:38:35.0283365Z ``GITHUB_TOKEN`` environment variable. Default is ``False``. 2024-08-20T21:38:35.0284571Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2024-08-20T21:38:35.0285521Z 2024-08-20T21:38:35.0285906Z Returns: 2024-08-20T21:38:35.0286619Z The output of the ``model`` callable when called with the given 2024-08-20T21:38:35.0287682Z ``*args`` and ``**kwargs``. 2024-08-20T21:38:35.0288296Z 2024-08-20T21:38:35.0288675Z Example: 2024-08-20T21:38:35.0289252Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-08-20T21:38:35.0290055Z >>> # from a github repo 2024-08-20T21:38:35.0290684Z >>> repo = "pytorch/vision" 2024-08-20T21:38:35.0291361Z >>> model = torch.hub.load( 2024-08-20T21:38:35.0292251Z ... repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1" 2024-08-20T21:38:35.0293116Z ... ) 2024-08-20T21:38:35.0293593Z >>> # from a local directory 2024-08-20T21:38:35.0294350Z >>> path = "/some/local/path/pytorch/vision" 2024-08-20T21:38:35.0295109Z >>> # xdoctest: +SKIP 2024-08-20T21:38:35.0296181Z >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT") 2024-08-20T21:38:35.0297227Z 2024-08-20T21:38:35.0298169Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.0299201Z 2024-08-20T21:38:35.0299629Z warnings.warn(msg) 2024-08-20T21:38:35.0300138Z 2024-08-20T21:38:35.0300723Z --- Parse Warning: 4 / 101 --- 2024-08-20T21:38:35.0303668Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=download_url_to_file in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=687. 2024-08-20T21:38:35.0306754Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.0307976Z Download object at the given URL to a local path. 2024-08-20T21:38:35.0308753Z 2024-08-20T21:38:35.0309141Z Args: 2024-08-20T21:38:35.0309686Z url (str): URL of the object to download 2024-08-20T21:38:35.0310843Z dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file`` 2024-08-20T21:38:35.0312545Z hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``. 2024-08-20T21:38:35.0313847Z Default: None 2024-08-20T21:38:35.0314837Z progress (bool, optional): whether or not to display a progress bar to stderr 2024-08-20T21:38:35.0315927Z Default: True 2024-08-20T21:38:35.0316462Z 2024-08-20T21:38:35.0316852Z Example: 2024-08-20T21:38:35.0317462Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-08-20T21:38:35.0318304Z >>> # xdoctest: +REQUIRES(POSIX) 2024-08-20T21:38:35.0319096Z >>> torch.hub.download_url_to_file( 2024-08-20T21:38:35.0320311Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth", 2024-08-20T21:38:35.0321332Z ... "/tmp/temporary_file", 2024-08-20T21:38:35.0322009Z ... ) 2024-08-20T21:38:35.0322448Z 2024-08-20T21:38:35.0322818Z 2024-08-20T21:38:35.0323779Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.0324813Z 2024-08-20T21:38:35.0325307Z warnings.warn(msg) 2024-08-20T21:38:35.0325823Z 2024-08-20T21:38:35.0326397Z --- Parse Warning: 5 / 101 --- 2024-08-20T21:38:35.0329463Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load_state_dict_from_url in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=812. 2024-08-20T21:38:35.0332694Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.0333944Z Loads the Torch serialized object at the given URL. 2024-08-20T21:38:35.0334735Z 2024-08-20T21:38:35.0335370Z If downloaded file is a zip file, it will be automatically 2024-08-20T21:38:35.0336282Z decompressed. 2024-08-20T21:38:35.0336766Z 2024-08-20T21:38:35.0337637Z If the object is already present in `model_dir`, it's deserialized and 2024-08-20T21:38:35.0338640Z returned. 2024-08-20T21:38:35.0339437Z The default value of ``model_dir`` is ``/checkpoints`` where 2024-08-20T21:38:35.0340703Z ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`. 2024-08-20T21:38:35.0341619Z 2024-08-20T21:38:35.0342001Z Args: 2024-08-20T21:38:35.0342549Z url (str): URL of the object to download 2024-08-20T21:38:35.0343581Z model_dir (str, optional): directory in which to save the object 2024-08-20T21:38:35.0345201Z map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load) 2024-08-20T21:38:35.0347193Z progress (bool, optional): whether or not to display a progress bar to stderr. 2024-08-20T21:38:35.0348282Z Default: True 2024-08-20T21:38:35.0349661Z check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention 2024-08-20T21:38:35.0351442Z ``filename-.ext`` where ```` is the first eight or more 2024-08-20T21:38:35.0352829Z digits of the SHA256 hash of the contents of the file. The hash is used to 2024-08-20T21:38:35.0354143Z ensure unique names and to verify the contents of the file. 2024-08-20T21:38:35.0355076Z Default: False 2024-08-20T21:38:35.0356287Z file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set. 2024-08-20T21:38:35.0358191Z weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects. 2024-08-20T21:38:35.0359870Z Recommended for untrusted sources. See :func:`~torch.load` for more details. 2024-08-20T21:38:35.0360938Z 2024-08-20T21:38:35.0361322Z Example: 2024-08-20T21:38:35.0361943Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-08-20T21:38:35.0362902Z >>> state_dict = torch.hub.load_state_dict_from_url( 2024-08-20T21:38:35.0364173Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth" 2024-08-20T21:38:35.0365150Z ... ) 2024-08-20T21:38:35.0365595Z 2024-08-20T21:38:35.0365963Z 2024-08-20T21:38:35.0366988Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.0368022Z 2024-08-20T21:38:35.0368425Z warnings.warn(msg) 2024-08-20T21:38:35.0368946Z 2024-08-20T21:38:35.0369537Z --- Parse Warning: 6 / 101 --- 2024-08-20T21:38:35.0372508Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Library.fallback in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=334. 2024-08-20T21:38:35.0375611Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-08-20T21:38:35.0377034Z Registers the function implementation as the fallback for the given key. 2024-08-20T21:38:35.0378032Z 2024-08-20T21:38:35.0378767Z This function only works for a library with global namespace ("_"). 2024-08-20T21:38:35.0379874Z 2024-08-20T21:38:35.0380252Z Args: 2024-08-20T21:38:35.0381252Z fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel` 2024-08-20T21:38:35.0382502Z to register a fallthrough. 2024-08-20T21:38:35.0383817Z dispatch_key: dispatch key that the input function should be registered for. By default, it uses 2024-08-20T21:38:35.0385302Z the dispatch key that the library was created with. 2024-08-20T21:38:35.0386899Z with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument 2024-08-20T21:38:35.0388931Z to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls. 2024-08-20T21:38:35.0390211Z 2024-08-20T21:38:35.0390631Z Example:: 2024-08-20T21:38:35.0391211Z >>> my_lib = Library("_", "IMPL") 2024-08-20T21:38:35.0392041Z >>> def fallback_kernel(op, *args, **kwargs): 2024-08-20T21:38:35.0392944Z >>> # Handle all autocast ops generically 2024-08-20T21:38:35.0393724Z >>> # ... 2024-08-20T21:38:35.0394423Z >>> my_lib.fallback(fallback_kernel, "Autocast") 2024-08-20T21:38:35.0395218Z 2024-08-20T21:38:35.0397307Z Original Error: IndentationError('expected an indented block after function definition on line 2', ('', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7)) 2024-08-20T21:38:35.0399259Z 2024-08-20T21:38:35.0399774Z my_lib.fallback(fallback_kernel, "Autocast") 2024-08-20T21:38:35.0400578Z ^ 2024-08-20T21:38:35.0400996Z warnings.warn(msg) 2024-08-20T21:38:35.0401518Z 2024-08-20T21:38:35.0402103Z --- Parse Warning: 7 / 101 --- 2024-08-20T21:38:35.0405042Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_fake in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=692. 2024-08-20T21:38:35.0408158Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-08-20T21:38:35.0409550Z Register a FakeTensor implementation ("fake impl") for this operator. 2024-08-20T21:38:35.0410528Z 2024-08-20T21:38:35.0411149Z Also sometimes known as a "meta kernel", "abstract impl". 2024-08-20T21:38:35.0411997Z 2024-08-20T21:38:35.0412794Z An "FakeTensor implementation" specifies the behavior of this operator on 2024-08-20T21:38:35.0414199Z Tensors that carry no data ("FakeTensor"). Given some input Tensors with 2024-08-20T21:38:35.0415599Z certain properties (sizes/strides/storage_offset/device), it specifies 2024-08-20T21:38:35.0416770Z what the properties of the output Tensors are. 2024-08-20T21:38:35.0417526Z 2024-08-20T21:38:35.0418296Z The FakeTensor implementation has the same signature as the operator. 2024-08-20T21:38:35.0419663Z It is run for both FakeTensors and meta tensors. To write a FakeTensor 2024-08-20T21:38:35.0420970Z implementation, assume that all Tensor inputs to the operator are 2024-08-20T21:38:35.0422245Z regular CPU/CUDA/Meta tensors, but they do not have storage, and 2024-08-20T21:38:35.0423522Z you are trying to return regular CPU/CUDA/Meta tensor(s) as output. 2024-08-20T21:38:35.0424861Z The FakeTensor implementation must consist of only PyTorch operations 2024-08-20T21:38:35.0426146Z (and may not directly access the storage or data of any input or 2024-08-20T21:38:35.0427118Z intermediate Tensors). 2024-08-20T21:38:35.0427698Z 2024-08-20T21:38:35.0428266Z This API may be used as a decorator (see examples). 2024-08-20T21:38:35.0429065Z 2024-08-20T21:38:35.0429610Z For a detailed guide on custom ops, please see 2024-08-20T21:38:35.0430890Z https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html 2024-08-20T21:38:35.0431874Z 2024-08-20T21:38:35.0432277Z Examples: 2024-08-20T21:38:35.0432747Z >>> import torch 2024-08-20T21:38:35.0433343Z >>> import numpy as np 2024-08-20T21:38:35.0434019Z >>> from torch import Tensor 2024-08-20T21:38:35.0434661Z >>> 2024-08-20T21:38:35.0435525Z >>> # Example 1: an operator without data-dependent output shape 2024-08-20T21:38:35.0436762Z >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=()) 2024-08-20T21:38:35.0438201Z >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2024-08-20T21:38:35.0439443Z >>> raise NotImplementedError("Implementation goes here") 2024-08-20T21:38:35.0440313Z >>> 2024-08-20T21:38:35.0440964Z >>> @torch.library.register_fake("mylib::custom_linear") 2024-08-20T21:38:35.0441856Z >>> def _(x, weight, bias): 2024-08-20T21:38:35.0442564Z >>> assert x.dim() == 2 2024-08-20T21:38:35.0443272Z >>> assert weight.dim() == 2 2024-08-20T21:38:35.0444023Z >>> assert bias.dim() == 1 2024-08-20T21:38:35.0444814Z >>> assert x.shape[1] == weight.shape[1] 2024-08-20T21:38:35.0445680Z >>> assert weight.shape[0] == bias.shape[0] 2024-08-20T21:38:35.0446541Z >>> assert x.device == weight.device 2024-08-20T21:38:35.0447480Z >>> 2024-08-20T21:38:35.0448001Z >>> return (x @ weight.t()) + bias 2024-08-20T21:38:35.0448713Z >>> 2024-08-20T21:38:35.0449385Z >>> with torch._subclasses.fake_tensor.FakeTensorMode(): 2024-08-20T21:38:35.0450452Z >>> x = torch.randn(2, 3) 2024-08-20T21:38:35.0451157Z >>> w = torch.randn(3, 3) 2024-08-20T21:38:35.0451863Z >>> b = torch.randn(3) 2024-08-20T21:38:35.0452650Z >>> y = torch.ops.mylib.custom_linear(x, w, b) 2024-08-20T21:38:35.0453429Z >>> 2024-08-20T21:38:35.0453928Z >>> assert y.shape == (2, 3) 2024-08-20T21:38:35.0454581Z >>> 2024-08-20T21:38:35.0455406Z >>> # Example 2: an operator with data-dependent output shape 2024-08-20T21:38:35.0456627Z >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=()) 2024-08-20T21:38:35.0457836Z >>> def custom_nonzero(x: Tensor) -> Tensor: 2024-08-20T21:38:35.0458653Z >>> x_np = x.numpy(force=True) 2024-08-20T21:38:35.0459481Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2024-08-20T21:38:35.0460380Z >>> return torch.tensor(res, device=x.device) 2024-08-20T21:38:35.0461144Z >>> 2024-08-20T21:38:35.0461822Z >>> @torch.library.register_fake("mylib::custom_nonzero") 2024-08-20T21:38:35.0462687Z >>> def _(x): 2024-08-20T21:38:35.0463489Z >>> # Number of nonzero-elements is data-dependent. 2024-08-20T21:38:35.0464497Z >>> # Since we cannot peek at the data in an fake impl, 2024-08-20T21:38:35.0465532Z >>> # we use the ctx object to construct a new symint that 2024-08-20T21:38:35.0466592Z >>> # represents the data-dependent size. 2024-08-20T21:38:35.0467430Z >>> ctx = torch.library.get_ctx() 2024-08-20T21:38:35.0468232Z >>> nnz = ctx.new_dynamic_size() 2024-08-20T21:38:35.0468978Z >>> shape = [nnz, x.dim()] 2024-08-20T21:38:35.0469819Z >>> result = x.new_empty(shape, dtype=torch.int64) 2024-08-20T21:38:35.0470657Z >>> return result 2024-08-20T21:38:35.0471250Z >>> 2024-08-20T21:38:35.0471942Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2024-08-20T21:38:35.0472821Z >>> 2024-08-20T21:38:35.0473360Z >>> x = torch.tensor([0, 1, 2, 3, 4, 0]) 2024-08-20T21:38:35.0474484Z >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x) 2024-08-20T21:38:35.0475727Z >>> trace.print_readable() 2024-08-20T21:38:35.0476371Z >>> 2024-08-20T21:38:35.0477158Z >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x)) 2024-08-20T21:38:35.0478143Z 2024-08-20T21:38:35.0478530Z 2024-08-20T21:38:35.0480290Z Original Error: IndentationError('expected an indented block after function definition on line 37', ('', 38, 1, '_._ = None\n', 38, 2)) 2024-08-20T21:38:35.0482004Z 2024-08-20T21:38:35.0482393Z _._ = None 2024-08-20T21:38:35.0482811Z ^ 2024-08-20T21:38:35.0483236Z warnings.warn(msg) 2024-08-20T21:38:35.0483749Z 2024-08-20T21:38:35.0484323Z --- Parse Warning: 8 / 101 --- 2024-08-20T21:38:35.0487409Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_autograd in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=813. 2024-08-20T21:38:35.0490553Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.0491770Z Register a backward formula for this custom op. 2024-08-20T21:38:35.0492533Z 2024-08-20T21:38:35.0493276Z In order for an operator to work with autograd, you need to register 2024-08-20T21:38:35.0494280Z a backward formula: 2024-08-20T21:38:35.0495185Z 1. You must tell us how to compute gradients during the backward pass 2024-08-20T21:38:35.0496272Z by providing us a "backward" function. 2024-08-20T21:38:35.0497343Z 2. If you need any values from the forward to compute gradients, you can 2024-08-20T21:38:35.0498589Z use `setup_context` to save values for backward. 2024-08-20T21:38:35.0499367Z 2024-08-20T21:38:35.0500129Z ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``: 2024-08-20T21:38:35.0501557Z - ``grads`` is one or more gradients. The number of gradients matches 2024-08-20T21:38:35.0502607Z the number of outputs of the operator. 2024-08-20T21:38:35.0503700Z The ``ctx`` object is `the same ctx object `_ used by 2024-08-20T21:38:35.0505101Z :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the 2024-08-20T21:38:35.0506332Z same as :meth:`torch.autograd.Function.backward`. 2024-08-20T21:38:35.0507137Z 2024-08-20T21:38:35.0507835Z ``setup_context(ctx, inputs, output)`` runs during the forward pass. 2024-08-20T21:38:35.0509149Z Please save quantities needed for backward onto the ``ctx`` object via 2024-08-20T21:38:35.0510550Z either :meth:`torch.autograd.function.FunctionCtx.save_for_backward` 2024-08-20T21:38:35.0511863Z or assigning them as attributes of ``ctx``. If your custom op has 2024-08-20T21:38:35.0513264Z kwarg-only arguments, we expect the signature of ``setup_context`` 2024-08-20T21:38:35.0514531Z to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``. 2024-08-20T21:38:35.0515432Z 2024-08-20T21:38:35.0516177Z Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is, 2024-08-20T21:38:35.0517552Z they may not directly access :meth:`torch.Tensor.data_ptr` and they must 2024-08-20T21:38:35.0519133Z not depend on or mutate global state. If you need a non-traceable backward, 2024-08-20T21:38:35.0520564Z you can make it a separate custom_op that you call inside ``backward_fn``. 2024-08-20T21:38:35.0521587Z 2024-08-20T21:38:35.0521984Z Examples: 2024-08-20T21:38:35.0522459Z >>> import torch 2024-08-20T21:38:35.0523056Z >>> import numpy as np 2024-08-20T21:38:35.0523741Z >>> from torch import Tensor 2024-08-20T21:38:35.0524387Z >>> 2024-08-20T21:38:35.0525129Z >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=()) 2024-08-20T21:38:35.0526255Z >>> def numpy_sin(x: Tensor) -> Tensor: 2024-08-20T21:38:35.0527198Z >>> x_np = x.cpu().numpy() 2024-08-20T21:38:35.0527909Z >>> y_np = np.sin(x_np) 2024-08-20T21:38:35.0528743Z >>> return torch.from_numpy(y_np).to(device=x.device) 2024-08-20T21:38:35.0529550Z >>> 2024-08-20T21:38:35.0530299Z >>> def setup_context(ctx, inputs, output) -> Tensor: 2024-08-20T21:38:35.0531130Z >>> x, = inputs 2024-08-20T21:38:35.0531770Z >>> ctx.save_for_backward(x) 2024-08-20T21:38:35.0532446Z >>> 2024-08-20T21:38:35.0532944Z >>> def backward(ctx, grad): 2024-08-20T21:38:35.0533647Z >>> x, = ctx.saved_tensors 2024-08-20T21:38:35.0534373Z >>> return grad * x.cos() 2024-08-20T21:38:35.0535026Z >>> 2024-08-20T21:38:35.0535563Z >>> torch.library.register_autograd( 2024-08-20T21:38:35.0536542Z ... "mylib::numpy_sin", backward, setup_context=setup_context 2024-08-20T21:38:35.0537422Z ... ) 2024-08-20T21:38:35.0537863Z >>> 2024-08-20T21:38:35.0538419Z >>> x = torch.randn(3, requires_grad=True) 2024-08-20T21:38:35.0539189Z >>> y = numpy_sin(x) 2024-08-20T21:38:35.0540001Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2024-08-20T21:38:35.0540983Z >>> assert torch.allclose(grad_x, x.cos()) 2024-08-20T21:38:35.0541726Z >>> 2024-08-20T21:38:35.0542356Z >>> # Example with a keyword-only arg 2024-08-20T21:38:35.0543380Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2024-08-20T21:38:35.0544613Z >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor: 2024-08-20T21:38:35.0545567Z >>> x_np = x.cpu().numpy() 2024-08-20T21:38:35.0546270Z >>> y_np = x_np * val 2024-08-20T21:38:35.0547245Z >>> return torch.from_numpy(y_np).to(device=x.device) 2024-08-20T21:38:35.0548064Z >>> 2024-08-20T21:38:35.0549010Z >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor: 2024-08-20T21:38:35.0550128Z >>> ctx.val = keyword_only_inputs["val"] 2024-08-20T21:38:35.0550868Z >>> 2024-08-20T21:38:35.0551352Z >>> def backward(ctx, grad): 2024-08-20T21:38:35.0552068Z >>> return grad * ctx.val 2024-08-20T21:38:35.0552721Z >>> 2024-08-20T21:38:35.0553254Z >>> torch.library.register_autograd( 2024-08-20T21:38:35.0554228Z ... "mylib::numpy_mul", backward, setup_context=setup_context 2024-08-20T21:38:35.0555102Z ... ) 2024-08-20T21:38:35.0555538Z >>> 2024-08-20T21:38:35.0556106Z >>> x = torch.randn(3, requires_grad=True) 2024-08-20T21:38:35.0556910Z >>> y = numpy_mul(x, val=3.14) 2024-08-20T21:38:35.0557800Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2024-08-20T21:38:35.0558903Z >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14)) 2024-08-20T21:38:35.0559757Z 2024-08-20T21:38:35.0560127Z 2024-08-20T21:38:35.0561089Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.0562118Z 2024-08-20T21:38:35.0562520Z warnings.warn(msg) 2024-08-20T21:38:35.0563034Z 2024-08-20T21:38:35.0563619Z --- Parse Warning: 9 / 101 --- 2024-08-20T21:38:35.0566489Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=opcheck in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=1221. 2024-08-20T21:38:35.0569589Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.0571015Z Given an operator and some sample arguments, tests if the operator is 2024-08-20T21:38:35.0572038Z registered correctly. 2024-08-20T21:38:35.0572586Z 2024-08-20T21:38:35.0573493Z That is, when you use the torch.library/TORCH_LIBRARY APIs to create a 2024-08-20T21:38:35.0574919Z custom op, you specified metadata (e.g. mutability info) about the custom op 2024-08-20T21:38:35.0576347Z and these APIs require that the functions you pass them satisfy certain 2024-08-20T21:38:35.0577745Z properties (e.g. no data pointer access in the fake/meta/abstract kernel) 2024-08-20T21:38:35.0578924Z ``opcheck`` tests these metadata and properties. 2024-08-20T21:38:35.0579683Z 2024-08-20T21:38:35.0580160Z Concretely, we test the following: 2024-08-20T21:38:35.0581142Z - test_schema: if the operator's schema is correct. 2024-08-20T21:38:35.0582378Z - test_autograd_registration: if autograd was registered correctly. 2024-08-20T21:38:35.0583687Z - test_faketensor: If the operator has a FakeTensor kernel 2024-08-20T21:38:35.0584826Z (and if it is correct). The FakeTensor kernel is necessary ( 2024-08-20T21:38:35.0586075Z but not sufficient) for the operator to work with PyTorch compilation 2024-08-20T21:38:35.0587134Z APIs (torch.compile/export/FX). 2024-08-20T21:38:35.0588222Z - test_aot_dispatch_dynamic: If the operator has correct behavior 2024-08-20T21:38:35.0589385Z with PyTorch compilation APIs (torch.compile/export/FX). 2024-08-20T21:38:35.0590600Z This checks that the outputs (and gradients, if applicable) are the 2024-08-20T21:38:35.0591843Z same under eager-mode PyTorch and torch.compile. 2024-08-20T21:38:35.0592794Z This test is a superset of ``test_faketensor``. 2024-08-20T21:38:35.0593542Z 2024-08-20T21:38:35.0594234Z For best results, please call ``opcheck`` multiple times with a 2024-08-20T21:38:35.0595505Z representative set of inputs. If your operator supports 2024-08-20T21:38:35.0596746Z autograd, please use ``opcheck`` with inputs with ``requires_grad = True``; 2024-08-20T21:38:35.0598149Z if your operator supports multiple devices (e.g. CPU and CUDA), please 2024-08-20T21:38:35.0599354Z use ``opcheck`` with inputs on all supported devices. 2024-08-20T21:38:35.0600147Z 2024-08-20T21:38:35.0600541Z Args: 2024-08-20T21:38:35.0601240Z op: The operator. Must either be a function decorated with 2024-08-20T21:38:35.0602453Z :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket 2024-08-20T21:38:35.0603782Z found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo) 2024-08-20T21:38:35.0604854Z args: The args to the operator 2024-08-20T21:38:35.0605630Z kwargs: The kwargs to the operator 2024-08-20T21:38:35.0606602Z test_utils: Tests that we should run. Default: all of them. 2024-08-20T21:38:35.0607714Z Example: ("test_schema", "test_faketensor") 2024-08-20T21:38:35.0608747Z raise_exception: If we should raise an exception on the first 2024-08-20T21:38:35.0609894Z error. If False, we will return a dict with information 2024-08-20T21:38:35.0610846Z on if each test passed or not. 2024-08-20T21:38:35.0645055Z 2024-08-20T21:38:35.0645484Z .. warning:: 2024-08-20T21:38:35.0645962Z 2024-08-20T21:38:35.0646882Z opcheck and :func:`torch.autograd.gradcheck` test different things; 2024-08-20T21:38:35.0648278Z opcheck tests if your usage of torch.library APIs is correct while 2024-08-20T21:38:35.0649612Z :func:`torch.autograd.gradcheck` tests if your autograd formula is 2024-08-20T21:38:35.0650912Z mathematically correct. Use both to test custom ops that support 2024-08-20T21:38:35.0651895Z gradient computation. 2024-08-20T21:38:35.0652492Z 2024-08-20T21:38:35.0652867Z Example: 2024-08-20T21:38:35.0653283Z 2024-08-20T21:38:35.0653814Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-20T21:38:35.0654874Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2024-08-20T21:38:35.0656382Z >>> def numpy_add(x: Tensor, y: float) -> Tensor: 2024-08-20T21:38:35.0657214Z >>> x_np = x.numpy(force=True) 2024-08-20T21:38:35.0657930Z >>> z_np = x_np + y 2024-08-20T21:38:35.0658664Z >>> return torch.from_numpy(z_np).to(x.device) 2024-08-20T21:38:35.0659418Z >>> 2024-08-20T21:38:35.0659908Z >>> @numpy_sin.register_fake 2024-08-20T21:38:35.0660563Z >>> def _(x, y): 2024-08-20T21:38:35.0661174Z >>> return torch.empty_like(x) 2024-08-20T21:38:35.0661837Z >>> 2024-08-20T21:38:35.0662371Z >>> def setup_context(ctx, inputs, output): 2024-08-20T21:38:35.0663140Z >>> y, = inputs 2024-08-20T21:38:35.0663705Z >>> ctx.y = y 2024-08-20T21:38:35.0664226Z >>> 2024-08-20T21:38:35.0664700Z >>> def backward(ctx, grad): 2024-08-20T21:38:35.0665413Z >>> return grad * ctx.y, None 2024-08-20T21:38:35.0666073Z >>> 2024-08-20T21:38:35.0666825Z >>> numpy_sin.register_autograd(backward, setup_context=setup_context) 2024-08-20T21:38:35.0667775Z >>> 2024-08-20T21:38:35.0668232Z >>> sample_inputs = [ 2024-08-20T21:38:35.0668875Z >>> (torch.randn(3), 3.14), 2024-08-20T21:38:35.0669788Z >>> (torch.randn(2, 3, device='cuda'), 2.718), 2024-08-20T21:38:35.0670703Z >>> (torch.randn(1, 10, requires_grad=True), 1.234), 2024-08-20T21:38:35.0671937Z >>> (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18), 2024-08-20T21:38:35.0672853Z >>> ] 2024-08-20T21:38:35.0673285Z >>> 2024-08-20T21:38:35.0673894Z >>> for args in sample_inputs: 2024-08-20T21:38:35.0674692Z >>> torch.library.opcheck(foo, args) 2024-08-20T21:38:35.0675414Z 2024-08-20T21:38:35.0675767Z 2024-08-20T21:38:35.0676721Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.0677726Z 2024-08-20T21:38:35.0678111Z warnings.warn(msg) 2024-08-20T21:38:35.0678610Z 2024-08-20T21:38:35.0679168Z --- Parse Warning: 10 / 101 --- 2024-08-20T21:38:35.0682075Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py line=1042. 2024-08-20T21:38:35.0685120Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.0686805Z load(f, map_location=None, pickle_module=pickle, *, weights_only=False, mmap=None, **pickle_load_args) 2024-08-20T21:38:35.0688123Z 2024-08-20T21:38:35.0688752Z Loads an object saved with :func:`torch.save` from a file. 2024-08-20T21:38:35.0689595Z 2024-08-20T21:38:35.0690503Z :func:`torch.load` uses Python's unpickling facilities but treats storages, 2024-08-20T21:38:35.0691898Z which underlie tensors, specially. They are first deserialized on the 2024-08-20T21:38:35.0693252Z CPU and are then moved to the device they were saved from. If this fails 2024-08-20T21:38:35.0694810Z (e.g. because the run time system doesn't have certain devices), an exception 2024-08-20T21:38:35.0696241Z is raised. However, storages can be dynamically remapped to an alternative 2024-08-20T21:38:35.0697480Z set of devices using the :attr:`map_location` argument. 2024-08-20T21:38:35.0698294Z 2024-08-20T21:38:35.0699107Z If :attr:`map_location` is a callable, it will be called once for each serialized 2024-08-20T21:38:35.0700529Z storage with two arguments: storage and location. The storage argument 2024-08-20T21:38:35.0701918Z will be the initial deserialization of the storage, residing on the CPU. 2024-08-20T21:38:35.0703265Z Each serialized storage has a location tag associated with it which 2024-08-20T21:38:35.0704649Z identifies the device it was saved from, and this tag is the second 2024-08-20T21:38:35.0706213Z argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'`` 2024-08-20T21:38:35.0707814Z for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors. 2024-08-20T21:38:35.0709140Z :attr:`map_location` should return either ``None`` or a storage. If 2024-08-20T21:38:35.0710496Z :attr:`map_location` returns a storage, it will be used as the final deserialized 2024-08-20T21:38:35.0711982Z object, already moved to the right device. Otherwise, :func:`torch.load` will 2024-08-20T21:38:35.0713618Z fall back to the default behavior, as if :attr:`map_location` wasn't specified. 2024-08-20T21:38:35.0714662Z 2024-08-20T21:38:35.0715461Z If :attr:`map_location` is a :class:`torch.device` object or a string containing 2024-08-20T21:38:35.0716913Z a device tag, it indicates the location where all tensors should be loaded. 2024-08-20T21:38:35.0717954Z 2024-08-20T21:38:35.0718808Z Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags 2024-08-20T21:38:35.0720221Z appearing in the file (keys), to ones that specify where to put the 2024-08-20T21:38:35.0721212Z storages (values). 2024-08-20T21:38:35.0721739Z 2024-08-20T21:38:35.0722473Z User extensions can register their own location tags and tagging and 2024-08-20T21:38:35.0723898Z deserialization methods using :func:`torch.serialization.register_package`. 2024-08-20T21:38:35.0724961Z 2024-08-20T21:38:35.0725340Z Args: 2024-08-20T21:38:35.0726687Z f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`), 2024-08-20T21:38:35.0728314Z or a string or os.PathLike object containing a file name 2024-08-20T21:38:35.0729790Z map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage 2024-08-20T21:38:35.0731051Z locations 2024-08-20T21:38:35.0731945Z pickle_module: module used for unpickling metadata and objects (has to 2024-08-20T21:38:35.0733186Z match the :attr:`pickle_module` used to serialize file) 2024-08-20T21:38:35.0734352Z weights_only: Indicates whether unpickler should be restricted to 2024-08-20T21:38:35.0735527Z loading only tensors, primitive types, dictionaries 2024-08-20T21:38:35.0736738Z and any types added via :func:`torch.serialization.add_safe_globals`. 2024-08-20T21:38:35.0738397Z mmap: Indicates whether the file should be mmaped rather than loading all the storages into memory. 2024-08-20T21:38:35.0740347Z Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they 2024-08-20T21:38:35.0742365Z are moved to the location that they were tagged with when saving, or specified by ``map_location``. This 2024-08-20T21:38:35.0744627Z second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the 2024-08-20T21:38:35.0746370Z tensor storages from disk to CPU memory in the first step, ``f`` is mmaped. 2024-08-20T21:38:35.0747960Z pickle_load_args: (Python 3 only) optional keyword arguments passed over to 2024-08-20T21:38:35.0749356Z :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g., 2024-08-20T21:38:35.0750377Z :attr:`errors=...`. 2024-08-20T21:38:35.0750985Z 2024-08-20T21:38:35.0751393Z .. warning:: 2024-08-20T21:38:35.0752219Z :func:`torch.load()` unless `weights_only` parameter is set to `True`, 2024-08-20T21:38:35.0753499Z uses ``pickle`` module implicitly, which is known to be insecure. 2024-08-20T21:38:35.0754948Z It is possible to construct malicious pickle data which will execute arbitrary code 2024-08-20T21:38:35.0756642Z during unpickling. Never load data that could have come from an untrusted 2024-08-20T21:38:35.0758289Z source in an unsafe mode, or that could have been tampered with. **Only load data you trust**. 2024-08-20T21:38:35.0759486Z 2024-08-20T21:38:35.0759857Z .. note:: 2024-08-20T21:38:35.0760789Z When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors 2024-08-20T21:38:35.0762603Z will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')`` 2024-08-20T21:38:35.0764252Z and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint. 2024-08-20T21:38:35.0765372Z 2024-08-20T21:38:35.0765772Z .. note:: 2024-08-20T21:38:35.0766851Z By default, we decode byte strings as ``utf-8``. This is to avoid a common error 2024-08-20T21:38:35.0768456Z case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...`` 2024-08-20T21:38:35.0769764Z when loading files saved by Python 2 in Python 3. If this default 2024-08-20T21:38:35.0771213Z is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how 2024-08-20T21:38:35.0772885Z these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them 2024-08-20T21:38:35.0774498Z to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them 2024-08-20T21:38:35.0775947Z as byte arrays which can be decoded later with ``byte_array.decode(...)``. 2024-08-20T21:38:35.0776978Z 2024-08-20T21:38:35.0777361Z Example: 2024-08-20T21:38:35.0778066Z >>> # xdoctest: +SKIP("undefined filepaths") 2024-08-20T21:38:35.0778977Z >>> torch.load("tensors.pt", weights_only=True) 2024-08-20T21:38:35.0779817Z # Load all tensors onto the CPU 2024-08-20T21:38:35.0780937Z >>> torch.load("tensors.pt", map_location=torch.device("cpu"), weights_only=True) 2024-08-20T21:38:35.0782175Z # Load all tensors onto the CPU, using a function 2024-08-20T21:38:35.0782981Z >>> torch.load( 2024-08-20T21:38:35.0783924Z ... "tensors.pt", map_location=lambda storage, loc: storage, weights_only=True 2024-08-20T21:38:35.0784954Z ... ) 2024-08-20T21:38:35.0785464Z # Load all tensors onto GPU 1 2024-08-20T21:38:35.0786158Z >>> torch.load( 2024-08-20T21:38:35.0786733Z ... "tensors.pt", 2024-08-20T21:38:35.0787107Z ... map_location=lambda storage, loc: storage.cuda(1), 2024-08-20T21:38:35.0787313Z ... weights_only=True, 2024-08-20T21:38:35.0787680Z ... ) # type: ignore[attr-defined] 2024-08-20T21:38:35.0787933Z # Map tensors from GPU 1 to GPU 0 2024-08-20T21:38:35.0788522Z >>> torch.load("tensors.pt", map_location={"cuda:1": "cuda:0"}, weights_only=True) 2024-08-20T21:38:35.0788805Z # Load tensor from io.BytesIO object 2024-08-20T21:38:35.0789399Z # Loading from a buffer setting weights_only=False, warning this can be unsafe 2024-08-20T21:38:35.0789670Z >>> with open("tensor.pt", "rb") as f: 2024-08-20T21:38:35.0789916Z ... buffer = io.BytesIO(f.read()) 2024-08-20T21:38:35.0790197Z >>> torch.load(buffer, weights_only=False) 2024-08-20T21:38:35.0790687Z # Load a module with 'ascii' encoding for unpickling 2024-08-20T21:38:35.0791274Z # Loading from a module setting weights_only=False, warning this can be unsafe 2024-08-20T21:38:35.0791725Z >>> torch.load("module.pt", encoding="ascii", weights_only=False) 2024-08-20T21:38:35.0791903Z 2024-08-20T21:38:35.0792620Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.0792769Z 2024-08-20T21:38:35.0792978Z warnings.warn(msg) 2024-08-20T21:38:35.0793125Z 2024-08-20T21:38:35.0793563Z --- Parse Warning: 11 / 101 --- 2024-08-20T21:38:35.0795960Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=cudart in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/__init__.py line=341. 2024-08-20T21:38:35.0796658Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-08-20T21:38:35.0796922Z Retrieves the CUDA runtime API module. 2024-08-20T21:38:35.0797070Z 2024-08-20T21:38:35.0797221Z 2024-08-20T21:38:35.0797814Z This function initializes the CUDA runtime environment if it is not already 2024-08-20T21:38:35.0798371Z initialized and returns the CUDA runtime API module (_cudart). The CUDA 2024-08-20T21:38:35.0798894Z runtime API module provides access to various CUDA runtime functions. 2024-08-20T21:38:35.0799058Z 2024-08-20T21:38:35.0799214Z Args: 2024-08-20T21:38:35.0799374Z ``None`` 2024-08-20T21:38:35.0799544Z 2024-08-20T21:38:35.0799705Z Returns: 2024-08-20T21:38:35.0800030Z module: The CUDA runtime API module (_cudart). 2024-08-20T21:38:35.0800195Z 2024-08-20T21:38:35.0800355Z Raises: 2024-08-20T21:38:35.0801026Z RuntimeError: If CUDA cannot be re-initialized in a forked subprocess. 2024-08-20T21:38:35.0801914Z AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable. 2024-08-20T21:38:35.0802067Z 2024-08-20T21:38:35.0802373Z Example of CUDA operations with profiling: 2024-08-20T21:38:35.0802561Z >>> import torch 2024-08-20T21:38:35.0802867Z >>> from torch.cuda import cudart, check_error 2024-08-20T21:38:35.0803130Z >>> import os 2024-08-20T21:38:35.0803289Z >>> 2024-08-20T21:38:35.0803634Z >>> os.environ['CUDA_PROFILE'] = '1' 2024-08-20T21:38:35.0803805Z >>> 2024-08-20T21:38:35.0804115Z >>> def perform_cuda_operations_with_streams(): 2024-08-20T21:38:35.0804380Z >>> stream = torch.cuda.Stream() 2024-08-20T21:38:35.0804653Z >>> with torch.cuda.stream(stream): 2024-08-20T21:38:35.0805069Z >>> x = torch.randn(100, 100, device='cuda') 2024-08-20T21:38:35.0805475Z >>> y = torch.randn(100, 100, device='cuda') 2024-08-20T21:38:35.0805709Z >>> z = torch.mul(x, y) 2024-08-20T21:38:35.0805887Z >>> return z 2024-08-20T21:38:35.0806057Z >>> 2024-08-20T21:38:35.0806290Z >>> torch.cuda.synchronize() 2024-08-20T21:38:35.0806595Z >>> print("====== Start nsys profiling ======") 2024-08-20T21:38:35.0806998Z >>> check_error(cudart().cudaProfilerStart()) 2024-08-20T21:38:35.0807322Z >>> with torch.autograd.profiler.emit_nvtx(): 2024-08-20T21:38:35.0807666Z >>> result = perform_cuda_operations_with_streams() 2024-08-20T21:38:35.0807971Z >>> print("CUDA operations completed.") 2024-08-20T21:38:35.0808344Z >>> check_error(torch.cuda.cudart().cudaProfilerStop()) 2024-08-20T21:38:35.0808632Z >>> print("====== End nsys profiling ======") 2024-08-20T21:38:35.0808794Z 2024-08-20T21:38:35.0809269Z To run this example and save the profiling information, execute: 2024-08-20T21:38:35.0810292Z >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2024-08-20T21:38:35.0810456Z 2024-08-20T21:38:35.0811039Z This command profiles the CUDA operations in the provided script and saves 2024-08-20T21:38:35.0811493Z the profiling information to a file named `trace_name.prof`. 2024-08-20T21:38:35.0812182Z The `--profile-from-start off` option ensures that profiling starts only 2024-08-20T21:38:35.0812525Z after the `cudaProfilerStart` call in the script. 2024-08-20T21:38:35.0813201Z The `--csv` and `--print-summary` options format the profiling output as a 2024-08-20T21:38:35.0813571Z CSV file and print a summary, respectively. 2024-08-20T21:38:35.0814296Z The `-o` option specifies the output file name, and the `-f` option forces the 2024-08-20T21:38:35.0814655Z overwrite of the output file if it already exists. 2024-08-20T21:38:35.0814810Z 2024-08-20T21:38:35.0816627Z Original Error: SyntaxError('invalid syntax', ('', 1, 1, '$ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py\n', 1, 2)) 2024-08-20T21:38:35.0816792Z 2024-08-20T21:38:35.0817778Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2024-08-20T21:38:35.0817950Z ^ 2024-08-20T21:38:35.0818140Z warnings.warn(msg) 2024-08-20T21:38:35.0818291Z 2024-08-20T21:38:35.0818663Z --- Parse Warning: 12 / 101 --- 2024-08-20T21:38:35.0821136Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Future.then in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py line=101. 2024-08-20T21:38:35.0821879Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.0822041Z 2024-08-20T21:38:35.0822581Z Append the given callback function to this ``Future``, which will be run 2024-08-20T21:38:35.0823075Z when the ``Future`` is completed. Multiple callbacks can be added to 2024-08-20T21:38:35.0823596Z the same ``Future``, but the order in which they will be executed cannot 2024-08-20T21:38:35.0824019Z be guaranteed (to enforce a certain order consider chaining: 2024-08-20T21:38:35.0824592Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which 2024-08-20T21:38:35.0825099Z is the reference to this ``Future``. The callback function can use the 2024-08-20T21:38:35.0825588Z :meth:`value` method to get the value. Note that if this ``Future`` is 2024-08-20T21:38:35.0826132Z already completed, the given callback will be run immediately inline. 2024-08-20T21:38:35.0826279Z 2024-08-20T21:38:35.0826873Z If the ``Future``'s value contains tensors that reside on GPUs, the 2024-08-20T21:38:35.0827416Z callback might be invoked while the async kernels that are populating 2024-08-20T21:38:35.0828089Z those tensors haven't yet finished executing on the device. However, the 2024-08-20T21:38:35.0828580Z callback will be invoked with some dedicated streams set as current 2024-08-20T21:38:35.0829076Z (fetched from a global pool) which will be synchronized with those 2024-08-20T21:38:35.0829620Z kernels. Hence any operation performed by the callback on these tensors 2024-08-20T21:38:35.0830145Z will be scheduled on the device after the kernels complete. In other 2024-08-20T21:38:35.0830759Z words, as long as the callback doesn't switch streams, it can safely 2024-08-20T21:38:35.0831292Z manipulate the result without any additional synchronization. This is 2024-08-20T21:38:35.0831769Z similar to the non-blocking behavior of :meth:`wait`. 2024-08-20T21:38:35.0831919Z 2024-08-20T21:38:35.0832428Z Similarly, if the callback returns a value that contains tensors that 2024-08-20T21:38:35.0832928Z reside on a GPU, it can do so even if the kernels that are producing 2024-08-20T21:38:35.0833442Z these tensors are still running on the device, as long as the callback 2024-08-20T21:38:35.0834038Z didn't change streams during its execution. If one wants to change 2024-08-20T21:38:35.0834683Z streams, one must be careful to re-synchronize them with the original 2024-08-20T21:38:35.0835216Z streams, that is, those that were current when the callback was invoked. 2024-08-20T21:38:35.0835380Z 2024-08-20T21:38:35.0835539Z Args: 2024-08-20T21:38:35.0836008Z callback(``Callable``): a ``Callable`` that takes this ``Future`` as 2024-08-20T21:38:35.0836334Z the only argument. 2024-08-20T21:38:35.0836482Z 2024-08-20T21:38:35.0836642Z Returns: 2024-08-20T21:38:35.0837051Z A new ``Future`` object that holds the return value of the 2024-08-20T21:38:35.0837449Z ``callback`` and will be marked as completed when the given 2024-08-20T21:38:35.0837644Z ``callback`` finishes. 2024-08-20T21:38:35.0837806Z 2024-08-20T21:38:35.0838207Z .. note:: Note that if the callback function throws, either 2024-08-20T21:38:35.0838693Z through the original future being completed with an exception and 2024-08-20T21:38:35.0839168Z calling ``fut.wait()``, or through other code in the callback, the 2024-08-20T21:38:35.0839647Z future returned by ``then`` will be marked appropriately with the 2024-08-20T21:38:35.0840103Z encountered error. However, if this callback later completes 2024-08-20T21:38:35.0840597Z additional futures, those futures are not marked as completed with 2024-08-20T21:38:35.0841122Z an error and the user is responsible for handling completion/waiting 2024-08-20T21:38:35.0841364Z on those futures independently. 2024-08-20T21:38:35.0841512Z 2024-08-20T21:38:35.0841683Z Example:: 2024-08-20T21:38:35.0842027Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2024-08-20T21:38:35.0842222Z >>> def callback(fut): 2024-08-20T21:38:35.0842540Z ... print(f"RPC return value is {fut.wait()}.") 2024-08-20T21:38:35.0842797Z >>> fut = torch.futures.Future() 2024-08-20T21:38:35.0843190Z >>> # The inserted callback will print the return value when 2024-08-20T21:38:35.0843464Z >>> # receiving the response from "worker1" 2024-08-20T21:38:35.0843779Z >>> cb_fut = fut.then(callback) 2024-08-20T21:38:35.0843999Z >>> chain_cb_fut = cb_fut.then( 2024-08-20T21:38:35.0844339Z ... lambda x : print(f"Chained cb done. {x.wait()}") 2024-08-20T21:38:35.0844512Z ... ) 2024-08-20T21:38:35.0844708Z >>> fut.set_result(5) 2024-08-20T21:38:35.0844924Z RPC return value is 5. 2024-08-20T21:38:35.0845122Z Chained cb done. None 2024-08-20T21:38:35.0845268Z 2024-08-20T21:38:35.0845998Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.0846148Z 2024-08-20T21:38:35.0846337Z warnings.warn(msg) 2024-08-20T21:38:35.0846502Z 2024-08-20T21:38:35.0847053Z --- Parse Warning: 13 / 101 --- 2024-08-20T21:38:35.0849608Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Future.set_result in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py line=209. 2024-08-20T21:38:35.0850369Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.0850516Z 2024-08-20T21:38:35.0851022Z Set the result for this ``Future``, which will mark this ``Future`` as 2024-08-20T21:38:35.0851552Z completed and trigger all attached callbacks. Note that a ``Future`` 2024-08-20T21:38:35.0851786Z cannot be marked completed twice. 2024-08-20T21:38:35.0851950Z 2024-08-20T21:38:35.0852470Z If the result contains tensors that reside on GPUs, this method can be 2024-08-20T21:38:35.0852952Z called even if the asynchronous kernels that are populating those 2024-08-20T21:38:35.0853617Z tensors haven't yet completed running on the device, provided that the 2024-08-20T21:38:35.0854161Z streams on which those kernels were enqueued are set as the current ones 2024-08-20T21:38:35.0854800Z when this method is called. Put simply, it's safe to call this method 2024-08-20T21:38:35.0855296Z immediately after launching those kernels, without any additional 2024-08-20T21:38:35.0855967Z synchronization, as long as one doesn't change streams in between. This 2024-08-20T21:38:35.0856483Z method will record events on all the relevant current streams and will 2024-08-20T21:38:35.0857116Z use them to ensure proper scheduling for all the consumers of this 2024-08-20T21:38:35.0857281Z ``Future``. 2024-08-20T21:38:35.0857432Z 2024-08-20T21:38:35.0857601Z Args: 2024-08-20T21:38:35.0857965Z result (object): the result object of this ``Future``. 2024-08-20T21:38:35.0858127Z 2024-08-20T21:38:35.0858298Z Example:: 2024-08-20T21:38:35.0858627Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2024-08-20T21:38:35.0858830Z >>> import threading 2024-08-20T21:38:35.0859009Z >>> import time 2024-08-20T21:38:35.0859252Z >>> def slow_set_future(fut, value): 2024-08-20T21:38:35.0859458Z ... time.sleep(0.5) 2024-08-20T21:38:35.0859673Z ... fut.set_result(value) 2024-08-20T21:38:35.0859904Z >>> fut = torch.futures.Future() 2024-08-20T21:38:35.0860127Z >>> t = threading.Thread( 2024-08-20T21:38:35.0860335Z ... target=slow_set_future, 2024-08-20T21:38:35.0860582Z ... args=(fut, torch.ones(2) * 3) 2024-08-20T21:38:35.0860750Z ... ) 2024-08-20T21:38:35.0860920Z >>> t.start() 2024-08-20T21:38:35.0861110Z >>> print(fut.wait()) 2024-08-20T21:38:35.0861303Z tensor([3., 3.]) 2024-08-20T21:38:35.0861469Z >>> t.join() 2024-08-20T21:38:35.0861619Z 2024-08-20T21:38:35.0862344Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.0862492Z 2024-08-20T21:38:35.0862681Z warnings.warn(msg) 2024-08-20T21:38:35.0862844Z 2024-08-20T21:38:35.0863204Z --- Parse Warning: 14 / 101 --- 2024-08-20T21:38:35.0865696Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=sum in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py line=201. 2024-08-20T21:38:35.0866440Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.0866814Z Return the sum of each row of the given sparse tensor. 2024-08-20T21:38:35.0866978Z 2024-08-20T21:38:35.0867549Z Returns the sum of each row of the sparse tensor :attr:`input` in the given 2024-08-20T21:38:35.0867995Z dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions, 2024-08-20T21:38:35.0868527Z reduce over all of them. When sum over all ``sparse_dim``, this method 2024-08-20T21:38:35.0868868Z returns a dense tensor instead of a sparse tensor. 2024-08-20T21:38:35.0869020Z 2024-08-20T21:38:35.0869665Z All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output 2024-08-20T21:38:35.0870106Z tensor having :attr:`dim` fewer dimensions than :attr:`input`. 2024-08-20T21:38:35.0870273Z 2024-08-20T21:38:35.0870782Z During backward, only gradients at ``nnz`` locations of :attr:`input` 2024-08-20T21:38:35.0871353Z will propagate back. Note that the gradients of :attr:`input` is coalesced. 2024-08-20T21:38:35.0871533Z 2024-08-20T21:38:35.0871693Z Args: 2024-08-20T21:38:35.0871971Z input (Tensor): the input sparse tensor 2024-08-20T21:38:35.0872681Z dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce 2024-08-20T21:38:35.0872868Z over all dims. 2024-08-20T21:38:35.0873479Z dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. 2024-08-20T21:38:35.0873747Z Default: dtype of :attr:`input`. 2024-08-20T21:38:35.0873899Z 2024-08-20T21:38:35.0874067Z Example:: 2024-08-20T21:38:35.0874226Z 2024-08-20T21:38:35.0874395Z >>> nnz = 3 2024-08-20T21:38:35.0874596Z >>> dims = [5, 5, 2, 3] 2024-08-20T21:38:35.0874976Z >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), 2024-08-20T21:38:35.0875422Z torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) 2024-08-20T21:38:35.0875782Z >>> V = torch.randn(nnz, dims[2], dims[3]) 2024-08-20T21:38:35.0875996Z >>> size = torch.Size(dims) 2024-08-20T21:38:35.0876437Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-08-20T21:38:35.0876726Z >>> S = torch.sparse_coo_tensor(I, V, size) 2024-08-20T21:38:35.0876888Z >>> S 2024-08-20T21:38:35.0877130Z tensor(indices=tensor([[2, 0, 3], 2024-08-20T21:38:35.0877363Z [2, 4, 1]]), 2024-08-20T21:38:35.0877765Z values=tensor([[[-0.6438, -1.6467, 1.4004], 2024-08-20T21:38:35.0878147Z [ 0.3411, 0.0918, -0.2312]], 2024-08-20T21:38:35.0878310Z 2024-08-20T21:38:35.0878699Z [[ 0.5348, 0.0634, -2.0494], 2024-08-20T21:38:35.0879075Z [-0.7125, -1.0646, 2.1844]], 2024-08-20T21:38:35.0879236Z 2024-08-20T21:38:35.0879610Z [[ 0.1276, 0.1874, -0.6334], 2024-08-20T21:38:35.0880011Z [-1.9682, -0.5340, 0.7483]]]), 2024-08-20T21:38:35.0880348Z size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo) 2024-08-20T21:38:35.0880497Z 2024-08-20T21:38:35.0880979Z # when sum over only part of sparse_dims, return a sparse tensor 2024-08-20T21:38:35.0881218Z >>> torch.sparse.sum(S, [1, 3]) 2024-08-20T21:38:35.0881466Z tensor(indices=tensor([[0, 2, 3]]), 2024-08-20T21:38:35.0881843Z values=tensor([[-1.4512, 0.4073], 2024-08-20T21:38:35.0882178Z [-0.8901, 0.2017], 2024-08-20T21:38:35.0882522Z [-0.3183, -1.7539]]), 2024-08-20T21:38:35.0882910Z size=(5, 2), nnz=3, layout=torch.sparse_coo) 2024-08-20T21:38:35.0883061Z 2024-08-20T21:38:35.0883423Z # when sum over all sparse dim, return a dense tensor 2024-08-20T21:38:35.0883654Z # with summed dims squeezed 2024-08-20T21:38:35.0883905Z >>> torch.sparse.sum(S, [0, 1, 3]) 2024-08-20T21:38:35.0884193Z tensor([-2.6596, -1.1450]) 2024-08-20T21:38:35.0884360Z 2024-08-20T21:38:35.0885077Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.0885238Z 2024-08-20T21:38:35.0885425Z warnings.warn(msg) 2024-08-20T21:38:35.0885573Z 2024-08-20T21:38:35.0885949Z --- Parse Warning: 15 / 101 --- 2024-08-20T21:38:35.0888420Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=vmap in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/apis.py line=40. 2024-08-20T21:38:35.0889167Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.0889330Z 2024-08-20T21:38:35.0889857Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that 2024-08-20T21:38:35.0890336Z maps ``func`` over some dimension of the inputs. Semantically, vmap 2024-08-20T21:38:35.0890877Z pushes the map into PyTorch operations called by ``func``, effectively 2024-08-20T21:38:35.0891097Z vectorizing those operations. 2024-08-20T21:38:35.0891249Z 2024-08-20T21:38:35.0891788Z vmap is useful for handling batch dimensions: one can write a function 2024-08-20T21:38:35.0892278Z ``func`` that runs on examples and then lift it to a function that can 2024-08-20T21:38:35.0892797Z take batches of examples with ``vmap(func)``. vmap can also be used to 2024-08-20T21:38:35.0893170Z compute batched gradients when composed with autograd. 2024-08-20T21:38:35.0893320Z 2024-08-20T21:38:35.0893504Z .. note:: 2024-08-20T21:38:35.0893930Z :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for 2024-08-20T21:38:35.0894313Z convenience. Use whichever one you'd like. 2024-08-20T21:38:35.0894480Z 2024-08-20T21:38:35.0894640Z Args: 2024-08-20T21:38:35.0895214Z func (function): A Python function that takes one or more arguments. 2024-08-20T21:38:35.0895473Z Must return one or more Tensors. 2024-08-20T21:38:35.0895961Z in_dims (int or nested structure): Specifies which dimension of the 2024-08-20T21:38:35.0896350Z inputs should be mapped over. ``in_dims`` should have a 2024-08-20T21:38:35.0896810Z structure like the inputs. If the ``in_dim`` for a particular 2024-08-20T21:38:35.0897253Z input is None, then that indicates there is no map dimension. 2024-08-20T21:38:35.0897441Z Default: 0. 2024-08-20T21:38:35.0897917Z out_dims (int or Tuple[int]): Specifies where the mapped dimension 2024-08-20T21:38:35.0898375Z should appear in the outputs. If ``out_dims`` is a Tuple, then 2024-08-20T21:38:35.0898740Z it should have one element per output. Default: 0. 2024-08-20T21:38:35.0899148Z randomness (str): Specifies whether the randomness in this 2024-08-20T21:38:35.0899809Z vmap should be the same or different across batches. If 'different', 2024-08-20T21:38:35.0900420Z the randomness for each batch will be different. If 'same', the 2024-08-20T21:38:35.0901068Z randomness will be the same across batches. If 'error', any calls to 2024-08-20T21:38:35.0901690Z random functions will error. Default: 'error'. WARNING: this flag 2024-08-20T21:38:35.0902173Z only applies to random PyTorch operations and does not apply to 2024-08-20T21:38:35.0902582Z Python's random module or numpy randomness. 2024-08-20T21:38:35.0903168Z chunk_size (None or int): If None (default), apply a single vmap over inputs. 2024-08-20T21:38:35.0903780Z If not None, then compute the vmap :attr:`chunk_size` samples at a time. 2024-08-20T21:38:35.0904573Z Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop. 2024-08-20T21:38:35.0905402Z If you run into memory issues computing the vmap, please try a non-None chunk_size. 2024-08-20T21:38:35.0905560Z 2024-08-20T21:38:35.0905724Z Returns: 2024-08-20T21:38:35.0906174Z Returns a new "batched" function. It takes the same inputs as 2024-08-20T21:38:35.0906598Z ``func``, except each input has an extra dimension at the index 2024-08-20T21:38:35.0907038Z specified by ``in_dims``. It takes returns the same outputs as 2024-08-20T21:38:35.0907485Z ``func``, except each output has an extra dimension at the index 2024-08-20T21:38:35.0907694Z specified by ``out_dims``. 2024-08-20T21:38:35.0907859Z 2024-08-20T21:38:35.0908024Z .. warning: 2024-08-20T21:38:35.0908625Z :func:`vmap` works best with functional-style code. Please do not 2024-08-20T21:38:35.0909182Z perform any side-effects in ``func``, with the exception of 2024-08-20T21:38:35.0909861Z in-place PyTorch operations. Examples of side-effects include mutating 2024-08-20T21:38:35.0910395Z Python data structures and assigning values to variables not captured 2024-08-20T21:38:35.0910579Z in ``func``. 2024-08-20T21:38:35.0910727Z 2024-08-20T21:38:35.0911305Z One example of using :func:`vmap` is to compute batched dot products. PyTorch 2024-08-20T21:38:35.0911966Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully 2024-08-20T21:38:35.0912472Z rummaging through docs, use :func:`vmap` to construct a new function. 2024-08-20T21:38:35.0912618Z 2024-08-20T21:38:35.0913083Z >>> torch.dot # [D], [D] -> [] 2024-08-20T21:38:35.0913694Z >>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N] 2024-08-20T21:38:35.0914003Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2024-08-20T21:38:35.0914197Z >>> batched_dot(x, y) 2024-08-20T21:38:35.0914347Z 2024-08-20T21:38:35.0914920Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler 2024-08-20T21:38:35.0915206Z model authoring experience. 2024-08-20T21:38:35.0915359Z 2024-08-20T21:38:35.0915609Z >>> batch_size, feature_size = 3, 5 2024-08-20T21:38:35.0915992Z >>> weights = torch.randn(feature_size, requires_grad=True) 2024-08-20T21:38:35.0916149Z >>> 2024-08-20T21:38:35.0916368Z >>> def model(feature_vec): 2024-08-20T21:38:35.0916677Z >>> # Very simple linear model with activation 2024-08-20T21:38:35.0916958Z >>> return feature_vec.dot(weights).relu() 2024-08-20T21:38:35.0917129Z >>> 2024-08-20T21:38:35.0917464Z >>> examples = torch.randn(batch_size, feature_size) 2024-08-20T21:38:35.0917725Z >>> result = torch.vmap(model)(examples) 2024-08-20T21:38:35.0917896Z 2024-08-20T21:38:35.0918506Z :func:`vmap` can also help vectorize computations that were previously difficult 2024-08-20T21:38:35.0919210Z or impossible to batch. One example is higher-order gradient computation. 2024-08-20T21:38:35.0919870Z The PyTorch autograd engine computes vjps (vector-Jacobian products). 2024-08-20T21:38:35.0920544Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually 2024-08-20T21:38:35.0921155Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`, 2024-08-20T21:38:35.0921724Z we can vectorize the whole computation, computing the Jacobian in a single 2024-08-20T21:38:35.0921928Z call to ``autograd.grad``. 2024-08-20T21:38:35.0922088Z 2024-08-20T21:38:35.0922253Z >>> # Setup 2024-08-20T21:38:35.0922415Z >>> N = 5 2024-08-20T21:38:35.0922627Z >>> f = lambda x: x ** 2 2024-08-20T21:38:35.0922892Z >>> x = torch.randn(N, requires_grad=True) 2024-08-20T21:38:35.0923149Z >>> y = f(x) 2024-08-20T21:38:35.0923354Z >>> I_N = torch.eye(N) 2024-08-20T21:38:35.0923508Z >>> 2024-08-20T21:38:35.0923713Z >>> # Sequential approach 2024-08-20T21:38:35.0924221Z >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0] 2024-08-20T21:38:35.0924473Z >>> for v in I_N.unbind()] 2024-08-20T21:38:35.0924752Z >>> jacobian = torch.stack(jacobian_rows) 2024-08-20T21:38:35.0924907Z >>> 2024-08-20T21:38:35.0925167Z >>> # vectorized gradient computation 2024-08-20T21:38:35.0925369Z >>> def get_vjp(v): 2024-08-20T21:38:35.0925643Z >>> return torch.autograd.grad(y, x, v) 2024-08-20T21:38:35.0925904Z >>> jacobian = torch.vmap(get_vjp)(I_N) 2024-08-20T21:38:35.0926070Z 2024-08-20T21:38:35.0926713Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions 2024-08-20T21:38:35.0926864Z 2024-08-20T21:38:35.0927398Z >>> torch.dot # [D], [D] -> [] 2024-08-20T21:38:35.0928239Z >>> batched_dot = torch.vmap(torch.vmap(torch.dot)) # [N1, N0, D], [N1, N0, D] -> [N1, N0] 2024-08-20T21:38:35.0928560Z >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5) 2024-08-20T21:38:35.0928865Z >>> batched_dot(x, y) # tensor of size [2, 3] 2024-08-20T21:38:35.0929016Z 2024-08-20T21:38:35.0929605Z If the inputs are not batched along the first dimension, ``in_dims`` specifies 2024-08-20T21:38:35.0929959Z the dimension that each inputs are batched along as 2024-08-20T21:38:35.0930109Z 2024-08-20T21:38:35.0930566Z >>> torch.dot # [N], [N] -> [] 2024-08-20T21:38:35.0931227Z >>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D] 2024-08-20T21:38:35.0931510Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2024-08-20T21:38:35.0932135Z >>> batched_dot(x, y) # output is [5] instead of [2] if batched along the 0th dimension 2024-08-20T21:38:35.0932291Z 2024-08-20T21:38:35.0932912Z If there are multiple inputs each of which is batched along different dimensions, 2024-08-20T21:38:35.0933411Z ``in_dims`` must be a tuple with the batch dimension for each input as 2024-08-20T21:38:35.0933638Z 2024-08-20T21:38:35.0934082Z >>> torch.dot # [D], [D] -> [] 2024-08-20T21:38:35.0934806Z >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N] 2024-08-20T21:38:35.0935075Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2024-08-20T21:38:35.0935860Z >>> batched_dot(x, y) # second arg doesn't have a batch dim because in_dim[1] was None 2024-08-20T21:38:35.0936007Z 2024-08-20T21:38:35.0936600Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct 2024-08-20T21:38:35.0936837Z matching the shape of the input: 2024-08-20T21:38:35.0936986Z 2024-08-20T21:38:35.0937419Z >>> f = lambda dict: torch.dot(dict['x'], dict['y']) 2024-08-20T21:38:35.0937708Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2024-08-20T21:38:35.0937990Z >>> input = {'x': x, 'y': y} 2024-08-20T21:38:35.0938520Z >>> batched_dot = torch.vmap(f, in_dims=({'x': 0, 'y': None},)) 2024-08-20T21:38:35.0938741Z >>> batched_dot(input) 2024-08-20T21:38:35.0938890Z 2024-08-20T21:38:35.0939569Z By default, the output is batched along the first dimension. However, it can be batched 2024-08-20T21:38:35.0939847Z along any dimension by using ``out_dims`` 2024-08-20T21:38:35.0939996Z 2024-08-20T21:38:35.0940192Z >>> f = lambda x: x ** 2 2024-08-20T21:38:35.0940404Z >>> x = torch.randn(2, 5) 2024-08-20T21:38:35.0940680Z >>> batched_pow = torch.vmap(f, out_dims=1) 2024-08-20T21:38:35.0940899Z >>> batched_pow(x) # [5, 2] 2024-08-20T21:38:35.0941043Z 2024-08-20T21:38:35.0941834Z For any function that uses kwargs, the returned function will not batch the kwargs but will 2024-08-20T21:38:35.0942021Z accept kwargs 2024-08-20T21:38:35.0942168Z 2024-08-20T21:38:35.0942374Z >>> x = torch.randn([2, 5]) 2024-08-20T21:38:35.0942583Z >>> def fn(x, scale=4.): 2024-08-20T21:38:35.0942774Z >>> return x * scale 2024-08-20T21:38:35.0942935Z >>> 2024-08-20T21:38:35.0943175Z >>> batched_pow = torch.vmap(fn) 2024-08-20T21:38:35.0943487Z >>> assert torch.allclose(batched_pow(x), x * 4) 2024-08-20T21:38:35.0944033Z >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5] 2024-08-20T21:38:35.0944197Z 2024-08-20T21:38:35.0944364Z .. note:: 2024-08-20T21:38:35.0945027Z vmap does not provide general autobatching or handle variable-length 2024-08-20T21:38:35.0945249Z sequences out of the box. 2024-08-20T21:38:35.0945398Z 2024-08-20T21:38:35.0946109Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.0946274Z 2024-08-20T21:38:35.0946470Z warnings.warn(msg) 2024-08-20T21:38:35.0946619Z 2024-08-20T21:38:35.0947174Z --- Parse Warning: 16 / 101 --- 2024-08-20T21:38:35.0949596Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=triton_op in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/triton.py line=17. 2024-08-20T21:38:35.0950355Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.0950948Z Create a custom operator whose implementation is backed by 1+ triton kernels. 2024-08-20T21:38:35.0951098Z 2024-08-20T21:38:35.0951690Z Use this instead of :func:`torch.library.custom_op` when the implementation 2024-08-20T21:38:35.0952207Z consists of 1+ triton kernels. :func:`torch.library.custom_op` treats 2024-08-20T21:38:35.0952576Z custom operators as opaque (:func:`torch.compile` and 2024-08-20T21:38:35.0953151Z :func:`torch.export.export` will never trace into them), but ``triton_op`` 2024-08-20T21:38:35.0953661Z makes the implementation visible to these subsystems, allowing them 2024-08-20T21:38:35.0953915Z to optimize the triton kernel(s). 2024-08-20T21:38:35.0954204Z 2024-08-20T21:38:35.0954795Z Note that ``fn`` must only consist of calls to PyTorch-understood 2024-08-20T21:38:35.0955334Z operators and triton kernels. Any triton kernels called inside ``fn`` 2024-08-20T21:38:35.0955833Z must be wrapped in a call to :func:`torch._library.capture_triton``. 2024-08-20T21:38:35.0955983Z 2024-08-20T21:38:35.0956152Z Args: 2024-08-20T21:38:35.0956707Z name (str): A name for the custom op that looks like "{namespace}::{name}", 2024-08-20T21:38:35.0957376Z e.g. "mylib::my_linear". The name is used as the op's stable identifier 2024-08-20T21:38:35.0957775Z in PyTorch subsystems (e.g. torch.export, FX graphs). 2024-08-20T21:38:35.0958346Z To avoid name collisions, please use your project name as the namespace; 2024-08-20T21:38:35.0958869Z e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace. 2024-08-20T21:38:35.0959527Z mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates. 2024-08-20T21:38:35.0960112Z This MUST be accurate, otherwise, the behavior is undefined. If "unknown", 2024-08-20T21:38:35.0960747Z it pessimistically assumes that all inputs to the operator are being mutated. 2024-08-20T21:38:35.0961188Z schema (None | str): A schema string for the operator. If None 2024-08-20T21:38:35.0961812Z (recommended) we'll infer a schema for the operator from its type 2024-08-20T21:38:35.0962293Z annotations. We recommend letting us infer a schema unless you 2024-08-20T21:38:35.0962541Z have a specific reason not to. 2024-08-20T21:38:35.0963101Z Example: "(Tensor x, int y) -> (Tensor, Tensor)". 2024-08-20T21:38:35.0963254Z 2024-08-20T21:38:35.0963426Z Example:: 2024-08-20T21:38:35.0963589Z 2024-08-20T21:38:35.0963907Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-20T21:38:35.0964101Z >>> import torch 2024-08-20T21:38:35.0964494Z >>> from torch._library import triton_op, capture_triton 2024-08-20T21:38:35.0964655Z >>> 2024-08-20T21:38:35.0964840Z >>> import triton 2024-08-20T21:38:35.0965116Z >>> from triton import language as tl 2024-08-20T21:38:35.0965273Z >>> 2024-08-20T21:38:35.0965451Z >>> @triton.jit 2024-08-20T21:38:35.0965654Z >>> def add_kernel( 2024-08-20T21:38:35.0965832Z >>> in_ptr0, 2024-08-20T21:38:35.0966006Z >>> in_ptr1, 2024-08-20T21:38:35.0966192Z >>> out_ptr, 2024-08-20T21:38:35.0966375Z >>> n_elements, 2024-08-20T21:38:35.0966631Z >>> BLOCK_SIZE: "tl.constexpr", 2024-08-20T21:38:35.0966801Z >>> ): 2024-08-20T21:38:35.0967129Z >>> pid = tl.program_id(axis=0) 2024-08-20T21:38:35.0967386Z >>> block_start = pid * BLOCK_SIZE 2024-08-20T21:38:35.0967752Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2024-08-20T21:38:35.0967996Z >>> mask = offsets < n_elements 2024-08-20T21:38:35.0968305Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2024-08-20T21:38:35.0968600Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2024-08-20T21:38:35.0968797Z >>> output = x + y 2024-08-20T21:38:35.0969150Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2024-08-20T21:38:35.0969310Z >>> 2024-08-20T21:38:35.0969598Z >>> @triton_op("mylib::add", mutates_args={}) 2024-08-20T21:38:35.0970176Z >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: 2024-08-20T21:38:35.0970435Z >>> output = torch.empty_like(x) 2024-08-20T21:38:35.0970683Z >>> n_elements = output.numel() 2024-08-20T21:38:35.0970862Z >>> 2024-08-20T21:38:35.0971054Z >>> def grid(meta): 2024-08-20T21:38:35.0971526Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2024-08-20T21:38:35.0971700Z >>> 2024-08-20T21:38:35.0972189Z >>> # NB: we need to wrap the triton kernel in a call to capture_triton 2024-08-20T21:38:35.0972667Z >>> capture_triton(add_kernel)[grid](x, y, output, n_elements, 16) 2024-08-20T21:38:35.0972861Z >>> return output 2024-08-20T21:38:35.0973019Z >>> 2024-08-20T21:38:35.0973221Z >>> @torch.compile 2024-08-20T21:38:35.0973405Z >>> def f(x, y): 2024-08-20T21:38:35.0973605Z >>> return add(x, y) 2024-08-20T21:38:35.0973780Z >>> 2024-08-20T21:38:35.0974042Z >>> x = torch.randn(3, device="cuda") 2024-08-20T21:38:35.0974293Z >>> y = torch.randn(3, device="cuda") 2024-08-20T21:38:35.0974463Z >>> 2024-08-20T21:38:35.0974643Z >>> z = f(x, y) 2024-08-20T21:38:35.0974899Z >>> assert torch.allclose(z, x + y) 2024-08-20T21:38:35.0975068Z 2024-08-20T21:38:35.0975226Z 2024-08-20T21:38:35.0975947Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.0976110Z 2024-08-20T21:38:35.0976302Z warnings.warn(msg) 2024-08-20T21:38:35.0976451Z 2024-08-20T21:38:35.0976828Z --- Parse Warning: 17 / 101 --- 2024-08-20T21:38:35.0979328Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=capture_triton in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/triton.py line=163. 2024-08-20T21:38:35.0980081Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.0980608Z Allows capture of a triton kernel into a graph via make_fx or 2024-08-20T21:38:35.0980929Z non-strict export (coming soon). 2024-08-20T21:38:35.0981094Z 2024-08-20T21:38:35.0981621Z These technologies perform Dispatcher-based tracing (via 2024-08-20T21:38:35.0982078Z ``__torch_dispatch__``) and cannot see calls to raw triton kernels. 2024-08-20T21:38:35.0982562Z The ``capture_triton`` API returns a new callable that can actually 2024-08-20T21:38:35.0982763Z be traced into a graph. 2024-08-20T21:38:35.0982912Z 2024-08-20T21:38:35.0983088Z Examples: 2024-08-20T21:38:35.0983234Z 2024-08-20T21:38:35.0983435Z >>> # xdoctest: +SKIP 2024-08-20T21:38:35.0983632Z >>> import torch 2024-08-20T21:38:35.0983821Z >>> import triton 2024-08-20T21:38:35.0984086Z >>> from triton import language as tl 2024-08-20T21:38:35.0984527Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2024-08-20T21:38:35.0985053Z >>> from torch._higher_order_ops.triton_kernel_wrap import capture_triton 2024-08-20T21:38:35.0985225Z >>> 2024-08-20T21:38:35.0985402Z >>> @triton.jit 2024-08-20T21:38:35.0985596Z >>> def add_kernel( 2024-08-20T21:38:35.0985782Z >>> in_ptr0, 2024-08-20T21:38:35.0985956Z >>> in_ptr1, 2024-08-20T21:38:35.0986129Z >>> out_ptr, 2024-08-20T21:38:35.0986321Z >>> n_elements, 2024-08-20T21:38:35.0986569Z >>> BLOCK_SIZE: "tl.constexpr", 2024-08-20T21:38:35.0986728Z >>> ): 2024-08-20T21:38:35.0986985Z >>> pid = tl.program_id(axis=0) 2024-08-20T21:38:35.0987239Z >>> block_start = pid * BLOCK_SIZE 2024-08-20T21:38:35.0987580Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2024-08-20T21:38:35.0987829Z >>> mask = offsets < n_elements 2024-08-20T21:38:35.0988133Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2024-08-20T21:38:35.0988427Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2024-08-20T21:38:35.0988634Z >>> output = x + y 2024-08-20T21:38:35.0988970Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2024-08-20T21:38:35.0989240Z >>> 2024-08-20T21:38:35.0989426Z >>> def add(x, y): 2024-08-20T21:38:35.0989675Z >>> output = torch.empty_like(x) 2024-08-20T21:38:35.0989935Z >>> n_elements = output.numel() 2024-08-20T21:38:35.0990094Z >>> 2024-08-20T21:38:35.0990295Z >>> def grid_fn(meta): 2024-08-20T21:38:35.0990699Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2024-08-20T21:38:35.0990858Z >>> 2024-08-20T21:38:35.0991337Z >>> capture_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16) 2024-08-20T21:38:35.0991540Z >>> return output 2024-08-20T21:38:35.0991707Z >>> 2024-08-20T21:38:35.0991958Z >>> x = torch.randn(3, device="cuda") 2024-08-20T21:38:35.0992219Z >>> y = torch.randn(3, device="cuda") 2024-08-20T21:38:35.0992432Z >>> gm = make_fx(add)(x, y) 2024-08-20T21:38:35.0992629Z >>> print(gm.code) 2024-08-20T21:38:35.0992883Z >>> # def forward(self, x_1, y_1): 2024-08-20T21:38:35.0993452Z >>> # empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False) 2024-08-20T21:38:35.0994004Z >>> # triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation( 2024-08-20T21:38:35.0994301Z >>> # kernel_idx = 0, constant_args_idx = 0, 2024-08-20T21:38:35.0994557Z >>> # grid = [(1, 1, 1)], kwargs = { 2024-08-20T21:38:35.0995101Z >>> # 'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like, 2024-08-20T21:38:35.0995501Z >>> # 'n_elements': 3, 'BLOCK_SIZE': 16 2024-08-20T21:38:35.0995749Z >>> # }) 2024-08-20T21:38:35.0995977Z >>> # return empty_like 2024-08-20T21:38:35.0996127Z 2024-08-20T21:38:35.0996280Z 2024-08-20T21:38:35.0997004Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.0997159Z 2024-08-20T21:38:35.0997351Z warnings.warn(msg) 2024-08-20T21:38:35.0997514Z 2024-08-20T21:38:35.0997871Z --- Parse Warning: 18 / 101 --- 2024-08-20T21:38:35.1000565Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_almost_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=330. 2024-08-20T21:38:35.1001302Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1001452Z 2024-08-20T21:38:35.1001952Z Raises an AssertionError if two items are not equal up to desired 2024-08-20T21:38:35.1002128Z precision. 2024-08-20T21:38:35.1002278Z 2024-08-20T21:38:35.1002694Z .. note:: It is recommended to use one of `assert_allclose`, 2024-08-20T21:38:35.1003098Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-08-20T21:38:35.1003548Z instead of this function for more consistent floating point 2024-08-20T21:38:35.1003748Z comparisons. 2024-08-20T21:38:35.1003896Z 2024-08-20T21:38:35.1004412Z The test verifies that the elements of `actual` and `desired` satisfy. 2024-08-20T21:38:35.1004576Z 2024-08-20T21:38:35.1005046Z ``abs(desired-actual) < float64(1.5 * 10**(-decimal))`` 2024-08-20T21:38:35.1005212Z 2024-08-20T21:38:35.1005772Z That is a looser test than originally documented, but agrees with what the 2024-08-20T21:38:35.1006304Z actual implementation in `assert_array_almost_equal` did up to rounding 2024-08-20T21:38:35.1006876Z vagaries. An exception is raised at conflicting values. For ndarrays this 2024-08-20T21:38:35.1007214Z delegates to assert_array_almost_equal 2024-08-20T21:38:35.1007367Z 2024-08-20T21:38:35.1007546Z Parameters 2024-08-20T21:38:35.1007755Z ---------- 2024-08-20T21:38:35.1007938Z actual : array_like 2024-08-20T21:38:35.1008240Z The object to check. 2024-08-20T21:38:35.1008430Z desired : array_like 2024-08-20T21:38:35.1008623Z The expected object. 2024-08-20T21:38:35.1008832Z decimal : int, optional 2024-08-20T21:38:35.1009071Z Desired precision, default is 7. 2024-08-20T21:38:35.1009264Z err_msg : str, optional 2024-08-20T21:38:35.1009628Z The error message to be printed in case of failure. 2024-08-20T21:38:35.1009825Z verbose : bool, optional 2024-08-20T21:38:35.1010315Z If True, the conflicting values are appended to the error message. 2024-08-20T21:38:35.1010478Z 2024-08-20T21:38:35.1010637Z Raises 2024-08-20T21:38:35.1010835Z ------ 2024-08-20T21:38:35.1011035Z AssertionError 2024-08-20T21:38:35.1011495Z If actual and desired are not equal up to specified precision. 2024-08-20T21:38:35.1011659Z 2024-08-20T21:38:35.1011821Z See Also 2024-08-20T21:38:35.1012020Z -------- 2024-08-20T21:38:35.1012577Z assert_allclose: Compare two array_like objects for equality with desired 2024-08-20T21:38:35.1012862Z relative and/or absolute precision. 2024-08-20T21:38:35.1013326Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-08-20T21:38:35.1013489Z 2024-08-20T21:38:35.1013648Z Examples 2024-08-20T21:38:35.1013847Z -------- 2024-08-20T21:38:35.1014221Z >>> from torch._numpy.testing import assert_almost_equal 2024-08-20T21:38:35.1014509Z >>> assert_almost_equal(2.3333333333333, 2.33333334) 2024-08-20T21:38:35.1014894Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) 2024-08-20T21:38:35.1015141Z Traceback (most recent call last): 2024-08-20T21:38:35.1015298Z ... 2024-08-20T21:38:35.1015559Z AssertionError: 2024-08-20T21:38:35.1015846Z Arrays are not almost equal to 10 decimals 2024-08-20T21:38:35.1016030Z ACTUAL: 2.3333333333333 2024-08-20T21:38:35.1016208Z DESIRED: 2.33333334 2024-08-20T21:38:35.1016379Z 2024-08-20T21:38:35.1016703Z >>> assert_almost_equal(np.array([1.0,2.3333333333333]), 2024-08-20T21:38:35.1016986Z ... np.array([1.0,2.33333334]), decimal=9) 2024-08-20T21:38:35.1017232Z Traceback (most recent call last): 2024-08-20T21:38:35.1017387Z ... 2024-08-20T21:38:35.1017580Z AssertionError: 2024-08-20T21:38:35.1017847Z Arrays are not almost equal to 9 decimals 2024-08-20T21:38:35.1018010Z 2024-08-20T21:38:35.1018240Z Mismatched elements: 1 / 2 (50%) 2024-08-20T21:38:35.1018626Z Max absolute difference: 6.666699636781459e-09 2024-08-20T21:38:35.1019023Z Max relative difference: 2.8571569790287484e-09 2024-08-20T21:38:35.1019343Z x: torch.ndarray([1.0000, 2.3333], dtype=float64) 2024-08-20T21:38:35.1019644Z y: torch.ndarray([1.0000, 2.3333], dtype=float64) 2024-08-20T21:38:35.1019791Z 2024-08-20T21:38:35.1019950Z 2024-08-20T21:38:35.1020658Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1020813Z 2024-08-20T21:38:35.1021017Z warnings.warn(msg) 2024-08-20T21:38:35.1021165Z 2024-08-20T21:38:35.1021521Z --- Parse Warning: 19 / 101 --- 2024-08-20T21:38:35.1024144Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_approx_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=455. 2024-08-20T21:38:35.1024874Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1025038Z 2024-08-20T21:38:35.1025555Z Raises an AssertionError if two items are not equal up to significant 2024-08-20T21:38:35.1025721Z digits. 2024-08-20T21:38:35.1025881Z 2024-08-20T21:38:35.1026281Z .. note:: It is recommended to use one of `assert_allclose`, 2024-08-20T21:38:35.1026683Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-08-20T21:38:35.1027227Z instead of this function for more consistent floating point 2024-08-20T21:38:35.1027410Z comparisons. 2024-08-20T21:38:35.1027557Z 2024-08-20T21:38:35.1027989Z Given two numbers, check that they are approximately equal. 2024-08-20T21:38:35.1028485Z Approximately equal is defined as the number of significant digits 2024-08-20T21:38:35.1028654Z that agree. 2024-08-20T21:38:35.1028816Z 2024-08-20T21:38:35.1028984Z Parameters 2024-08-20T21:38:35.1029187Z ---------- 2024-08-20T21:38:35.1029377Z actual : scalar 2024-08-20T21:38:35.1029570Z The object to check. 2024-08-20T21:38:35.1029746Z desired : scalar 2024-08-20T21:38:35.1029954Z The expected object. 2024-08-20T21:38:35.1030170Z significant : int, optional 2024-08-20T21:38:35.1030422Z Desired precision, default is 7. 2024-08-20T21:38:35.1030617Z err_msg : str, optional 2024-08-20T21:38:35.1030968Z The error message to be printed in case of failure. 2024-08-20T21:38:35.1031190Z verbose : bool, optional 2024-08-20T21:38:35.1031677Z If True, the conflicting values are appended to the error message. 2024-08-20T21:38:35.1031826Z 2024-08-20T21:38:35.1031997Z Raises 2024-08-20T21:38:35.1032196Z ------ 2024-08-20T21:38:35.1032372Z AssertionError 2024-08-20T21:38:35.1032842Z If actual and desired are not equal up to specified precision. 2024-08-20T21:38:35.1032993Z 2024-08-20T21:38:35.1033152Z See Also 2024-08-20T21:38:35.1033367Z -------- 2024-08-20T21:38:35.1033907Z assert_allclose: Compare two array_like objects for equality with desired 2024-08-20T21:38:35.1034196Z relative and/or absolute precision. 2024-08-20T21:38:35.1034797Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-08-20T21:38:35.1034948Z 2024-08-20T21:38:35.1035109Z Examples 2024-08-20T21:38:35.1035320Z -------- 2024-08-20T21:38:35.1036125Z >>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20) # doctest: +SKIP 2024-08-20T21:38:35.1036888Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20, # doctest: +SKIP 2024-08-20T21:38:35.1037150Z ... significant=8) 2024-08-20T21:38:35.1037896Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20, # doctest: +SKIP 2024-08-20T21:38:35.1038163Z ... significant=8) 2024-08-20T21:38:35.1038397Z Traceback (most recent call last): 2024-08-20T21:38:35.1038547Z ... 2024-08-20T21:38:35.1038742Z AssertionError: 2024-08-20T21:38:35.1039023Z Items are not equal to 8 significant digits: 2024-08-20T21:38:35.1039271Z ACTUAL: 1.234567e-21 2024-08-20T21:38:35.1039547Z DESIRED: 1.2345672e-21 2024-08-20T21:38:35.1039700Z 2024-08-20T21:38:35.1040059Z the evaluated condition that raises the exception is 2024-08-20T21:38:35.1040220Z 2024-08-20T21:38:35.1040746Z >>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1) 2024-08-20T21:38:35.1040908Z True 2024-08-20T21:38:35.1041071Z 2024-08-20T21:38:35.1041218Z 2024-08-20T21:38:35.1041924Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1042089Z 2024-08-20T21:38:35.1042278Z warnings.warn(msg) 2024-08-20T21:38:35.1042429Z 2024-08-20T21:38:35.1042799Z --- Parse Warning: 20 / 101 --- 2024-08-20T21:38:35.1045390Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_array_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=734. 2024-08-20T21:38:35.1046152Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1046301Z 2024-08-20T21:38:35.1047033Z Raises an AssertionError if two array_like objects are not equal. 2024-08-20T21:38:35.1047197Z 2024-08-20T21:38:35.1047879Z Given two array_like objects, check that the shape is equal and all 2024-08-20T21:38:35.1048398Z elements of these objects are equal (but see the Notes for the special 2024-08-20T21:38:35.1048896Z handling of a scalar). An exception is raised at shape mismatch or 2024-08-20T21:38:35.1049411Z conflicting values. In contrast to the standard usage in numpy, NaNs 2024-08-20T21:38:35.1049937Z are compared like numbers, no assertion is raised if both objects have 2024-08-20T21:38:35.1050161Z NaNs in the same positions. 2024-08-20T21:38:35.1050312Z 2024-08-20T21:38:35.1050862Z The usual caution for verifying equality with floating point numbers is 2024-08-20T21:38:35.1051024Z advised. 2024-08-20T21:38:35.1051174Z 2024-08-20T21:38:35.1051362Z Parameters 2024-08-20T21:38:35.1051580Z ---------- 2024-08-20T21:38:35.1051752Z x : array_like 2024-08-20T21:38:35.1051977Z The actual object to check. 2024-08-20T21:38:35.1052143Z y : array_like 2024-08-20T21:38:35.1052375Z The desired, expected object. 2024-08-20T21:38:35.1052581Z err_msg : str, optional 2024-08-20T21:38:35.1052935Z The error message to be printed in case of failure. 2024-08-20T21:38:35.1053140Z verbose : bool, optional 2024-08-20T21:38:35.1053634Z If True, the conflicting values are appended to the error message. 2024-08-20T21:38:35.1053831Z strict : bool, optional 2024-08-20T21:38:35.1054315Z If True, raise an AssertionError when either the shape or the data 2024-08-20T21:38:35.1054735Z type of the array_like objects does not match. The special 2024-08-20T21:38:35.1055221Z handling for scalars mentioned in the Notes section is disabled. 2024-08-20T21:38:35.1055366Z 2024-08-20T21:38:35.1055668Z Raises 2024-08-20T21:38:35.1055867Z ------ 2024-08-20T21:38:35.1056045Z AssertionError 2024-08-20T21:38:35.1056356Z If actual and desired objects are not equal. 2024-08-20T21:38:35.1056507Z 2024-08-20T21:38:35.1056687Z See Also 2024-08-20T21:38:35.1056893Z -------- 2024-08-20T21:38:35.1057437Z assert_allclose: Compare two array_like objects for equality with desired 2024-08-20T21:38:35.1057736Z relative and/or absolute precision. 2024-08-20T21:38:35.1058199Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-08-20T21:38:35.1058344Z 2024-08-20T21:38:35.1058512Z Notes 2024-08-20T21:38:35.1058706Z ----- 2024-08-20T21:38:35.1059177Z When one of `x` and `y` is a scalar and the other is array_like, the 2024-08-20T21:38:35.1059711Z function checks that each element of the array_like object is equal to 2024-08-20T21:38:35.1060251Z the scalar. This behaviour can be disabled with the `strict` parameter. 2024-08-20T21:38:35.1060401Z 2024-08-20T21:38:35.1060574Z Examples 2024-08-20T21:38:35.1060780Z -------- 2024-08-20T21:38:35.1061074Z The first assert does not raise an exception: 2024-08-20T21:38:35.1061235Z 2024-08-20T21:38:35.1061580Z >>> np.testing.assert_array_equal([1.0,2.33333,np.nan], 2024-08-20T21:38:35.1061859Z ... [np.exp(0),2.33333, np.nan]) 2024-08-20T21:38:35.1062022Z 2024-08-20T21:38:35.1062564Z Use `assert_allclose` or one of the nulp (number of floating point values) 2024-08-20T21:38:35.1062810Z functions for these cases instead: 2024-08-20T21:38:35.1062955Z 2024-08-20T21:38:35.1063272Z >>> np.testing.assert_allclose([1.0,np.pi,np.nan], 2024-08-20T21:38:35.1063574Z ... [1, np.sqrt(np.pi)**2, np.nan], 2024-08-20T21:38:35.1063936Z ... rtol=1e-10, atol=0) 2024-08-20T21:38:35.1064086Z 2024-08-20T21:38:35.1064594Z As mentioned in the Notes section, `assert_array_equal` has special 2024-08-20T21:38:35.1065128Z handling for scalars. Here the test checks that each value in `x` is 3: 2024-08-20T21:38:35.1065281Z 2024-08-20T21:38:35.1065516Z >>> x = np.full((2, 5), fill_value=3) 2024-08-20T21:38:35.1065844Z >>> np.testing.assert_array_equal(x, 3) 2024-08-20T21:38:35.1065995Z 2024-08-20T21:38:35.1066530Z Use `strict` to raise an AssertionError when comparing a scalar with an 2024-08-20T21:38:35.1066686Z array: 2024-08-20T21:38:35.1066830Z 2024-08-20T21:38:35.1067175Z >>> np.testing.assert_array_equal(x, 3, strict=True) 2024-08-20T21:38:35.1067410Z Traceback (most recent call last): 2024-08-20T21:38:35.1067564Z ... 2024-08-20T21:38:35.1067758Z AssertionError: 2024-08-20T21:38:35.1067950Z Arrays are not equal 2024-08-20T21:38:35.1068115Z 2024-08-20T21:38:35.1068328Z (shapes (2, 5), () mismatch) 2024-08-20T21:38:35.1068542Z x: torch.ndarray([[3, 3, 3, 3, 3], 2024-08-20T21:38:35.1068740Z [3, 3, 3, 3, 3]]) 2024-08-20T21:38:35.1068936Z y: torch.ndarray(3) 2024-08-20T21:38:35.1069082Z 2024-08-20T21:38:35.1069590Z The `strict` parameter also ensures that the array data types match: 2024-08-20T21:38:35.1069748Z 2024-08-20T21:38:35.1069941Z >>> x = np.array([2, 2, 2]) 2024-08-20T21:38:35.1070233Z >>> y = np.array([2., 2., 2.], dtype=np.float32) 2024-08-20T21:38:35.1070561Z >>> np.testing.assert_array_equal(x, y, strict=True) 2024-08-20T21:38:35.1070794Z Traceback (most recent call last): 2024-08-20T21:38:35.1070959Z ... 2024-08-20T21:38:35.1071137Z AssertionError: 2024-08-20T21:38:35.1071331Z Arrays are not equal 2024-08-20T21:38:35.1071514Z 2024-08-20T21:38:35.1071826Z (dtypes dtype("int64"), dtype("float32") mismatch) 2024-08-20T21:38:35.1072028Z x: torch.ndarray([2, 2, 2]) 2024-08-20T21:38:35.1072251Z y: torch.ndarray([2., 2., 2.]) 2024-08-20T21:38:35.1072399Z 2024-08-20T21:38:35.1073210Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1073377Z 2024-08-20T21:38:35.1073565Z warnings.warn(msg) 2024-08-20T21:38:35.1073711Z 2024-08-20T21:38:35.1074086Z --- Parse Warning: 21 / 101 --- 2024-08-20T21:38:35.1076756Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_array_almost_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=840. 2024-08-20T21:38:35.1077506Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1077658Z 2024-08-20T21:38:35.1078156Z Raises an AssertionError if two objects are not equal up to desired 2024-08-20T21:38:35.1078335Z precision. 2024-08-20T21:38:35.1078481Z 2024-08-20T21:38:35.1078885Z .. note:: It is recommended to use one of `assert_allclose`, 2024-08-20T21:38:35.1079309Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-08-20T21:38:35.1079749Z instead of this function for more consistent floating point 2024-08-20T21:38:35.1079931Z comparisons. 2024-08-20T21:38:35.1080102Z 2024-08-20T21:38:35.1080665Z The test verifies identical shapes and that the elements of ``actual`` and 2024-08-20T21:38:35.1080847Z ``desired`` satisfy. 2024-08-20T21:38:35.1081008Z 2024-08-20T21:38:35.1081396Z ``abs(desired-actual) < 1.5 * 10**(-decimal)`` 2024-08-20T21:38:35.1081560Z 2024-08-20T21:38:35.1082126Z That is a looser test than originally documented, but agrees with what the 2024-08-20T21:38:35.1082695Z actual implementation did up to rounding vagaries. An exception is raised 2024-08-20T21:38:35.1083272Z at shape mismatch or conflicting values. In contrast to the standard usage 2024-08-20T21:38:35.1083808Z in numpy, NaNs are compared like numbers, no assertion is raised if both 2024-08-20T21:38:35.1084085Z objects have NaNs in the same positions. 2024-08-20T21:38:35.1084246Z 2024-08-20T21:38:35.1084413Z Parameters 2024-08-20T21:38:35.1084621Z ---------- 2024-08-20T21:38:35.1084802Z x : array_like 2024-08-20T21:38:35.1085016Z The actual object to check. 2024-08-20T21:38:35.1085264Z y : array_like 2024-08-20T21:38:35.1085507Z The desired, expected object. 2024-08-20T21:38:35.1085703Z decimal : int, optional 2024-08-20T21:38:35.1085940Z Desired precision, default is 6. 2024-08-20T21:38:35.1086142Z err_msg : str, optional 2024-08-20T21:38:35.1086491Z The error message to be printed in case of failure. 2024-08-20T21:38:35.1086692Z verbose : bool, optional 2024-08-20T21:38:35.1087303Z If True, the conflicting values are appended to the error message. 2024-08-20T21:38:35.1087455Z 2024-08-20T21:38:35.1087624Z Raises 2024-08-20T21:38:35.1087827Z ------ 2024-08-20T21:38:35.1088001Z AssertionError 2024-08-20T21:38:35.1088484Z If actual and desired are not equal up to specified precision. 2024-08-20T21:38:35.1088629Z 2024-08-20T21:38:35.1088795Z See Also 2024-08-20T21:38:35.1089010Z -------- 2024-08-20T21:38:35.1089547Z assert_allclose: Compare two array_like objects for equality with desired 2024-08-20T21:38:35.1089844Z relative and/or absolute precision. 2024-08-20T21:38:35.1090320Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-08-20T21:38:35.1090470Z 2024-08-20T21:38:35.1090630Z Examples 2024-08-20T21:38:35.1090839Z -------- 2024-08-20T21:38:35.1091122Z the first assert does not raise an exception 2024-08-20T21:38:35.1091268Z 2024-08-20T21:38:35.1091663Z >>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan], 2024-08-20T21:38:35.1091929Z ... [1.0,2.333,np.nan]) 2024-08-20T21:38:35.1092074Z 2024-08-20T21:38:35.1092484Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2024-08-20T21:38:35.1092861Z ... [1.0,2.33339,np.nan], decimal=5) 2024-08-20T21:38:35.1093111Z Traceback (most recent call last): 2024-08-20T21:38:35.1093268Z ... 2024-08-20T21:38:35.1093453Z AssertionError: 2024-08-20T21:38:35.1093733Z Arrays are not almost equal to 5 decimals 2024-08-20T21:38:35.1093900Z 2024-08-20T21:38:35.1094125Z Mismatched elements: 1 / 3 (33.3%) 2024-08-20T21:38:35.1094521Z Max absolute difference: 5.999999999994898e-05 2024-08-20T21:38:35.1094908Z Max relative difference: 2.5713661239633743e-05 2024-08-20T21:38:35.1095271Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2024-08-20T21:38:35.1095644Z y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64) 2024-08-20T21:38:35.1095795Z 2024-08-20T21:38:35.1096194Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2024-08-20T21:38:35.1096500Z ... [1.0,2.33333, 5], decimal=5) 2024-08-20T21:38:35.1096731Z Traceback (most recent call last): 2024-08-20T21:38:35.1096887Z ... 2024-08-20T21:38:35.1097081Z AssertionError: 2024-08-20T21:38:35.1097348Z Arrays are not almost equal to 5 decimals 2024-08-20T21:38:35.1097520Z 2024-08-20T21:38:35.1097750Z x and y nan location mismatch: 2024-08-20T21:38:35.1098110Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2024-08-20T21:38:35.1098477Z y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64) 2024-08-20T21:38:35.1098626Z 2024-08-20T21:38:35.1098770Z 2024-08-20T21:38:35.1099498Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1099647Z 2024-08-20T21:38:35.1099831Z warnings.warn(msg) 2024-08-20T21:38:35.1099995Z 2024-08-20T21:38:35.1100353Z --- Parse Warning: 22 / 101 --- 2024-08-20T21:38:35.1103020Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=clear_and_catch_warnings in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=1790. 2024-08-20T21:38:35.1103775Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1104341Z Context manager that resets warning registry for catching warnings 2024-08-20T21:38:35.1104491Z 2024-08-20T21:38:35.1105080Z Warnings can be slippery, because, whenever a warning is triggered, Python 2024-08-20T21:38:35.1105597Z adds a ``__warningregistry__`` member to the *calling* module. This makes 2024-08-20T21:38:35.1106174Z it impossible to retrigger the warning in this module, whatever you put in 2024-08-20T21:38:35.1106742Z the warnings filters. This context manager accepts a sequence of `modules` 2024-08-20T21:38:35.1107047Z as a keyword argument to its constructor and: 2024-08-20T21:38:35.1107209Z 2024-08-20T21:38:35.1107758Z * stores and removes any ``__warningregistry__`` entries in given `modules` 2024-08-20T21:38:35.1107923Z on entry; 2024-08-20T21:38:35.1108358Z * resets ``__warningregistry__`` to its previous state on exit. 2024-08-20T21:38:35.1108508Z 2024-08-20T21:38:35.1109051Z This makes it possible to trigger any warning afresh inside the context 2024-08-20T21:38:35.1109470Z manager without disturbing the state of warnings outside. 2024-08-20T21:38:35.1109617Z 2024-08-20T21:38:35.1110157Z For compatibility with Python 3.0, please consider all arguments to be 2024-08-20T21:38:35.1110407Z keyword-only. 2024-08-20T21:38:35.1110554Z 2024-08-20T21:38:35.1110737Z Parameters 2024-08-20T21:38:35.1110947Z ---------- 2024-08-20T21:38:35.1111145Z record : bool, optional 2024-08-20T21:38:35.1111577Z Specifies whether warnings should be captured by a custom 2024-08-20T21:38:35.1112194Z implementation of ``warnings.showwarning()`` and be appended to a list 2024-08-20T21:38:35.1112692Z returned by the context manager. Otherwise None is returned by the 2024-08-20T21:38:35.1113232Z context manager. The objects appended to the list are arguments whose 2024-08-20T21:38:35.1113615Z attributes mirror the arguments to ``showwarning()``. 2024-08-20T21:38:35.1113838Z modules : sequence, optional 2024-08-20T21:38:35.1114379Z Sequence of modules for which to reset warnings registry on entry and 2024-08-20T21:38:35.1114976Z restore on exit. To work correctly, all 'ignore' filters should 2024-08-20T21:38:35.1115214Z filter by one of these modules. 2024-08-20T21:38:35.1115374Z 2024-08-20T21:38:35.1115536Z Examples 2024-08-20T21:38:35.1115743Z -------- 2024-08-20T21:38:35.1115945Z >>> import warnings 2024-08-20T21:38:35.1116383Z >>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP 2024-08-20T21:38:35.1116675Z ... modules=[np.core.fromnumeric]): 2024-08-20T21:38:35.1117037Z ... warnings.simplefilter('always') 2024-08-20T21:38:35.1117675Z ... warnings.filterwarnings('ignore', module='np.core.fromnumeric') 2024-08-20T21:38:35.1118108Z ... # do something that raises a warning but ignore those in 2024-08-20T21:38:35.1118324Z ... # np.core.fromnumeric 2024-08-20T21:38:35.1118485Z 2024-08-20T21:38:35.1119203Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1119351Z 2024-08-20T21:38:35.1119541Z warnings.warn(msg) 2024-08-20T21:38:35.1119703Z 2024-08-20T21:38:35.1120061Z --- Parse Warning: 23 / 101 --- 2024-08-20T21:38:35.1122644Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Conv1d in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py line=355. 2024-08-20T21:38:35.1123396Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1123894Z Applies a 1D convolution over a quantized input signal composed of 2024-08-20T21:38:35.1124139Z several quantized input planes. 2024-08-20T21:38:35.1124362Z 2024-08-20T21:38:35.1124864Z For details on input arguments, parameters, and implementation see 2024-08-20T21:38:35.1125082Z :class:`~torch.nn.Conv1d`. 2024-08-20T21:38:35.1125228Z 2024-08-20T21:38:35.1125399Z .. note:: 2024-08-20T21:38:35.1125871Z Only `zeros` is supported for the :attr:`padding_mode` argument. 2024-08-20T21:38:35.1126020Z 2024-08-20T21:38:35.1126186Z .. note:: 2024-08-20T21:38:35.1126612Z Only `torch.quint8` is supported for the input data type. 2024-08-20T21:38:35.1126763Z 2024-08-20T21:38:35.1127007Z 2024-08-20T21:38:35.1127199Z Attributes: 2024-08-20T21:38:35.1127697Z weight (Tensor): packed tensor derived from the learnable weight 2024-08-20T21:38:35.1127905Z parameter. 2024-08-20T21:38:35.1128242Z scale (Tensor): scalar for the output scale 2024-08-20T21:38:35.1128605Z zero_point (Tensor): scalar for the output zero point 2024-08-20T21:38:35.1128773Z 2024-08-20T21:38:35.1129110Z See :class:`~torch.nn.Conv1d` for other attributes. 2024-08-20T21:38:35.1129255Z 2024-08-20T21:38:35.1129439Z Examples:: 2024-08-20T21:38:35.1129587Z 2024-08-20T21:38:35.1129918Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) 2024-08-20T21:38:35.1130234Z >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2) 2024-08-20T21:38:35.1130479Z >>> input = torch.randn(20, 16, 100) 2024-08-20T21:38:35.1130711Z >>> # quantize input to quint8 2024-08-20T21:38:35.1130925Z >>> # xdoctest: +SKIP 2024-08-20T21:38:35.1131503Z >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, 2024-08-20T21:38:35.1131802Z ... dtype=torch.quint8) 2024-08-20T21:38:35.1132017Z >>> output = m(q_input) 2024-08-20T21:38:35.1132164Z 2024-08-20T21:38:35.1132323Z 2024-08-20T21:38:35.1133066Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1133212Z 2024-08-20T21:38:35.1133403Z warnings.warn(msg) 2024-08-20T21:38:35.1133564Z 2024-08-20T21:38:35.1133925Z --- Parse Warning: 24 / 101 --- 2024-08-20T21:38:35.1136465Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=LSTM in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/rnn.py line=11. 2024-08-20T21:38:35.1137202Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1137570Z A quantized long short-term memory (LSTM). 2024-08-20T21:38:35.1137741Z 2024-08-20T21:38:35.1138404Z For the description and the argument types, please, refer to :class:`~torch.nn.LSTM` 2024-08-20T21:38:35.1138555Z 2024-08-20T21:38:35.1138735Z Attributes: 2024-08-20T21:38:35.1139011Z layers : instances of the `_LSTMLayer` 2024-08-20T21:38:35.1139160Z 2024-08-20T21:38:35.1139344Z .. note:: 2024-08-20T21:38:35.1139862Z To access the weights and biases, you need to access them per layer. 2024-08-20T21:38:35.1140257Z See examples in :class:`~torch.ao.nn.quantizable.LSTM` 2024-08-20T21:38:35.1140421Z 2024-08-20T21:38:35.1140592Z Examples:: 2024-08-20T21:38:35.1140802Z >>> # xdoctest: +SKIP 2024-08-20T21:38:35.1141021Z >>> custom_module_config = { 2024-08-20T21:38:35.1141446Z ... 'float_to_observed_custom_module_class': { 2024-08-20T21:38:35.1141739Z ... nn.LSTM: nn.quantizable.LSTM, 2024-08-20T21:38:35.1141903Z ... }, 2024-08-20T21:38:35.1142372Z ... 'observed_to_quantized_custom_module_class': { 2024-08-20T21:38:35.1142707Z ... nn.quantizable.LSTM: nn.quantized.LSTM, 2024-08-20T21:38:35.1142871Z ... } 2024-08-20T21:38:35.1143105Z ... } 2024-08-20T21:38:35.1143624Z >>> tq.prepare(model, prepare_custom_module_class=custom_module_config) 2024-08-20T21:38:35.1144119Z >>> tq.convert(model, convert_custom_module_class=custom_module_config) 2024-08-20T21:38:35.1144272Z 2024-08-20T21:38:35.1144996Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1145146Z 2024-08-20T21:38:35.1145338Z warnings.warn(msg) 2024-08-20T21:38:35.1145497Z 2024-08-20T21:38:35.1145856Z --- Parse Warning: 25 / 101 --- 2024-08-20T21:38:35.1148996Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=BaseSparsifier.squash_mask in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py line=227. 2024-08-20T21:38:35.1149741Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1150130Z Squashes the sparse masks into the appropriate tensors. 2024-08-20T21:38:35.1150296Z 2024-08-20T21:38:35.1150790Z If either the `params_to_keep` or `params_to_keep_per_layer` is set, 2024-08-20T21:38:35.1151222Z the module will have a `sparse_params` dict attached to it. 2024-08-20T21:38:35.1151387Z 2024-08-20T21:38:35.1151551Z Args: 2024-08-20T21:38:35.1151982Z params_to_keep: List of keys to save in the module or a dict 2024-08-20T21:38:35.1152374Z representing the modules and keys that will have 2024-08-20T21:38:35.1152654Z sparsity parameters saved 2024-08-20T21:38:35.1153320Z params_to_keep_per_layer: Dict to specify the params that should be 2024-08-20T21:38:35.1153683Z saved for specific layers. The keys in the dict 2024-08-20T21:38:35.1154054Z should be the module fqn, while the values should 2024-08-20T21:38:35.1154465Z be a list of strings with the names of the variables 2024-08-20T21:38:35.1154749Z to save in the `sparse_params` 2024-08-20T21:38:35.1154894Z 2024-08-20T21:38:35.1155078Z Examples: 2024-08-20T21:38:35.1155385Z >>> # xdoctest: +SKIP("locals are undefined") 2024-08-20T21:38:35.1155738Z >>> # Don't save any sparse params 2024-08-20T21:38:35.1155996Z >>> sparsifier.squash_mask() 2024-08-20T21:38:35.1156431Z >>> hasattr(model.submodule1, 'sparse_params') 2024-08-20T21:38:35.1156598Z False 2024-08-20T21:38:35.1156758Z 2024-08-20T21:38:35.1157026Z >>> # Keep sparse params per layer 2024-08-20T21:38:35.1157277Z >>> sparsifier.squash_mask( 2024-08-20T21:38:35.1157520Z ... params_to_keep_per_layer={ 2024-08-20T21:38:35.1157941Z ... 'submodule1.linear1': ('foo', 'bar'), 2024-08-20T21:38:35.1158364Z ... 'submodule2.linear42': ('baz',) 2024-08-20T21:38:35.1158536Z ... }) 2024-08-20T21:38:35.1158894Z >>> print(model.submodule1.linear1.sparse_params) 2024-08-20T21:38:35.1159198Z {'foo': 42, 'bar': 24} 2024-08-20T21:38:35.1159556Z >>> print(model.submodule2.linear42.sparse_params) 2024-08-20T21:38:35.1159807Z {'baz': 0.1} 2024-08-20T21:38:35.1159969Z 2024-08-20T21:38:35.1160249Z >>> # Keep sparse params for all layers 2024-08-20T21:38:35.1160770Z >>> sparsifier.squash_mask(params_to_keep=('foo', 'bar')) 2024-08-20T21:38:35.1161142Z >>> print(model.submodule1.linear1.sparse_params) 2024-08-20T21:38:35.1161425Z {'foo': 42, 'bar': 24} 2024-08-20T21:38:35.1161802Z >>> print(model.submodule2.linear42.sparse_params) 2024-08-20T21:38:35.1162087Z {'foo': 42, 'bar': 24} 2024-08-20T21:38:35.1162342Z 2024-08-20T21:38:35.1162826Z >>> # Keep some sparse params for all layers, and specific ones for 2024-08-20T21:38:35.1163037Z >>> # some other layers 2024-08-20T21:38:35.1163266Z >>> sparsifier.squash_mask( 2024-08-20T21:38:35.1163642Z ... params_to_keep=('foo', 'bar'), 2024-08-20T21:38:35.1163889Z ... params_to_keep_per_layer={ 2024-08-20T21:38:35.1164286Z ... 'submodule2.linear42': ('baz',) 2024-08-20T21:38:35.1164473Z ... }) 2024-08-20T21:38:35.1164829Z >>> print(model.submodule1.linear1.sparse_params) 2024-08-20T21:38:35.1165116Z {'foo': 42, 'bar': 24} 2024-08-20T21:38:35.1165489Z >>> print(model.submodule2.linear42.sparse_params) 2024-08-20T21:38:35.1165827Z {'foo': 42, 'bar': 24, 'baz': 0.1} 2024-08-20T21:38:35.1165987Z 2024-08-20T21:38:35.1166721Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1166871Z 2024-08-20T21:38:35.1167147Z warnings.warn(msg) 2024-08-20T21:38:35.1167297Z 2024-08-20T21:38:35.1167662Z --- Parse Warning: 26 / 101 --- 2024-08-20T21:38:35.1170554Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DTypeConfig in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/backend_config/backend_config.py line=181. 2024-08-20T21:38:35.1171286Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1171437Z 2024-08-20T21:38:35.1172129Z Config object that specifies the supported data types passed as arguments to 2024-08-20T21:38:35.1172710Z quantize ops in the reference model spec, for input and output activations, 2024-08-20T21:38:35.1172899Z weights, and biases. 2024-08-20T21:38:35.1173069Z 2024-08-20T21:38:35.1173421Z For example, consider the following reference model: 2024-08-20T21:38:35.1173582Z 2024-08-20T21:38:35.1174036Z quant1 - [dequant1 - fp32_linear - quant2] - dequant2 2024-08-20T21:38:35.1174185Z 2024-08-20T21:38:35.1174717Z The pattern in the square brackets refers to the reference pattern of 2024-08-20T21:38:35.1175249Z statically quantized linear. Setting the input dtype as `torch.quint8` 2024-08-20T21:38:35.1175791Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument 2024-08-20T21:38:35.1176346Z to the first quantize op (quant1). Similarly, setting the output dtype as 2024-08-20T21:38:35.1176866Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to 2024-08-20T21:38:35.1177098Z the second quantize op (quant2). 2024-08-20T21:38:35.1177255Z 2024-08-20T21:38:35.1177778Z Note that the dtype here does not refer to the interface dtypes of the 2024-08-20T21:38:35.1178275Z op. For example, the "input dtype" here is not the dtype of the input 2024-08-20T21:38:35.1178812Z tensor passed to the quantized linear op. Though it can still be the 2024-08-20T21:38:35.1179293Z same as the interface dtype, this is not always the case, e.g. the 2024-08-20T21:38:35.1179827Z interface dtype is fp32 in dynamic quantization but the "input dtype" 2024-08-20T21:38:35.1180340Z specified in the DTypeConfig would still be quint8. The semantics of 2024-08-20T21:38:35.1180851Z dtypes here are the same as the semantics of the dtypes specified in 2024-08-20T21:38:35.1181040Z the observers. 2024-08-20T21:38:35.1181189Z 2024-08-20T21:38:35.1181731Z These dtypes are matched against the ones specified in the user's 2024-08-20T21:38:35.1182277Z QConfig. If there is a match, and the QConfig satisfies the constraints 2024-08-20T21:38:35.1182802Z specified in the DTypeConfig (if any), then we will quantize the given 2024-08-20T21:38:35.1183323Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and 2024-08-20T21:38:35.1183654Z the pattern will not be quantized. 2024-08-20T21:38:35.1183801Z 2024-08-20T21:38:35.1183990Z Example usage:: 2024-08-20T21:38:35.1184153Z 2024-08-20T21:38:35.1184368Z >>> # xdoctest: +SKIP(failing) 2024-08-20T21:38:35.1184601Z >>> dtype_config1 = DTypeConfig( 2024-08-20T21:38:35.1184838Z ... input_dtype=torch.quint8, 2024-08-20T21:38:35.1185061Z ... output_dtype=torch.quint8, 2024-08-20T21:38:35.1185295Z ... weight_dtype=torch.qint8, 2024-08-20T21:38:35.1185508Z ... bias_dtype=torch.float) 2024-08-20T21:38:35.1185659Z 2024-08-20T21:38:35.1185903Z >>> dtype_config2 = DTypeConfig( 2024-08-20T21:38:35.1186189Z ... input_dtype=DTypeWithConstraints( 2024-08-20T21:38:35.1186398Z ... dtype=torch.quint8, 2024-08-20T21:38:35.1186643Z ... quant_min_lower_bound=0, 2024-08-20T21:38:35.1186883Z ... quant_max_upper_bound=255, 2024-08-20T21:38:35.1187051Z ... ), 2024-08-20T21:38:35.1187343Z ... output_dtype=DTypeWithConstraints( 2024-08-20T21:38:35.1187547Z ... dtype=torch.quint8, 2024-08-20T21:38:35.1187775Z ... quant_min_lower_bound=0, 2024-08-20T21:38:35.1188022Z ... quant_max_upper_bound=255, 2024-08-20T21:38:35.1188181Z ... ), 2024-08-20T21:38:35.1188457Z ... weight_dtype=DTypeWithConstraints( 2024-08-20T21:38:35.1188678Z ... dtype=torch.qint8, 2024-08-20T21:38:35.1189017Z ... quant_min_lower_bound=-128, 2024-08-20T21:38:35.1189269Z ... quant_max_upper_bound=127, 2024-08-20T21:38:35.1189429Z ... ), 2024-08-20T21:38:35.1189712Z ... bias_dtype=torch.float) 2024-08-20T21:38:35.1189874Z 2024-08-20T21:38:35.1190096Z >>> dtype_config1.input_dtype 2024-08-20T21:38:35.1190271Z torch.quint8 2024-08-20T21:38:35.1190434Z 2024-08-20T21:38:35.1190653Z >>> dtype_config2.input_dtype 2024-08-20T21:38:35.1190825Z torch.quint8 2024-08-20T21:38:35.1190986Z 2024-08-20T21:38:35.1191284Z >>> dtype_config2.input_dtype_with_constraints 2024-08-20T21:38:35.1192541Z DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None) 2024-08-20T21:38:35.1192703Z 2024-08-20T21:38:35.1193417Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1193565Z 2024-08-20T21:38:35.1193767Z warnings.warn(msg) 2024-08-20T21:38:35.1193916Z 2024-08-20T21:38:35.1194271Z --- Parse Warning: 27 / 101 --- 2024-08-20T21:38:35.1197705Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_filtered_tables in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=301. 2024-08-20T21:38:35.1198454Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1198614Z 2024-08-20T21:38:35.1199257Z Takes in optional filter values and generates two tables with desired information. 2024-08-20T21:38:35.1199405Z 2024-08-20T21:38:35.1200024Z The generated tables are presented in both a list-of-lists format 2024-08-20T21:38:35.1200174Z 2024-08-20T21:38:35.1200676Z The reason for the two tables are that they handle different things: 2024-08-20T21:38:35.1201062Z 1.) the first table handles all tensor level information 2024-08-20T21:38:35.1201591Z 2.) the second table handles and displays all channel based information 2024-08-20T21:38:35.1201741Z 2024-08-20T21:38:35.1202578Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2024-08-20T21:38:35.1203550Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2024-08-20T21:38:35.1204530Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2024-08-20T21:38:35.1204678Z 2024-08-20T21:38:35.1204871Z Tensor table columns: 2024-08-20T21:38:35.1205333Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2024-08-20T21:38:35.1205781Z ---- --------- --------- --------- --------- --------- 2024-08-20T21:38:35.1205929Z 2024-08-20T21:38:35.1206224Z Per-Channel table columns: 2024-08-20T21:38:35.1206750Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2024-08-20T21:38:35.1207324Z ---- --------- ------- --------- --------- --------- --------- 2024-08-20T21:38:35.1207488Z 2024-08-20T21:38:35.1207646Z Args: 2024-08-20T21:38:35.1208265Z feature_filter (str, optional): Filters the features presented to only those that 2024-08-20T21:38:35.1208521Z contain this filter substring 2024-08-20T21:38:35.1208894Z Default = "", results in all the features being printed 2024-08-20T21:38:35.1209515Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-08-20T21:38:35.1210120Z Default = "", results in all the modules in the reports to be visible in the table 2024-08-20T21:38:35.1210267Z 2024-08-20T21:38:35.1210512Z Returns a dictionary with two keys: 2024-08-20T21:38:35.1210904Z (Dict[str, Tuple[List, List]]) A dict containing two keys: 2024-08-20T21:38:35.1211165Z "tensor_level_info", "channel_level_info" 2024-08-20T21:38:35.1211413Z Each key maps to a tuple with: 2024-08-20T21:38:35.1211752Z A list of the headers of each table 2024-08-20T21:38:35.1212185Z A list of lists containing the table information row by row 2024-08-20T21:38:35.1212608Z The 0th index row will contain the headers of the columns 2024-08-20T21:38:35.1212897Z The rest of the rows will contain data 2024-08-20T21:38:35.1213046Z 2024-08-20T21:38:35.1213230Z Example Use: 2024-08-20T21:38:35.1213512Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:38:35.1213865Z >>> mod_report_visualizer.generate_filtered_tables( 2024-08-20T21:38:35.1214131Z ... feature_filter = "per_channel_min", 2024-08-20T21:38:35.1214367Z ... module_fqn_filter = "block1" 2024-08-20T21:38:35.1215030Z ... ) # generates table with per_channel_min info for all modules in block 1 of the model 2024-08-20T21:38:35.1215182Z 2024-08-20T21:38:35.1215908Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1216074Z 2024-08-20T21:38:35.1216267Z warnings.warn(msg) 2024-08-20T21:38:35.1216418Z 2024-08-20T21:38:35.1216794Z --- Parse Warning: 28 / 101 --- 2024-08-20T21:38:35.1220264Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_table_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=400. 2024-08-20T21:38:35.1221026Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1221179Z 2024-08-20T21:38:35.1221831Z Takes in optional filter values and prints out formatted tables of the information. 2024-08-20T21:38:35.1221994Z 2024-08-20T21:38:35.1222868Z The reason for the two tables printed out instead of one large one are that they handle different things: 2024-08-20T21:38:35.1223252Z 1.) the first table handles all tensor level information 2024-08-20T21:38:35.1223789Z 2.) the second table handles and displays all channel based information 2024-08-20T21:38:35.1223939Z 2024-08-20T21:38:35.1224772Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2024-08-20T21:38:35.1225840Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2024-08-20T21:38:35.1226709Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2024-08-20T21:38:35.1226871Z 2024-08-20T21:38:35.1227067Z Tensor table columns: 2024-08-20T21:38:35.1227515Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2024-08-20T21:38:35.1227960Z ---- --------- --------- --------- --------- --------- 2024-08-20T21:38:35.1228108Z 2024-08-20T21:38:35.1228382Z Per-Channel table columns: 2024-08-20T21:38:35.1228544Z 2024-08-20T21:38:35.1229076Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2024-08-20T21:38:35.1229556Z ---- --------- ------- --------- --------- --------- --------- 2024-08-20T21:38:35.1229717Z 2024-08-20T21:38:35.1229880Z Args: 2024-08-20T21:38:35.1230497Z feature_filter (str, optional): Filters the features presented to only those that 2024-08-20T21:38:35.1230746Z contain this filter substring 2024-08-20T21:38:35.1231125Z Default = "", results in all the features being printed 2024-08-20T21:38:35.1231736Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-08-20T21:38:35.1232357Z Default = "", results in all the modules in the reports to be visible in the table 2024-08-20T21:38:35.1232505Z 2024-08-20T21:38:35.1232694Z Example Use: 2024-08-20T21:38:35.1232982Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:38:35.1233418Z >>> mod_report_visualizer.generate_table_visualization( 2024-08-20T21:38:35.1233704Z ... feature_filter = "per_channel_min", 2024-08-20T21:38:35.1233938Z ... module_fqn_filter = "block1" 2024-08-20T21:38:35.1234096Z ... ) 2024-08-20T21:38:35.1234556Z >>> # prints out neatly formatted table with per_channel_min info 2024-08-20T21:38:35.1234842Z >>> # for all modules in block 1 of the model 2024-08-20T21:38:35.1234993Z 2024-08-20T21:38:35.1235715Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1235866Z 2024-08-20T21:38:35.1236055Z warnings.warn(msg) 2024-08-20T21:38:35.1236216Z 2024-08-20T21:38:35.1236573Z --- Parse Warning: 29 / 101 --- 2024-08-20T21:38:35.1240035Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_plot_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=565. 2024-08-20T21:38:35.1240774Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1240923Z 2024-08-20T21:38:35.1241525Z Takes in a feature and optional module_filter and plots of the desired data. 2024-08-20T21:38:35.1241676Z 2024-08-20T21:38:35.1242343Z For per channel features, it averages the value across the channels and plots a point 2024-08-20T21:38:35.1243005Z per module. The reason for this is that for models with hundreds of channels, it can 2024-08-20T21:38:35.1243688Z be hard to differentiate one channel line from another, and so the point of generating 2024-08-20T21:38:35.1244358Z a single average point per module is to give a sense of general trends that encourage 2024-08-20T21:38:35.1244561Z further deep dives. 2024-08-20T21:38:35.1244710Z 2024-08-20T21:38:35.1244877Z Note: 2024-08-20T21:38:35.1245543Z Only features in the report that have tensor value data are plottable by this class 2024-08-20T21:38:35.1245917Z When the tensor information is plotted, it will plot: 2024-08-20T21:38:35.1246239Z idx as the x val, feature value as the y_val 2024-08-20T21:38:35.1246849Z When the channel information is plotted, it will plot: 2024-08-20T21:38:35.1247598Z the first idx of each module as the x val, feature value as the y_val [for each channel] 2024-08-20T21:38:35.1248198Z The reason for this is that we want to be able to compare values across the 2024-08-20T21:38:35.1248776Z channels for same layer, and it will be hard if values are staggered by idx 2024-08-20T21:38:35.1249165Z This means each module is represented by only 1 x value 2024-08-20T21:38:35.1249339Z Args: 2024-08-20T21:38:35.1249861Z feature_filter (str): Filters the features presented to only those that 2024-08-20T21:38:35.1250109Z contain this filter substring 2024-08-20T21:38:35.1250728Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-08-20T21:38:35.1251339Z Default = "", results in all the modules in the reports to be visible in the table 2024-08-20T21:38:35.1251508Z 2024-08-20T21:38:35.1251680Z Example Use: 2024-08-20T21:38:35.1251965Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:38:35.1252344Z >>> mod_report_visualizer.generate_plot_visualization( 2024-08-20T21:38:35.1252617Z ... feature_filter = "per_channel_min", 2024-08-20T21:38:35.1252851Z ... module_fqn_filter = "block1" 2024-08-20T21:38:35.1253024Z ... ) 2024-08-20T21:38:35.1253440Z >>> # outputs line plot of per_channel_min information for all 2024-08-20T21:38:35.1254018Z >>> # modules in block1 of model each channel gets it's own line, 2024-08-20T21:38:35.1254580Z >>> # and it's plotted across the in-order modules on the x-axis 2024-08-20T21:38:35.1254870Z 2024-08-20T21:38:35.1255601Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1255745Z 2024-08-20T21:38:35.1255936Z warnings.warn(msg) 2024-08-20T21:38:35.1256098Z 2024-08-20T21:38:35.1256469Z --- Parse Warning: 30 / 101 --- 2024-08-20T21:38:35.1259985Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_histogram_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=645. 2024-08-20T21:38:35.1260737Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1260892Z 2024-08-20T21:38:35.1261570Z Takes in a feature and optional module_filter and plots the histogram of desired data. 2024-08-20T21:38:35.1261738Z 2024-08-20T21:38:35.1261896Z Note: 2024-08-20T21:38:35.1262565Z Only features in the report that have tensor value data can be viewed as a histogram 2024-08-20T21:38:35.1263247Z If you want to plot a histogram from all the channel values of a specific feature for 2024-08-20T21:38:35.1263851Z a specific model, make sure to specify both the model and the feature properly 2024-08-20T21:38:35.1264497Z in the filters and you should be able to see a distribution of the channel data 2024-08-20T21:38:35.1264649Z 2024-08-20T21:38:35.1264806Z Args: 2024-08-20T21:38:35.1265435Z feature_filter (str, optional): Filters the features presented to only those that 2024-08-20T21:38:35.1265672Z contain this filter substring 2024-08-20T21:38:35.1266043Z Default = "", results in all the features being printed 2024-08-20T21:38:35.1266666Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-08-20T21:38:35.1267281Z Default = "", results in all the modules in the reports to be visible in the table 2024-08-20T21:38:35.1267824Z num_bins (int, optional): The number of bins to create the histogram with 2024-08-20T21:38:35.1268268Z Default = 10, the values will be split into 10 equal sized bins 2024-08-20T21:38:35.1268528Z 2024-08-20T21:38:35.1268715Z Example Use: 2024-08-20T21:38:35.1268909Z >>> # xdoctest: +SKIP 2024-08-20T21:38:35.1269591Z >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization( 2024-08-20T21:38:35.1269875Z ... feature_filter = "per_channel_min", 2024-08-20T21:38:35.1270109Z ... module_fqn_filter = "block1" 2024-08-20T21:38:35.1270266Z ... ) 2024-08-20T21:38:35.1270941Z # outputs histogram of per_channel_min information for all modules in block1 of model 2024-08-20T21:38:35.1271558Z information is gathered across all channels for all modules in block 1 for the 2024-08-20T21:38:35.1272082Z per_channel_min and is displayed in a histogram of equally sized bins 2024-08-20T21:38:35.1272247Z 2024-08-20T21:38:35.1272964Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1273112Z 2024-08-20T21:38:35.1273325Z warnings.warn(msg) 2024-08-20T21:38:35.1273472Z 2024-08-20T21:38:35.1273847Z --- Parse Warning: 31 / 101 --- 2024-08-20T21:38:35.1276520Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DeviceMesh.__getitem__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py line=473. 2024-08-20T21:38:35.1277225Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-08-20T21:38:35.1277392Z 2024-08-20T21:38:35.1278035Z Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh. 2024-08-20T21:38:35.1278746Z The submesh created consists of the dimensions and the communicators indicated by 2024-08-20T21:38:35.1278940Z ``mesh_dim_names`` 2024-08-20T21:38:35.1279092Z 2024-08-20T21:38:35.1279247Z Args: 2024-08-20T21:38:35.1279828Z mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the 2024-08-20T21:38:35.1280265Z mesh dimension of the DeviceMesh to create the submesh for. 2024-08-20T21:38:35.1280446Z Returns: 2024-08-20T21:38:35.1280665Z A :class:`DeviceMesh` object 2024-08-20T21:38:35.1280813Z 2024-08-20T21:38:35.1281524Z The following program runs on each process/rank in an SPMD manner in a world size of 8. 2024-08-20T21:38:35.1281722Z In the first example: 2024-08-20T21:38:35.1282377Z Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]). 2024-08-20T21:38:35.1283059Z Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of DeviceMesh:([4, 5, 6, 7]). 2024-08-20T21:38:35.1283642Z Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of DeviceMesh:([0, 4]). 2024-08-20T21:38:35.1284213Z Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of DeviceMesh:([1, 5]). 2024-08-20T21:38:35.1284796Z Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of DeviceMesh:([2, 6]). 2024-08-20T21:38:35.1285374Z Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of DeviceMesh:([3, 7]). 2024-08-20T21:38:35.1285539Z 2024-08-20T21:38:35.1285739Z In the second example: 2024-08-20T21:38:35.1286455Z Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]). 2024-08-20T21:38:35.1287278Z Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]). 2024-08-20T21:38:35.1287997Z Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]). 2024-08-20T21:38:35.1288711Z Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]). 2024-08-20T21:38:35.1288885Z 2024-08-20T21:38:35.1289065Z Example:: 2024-08-20T21:38:35.1289293Z >>> # xdoctest: +SKIP("no rank") 2024-08-20T21:38:35.1289693Z >>> from torch.distributed.device_mesh import DeviceMesh 2024-08-20T21:38:35.1289947Z >>> 2024-08-20T21:38:35.1290417Z >>> # Initialize a 2D device mesh as (2, 4) to represent the topology 2024-08-20T21:38:35.1290884Z >>> # of cross-host(dim 0), and within-host (dim 1). 2024-08-20T21:38:35.1291482Z >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2024-08-20T21:38:35.1291700Z >>> tp_mesh = mesh_2d["tp"] 2024-08-20T21:38:35.1291900Z >>> dp_mesh = mesh_2d["dp"] 2024-08-20T21:38:35.1292059Z >>> 2024-08-20T21:38:35.1292282Z >>> # Initialize a 3D mesh. 2024-08-20T21:38:35.1292948Z >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp")) 2024-08-20T21:38:35.1293706Z >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh. 2024-08-20T21:38:35.1293961Z >>> dp_cp_mesh = mesh_3d["dp", "cp"] 2024-08-20T21:38:35.1294201Z >>> cp_dp_mesh = mesh_3d["cp", "dp"] 2024-08-20T21:38:35.1294365Z 2024-08-20T21:38:35.1296293Z Original Error: SyntaxError('positional argument follows keyword argument', ('', 6, 82, 'mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))\n', 6, 83)) 2024-08-20T21:38:35.1296445Z 2024-08-20T21:38:35.1297034Z mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2024-08-20T21:38:35.1297322Z ^ 2024-08-20T21:38:35.1297514Z warnings.warn(msg) 2024-08-20T21:38:35.1297677Z 2024-08-20T21:38:35.1298037Z --- Parse Warning: 32 / 101 --- 2024-08-20T21:38:35.1300788Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=gather_object in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py line=2745. 2024-08-20T21:38:35.1301539Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1301698Z 2024-08-20T21:38:35.1302204Z Gathers picklable objects from the whole group in a single process. 2024-08-20T21:38:35.1302366Z 2024-08-20T21:38:35.1302933Z Similar to :func:`gather`, but Python objects can be passed in. Note that the 2024-08-20T21:38:35.1303277Z object must be picklable in order to be gathered. 2024-08-20T21:38:35.1303428Z 2024-08-20T21:38:35.1303583Z Args: 2024-08-20T21:38:35.1303882Z obj (Any): Input object. Must be picklable. 2024-08-20T21:38:35.1304363Z object_gather_list (list[Any]): Output list. On the ``dst`` rank, it 2024-08-20T21:38:35.1304792Z should be correctly sized as the size of the group for this 2024-08-20T21:38:35.1305453Z collective and will contain the output. Must be ``None`` on non-dst 2024-08-20T21:38:35.1305674Z ranks. (default is ``None``) 2024-08-20T21:38:35.1306594Z dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). (default is 0) 2024-08-20T21:38:35.1307146Z group: (ProcessGroup, optional): The process group to work on. If None, 2024-08-20T21:38:35.1307581Z the default process group will be used. Default is ``None``. 2024-08-20T21:38:35.1307732Z 2024-08-20T21:38:35.1307912Z Returns: 2024-08-20T21:38:35.1308362Z None. On the ``dst`` rank, ``object_gather_list`` will contain the 2024-08-20T21:38:35.1308587Z output of the collective. 2024-08-20T21:38:35.1308736Z 2024-08-20T21:38:35.1309257Z .. note:: Note that this API differs slightly from the gather collective 2024-08-20T21:38:35.1309816Z since it does not provide an async_op handle and thus will be a blocking 2024-08-20T21:38:35.1309982Z call. 2024-08-20T21:38:35.1310131Z 2024-08-20T21:38:35.1310825Z .. note:: For NCCL-based processed groups, internal tensor representations 2024-08-20T21:38:35.1311343Z of objects must be moved to the GPU device before communication takes 2024-08-20T21:38:35.1311750Z place. In this case, the device used is given by 2024-08-20T21:38:35.1312408Z ``torch.cuda.current_device()`` and it is the user's responsiblity to 2024-08-20T21:38:35.1312923Z ensure that this is set so that each rank has an individual GPU, via 2024-08-20T21:38:35.1313150Z ``torch.cuda.set_device()``. 2024-08-20T21:38:35.1313303Z 2024-08-20T21:38:35.1313473Z .. warning:: 2024-08-20T21:38:35.1313942Z :func:`gather_object` uses ``pickle`` module implicitly, which is 2024-08-20T21:38:35.1314491Z known to be insecure. It is possible to construct malicious pickle data 2024-08-20T21:38:35.1315003Z which will execute arbitrary code during unpickling. Only call this 2024-08-20T21:38:35.1315246Z function with data you trust. 2024-08-20T21:38:35.1315399Z 2024-08-20T21:38:35.1315568Z .. warning:: 2024-08-20T21:38:35.1316072Z Calling :func:`gather_object` with GPU tensors is not well supported 2024-08-20T21:38:35.1316756Z and inefficient as it incurs GPU -> CPU transfer since tensors would be 2024-08-20T21:38:35.1317134Z pickled. Please consider using :func:`gather` instead. 2024-08-20T21:38:35.1317295Z 2024-08-20T21:38:35.1317462Z Example:: 2024-08-20T21:38:35.1317774Z >>> # xdoctest: +SKIP("need process group init") 2024-08-20T21:38:35.1318204Z >>> # Note: Process group initialization omitted on each rank. 2024-08-20T21:38:35.1318461Z >>> import torch.distributed as dist 2024-08-20T21:38:35.1318695Z >>> # Assumes world_size of 3. 2024-08-20T21:38:35.1319101Z >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object 2024-08-20T21:38:35.1319442Z >>> output = [None for _ in gather_objects] 2024-08-20T21:38:35.1319655Z >>> dist.gather_object( 2024-08-20T21:38:35.1319911Z ... gather_objects[dist.get_rank()], 2024-08-20T21:38:35.1320207Z ... output if dist.get_rank() == 0 else None, 2024-08-20T21:38:35.1320393Z ... dst=0 2024-08-20T21:38:35.1320547Z ... ) 2024-08-20T21:38:35.1320720Z >>> # On rank 0 2024-08-20T21:38:35.1321217Z >>> output 2024-08-20T21:38:35.1321552Z ['foo', 12, {1: 2}] 2024-08-20T21:38:35.1321766Z 2024-08-20T21:38:35.1334947Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1335089Z 2024-08-20T21:38:35.1335221Z warnings.warn(msg) 2024-08-20T21:38:35.1335323Z 2024-08-20T21:38:35.1335617Z --- Parse Warning: 33 / 101 --- 2024-08-20T21:38:35.1336978Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=__doc__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/launch.py line=2. 2024-08-20T21:38:35.1337418Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1337506Z 2024-08-20T21:38:35.1337664Z Module ``torch.distributed.launch``. 2024-08-20T21:38:35.1337754Z 2024-08-20T21:38:35.1338091Z ``torch.distributed.launch`` is a module that spawns up multiple distributed 2024-08-20T21:38:35.1338297Z training processes on each of the training nodes. 2024-08-20T21:38:35.1338384Z 2024-08-20T21:38:35.1338498Z .. warning:: 2024-08-20T21:38:35.1338596Z 2024-08-20T21:38:35.1339040Z This module is going to be deprecated in favor of :ref:`torchrun `. 2024-08-20T21:38:35.1339128Z 2024-08-20T21:38:35.1339551Z The utility can be used for single-node distributed training, in which one or 2024-08-20T21:38:35.1339877Z more processes per node will be spawned. The utility can be used for either 2024-08-20T21:38:35.1340180Z CPU training or GPU training. If the utility is used for GPU training, 2024-08-20T21:38:35.1340528Z each distributed process will be operating on a single GPU. This can achieve 2024-08-20T21:38:35.1340904Z well-improved single-node training performance. It can also be used in 2024-08-20T21:38:35.1341490Z multi-node distributed training, by spawning up multiple processes on each node 2024-08-20T21:38:35.1341866Z for well-improved multi-node distributed training performance as well. 2024-08-20T21:38:35.1342173Z This will especially be beneficial for systems with multiple Infiniband 2024-08-20T21:38:35.1342603Z interfaces that have direct-GPU support, since all of them can be utilized for 2024-08-20T21:38:35.1342742Z aggregated communication bandwidth. 2024-08-20T21:38:35.1342829Z 2024-08-20T21:38:35.1343243Z In both cases of single-node distributed training or multi-node distributed 2024-08-20T21:38:35.1343561Z training, this utility will launch the given number of processes per node 2024-08-20T21:38:35.1343952Z (``--nproc-per-node``). If used for GPU training, this number needs to be less 2024-08-20T21:38:35.1344282Z or equal to the number of GPUs on the current system (``nproc_per_node``), 2024-08-20T21:38:35.1344562Z and each process will be operating on a single GPU from *GPU 0 to 2024-08-20T21:38:35.1344737Z GPU (nproc_per_node - 1)*. 2024-08-20T21:38:35.1344824Z 2024-08-20T21:38:35.1344943Z **How to use this module:** 2024-08-20T21:38:35.1345041Z 2024-08-20T21:38:35.1345290Z 1. Single-Node multi-process distributed training 2024-08-20T21:38:35.1345378Z 2024-08-20T21:38:35.1345481Z :: 2024-08-20T21:38:35.1345566Z 2024-08-20T21:38:35.1345947Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-08-20T21:38:35.1346283Z YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other 2024-08-20T21:38:35.1346508Z arguments of your training script) 2024-08-20T21:38:35.1346595Z 2024-08-20T21:38:35.1347127Z 2. Multi-Node multi-process distributed training: (e.g. two nodes) 2024-08-20T21:38:35.1347215Z 2024-08-20T21:38:35.1347300Z 2024-08-20T21:38:35.1347509Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)* 2024-08-20T21:38:35.1347602Z 2024-08-20T21:38:35.1347692Z :: 2024-08-20T21:38:35.1347791Z 2024-08-20T21:38:35.1348167Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-08-20T21:38:35.1348456Z --nnodes=2 --node-rank=0 --master-addr="192.168.1.1" 2024-08-20T21:38:35.1348805Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2024-08-20T21:38:35.1349008Z and all other arguments of your training script) 2024-08-20T21:38:35.1349109Z 2024-08-20T21:38:35.1349202Z Node 2: 2024-08-20T21:38:35.1349289Z 2024-08-20T21:38:35.1349390Z :: 2024-08-20T21:38:35.1349474Z 2024-08-20T21:38:35.1349854Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-08-20T21:38:35.1350142Z --nnodes=2 --node-rank=1 --master-addr="192.168.1.1" 2024-08-20T21:38:35.1350488Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2024-08-20T21:38:35.1350695Z and all other arguments of your training script) 2024-08-20T21:38:35.1350793Z 2024-08-20T21:38:35.1351020Z 3. To look up what optional arguments this module offers: 2024-08-20T21:38:35.1351107Z 2024-08-20T21:38:35.1351207Z :: 2024-08-20T21:38:35.1351293Z 2024-08-20T21:38:35.1351518Z python -m torch.distributed.launch --help 2024-08-20T21:38:35.1351616Z 2024-08-20T21:38:35.1351702Z 2024-08-20T21:38:35.1351814Z **Important Notices:** 2024-08-20T21:38:35.1351912Z 2024-08-20T21:38:35.1352225Z 1. This utility and multi-process distributed (single-node or 2024-08-20T21:38:35.1352640Z multi-node) GPU training currently only achieves the best performance using 2024-08-20T21:38:35.1352977Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to 2024-08-20T21:38:35.1353091Z use for GPU training. 2024-08-20T21:38:35.1353189Z 2024-08-20T21:38:35.1354146Z 2. In your training program, you must parse the command-line argument: 2024-08-20T21:38:35.1354522Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module. 2024-08-20T21:38:35.1354851Z If your training program uses GPUs, you should ensure that your code only 2024-08-20T21:38:35.1355124Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by: 2024-08-20T21:38:35.1355211Z 2024-08-20T21:38:35.1355352Z Parsing the local_rank argument 2024-08-20T21:38:35.1355438Z 2024-08-20T21:38:35.1355527Z :: 2024-08-20T21:38:35.1355624Z 2024-08-20T21:38:35.1355736Z >>> # xdoctest: +SKIP 2024-08-20T21:38:35.1355846Z >>> import argparse 2024-08-20T21:38:35.1356027Z >>> parser = argparse.ArgumentParser() 2024-08-20T21:38:35.1356347Z >>> parser.add_argument("--local-rank", "--local_rank", type=int) 2024-08-20T21:38:35.1356490Z >>> args = parser.parse_args() 2024-08-20T21:38:35.1356576Z 2024-08-20T21:38:35.1356740Z Set your device to local rank using either 2024-08-20T21:38:35.1356838Z 2024-08-20T21:38:35.1356926Z :: 2024-08-20T21:38:35.1357012Z 2024-08-20T21:38:35.1357297Z >>> torch.cuda.set_device(args.local_rank) # before your code runs 2024-08-20T21:38:35.1357384Z 2024-08-20T21:38:35.1357472Z or 2024-08-20T21:38:35.1357573Z 2024-08-20T21:38:35.1357660Z :: 2024-08-20T21:38:35.1357745Z 2024-08-20T21:38:35.1357928Z >>> with torch.cuda.device(args.local_rank): 2024-08-20T21:38:35.1358041Z >>> # your code to run 2024-08-20T21:38:35.1358136Z >>> ... 2024-08-20T21:38:35.1358235Z 2024-08-20T21:38:35.1358354Z .. versionchanged:: 2.0.0 2024-08-20T21:38:35.1358440Z 2024-08-20T21:38:35.1358944Z The launcher will passes the ``--local-rank=`` argument to your script. 2024-08-20T21:38:35.1359358Z From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the 2024-08-20T21:38:35.1359597Z previously used underscored ``--local_rank``. 2024-08-20T21:38:35.1359698Z 2024-08-20T21:38:35.1360016Z For backward compatibility, it may be necessary for users to handle both 2024-08-20T21:38:35.1360462Z cases in their argument parsing code. This means including both ``"--local-rank"`` 2024-08-20T21:38:35.1360849Z and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is 2024-08-20T21:38:35.1361182Z provided, the launcher will trigger an error: "error: unrecognized arguments: 2024-08-20T21:38:35.1361581Z --local-rank=". For training code that only supports PyTorch 2.0.0+, 2024-08-20T21:38:35.1361829Z including ``"--local-rank"`` should be sufficient. 2024-08-20T21:38:35.1361921Z 2024-08-20T21:38:35.1362263Z 3. In your training program, you are supposed to call the following function 2024-08-20T21:38:35.1362598Z at the beginning to start the distributed backend. It is strongly recommended 2024-08-20T21:38:35.1362901Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work, 2024-08-20T21:38:35.1363186Z but ``env://`` is the one that is officially supported by this module. 2024-08-20T21:38:35.1363272Z 2024-08-20T21:38:35.1363361Z :: 2024-08-20T21:38:35.1363459Z 2024-08-20T21:38:35.1363795Z >>> torch.distributed.init_process_group(backend='YOUR BACKEND', 2024-08-20T21:38:35.1364058Z >>> init_method='env://') 2024-08-20T21:38:35.1364147Z 2024-08-20T21:38:35.1364474Z 4. In your training program, you can either use regular distributed functions 2024-08-20T21:38:35.1364819Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your 2024-08-20T21:38:35.1365097Z training program uses GPUs for training and you would like to use 2024-08-20T21:38:35.1365351Z :func:`torch.nn.parallel.DistributedDataParallel` module, 2024-08-20T21:38:35.1365490Z here is how to configure it. 2024-08-20T21:38:35.1365632Z 2024-08-20T21:38:35.1365721Z :: 2024-08-20T21:38:35.1365820Z 2024-08-20T21:38:35.1366084Z >>> model = torch.nn.parallel.DistributedDataParallel(model, 2024-08-20T21:38:35.1366282Z >>> device_ids=[args.local_rank], 2024-08-20T21:38:35.1366494Z >>> output_device=args.local_rank) 2024-08-20T21:38:35.1366583Z 2024-08-20T21:38:35.1367013Z Please ensure that ``device_ids`` argument is set to be the only GPU device id 2024-08-20T21:38:35.1367364Z that your code will be operating on. This is generally the local rank of the 2024-08-20T21:38:35.1367691Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``, 2024-08-20T21:38:35.1368001Z and ``output_device`` needs to be ``args.local_rank`` in order to use this 2024-08-20T21:38:35.1368094Z utility 2024-08-20T21:38:35.1368181Z 2024-08-20T21:38:35.1368537Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable 2024-08-20T21:38:35.1368831Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with 2024-08-20T21:38:35.1369220Z ``--use-env=True``. You must adjust the subprocess example above to replace 2024-08-20T21:38:35.1369561Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher 2024-08-20T21:38:35.1369848Z will not pass ``--local-rank`` when you specify this flag. 2024-08-20T21:38:35.1369937Z 2024-08-20T21:38:35.1370050Z .. warning:: 2024-08-20T21:38:35.1370136Z 2024-08-20T21:38:35.1370415Z ``local_rank`` is NOT globally unique: it is only unique per process 2024-08-20T21:38:35.1370824Z on a machine. Thus, don't use it to decide if you should, e.g., 2024-08-20T21:38:35.1370979Z write to a networked filesystem. See 2024-08-20T21:38:35.1371277Z https://github.com/pytorch/pytorch/issues/12042 for an example of 2024-08-20T21:38:35.1371560Z how things can go wrong if you don't do this correctly. 2024-08-20T21:38:35.1371657Z 2024-08-20T21:38:35.1371755Z 2024-08-20T21:38:35.1371841Z 2024-08-20T21:38:35.1371926Z 2024-08-20T21:38:35.1372341Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1372429Z 2024-08-20T21:38:35.1372538Z warnings.warn(msg) 2024-08-20T21:38:35.1372635Z 2024-08-20T21:38:35.1372845Z --- Parse Warning: 34 / 101 --- 2024-08-20T21:38:35.1374453Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=init_from_local_shards in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py line=361. 2024-08-20T21:38:35.1374887Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1374974Z 2024-08-20T21:38:35.1375308Z Creates an :class:`ShardedTensor` from local shards and the global metadata. 2024-08-20T21:38:35.1375509Z Needs to be called on all ranks in an SPMD fashion. 2024-08-20T21:38:35.1375597Z 2024-08-20T21:38:35.1375700Z Args: 2024-08-20T21:38:35.1376061Z local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list 2024-08-20T21:38:35.1376287Z of shards that represent the local shards on this rank. 2024-08-20T21:38:35.1376613Z global_size (int...): a list, tuple, or `torch.Size` of integers defining the 2024-08-20T21:38:35.1376767Z shape of the overall sharded tensor. 2024-08-20T21:38:35.1376855Z 2024-08-20T21:38:35.1376968Z Keyword args: 2024-08-20T21:38:35.1377306Z process_group (ProcessGroup, optional): The process group to work on. If None, 2024-08-20T21:38:35.1377473Z the default process group will be used. 2024-08-20T21:38:35.1377708Z init_rrefs (bool, optional): Whether or not to initialize 2024-08-20T21:38:35.1377985Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2024-08-20T21:38:35.1378321Z Need to initialize the RPC Framework if specified as ``True``. 2024-08-20T21:38:35.1378435Z Default: ``False``. 2024-08-20T21:38:35.1378522Z 2024-08-20T21:38:35.1378630Z Returns: 2024-08-20T21:38:35.1378829Z A :class:`ShardedTensor` object handle on this rank 2024-08-20T21:38:35.1378916Z 2024-08-20T21:38:35.1379014Z 2024-08-20T21:38:35.1379109Z Examples: 2024-08-20T21:38:35.1379456Z Suppose we want construct a sharded tensor on two ranks, global size = (10, 5), 2024-08-20T21:38:35.1379727Z each shard have a (5, 5) local tensor, we can do it like below: 2024-08-20T21:38:35.1379813Z 2024-08-20T21:38:35.1379911Z on rank 0: 2024-08-20T21:38:35.1380086Z >>> # xdoctest: +SKIP("not distributed") 2024-08-20T21:38:35.1380244Z >>> local_shard_metadata = ShardMetadata( 2024-08-20T21:38:35.1380366Z >>> shard_offsets=[0, 0], 2024-08-20T21:38:35.1380503Z >>> shard_lengths=[5, 5], 2024-08-20T21:38:35.1380633Z >>> placement="rank:0/cuda:0" 2024-08-20T21:38:35.1380724Z >>> ) 2024-08-20T21:38:35.1380993Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2024-08-20T21:38:35.1381243Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2024-08-20T21:38:35.1381342Z 2024-08-20T21:38:35.1381436Z on rank 1: 2024-08-20T21:38:35.1381591Z >>> # xdoctest: +SKIP("not distributed") 2024-08-20T21:38:35.1381759Z >>> local_shard_metadata = ShardMetadata( 2024-08-20T21:38:35.1381881Z >>> shard_offsets=[5, 0], 2024-08-20T21:38:35.1381998Z >>> shard_lengths=[5, 5], 2024-08-20T21:38:35.1382204Z >>> placement="rank:1/cuda:1" 2024-08-20T21:38:35.1382297Z >>> ) 2024-08-20T21:38:35.1382550Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2024-08-20T21:38:35.1382811Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2024-08-20T21:38:35.1382903Z 2024-08-20T21:38:35.1383311Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1383413Z 2024-08-20T21:38:35.1383523Z warnings.warn(msg) 2024-08-20T21:38:35.1383609Z 2024-08-20T21:38:35.1383834Z --- Parse Warning: 35 / 101 --- 2024-08-20T21:38:35.1385503Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardedTensor._init_from_local_tensor in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=784. 2024-08-20T21:38:35.1385940Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1386028Z 2024-08-20T21:38:35.1386363Z Initialize a ShardedTensor given only one local tensor, global sharded tensor 2024-08-20T21:38:35.1386520Z size and sharding spec on each rank. 2024-08-20T21:38:35.1386611Z 2024-08-20T21:38:35.1386703Z Args: 2024-08-20T21:38:35.1387019Z local_tensor (Tensor): Single tensor of local shard stored in each rank. 2024-08-20T21:38:35.1387360Z sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): 2024-08-20T21:38:35.1387579Z The specification describing how to shard the Tensor. 2024-08-20T21:38:35.1387811Z global_size (Sequence[int]): Size of the sharded tensor. 2024-08-20T21:38:35.1388133Z process_group (ProcessGroup, optional): The process group to aggregate on. 2024-08-20T21:38:35.1388253Z Default: None 2024-08-20T21:38:35.1388478Z init_rrefs (bool, optional): Whether or not to initialize 2024-08-20T21:38:35.1388759Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2024-08-20T21:38:35.1389037Z Need to initialize the RPC Framework if specified as ``True``. 2024-08-20T21:38:35.1389150Z Default: ``False``. 2024-08-20T21:38:35.1389296Z 2024-08-20T21:38:35.1389404Z Returns: 2024-08-20T21:38:35.1389734Z A :class:`ShardedTensor` sharded based on the given sharding_spec with local 2024-08-20T21:38:35.1389888Z tensor stored in the current rank. 2024-08-20T21:38:35.1389986Z 2024-08-20T21:38:35.1390082Z Examples: 2024-08-20T21:38:35.1390196Z >>> # xdoctest: +SKIP 2024-08-20T21:38:35.1390390Z >>> # All tensors below are of torch.int64 type. 2024-08-20T21:38:35.1390544Z >>> # We have 2 process groups, 2 ranks. 2024-08-20T21:38:35.1390782Z >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank 2024-08-20T21:38:35.1391056Z >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2])) 2024-08-20T21:38:35.1391164Z >>> local_tensor 2024-08-20T21:38:35.1391296Z tensor([[1, 2, 3, 4]]) # Rank 0 2024-08-20T21:38:35.1391413Z tensor([[3, 4, 5, 6]]) # Rank 1 2024-08-20T21:38:35.1391525Z >>> sharding_dim = 0 2024-08-20T21:38:35.1391700Z >>> sharding_spec = ChunkShardingSpec( 2024-08-20T21:38:35.1391813Z dim=sharding_dim, 2024-08-20T21:38:35.1391921Z placements=[ 2024-08-20T21:38:35.1392043Z "rank:0/cuda:0", 2024-08-20T21:38:35.1392152Z "rank:1/cuda:1", 2024-08-20T21:38:35.1392245Z ], 2024-08-20T21:38:35.1392347Z ) 2024-08-20T21:38:35.1392684Z >>> st = ShardedTensor._init_from_local_tensor(local_tensor, sharding_spec, [2, 4]) 2024-08-20T21:38:35.1392779Z >>> st 2024-08-20T21:38:35.1392897Z ShardedTensor( 2024-08-20T21:38:35.1393024Z ShardedTensorMetadata( 2024-08-20T21:38:35.1393136Z shards_metadata=[ 2024-08-20T21:38:35.1393551Z ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0), 2024-08-20T21:38:35.1393894Z ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1), 2024-08-20T21:38:35.1394005Z ], 2024-08-20T21:38:35.1394128Z size=torch.Size([2, 4]) 2024-08-20T21:38:35.1394220Z ) 2024-08-20T21:38:35.1394341Z >>> st.local_tensor() 2024-08-20T21:38:35.1394459Z tensor([1, 2, 3, 4]) # Rank 0 2024-08-20T21:38:35.1394574Z tensor([3, 4, 5, 6]) # Rank 1 2024-08-20T21:38:35.1394672Z 2024-08-20T21:38:35.1395043Z Warning: This API is experimental and subject to change. It lacks of a fully across 2024-08-20T21:38:35.1395375Z rank validations, and we only validate the local shard on the current rank. 2024-08-20T21:38:35.1395706Z We fully rely on the user to ensure local tensor is sharded based on the 2024-08-20T21:38:35.1395813Z sharding spec. 2024-08-20T21:38:35.1395903Z 2024-08-20T21:38:35.1396321Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1396408Z 2024-08-20T21:38:35.1396517Z warnings.warn(msg) 2024-08-20T21:38:35.1396619Z 2024-08-20T21:38:35.1396830Z --- Parse Warning: 36 / 101 --- 2024-08-20T21:38:35.1398437Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardedTensor.reshard in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=1023. 2024-08-20T21:38:35.1398849Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1398937Z 2024-08-20T21:38:35.1399299Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support 2024-08-20T21:38:35.1399409Z single local shard. 2024-08-20T21:38:35.1399495Z 2024-08-20T21:38:35.1399892Z If ``resharding_spec`` is same as the original one, this becomes a no-op. 2024-08-20T21:38:35.1400219Z If only ``resharding_spec`` shares the same sharding dim with the original one, 2024-08-20T21:38:35.1400344Z we swap local shards directly. 2024-08-20T21:38:35.1400771Z For more generic cases, we merge different shards across different ranks and split 2024-08-20T21:38:35.1401121Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API. 2024-08-20T21:38:35.1401220Z 2024-08-20T21:38:35.1401313Z Args: 2024-08-20T21:38:35.1401690Z resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The 2024-08-20T21:38:35.1401917Z specification describing how the tensor is sharded. 2024-08-20T21:38:35.1402004Z 2024-08-20T21:38:35.1402098Z Returns: 2024-08-20T21:38:35.1402380Z A :class:`ShardedTensor` object whose local shards are resharded. 2024-08-20T21:38:35.1402467Z 2024-08-20T21:38:35.1402562Z Examples: 2024-08-20T21:38:35.1402692Z >>> # xdoctest: +SKIP 2024-08-20T21:38:35.1402846Z >>> # We have 2 process groups, 2 ranks. 2024-08-20T21:38:35.1403083Z >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank 2024-08-20T21:38:35.1403254Z >>> tensor = torch.stack([tensor, tensor]) 2024-08-20T21:38:35.1403350Z >>> tensor 2024-08-20T21:38:35.1403509Z tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0 2024-08-20T21:38:35.1403674Z tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1 2024-08-20T21:38:35.1403823Z tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2 2024-08-20T21:38:35.1403986Z tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3 2024-08-20T21:38:35.1404109Z >>> sharding_dim = 0 2024-08-20T21:38:35.1404243Z >>> spec = ChunkShardingSpec( 2024-08-20T21:38:35.1404368Z dim=sharding_dim, 2024-08-20T21:38:35.1404478Z placements=[ 2024-08-20T21:38:35.1404591Z "rank:0/cuda:0", 2024-08-20T21:38:35.1404758Z "rank:1/cuda:1", 2024-08-20T21:38:35.1404867Z "rank:2/cuda:2", 2024-08-20T21:38:35.1404972Z "rank:3/cuda:3", 2024-08-20T21:38:35.1405077Z ], 2024-08-20T21:38:35.1405173Z ) 2024-08-20T21:38:35.1405298Z >>> current_offsets = [0] * 2 2024-08-20T21:38:35.1405441Z >>> current_offsets[0] = rank * 2 2024-08-20T21:38:35.1405583Z >>> shard_metadata = ShardMetadata( 2024-08-20T21:38:35.1405775Z shard_offsets=copy.deepcopy(current_offsets), 2024-08-20T21:38:35.1405920Z shard_sizes=tensor.size(), 2024-08-20T21:38:35.1406075Z placement=spec.placements[rank], 2024-08-20T21:38:35.1406168Z ) 2024-08-20T21:38:35.1406288Z >>> local_shards = [ 2024-08-20T21:38:35.1406384Z Shard( 2024-08-20T21:38:35.1406494Z tensor=tensor, 2024-08-20T21:38:35.1406642Z metadata=shard_metadata, 2024-08-20T21:38:35.1406739Z ) 2024-08-20T21:38:35.1406829Z ] 2024-08-20T21:38:35.1407260Z >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size()) 2024-08-20T21:38:35.1407372Z >>> sharding_dim = 1 2024-08-20T21:38:35.1407550Z >>> resharding_spec = ChunkShardingSpec( 2024-08-20T21:38:35.1407663Z dim=sharding_dim, 2024-08-20T21:38:35.1407771Z placements=[ 2024-08-20T21:38:35.1407893Z "rank:0/cuda:0", 2024-08-20T21:38:35.1408000Z "rank:1/cuda:1", 2024-08-20T21:38:35.1408106Z "rank:2/cuda:2", 2024-08-20T21:38:35.1408227Z "rank:3/cuda:3", 2024-08-20T21:38:35.1408319Z ], 2024-08-20T21:38:35.1408407Z ) 2024-08-20T21:38:35.1408549Z >>> st.reshard(resharding_spec) 2024-08-20T21:38:35.1408696Z >>> tensor = st.local_shards()[0].tensor 2024-08-20T21:38:35.1408790Z >>> tensor 2024-08-20T21:38:35.1409002Z tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0 2024-08-20T21:38:35.1409191Z tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1 2024-08-20T21:38:35.1409376Z tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2 2024-08-20T21:38:35.1409646Z tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3 2024-08-20T21:38:35.1409733Z 2024-08-20T21:38:35.1410168Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1410255Z 2024-08-20T21:38:35.1410364Z warnings.warn(msg) 2024-08-20T21:38:35.1410462Z 2024-08-20T21:38:35.1410672Z --- Parse Warning: 37 / 101 --- 2024-08-20T21:38:35.1412188Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardingPlan in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharding_plan/api.py line=12. 2024-08-20T21:38:35.1412622Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1412710Z 2024-08-20T21:38:35.1412995Z Representation of a sharding plan, describes how to shard a module 2024-08-20T21:38:35.1413389Z across hosts. `plan` is used to shard module parameters according to the spec provided, 2024-08-20T21:38:35.1413774Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output 2024-08-20T21:38:35.1414142Z layout of a module with a spec, and when to convert back to data parallel fashion. 2024-08-20T21:38:35.1414229Z 2024-08-20T21:38:35.1414321Z Args: 2024-08-20T21:38:35.1414699Z plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`, 2024-08-20T21:38:35.1414922Z :class:`torch.distributed._shard.sharder.Sharder`]): 2024-08-20T21:38:35.1415400Z a dict describes how to shard a module, there're currently two ways to shard a module: 2024-08-20T21:38:35.1415820Z 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of 2024-08-20T21:38:35.1415979Z a parameter to a `ShardingSpec`. 2024-08-20T21:38:35.1416338Z 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module 2024-08-20T21:38:35.1416481Z to a `Sharder` object. 2024-08-20T21:38:35.1416921Z output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional): 2024-08-20T21:38:35.1417379Z a dict specifies the layout of a module's output which produces a ShardedTensor, 2024-08-20T21:38:35.1417715Z keyed by the name of module to ShardingSpec("" in key means the root module). 2024-08-20T21:38:35.1417823Z Default: `None` 2024-08-20T21:38:35.1418177Z return_local_tensor (List[str], optional): a list of string, each element enables 2024-08-20T21:38:35.1418593Z a module's sharded output to be returned as a Tensor from its local shards to 2024-08-20T21:38:35.1418926Z ensure further processing in a data parallel fashion. ("" in list means the 2024-08-20T21:38:35.1419044Z root module). 2024-08-20T21:38:35.1419147Z Default: None 2024-08-20T21:38:35.1419248Z Example: 2024-08-20T21:38:35.1419661Z Suppose we want to shard a module with two linear layers and then run it with DDP, we also 2024-08-20T21:38:35.1420069Z want to convert the output of the second linear layer back to DDP, we can do it as follows: 2024-08-20T21:38:35.1420169Z 2024-08-20T21:38:35.1420391Z >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d) 2024-08-20T21:38:35.1420518Z >>> class MyModule(nn.Module): 2024-08-20T21:38:35.1420715Z >>> def __init__(self) -> None: 2024-08-20T21:38:35.1420831Z >>> super().__init__() 2024-08-20T21:38:35.1420958Z >>> self.fc1 = nn.Linear() 2024-08-20T21:38:35.1421097Z >>> self.gelu = nn.GELU() 2024-08-20T21:38:35.1421225Z >>> self.fc2 = nn.Linear() 2024-08-20T21:38:35.1421354Z >>> self.relu = nn.Linear() 2024-08-20T21:38:35.1421457Z >>> 2024-08-20T21:38:35.1421584Z >>> def forward(self, input): 2024-08-20T21:38:35.1421884Z >>> return self.relu(self.fc2(self.gelu(self.fc1(input)))) 2024-08-20T21:38:35.1421984Z 2024-08-20T21:38:35.1422070Z 2024-08-20T21:38:35.1422241Z >>> # xdoctest: +SKIP("Undefined spec1, spec2) 2024-08-20T21:38:35.1422390Z >>> sharding_plan = ShardingPlan( 2024-08-20T21:38:35.1422491Z >>> plan={ 2024-08-20T21:38:35.1422613Z >>> "fc1.weight": spec1, 2024-08-20T21:38:35.1422745Z >>> "fc2.weight": spec2 2024-08-20T21:38:35.1422839Z >>> }, 2024-08-20T21:38:35.1422963Z >>> output_plan={ 2024-08-20T21:38:35.1423079Z >>> "fc2": output_spec 2024-08-20T21:38:35.1423172Z >>> }, 2024-08-20T21:38:35.1423320Z >>> return_local_tensor=["fc2"] 2024-08-20T21:38:35.1423411Z >>> ) 2024-08-20T21:38:35.1423497Z 2024-08-20T21:38:35.1423916Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1424004Z 2024-08-20T21:38:35.1424117Z warnings.warn(msg) 2024-08-20T21:38:35.1424217Z 2024-08-20T21:38:35.1424427Z --- Parse Warning: 38 / 101 --- 2024-08-20T21:38:35.1425947Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=local_map in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_tensor/experimental/func_map.py line=33. 2024-08-20T21:38:35.1426377Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1426467Z 2024-08-20T21:38:35.1426823Z ``local_map`` is an experimental API that allows users to apply on :class:`DTensors` 2024-08-20T21:38:35.1427180Z a function that is written to be applied on :class:`~torch.Tensors`. 2024-08-20T21:38:35.1427269Z 2024-08-20T21:38:35.1427374Z Args: 2024-08-20T21:38:35.1427651Z func (Callable): the function to be applied on each local shard of 2024-08-20T21:38:35.1427763Z :class:`DTensor`s. 2024-08-20T21:38:35.1428071Z out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]): 2024-08-20T21:38:35.1428501Z the desired placements of the :class:`DTensor`s in ``func``'s flattened output. 2024-08-20T21:38:35.1428836Z If the flattened ``output`` is a single value, the ``out_placements`` should be 2024-08-20T21:38:35.1429175Z of type `PlacementType`. Otherwise if the flattened ``output`` has multiple 2024-08-20T21:38:35.1429505Z values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1 2024-08-20T21:38:35.1429661Z mapping to the flattened ``output``. 2024-08-20T21:38:35.1429948Z Besides, for :class:`Tensor` output, we use `PlacementType` as its 2024-08-20T21:38:35.1430340Z placements (a `Tuple[Placement]` value). For non-:class:`Tensor` output, 2024-08-20T21:38:35.1430508Z the `PlacementType` should be `None`. 2024-08-20T21:38:35.1430837Z Note that the only exception is when no :class:`DTensor` argument is passed 2024-08-20T21:38:35.1431166Z in. In this case, even if `out_placements` is not `None`, the result function 2024-08-20T21:38:35.1431488Z should ignore the desired placements because the application is not on 2024-08-20T21:38:35.1431601Z :class:`DTensors`. 2024-08-20T21:38:35.1431813Z in_placements (Tuple[`PlacementType`, ...], optional): 2024-08-20T21:38:35.1432256Z the required placements of the :class:`DTensor`s in ``func``'s flattened input. 2024-08-20T21:38:35.1432558Z If ``in_placements`` is specified, ``local_map`` would examine whether the 2024-08-20T21:38:35.1432881Z placements of each :class:`DTensor` argument is the same as the required 2024-08-20T21:38:35.1433122Z placements or not. If the placements are not the same and 2024-08-20T21:38:35.1433451Z ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if 2024-08-20T21:38:35.1433853Z ``redistribute_inputs`` is `True`, the argument will be first redistributed to 2024-08-20T21:38:35.1434195Z the required sharding placements before passing its local tensor to ``func``. 2024-08-20T21:38:35.1434498Z The only exception is when required placements are not ``None`` and the 2024-08-20T21:38:35.1434843Z argument is a :class:`torch.Tensor`. In this case, the placements examination 2024-08-20T21:38:35.1435141Z will be skipped and the argument will be directly passed to ``func``. 2024-08-20T21:38:35.1435472Z If ``in_placements`` is ``None``, no placements examination will be performed. 2024-08-20T21:38:35.1435578Z Default: None 2024-08-20T21:38:35.1435757Z device_mesh (:class:`DeviceMesh`, optional): 2024-08-20T21:38:35.1436063Z the device mesh that all the :class:`DTensor`s are placed on. If not 2024-08-20T21:38:35.1436462Z specified, this will be inferred from the input :class:`DTensor`s' device 2024-08-20T21:38:35.1436781Z mesh. `local_map` requires every :class:`DTensor`s to be placed on the same 2024-08-20T21:38:35.1436925Z device mesh. Default: None. 2024-08-20T21:38:35.1437081Z redistribute_inputs (bool, optional): 2024-08-20T21:38:35.1437421Z the bool value indicating whether to reshard the input :class:`DTensor`s when 2024-08-20T21:38:35.1437764Z their placements are different from the required input placements. If this 2024-08-20T21:38:35.1438083Z value is ``False`` and some :class:`DTensor` input has a different placement, 2024-08-20T21:38:35.1438275Z an exception will be raised. Default: False. 2024-08-20T21:38:35.1438363Z 2024-08-20T21:38:35.1438515Z Returns: 2024-08-20T21:38:35.1438889Z A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor` 2024-08-20T21:38:35.1439215Z and returns a :class:`DTensor` constructed from the return value of ``func``. 2024-08-20T21:38:35.1439307Z 2024-08-20T21:38:35.1439411Z Raises: 2024-08-20T21:38:35.1439763Z AssertionError: If the input :class:`DTensor`s are not placed on the same device 2024-08-20T21:38:35.1440104Z mesh, or if they are placed on a different device mesh than the ``device_mesh`` 2024-08-20T21:38:35.1440229Z argument passed in. 2024-08-20T21:38:35.1440316Z 2024-08-20T21:38:35.1440752Z AssertionError: For any non-:class:`DTensor` output, we require its corresponding 2024-08-20T21:38:35.1441108Z output placement in ``out_placements`` be None. An AssertionError will be raised 2024-08-20T21:38:35.1441226Z if this is not the case. 2024-08-20T21:38:35.1441315Z 2024-08-20T21:38:35.1441676Z ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs 2024-08-20T21:38:35.1441870Z a redistribution according to ``in_placements``. 2024-08-20T21:38:35.1441969Z 2024-08-20T21:38:35.1442067Z Example: 2024-08-20T21:38:35.1442215Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:38:35.1442402Z >>> def mm_allreduce_forward(device_mesh, W, X): 2024-08-20T21:38:35.1442561Z >>> partial_sum_tensor = torch.mm(W, X) 2024-08-20T21:38:35.1442878Z >>> reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh) 2024-08-20T21:38:35.1443013Z >>> return reduced_tensor 2024-08-20T21:38:35.1443104Z >>> 2024-08-20T21:38:35.1443272Z >>> W = torch.randn(12, 8, requires_grad=False) 2024-08-20T21:38:35.1443452Z >>> X = torch.randn(8, 16, requires_grad=False) 2024-08-20T21:38:35.1443564Z >>> Y = torch.mm(W, X) 2024-08-20T21:38:35.1443907Z >>> row_wise = [Shard(0)] # row-wise sharding placements on 1-d mesh 2024-08-20T21:38:35.1444249Z >>> col_wise = [Shard(1)] # col-wise sharding placements on 1-d mesh 2024-08-20T21:38:35.1444339Z >>> 2024-08-20T21:38:35.1444713Z >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor convertion 2024-08-20T21:38:35.1444931Z >>> local_mm_allreduce_forward = local_map( 2024-08-20T21:38:35.1445054Z >>> mm_allreduce_forward, 2024-08-20T21:38:35.1445212Z >>> out_placements=[Replicate()], 2024-08-20T21:38:35.1445361Z >>> in_placements=[col_wise, row_wise], 2024-08-20T21:38:35.1445488Z >>> device_mesh=device_mesh, 2024-08-20T21:38:35.1445591Z >>> ) 2024-08-20T21:38:35.1445681Z >>> 2024-08-20T21:38:35.1446125Z >>> W_dt = distribute_tensor(W, device_mesh, (col_wise)) # col-wisely sharded W tensor 2024-08-20T21:38:35.1446578Z >>> X_dt = distribute_tensor(X, device_mesh, (row_wise)) # row-wisely sharded X tensor 2024-08-20T21:38:35.1447321Z >>> Y_dt = local_mm_allreduce_forward(device_mesh, W_dt, X_dt) # apply local_mm_allreduce_forward to DTensors 2024-08-20T21:38:35.1447413Z 2024-08-20T21:38:35.1447686Z NOTE: This API is currently experimental and subject to change 2024-08-20T21:38:35.1447778Z 2024-08-20T21:38:35.1448204Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1448297Z 2024-08-20T21:38:35.1448408Z warnings.warn(msg) 2024-08-20T21:38:35.1448507Z 2024-08-20T21:38:35.1448718Z --- Parse Warning: 39 / 101 --- 2024-08-20T21:38:35.1450335Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_sharding in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_tensor/experimental/register_sharding.py line=25. 2024-08-20T21:38:35.1450886Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1450977Z 2024-08-20T21:38:35.1451330Z ``register_sharding`` is an experimental API that allows users to register sharding 2024-08-20T21:38:35.1451716Z strategies for an operator when the tensor inputs and outputs are :class:`DTensor`s. 2024-08-20T21:38:35.1452172Z It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``, 2024-08-20T21:38:35.1452535Z e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2) 2024-08-20T21:38:35.1452911Z when users would like to overwrite default sharding strategies of existing operators. 2024-08-20T21:38:35.1452999Z 2024-08-20T21:38:35.1453103Z Args: 2024-08-20T21:38:35.1453267Z op (Union[OpOverload, List[OpOverload]]): 2024-08-20T21:38:35.1453557Z An op or a list of ops to register the customized sharding function. 2024-08-20T21:38:35.1453658Z 2024-08-20T21:38:35.1453751Z Returns: 2024-08-20T21:38:35.1454127Z A function decorator which can be used to wrap a function that defines the sharding 2024-08-20T21:38:35.1454510Z strategy for the operator specified in ``op``. The defined sharding strategy will be 2024-08-20T21:38:35.1454888Z registered to DTensor and will override the default sharding strategy if DTensor has 2024-08-20T21:38:35.1455294Z already implemented the operator. The customized sharding function takes the same inputs 2024-08-20T21:38:35.1455650Z as the original op (except that if an arg is a :class:`torch.Tensor`, it will be 2024-08-20T21:38:35.1456108Z replaced by a tensor-like object that DTensor uses internally). The function should 2024-08-20T21:38:35.1456573Z return a sequence of 2-tuples, each specifying acceptable output placements and its 2024-08-20T21:38:35.1456715Z corresponding intput placements. 2024-08-20T21:38:35.1456802Z 2024-08-20T21:38:35.1456908Z Example: 2024-08-20T21:38:35.1457054Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:38:35.1457231Z >>> @register_sharding(aten._softmax.default) 2024-08-20T21:38:35.1457443Z >>> def custom_softmax_sharding(x, dim, half_to_float): 2024-08-20T21:38:35.1457635Z >>> softmax_dim = dim if dim >= 0 else dim + x.ndim 2024-08-20T21:38:35.1457847Z >>> acceptable_shardings = [] 2024-08-20T21:38:35.1457950Z >>> 2024-08-20T21:38:35.1458186Z >>> all_replicate = ([Replicate()], [Replicate(), None, None]) 2024-08-20T21:38:35.1458372Z >>> acceptable_shardings.append(all_replicate) 2024-08-20T21:38:35.1458478Z >>> 2024-08-20T21:38:35.1458630Z >>> for sharding_dim in range(x.ndim): 2024-08-20T21:38:35.1458793Z >>> if sharding_dim != softmax_dim: 2024-08-20T21:38:35.1458913Z >>> all_sharded = ( 2024-08-20T21:38:35.1459056Z >>> [Shard(sharding_dim)], 2024-08-20T21:38:35.1459239Z >>> [Shard(sharding_dim), None, None], 2024-08-20T21:38:35.1459343Z >>> ) 2024-08-20T21:38:35.1459531Z >>> acceptable_shardings.append(all_sharded) 2024-08-20T21:38:35.1459635Z >>> 2024-08-20T21:38:35.1459771Z >>> return acceptable_shardings 2024-08-20T21:38:35.1459861Z 2024-08-20T21:38:35.1460274Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1460361Z 2024-08-20T21:38:35.1460471Z warnings.warn(msg) 2024-08-20T21:38:35.1460569Z 2024-08-20T21:38:35.1460780Z --- Parse Warning: 40 / 101 --- 2024-08-20T21:38:35.1462460Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=post_localSGD_hook in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/post_localSGD_hook.py line=72. 2024-08-20T21:38:35.1462890Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1463032Z 2024-08-20T21:38:35.1463215Z Run post-localSGD algorithm. 2024-08-20T21:38:35.1463301Z 2024-08-20T21:38:35.1463689Z This DDP communication hook is used for running post-localSGD algorithm, 2024-08-20T21:38:35.1463910Z by combining with a model averaging component (e.g., 2024-08-20T21:38:35.1464351Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`) 2024-08-20T21:38:35.1464489Z that runs after the optimizer step. 2024-08-20T21:38:35.1464589Z 2024-08-20T21:38:35.1464679Z Args: 2024-08-20T21:38:35.1465035Z state (PostLocalSGDState): State information to run post-localSGD. 2024-08-20T21:38:35.1465426Z Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD. 2024-08-20T21:38:35.1466107Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2024-08-20T21:38:35.1466476Z Note that since DDP comm hook only supports single process single device mode, 2024-08-20T21:38:35.1466674Z only exactly one tensor is stored in this bucket. 2024-08-20T21:38:35.1466763Z 2024-08-20T21:38:35.1466868Z Returns: 2024-08-20T21:38:35.1467199Z Future handler of the communication, which updates the gradients in place. 2024-08-20T21:38:35.1467287Z 2024-08-20T21:38:35.1467403Z Example:: 2024-08-20T21:38:35.1467515Z >>> # xdoctest: +SKIP 2024-08-20T21:38:35.1467835Z >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup, 2024-08-20T21:38:35.1468004Z start_localSGD_iter=10) 2024-08-20T21:38:35.1468232Z >>> ddp_model.register_comm_hook(state, post_localSGD_hook) 2024-08-20T21:38:35.1468704Z >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``. 2024-08-20T21:38:35.1469204Z >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module. 2024-08-20T21:38:35.1469294Z 2024-08-20T21:38:35.1469712Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1469799Z 2024-08-20T21:38:35.1469908Z warnings.warn(msg) 2024-08-20T21:38:35.1470077Z 2024-08-20T21:38:35.1470289Z --- Parse Warning: 41 / 101 --- 2024-08-20T21:38:35.1471921Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=powerSGD_hook in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py line=342. 2024-08-20T21:38:35.1472351Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1472438Z 2024-08-20T21:38:35.1472567Z Implement PowerSGD algorithm. 2024-08-20T21:38:35.1472666Z 2024-08-20T21:38:35.1472963Z This DDP communication hook implements PowerSGD gradient compression 2024-08-20T21:38:35.1473296Z algorithm described in the `paper `_. 2024-08-20T21:38:35.1473618Z Once gradient tensors are aggregated across all workers, this hook applies 2024-08-20T21:38:35.1473737Z compression as follows: 2024-08-20T21:38:35.1473842Z 2024-08-20T21:38:35.1474578Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups: 2024-08-20T21:38:35.1474667Z 2024-08-20T21:38:35.1475260Z 1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth. 2024-08-20T21:38:35.1475348Z 2024-08-20T21:38:35.1475916Z 1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases). 2024-08-20T21:38:35.1476015Z 2024-08-20T21:38:35.1476146Z 2. Handles uncompressed tensors: 2024-08-20T21:38:35.1476233Z 2024-08-20T21:38:35.1476982Z 2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression; 2024-08-20T21:38:35.1477071Z 2024-08-20T21:38:35.1477542Z 2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor. 2024-08-20T21:38:35.1477633Z 2024-08-20T21:38:35.1477946Z 3. Handles the tensors that should be compressed by PowerSGD compression: 2024-08-20T21:38:35.1478048Z 2024-08-20T21:38:35.1478473Z 3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M, 2024-08-20T21:38:35.1478919Z such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized; 2024-08-20T21:38:35.1479018Z 2024-08-20T21:38:35.1479212Z 3.2. Computes each P in Ps, which is equal to MQ; 2024-08-20T21:38:35.1479299Z 2024-08-20T21:38:35.1479442Z 3.3. Allreduces Ps as a batch; 2024-08-20T21:38:35.1479529Z 2024-08-20T21:38:35.1479672Z 3.4. Orthogonalizes each P in Ps; 2024-08-20T21:38:35.1479770Z 2024-08-20T21:38:35.1480046Z 3.5. Computes each Q in Qs, which is approximately equal to M^TP; 2024-08-20T21:38:35.1480132Z 2024-08-20T21:38:35.1480271Z 3.6. Allreduces Qs as a batch; 2024-08-20T21:38:35.1480361Z 2024-08-20T21:38:35.1480784Z 3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T. 2024-08-20T21:38:35.1480882Z 2024-08-20T21:38:35.1481435Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations. 2024-08-20T21:38:35.1481844Z This not only gives the user more control over the tradeoff between speedup and accuracy, 2024-08-20T21:38:35.1482441Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers. 2024-08-20T21:38:35.1482528Z 2024-08-20T21:38:35.1482631Z Args: 2024-08-20T21:38:35.1483205Z state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc. 2024-08-20T21:38:35.1483695Z To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter`` 2024-08-20T21:38:35.1483899Z and ``min_compression_rate``. 2024-08-20T21:38:35.1484577Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2024-08-20T21:38:35.1484938Z Note that since DDP comm hook only supports single process single device mode, 2024-08-20T21:38:35.1485135Z only exactly one tensor is stored in this bucket. 2024-08-20T21:38:35.1485223Z 2024-08-20T21:38:35.1485330Z Returns: 2024-08-20T21:38:35.1485656Z Future handler of the communication, which updates the gradients in place. 2024-08-20T21:38:35.1485743Z 2024-08-20T21:38:35.1485856Z Example:: 2024-08-20T21:38:35.1485970Z >>> # xdoctest: +SKIP 2024-08-20T21:38:35.1486326Z >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, 2024-08-20T21:38:35.1486560Z start_powerSGD_iter=10, min_compression_rate=0.5) 2024-08-20T21:38:35.1486768Z >>> ddp_model.register_comm_hook(state, powerSGD_hook) 2024-08-20T21:38:35.1486855Z 2024-08-20T21:38:35.1487366Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1487455Z 2024-08-20T21:38:35.1487580Z warnings.warn(msg) 2024-08-20T21:38:35.1487666Z 2024-08-20T21:38:35.1487879Z --- Parse Warning: 42 / 101 --- 2024-08-20T21:38:35.1489563Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PeriodicModelAverager in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/model_averaging/averagers.py line=36. 2024-08-20T21:38:35.1490037Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1490125Z 2024-08-20T21:38:35.1490432Z Averages parameters periodically after the warm-up stage. 2024-08-20T21:38:35.1490519Z 2024-08-20T21:38:35.1490959Z This can be used for running `post-local SGD `_, 2024-08-20T21:38:35.1491231Z by running :class:`~torch.nn.DistributedDataParallel` (DDP) 2024-08-20T21:38:35.1491545Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`. 2024-08-20T21:38:35.1491632Z 2024-08-20T21:38:35.1491735Z Args: 2024-08-20T21:38:35.1491951Z period (int): The number of steps per model averaging. 2024-08-20T21:38:35.1492337Z Usually the period should be greater than ``1`` to reduce the communication cost. 2024-08-20T21:38:35.1492510Z Otherwise, only DDP needs to be used. 2024-08-20T21:38:35.1492859Z warmup_steps (int): The number of warm-up steps. During this stage, 2024-08-20T21:38:35.1493032Z model averaging is skipped. 2024-08-20T21:38:35.1493342Z process_group: The process group to be used for all-reduce. 2024-08-20T21:38:35.1493537Z If ``None``, the default process group, which 2024-08-20T21:38:35.1493811Z is created by :func:`torch.distributed.init_process_group`, 2024-08-20T21:38:35.1493972Z will be used. (default: ``None``) 2024-08-20T21:38:35.1494060Z 2024-08-20T21:38:35.1494174Z Example:: 2024-08-20T21:38:35.1494259Z 2024-08-20T21:38:35.1494428Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:38:35.1494546Z >>> import torch 2024-08-20T21:38:35.1494693Z >>> import torch.distributed as dist 2024-08-20T21:38:35.1495117Z >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD 2024-08-20T21:38:35.1495476Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2024-08-20T21:38:35.1495598Z >>> import torch.nn as nn 2024-08-20T21:38:35.1495701Z >>> 2024-08-20T21:38:35.1495930Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2024-08-20T21:38:35.1496058Z >>> torch.cuda.set_device(rank) 2024-08-20T21:38:35.1496296Z >>> module = nn.Linear(1, 1, bias=False).cuda() 2024-08-20T21:38:35.1496501Z >>> model = nn.parallel.DistributedDataParallel( 2024-08-20T21:38:35.1496686Z >>> module, device_ids=[rank], output_device=rank 2024-08-20T21:38:35.1496790Z >>> ) 2024-08-20T21:38:35.1497046Z >>> # Register a post-localSGD communication hook. 2024-08-20T21:38:35.1497427Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2024-08-20T21:38:35.1497649Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-08-20T21:38:35.1497742Z >>> 2024-08-20T21:38:35.1498143Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2024-08-20T21:38:35.1498355Z >>> # After 100 steps, run model averaging every 4 steps. 2024-08-20T21:38:35.1498803Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-08-20T21:38:35.1499148Z >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2024-08-20T21:38:35.1499275Z >>> for step in range(0, 200): 2024-08-20T21:38:35.1499397Z >>> optimizer.zero_grad() 2024-08-20T21:38:35.1499552Z >>> loss = loss_fn(output, labels) 2024-08-20T21:38:35.1499664Z >>> loss.backward() 2024-08-20T21:38:35.1499779Z >>> optimizer.step() 2024-08-20T21:38:35.1500051Z >>> # Will average model parameters globally every 4 steps. Thus, 2024-08-20T21:38:35.1500394Z >>> # inter-node communication only occurs every 4 iterations after 2024-08-20T21:38:35.1500567Z >>> # the initial ``warmup_steps`` period. 2024-08-20T21:38:35.1500829Z >>> averager.average_parameters(model.parameters()) 2024-08-20T21:38:35.1500919Z 2024-08-20T21:38:35.1501331Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1501423Z 2024-08-20T21:38:35.1501532Z warnings.warn(msg) 2024-08-20T21:38:35.1501631Z 2024-08-20T21:38:35.1501841Z --- Parse Warning: 43 / 101 --- 2024-08-20T21:38:35.1503723Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=HierarchicalModelAverager in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/model_averaging/hierarchical_model_averager.py line=18. 2024-08-20T21:38:35.1504152Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1504240Z 2024-08-20T21:38:35.1504685Z Runs hierarchical model averaging (`hierarchical SGD `_). 2024-08-20T21:38:35.1504776Z 2024-08-20T21:38:35.1505190Z Process groups of different sizes are organized in a hierarchy, and they average parameters 2024-08-20T21:38:35.1505545Z by using different periods concurrently after the warm-up stage. 2024-08-20T21:38:35.1506115Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager` 2024-08-20T21:38:35.1506663Z that supports `post-local SGD `_, which essentially only supports 2024-08-20T21:38:35.1507185Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine 2024-08-20T21:38:35.1507663Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`. 2024-08-20T21:38:35.1508145Z Similarly, the process groups within this class do not have such an intra-machine process 2024-08-20T21:38:35.1508607Z subgroup, which should be embedded by the post-local SGD communication hook instead. 2024-08-20T21:38:35.1508699Z 2024-08-20T21:38:35.1508803Z Args: 2024-08-20T21:38:35.1509155Z period_group_size_dict: An ordered dict mapping keys of model averaging period to 2024-08-20T21:38:35.1509428Z process group size, used for initializing process groups of 2024-08-20T21:38:35.1509814Z different sizes in a hierarchy to average parameters concurrently. 2024-08-20T21:38:35.1510103Z Particularly, at each iteration, there will be at most a single 2024-08-20T21:38:35.1510508Z process group that runs averaging -- the period of such group should 2024-08-20T21:38:35.1510818Z have the largest period which the current step can be divided by. 2024-08-20T21:38:35.1511045Z For example, if the dict has three keys: 2, 4, and 8, 2024-08-20T21:38:35.1511348Z then this means totally three process groups will be created to 2024-08-20T21:38:35.1511633Z average parameters every 2, 4, and 8 iterations, respectively. 2024-08-20T21:38:35.1511902Z At the 4th iteration, only the second process group will run 2024-08-20T21:38:35.1512159Z averaging, because the first process group should be a 2024-08-20T21:38:35.1512470Z subset of the second process group, and no need to execute the first 2024-08-20T21:38:35.1512635Z process group redundantly. 2024-08-20T21:38:35.1512936Z On the other hand, the third process group can only be triggered 2024-08-20T21:38:35.1513251Z every 8 iterations, so it will not be triggered at the 4th iteration. 2024-08-20T21:38:35.1513780Z warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped. 2024-08-20T21:38:35.1514453Z process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging. 2024-08-20T21:38:35.1514711Z If ``None``, the default process group, which is created 2024-08-20T21:38:35.1515020Z by :func:`torch.distributed.init_process_group`, will be used. 2024-08-20T21:38:35.1515184Z (default: ``None``) 2024-08-20T21:38:35.1515271Z 2024-08-20T21:38:35.1515388Z Example:: 2024-08-20T21:38:35.1515599Z >>> # xdoctest: +SKIP('undefined rank') 2024-08-20T21:38:35.1515767Z >>> from collections import OrderedDict 2024-08-20T21:38:35.1515873Z >>> import torch 2024-08-20T21:38:35.1516023Z >>> import torch.distributed as dist 2024-08-20T21:38:35.1516397Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2024-08-20T21:38:35.1516525Z >>> PostLocalSGDState, 2024-08-20T21:38:35.1516643Z >>> post_localSGD_hook, 2024-08-20T21:38:35.1516746Z >>> ) 2024-08-20T21:38:35.1517236Z >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD 2024-08-20T21:38:35.1517358Z >>> import torch.nn as nn 2024-08-20T21:38:35.1517462Z >>> 2024-08-20T21:38:35.1517689Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2024-08-20T21:38:35.1517818Z >>> torch.cuda.set_device(rank) 2024-08-20T21:38:35.1518007Z >>> module = nn.Linear(1, 1, bias=False).to(rank) 2024-08-20T21:38:35.1518214Z >>> model = nn.parallel.DistributedDataParallel( 2024-08-20T21:38:35.1518398Z >>> module, device_ids=[rank], output_device=rank 2024-08-20T21:38:35.1518502Z >>> ) 2024-08-20T21:38:35.1518756Z >>> # Register a post-localSGD communication hook. 2024-08-20T21:38:35.1519254Z >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4. 2024-08-20T21:38:35.1519405Z >>> subgroup, _ = dist.new_subgroups() 2024-08-20T21:38:35.1519811Z >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100) 2024-08-20T21:38:35.1520089Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-08-20T21:38:35.1520182Z >>> 2024-08-20T21:38:35.1520572Z >>> # Average parameters among each group of 8 processes every 4 iterations, and among all 2024-08-20T21:38:35.1520742Z >>> # the 16 processes every 16 iterations. 2024-08-20T21:38:35.1520995Z >>> averager = hierarchicalSGD.HierarchicalModelAverager( 2024-08-20T21:38:35.1521298Z >>> period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100) 2024-08-20T21:38:35.1521756Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-08-20T21:38:35.1522138Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2024-08-20T21:38:35.1522360Z >>> # After 100 steps, run model averaging at two levels. 2024-08-20T21:38:35.1522488Z >>> for step in range(0, 200): 2024-08-20T21:38:35.1522617Z >>> optimizer.zero_grad() 2024-08-20T21:38:35.1522773Z >>> loss = loss_fn(output, labels) 2024-08-20T21:38:35.1522886Z >>> loss.backward() 2024-08-20T21:38:35.1523003Z >>> optimizer.step() 2024-08-20T21:38:35.1523217Z >>> # Average parameters after ``optimizer.step()``. 2024-08-20T21:38:35.1523695Z >>> # Thus, the inter-node communication only occurs periodically after ``warmup_steps``. 2024-08-20T21:38:35.1523901Z >>> averager.average_parameters(model.parameters()) 2024-08-20T21:38:35.1524001Z 2024-08-20T21:38:35.1524103Z .. warning :: 2024-08-20T21:38:35.1524470Z The last group size in the dict must be the size of the provided ``process_group``, 2024-08-20T21:38:35.1524847Z which indicates model averaging at the highest level of the hierarchy. 2024-08-20T21:38:35.1525275Z If ``process_group`` is not provided, then the last group size should be equal to the world size. 2024-08-20T21:38:35.1525379Z 2024-08-20T21:38:35.1525481Z .. warning :: 2024-08-20T21:38:35.1525774Z `HierarchicalModelAverager` is experimental and subject to change. 2024-08-20T21:38:35.1525873Z 2024-08-20T21:38:35.1526276Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1526363Z 2024-08-20T21:38:35.1526486Z warnings.warn(msg) 2024-08-20T21:38:35.1526571Z 2024-08-20T21:38:35.1526783Z --- Parse Warning: 44 / 101 --- 2024-08-20T21:38:35.1528533Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=BroadcastingTorchSaveReader in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/format_utils.py line=40. 2024-08-20T21:38:35.1528953Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1529056Z 2024-08-20T21:38:35.1529453Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint 2024-08-20T21:38:35.1529800Z on the coordinator rank, and then broadcast and shard each tensor to all ranks. 2024-08-20T21:38:35.1529903Z 2024-08-20T21:38:35.1530119Z . N.B. Intended to be used with DynamicMetaLoadPlanner 2024-08-20T21:38:35.1530207Z 2024-08-20T21:38:35.1530321Z .. warning:: 2024-08-20T21:38:35.1530543Z Current implementation only supports loading Tensors. 2024-08-20T21:38:35.1530629Z 2024-08-20T21:38:35.1530784Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:38:35.1530896Z >>> sd = {"mode": model} 2024-08-20T21:38:35.1530994Z >>> dcp.load( 2024-08-20T21:38:35.1531100Z >>> sd, 2024-08-20T21:38:35.1531303Z >>> storage_reader=BroadcastingTorchSaveReader(), 2024-08-20T21:38:35.1531462Z >>> planner=DynamicMetaLoadPlanner(), 2024-08-20T21:38:35.1531614Z >>> checkpoint_id="path_to_model.pt" 2024-08-20T21:38:35.1531705Z >>> ) 2024-08-20T21:38:35.1531792Z 2024-08-20T21:38:35.1532282Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1532370Z 2024-08-20T21:38:35.1532491Z warnings.warn(msg) 2024-08-20T21:38:35.1532577Z 2024-08-20T21:38:35.1532787Z --- Parse Warning: 45 / 101 --- 2024-08-20T21:38:35.1534388Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DynamicMetaLoadPlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/format_utils.py line=151. 2024-08-20T21:38:35.1534804Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1534891Z 2024-08-20T21:38:35.1535396Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, 2024-08-20T21:38:35.1535951Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a 2024-08-20T21:38:35.1536097Z metadata file, like Torch Save files. 2024-08-20T21:38:35.1536195Z 2024-08-20T21:38:35.1536443Z . N.B. Intended to be used with BroadcastingTorchSaveReader 2024-08-20T21:38:35.1536543Z 2024-08-20T21:38:35.1536642Z .. warning:: 2024-08-20T21:38:35.1536862Z Current implementation only supports loading Tensors. 2024-08-20T21:38:35.1536959Z 2024-08-20T21:38:35.1537101Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:38:35.1537212Z >>> sd = {"mode": model} 2024-08-20T21:38:35.1537321Z >>> dcp.load( 2024-08-20T21:38:35.1537416Z >>> sd, 2024-08-20T21:38:35.1537612Z >>> storage_reader=BroadcastingTorchSaveReader(), 2024-08-20T21:38:35.1537781Z >>> planner=DynamicMetaLoadPlanner(), 2024-08-20T21:38:35.1537972Z >>> checkpoint_id="path_to_model.pt" 2024-08-20T21:38:35.1538064Z >>> ) 2024-08-20T21:38:35.1538162Z 2024-08-20T21:38:35.1538565Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1538656Z 2024-08-20T21:38:35.1538777Z warnings.warn(msg) 2024-08-20T21:38:35.1538863Z 2024-08-20T21:38:35.1539073Z --- Parse Warning: 46 / 101 --- 2024-08-20T21:38:35.1540699Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load_sharded_optimizer_state_dict in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/optimizer.py line=220. 2024-08-20T21:38:35.1541114Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1541214Z 2024-08-20T21:38:35.1541495Z Load a state_dict in conjunction with FSDP sharded optimizer state. 2024-08-20T21:38:35.1541583Z 2024-08-20T21:38:35.1541819Z This is the current recommended way to checkpoint FSDP. 2024-08-20T21:38:35.1541931Z >>> # xdoctest: +SKIP 2024-08-20T21:38:35.1542131Z >>> import torch.distributed.checkpoint as dist_cp 2024-08-20T21:38:35.1542238Z >>> # Save 2024-08-20T21:38:35.1542356Z >>> model: torch.nn.Model 2024-08-20T21:38:35.1542500Z >>> optim_params = model.parameters() 2024-08-20T21:38:35.1542694Z >>> optim = torch.optim.SGD(optim_params, lr=0.01) 2024-08-20T21:38:35.1542786Z >>> # Save 2024-08-20T21:38:35.1543080Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT): 2024-08-20T21:38:35.1543197Z >>> state_dict = { 2024-08-20T21:38:35.1543400Z >>> "optimizer": FSDP.optim_state_dict(model, optim), 2024-08-20T21:38:35.1543533Z >>> "model": model.state_dict() 2024-08-20T21:38:35.1543637Z >>> } 2024-08-20T21:38:35.1543758Z >>> dist_cp.save_state_dict( 2024-08-20T21:38:35.1543893Z >>> state_dict=optim_state, 2024-08-20T21:38:35.1544127Z >>> storage_writer=dist_cp.FileSystemWriter("checkpoint"), 2024-08-20T21:38:35.1544298Z >>> planner=dist_cp.DefaultSavePlanner(), 2024-08-20T21:38:35.1544403Z >>> ) 2024-08-20T21:38:35.1544551Z >>> 2024-08-20T21:38:35.1544644Z >>> # Load 2024-08-20T21:38:35.1544964Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT): 2024-08-20T21:38:35.1545130Z >>> model_state_dict = model_tp.state_dict() 2024-08-20T21:38:35.1545238Z >>> checkpoint = { 2024-08-20T21:38:35.1545377Z >>> "model": model_state_dict 2024-08-20T21:38:35.1545468Z >>> } 2024-08-20T21:38:35.1545587Z >>> dist_cp.load_state_dict( 2024-08-20T21:38:35.1545724Z >>> state_dict=checkpoint, 2024-08-20T21:38:35.1545967Z >>> storage_reader=dist_cp.FileSystemReader(checkpoint_file), 2024-08-20T21:38:35.1546138Z >>> planner=dist_cp.DefaultLoadPlanner(), 2024-08-20T21:38:35.1546252Z >>> ) 2024-08-20T21:38:35.1546447Z >>> model.load_state_dict(checkpoint["model_state"]) 2024-08-20T21:38:35.1546538Z >>> 2024-08-20T21:38:35.1546997Z >>> optim_state = dist_cp.load_sharded_optimizer_state_dict( 2024-08-20T21:38:35.1547115Z >>> model_state_dict, 2024-08-20T21:38:35.1547265Z >>> optimizer_key="optimizer", 2024-08-20T21:38:35.1547494Z >>> storage_reader=dist_cp.FileSystemReader("checkpoint"), 2024-08-20T21:38:35.1547586Z >>> ) 2024-08-20T21:38:35.1547688Z >>> 2024-08-20T21:38:35.1547875Z >>> flattened_osd = FSDP.optim_state_dict_to_load( 2024-08-20T21:38:35.1548039Z >>> model, optim, optim_state["optimizer"] 2024-08-20T21:38:35.1548143Z >>> ) 2024-08-20T21:38:35.1548233Z >>> 2024-08-20T21:38:35.1548384Z >>> optim.load_state_dict(flattened_osd) 2024-08-20T21:38:35.1548483Z 2024-08-20T21:38:35.1548900Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1549094Z 2024-08-20T21:38:35.1549222Z warnings.warn(msg) 2024-08-20T21:38:35.1549308Z 2024-08-20T21:38:35.1549522Z --- Parse Warning: 47 / 101 --- 2024-08-20T21:38:35.1551007Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SavePlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/planner.py line=110. 2024-08-20T21:38:35.1551429Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1551530Z 2024-08-20T21:38:35.1551914Z Abstract class defining the protocol used by save_state_dict to plan the save process. 2024-08-20T21:38:35.1552000Z 2024-08-20T21:38:35.1552404Z SavePlanners are stateful objects that can be used to customize the whole save process. 2024-08-20T21:38:35.1552490Z 2024-08-20T21:38:35.1552885Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it 2024-08-20T21:38:35.1553040Z will be visible to the whole process. 2024-08-20T21:38:35.1553125Z 2024-08-20T21:38:35.1553503Z A planner subclass can expect the following sequence of calls during save_state_dict: 2024-08-20T21:38:35.1553605Z 2024-08-20T21:38:35.1553808Z 1) set_up_planner - called on all ranks. 2024-08-20T21:38:35.1553969Z Signals the start of a checkpoint save. 2024-08-20T21:38:35.1554066Z 2024-08-20T21:38:35.1554275Z 2) create_local_plan - called on all ranks. 2024-08-20T21:38:35.1554680Z Process the state_dict and produces a `SavePlan` that will be sent for global planning. 2024-08-20T21:38:35.1554768Z 2024-08-20T21:38:35.1555067Z 3) create_global_plan - called on the coordinator rank only. 2024-08-20T21:38:35.1555342Z Takes the SavePlan from all ranks and make any global decision. 2024-08-20T21:38:35.1555429Z 2024-08-20T21:38:35.1555613Z 4) finish_plan - called on all ranks. 2024-08-20T21:38:35.1555929Z This gives each rank a chance to adjust to global planning decisions. 2024-08-20T21:38:35.1556016Z 2024-08-20T21:38:35.1556272Z 5) resolve_data - called multiple times on each rank 2024-08-20T21:38:35.1556570Z Lookups a value on the `state_dict` for the storage layer to write. 2024-08-20T21:38:35.1556747Z 2024-08-20T21:38:35.1557156Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as 2024-08-20T21:38:35.1557415Z most changes can be expressed by changes in a single method. 2024-08-20T21:38:35.1557502Z 2024-08-20T21:38:35.1557658Z There are 3 usual patterns of extension: 2024-08-20T21:38:35.1557759Z 2024-08-20T21:38:35.1558101Z Rewriting state_dict. This is the simplest way to extend the save process as it 2024-08-20T21:38:35.1558485Z doesn't requite understanding the intrincacies of how SavePlan works: 2024-08-20T21:38:35.1558573Z 2024-08-20T21:38:35.1558716Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:38:35.1558905Z >>> class RenamePlanner(DefaultSavePlanner): 2024-08-20T21:38:35.1559020Z >>> def set_up_planner( 2024-08-20T21:38:35.1559117Z >>> self, 2024-08-20T21:38:35.1559271Z >>> state_dict: STATE_DICT_TYPE, 2024-08-20T21:38:35.1559441Z >>> storage_meta: Optional[StorageMeta], 2024-08-20T21:38:35.1559564Z >>> is_coordinator: bool, 2024-08-20T21:38:35.1559711Z >>> ) -> None: 2024-08-20T21:38:35.1559859Z >>> # prefix all keys with `foo_`` 2024-08-20T21:38:35.1560282Z >>> super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator) 2024-08-20T21:38:35.1560381Z 2024-08-20T21:38:35.1560840Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted 2024-08-20T21:38:35.1560928Z 2024-08-20T21:38:35.1561083Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:38:35.1561299Z >>> class FP16Planner(DefaultSavePlanner): 2024-08-20T21:38:35.1561447Z >>> def create_local_plan(self): 2024-08-20T21:38:35.1561601Z >>> plan = super().create_local_plan() 2024-08-20T21:38:35.1561714Z >>> for p in plan: 2024-08-20T21:38:35.1561881Z >>> if p.tensor_data is not None: 2024-08-20T21:38:35.1562098Z >>> p.tensor_data.properties.dtype = torch.float16 2024-08-20T21:38:35.1562205Z >>> return plan 2024-08-20T21:38:35.1562307Z >>> 2024-08-20T21:38:35.1562457Z >>> def resolve_data(self, write_item): 2024-08-20T21:38:35.1562619Z >>> item = super().resolve_data(write_item) 2024-08-20T21:38:35.1563011Z >>> return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16) 2024-08-20T21:38:35.1563099Z 2024-08-20T21:38:35.1563667Z Using the global planning step to make central decisions that can't be made individually by each rank 2024-08-20T21:38:35.1563768Z 2024-08-20T21:38:35.1563913Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:38:35.1564042Z >>> from itertools import islice 2024-08-20T21:38:35.1564188Z >>> from dataclasses import replace 2024-08-20T21:38:35.1564409Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner): 2024-08-20T21:38:35.1564902Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0 2024-08-20T21:38:35.1565138Z >>> # This sample doesn't handle ShardedTensors 2024-08-20T21:38:35.1565302Z >>> def create_global_plan(self, all_plans): 2024-08-20T21:38:35.1565433Z >>> def chunk(it, size): 2024-08-20T21:38:35.1565546Z >>> it = iter(it) 2024-08-20T21:38:35.1565768Z >>> return list(iter(lambda: tuple(islice(it, size)), ())) 2024-08-20T21:38:35.1565890Z >>> all_plans = [ 2024-08-20T21:38:35.1566082Z >>> replace(plan, items=items) for plan, items in 2024-08-20T21:38:35.1566328Z >>> zip(all_plans, chunk(all_plans[0].items, len(all_plans))) 2024-08-20T21:38:35.1566435Z >>> ] 2024-08-20T21:38:35.1566620Z >>> return super().create_global_plan(all_plans) 2024-08-20T21:38:35.1566707Z 2024-08-20T21:38:35.1567176Z Finally, some planners need to save additional metadata in the checkpoint, this is 2024-08-20T21:38:35.1567608Z accomplished by having each rank contribute their data items in the local plan and 2024-08-20T21:38:35.1567758Z the global planner aggregate them: 2024-08-20T21:38:35.1567844Z 2024-08-20T21:38:35.1567986Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:38:35.1568200Z >>> class SaveExtraDataPlanner(DefaultSavePlanner): 2024-08-20T21:38:35.1568432Z >>> def create_local_plan(self) -> SavePlan: 2024-08-20T21:38:35.1568584Z >>> plan = super().create_local_plan() 2024-08-20T21:38:35.1568875Z >>> return replace(plan, planner_data="per-rank-data") 2024-08-20T21:38:35.1568969Z >>> 2024-08-20T21:38:35.1569471Z >>> def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]: 2024-08-20T21:38:35.1569737Z >>> global_plan, metadata = super().create_global_plan(all_plans) 2024-08-20T21:38:35.1569949Z >>> merged_data = [p.planner_data for p in global_plan] 2024-08-20T21:38:35.1570176Z >>> metadata = replace(metadata, planner_data=merged_data) 2024-08-20T21:38:35.1570331Z >>> return global_plan, metadata 2024-08-20T21:38:35.1570419Z 2024-08-20T21:38:35.1570833Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1570920Z 2024-08-20T21:38:35.1571030Z warnings.warn(msg) 2024-08-20T21:38:35.1571127Z 2024-08-20T21:38:35.1571337Z --- Parse Warning: 48 / 101 --- 2024-08-20T21:38:35.1572866Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=LoadPlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/planner.py line=270. 2024-08-20T21:38:35.1573297Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1573384Z 2024-08-20T21:38:35.1573767Z Abstract class defining the protocol used by load_state_dict to plan the load process. 2024-08-20T21:38:35.1573870Z 2024-08-20T21:38:35.1574256Z LoadPlanner are stateful objects that can be used to customize the whole load process. 2024-08-20T21:38:35.1574342Z 2024-08-20T21:38:35.1574743Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it 2024-08-20T21:38:35.1574885Z will be visible to the whole process. 2024-08-20T21:38:35.1574985Z 2024-08-20T21:38:35.1575364Z A planner subclass can expect the following sequence of calls during load_state_dict: 2024-08-20T21:38:35.1575452Z 2024-08-20T21:38:35.1575663Z 1) set_up_planner - called on all ranks. 2024-08-20T21:38:35.1575836Z Signals the start of loading a checkpoint. 2024-08-20T21:38:35.1575922Z 2024-08-20T21:38:35.1576144Z 2) create_local_plan - called on all ranks. 2024-08-20T21:38:35.1576537Z Process the state_dict and produces a `LoadPlan` that will be sent for global planning. 2024-08-20T21:38:35.1576628Z 2024-08-20T21:38:35.1576940Z 3) create_global_plan - called on the coordinator rank only. 2024-08-20T21:38:35.1577204Z Takes the LoadPlan from all ranks and make any global decision. 2024-08-20T21:38:35.1577292Z 2024-08-20T21:38:35.1577548Z 4) load_bytes - called multiple times on each rank 2024-08-20T21:38:35.1577829Z This is called once per non-tensor value in state_dict. 2024-08-20T21:38:35.1577916Z 2024-08-20T21:38:35.1578299Z 5) resolve_tensor and commit_tensor - called multiple times on each rank 2024-08-20T21:38:35.1578550Z They are called in pair for each Tensor value in state_dict. 2024-08-20T21:38:35.1578649Z 2024-08-20T21:38:35.1579061Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as 2024-08-20T21:38:35.1579308Z most changes can be expressed by changes in a single method. 2024-08-20T21:38:35.1579407Z 2024-08-20T21:38:35.1579571Z There are two usual patterns of extension: 2024-08-20T21:38:35.1579712Z 2024-08-20T21:38:35.1580065Z Rewriting state_dict. This is the simplest way to extend the load process as it 2024-08-20T21:38:35.1580477Z doesn't requite understanding the intrincacies of how LoadPlan works. We need 2024-08-20T21:38:35.1580797Z to keep a reference to the original state_dict as load happens in place so 2024-08-20T21:38:35.1580967Z we need to be able to perform it in place 2024-08-20T21:38:35.1581054Z 2024-08-20T21:38:35.1581197Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:38:35.1581382Z >>> class RenamePlanner(DefaultLoadPlanner): 2024-08-20T21:38:35.1581497Z >>> def set_up_planner( 2024-08-20T21:38:35.1581594Z >>> self, 2024-08-20T21:38:35.1581751Z >>> state_dict: STATE_DICT_TYPE, 2024-08-20T21:38:35.1581867Z >>> metadata: Metadata, 2024-08-20T21:38:35.1582002Z >>> is_coordinator: bool, 2024-08-20T21:38:35.1582136Z >>> ) -> None: 2024-08-20T21:38:35.1582304Z >>> self.original_state_dict = state_dict 2024-08-20T21:38:35.1582550Z >>> state_dict = {"foo_" + k: v for k, v in state_dict.items()} 2024-08-20T21:38:35.1582640Z >>> 2024-08-20T21:38:35.1582790Z >>> if self.flatten_sharded_tensors: 2024-08-20T21:38:35.1583007Z >>> state_dict = _flatten_sharded_tensors(state_dict) 2024-08-20T21:38:35.1583097Z >>> 2024-08-20T21:38:35.1583232Z >>> if self.flatten_state_dict: 2024-08-20T21:38:35.1583490Z >>> state_dict, self.mappings = flatten_state_dict(state_dict) 2024-08-20T21:38:35.1583578Z >>> 2024-08-20T21:38:35.1583719Z >>> self.state_dict = state_dict 2024-08-20T21:38:35.1583912Z >>> self.metadata = metadata 2024-08-20T21:38:35.1584073Z >>> self.is_coordinator = is_coordinator 2024-08-20T21:38:35.1584162Z >>> 2024-08-20T21:38:35.1584333Z >>> def load_bytes(self, read_item, value): 2024-08-20T21:38:35.1584469Z >>> # Remove the "foo_" prefix 2024-08-20T21:38:35.1584914Z >>> self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False) 2024-08-20T21:38:35.1585001Z 2024-08-20T21:38:35.1585086Z 2024-08-20T21:38:35.1585434Z Modifying resolve_tensor and commit_tensor to handle load time transformation. 2024-08-20T21:38:35.1585521Z 2024-08-20T21:38:35.1585661Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:38:35.1585874Z >>> class MetaModelMaterialize(DefaultSavePlanner): 2024-08-20T21:38:35.1586025Z >>> def resolve_tensor(self, read_item): 2024-08-20T21:38:35.1586198Z >>> tensor = super().resolve_tensor(read_item) 2024-08-20T21:38:35.1586405Z >>> return torch.empty_like(tensor, device="cpu") 2024-08-20T21:38:35.1586495Z >>> 2024-08-20T21:38:35.1586667Z >>> def commit_tensor(self, read_item, tensor): 2024-08-20T21:38:35.1586882Z >>> self.state_dict[read_item.dest_index.fqn] = tensor 2024-08-20T21:38:35.1586974Z 2024-08-20T21:38:35.1587376Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1587475Z 2024-08-20T21:38:35.1587585Z warnings.warn(msg) 2024-08-20T21:38:35.1587672Z 2024-08-20T21:38:35.1587894Z --- Parse Warning: 49 / 101 --- 2024-08-20T21:38:35.1589388Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_loader.py line=61. 2024-08-20T21:38:35.1589813Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1589900Z 2024-08-20T21:38:35.1590091Z Load a distributed ``state_dict`` in SPMD style. 2024-08-20T21:38:35.1590190Z 2024-08-20T21:38:35.1590441Z Each rank will try to read the least amount of data necessary 2024-08-20T21:38:35.1590761Z to fullfill the requested `state_dict`. When loading :class:`ShardedTensor` 2024-08-20T21:38:35.1591164Z or :class:`DTensor` instances, each rank only reads data for their local shards. 2024-08-20T21:38:35.1591252Z 2024-08-20T21:38:35.1591606Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2024-08-20T21:38:35.1591968Z load will first call ``state_dict`` before attempting deserialization, followed by 2024-08-20T21:38:35.1592183Z ``load_state_dict`` once the deserialization is complete. 2024-08-20T21:38:35.1592282Z 2024-08-20T21:38:35.1592387Z .. warning:: 2024-08-20T21:38:35.1592610Z All tensors in ``state_dict`` must be allocated on their 2024-08-20T21:38:35.1592835Z destination device *prior to* calling this function. 2024-08-20T21:38:35.1592923Z 2024-08-20T21:38:35.1593312Z All non-tensor data is loaded using `torch.load()` and modified in place 2024-08-20T21:38:35.1593427Z on state_dict. 2024-08-20T21:38:35.1593518Z 2024-08-20T21:38:35.1593616Z .. warning:: 2024-08-20T21:38:35.1593907Z Users must call `load_state_dict` on the root module to ensure load 2024-08-20T21:38:35.1594204Z pos-processing and non-tensor data properly propagates. 2024-08-20T21:38:35.1594290Z 2024-08-20T21:38:35.1594395Z .. note: 2024-08-20T21:38:35.1594707Z If no process group is initialized, this function will assume the intent 2024-08-20T21:38:35.1595019Z is to load a checkpoint into the local process. This can be useful in the 2024-08-20T21:38:35.1595372Z case of local inference, and when using regular Tensors (as opposed to DTensor 2024-08-20T21:38:35.1595488Z or ShardedTensor) 2024-08-20T21:38:35.1595587Z 2024-08-20T21:38:35.1595783Z .. note: 2024-08-20T21:38:35.1595967Z Rank 0 is assumed to be the coordinator rank. 2024-08-20T21:38:35.1596070Z 2024-08-20T21:38:35.1596163Z Args: 2024-08-20T21:38:35.1596362Z state_dict (Dict[str, Any]): The state_dict to save. 2024-08-20T21:38:35.1596568Z checkpoint_id (Union[str, os.PathLike, None]): 2024-08-20T21:38:35.1596863Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-08-20T21:38:35.1597150Z depends on the storage. It can be a path to a folder or to a file. 2024-08-20T21:38:35.1597467Z It can also be a key if the storage is a key-value store. 2024-08-20T21:38:35.1597577Z (Default: ``None``) 2024-08-20T21:38:35.1597741Z storage_reader (Optional[StorageReader]): 2024-08-20T21:38:35.1598027Z Instance of StorageWriter used to perform reads. If this is not 2024-08-20T21:38:35.1598299Z specified, DCP will automatically infer the reader based on the 2024-08-20T21:38:35.1598576Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-08-20T21:38:35.1598711Z be raised. (Default: ``None``) 2024-08-20T21:38:35.1598853Z planner (Optional[LoadPlanner]): 2024-08-20T21:38:35.1599137Z Instance of LoadPlanner. If this is not specificed, the default 2024-08-20T21:38:35.1599303Z planner will be used. (Default: ``None``) 2024-08-20T21:38:35.1599466Z process_group (Optional[ProcessGroup]): 2024-08-20T21:38:35.1599780Z ProcessGroup to be used for cross-rank synchronization. 2024-08-20T21:38:35.1599890Z (Default: ``None``) 2024-08-20T21:38:35.1599978Z 2024-08-20T21:38:35.1600084Z Returns: 2024-08-20T21:38:35.1600177Z None. 2024-08-20T21:38:35.1600262Z 2024-08-20T21:38:35.1600368Z Examples 2024-08-20T21:38:35.1600480Z >>> # xdoctest: +SKIP 2024-08-20T21:38:35.1600597Z >>> my_model = MyModule() 2024-08-20T21:38:35.1600792Z >>> optimizer = Adagrad(my_model.parameters()) 2024-08-20T21:38:35.1600958Z >>> model_state_dict = my_model.state_dict() 2024-08-20T21:38:35.1601373Z >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader("/checkpoint/1") 2024-08-20T21:38:35.1601535Z 2024-08-20T21:38:35.1601741Z >>> torch.distributed.checkpoint.load_state_dict( 2024-08-20T21:38:35.1601893Z >>> state_dict=model_state_dict, 2024-08-20T21:38:35.1602044Z >>> storage_reader=fs_storage_reader, 2024-08-20T21:38:35.1602136Z >>> ) 2024-08-20T21:38:35.1602235Z 2024-08-20T21:38:35.1602496Z >>> # module.load_state_dict() function might have customized steps 2024-08-20T21:38:35.1602664Z >>> # to flush the state_dict, must call it to 2024-08-20T21:38:35.1602805Z >>> # ensure correct behavior. 2024-08-20T21:38:35.1602972Z >>> my_model.load_state_dict(model_state_dict) 2024-08-20T21:38:35.1603058Z 2024-08-20T21:38:35.1603168Z .. note:: 2024-08-20T21:38:35.1603449Z load_state_dict uses collectives to coordinate reads across ranks. 2024-08-20T21:38:35.1603807Z For NCCL-based process groups, internal tensor representations of 2024-08-20T21:38:35.1604140Z objects must be moved to the GPU device before communication takes place. 2024-08-20T21:38:35.1604457Z In this case, the device used is given by ``torch.cuda.current_device()`` 2024-08-20T21:38:35.1604876Z and it is the user's responsibility to ensure that this is set so that each 2024-08-20T21:38:35.1605127Z rank has an individual GPU, via ``torch.cuda.set_device()``. 2024-08-20T21:38:35.1605217Z 2024-08-20T21:38:35.1605631Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1605718Z 2024-08-20T21:38:35.1605828Z warnings.warn(msg) 2024-08-20T21:38:35.1605926Z 2024-08-20T21:38:35.1606138Z --- Parse Warning: 50 / 101 --- 2024-08-20T21:38:35.1607782Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=save in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=67. 2024-08-20T21:38:35.1608223Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1608311Z 2024-08-20T21:38:35.1608462Z Save a distributed model in SPMD style. 2024-08-20T21:38:35.1608561Z 2024-08-20T21:38:35.1608811Z This function is different from ``torch.save()`` as it handles 2024-08-20T21:38:35.1609170Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards. 2024-08-20T21:38:35.1609259Z 2024-08-20T21:38:35.1609614Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2024-08-20T21:38:35.1609823Z save will call ``state_dict`` before serialization. 2024-08-20T21:38:35.1609910Z 2024-08-20T21:38:35.1610010Z .. warning:: 2024-08-20T21:38:35.1610351Z There is no guarantees of Backwards Compatibility across PyTorch versions 2024-08-20T21:38:35.1610467Z for saved state_dicts. 2024-08-20T21:38:35.1610555Z 2024-08-20T21:38:35.1610665Z .. warning:: 2024-08-20T21:38:35.1610956Z If using the `process_group` argument, make sure that only its ranks 2024-08-20T21:38:35.1611235Z call `save_state_dict` and that all data in state_dict belong to it. 2024-08-20T21:38:35.1611334Z 2024-08-20T21:38:35.1611429Z .. note:: 2024-08-20T21:38:35.1611870Z When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of 2024-08-20T21:38:35.1612238Z the shard_group should be calling `save_state_dict` and the corresponding process 2024-08-20T21:38:35.1612364Z group needs to be passed in. 2024-08-20T21:38:35.1612463Z 2024-08-20T21:38:35.1612558Z .. note:: 2024-08-20T21:38:35.1612933Z If no process group is available, this function assumes the intention is to save the 2024-08-20T21:38:35.1613085Z state_dict in the local process. 2024-08-20T21:38:35.1613171Z 2024-08-20T21:38:35.1613266Z .. note: 2024-08-20T21:38:35.1613456Z Rank 0 is assumed to be the coordinator rank. 2024-08-20T21:38:35.1613601Z 2024-08-20T21:38:35.1613687Z 2024-08-20T21:38:35.1613790Z Args: 2024-08-20T21:38:35.1613990Z state_dict (Dict[str, Any]): The state_dict to save. 2024-08-20T21:38:35.1614172Z checkpoint_id (Union[str, os.PathLike, None]): 2024-08-20T21:38:35.1614477Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-08-20T21:38:35.1614761Z depends on the storage. It can be a path to a folder or to a file. 2024-08-20T21:38:35.1615067Z It can also be a key if the storage is a key-value store. 2024-08-20T21:38:35.1615191Z (Default: ``None``) 2024-08-20T21:38:35.1615355Z storage_writer (Optional[StorageWriter]): 2024-08-20T21:38:35.1615652Z Instance of StorageWriter used to perform writes. If this is not 2024-08-20T21:38:35.1615922Z specified, DCP will automatically infer the writer based on the 2024-08-20T21:38:35.1616189Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-08-20T21:38:35.1616339Z be raised. (Default: ``None``) 2024-08-20T21:38:35.1616477Z planner (Optional[SavePlanner]): 2024-08-20T21:38:35.1616747Z Instance of SavePlanner. If this is not specificed, the default 2024-08-20T21:38:35.1616926Z planner will be used. (Default: ``None``) 2024-08-20T21:38:35.1617087Z process_group (Optional[ProcessGroup]): 2024-08-20T21:38:35.1617388Z ProcessGroup to be used for cross-rank synchronization. 2024-08-20T21:38:35.1617511Z (Default: ``None``) 2024-08-20T21:38:35.1617597Z 2024-08-20T21:38:35.1617690Z Returns: 2024-08-20T21:38:35.1617905Z Metadata: Metadata object for the saved checkpoint. 2024-08-20T21:38:35.1617993Z 2024-08-20T21:38:35.1618144Z Example: 2024-08-20T21:38:35.1618271Z >>> # xdoctest: +SKIP 2024-08-20T21:38:35.1618387Z >>> my_model = MyModule() 2024-08-20T21:38:35.1618484Z 2024-08-20T21:38:35.1618623Z >>> state_dict = {"model": my_model} 2024-08-20T21:38:35.1618714Z 2024-08-20T21:38:35.1619129Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2024-08-20T21:38:35.1619302Z >>> torch.distributed.checkpoint.save( 2024-08-20T21:38:35.1619426Z >>> state_dict=state_dict, 2024-08-20T21:38:35.1619587Z >>> storage_writer=fs_storage_writer, 2024-08-20T21:38:35.1619677Z >>> ) 2024-08-20T21:38:35.1619763Z 2024-08-20T21:38:35.1619869Z .. note:: 2024-08-20T21:38:35.1620151Z save_state_dict uses collectives to coordinate writes across ranks. 2024-08-20T21:38:35.1620509Z For NCCL-based process groups, internal tensor representations of 2024-08-20T21:38:35.1620848Z objects must be moved to the GPU device before communication takes place. 2024-08-20T21:38:35.1621158Z In this case, the device used is given by ``torch.cuda.current_device()`` 2024-08-20T21:38:35.1621544Z and it is the user's responsibility to ensure that this is set so that 2024-08-20T21:38:35.1621821Z each rank has an individual GPU, via ``torch.cuda.set_device()``. 2024-08-20T21:38:35.1621909Z 2024-08-20T21:38:35.1622325Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1622412Z 2024-08-20T21:38:35.1622521Z warnings.warn(msg) 2024-08-20T21:38:35.1622619Z 2024-08-20T21:38:35.1622828Z --- Parse Warning: 51 / 101 --- 2024-08-20T21:38:35.1624350Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=async_save in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=170. 2024-08-20T21:38:35.1624779Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1625216Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the 2024-08-20T21:38:35.1625687Z staging storage (defaults to CPU memory), and then calls the `save` in a separate thread. 2024-08-20T21:38:35.1625775Z 2024-08-20T21:38:35.1625877Z .. warning:: 2024-08-20T21:38:35.1626100Z This feature is experimental and subject to change. 2024-08-20T21:38:35.1626186Z 2024-08-20T21:38:35.1626279Z Args: 2024-08-20T21:38:35.1626498Z state_dict (Dict[str, Any]): The state_dict to save. 2024-08-20T21:38:35.1626688Z checkpoint_id (Union[str, os.PathLike, None]): 2024-08-20T21:38:35.1626988Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-08-20T21:38:35.1627291Z depends on the storage. It can be a path to a folder or to a file. 2024-08-20T21:38:35.1627605Z It can also be a key if the storage is a key-value store. 2024-08-20T21:38:35.1627720Z (Default: ``None``) 2024-08-20T21:38:35.1627903Z storage_writer (Optional[StorageWriter]): 2024-08-20T21:38:35.1628266Z Instance of StorageWriter used to perform 'stage' and 'save'. If 2024-08-20T21:38:35.1628612Z this is not specified, DCP will automatically infer the writer based on the 2024-08-20T21:38:35.1628884Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-08-20T21:38:35.1629026Z be raised. (Default: ``None``) 2024-08-20T21:38:35.1629185Z planner (Optional[SavePlanner]): 2024-08-20T21:38:35.1629458Z Instance of SavePlanner. If this is not specificed, the default 2024-08-20T21:38:35.1629628Z planner will be used. (Default: ``None``) 2024-08-20T21:38:35.1629804Z process_group (Optional[ProcessGroup]): 2024-08-20T21:38:35.1630165Z ProcessGroup to be used for cross-rank synchronization. 2024-08-20T21:38:35.1630282Z (Default: ``None``) 2024-08-20T21:38:35.1630380Z 2024-08-20T21:38:35.1630476Z Returns: 2024-08-20T21:38:35.1630765Z Future: A future holding the resultant Metadata object from `save`. 2024-08-20T21:38:35.1630862Z 2024-08-20T21:38:35.1630959Z Example: 2024-08-20T21:38:35.1631075Z >>> # xdoctest: +SKIP 2024-08-20T21:38:35.1631208Z >>> my_model = MyModule() 2024-08-20T21:38:35.1631293Z 2024-08-20T21:38:35.1631451Z >>> state_dict = {"model": my_model} 2024-08-20T21:38:35.1631536Z 2024-08-20T21:38:35.1631942Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2024-08-20T21:38:35.1632231Z >>> checkpoint_future = torch.distributed.checkpoint.async_save( 2024-08-20T21:38:35.1632360Z >>> state_dict=state_dict, 2024-08-20T21:38:35.1632523Z >>> storage_writer=fs_storage_writer, 2024-08-20T21:38:35.1632628Z >>> ) 2024-08-20T21:38:35.1632721Z >>> 2024-08-20T21:38:35.1632840Z >>> # ... do some work ... 2024-08-20T21:38:35.1632943Z >>> 2024-08-20T21:38:35.1633082Z >>> checkpoint_future.result() 2024-08-20T21:38:35.1633168Z 2024-08-20T21:38:35.1633268Z 2024-08-20T21:38:35.1633671Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1633758Z 2024-08-20T21:38:35.1633879Z warnings.warn(msg) 2024-08-20T21:38:35.1633965Z 2024-08-20T21:38:35.1634175Z --- Parse Warning: 52 / 101 --- 2024-08-20T21:38:35.1635793Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=construct_and_record_rdzv_event in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/elastic/events/__init__.py line=91. 2024-08-20T21:38:35.1636216Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1636317Z 2024-08-20T21:38:35.1636568Z Initialize rendezvous event object and record its operations. 2024-08-20T21:38:35.1636717Z 2024-08-20T21:38:35.1636820Z Args: 2024-08-20T21:38:35.1636986Z run_id (str): The run id of the rendezvous. 2024-08-20T21:38:35.1637179Z message (str): The message describing the event. 2024-08-20T21:38:35.1637539Z node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED). 2024-08-20T21:38:35.1637790Z name (str): Event name. (E.g. Current action being performed). 2024-08-20T21:38:35.1637941Z hostname (str): Hostname of the node. 2024-08-20T21:38:35.1638142Z pid (Optional[int]): The process id of the node. 2024-08-20T21:38:35.1638472Z master_endpoint (str): The master endpoint for the rendezvous store, if known. 2024-08-20T21:38:35.1638866Z local_id (Optional[int]): The local_id of the node, if defined in dynamic_rendezvous.py 2024-08-20T21:38:35.1639072Z rank (Optional[int]): The rank of the node, if known. 2024-08-20T21:38:35.1639168Z Returns: 2024-08-20T21:38:35.1639274Z None 2024-08-20T21:38:35.1639371Z Example: 2024-08-20T21:38:35.1639539Z >>> # See DynamicRendezvousHandler class 2024-08-20T21:38:35.1639656Z >>> def _record( 2024-08-20T21:38:35.1639752Z ... self, 2024-08-20T21:38:35.1639861Z ... message: str, 2024-08-20T21:38:35.1640058Z ... node_state: NodeState = NodeState.RUNNING, 2024-08-20T21:38:35.1640192Z ... rank: Optional[int] = None, 2024-08-20T21:38:35.1640325Z ... ) -> None: 2024-08-20T21:38:35.1640489Z ... construct_and_record_rdzv_event( 2024-08-20T21:38:35.1640708Z ... name=f"{self.__class__.__name__}.{get_method_name()}", 2024-08-20T21:38:35.1640857Z ... run_id=self._settings.run_id, 2024-08-20T21:38:35.1641049Z ... message=message, 2024-08-20T21:38:35.1641176Z ... node_state=node_state, 2024-08-20T21:38:35.1641325Z ... hostname=self._this_node.addr, 2024-08-20T21:38:35.1641474Z ... pid=self._this_node.pid, 2024-08-20T21:38:35.1641638Z ... local_id=self._this_node.local_id, 2024-08-20T21:38:35.1641756Z ... rank=rank, 2024-08-20T21:38:35.1641849Z ... ) 2024-08-20T21:38:35.1641937Z 2024-08-20T21:38:35.1642354Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1642442Z 2024-08-20T21:38:35.1642551Z warnings.warn(msg) 2024-08-20T21:38:35.1642651Z 2024-08-20T21:38:35.1642861Z --- Parse Warning: 53 / 101 --- 2024-08-20T21:38:35.1644279Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MixedPrecision in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/api.py line=113. 2024-08-20T21:38:35.1644710Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1644797Z 2024-08-20T21:38:35.1645068Z This configures FSDP-native mixed precision training. 2024-08-20T21:38:35.1645176Z 2024-08-20T21:38:35.1645275Z Attributes: 2024-08-20T21:38:35.1645594Z param_dtype (Optional[torch.dtype]): This specifies the dtype for model 2024-08-20T21:38:35.1645860Z parameters during forward and backward and thus the dtype for 2024-08-20T21:38:35.1646156Z forward and backward computation. Outside forward and backward, the 2024-08-20T21:38:35.1646423Z *sharded* parameters are kept in full precision (e.g. for the 2024-08-20T21:38:35.1646900Z optimizer step), and for model checkpointing, the parameters are 2024-08-20T21:38:35.1647183Z always saved in full precision. (Default: ``None``) 2024-08-20T21:38:35.1647482Z reduce_dtype (Optional[torch.dtype]): This specifies the dtype for 2024-08-20T21:38:35.1647847Z gradient reduction (i.e. reduce-scatter or all-reduce). If this is 2024-08-20T21:38:35.1648095Z ``None`` but ``param_dtype`` is not ``None``, then this takes on 2024-08-20T21:38:35.1648500Z the ``param_dtype`` value, still running gradient reduction in low 2024-08-20T21:38:35.1648781Z precision. This is permitted to differ from ``param_dtype``, e.g. 2024-08-20T21:38:35.1649062Z to force gradient reduction to run in full precision. (Default: 2024-08-20T21:38:35.1649158Z ``None``) 2024-08-20T21:38:35.1649438Z buffer_dtype (Optional[torch.dtype]): This specifies the dtype for 2024-08-20T21:38:35.1649727Z buffers. FSDP does not shard buffers. Rather, FSDP casts them to 2024-08-20T21:38:35.1649987Z ``buffer_dtype`` in the first forward pass and keeps them in that 2024-08-20T21:38:35.1650268Z dtype thereafter. For model checkpointing, the buffers are saved 2024-08-20T21:38:35.1650526Z in full precision except for ``LOCAL_STATE_DICT``. (Default: 2024-08-20T21:38:35.1650623Z ``None``) 2024-08-20T21:38:35.1650881Z keep_low_precision_grads (bool): If ``False``, then FSDP upcasts 2024-08-20T21:38:35.1651183Z gradients to full precision after the backward pass in preparation 2024-08-20T21:38:35.1651462Z for the optimizer step. If ``True``, then FSDP keeps the gradients 2024-08-20T21:38:35.1651757Z in the dtype used for gradient reduction, which can save memory if 2024-08-20T21:38:35.1652035Z using a custom optimizer that supports running in low precision. 2024-08-20T21:38:35.1652148Z (Default: ``False``) 2024-08-20T21:38:35.1652438Z cast_forward_inputs (bool): If ``True``, then this FSDP module casts 2024-08-20T21:38:35.1652711Z its forward args and kwargs to ``param_dtype``. This is to ensure 2024-08-20T21:38:35.1653063Z that parameter and input dtypes match for forward computation, as 2024-08-20T21:38:35.1653364Z required by many ops. This may need to be set to ``True`` when only 2024-08-20T21:38:35.1653653Z applying mixed precision to some but not all FSDP modules, in which 2024-08-20T21:38:35.1654034Z case a mixed-precision FSDP submodule needs to recast its inputs. 2024-08-20T21:38:35.1654146Z (Default: ``False``) 2024-08-20T21:38:35.1654433Z cast_root_forward_inputs (bool): If ``True``, then the root FSDP module 2024-08-20T21:38:35.1654716Z casts its forward args and kwargs to ``param_dtype``, overriding 2024-08-20T21:38:35.1655052Z the value of ``cast_forward_inputs``. For non-root FSDP modules, 2024-08-20T21:38:35.1655236Z this does not do anything. (Default: ``True``) 2024-08-20T21:38:35.1655535Z _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies 2024-08-20T21:38:35.1655784Z module classes to ignore for mixed precision when using an 2024-08-20T21:38:35.1656026Z ``auto_wrap_policy``: Modules of these classes will have FSDP 2024-08-20T21:38:35.1656318Z applied to them separately with mixed precision disabled (meaning 2024-08-20T21:38:35.1656599Z that the final FSDP construction would deviate from the specified 2024-08-20T21:38:35.1656871Z policy). If ``auto_wrap_policy`` is not specified, then this does 2024-08-20T21:38:35.1657143Z not do anything. This API is experimental and subject to change. 2024-08-20T21:38:35.1657272Z (Default: ``(_BatchNorm,)``) 2024-08-20T21:38:35.1657372Z 2024-08-20T21:38:35.1657592Z .. note:: This API is experimental and subject to change. 2024-08-20T21:38:35.1657683Z 2024-08-20T21:38:35.1657994Z .. note:: Only floating point tensors are cast to their specified dtypes. 2024-08-20T21:38:35.1658080Z 2024-08-20T21:38:35.1658333Z .. note:: In ``summon_full_params``, parameters are forced to full 2024-08-20T21:38:35.1658479Z precision, but buffers are not. 2024-08-20T21:38:35.1658563Z 2024-08-20T21:38:35.1658842Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when 2024-08-20T21:38:35.1659200Z their inputs are in a low precision like ``float16`` or ``bfloat16``. 2024-08-20T21:38:35.1659583Z Disabling FSDP's mixed precision for those norm modules only means that 2024-08-20T21:38:35.1659878Z the affine parameters are kept in ``float32``. However, this incurs 2024-08-20T21:38:35.1660263Z separate all-gathers and reduce-scatters for those norm modules, which 2024-08-20T21:38:35.1660563Z may be inefficient, so if the workload permits, the user should prefer 2024-08-20T21:38:35.1660768Z to still apply mixed precision to those modules. 2024-08-20T21:38:35.1660855Z 2024-08-20T21:38:35.1661148Z .. note:: By default, if the user passes a model with any ``_BatchNorm`` 2024-08-20T21:38:35.1661439Z modules and specifies an ``auto_wrap_policy``, then the batch norm 2024-08-20T21:38:35.1661740Z modules will have FSDP applied to them separately with mixed precision 2024-08-20T21:38:35.1661979Z disabled. See the ``_module_classes_to_ignore`` argument. 2024-08-20T21:38:35.1662069Z 2024-08-20T21:38:35.1662340Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and 2024-08-20T21:38:35.1662629Z ``cast_forward_inputs=False`` by default. For the root FSDP instance, 2024-08-20T21:38:35.1662854Z its ``cast_root_forward_inputs`` takes precedence over its 2024-08-20T21:38:35.1663148Z ``cast_forward_inputs``. For non-root FSDP instances, their 2024-08-20T21:38:35.1663445Z ``cast_root_forward_inputs`` values are ignored. The default setting is 2024-08-20T21:38:35.1663738Z sufficient for the typical case where each FSDP instance has the same 2024-08-20T21:38:35.1664083Z ``MixedPrecision`` configuration and only needs to cast inputs to the 2024-08-20T21:38:35.1664405Z ``param_dtype`` at the beginning of the model's forward pass. 2024-08-20T21:38:35.1664492Z 2024-08-20T21:38:35.1664789Z .. note:: For nested FSDP instances with different ``MixedPrecision`` 2024-08-20T21:38:35.1665101Z configurations, we recommend setting individual ``cast_forward_inputs`` 2024-08-20T21:38:35.1665405Z values to configure casting inputs or not before each instance's 2024-08-20T21:38:35.1665680Z forward. In such a case, since the casts happen before each FSDP 2024-08-20T21:38:35.1666036Z instance's forward, a parent FSDP instance should have its non-FSDP 2024-08-20T21:38:35.1666346Z submodules run before its FSDP submodules to avoid the activation dtype 2024-08-20T21:38:35.1666640Z being changed due to a different ``MixedPrecision`` configuration. 2024-08-20T21:38:35.1666726Z 2024-08-20T21:38:35.1666826Z Example:: 2024-08-20T21:38:35.1666923Z 2024-08-20T21:38:35.1667101Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:38:35.1667326Z >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3)) 2024-08-20T21:38:35.1667452Z >>> model[1] = FSDP( 2024-08-20T21:38:35.1667560Z >>> model[1], 2024-08-20T21:38:35.1667959Z >>> mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True), 2024-08-20T21:38:35.1668053Z >>> ) 2024-08-20T21:38:35.1668159Z >>> model = FSDP( 2024-08-20T21:38:35.1668271Z >>> model, 2024-08-20T21:38:35.1668662Z >>> mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), 2024-08-20T21:38:35.1668754Z >>> ) 2024-08-20T21:38:35.1668852Z 2024-08-20T21:38:35.1669147Z The above shows a working example. On the other hand, if ``model[1]`` 2024-08-20T21:38:35.1669416Z were replaced with ``model[0]``, meaning that the submodule using 2024-08-20T21:38:35.1669720Z different ``MixedPrecision`` ran its forward first, then ``model[1]`` 2024-08-20T21:38:35.1670008Z would incorrectly see ``float16`` activations instead of ``bfloat16`` 2024-08-20T21:38:35.1670101Z ones. 2024-08-20T21:38:35.1670257Z 2024-08-20T21:38:35.1670343Z 2024-08-20T21:38:35.1670751Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1670850Z 2024-08-20T21:38:35.1670959Z warnings.warn(msg) 2024-08-20T21:38:35.1671058Z 2024-08-20T21:38:35.1671271Z --- Parse Warning: 54 / 101 --- 2024-08-20T21:38:35.1673060Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.set_state_dict_type in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=648. 2024-08-20T21:38:35.1673491Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1673839Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2024-08-20T21:38:35.1673926Z 2024-08-20T21:38:35.1674370Z Also takes (optional) configuration for the model's and optimizer's state dict. 2024-08-20T21:38:35.1674660Z The target module does not have to be a FSDP module. If the target 2024-08-20T21:38:35.1674965Z module is a FSDP module, its ``state_dict_type`` will also be changed. 2024-08-20T21:38:35.1675053Z 2024-08-20T21:38:35.1675395Z .. note:: This API should be called for only the top-level (root) 2024-08-20T21:38:35.1675506Z module. 2024-08-20T21:38:35.1675591Z 2024-08-20T21:38:35.1675887Z .. note:: This API enables users to transparently use the conventional 2024-08-20T21:38:35.1676161Z ``state_dict`` API to take model checkpoints in cases where the 2024-08-20T21:38:35.1676499Z root FSDP module is wrapped by another ``nn.Module``. For example, 2024-08-20T21:38:35.1676856Z the following will ensure ``state_dict`` is called on all non-FSDP 2024-08-20T21:38:35.1677171Z instances, while dispatching into `sharded_state_dict` implementation 2024-08-20T21:38:35.1677278Z for FSDP: 2024-08-20T21:38:35.1677376Z 2024-08-20T21:38:35.1677478Z Example:: 2024-08-20T21:38:35.1677564Z 2024-08-20T21:38:35.1677754Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:38:35.1677885Z >>> model = DDP(FSDP(...)) 2024-08-20T21:38:35.1678025Z >>> FSDP.set_state_dict_type( 2024-08-20T21:38:35.1678141Z >>> model, 2024-08-20T21:38:35.1678319Z >>> StateDictType.SHARDED_STATE_DICT, 2024-08-20T21:38:35.1678605Z >>> state_dict_config = ShardedStateDictConfig(offload_to_cpu=True), 2024-08-20T21:38:35.1678928Z >>> optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True), 2024-08-20T21:38:35.1679026Z >>> ) 2024-08-20T21:38:35.1679195Z >>> param_state_dict = model.state_dict() 2024-08-20T21:38:35.1679433Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2024-08-20T21:38:35.1679523Z 2024-08-20T21:38:35.1679617Z Args: 2024-08-20T21:38:35.1679794Z module (torch.nn.Module): Root module. 2024-08-20T21:38:35.1680100Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2024-08-20T21:38:35.1680423Z state_dict_config (Optional[StateDictConfig]): the configuration for the 2024-08-20T21:38:35.1680565Z target ``state_dict_type``. 2024-08-20T21:38:35.1680896Z optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration 2024-08-20T21:38:35.1681058Z for the optimizer state dict. 2024-08-20T21:38:35.1681144Z 2024-08-20T21:38:35.1681245Z Returns: 2024-08-20T21:38:35.1681545Z A StateDictSettings that include the previous state_dict type and 2024-08-20T21:38:35.1681689Z configuration for the module. 2024-08-20T21:38:35.1681835Z 2024-08-20T21:38:35.1682250Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1682336Z 2024-08-20T21:38:35.1682446Z warnings.warn(msg) 2024-08-20T21:38:35.1682545Z 2024-08-20T21:38:35.1682755Z --- Parse Warning: 55 / 101 --- 2024-08-20T21:38:35.1684534Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.state_dict_type in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=804. 2024-08-20T21:38:35.1684952Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1685305Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2024-08-20T21:38:35.1685405Z 2024-08-20T21:38:35.1685850Z This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of 2024-08-20T21:38:35.1686022Z :meth:`set_state_dict_type` for the detail. 2024-08-20T21:38:35.1686120Z 2024-08-20T21:38:35.1686222Z Example:: 2024-08-20T21:38:35.1686307Z 2024-08-20T21:38:35.1686498Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:38:35.1686629Z >>> model = DDP(FSDP(...)) 2024-08-20T21:38:35.1686775Z >>> with FSDP.state_dict_type( 2024-08-20T21:38:35.1686889Z >>> model, 2024-08-20T21:38:35.1687199Z >>> StateDictType.SHARDED_STATE_DICT, 2024-08-20T21:38:35.1687310Z >>> ): 2024-08-20T21:38:35.1687471Z >>> checkpoint = model.state_dict() 2024-08-20T21:38:35.1687558Z 2024-08-20T21:38:35.1687744Z Args: 2024-08-20T21:38:35.1687911Z module (torch.nn.Module): Root module. 2024-08-20T21:38:35.1688219Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2024-08-20T21:38:35.1688542Z state_dict_config (Optional[StateDictConfig]): the model ``state_dict`` 2024-08-20T21:38:35.1688751Z configuration for the target ``state_dict_type``. 2024-08-20T21:38:35.1689056Z optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer 2024-08-20T21:38:35.1689333Z ``state_dict`` configuration for the target ``state_dict_type``. 2024-08-20T21:38:35.1689425Z 2024-08-20T21:38:35.1689839Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1689939Z 2024-08-20T21:38:35.1690054Z warnings.warn(msg) 2024-08-20T21:38:35.1690156Z 2024-08-20T21:38:35.1690373Z --- Parse Warning: 56 / 101 --- 2024-08-20T21:38:35.1692156Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.optim_state_dict in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1801. 2024-08-20T21:38:35.1692590Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1692676Z 2024-08-20T21:38:35.1693073Z Transform the state-dict of an optimizer corresponding to a sharded model. 2024-08-20T21:38:35.1693174Z 2024-08-20T21:38:35.1693494Z The given state-dict can be transformed to one of three types: 2024-08-20T21:38:35.1693896Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict. 2024-08-20T21:38:35.1693995Z 2024-08-20T21:38:35.1694312Z For full optimizer state_dict, all states are unflattened and not sharded. 2024-08-20T21:38:35.1694626Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to 2024-08-20T21:38:35.1694724Z avoid OOM. 2024-08-20T21:38:35.1694814Z 2024-08-20T21:38:35.1695137Z For sharded optimizer state_dict, all states are unflattened but sharded. 2024-08-20T21:38:35.1695504Z CPU only can be specified via :meth:`state_dict_type` to further save 2024-08-20T21:38:35.1695598Z memory. 2024-08-20T21:38:35.1695696Z 2024-08-20T21:38:35.1695992Z For local state_dict, no transformation will be performed. But a state 2024-08-20T21:38:35.1696315Z will be converted from nn.Tensor to ShardedTensor to represent its sharding 2024-08-20T21:38:35.1696464Z nature (this is not supported yet). 2024-08-20T21:38:35.1696550Z 2024-08-20T21:38:35.1696650Z Example:: 2024-08-20T21:38:35.1696748Z 2024-08-20T21:38:35.1696918Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:38:35.1697234Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2024-08-20T21:38:35.1697464Z >>> from torch.distributed.fsdp import StateDictType 2024-08-20T21:38:35.1697702Z >>> from torch.distributed.fsdp import FullStateDictConfig 2024-08-20T21:38:35.1697979Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2024-08-20T21:38:35.1698098Z >>> # Save a checkpoint 2024-08-20T21:38:35.1698212Z >>> model, optim = ... 2024-08-20T21:38:35.1698350Z >>> FSDP.set_state_dict_type( 2024-08-20T21:38:35.1698446Z >>> model, 2024-08-20T21:38:35.1698600Z >>> StateDictType.FULL_STATE_DICT, 2024-08-20T21:38:35.1698785Z >>> FullStateDictConfig(rank0_only=False), 2024-08-20T21:38:35.1698980Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-08-20T21:38:35.1699073Z >>> ) 2024-08-20T21:38:35.1699224Z >>> state_dict = model.state_dict() 2024-08-20T21:38:35.1699443Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2024-08-20T21:38:35.1699714Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2024-08-20T21:38:35.1699840Z >>> # Load a checkpoint 2024-08-20T21:38:35.1699951Z >>> model, optim = ... 2024-08-20T21:38:35.1700149Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2024-08-20T21:38:35.1700288Z >>> FSDP.set_state_dict_type( 2024-08-20T21:38:35.1700385Z >>> model, 2024-08-20T21:38:35.1700548Z >>> StateDictType.FULL_STATE_DICT, 2024-08-20T21:38:35.1700719Z >>> FullStateDictConfig(rank0_only=False), 2024-08-20T21:38:35.1700910Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-08-20T21:38:35.1701014Z >>> ) 2024-08-20T21:38:35.1701159Z >>> model.load_state_dict(state_dict) 2024-08-20T21:38:35.1701355Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2024-08-20T21:38:35.1701510Z >>> model, optim, optim_state_dict 2024-08-20T21:38:35.1701600Z >>> ) 2024-08-20T21:38:35.1701762Z >>> optim.load_state_dict(optim_state_dict) 2024-08-20T21:38:35.1701861Z 2024-08-20T21:38:35.1701953Z Args: 2024-08-20T21:38:35.1702217Z model (torch.nn.Module): Root module (which may or may not be a 2024-08-20T21:38:35.1702492Z :class:`FullyShardedDataParallel` instance) whose parameters 2024-08-20T21:38:35.1702666Z were passed into the optimizer ``optim``. 2024-08-20T21:38:35.1702944Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2024-08-20T21:38:35.1703061Z parameters. 2024-08-20T21:38:35.1703343Z optim_state_dict (Dict[str, Any]): the target optimizer state_dict to 2024-08-20T21:38:35.1703636Z transform. If the value is None, optim.state_dict() will be used. ( 2024-08-20T21:38:35.1703747Z Default: ``None``) 2024-08-20T21:38:35.1704139Z group (dist.ProcessGroup): Model's process group across which parameters 2024-08-20T21:38:35.1704405Z are sharded or ``None`` if using the default process group. ( 2024-08-20T21:38:35.1704520Z Default: ``None``) 2024-08-20T21:38:35.1704609Z 2024-08-20T21:38:35.1704715Z Returns: 2024-08-20T21:38:35.1704984Z Dict[str, Any]: A :class:`dict` containing the optimizer state for 2024-08-20T21:38:35.1705263Z ``model``. The sharding of the optimizer state is based on 2024-08-20T21:38:35.1705383Z ``state_dict_type``. 2024-08-20T21:38:35.1705470Z 2024-08-20T21:38:35.1705872Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1705970Z 2024-08-20T21:38:35.1706077Z warnings.warn(msg) 2024-08-20T21:38:35.1706163Z 2024-08-20T21:38:35.1706390Z --- Parse Warning: 57 / 101 --- 2024-08-20T21:38:35.1708205Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.optim_state_dict_to_load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1899. 2024-08-20T21:38:35.1708632Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1708718Z 2024-08-20T21:38:35.1709321Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model. 2024-08-20T21:38:35.1709426Z 2024-08-20T21:38:35.1709643Z Given a ``optim_state_dict`` that is transformed through 2024-08-20T21:38:35.1709924Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer 2024-08-20T21:38:35.1710226Z state_dict that can be loaded to ``optim`` which is the optimizer for 2024-08-20T21:38:35.1710480Z ``model``. ``model`` must be sharded by FullyShardedDataParallel. 2024-08-20T21:38:35.1710579Z 2024-08-20T21:38:35.1710747Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:38:35.1711064Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2024-08-20T21:38:35.1711339Z >>> from torch.distributed.fsdp import StateDictType 2024-08-20T21:38:35.1711579Z >>> from torch.distributed.fsdp import FullStateDictConfig 2024-08-20T21:38:35.1711846Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2024-08-20T21:38:35.1711979Z >>> # Save a checkpoint 2024-08-20T21:38:35.1712090Z >>> model, optim = ... 2024-08-20T21:38:35.1712216Z >>> FSDP.set_state_dict_type( 2024-08-20T21:38:35.1712324Z >>> model, 2024-08-20T21:38:35.1712477Z >>> StateDictType.FULL_STATE_DICT, 2024-08-20T21:38:35.1712651Z >>> FullStateDictConfig(rank0_only=False), 2024-08-20T21:38:35.1712858Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-08-20T21:38:35.1712950Z >>> ) 2024-08-20T21:38:35.1713088Z >>> state_dict = model.state_dict() 2024-08-20T21:38:35.1713246Z >>> original_osd = optim.state_dict() 2024-08-20T21:38:35.1713413Z >>> optim_state_dict = FSDP.optim_state_dict( 2024-08-20T21:38:35.1713528Z >>> model, 2024-08-20T21:38:35.1713625Z >>> optim, 2024-08-20T21:38:35.1713767Z >>> optim_state_dict=original_osd 2024-08-20T21:38:35.1713869Z >>> ) 2024-08-20T21:38:35.1714058Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2024-08-20T21:38:35.1714176Z >>> # Load a checkpoint 2024-08-20T21:38:35.1714299Z >>> model, optim = ... 2024-08-20T21:38:35.1714498Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2024-08-20T21:38:35.1714623Z >>> FSDP.set_state_dict_type( 2024-08-20T21:38:35.1714730Z >>> model, 2024-08-20T21:38:35.1714880Z >>> StateDictType.FULL_STATE_DICT, 2024-08-20T21:38:35.1715050Z >>> FullStateDictConfig(rank0_only=False), 2024-08-20T21:38:35.1715255Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-08-20T21:38:35.1715346Z >>> ) 2024-08-20T21:38:35.1715490Z >>> model.load_state_dict(state_dict) 2024-08-20T21:38:35.1715699Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2024-08-20T21:38:35.1715842Z >>> model, optim, optim_state_dict 2024-08-20T21:38:35.1715944Z >>> ) 2024-08-20T21:38:35.1716102Z >>> optim.load_state_dict(optim_state_dict) 2024-08-20T21:38:35.1716240Z 2024-08-20T21:38:35.1716343Z Args: 2024-08-20T21:38:35.1716607Z model (torch.nn.Module): Root module (which may or may not be a 2024-08-20T21:38:35.1716868Z :class:`FullyShardedDataParallel` instance) whose parameters 2024-08-20T21:38:35.1717051Z were passed into the optimizer ``optim``. 2024-08-20T21:38:35.1717327Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2024-08-20T21:38:35.1717431Z parameters. 2024-08-20T21:38:35.1717729Z optim_state_dict (Dict[str, Any]): The optimizer states to be loaded. 2024-08-20T21:38:35.1717989Z is_named_optimizer (bool): Is this optimizer a NamedOptimizer or 2024-08-20T21:38:35.1718279Z KeyedOptimizer. Only set to True if ``optim`` is TorchRec's 2024-08-20T21:38:35.1718584Z KeyedOptimizer or torch.distributed's NamedOptimizer. 2024-08-20T21:38:35.1718845Z load_directly (bool): If this is set to True, this API will also 2024-08-20T21:38:35.1719124Z call optim.load_state_dict(result) before returning the result. 2024-08-20T21:38:35.1719414Z Otherwise, users are responsible to call ``optim.load_state_dict()`` 2024-08-20T21:38:35.1719526Z (Default: ``False``) 2024-08-20T21:38:35.1719926Z group (dist.ProcessGroup): Model's process group across which parameters 2024-08-20T21:38:35.1720178Z are sharded or ``None`` if using the default process group. ( 2024-08-20T21:38:35.1720289Z Default: ``None``) 2024-08-20T21:38:35.1720388Z 2024-08-20T21:38:35.1720788Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1720875Z 2024-08-20T21:38:35.1720997Z warnings.warn(msg) 2024-08-20T21:38:35.1721135Z 2024-08-20T21:38:35.1721423Z --- Parse Warning: 58 / 101 --- 2024-08-20T21:38:35.1734458Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_RemoteModule.__init__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=137. 2024-08-20T21:38:35.1735015Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1735129Z 2024-08-20T21:38:35.1735429Z RemoteModule instance can only be created after RPC initialization. 2024-08-20T21:38:35.1735521Z 2024-08-20T21:38:35.1735869Z It creates a user-specified module on a specified remote node. 2024-08-20T21:38:35.1736201Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2024-08-20T21:38:35.1736331Z executed on the remote node. 2024-08-20T21:38:35.1736663Z It takes care of autograd recording to ensure the backward pass propagates 2024-08-20T21:38:35.1736869Z gradients back to the corresponding remote module. 2024-08-20T21:38:35.1737369Z It can be shared across processors using `RPC framework `__, 2024-08-20T21:38:35.1737661Z without incurring any overheads of copying the actual module, 2024-08-20T21:38:35.1737935Z which is equivalent to an :class:`~torch.distributed.rpc.RRef` 2024-08-20T21:38:35.1738080Z pointing to the remote module. 2024-08-20T21:38:35.1738173Z 2024-08-20T21:38:35.1738439Z The arguments of ``forward_async`` and ``forward`` are the same as 2024-08-20T21:38:35.1738724Z the ``forward`` method of the module returned by the ``module_cls``. 2024-08-20T21:38:35.1738814Z 2024-08-20T21:38:35.1739247Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now. 2024-08-20T21:38:35.1739352Z 2024-08-20T21:38:35.1739701Z Particularly, to create a hybrid model, typically the local modules should be 2024-08-20T21:38:35.1740222Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``). 2024-08-20T21:38:35.1740344Z Hybrid Example: 2024-08-20T21:38:35.1740496Z >>> class HybridModel(nn.Module): 2024-08-20T21:38:35.1740842Z >>> def __init__(self) -> None: 2024-08-20T21:38:35.1741005Z >>> nn.Module.__init__(self) 2024-08-20T21:38:35.1741197Z >>> self.remote_embedding = RemoteModule(...) 2024-08-20T21:38:35.1741384Z >>> self.local_linear = nn.Linear(...) 2024-08-20T21:38:35.1741476Z 2024-08-20T21:38:35.1741756Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2024-08-20T21:38:35.1742197Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``, 2024-08-20T21:38:35.1742476Z the generated ``RemoteModule`` will have 2 methods in signature of 2024-08-20T21:38:35.1742703Z ``def forward(input: Tensor) -> Tensor:`` and 2024-08-20T21:38:35.1742996Z ``def forward_async(input: Tensor) -> Future[Tensor]:``. 2024-08-20T21:38:35.1743087Z 2024-08-20T21:38:35.1743198Z .. note:: 2024-08-20T21:38:35.1743407Z If the remote module is placed on a cuda device, 2024-08-20T21:38:35.1743739Z any input CPU tensors will be automatically moved to the same cuda device, 2024-08-20T21:38:35.1744314Z and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend. 2024-08-20T21:38:35.1744420Z 2024-08-20T21:38:35.1744517Z Args: 2024-08-20T21:38:35.1745031Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-08-20T21:38:35.1745464Z The device can be a local device or a remote device specified by one of the following remote 2024-08-20T21:38:35.1745569Z formats: 2024-08-20T21:38:35.1745674Z 2024-08-20T21:38:35.1745927Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2024-08-20T21:38:35.1746133Z 2. "/" (ex: "trainer0/cuda:0"). 2024-08-20T21:38:35.1746238Z 2024-08-20T21:38:35.1746582Z In addition, the device field can be optional and the default value is "cpu". 2024-08-20T21:38:35.1746998Z module_cls (nn.Module): For example, 2024-08-20T21:38:35.1747156Z >>> class MyModule(nn.Module): 2024-08-20T21:38:35.1747283Z >>> def forward(input): 2024-08-20T21:38:35.1747412Z >>> return input + 1 2024-08-20T21:38:35.1747523Z >>> 2024-08-20T21:38:35.1747646Z >>> module_cls = MyModule 2024-08-20T21:38:35.1747909Z args (Sequence, optional): args to be passed to ``module_cls``. 2024-08-20T21:38:35.1748182Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2024-08-20T21:38:35.1748551Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2024-08-20T21:38:35.1748909Z to be created. The type object should be decorated by @torch.jit.interface. 2024-08-20T21:38:35.1749309Z If not provided, the generated RemoteModule is not torchscript-able. 2024-08-20T21:38:35.1749641Z Warning, this is an experimental API and susceptible to frequent changes. 2024-08-20T21:38:35.1749748Z 2024-08-20T21:38:35.1749845Z Returns: 2024-08-20T21:38:35.1750176Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-08-20T21:38:35.1750580Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2024-08-20T21:38:35.1750936Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-08-20T21:38:35.1751198Z on the user-provided module on the remote side. 2024-08-20T21:38:35.1751290Z 2024-08-20T21:38:35.1751397Z Example:: 2024-08-20T21:38:35.1751616Z Run the following code in two different processes: 2024-08-20T21:38:35.1751714Z 2024-08-20T21:38:35.1751864Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:38:35.1751991Z >>> # On worker 0: 2024-08-20T21:38:35.1752100Z >>> import torch 2024-08-20T21:38:35.1752267Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:38:35.1752542Z >>> from torch import nn, Tensor 2024-08-20T21:38:35.1752843Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-08-20T21:38:35.1752940Z >>> 2024-08-20T21:38:35.1753140Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-08-20T21:38:35.1753299Z >>> remote_linear_module = RemoteModule( 2024-08-20T21:38:35.1753471Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-08-20T21:38:35.1753585Z >>> ) 2024-08-20T21:38:35.1753717Z >>> input = torch.randn(128, 20) 2024-08-20T21:38:35.1753924Z >>> ret_fut = remote_linear_module.forward_async(input) 2024-08-20T21:38:35.1754057Z >>> ret = ret_fut.wait() 2024-08-20T21:38:35.1754174Z >>> rpc.shutdown() 2024-08-20T21:38:35.1754264Z 2024-08-20T21:38:35.1754388Z >>> # On worker 1: 2024-08-20T21:38:35.1754495Z >>> import torch 2024-08-20T21:38:35.1754675Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:38:35.1754798Z >>> 2024-08-20T21:38:35.1754980Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-08-20T21:38:35.1755106Z >>> rpc.shutdown() 2024-08-20T21:38:35.1755197Z 2024-08-20T21:38:35.1755616Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1755719Z 2024-08-20T21:38:35.1755834Z warnings.warn(msg) 2024-08-20T21:38:35.1755924Z 2024-08-20T21:38:35.1756158Z --- Parse Warning: 59 / 101 --- 2024-08-20T21:38:35.1757900Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_RemoteModule.init_from_module_rref in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=514. 2024-08-20T21:38:35.1758448Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1763404Z 2024-08-20T21:38:35.1763841Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef. 2024-08-20T21:38:35.1763947Z 2024-08-20T21:38:35.1764381Z This alternate initialization method can be particularly useful if we want to create multiple 2024-08-20T21:38:35.1764797Z RemoteModule instances that share the same underlying module and reduce memory consumption. 2024-08-20T21:38:35.1764897Z 2024-08-20T21:38:35.1765265Z Moreover, this also provides a workaround for passing script RemoteModule over RPC, 2024-08-20T21:38:35.1765500Z which is not supported. The recommended way is as follows: 2024-08-20T21:38:35.1765600Z 2024-08-20T21:38:35.1765753Z 1. the sender creates a RemoteModule; 2024-08-20T21:38:35.1765951Z 2. the sender sends its ``module_rref`` over RPC; 2024-08-20T21:38:35.1766423Z 3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``. 2024-08-20T21:38:35.1766508Z 2024-08-20T21:38:35.1766608Z Example:: 2024-08-20T21:38:35.1766826Z Run the following code in two different processes: 2024-08-20T21:38:35.1767007Z 2024-08-20T21:38:35.1767172Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:38:35.1767280Z >>> # On worker 0: 2024-08-20T21:38:35.1767386Z >>> import torch 2024-08-20T21:38:35.1767558Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:38:35.1767685Z >>> from torch import nn, Tensor 2024-08-20T21:38:35.1767971Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-08-20T21:38:35.1768066Z >>> 2024-08-20T21:38:35.1768235Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-08-20T21:38:35.1768366Z >>> remote_module = RemoteModule( 2024-08-20T21:38:35.1768537Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-08-20T21:38:35.1768621Z >>> ) 2024-08-20T21:38:35.1768705Z >>> 2024-08-20T21:38:35.1768841Z >>> remote_module1 = rpc.rpc_sync( 2024-08-20T21:38:35.1769012Z >>> "worker1/cpu", 2024-08-20T21:38:35.1769165Z >>> RemoteModule.init_from_module_rref, 2024-08-20T21:38:35.1769365Z >>> ("worker1/cpu", remote_module1.get_module_rref()), 2024-08-20T21:38:35.1769449Z >>> ) 2024-08-20T21:38:35.1769557Z >>> rpc.shutdown() 2024-08-20T21:38:35.1769637Z 2024-08-20T21:38:35.1769735Z >>> # On worker 1: 2024-08-20T21:38:35.1769837Z >>> import torch 2024-08-20T21:38:35.1769992Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:38:35.1770075Z >>> 2024-08-20T21:38:35.1770252Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-08-20T21:38:35.1770352Z >>> rpc.shutdown() 2024-08-20T21:38:35.1770432Z 2024-08-20T21:38:35.1770528Z Args: 2024-08-20T21:38:35.1771032Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-08-20T21:38:35.1771456Z The device can be a local device or a remote device specified by one of the following remote 2024-08-20T21:38:35.1771565Z formats: 2024-08-20T21:38:35.1771643Z 2024-08-20T21:38:35.1771823Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2024-08-20T21:38:35.1772021Z 2. "/" (ex: "trainer0/cuda:0"). 2024-08-20T21:38:35.1772100Z 2024-08-20T21:38:35.1772433Z In addition, the device field can be optional and the default value is "cpu". 2024-08-20T21:38:35.1772775Z module_rref (RRef[nn.Module]): The module reference shared by both the caller and 2024-08-20T21:38:35.1772894Z the created remote module. 2024-08-20T21:38:35.1773256Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2024-08-20T21:38:35.1773638Z to be created. The type object should be decorated by @torch.jit.interface. 2024-08-20T21:38:35.1774013Z If not provided, the generated RemoteModule is not torchscript-able. 2024-08-20T21:38:35.1774340Z Warning, this is an experimental API and susceptible to frequent changes. 2024-08-20T21:38:35.1774421Z 2024-08-20T21:38:35.1774509Z Returns: 2024-08-20T21:38:35.1774832Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-08-20T21:38:35.1775212Z user-provided ``module_rref``, it has a blocking ``forward`` method and an 2024-08-20T21:38:35.1775566Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-08-20T21:38:35.1775803Z on the user-provided module on the remote side. 2024-08-20T21:38:35.1775882Z 2024-08-20T21:38:35.1776283Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1776363Z 2024-08-20T21:38:35.1776469Z warnings.warn(msg) 2024-08-20T21:38:35.1776552Z 2024-08-20T21:38:35.1776755Z --- Parse Warning: 60 / 101 --- 2024-08-20T21:38:35.1778234Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RemoteModule in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=606. 2024-08-20T21:38:35.1778656Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1778737Z 2024-08-20T21:38:35.1779033Z A RemoteModule instance can only be created after RPC initialization. 2024-08-20T21:38:35.1779120Z 2024-08-20T21:38:35.1779449Z It creates a user-specified module on a specified remote node. 2024-08-20T21:38:35.1779776Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2024-08-20T21:38:35.1779895Z executed on the remote node. 2024-08-20T21:38:35.1780215Z It takes care of autograd recording to ensure the backward pass propagates 2024-08-20T21:38:35.1780417Z gradients back to the corresponding remote module. 2024-08-20T21:38:35.1780497Z 2024-08-20T21:38:35.1780784Z It generates two methods ``forward_async`` and ``forward`` based on the 2024-08-20T21:38:35.1781150Z signature of the ``forward`` method of ``module_cls``. ``forward_async`` 2024-08-20T21:38:35.1781475Z runs asynchronously and returns a Future. The arguments of ``forward_async`` 2024-08-20T21:38:35.1781741Z and ``forward`` are the same as the ``forward`` method of the module 2024-08-20T21:38:35.1781870Z returned by the ``module_cls``. 2024-08-20T21:38:35.1781948Z 2024-08-20T21:38:35.1782218Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2024-08-20T21:38:35.1782644Z that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``, 2024-08-20T21:38:35.1782942Z the generated ``RemoteModule`` will have 2 methods with the signatures: 2024-08-20T21:38:35.1783029Z 2024-08-20T21:38:35.1783238Z | ``def forward(input: Tensor) -> Tensor:`` 2024-08-20T21:38:35.1783505Z | ``def forward_async(input: Tensor) -> Future[Tensor]:`` 2024-08-20T21:38:35.1783603Z 2024-08-20T21:38:35.1783689Z Args: 2024-08-20T21:38:35.1784176Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-08-20T21:38:35.1784641Z The format should be "/", where the device field can be parsed as torch.device type. 2024-08-20T21:38:35.1784819Z E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0". 2024-08-20T21:38:35.1785152Z In addition, the device field can be optional and the default value is "cpu". 2024-08-20T21:38:35.1785500Z module_cls (nn.Module): Class for the module to be created remotely. For example, 2024-08-20T21:38:35.1785577Z 2024-08-20T21:38:35.1785769Z >>> class MyModule(nn.Module): 2024-08-20T21:38:35.1785885Z >>> def forward(input): 2024-08-20T21:38:35.1806646Z >>> return input + 1 2024-08-20T21:38:35.1806766Z >>> 2024-08-20T21:38:35.1806891Z >>> module_cls = MyModule 2024-08-20T21:38:35.1807058Z 2024-08-20T21:38:35.1807324Z args (Sequence, optional): args to be passed to ``module_cls``. 2024-08-20T21:38:35.1807576Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2024-08-20T21:38:35.1807656Z 2024-08-20T21:38:35.1807751Z Returns: 2024-08-20T21:38:35.1808068Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-08-20T21:38:35.1808469Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2024-08-20T21:38:35.1808823Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-08-20T21:38:35.1809064Z on the user-provided module on the remote side. 2024-08-20T21:38:35.1809148Z 2024-08-20T21:38:35.1809256Z Example:: 2024-08-20T21:38:35.1809452Z Run the following code in two different processes: 2024-08-20T21:38:35.1809540Z 2024-08-20T21:38:35.1809681Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:38:35.1809785Z >>> # On worker 0: 2024-08-20T21:38:35.1809904Z >>> import torch 2024-08-20T21:38:35.1810066Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:38:35.1810199Z >>> from torch import nn, Tensor 2024-08-20T21:38:35.1810503Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-08-20T21:38:35.1810594Z >>> 2024-08-20T21:38:35.1810774Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-08-20T21:38:35.1810943Z >>> remote_linear_module = RemoteModule( 2024-08-20T21:38:35.1811112Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-08-20T21:38:35.1811204Z >>> ) 2024-08-20T21:38:35.1811350Z >>> input = torch.randn(128, 20) 2024-08-20T21:38:35.1811554Z >>> ret_fut = remote_linear_module.forward_async(input) 2024-08-20T21:38:35.1811669Z >>> ret = ret_fut.wait() 2024-08-20T21:38:35.1811788Z >>> rpc.shutdown() 2024-08-20T21:38:35.1812002Z 2024-08-20T21:38:35.1812111Z >>> # On worker 1: 2024-08-20T21:38:35.1812228Z >>> import torch 2024-08-20T21:38:35.1812390Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:38:35.1812495Z >>> 2024-08-20T21:38:35.1812672Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-08-20T21:38:35.1812778Z >>> rpc.shutdown() 2024-08-20T21:38:35.1812878Z 2024-08-20T21:38:35.1813123Z Furthermore, a more practical example that is combined with 2024-08-20T21:38:35.1813751Z `DistributedDataParallel `__ (DDP) 2024-08-20T21:38:35.1814220Z can be found in this `tutorial `__. 2024-08-20T21:38:35.1814307Z 2024-08-20T21:38:35.1814716Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1814814Z 2024-08-20T21:38:35.1814924Z warnings.warn(msg) 2024-08-20T21:38:35.1815015Z 2024-08-20T21:38:35.1815238Z --- Parse Warning: 61 / 101 --- 2024-08-20T21:38:35.1816757Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/optimizer.py line=130. 2024-08-20T21:38:35.1817191Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1817278Z 2024-08-20T21:38:35.1817576Z DistributedOptimizer takes remote references to parameters scattered 2024-08-20T21:38:35.1817916Z across workers and applies the given optimizer locally for each parameter. 2024-08-20T21:38:35.1818060Z 2024-08-20T21:38:35.1818386Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order 2024-08-20T21:38:35.1818594Z to retrieve the gradients for specific parameters. 2024-08-20T21:38:35.1818682Z 2024-08-20T21:38:35.1818798Z Concurrent calls to 2024-08-20T21:38:35.1819093Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`, 2024-08-20T21:38:35.1819271Z either from the same or different clients, will 2024-08-20T21:38:35.1819653Z be serialized on each worker -- as each worker's optimizer can only work 2024-08-20T21:38:35.1819971Z on one set of gradients at a time. However, there is no guarantee that 2024-08-20T21:38:35.1820371Z the full forward-backward-optimizer sequence will execute for one client 2024-08-20T21:38:35.1820696Z at a time. This means that the gradients being applied may not correspond 2024-08-20T21:38:35.1821000Z to the latest forward pass executed on a given worker. Also, there is no 2024-08-20T21:38:35.1821143Z guaranteed ordering across workers. 2024-08-20T21:38:35.1821242Z 2024-08-20T21:38:35.1821572Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled 2024-08-20T21:38:35.1821887Z by default, so that optimizer updates are not blocked by the Python Global 2024-08-20T21:38:35.1822240Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed 2024-08-20T21:38:35.1822559Z Model Parallel). This feature is currently enabled for most optimizers. You 2024-08-20T21:38:35.1822907Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support 2024-08-20T21:38:35.1823052Z for your own custom optimizers. 2024-08-20T21:38:35.1823138Z 2024-08-20T21:38:35.1823242Z Args: 2024-08-20T21:38:35.1823498Z optimizer_class (optim.Optimizer): the class of optimizer to 2024-08-20T21:38:35.1823627Z instantiate on each worker. 2024-08-20T21:38:35.1823930Z params_rref (list[RRef]): list of RRefs to local or remote parameters 2024-08-20T21:38:35.1824033Z to optimize. 2024-08-20T21:38:35.1824323Z args: arguments to pass to the optimizer constructor on each worker. 2024-08-20T21:38:35.1824635Z kwargs: arguments to pass to the optimizer constructor on each worker. 2024-08-20T21:38:35.1824819Z 2024-08-20T21:38:35.1824934Z Example:: 2024-08-20T21:38:35.1825096Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:38:35.1825316Z >>> import torch.distributed.autograd as dist_autograd 2024-08-20T21:38:35.1825479Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:38:35.1825616Z >>> from torch import optim 2024-08-20T21:38:35.1825865Z >>> from torch.distributed.optim import DistributedOptimizer 2024-08-20T21:38:35.1825958Z >>> 2024-08-20T21:38:35.1826149Z >>> with dist_autograd.context() as context_id: 2024-08-20T21:38:35.1826260Z >>> # Forward pass. 2024-08-20T21:38:35.1826551Z >>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3)) 2024-08-20T21:38:35.1826816Z >>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1)) 2024-08-20T21:38:35.1826980Z >>> loss = rref1.to_here() + rref2.to_here() 2024-08-20T21:38:35.1827092Z >>> 2024-08-20T21:38:35.1827206Z >>> # Backward pass. 2024-08-20T21:38:35.1827407Z >>> dist_autograd.backward(context_id, [loss.sum()]) 2024-08-20T21:38:35.1827509Z >>> 2024-08-20T21:38:35.1827615Z >>> # Optimizer. 2024-08-20T21:38:35.1827777Z >>> dist_optim = DistributedOptimizer( 2024-08-20T21:38:35.1827893Z >>> optim.SGD, 2024-08-20T21:38:35.1828001Z >>> [rref1, rref2], 2024-08-20T21:38:35.1828100Z >>> lr=0.05, 2024-08-20T21:38:35.1848844Z >>> ) 2024-08-20T21:38:35.1849016Z >>> dist_optim.step(context_id) 2024-08-20T21:38:35.1849104Z 2024-08-20T21:38:35.1849317Z __ https://github.com/pytorch/tutorials/pull/1465 2024-08-20T21:38:35.1849548Z 2024-08-20T21:38:35.1850003Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1850105Z 2024-08-20T21:38:35.1850214Z warnings.warn(msg) 2024-08-20T21:38:35.1850318Z 2024-08-20T21:38:35.1850536Z --- Parse Warning: 62 / 101 --- 2024-08-20T21:38:35.1852157Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PostLocalSGDOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/post_localSGD_optimizer.py line=9. 2024-08-20T21:38:35.1852586Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1852674Z 2024-08-20T21:38:35.1853313Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD `_, 2024-08-20T21:38:35.1853524Z This optimizer runs local optimizer at every step. 2024-08-20T21:38:35.1854068Z After the warm-up stage, it averages parameters periodically afer the local optimizer is applied. 2024-08-20T21:38:35.1854157Z 2024-08-20T21:38:35.1854262Z Args: 2024-08-20T21:38:35.1854390Z optim: The local optimizer. 2024-08-20T21:38:35.1854768Z averager: A model averager instance to run post-localSGD algorithm. 2024-08-20T21:38:35.1854856Z 2024-08-20T21:38:35.1854960Z Example:: 2024-08-20T21:38:35.1855059Z 2024-08-20T21:38:35.1855229Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:38:35.1855335Z >>> import torch 2024-08-20T21:38:35.1855497Z >>> import torch.distributed as dist 2024-08-20T21:38:35.1855861Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2024-08-20T21:38:35.1855981Z >>> import torch.nn as nn 2024-08-20T21:38:35.1856252Z >>> from torch.distributed.optim import PostLocalSGDOptimizer 2024-08-20T21:38:35.1856617Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2024-08-20T21:38:35.1856740Z >>> PostLocalSGDState, 2024-08-20T21:38:35.1856867Z >>> post_localSGD_hook, 2024-08-20T21:38:35.1856959Z >>> ) 2024-08-20T21:38:35.1857050Z >>> 2024-08-20T21:38:35.1857392Z >>> model = nn.parallel.DistributedDataParallel( 2024-08-20T21:38:35.1857587Z >>> module, device_ids=[rank], output_device=rank 2024-08-20T21:38:35.1857678Z >>> ) 2024-08-20T21:38:35.1857784Z >>> 2024-08-20T21:38:35.1858046Z >>> # Register a post-localSGD communication hook. 2024-08-20T21:38:35.1858444Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2024-08-20T21:38:35.1858652Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-08-20T21:38:35.1858744Z >>> 2024-08-20T21:38:35.1859107Z >>> # Create a post-localSGD optimizer that wraps a local optimizer. 2024-08-20T21:38:35.1859463Z >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as 2024-08-20T21:38:35.1859681Z >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-08-20T21:38:35.1859967Z >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01) 2024-08-20T21:38:35.1860116Z >>> opt = PostLocalSGDOptimizer( 2024-08-20T21:38:35.1860232Z >>> optim=local_optim, 2024-08-20T21:38:35.1860566Z >>> averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2024-08-20T21:38:35.1860659Z >>> ) 2024-08-20T21:38:35.1860761Z >>> 2024-08-20T21:38:35.1861081Z >>> # In the first 100 steps, DDP runs global gradient averaging at every step. 2024-08-20T21:38:35.1861592Z >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default), 2024-08-20T21:38:35.1862217Z >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer. 2024-08-20T21:38:35.1862406Z >>> for step in range(0, 200): 2024-08-20T21:38:35.1862518Z >>> opt.zero_grad() 2024-08-20T21:38:35.1862673Z >>> loss = loss_fn(output, labels) 2024-08-20T21:38:35.1862784Z >>> loss.backward() 2024-08-20T21:38:35.1862893Z >>> opt.step() 2024-08-20T21:38:35.1862992Z 2024-08-20T21:38:35.1863392Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1863479Z 2024-08-20T21:38:35.1863602Z warnings.warn(msg) 2024-08-20T21:38:35.1863687Z 2024-08-20T21:38:35.1863895Z --- Parse Warning: 63 / 101 --- 2024-08-20T21:38:35.1865564Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ZeroRedundancyOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/zero_redundancy_optimizer.py line=282. 2024-08-20T21:38:35.1865983Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1866084Z 2024-08-20T21:38:35.1866627Z Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group. 2024-08-20T21:38:35.1866715Z 2024-08-20T21:38:35.1866892Z The sharing is done as described by ZeRO_. 2024-08-20T21:38:35.1866979Z 2024-08-20T21:38:35.1867167Z The local optimizer instance in each rank is only 2024-08-20T21:38:35.1867494Z responsible for updating approximately ``1 / world_size`` parameters and 2024-08-20T21:38:35.1867770Z hence only needs to keep ``1 / world_size`` optimizer states. After 2024-08-20T21:38:35.1868096Z parameters are updated locally, each rank will broadcast its parameters to 2024-08-20T21:38:35.1868360Z all other peers to keep all model replicas in the same state. 2024-08-20T21:38:35.1868606Z ``ZeroRedundancyOptimizer`` can be used in conjunction with 2024-08-20T21:38:35.1869050Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak 2024-08-20T21:38:35.1869160Z memory consumption. 2024-08-20T21:38:35.1869247Z 2024-08-20T21:38:35.1869661Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number 2024-08-20T21:38:35.1869981Z of parameters at each rank. Each parameter belongs to a single rank and is 2024-08-20T21:38:35.1870381Z not divided among ranks. The partition is arbitrary and might not match the 2024-08-20T21:38:35.1870559Z the parameter registration or usage order. 2024-08-20T21:38:35.1870646Z 2024-08-20T21:38:35.1870746Z Arguments: 2024-08-20T21:38:35.1871016Z params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s 2024-08-20T21:38:35.1871274Z or :class:`dict` s giving all parameters, which will be sharded 2024-08-20T21:38:35.1871382Z across ranks. 2024-08-20T21:38:35.1871486Z 2024-08-20T21:38:35.1871587Z Keyword Args: 2024-08-20T21:38:35.1871896Z optimizer_class (:class:`torch.nn.Optimizer`): the class of the local 2024-08-20T21:38:35.1871996Z optimizer. 2024-08-20T21:38:35.1872269Z process_group (``ProcessGroup``, optional): ``torch.distributed`` 2024-08-20T21:38:35.1872537Z ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by 2024-08-20T21:38:35.1872729Z :meth:`torch.distributed.init_process_group`). 2024-08-20T21:38:35.1873024Z parameters_as_bucket_view (bool, optional): if ``True``, parameters are 2024-08-20T21:38:35.1873323Z packed into buckets to speed up communication, and ``param.data`` 2024-08-20T21:38:35.1873597Z fields point to bucket views at different offsets; if ``False``, 2024-08-20T21:38:35.1873868Z each individual parameter is communicated separately, and each 2024-08-20T21:38:35.1874077Z ``params.data`` stays intact (default: ``False``). 2024-08-20T21:38:35.1874331Z overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is 2024-08-20T21:38:35.1895377Z overlapped with :class:`DistributedDataParallel` 's gradient 2024-08-20T21:38:35.1895705Z synchronization; this requires (1) either a functional optimizer 2024-08-20T21:38:35.1895953Z for the ``optimizer_class`` argument or one with a functional 2024-08-20T21:38:35.1896199Z equivalent and (2) registering a DDP communication hook 2024-08-20T21:38:35.1896461Z constructed from one of the functions in ``ddp_zero_hook.py``; 2024-08-20T21:38:35.1896676Z parameters are packed into buckets matching those in 2024-08-20T21:38:35.1896901Z :class:`DistributedDataParallel`, meaning that the 2024-08-20T21:38:35.1897096Z ``parameters_as_bucket_view`` argument is ignored. 2024-08-20T21:38:35.1897356Z If ``False``, :meth:`step` runs disjointly after the backward pass 2024-08-20T21:38:35.1897474Z (per normal). 2024-08-20T21:38:35.1897585Z (default: ``False``) 2024-08-20T21:38:35.1897884Z **defaults: any trailing arguments, which are forwarded to the local 2024-08-20T21:38:35.1897985Z optimizer. 2024-08-20T21:38:35.1898072Z 2024-08-20T21:38:35.1898192Z Example:: 2024-08-20T21:38:35.1898279Z 2024-08-20T21:38:35.1898392Z >>> # xdoctest: +SKIP 2024-08-20T21:38:35.1898528Z >>> import torch.nn as nn 2024-08-20T21:38:35.1898796Z >>> from torch.distributed.optim import ZeroRedundancyOptimizer 2024-08-20T21:38:35.1899072Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2024-08-20T21:38:35.1899393Z >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)]) 2024-08-20T21:38:35.1899544Z >>> ddp = DDP(model, device_ids=[rank]) 2024-08-20T21:38:35.1899694Z >>> opt = ZeroRedundancyOptimizer( 2024-08-20T21:38:35.1899824Z >>> ddp.parameters(), 2024-08-20T21:38:35.1899979Z >>> optimizer_class=torch.optim.Adam, 2024-08-20T21:38:35.1900077Z >>> lr=0.01 2024-08-20T21:38:35.1900184Z >>> ) 2024-08-20T21:38:35.1900314Z >>> ddp(inputs).sum().backward() 2024-08-20T21:38:35.1900416Z >>> opt.step() 2024-08-20T21:38:35.1900514Z 2024-08-20T21:38:35.1900612Z .. warning:: 2024-08-20T21:38:35.1900897Z Currently, ``ZeroRedundancyOptimizer`` requires that all of the 2024-08-20T21:38:35.1901229Z passed-in parameters are the same dense type. 2024-08-20T21:38:35.1901316Z 2024-08-20T21:38:35.1901429Z .. warning:: 2024-08-20T21:38:35.1901722Z If you pass ``overlap_with_ddp=True``, be wary of the following: Given 2024-08-20T21:38:35.1901991Z the way that overlapping :class:`DistributedDataParallel` with 2024-08-20T21:38:35.1902305Z :class:`ZeroRedundancyOptimizer` is currently implemented, the first 2024-08-20T21:38:35.1902598Z two or three training iterations do not perform parameter updates in 2024-08-20T21:38:35.1902848Z the optimizer step, depending on if ``static_graph=False`` or 2024-08-20T21:38:35.1903102Z ``static_graph=True``, respectively. This is because it needs 2024-08-20T21:38:35.1903337Z information about the gradient bucketing strategy used by 2024-08-20T21:38:35.1903621Z :class:`DistributedDataParallel`, which is not finalized until the 2024-08-20T21:38:35.1903904Z second forward pass if ``static_graph=False`` or until the third 2024-08-20T21:38:35.1904188Z forward pass if ``static_graph=True``. To adjust for this, one option 2024-08-20T21:38:35.1904328Z is to prepend dummy inputs. 2024-08-20T21:38:35.1904415Z 2024-08-20T21:38:35.1904740Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change. 2024-08-20T21:38:35.1904838Z 2024-08-20T21:38:35.1905004Z .. _ZeRO: https://arxiv.org/abs/1910.02054 2024-08-20T21:38:35.1905090Z 2024-08-20T21:38:35.1905188Z 2024-08-20T21:38:35.1905592Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1905678Z 2024-08-20T21:38:35.1905852Z warnings.warn(msg) 2024-08-20T21:38:35.1905939Z 2024-08-20T21:38:35.1906152Z --- Parse Warning: 64 / 101 --- 2024-08-20T21:38:35.1907684Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_CustomReducer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/pipelining/microbatch.py line=28. 2024-08-20T21:38:35.1908105Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1908204Z 2024-08-20T21:38:35.1908518Z Custom reducer class that can be used to specify a custom operation that 2024-08-20T21:38:35.1908738Z reduces losses of multiple microbatches into one value. 2024-08-20T21:38:35.1908837Z 2024-08-20T21:38:35.1908932Z Example: 2024-08-20T21:38:35.1909043Z >>> # xdoctest: +SKIP 2024-08-20T21:38:35.1909188Z >>> sum_reducer = _CustomReducer( 2024-08-20T21:38:35.1909302Z >>> torch.tensor(0.0), 2024-08-20T21:38:35.1909420Z >>> lambda a, b: a + b 2024-08-20T21:38:35.1909521Z >>> ) 2024-08-20T21:38:35.1909607Z 2024-08-20T21:38:35.1910008Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1910110Z 2024-08-20T21:38:35.1910219Z warnings.warn(msg) 2024-08-20T21:38:35.1910306Z 2024-08-20T21:38:35.1910528Z --- Parse Warning: 65 / 101 --- 2024-08-20T21:38:35.1911968Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=async_execution in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/functions.py line=6. 2024-08-20T21:38:35.1912395Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1912482Z 2024-08-20T21:38:35.1912809Z A decorator for a function indicating that the return value of the function 2024-08-20T21:38:35.1913113Z is guaranteed to be a :class:`~torch.futures.Future` object and this 2024-08-20T21:38:35.1913434Z function can run asynchronously on the RPC callee. More specifically, the 2024-08-20T21:38:35.1913751Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped 2024-08-20T21:38:35.1914124Z function and installs subsequent processing steps as a callback to that 2024-08-20T21:38:35.1914437Z :class:`~torch.futures.Future`. The installed callback will read the value 2024-08-20T21:38:35.1914716Z from the :class:`~torch.futures.Future` when completed and send the 2024-08-20T21:38:35.1914972Z value back as the RPC response. That also means the returned 2024-08-20T21:38:35.1915283Z :class:`~torch.futures.Future` only exists on the callee side and is never 2024-08-20T21:38:35.1915631Z sent through RPC. This decorator is useful when the wrapped function's 2024-08-20T21:38:35.1915896Z (``fn``) execution needs to pause and resume due to, e.g., containing 2024-08-20T21:38:35.1916199Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals. 2024-08-20T21:38:35.1916302Z 2024-08-20T21:38:35.1916588Z .. note:: To enable asynchronous execution, applications must pass the 2024-08-20T21:38:35.1916902Z function object returned by this decorator to RPC APIs. If RPC detected 2024-08-20T21:38:35.1917209Z attributes installed by this decorator, it knows that this function 2024-08-20T21:38:35.1917456Z returns a ``Future`` object and will handle that accordingly. 2024-08-20T21:38:35.1917744Z However, this does not mean this decorator has to be outmost one when 2024-08-20T21:38:35.1918061Z defining a function. For example, when combined with ``@staticmethod`` 2024-08-20T21:38:35.1918345Z or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the 2024-08-20T21:38:35.1918659Z inner decorator to allow the target function be recognized as a static 2024-08-20T21:38:35.1919021Z or class function. This target function can still execute asynchronously 2024-08-20T21:38:35.1919325Z because, when accessed, the static or class method preserves attributes 2024-08-20T21:38:35.1919532Z installed by ``@rpc.functions.async_execution``. 2024-08-20T21:38:35.1919625Z 2024-08-20T21:38:35.1919711Z 2024-08-20T21:38:35.1919822Z Example:: 2024-08-20T21:38:35.1920095Z The returned :class:`~torch.futures.Future` object can come from 2024-08-20T21:38:35.1920263Z :meth:`~torch.distributed.rpc.rpc_async`, 2024-08-20T21:38:35.1920574Z :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future` 2024-08-20T21:38:35.1920803Z constructor. The example below shows directly using the 2024-08-20T21:38:35.1920985Z :class:`~torch.futures.Future` returned by 2024-08-20T21:38:35.1921136Z :meth:`~torch.futures.Future.then`. 2024-08-20T21:38:35.1921224Z 2024-08-20T21:38:35.1921392Z >>> from torch.distributed import rpc 2024-08-20T21:38:35.1921483Z >>> 2024-08-20T21:38:35.1921638Z >>> # omitting setup and shutdown RPC 2024-08-20T21:38:35.1921743Z >>> 2024-08-20T21:38:35.1921852Z >>> # On all workers 2024-08-20T21:38:35.1921992Z >>> @rpc.functions.async_execution 2024-08-20T21:38:35.1922157Z >>> def async_add_chained(to, x, y, z): 2024-08-20T21:38:35.1922418Z >>> # This function runs on "worker1" and returns immediately when 2024-08-20T21:38:35.1922673Z >>> # the callback is installed through the `then(cb)` API. In the 2024-08-20T21:38:35.1922942Z >>> # mean time, the `rpc_async` to "worker2" can run concurrently. 2024-08-20T21:38:35.1923161Z >>> # When the return value of that `rpc_async` arrives at 2024-08-20T21:38:35.1923410Z >>> # "worker1", "worker1" will run the lambda function accordingly 2024-08-20T21:38:35.1923690Z >>> # and set the value for the previously returned `Future`, which 2024-08-20T21:38:35.1923946Z >>> # will then trigger RPC to send the result back to "worker0". 2024-08-20T21:38:35.1924183Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-08-20T21:38:35.1924327Z >>> lambda fut: fut.wait() + z 2024-08-20T21:38:35.1924476Z >>> ) 2024-08-20T21:38:35.1924579Z >>> 2024-08-20T21:38:35.1924681Z >>> # On worker0 2024-08-20T21:38:35.1924793Z >>> # xdoctest: +SKIP 2024-08-20T21:38:35.1924917Z >>> ret = rpc.rpc_sync( 2024-08-20T21:38:35.1925018Z >>> "worker1", 2024-08-20T21:38:35.1925134Z >>> async_add_chained, 2024-08-20T21:38:35.1925306Z >>> args=("worker2", torch.ones(2), 1, 1) 2024-08-20T21:38:35.1925398Z >>> ) 2024-08-20T21:38:35.1925551Z >>> print(ret) # prints tensor([3., 3.]) 2024-08-20T21:38:35.1925652Z 2024-08-20T21:38:35.1925957Z When combined with TorchScript decorators, this decorator must be the 2024-08-20T21:38:35.1926058Z outmost one. 2024-08-20T21:38:35.1926164Z 2024-08-20T21:38:35.1926286Z >>> from torch import Tensor 2024-08-20T21:38:35.1926447Z >>> from torch.futures import Future 2024-08-20T21:38:35.1926599Z >>> from torch.distributed import rpc 2024-08-20T21:38:35.1926693Z >>> 2024-08-20T21:38:35.1926854Z >>> # omitting setup and shutdown RPC 2024-08-20T21:38:35.1927043Z >>> 2024-08-20T21:38:35.1927154Z >>> # On all workers 2024-08-20T21:38:35.1927281Z >>> @torch.jit.script 2024-08-20T21:38:35.1927554Z >>> def script_add(x: Tensor, y: Tensor) -> Tensor: 2024-08-20T21:38:35.1927660Z >>> return x + y 2024-08-20T21:38:35.1927763Z >>> 2024-08-20T21:38:35.1927904Z >>> @rpc.functions.async_execution 2024-08-20T21:38:35.1928016Z >>> @torch.jit.script 2024-08-20T21:38:35.1928360Z >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]: 2024-08-20T21:38:35.1928544Z >>> return rpc.rpc_async(to, script_add, (x, y)) 2024-08-20T21:38:35.1928703Z >>> 2024-08-20T21:38:35.1928819Z >>> # On worker0 2024-08-20T21:38:35.1928929Z >>> ret = rpc.rpc_sync( 2024-08-20T21:38:35.1929033Z >>> "worker1", 2024-08-20T21:38:35.1929149Z >>> async_add, 2024-08-20T21:38:35.1929305Z >>> args=("worker2", torch.ones(2), 1) 2024-08-20T21:38:35.1929395Z >>> ) 2024-08-20T21:38:35.1929561Z >>> print(ret) # prints tensor([2., 2.]) 2024-08-20T21:38:35.1929648Z 2024-08-20T21:38:35.1929962Z When combined with static or class method, this decorator must be the 2024-08-20T21:38:35.1930059Z inner one. 2024-08-20T21:38:35.1930145Z 2024-08-20T21:38:35.1930311Z >>> from torch.distributed import rpc 2024-08-20T21:38:35.1930402Z >>> 2024-08-20T21:38:35.1930550Z >>> # omitting setup and shutdown RPC 2024-08-20T21:38:35.1930651Z >>> 2024-08-20T21:38:35.1930759Z >>> # On all workers 2024-08-20T21:38:35.1930899Z >>> class AsyncExecutionClass: 2024-08-20T21:38:35.1931001Z >>> 2024-08-20T21:38:35.1931107Z >>> @staticmethod 2024-08-20T21:38:35.1931256Z >>> @rpc.functions.async_execution 2024-08-20T21:38:35.1931418Z >>> def static_async_add(to, x, y, z): 2024-08-20T21:38:35.1931650Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-08-20T21:38:35.1931794Z >>> lambda fut: fut.wait() + z 2024-08-20T21:38:35.1931902Z >>> ) 2024-08-20T21:38:35.1931992Z >>> 2024-08-20T21:38:35.1932096Z >>> @classmethod 2024-08-20T21:38:35.1932254Z >>> @rpc.functions.async_execution 2024-08-20T21:38:35.1932415Z >>> def class_async_add(cls, to, x, y, z): 2024-08-20T21:38:35.1932578Z >>> ret_fut = torch.futures.Future() 2024-08-20T21:38:35.1932789Z >>> rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-08-20T21:38:35.1932993Z >>> lambda fut: ret_fut.set_result(fut.wait() + z) 2024-08-20T21:38:35.1933102Z >>> ) 2024-08-20T21:38:35.1933213Z >>> return ret_fut 2024-08-20T21:38:35.1933303Z >>> 2024-08-20T21:38:35.1933462Z >>> @rpc.functions.async_execution 2024-08-20T21:38:35.1933682Z >>> def bound_async_add(self, to, x, y, z): 2024-08-20T21:38:35.1933907Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-08-20T21:38:35.1934065Z >>> lambda fut: fut.wait() + z 2024-08-20T21:38:35.1934161Z >>> ) 2024-08-20T21:38:35.1934250Z >>> 2024-08-20T21:38:35.1934362Z >>> # On worker0 2024-08-20T21:38:35.1934474Z >>> ret = rpc.rpc_sync( 2024-08-20T21:38:35.1934578Z >>> "worker1", 2024-08-20T21:38:35.1934770Z >>> AsyncExecutionClass.static_async_add, 2024-08-20T21:38:35.1934925Z >>> args=("worker2", torch.ones(2), 1, 2) 2024-08-20T21:38:35.1935015Z >>> ) 2024-08-20T21:38:35.1935186Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-20T21:38:35.1935277Z >>> 2024-08-20T21:38:35.1935390Z >>> ret = rpc.rpc_sync( 2024-08-20T21:38:35.1935502Z >>> "worker1", 2024-08-20T21:38:35.1935676Z >>> AsyncExecutionClass.class_async_add, 2024-08-20T21:38:35.1935847Z >>> args=("worker2", torch.ones(2), 1, 2) 2024-08-20T21:38:35.1935936Z >>> ) 2024-08-20T21:38:35.1936087Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-20T21:38:35.1936187Z 2024-08-20T21:38:35.1936399Z This decorator also works with RRef helpers, i.e., . 2024-08-20T21:38:35.1936583Z :meth:`torch.distributed.rpc.RRef.rpc_sync`, 2024-08-20T21:38:35.1936801Z :meth:`torch.distributed.rpc.RRef.rpc_async`, and 2024-08-20T21:38:35.1936980Z :meth:`torch.distributed.rpc.RRef.remote`. 2024-08-20T21:38:35.1937068Z 2024-08-20T21:38:35.1937231Z >>> from torch.distributed import rpc 2024-08-20T21:38:35.1937320Z >>> 2024-08-20T21:38:35.1937552Z >>> # reuse the AsyncExecutionClass class above 2024-08-20T21:38:35.1937773Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-08-20T21:38:35.1938058Z >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2) 2024-08-20T21:38:35.1938215Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-20T21:38:35.1938318Z >>> 2024-08-20T21:38:35.1938522Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-08-20T21:38:35.1938855Z >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait() 2024-08-20T21:38:35.1939004Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-20T21:38:35.1939095Z >>> 2024-08-20T21:38:35.1939306Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-08-20T21:38:35.1939622Z >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here() 2024-08-20T21:38:35.1939771Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-20T21:38:35.1939875Z 2024-08-20T21:38:35.1940287Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1940372Z 2024-08-20T21:38:35.1940493Z warnings.warn(msg) 2024-08-20T21:38:35.1940584Z 2024-08-20T21:38:35.1940796Z --- Parse Warning: 66 / 101 --- 2024-08-20T21:38:35.1942443Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=TensorPipeRpcBackendOptions.set_device_map in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/options.py line=108. 2024-08-20T21:38:35.1942856Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1942956Z 2024-08-20T21:38:35.1943230Z Set device mapping between each RPC caller and callee pair. This 2024-08-20T21:38:35.1943465Z function can be called multiple times to incrementally add 2024-08-20T21:38:35.1943615Z device placement configurations. 2024-08-20T21:38:35.1943701Z 2024-08-20T21:38:35.1943794Z Args: 2024-08-20T21:38:35.1943920Z to (str): Callee name. 2024-08-20T21:38:35.1944185Z device_map (Dict of int, str, or torch.device): Device placement 2024-08-20T21:38:35.1944479Z mappings from this worker to the callee. This map must be 2024-08-20T21:38:35.1944592Z invertible. 2024-08-20T21:38:35.1944677Z 2024-08-20T21:38:35.1944773Z Example: 2024-08-20T21:38:35.1944932Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:38:35.1945038Z >>> # both workers 2024-08-20T21:38:35.1945144Z >>> def add(x, y): 2024-08-20T21:38:35.1945406Z >>> print(x) # tensor([1., 1.], device='cuda:1') 2024-08-20T21:38:35.1945541Z >>> return x + y, (x + y).to(2) 2024-08-20T21:38:35.1945645Z >>> 2024-08-20T21:38:35.1945751Z >>> # on worker 0 2024-08-20T21:38:35.1945931Z >>> options = TensorPipeRpcBackendOptions( 2024-08-20T21:38:35.1946072Z >>> num_worker_threads=8, 2024-08-20T21:38:35.1946215Z >>> device_maps={"worker1": {0: 1}} 2024-08-20T21:38:35.1946455Z >>> # maps worker0's cuda:0 to worker1's cuda:1 2024-08-20T21:38:35.1946559Z >>> ) 2024-08-20T21:38:35.1946942Z >>> options.set_device_map("worker1", {1: 2}) 2024-08-20T21:38:35.1947186Z >>> # maps worker0's cuda:1 to worker1's cuda:2 2024-08-20T21:38:35.1947291Z >>> 2024-08-20T21:38:35.1947398Z >>> rpc.init_rpc( 2024-08-20T21:38:35.1947503Z >>> "worker0", 2024-08-20T21:38:35.1947614Z >>> rank=0, 2024-08-20T21:38:35.1947719Z >>> world_size=2, 2024-08-20T21:38:35.1947888Z >>> backend=rpc.BackendType.TENSORPIPE, 2024-08-20T21:38:35.1948037Z >>> rpc_backend_options=options 2024-08-20T21:38:35.1948128Z >>> ) 2024-08-20T21:38:35.1948218Z >>> 2024-08-20T21:38:35.1948343Z >>> x = torch.ones(2) 2024-08-20T21:38:35.1948676Z >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1)) 2024-08-20T21:38:35.1948944Z >>> # The first argument will be moved to cuda:1 on worker1. When 2024-08-20T21:38:35.1949194Z >>> # sending the return value back, it will follow the invert of 2024-08-20T21:38:35.1949443Z >>> # the device map, and hence will be moved back to cuda:0 and 2024-08-20T21:38:35.1949570Z >>> # cuda:1 on worker0 2024-08-20T21:38:35.1949836Z >>> print(rets[0]) # tensor([2., 2.], device='cuda:0') 2024-08-20T21:38:35.1950092Z >>> print(rets[1]) # tensor([2., 2.], device='cuda:1') 2024-08-20T21:38:35.1950194Z 2024-08-20T21:38:35.1950597Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1950684Z 2024-08-20T21:38:35.1950808Z warnings.warn(msg) 2024-08-20T21:38:35.1950894Z 2024-08-20T21:38:35.1951103Z --- Parse Warning: 67 / 101 --- 2024-08-20T21:38:35.1952674Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PrepareModuleInput in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py line=378. 2024-08-20T21:38:35.1953092Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1953196Z 2024-08-20T21:38:35.1953841Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to 2024-08-20T21:38:35.1954262Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``. 2024-08-20T21:38:35.1954369Z 2024-08-20T21:38:35.1954470Z Keyword Args: 2024-08-20T21:38:35.1954723Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2024-08-20T21:38:35.1955199Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2024-08-20T21:38:35.1955725Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2024-08-20T21:38:35.1955874Z as a placeholder. default: None. 2024-08-20T21:38:35.1956182Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2024-08-20T21:38:35.1956797Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2024-08-20T21:38:35.1957358Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2024-08-20T21:38:35.1957525Z input_kwarg_layouts (Dict[str, Placement]): 2024-08-20T21:38:35.1958066Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2024-08-20T21:38:35.1958186Z default: None 2024-08-20T21:38:35.1958390Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2024-08-20T21:38:35.1958922Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2024-08-20T21:38:35.1959132Z have the desired DTensor layouts. default: None. 2024-08-20T21:38:35.1959273Z use_local_output (bool, optional): 2024-08-20T21:38:35.1959792Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2024-08-20T21:38:35.1959887Z Returns: 2024-08-20T21:38:35.1960406Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs. 2024-08-20T21:38:35.1960510Z 2024-08-20T21:38:35.1960613Z Example:: 2024-08-20T21:38:35.1960743Z >>> # xdoctest: +SKIP(failing) 2024-08-20T21:38:35.1961170Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput 2024-08-20T21:38:35.1961424Z >>> from torch.distributed.device_mesh import init_device_mesh 2024-08-20T21:38:35.1961519Z >>> ... 2024-08-20T21:38:35.1962004Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2024-08-20T21:38:35.1962166Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2024-08-20T21:38:35.1962270Z >>> 2024-08-20T21:38:35.1962743Z >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor 2024-08-20T21:38:35.1962942Z >>> # and then redistributed to Replicated DTensor. 2024-08-20T21:38:35.1963072Z >>> parallelize_module( 2024-08-20T21:38:35.1963255Z >>> block, # this can be a submodule or module 2024-08-20T21:38:35.1963358Z >>> tp_mesh, 2024-08-20T21:38:35.1963486Z >>> parallelize_plan={ 2024-08-20T21:38:35.1963643Z >>> "attn": PrepareModuleInput( 2024-08-20T21:38:35.1963827Z >>> input_layouts=(Shard(0), None, None, ...), 2024-08-20T21:38:35.1964060Z >>> desired_input_layouts=(Replicate(), None, None, ...) 2024-08-20T21:38:35.1964161Z >>> ), 2024-08-20T21:38:35.1964253Z >>> } 2024-08-20T21:38:35.1964355Z >>> ) 2024-08-20T21:38:35.1964441Z 2024-08-20T21:38:35.1964847Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1964950Z 2024-08-20T21:38:35.1965059Z warnings.warn(msg) 2024-08-20T21:38:35.1965146Z 2024-08-20T21:38:35.1965370Z --- Parse Warning: 68 / 101 --- 2024-08-20T21:38:35.1966991Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PrepareModuleOutput in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py line=533. 2024-08-20T21:38:35.1967430Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1967518Z 2024-08-20T21:38:35.1968180Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to 2024-08-20T21:38:35.1968623Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``. 2024-08-20T21:38:35.1968712Z 2024-08-20T21:38:35.1968812Z Keyword Args: 2024-08-20T21:38:35.1969091Z output_layouts (Union[Placement, Tuple[Placement]]): 2024-08-20T21:38:35.1969565Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2024-08-20T21:38:35.1970120Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2024-08-20T21:38:35.1970308Z ``None`` need to be specified as a placeholder. 2024-08-20T21:38:35.1970556Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2024-08-20T21:38:35.1971120Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2024-08-20T21:38:35.1971273Z have the desired DTensor layouts. 2024-08-20T21:38:35.1971413Z use_local_output (bool, optional): 2024-08-20T21:38:35.1971928Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2024-08-20T21:38:35.1972029Z Returns: 2024-08-20T21:38:35.1972516Z A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs. 2024-08-20T21:38:35.1972605Z 2024-08-20T21:38:35.1972706Z Example:: 2024-08-20T21:38:35.1972844Z >>> # xdoctest: +SKIP(failing) 2024-08-20T21:38:35.1973263Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput 2024-08-20T21:38:35.1973516Z >>> from torch.distributed.device_mesh import init_device_mesh 2024-08-20T21:38:35.1973620Z >>> ... 2024-08-20T21:38:35.1974040Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2024-08-20T21:38:35.1974273Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2024-08-20T21:38:35.1974379Z >>> 2024-08-20T21:38:35.1974931Z >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor 2024-08-20T21:38:35.1975115Z >>> # and then redistributed to Sharded DTensor. 2024-08-20T21:38:35.1975244Z >>> parallelize_module( 2024-08-20T21:38:35.1975426Z >>> block, # this can be a submodule or module 2024-08-20T21:38:35.1975541Z >>> tp_mesh, 2024-08-20T21:38:35.1975725Z >>> parallelize_plan = PrepareModuleOutput( 2024-08-20T21:38:35.1975871Z >>> output_layouts=Replicate(), 2024-08-20T21:38:35.1976035Z >>> desired_output_layouts=Shard(0) 2024-08-20T21:38:35.1976130Z >>> ) 2024-08-20T21:38:35.1976221Z >>> ) 2024-08-20T21:38:35.1976320Z 2024-08-20T21:38:35.1976725Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1976813Z 2024-08-20T21:38:35.1976940Z warnings.warn(msg) 2024-08-20T21:38:35.1977026Z 2024-08-20T21:38:35.1977239Z --- Parse Warning: 69 / 101 --- 2024-08-20T21:38:35.1978787Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MixtureSameFamily in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/mixture_same_family.py line=13. 2024-08-20T21:38:35.1979208Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1979297Z 2024-08-20T21:38:35.1979596Z The `MixtureSameFamily` distribution implements a (batch of) mixture 2024-08-20T21:38:35.1979913Z distribution where all component are from different parameterizations of 2024-08-20T21:38:35.1980207Z the same distribution type. It is parameterized by a `Categorical` 2024-08-20T21:38:35.1980452Z "selecting distribution" (over `k` component) and a component 2024-08-20T21:38:35.1980729Z distribution, i.e., a `Distribution` with a rightmost batch shape 2024-08-20T21:38:35.1980956Z (equal to `[k]`) which indexes each (batch of) component. 2024-08-20T21:38:35.1981043Z 2024-08-20T21:38:35.1981144Z Examples:: 2024-08-20T21:38:35.1981298Z 2024-08-20T21:38:35.1981451Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:38:35.1981719Z >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally 2024-08-20T21:38:35.1981873Z >>> # weighted normal distributions 2024-08-20T21:38:35.1982023Z >>> mix = D.Categorical(torch.ones(5,)) 2024-08-20T21:38:35.1982217Z >>> comp = D.Normal(torch.randn(5,), torch.rand(5,)) 2024-08-20T21:38:35.1982385Z >>> gmm = MixtureSameFamily(mix, comp) 2024-08-20T21:38:35.1982473Z 2024-08-20T21:38:35.1982752Z >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally 2024-08-20T21:38:35.1982922Z >>> # weighted bivariate normal distributions 2024-08-20T21:38:35.1983075Z >>> mix = D.Categorical(torch.ones(5,)) 2024-08-20T21:38:35.1983225Z >>> comp = D.Independent(D.Normal( 2024-08-20T21:38:35.1983396Z ... torch.randn(5,2), torch.rand(5,2)), 1) 2024-08-20T21:38:35.1983550Z >>> gmm = MixtureSameFamily(mix, comp) 2024-08-20T21:38:35.1983653Z 2024-08-20T21:38:35.1983895Z >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each 2024-08-20T21:38:35.1984164Z >>> # consisting of 5 random weighted bivariate normal distributions 2024-08-20T21:38:35.1984329Z >>> mix = D.Categorical(torch.rand(3,5)) 2024-08-20T21:38:35.1984465Z >>> comp = D.Independent(D.Normal( 2024-08-20T21:38:35.1984646Z ... torch.randn(3,5,2), torch.rand(3,5,2)), 1) 2024-08-20T21:38:35.1984811Z >>> gmm = MixtureSameFamily(mix, comp) 2024-08-20T21:38:35.1984899Z 2024-08-20T21:38:35.1984991Z Args: 2024-08-20T21:38:35.1985335Z mixture_distribution: `torch.distributions.Categorical`-like 2024-08-20T21:38:35.1985629Z instance. Manages the probability of selecting component. 2024-08-20T21:38:35.1985871Z The number of categories must match the rightmost batch 2024-08-20T21:38:35.1986117Z dimension of the `component_distribution`. Must have either 2024-08-20T21:38:35.1986308Z scalar `batch_shape` or `batch_shape` matching 2024-08-20T21:38:35.1986553Z `component_distribution.batch_shape[:-1]` 2024-08-20T21:38:35.1986901Z component_distribution: `torch.distributions.Distribution`-like 2024-08-20T21:38:35.1987194Z instance. Right-most batch dimension indexes component. 2024-08-20T21:38:35.1987293Z 2024-08-20T21:38:35.1987689Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1987776Z 2024-08-20T21:38:35.1987899Z warnings.warn(msg) 2024-08-20T21:38:35.1987986Z 2024-08-20T21:38:35.1988194Z --- Parse Warning: 70 / 101 --- 2024-08-20T21:38:35.1989723Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RelaxedBernoulli in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/relaxed_bernoulli.py line=111. 2024-08-20T21:38:35.1990143Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1990242Z 2024-08-20T21:38:35.1990475Z Creates a RelaxedBernoulli distribution, parametrized by 2024-08-20T21:38:35.1990720Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits` 2024-08-20T21:38:35.1991039Z (but not both). This is a relaxed version of the `Bernoulli` distribution, 2024-08-20T21:38:35.1991290Z so the values are in (0, 1), and has reparametrizable samples. 2024-08-20T21:38:35.1991376Z 2024-08-20T21:38:35.1991487Z Example:: 2024-08-20T21:38:35.1991572Z 2024-08-20T21:38:35.1991817Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-08-20T21:38:35.1992004Z >>> m = RelaxedBernoulli(torch.tensor([2.2]), 2024-08-20T21:38:35.1992179Z ... torch.tensor([0.1, 0.2, 0.3, 0.99])) 2024-08-20T21:38:35.1992294Z >>> m.sample() 2024-08-20T21:38:35.1992445Z tensor([ 0.2951, 0.3442, 0.8918, 0.9021]) 2024-08-20T21:38:35.1992584Z 2024-08-20T21:38:35.1992688Z Args: 2024-08-20T21:38:35.1992867Z temperature (Tensor): relaxation temperature 2024-08-20T21:38:35.1993087Z probs (Number, Tensor): the probability of sampling `1` 2024-08-20T21:38:35.1993370Z logits (Number, Tensor): the log-odds of sampling `1` 2024-08-20T21:38:35.1993457Z 2024-08-20T21:38:35.1993858Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.1993956Z 2024-08-20T21:38:35.1994065Z warnings.warn(msg) 2024-08-20T21:38:35.1994151Z 2024-08-20T21:38:35.1994373Z --- Parse Warning: 71 / 101 --- 2024-08-20T21:38:35.1995959Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RelaxedOneHotCategorical in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/relaxed_categorical.py line=99. 2024-08-20T21:38:35.1996378Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.1996481Z 2024-08-20T21:38:35.1996764Z Creates a RelaxedOneHotCategorical distribution parametrized by 2024-08-20T21:38:35.1997029Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`. 2024-08-20T21:38:35.1997356Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so 2024-08-20T21:38:35.1997566Z its samples are on simplex, and are reparametrizable. 2024-08-20T21:38:35.1997663Z 2024-08-20T21:38:35.1997764Z Example:: 2024-08-20T21:38:35.1997851Z 2024-08-20T21:38:35.1998109Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-08-20T21:38:35.1998369Z >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]), 2024-08-20T21:38:35.1998553Z ... torch.tensor([0.1, 0.2, 0.3, 0.4])) 2024-08-20T21:38:35.1998667Z >>> m.sample() 2024-08-20T21:38:35.1998821Z tensor([ 0.1294, 0.2324, 0.3859, 0.2523]) 2024-08-20T21:38:35.1998914Z 2024-08-20T21:38:35.1999020Z Args: 2024-08-20T21:38:35.1999196Z temperature (Tensor): relaxation temperature 2024-08-20T21:38:35.1999340Z probs (Tensor): event probabilities 2024-08-20T21:38:35.1999598Z logits (Tensor): unnormalized log probability for each event 2024-08-20T21:38:35.1999686Z 2024-08-20T21:38:35.2000100Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.2000189Z 2024-08-20T21:38:35.2000297Z warnings.warn(msg) 2024-08-20T21:38:35.2000398Z 2024-08-20T21:38:35.2000605Z --- Parse Warning: 72 / 101 --- 2024-08-20T21:38:35.2002181Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assoc_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=230. 2024-08-20T21:38:35.2002607Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.2002869Z Return a new dict with new, potentially nested, key value pair 2024-08-20T21:38:35.2002956Z 2024-08-20T21:38:35.2003143Z >>> purchase = {'name': 'Alice', 2024-08-20T21:38:35.2003381Z ... 'order': {'items': ['Apple', 'Orange'], 2024-08-20T21:38:35.2003624Z ... 'costs': [0.50, 1.25]}, 2024-08-20T21:38:35.2003951Z ... 'credit card': '5555-1234-1234-1234'} 2024-08-20T21:38:35.2004310Z >>> assoc_in(purchase, ['order', 'costs'], [0.25, 1.00]) # doctest: +SKIP 2024-08-20T21:38:35.2004515Z {'credit card': '5555-1234-1234-1234', 2024-08-20T21:38:35.2004653Z 'name': 'Alice', 2024-08-20T21:38:35.2004959Z 'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}} 2024-08-20T21:38:35.2005064Z 2024-08-20T21:38:35.2005467Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.2005623Z 2024-08-20T21:38:35.2005744Z warnings.warn(msg) 2024-08-20T21:38:35.2005831Z 2024-08-20T21:38:35.2006042Z --- Parse Warning: 73 / 101 --- 2024-08-20T21:38:35.2007718Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=update_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=245. 2024-08-20T21:38:35.2008140Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.2008347Z Update value in a (potentially) nested dictionary 2024-08-20T21:38:35.2008439Z 2024-08-20T21:38:35.2008533Z inputs: 2024-08-20T21:38:35.2008740Z d - dictionary on which to operate 2024-08-20T21:38:35.2009119Z keys - list or tuple giving the location of the value to be changed in d 2024-08-20T21:38:35.2009328Z func - function to operate on that value 2024-08-20T21:38:35.2009434Z 2024-08-20T21:38:35.2009727Z If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the 2024-08-20T21:38:35.2010034Z original dictionary with v replaced by func(v), but does not mutate the 2024-08-20T21:38:35.2010160Z original dictionary. 2024-08-20T21:38:35.2010246Z 2024-08-20T21:38:35.2010565Z If k0 is not a key in d, update_in creates nested dictionaries to the depth 2024-08-20T21:38:35.2010875Z specified by the keys, with the innermost value set to func(default). 2024-08-20T21:38:35.2010964Z 2024-08-20T21:38:35.2011080Z >>> inc = lambda x: x + 1 2024-08-20T21:38:35.2011278Z >>> update_in({'a': 0}, ['a'], inc) 2024-08-20T21:38:35.2011402Z {'a': 1} 2024-08-20T21:38:35.2011501Z 2024-08-20T21:38:35.2011740Z >>> transaction = {'name': 'Alice', 2024-08-20T21:38:35.2011999Z ... 'purchase': {'items': ['Apple', 'Orange'], 2024-08-20T21:38:35.2012236Z ... 'costs': [0.50, 1.25]}, 2024-08-20T21:38:35.2012471Z ... 'credit card': '5555-1234-1234-1234'} 2024-08-20T21:38:35.2012826Z >>> update_in(transaction, ['purchase', 'costs'], sum) # doctest: +SKIP 2024-08-20T21:38:35.2013029Z {'credit card': '5555-1234-1234-1234', 2024-08-20T21:38:35.2013169Z 'name': 'Alice', 2024-08-20T21:38:35.2013449Z 'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}} 2024-08-20T21:38:35.2013551Z 2024-08-20T21:38:35.2013713Z >>> # updating a value when k0 is not in d 2024-08-20T21:38:35.2013880Z >>> update_in({}, [1, 2, 3], str, default="bar") 2024-08-20T21:38:35.2014037Z {1: {2: {3: 'bar'}}} 2024-08-20T21:38:35.2014239Z >>> update_in({1: 'foo'}, [2, 3, 4], inc, 0) 2024-08-20T21:38:35.2014455Z {1: 'foo', 2: {3: {4: 1}}} 2024-08-20T21:38:35.2014609Z 2024-08-20T21:38:35.2015112Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.2015216Z 2024-08-20T21:38:35.2015330Z warnings.warn(msg) 2024-08-20T21:38:35.2015417Z 2024-08-20T21:38:35.2015645Z --- Parse Warning: 74 / 101 --- 2024-08-20T21:38:35.2017200Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=get_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=303. 2024-08-20T21:38:35.2017619Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.2017848Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys. 2024-08-20T21:38:35.2017936Z 2024-08-20T21:38:35.2018204Z If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless 2024-08-20T21:38:35.2018486Z ``no_default`` is specified, then it raises KeyError or IndexError. 2024-08-20T21:38:35.2018573Z 2024-08-20T21:38:35.2018858Z ``get_in`` is a generalization of ``operator.getitem`` for nested data 2024-08-20T21:38:35.2019124Z structures such as dictionaries and lists. 2024-08-20T21:38:35.2019212Z 2024-08-20T21:38:35.2019412Z >>> transaction = {'name': 'Alice', 2024-08-20T21:38:35.2019670Z ... 'purchase': {'items': ['Apple', 'Orange'], 2024-08-20T21:38:35.2019898Z ... 'costs': [0.50, 1.25]}, 2024-08-20T21:38:35.2020144Z ... 'credit card': '5555-1234-1234-1234'} 2024-08-20T21:38:35.2020379Z >>> get_in(['purchase', 'items', 0], transaction) 2024-08-20T21:38:35.2020501Z 'Apple' 2024-08-20T21:38:35.2020690Z >>> get_in(['name'], transaction) 2024-08-20T21:38:35.2020808Z 'Alice' 2024-08-20T21:38:35.2021034Z >>> get_in(['purchase', 'total'], transaction) 2024-08-20T21:38:35.2021303Z >>> get_in(['purchase', 'items', 'apple'], transaction) 2024-08-20T21:38:35.2021536Z >>> get_in(['purchase', 'items', 10], transaction) 2024-08-20T21:38:35.2021770Z >>> get_in(['purchase', 'total'], transaction, 0) 2024-08-20T21:38:35.2021879Z 0 2024-08-20T21:38:35.2022065Z >>> get_in(['y'], {}, no_default=True) 2024-08-20T21:38:35.2022217Z Traceback (most recent call last): 2024-08-20T21:38:35.2022311Z ... 2024-08-20T21:38:35.2022444Z KeyError: 'y' 2024-08-20T21:38:35.2022542Z 2024-08-20T21:38:35.2022636Z See Also: 2024-08-20T21:38:35.2022740Z itertoolz.get 2024-08-20T21:38:35.2022863Z operator.getitem 2024-08-20T21:38:35.2022953Z 2024-08-20T21:38:35.2023354Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.2023452Z 2024-08-20T21:38:35.2023561Z warnings.warn(msg) 2024-08-20T21:38:35.2023702Z 2024-08-20T21:38:35.2023928Z --- Parse Warning: 75 / 101 --- 2024-08-20T21:38:35.2025533Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=groupby in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=355. 2024-08-20T21:38:35.2026024Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.2026178Z Group a collection by a key function 2024-08-20T21:38:35.2026264Z 2024-08-20T21:38:35.2026569Z >>> names = ['Alice', 'Bob', 'Charlie', 'Dan', 'Edith', 'Frank'] 2024-08-20T21:38:35.2026738Z >>> groupby(len, names) # doctest: +SKIP 2024-08-20T21:38:35.2027040Z {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} 2024-08-20T21:38:35.2027138Z 2024-08-20T21:38:35.2027269Z >>> iseven = lambda x: x % 2 == 0 2024-08-20T21:38:35.2027506Z >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP 2024-08-20T21:38:35.2027662Z {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} 2024-08-20T21:38:35.2027749Z 2024-08-20T21:38:35.2027988Z Non-callable keys imply grouping on a member. 2024-08-20T21:38:35.2028090Z 2024-08-20T21:38:35.2028357Z >>> groupby('gender', [{'name': 'Alice', 'gender': 'F'}, 2024-08-20T21:38:35.2028587Z ... {'name': 'Bob', 'gender': 'M'}, 2024-08-20T21:38:35.2028892Z ... {'name': 'Charlie', 'gender': 'M'}]) # doctest:+SKIP 2024-08-20T21:38:35.2029089Z {'F': [{'gender': 'F', 'name': 'Alice'}], 2024-08-20T21:38:35.2029277Z 'M': [{'gender': 'M', 'name': 'Bob'}, 2024-08-20T21:38:35.2029489Z {'gender': 'M', 'name': 'Charlie'}]} 2024-08-20T21:38:35.2029576Z 2024-08-20T21:38:35.2029763Z Not to be confused with ``itertools.groupby`` 2024-08-20T21:38:35.2029861Z 2024-08-20T21:38:35.2029959Z See Also: 2024-08-20T21:38:35.2030070Z countby 2024-08-20T21:38:35.2030161Z 2024-08-20T21:38:35.2030561Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.2030658Z 2024-08-20T21:38:35.2030766Z warnings.warn(msg) 2024-08-20T21:38:35.2030922Z 2024-08-20T21:38:35.2031142Z --- Parse Warning: 76 / 101 --- 2024-08-20T21:38:35.2032554Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SyncBatchNorm in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py line=601. 2024-08-20T21:38:35.2032964Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.2033261Z Applies Batch Normalization over a N-Dimensional input. 2024-08-20T21:38:35.2033348Z 2024-08-20T21:38:35.2033952Z The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper 2024-08-20T21:38:35.2034263Z `Batch Normalization: Accelerating Deep Network Training by Reducing 2024-08-20T21:38:35.2034538Z Internal Covariate Shift `__ . 2024-08-20T21:38:35.2034636Z 2024-08-20T21:38:35.2034743Z .. math:: 2024-08-20T21:38:35.2034828Z 2024-08-20T21:38:35.2035259Z y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta 2024-08-20T21:38:35.2035346Z 2024-08-20T21:38:35.2035728Z The mean and standard-deviation are calculated per-dimension over all 2024-08-20T21:38:35.2036131Z mini-batches of the same process groups. :math:`\gamma` and :math:`\beta` 2024-08-20T21:38:35.2036449Z are learnable parameter vectors of size `C` (where `C` is the input size). 2024-08-20T21:38:35.2036690Z By default, the elements of :math:`\gamma` are sampled from 2024-08-20T21:38:35.2036996Z :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. 2024-08-20T21:38:35.2037499Z The standard-deviation is calculated via the biased estimator, equivalent to 2024-08-20T21:38:35.2037658Z `torch.var(input, unbiased=False)`. 2024-08-20T21:38:35.2037745Z 2024-08-20T21:38:35.2038065Z Also by default, during training this layer keeps running estimates of its 2024-08-20T21:38:35.2038395Z computed mean and variance, which are then used for normalization during 2024-08-20T21:38:35.2038716Z evaluation. The running estimates are kept with a default :attr:`momentum` 2024-08-20T21:38:35.2038811Z of 0.1. 2024-08-20T21:38:35.2038913Z 2024-08-20T21:38:35.2039225Z If :attr:`track_running_stats` is set to ``False``, this layer then does not 2024-08-20T21:38:35.2039518Z keep running estimates, and batch statistics are instead used during 2024-08-20T21:38:35.2039653Z evaluation time as well. 2024-08-20T21:38:35.2039740Z 2024-08-20T21:38:35.2039837Z .. note:: 2024-08-20T21:38:35.2040153Z This :attr:`momentum` argument is different from one used in optimizer 2024-08-20T21:38:35.2040458Z classes and the conventional notion of momentum. Mathematically, the 2024-08-20T21:38:35.2040632Z update rule for running statistics here is 2024-08-20T21:38:35.2041149Z :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, 2024-08-20T21:38:35.2041442Z where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the 2024-08-20T21:38:35.2041571Z new observed value. 2024-08-20T21:38:35.2041656Z 2024-08-20T21:38:35.2042069Z Because the Batch Normalization is done for each channel in the ``C`` dimension, computing 2024-08-20T21:38:35.2042516Z statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch 2024-08-20T21:38:35.2042798Z Normalization or Spatio-temporal Batch Normalization. 2024-08-20T21:38:35.2042888Z 2024-08-20T21:38:35.2043088Z Currently :class:`SyncBatchNorm` only supports 2024-08-20T21:38:35.2043463Z :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use 2024-08-20T21:38:35.2043748Z :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert 2024-08-20T21:38:35.2044086Z :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping 2024-08-20T21:38:35.2044198Z Network with DDP. 2024-08-20T21:38:35.2044296Z 2024-08-20T21:38:35.2044389Z Args: 2024-08-20T21:38:35.2044603Z num_features: :math:`C` from an expected input of size 2024-08-20T21:38:35.2044728Z :math:`(N, C, +)` 2024-08-20T21:38:35.2044989Z eps: a value added to the denominator for numerical stability. 2024-08-20T21:38:35.2045145Z Default: ``1e-5`` 2024-08-20T21:38:35.2045410Z momentum: the value used for the running_mean and running_var 2024-08-20T21:38:35.2045691Z computation. Can be set to ``None`` for cumulative moving average 2024-08-20T21:38:35.2045844Z (i.e. simple average). Default: 0.1 2024-08-20T21:38:35.2046147Z affine: a boolean value that when set to ``True``, this module has 2024-08-20T21:38:35.2046406Z learnable affine parameters. Default: ``True`` 2024-08-20T21:38:35.2046893Z track_running_stats: a boolean value that when set to ``True``, this 2024-08-20T21:38:35.2047313Z module tracks the running mean and variance, and when set to ``False``, 2024-08-20T21:38:35.2047620Z this module does not track such statistics, and initializes statistics 2024-08-20T21:38:35.2047901Z buffers :attr:`running_mean` and :attr:`running_var` as ``None``. 2024-08-20T21:38:35.2048225Z When these buffers are ``None``, this module always uses batch statistics. 2024-08-20T21:38:35.2048430Z in both training and eval modes. Default: ``True`` 2024-08-20T21:38:35.2048879Z process_group: synchronization of stats happen within each process group 2024-08-20T21:38:35.2049173Z individually. Default behavior is synchronization across the whole 2024-08-20T21:38:35.2049271Z world 2024-08-20T21:38:35.2049376Z 2024-08-20T21:38:35.2049470Z Shape: 2024-08-20T21:38:35.2049660Z - Input: :math:`(N, C, +)` 2024-08-20T21:38:35.2049928Z - Output: :math:`(N, C, +)` (same shape as input) 2024-08-20T21:38:35.2050015Z 2024-08-20T21:38:35.2050129Z .. note:: 2024-08-20T21:38:35.2050452Z Synchronization of batchnorm statistics occurs only while training, i.e. 2024-08-20T21:38:35.2050717Z synchronization is disabled when ``model.eval()`` is set or if 2024-08-20T21:38:35.2050896Z ``self.training`` is otherwise ``False``. 2024-08-20T21:38:35.2050986Z 2024-08-20T21:38:35.2051088Z Examples:: 2024-08-20T21:38:35.2051189Z 2024-08-20T21:38:35.2051306Z >>> # xdoctest: +SKIP 2024-08-20T21:38:35.2051450Z >>> # With Learnable Parameters 2024-08-20T21:38:35.2051598Z >>> m = nn.SyncBatchNorm(100) 2024-08-20T21:38:35.2051757Z >>> # creating process group (optional) 2024-08-20T21:38:35.2051954Z >>> # ranks is a list of int identifying rank ids. 2024-08-20T21:38:35.2052087Z >>> ranks = list(range(8)) 2024-08-20T21:38:35.2052225Z >>> r1, r2 = ranks[:4], ranks[4:] 2024-08-20T21:38:35.2052421Z >>> # Note: every rank calls into new_group for every 2024-08-20T21:38:35.2052636Z >>> # process group created, even if that rank is not 2024-08-20T21:38:35.2052757Z >>> # part of the group. 2024-08-20T21:38:35.2053098Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2024-08-20T21:38:35.2053366Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2024-08-20T21:38:35.2053518Z >>> # Without Learnable Parameters 2024-08-20T21:38:35.2053814Z >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group) 2024-08-20T21:38:35.2053978Z >>> input = torch.randn(20, 100, 35, 45, 10) 2024-08-20T21:38:35.2054095Z >>> output = m(input) 2024-08-20T21:38:35.2054272Z 2024-08-20T21:38:35.2054423Z >>> # network is nn.BatchNorm layer 2024-08-20T21:38:35.2054790Z >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group) 2024-08-20T21:38:35.2055024Z >>> # only single gpu per process is currently supported 2024-08-20T21:38:35.2055325Z >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel( 2024-08-20T21:38:35.2055491Z >>> sync_bn_network, 2024-08-20T21:38:35.2055664Z >>> device_ids=[args.local_rank], 2024-08-20T21:38:35.2055841Z >>> output_device=args.local_rank) 2024-08-20T21:38:35.2055948Z 2024-08-20T21:38:35.2056361Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.2056449Z 2024-08-20T21:38:35.2056573Z warnings.warn(msg) 2024-08-20T21:38:35.2056659Z 2024-08-20T21:38:35.2056944Z --- Parse Warning: 77 / 101 --- 2024-08-20T21:38:35.2058571Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SyncBatchNorm.convert_sync_batchnorm in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py line=824. 2024-08-20T21:38:35.2058988Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.2059430Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers. 2024-08-20T21:38:35.2059521Z 2024-08-20T21:38:35.2059616Z Args: 2024-08-20T21:38:35.2060031Z module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers 2024-08-20T21:38:35.2060321Z process_group (optional): process group to scope synchronization, 2024-08-20T21:38:35.2060465Z default is the whole world 2024-08-20T21:38:35.2060565Z 2024-08-20T21:38:35.2060667Z Returns: 2024-08-20T21:38:35.2061017Z The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm` 2024-08-20T21:38:35.2061331Z layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer, 2024-08-20T21:38:35.2061624Z a new :class:`torch.nn.SyncBatchNorm` layer object will be returned 2024-08-20T21:38:35.2061724Z instead. 2024-08-20T21:38:35.2061823Z 2024-08-20T21:38:35.2061927Z Example:: 2024-08-20T21:38:35.2062013Z 2024-08-20T21:38:35.2062188Z >>> # Network with nn.BatchNorm layer 2024-08-20T21:38:35.2062377Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-20T21:38:35.2062546Z >>> module = torch.nn.Sequential( 2024-08-20T21:38:35.2062707Z >>> torch.nn.Linear(20, 100), 2024-08-20T21:38:35.2062872Z >>> torch.nn.BatchNorm1d(100), 2024-08-20T21:38:35.2062995Z >>> ).cuda() 2024-08-20T21:38:35.2063167Z >>> # creating process group (optional) 2024-08-20T21:38:35.2063362Z >>> # ranks is a list of int identifying rank ids. 2024-08-20T21:38:35.2063508Z >>> ranks = list(range(8)) 2024-08-20T21:38:35.2063654Z >>> r1, r2 = ranks[:4], ranks[4:] 2024-08-20T21:38:35.2063856Z >>> # Note: every rank calls into new_group for every 2024-08-20T21:38:35.2064070Z >>> # process group created, even if that rank is not 2024-08-20T21:38:35.2064199Z >>> # part of the group. 2024-08-20T21:38:35.2064355Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:38:35.2064702Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2024-08-20T21:38:35.2064970Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2024-08-20T21:38:35.2065381Z >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group) 2024-08-20T21:38:35.2065538Z 2024-08-20T21:38:35.2065632Z 2024-08-20T21:38:35.2066053Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.2066141Z 2024-08-20T21:38:35.2066250Z warnings.warn(msg) 2024-08-20T21:38:35.2066349Z 2024-08-20T21:38:35.2066560Z --- Parse Warning: 78 / 101 --- 2024-08-20T21:38:35.2067917Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Unflatten in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py line=60. 2024-08-20T21:38:35.2068352Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.2068439Z 2024-08-20T21:38:35.2068882Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`. 2024-08-20T21:38:35.2068969Z 2024-08-20T21:38:35.2069342Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can 2024-08-20T21:38:35.2069677Z be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively. 2024-08-20T21:38:35.2069764Z 2024-08-20T21:38:35.2070201Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be 2024-08-20T21:38:35.2070592Z a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input; a `NamedShape` 2024-08-20T21:38:35.2070810Z (tuple of `(name, size)` tuples) for `NamedTensor` input. 2024-08-20T21:38:35.2070897Z 2024-08-20T21:38:35.2071001Z Shape: 2024-08-20T21:38:35.2071456Z - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at 2024-08-20T21:38:35.2071800Z dimension :attr:`dim` and :math:`*` means any number of dimensions including none. 2024-08-20T21:38:35.2072203Z - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and 2024-08-20T21:38:35.2072374Z :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`. 2024-08-20T21:38:35.2072474Z 2024-08-20T21:38:35.2072566Z Args: 2024-08-20T21:38:35.2072760Z dim (Union[int, str]): Dimension to be unflattened 2024-08-20T21:38:35.2073226Z unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension 2024-08-20T21:38:35.2073313Z 2024-08-20T21:38:35.2073409Z Examples: 2024-08-20T21:38:35.2073550Z >>> input = torch.randn(2, 50) 2024-08-20T21:38:35.2073666Z >>> # With tuple of ints 2024-08-20T21:38:35.2073779Z >>> m = nn.Sequential( 2024-08-20T21:38:35.2073904Z >>> nn.Linear(50, 50), 2024-08-20T21:38:35.2074039Z >>> nn.Unflatten(1, (2, 5, 5)) 2024-08-20T21:38:35.2074130Z >>> ) 2024-08-20T21:38:35.2074252Z >>> output = m(input) 2024-08-20T21:38:35.2074359Z >>> output.size() 2024-08-20T21:38:35.2074471Z torch.Size([2, 2, 5, 5]) 2024-08-20T21:38:35.2074598Z >>> # With torch.Size 2024-08-20T21:38:35.2074708Z >>> m = nn.Sequential( 2024-08-20T21:38:35.2074821Z >>> nn.Linear(50, 50), 2024-08-20T21:38:35.2074996Z >>> nn.Unflatten(1, torch.Size([2, 5, 5])) 2024-08-20T21:38:35.2075088Z >>> ) 2024-08-20T21:38:35.2075208Z >>> output = m(input) 2024-08-20T21:38:35.2075315Z >>> output.size() 2024-08-20T21:38:35.2075427Z torch.Size([2, 2, 5, 5]) 2024-08-20T21:38:35.2075593Z >>> # With namedshape (tuple of tuples) 2024-08-20T21:38:35.2075860Z >>> input = torch.randn(2, 50, names=('N', 'features')) 2024-08-20T21:38:35.2076212Z >>> unflatten = nn.Unflatten('features', (('C', 2), ('H', 5), ('W', 5))) 2024-08-20T21:38:35.2076356Z >>> output = unflatten(input) 2024-08-20T21:38:35.2076463Z >>> output.size() 2024-08-20T21:38:35.2076575Z torch.Size([2, 2, 5, 5]) 2024-08-20T21:38:35.2076673Z 2024-08-20T21:38:35.2077072Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.2077211Z 2024-08-20T21:38:35.2077332Z warnings.warn(msg) 2024-08-20T21:38:35.2077417Z 2024-08-20T21:38:35.2077628Z --- Parse Warning: 79 / 101 --- 2024-08-20T21:38:35.2079125Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=TripletMarginWithDistanceLoss in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py line=1696. 2024-08-20T21:38:35.2079543Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.2079816Z Creates a criterion that measures the triplet loss given input 2024-08-20T21:38:35.2080077Z tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor, 2024-08-20T21:38:35.2080361Z positive, and negative examples, respectively), and a nonnegative, 2024-08-20T21:38:35.2080778Z real-valued function ("distance function") used to compute the relationship 2024-08-20T21:38:35.2081079Z between the anchor and positive example ("positive distance") and the 2024-08-20T21:38:35.2081276Z anchor and negative example ("negative distance"). 2024-08-20T21:38:35.2081375Z 2024-08-20T21:38:35.2081733Z The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``) 2024-08-20T21:38:35.2081860Z can be described as: 2024-08-20T21:38:35.2081947Z 2024-08-20T21:38:35.2082046Z .. math:: 2024-08-20T21:38:35.2082252Z \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad 2024-08-20T21:38:35.2082545Z l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} 2024-08-20T21:38:35.2082634Z 2024-08-20T21:38:35.2083135Z where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function 2024-08-20T21:38:35.2083526Z quantifying the closeness of two tensors, referred to as the :attr:`distance_function`; 2024-08-20T21:38:35.2083862Z and :math:`margin` is a nonnegative margin representing the minimum difference 2024-08-20T21:38:35.2084207Z between the positive and negative distances that is required for the loss to 2024-08-20T21:38:35.2084527Z be 0. The input tensors have :math:`N` elements each and can be of any shape 2024-08-20T21:38:35.2084683Z that the distance function can handle. 2024-08-20T21:38:35.2084783Z 2024-08-20T21:38:35.2084985Z If :attr:`reduction` is not ``'none'`` 2024-08-20T21:38:35.2085163Z (default ``'mean'``), then: 2024-08-20T21:38:35.2085251Z 2024-08-20T21:38:35.2085348Z .. math:: 2024-08-20T21:38:35.2085461Z \ell(x, y) = 2024-08-20T21:38:35.2085565Z \begin{cases} 2024-08-20T21:38:35.2085922Z \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ 2024-08-20T21:38:35.2086267Z \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} 2024-08-20T21:38:35.2086367Z \end{cases} 2024-08-20T21:38:35.2086458Z 2024-08-20T21:38:35.2086792Z See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet 2024-08-20T21:38:35.2087226Z loss for input tensors using the :math:`l_p` distance as the distance function. 2024-08-20T21:38:35.2087315Z 2024-08-20T21:38:35.2087420Z Args: 2024-08-20T21:38:35.2087865Z distance_function (Callable, optional): A nonnegative, real-valued function that 2024-08-20T21:38:35.2088115Z quantifies the closeness of two tensors. If not specified, 2024-08-20T21:38:35.2088356Z `nn.PairwiseDistance` will be used. Default: ``None`` 2024-08-20T21:38:35.2088716Z margin (float, optional): A nonnegative margin representing the minimum difference 2024-08-20T21:38:35.2089102Z between the positive and negative distances required for the loss to be 0. Larger 2024-08-20T21:38:35.2089477Z margins penalize cases where the negative examples are not distant enough from the 2024-08-20T21:38:35.2089768Z anchors, relative to the positives. Default: :math:`1`. 2024-08-20T21:38:35.2090124Z swap (bool, optional): Whether to use the distance swap described in the paper 2024-08-20T21:38:35.2090465Z `Learning shallow convolutional feature descriptors with triplet losses` by 2024-08-20T21:38:35.2090815Z V. Balntas, E. Riba et al. If True, and if the positive example is closer to the 2024-08-20T21:38:35.2091197Z negative example than the anchor is, swaps the positive example and the anchor in 2024-08-20T21:38:35.2091371Z the loss computation. Default: ``False``. 2024-08-20T21:38:35.2091763Z reduction (str, optional): Specifies the (optional) reduction to apply to the output: 2024-08-20T21:38:35.2092121Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2024-08-20T21:38:35.2092455Z ``'mean'``: the sum of the output will be divided by the number of 2024-08-20T21:38:35.2092896Z elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'`` 2024-08-20T21:38:35.2092985Z 2024-08-20T21:38:35.2093072Z 2024-08-20T21:38:35.2093180Z Shape: 2024-08-20T21:38:35.2093601Z - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions 2024-08-20T21:38:35.2093768Z as supported by the distance function. 2024-08-20T21:38:35.2094224Z - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar 2024-08-20T21:38:35.2094326Z otherwise. 2024-08-20T21:38:35.2094428Z 2024-08-20T21:38:35.2094531Z Examples:: 2024-08-20T21:38:35.2094667Z 2024-08-20T21:38:35.2094800Z >>> # Initialize embeddings 2024-08-20T21:38:35.2094954Z >>> embedding = nn.Embedding(1000, 128) 2024-08-20T21:38:35.2095120Z >>> anchor_ids = torch.randint(0, 1000, (1,)) 2024-08-20T21:38:35.2095310Z >>> positive_ids = torch.randint(0, 1000, (1,)) 2024-08-20T21:38:35.2095478Z >>> negative_ids = torch.randint(0, 1000, (1,)) 2024-08-20T21:38:35.2095618Z >>> anchor = embedding(anchor_ids) 2024-08-20T21:38:35.2095785Z >>> positive = embedding(positive_ids) 2024-08-20T21:38:35.2095934Z >>> negative = embedding(negative_ids) 2024-08-20T21:38:35.2096026Z >>> 2024-08-20T21:38:35.2096221Z >>> # Built-in Distance Function 2024-08-20T21:38:35.2096333Z >>> triplet_loss = \ 2024-08-20T21:38:35.2096693Z >>> nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance()) 2024-08-20T21:38:35.2096906Z >>> output = triplet_loss(anchor, positive, negative) 2024-08-20T21:38:35.2097025Z >>> output.backward() 2024-08-20T21:38:35.2097116Z >>> 2024-08-20T21:38:35.2097256Z >>> # Custom Distance Function 2024-08-20T21:38:35.2097376Z >>> def l_infinity(x1, x2): 2024-08-20T21:38:35.2097666Z >>> return torch.max(torch.abs(x1 - x2), dim=1).values 2024-08-20T21:38:35.2097755Z >>> 2024-08-20T21:38:35.2098011Z >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time") 2024-08-20T21:38:35.2098136Z >>> triplet_loss = ( 2024-08-20T21:38:35.2098492Z >>> nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5)) 2024-08-20T21:38:35.2098692Z >>> output = triplet_loss(anchor, positive, negative) 2024-08-20T21:38:35.2098821Z >>> output.backward() 2024-08-20T21:38:35.2098910Z >>> 2024-08-20T21:38:35.2099063Z >>> # Custom Distance Function (Lambda) 2024-08-20T21:38:35.2099185Z >>> triplet_loss = ( 2024-08-20T21:38:35.2099359Z >>> nn.TripletMarginWithDistanceLoss( 2024-08-20T21:38:35.2099715Z >>> distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y))) 2024-08-20T21:38:35.2099924Z >>> output = triplet_loss(anchor, positive, negative) 2024-08-20T21:38:35.2100094Z >>> output.backward() 2024-08-20T21:38:35.2100181Z 2024-08-20T21:38:35.2100291Z Reference: 2024-08-20T21:38:35.2100701Z V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses: 2024-08-20T21:38:35.2100953Z http://www.bmva.org/bmvc/2016/papers/paper119/index.html 2024-08-20T21:38:35.2101046Z 2024-08-20T21:38:35.2101455Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17)) 2024-08-20T21:38:35.2101554Z 2024-08-20T21:38:35.2101663Z warnings.warn(msg) 2024-08-20T21:38:35.2101750Z 2024-08-20T21:38:35.2101973Z --- Parse Warning: 80 / 101 --- 2024-08-20T21:38:35.2103347Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MaxUnpool2d in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py line=395. 2024-08-20T21:38:35.2103764Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.2103972Z Computes a partial inverse of :class:`MaxPool2d`. 2024-08-20T21:38:35.2104059Z 2024-08-20T21:38:35.2104511Z :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost. 2024-08-20T21:38:35.2104599Z 2024-08-20T21:38:35.2104898Z :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d` 2024-08-20T21:38:35.2105233Z including the indices of the maximal values and computes a partial inverse 2024-08-20T21:38:35.2105479Z in which all non-maximal values are set to zero. 2024-08-20T21:38:35.2105566Z 2024-08-20T21:38:35.2105671Z Note: 2024-08-20T21:38:35.2106146Z This operation may behave nondeterministically when the input indices has repeat values. 2024-08-20T21:38:35.2106647Z See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. 2024-08-20T21:38:35.2106745Z 2024-08-20T21:38:35.2107052Z .. note:: :class:`MaxPool2d` can map several input sizes to the same output 2024-08-20T21:38:35.2107280Z sizes. Hence, the inversion process can get ambiguous. 2024-08-20T21:38:35.2107550Z To accommodate this, you can provide the needed output size 2024-08-20T21:38:35.2107834Z as an additional argument :attr:`output_size` in the forward call. 2024-08-20T21:38:35.2108007Z See the Inputs and Example below. 2024-08-20T21:38:35.2108094Z 2024-08-20T21:38:35.2108187Z Args: 2024-08-20T21:38:35.2108443Z kernel_size (int or tuple): Size of the max pooling window. 2024-08-20T21:38:35.2108678Z stride (int or tuple): Stride of the max pooling window. 2024-08-20T21:38:35.2108858Z It is set to :attr:`kernel_size` by default. 2024-08-20T21:38:35.2109120Z padding (int or tuple): Padding that was added to the input 2024-08-20T21:38:35.2109207Z 2024-08-20T21:38:35.2109306Z Inputs: 2024-08-20T21:38:35.2109525Z - `input`: the input Tensor to invert 2024-08-20T21:38:35.2109869Z - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d` 2024-08-20T21:38:35.2110133Z - `output_size` (optional): the targeted output size 2024-08-20T21:38:35.2110232Z 2024-08-20T21:38:35.2110325Z Shape: 2024-08-20T21:38:35.2110677Z - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. 2024-08-20T21:38:35.2111065Z - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where 2024-08-20T21:38:35.2111154Z 2024-08-20T21:38:35.2111268Z .. math:: 2024-08-20T21:38:35.2111790Z H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} 2024-08-20T21:38:35.2111879Z 2024-08-20T21:38:35.2111991Z .. math:: 2024-08-20T21:38:35.2112491Z W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} 2024-08-20T21:38:35.2112631Z 2024-08-20T21:38:35.2112869Z or as given by :attr:`output_size` in the call operator 2024-08-20T21:38:35.2112956Z 2024-08-20T21:38:35.2113056Z Example:: 2024-08-20T21:38:35.2113154Z 2024-08-20T21:38:35.2113374Z >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True) 2024-08-20T21:38:35.2113537Z >>> unpool = nn.MaxUnpool2d(2, stride=2) 2024-08-20T21:38:35.2113726Z >>> input = torch.tensor([[[[ 1., 2., 3., 4.], 2024-08-20T21:38:35.2113876Z [ 5., 6., 7., 8.], 2024-08-20T21:38:35.2114036Z [ 9., 10., 11., 12.], 2024-08-20T21:38:35.2114196Z [13., 14., 15., 16.]]]]) 2024-08-20T21:38:35.2114339Z >>> output, indices = pool(input) 2024-08-20T21:38:35.2114477Z >>> unpool(output, indices) 2024-08-20T21:38:35.2114620Z tensor([[[[ 0., 0., 0., 0.], 2024-08-20T21:38:35.2114749Z [ 0., 6., 0., 8.], 2024-08-20T21:38:35.2114888Z [ 0., 0., 0., 0.], 2024-08-20T21:38:35.2115018Z [ 0., 14., 0., 16.]]]]) 2024-08-20T21:38:35.2115307Z >>> # Now using output_size to resolve an ambiguous size for the inverse 2024-08-20T21:38:35.2115509Z >>> input = torch.tensor([[[[ 1., 2., 3., 4., 5.], 2024-08-20T21:38:35.2115663Z [ 6., 7., 8., 9., 10.], 2024-08-20T21:38:35.2115825Z [11., 12., 13., 14., 15.], 2024-08-20T21:38:35.2116034Z [16., 17., 18., 19., 20.]]]]) 2024-08-20T21:38:35.2116180Z >>> output, indices = pool(input) 2024-08-20T21:38:35.2116423Z >>> # This call will not work without specifying output_size 2024-08-20T21:38:35.2116627Z >>> unpool(output, indices, output_size=input.size()) 2024-08-20T21:38:35.2116768Z tensor([[[[ 0., 0., 0., 0., 0.], 2024-08-20T21:38:35.2116910Z [ 0., 7., 0., 9., 0.], 2024-08-20T21:38:35.2117038Z [ 0., 0., 0., 0., 0.], 2024-08-20T21:38:35.2117171Z [ 0., 17., 0., 19., 0.]]]]) 2024-08-20T21:38:35.2117270Z 2024-08-20T21:38:35.2117354Z 2024-08-20T21:38:35.2117446Z 2024-08-20T21:38:35.2117862Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.2117950Z 2024-08-20T21:38:35.2118058Z warnings.warn(msg) 2024-08-20T21:38:35.2118156Z 2024-08-20T21:38:35.2118364Z --- Parse Warning: 81 / 101 --- 2024-08-20T21:38:35.2119768Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=EmbeddingBag in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py line=270. 2024-08-20T21:38:35.2120189Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.2120721Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings. 2024-08-20T21:38:35.2120821Z 2024-08-20T21:38:35.2121267Z For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`, 2024-08-20T21:38:35.2121398Z and with 2D inputs, this class 2024-08-20T21:38:35.2121496Z 2024-08-20T21:38:35.2121933Z * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``, 2024-08-20T21:38:35.2122376Z * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``, 2024-08-20T21:38:35.2122814Z * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``. 2024-08-20T21:38:35.2122902Z 2024-08-20T21:38:35.2123405Z However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these 2024-08-20T21:38:35.2123560Z operations. 2024-08-20T21:38:35.2123648Z 2024-08-20T21:38:35.2124071Z EmbeddingBag also supports per-sample weights as an argument to the forward 2024-08-20T21:38:35.2124394Z pass. This scales the output of the Embedding before performing a weighted 2024-08-20T21:38:35.2124736Z reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the 2024-08-20T21:38:35.2125073Z only supported ``mode`` is ``"sum"``, which computes a weighted sum according to 2024-08-20T21:38:35.2125196Z :attr:`per_sample_weights`. 2024-08-20T21:38:35.2125281Z 2024-08-20T21:38:35.2125391Z Args: 2024-08-20T21:38:35.2125633Z num_embeddings (int): size of the dictionary of embeddings 2024-08-20T21:38:35.2125864Z embedding_dim (int): the size of each embedding vector 2024-08-20T21:38:35.2126299Z max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm` 2024-08-20T21:38:35.2126514Z is renormalized to have norm :attr:`max_norm`. 2024-08-20T21:38:35.2127213Z norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``. 2024-08-20T21:38:35.2127670Z scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of 2024-08-20T21:38:35.2127995Z the words in the mini-batch. Default ``False``. 2024-08-20T21:38:35.2128326Z Note: this option is not supported when ``mode="max"``. 2024-08-20T21:38:35.2128684Z mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag. 2024-08-20T21:38:35.2128999Z ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights` 2024-08-20T21:38:35.2129305Z into consideration. ``"mean"`` computes the average of the values 2024-08-20T21:38:35.2129562Z in the bag, ``"max"`` computes the max value over each bag. 2024-08-20T21:38:35.2129727Z Default: ``"mean"`` 2024-08-20T21:38:35.2130183Z sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See 2024-08-20T21:38:35.2130536Z Notes for more details regarding sparse gradients. Note: this option is not 2024-08-20T21:38:35.2130719Z supported when ``mode="max"``. 2024-08-20T21:38:35.2131232Z include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element 2024-08-20T21:38:35.2131564Z is equivalent to the size of `indices`. This matches the CSR format. 2024-08-20T21:38:35.2132030Z padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the 2024-08-20T21:38:35.2132402Z gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated 2024-08-20T21:38:35.2132760Z during training, i.e. it remains as a fixed "pad". For a newly constructed 2024-08-20T21:38:35.2133124Z EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all 2024-08-20T21:38:35.2133493Z zeros, but can be updated to another value to be used as the padding vector. 2024-08-20T21:38:35.2133842Z Note that the embedding vector at :attr:`padding_idx` is excluded from the 2024-08-20T21:38:35.2133977Z reduction. 2024-08-20T21:38:35.2134131Z 2024-08-20T21:38:35.2134231Z Attributes: 2024-08-20T21:38:35.2134661Z weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)` 2024-08-20T21:38:35.2134868Z initialized from :math:`\mathcal{N}(0, 1)`. 2024-08-20T21:38:35.2134955Z 2024-08-20T21:38:35.2135059Z Examples:: 2024-08-20T21:38:35.2135160Z 2024-08-20T21:38:35.2135395Z >>> # an EmbeddingBag module containing 10 tensors of size 3 2024-08-20T21:38:35.2135677Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum') 2024-08-20T21:38:35.2135862Z >>> # a batch of 2 samples of 4 indices each 2024-08-20T21:38:35.2136124Z >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long) 2024-08-20T21:38:35.2136333Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2024-08-20T21:38:35.2136585Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-08-20T21:38:35.2136727Z >>> embedding_sum(input, offsets) 2024-08-20T21:38:35.2136937Z tensor([[-0.8861, -5.4350, -0.0523], 2024-08-20T21:38:35.2137128Z [ 1.1306, -2.5798, -1.0044]]) 2024-08-20T21:38:35.2137218Z 2024-08-20T21:38:35.2137366Z >>> # Example with padding_idx 2024-08-20T21:38:35.2137725Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2) 2024-08-20T21:38:35.2137980Z >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long) 2024-08-20T21:38:35.2138186Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2024-08-20T21:38:35.2138328Z >>> embedding_sum(input, offsets) 2024-08-20T21:38:35.2138462Z tensor([[ 0.0000, 0.0000, 0.0000], 2024-08-20T21:38:35.2138714Z [-0.7082, 3.2145, -2.6251]]) 2024-08-20T21:38:35.2138803Z 2024-08-20T21:38:35.2139051Z >>> # An EmbeddingBag can be loaded from an Embedding like so 2024-08-20T21:38:35.2139244Z >>> embedding = nn.Embedding(10, 3, padding_idx=2) 2024-08-20T21:38:35.2139454Z >>> embedding_sum = nn.EmbeddingBag.from_pretrained( 2024-08-20T21:38:35.2139593Z embedding.weight, 2024-08-20T21:38:35.2139759Z padding_idx=embedding.padding_idx, 2024-08-20T21:38:35.2139909Z mode='sum') 2024-08-20T21:38:35.2140014Z 2024-08-20T21:38:35.2140413Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.2140499Z 2024-08-20T21:38:35.2140621Z warnings.warn(msg) 2024-08-20T21:38:35.2140707Z 2024-08-20T21:38:35.2140920Z --- Parse Warning: 82 / 101 --- 2024-08-20T21:38:35.2142485Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedDataParallel.join in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py line=1748. 2024-08-20T21:38:35.2142903Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.2143009Z 2024-08-20T21:38:35.2143323Z Context manager for training with uneven inputs across processes in DDP. 2024-08-20T21:38:35.2143410Z 2024-08-20T21:38:35.2143790Z This context manager will keep track of already-joined DDP processes, 2024-08-20T21:38:35.2144078Z and "shadow" the forward and backward passes by inserting collective 2024-08-20T21:38:35.2144444Z communication operations to match with the ones created by non-joined 2024-08-20T21:38:35.2144766Z DDP processes. This will ensure each collective call has a corresponding 2024-08-20T21:38:35.2145132Z call by already-joined DDP processes, preventing hangs or errors that 2024-08-20T21:38:35.2145394Z would otherwise happen when training with uneven inputs across 2024-08-20T21:38:35.2145705Z processes. Alternatively, if the flag ``throw_on_early_termination`` is 2024-08-20T21:38:35.2145997Z specified to be ``True``, all trainers will throw an error once one rank 2024-08-20T21:38:35.2146337Z runs out of inputs, allowing these errors to be caught and handled 2024-08-20T21:38:35.2146467Z according to application logic. 2024-08-20T21:38:35.2146553Z 2024-08-20T21:38:35.2147088Z Once all DDP processes have joined, the context manager will broadcast 2024-08-20T21:38:35.2147393Z the model corresponding to the last joined process to all processes to 2024-08-20T21:38:35.2147583Z ensure the model is the same across all processes 2024-08-20T21:38:35.2147721Z (which is guaranteed by DDP). 2024-08-20T21:38:35.2147807Z 2024-08-20T21:38:35.2148090Z To use this to enable training with uneven inputs across processes, 2024-08-20T21:38:35.2148406Z simply wrap this context manager around your training loop. No further 2024-08-20T21:38:35.2148625Z modifications to the model or data loading is required. 2024-08-20T21:38:35.2148713Z 2024-08-20T21:38:35.2148832Z .. warning:: 2024-08-20T21:38:35.2149130Z If the model or training loop this context manager is wrapped around 2024-08-20T21:38:35.2149383Z has additional distributed collective operations, such as 2024-08-20T21:38:35.2149703Z ``SyncBatchNorm`` in the model's forward pass, then the flag 2024-08-20T21:38:35.2149972Z ``throw_on_early_termination`` must be enabled. This is because this 2024-08-20T21:38:35.2150334Z context manager is not aware of non-DDP collective communication. 2024-08-20T21:38:35.2150564Z This flag will cause all ranks to throw when any one rank 2024-08-20T21:38:35.2150838Z exhausts inputs, allowing these errors to be caught and recovered 2024-08-20T21:38:35.2150966Z from across all ranks. 2024-08-20T21:38:35.2151176Z 2024-08-20T21:38:35.2151271Z Args: 2024-08-20T21:38:35.2151527Z divide_by_initial_world_size (bool): If ``True``, will divide 2024-08-20T21:38:35.2151804Z gradients by the initial ``world_size`` DDP training was launched 2024-08-20T21:38:35.2152037Z with. If ``False``, will compute the effective world size 2024-08-20T21:38:35.2152303Z (number of ranks that have not depleted their inputs yet) and 2024-08-20T21:38:35.2152493Z divide gradients by that during allreduce. Set 2024-08-20T21:38:35.2152740Z ``divide_by_initial_world_size=True`` to ensure every input 2024-08-20T21:38:35.2153017Z sample including the uneven inputs have equal weight in terms of 2024-08-20T21:38:35.2153249Z how much they contribute to the global gradient. This is 2024-08-20T21:38:35.2153491Z achieved by always dividing the gradient by the initial 2024-08-20T21:38:35.2153745Z ``world_size`` even when we encounter uneven inputs. If you set 2024-08-20T21:38:35.2153978Z this to ``False``, we divide the gradient by the remaining 2024-08-20T21:38:35.2154256Z number of nodes. This ensures parity with training on a smaller 2024-08-20T21:38:35.2154499Z ``world_size`` although it also means the uneven inputs would 2024-08-20T21:38:35.2154763Z contribute more towards the global gradient. Typically, you 2024-08-20T21:38:35.2155024Z would want to set this to ``True`` for cases where the last few 2024-08-20T21:38:35.2155291Z inputs of your training job are uneven. In extreme cases, where 2024-08-20T21:38:35.2155558Z there is a large discrepancy in the number of inputs, setting 2024-08-20T21:38:35.2155744Z this to ``False`` might provide better results. 2024-08-20T21:38:35.2156029Z enable (bool): Whether to enable uneven input detection or not. Pass 2024-08-20T21:38:35.2156280Z in ``enable=False`` to disable in cases where you know that 2024-08-20T21:38:35.2156529Z inputs are even across participating processes. Default is 2024-08-20T21:38:35.2156628Z ``True``. 2024-08-20T21:38:35.2156881Z throw_on_early_termination (bool): Whether to throw an error 2024-08-20T21:38:35.2157191Z or continue training when at least one rank has exhausted 2024-08-20T21:38:35.2157462Z inputs. If ``True``, will throw upon the first rank reaching end 2024-08-20T21:38:35.2157703Z of data. If ``False``, will continue training with a smaller 2024-08-20T21:38:35.2157960Z effective world size until all ranks are joined. Note that if 2024-08-20T21:38:35.2158128Z this flag is specified, then the flag 2024-08-20T21:38:35.2158356Z ``divide_by_initial_world_size`` would be ignored. Default 2024-08-20T21:38:35.2158460Z is ``False``. 2024-08-20T21:38:35.2158561Z 2024-08-20T21:38:35.2158647Z 2024-08-20T21:38:35.2158753Z Example:: 2024-08-20T21:38:35.2158853Z 2024-08-20T21:38:35.2158999Z >>> # xdoctest: +SKIP("Distributed") 2024-08-20T21:38:35.2159104Z >>> import torch 2024-08-20T21:38:35.2159264Z >>> import torch.distributed as dist 2024-08-20T21:38:35.2159371Z >>> import os 2024-08-20T21:38:35.2159532Z >>> import torch.multiprocessing as mp 2024-08-20T21:38:35.2159660Z >>> import torch.nn as nn 2024-08-20T21:38:35.2159783Z >>> # On each spawned worker 2024-08-20T21:38:35.2159892Z >>> def worker(rank): 2024-08-20T21:38:35.2160133Z >>> dist.init_process_group("nccl", rank=rank, world_size=2) 2024-08-20T21:38:35.2160270Z >>> torch.cuda.set_device(rank) 2024-08-20T21:38:35.2160460Z >>> model = nn.Linear(1, 1, bias=False).to(rank) 2024-08-20T21:38:35.2160702Z >>> model = torch.nn.parallel.DistributedDataParallel( 2024-08-20T21:38:35.2160886Z >>> model, device_ids=[rank], output_device=rank 2024-08-20T21:38:35.2161042Z >>> ) 2024-08-20T21:38:35.2161216Z >>> # Rank 1 gets one more input than rank 0. 2024-08-20T21:38:35.2161468Z >>> inputs = [torch.tensor([1]).float() for _ in range(10 + rank)] 2024-08-20T21:38:35.2161599Z >>> with model.join(): 2024-08-20T21:38:35.2161718Z >>> for _ in range(5): 2024-08-20T21:38:35.2161847Z >>> for inp in inputs: 2024-08-20T21:38:35.2162004Z >>> loss = model(inp).sum() 2024-08-20T21:38:35.2162131Z >>> loss.backward() 2024-08-20T21:38:35.2162393Z >>> # Without the join() API, the below synchronization will hang 2024-08-20T21:38:35.2162671Z >>> # blocking for rank 1's allreduce to complete. 2024-08-20T21:38:35.2162833Z >>> torch.cuda.synchronize(device=rank) 2024-08-20T21:38:35.2162932Z 2024-08-20T21:38:35.2163334Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.2163426Z 2024-08-20T21:38:35.2163549Z warnings.warn(msg) 2024-08-20T21:38:35.2163634Z 2024-08-20T21:38:35.2163845Z --- Parse Warning: 83 / 101 --- 2024-08-20T21:38:35.2165493Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedDataParallel._register_fused_optim in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py line=2039. 2024-08-20T21:38:35.2165914Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.2166002Z 2024-08-20T21:38:35.2166436Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction. 2024-08-20T21:38:35.2166526Z 2024-08-20T21:38:35.2166798Z Registers an optimizer with DDP such that the optimization for a 2024-08-20T21:38:35.2167235Z parameter will run immediately when that parameter's gradient is 2024-08-20T21:38:35.2167504Z finished with reduction, instead of waiting for all parameters' 2024-08-20T21:38:35.2167808Z gradients to finish reduction. This can result in a training speedup 2024-08-20T21:38:35.2168104Z depending on your workload since the optimizer can run while gradient 2024-08-20T21:38:35.2168474Z reduction for other parameters are still ongoing. In addition, this has 2024-08-20T21:38:35.2168784Z the potential to reduce peak memory consumption during training, as it 2024-08-20T21:38:35.2169131Z only needs to load the per-parameter optimizer states of a single 2024-08-20T21:38:35.2169483Z parameter at a time, instead of loading all per-parameter optimizer 2024-08-20T21:38:35.2169597Z states at once. 2024-08-20T21:38:35.2169683Z 2024-08-20T21:38:35.2169775Z Args: 2024-08-20T21:38:35.2170051Z optim (Type): a ``torch.optim.Optimizer`` class to be registered 2024-08-20T21:38:35.2170166Z as a fused optimizer. 2024-08-20T21:38:35.2170398Z *args (Sequence[Any]): Arguments to forward to `optim`. 2024-08-20T21:38:35.2170675Z optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters 2024-08-20T21:38:35.2170975Z to optimize, similar to `params` argument of traditional `torch.optim` 2024-08-20T21:38:35.2171258Z Optimizers. If this is omitted, all DDP model parameters will be 2024-08-20T21:38:35.2171358Z optimized. 2024-08-20T21:38:35.2171628Z **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`. 2024-08-20T21:38:35.2171728Z 2024-08-20T21:38:35.2171832Z .. warning :: 2024-08-20T21:38:35.2172108Z _register_fused_optim should only be called once on a DDP instance, 2024-08-20T21:38:35.2172393Z and registering multiple fused optimizers for the same DDP model 2024-08-20T21:38:35.2172555Z is not currently supported. Please ping 2024-08-20T21:38:35.2172858Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-08-20T21:38:35.2172980Z for your use case. 2024-08-20T21:38:35.2173115Z 2024-08-20T21:38:35.2173217Z .. warning :: 2024-08-20T21:38:35.2173473Z _register_fused_optim and register_comm_hook currently do not 2024-08-20T21:38:35.2173750Z compose together, meaning that custom DDP communication hooks are 2024-08-20T21:38:35.2173985Z not supported with overlapped optimizers. Please ping 2024-08-20T21:38:35.2174279Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-08-20T21:38:35.2174389Z for your use case. 2024-08-20T21:38:35.2174490Z 2024-08-20T21:38:35.2174592Z .. warning :: 2024-08-20T21:38:35.2174879Z Gradient accumulation and DDP `no_sync` are currently not supported 2024-08-20T21:38:35.2175050Z with overlapped optimizer. Please ping 2024-08-20T21:38:35.2175343Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-08-20T21:38:35.2175451Z for your use case. 2024-08-20T21:38:35.2175550Z 2024-08-20T21:38:35.2175652Z Example:: 2024-08-20T21:38:35.2175738Z 2024-08-20T21:38:35.2175926Z >>> # xdoctest: +SKIP("No rendezvous handler") 2024-08-20T21:38:35.2176400Z >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...') 2024-08-20T21:38:35.2176691Z >>> net = torch.nn.parallel.DistributedDataParallel(model, pg) 2024-08-20T21:38:35.2176808Z >>> lr = 1e-2 2024-08-20T21:38:35.2176918Z >>> betas = (0.9, 0.99) 2024-08-20T21:38:35.2177049Z >>> eps = 1e-6 2024-08-20T21:38:35.2177343Z >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps) 2024-08-20T21:38:35.2177499Z >>> # Example with subset of parameters 2024-08-20T21:38:35.2177688Z >>> params_to_opt = [list(net.parameters())[0]] 2024-08-20T21:38:35.2177816Z >>> net._register_fused_optim( 2024-08-20T21:38:35.2178134Z ... torch.optim.Adam, lr, optim_params=params_to_opt, betas=betas, eps=eps 2024-08-20T21:38:35.2178241Z ... ) 2024-08-20T21:38:35.2178332Z 2024-08-20T21:38:35.2178734Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.2178833Z 2024-08-20T21:38:35.2178947Z warnings.warn(msg) 2024-08-20T21:38:35.2179090Z 2024-08-20T21:38:35.2179316Z --- Parse Warning: 84 / 101 --- 2024-08-20T21:38:35.2180840Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=convert_conv2d_weight_memory_format in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/memory_format.py line=6. 2024-08-20T21:38:35.2181266Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.2181547Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``. 2024-08-20T21:38:35.2181637Z 2024-08-20T21:38:35.2182012Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2024-08-20T21:38:35.2182398Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2024-08-20T21:38:35.2182754Z This function is used to facilitate the computation to adopt NHWC kernels, which 2024-08-20T21:38:35.2183189Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2024-08-20T21:38:35.2183282Z 2024-08-20T21:38:35.2183394Z .. note:: 2024-08-20T21:38:35.2183711Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2024-08-20T21:38:35.2184008Z than the utility function ``convert_conv2d_weight_memory_format``. Any 2024-08-20T21:38:35.2184316Z layer with 4d weight will be affected by ``model.to``, which does not 2024-08-20T21:38:35.2184606Z necessarily benefit from conversion to specified ``memory_format``. 2024-08-20T21:38:35.2184920Z One place we are confident in is that NHWC(channels_last) conversion for 2024-08-20T21:38:35.2185279Z convolution in cuDNN, as it is beneficial to run convolution in NHWC, 2024-08-20T21:38:35.2185564Z even in cases where we have to apply permutation to input tensors. 2024-08-20T21:38:35.2185653Z 2024-08-20T21:38:35.2185976Z Hence our strategy here is to convert only the weight of convolution to 2024-08-20T21:38:35.2186127Z channels_last. This ensures that; 2024-08-20T21:38:35.2186429Z 1. Fast convolution kernels will be used, the benefit of which could 2024-08-20T21:38:35.2186737Z outweigh overhead of permutation (if input is not in the same format). 2024-08-20T21:38:35.2187052Z 2. No unnecessary permutations are applied on layers that do not benefit 2024-08-20T21:38:35.2187200Z from memory_format conversion. 2024-08-20T21:38:35.2187285Z 2024-08-20T21:38:35.2187599Z The optimal case is that, layers between convolution layers are channels 2024-08-20T21:38:35.2187931Z last compatible. Input tensor would be permuted to channels last when it 2024-08-20T21:38:35.2188233Z encounters the first convolution layer and stay in that memory format. 2024-08-20T21:38:35.2188545Z Hence following convolutions will not need to permute its input tensor. 2024-08-20T21:38:35.2188649Z 2024-08-20T21:38:35.2188958Z In case where a channels last incompatible layer is between convolution 2024-08-20T21:38:35.2189262Z layers, we need to permute the input tensor back to contiguous format 2024-08-20T21:38:35.2189576Z for that layer. The input tensor will go through the remaining layers in 2024-08-20T21:38:35.2189881Z contiguous format and be permuted to channels last when it encounters 2024-08-20T21:38:35.2190245Z another convolution layer. There's no point in propagating that 2024-08-20T21:38:35.2190548Z permutation to an earlier layer, as most layers are quite agnostic to 2024-08-20T21:38:35.2206216Z ``memory_format``. 2024-08-20T21:38:35.2206381Z 2024-08-20T21:38:35.2206745Z This claim might change when PyTorch supports fusion of permutation, as 2024-08-20T21:38:35.2207155Z there might have been a better spot to fuse the permutation other than 2024-08-20T21:38:35.2207467Z immediately before a convolution. 2024-08-20T21:38:35.2207549Z 2024-08-20T21:38:35.2207638Z Args: 2024-08-20T21:38:35.2207952Z module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container 2024-08-20T21:38:35.2208079Z ``nn.Module`` 2024-08-20T21:38:35.2208269Z memory_format: user specified ``memory_format``, 2024-08-20T21:38:35.2208514Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2024-08-20T21:38:35.2208594Z 2024-08-20T21:38:35.2208682Z Returns: 2024-08-20T21:38:35.2208866Z The original module with updated ``nn.Conv2d`` 2024-08-20T21:38:35.2208946Z 2024-08-20T21:38:35.2209038Z Example: 2024-08-20T21:38:35.2209220Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-20T21:38:35.2209413Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2024-08-20T21:38:35.2209735Z >>> input = torch.randint(1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda") 2024-08-20T21:38:35.2209858Z >>> model = nn.Sequential( 2024-08-20T21:38:35.2210010Z >>> nn.Conv2d(8, 4, 3)).cuda().half() 2024-08-20T21:38:35.2210152Z >>> # This is identical to: 2024-08-20T21:38:35.2210472Z >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2024-08-20T21:38:35.2210822Z >>> model = nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2024-08-20T21:38:35.2210933Z >>> out = model(input) 2024-08-20T21:38:35.2211016Z 2024-08-20T21:38:35.2211476Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.2211620Z 2024-08-20T21:38:35.2211725Z warnings.warn(msg) 2024-08-20T21:38:35.2211804Z 2024-08-20T21:38:35.2212019Z --- Parse Warning: 85 / 101 --- 2024-08-20T21:38:35.2213541Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=convert_conv3d_weight_memory_format in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/memory_format.py line=81. 2024-08-20T21:38:35.2213965Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.2214235Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format`` 2024-08-20T21:38:35.2214591Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2024-08-20T21:38:35.2214970Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2024-08-20T21:38:35.2215325Z This function is used to facilitate the computation to adopt NHWC kernels, which 2024-08-20T21:38:35.2215736Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2024-08-20T21:38:35.2215822Z 2024-08-20T21:38:35.2215921Z .. note:: 2024-08-20T21:38:35.2216253Z Calling ``model.to(memory_format=torch.channels_last_3d)`` is more aggressive 2024-08-20T21:38:35.2216543Z than the utility function ``convert_conv3d_weight_memory_format``. Any 2024-08-20T21:38:35.2216830Z layer with 4d weight will be affected by ``model.to``, which does not 2024-08-20T21:38:35.2217114Z necessarily benefit from conversion to specified ``memory_format``. 2024-08-20T21:38:35.2217438Z One place we are confident in is that NDHWC(channels_last_3d) conversion for 2024-08-20T21:38:35.2217735Z convolution in cuDNN, as it is beneficial to run convolution in NDHWC, 2024-08-20T21:38:35.2218013Z even in cases where we have to apply permutation to input tensors. 2024-08-20T21:38:35.2218098Z 2024-08-20T21:38:35.2218400Z Hence our strategy here is to convert only the weight of convolution to 2024-08-20T21:38:35.2218547Z channels_last_3d. This ensures that; 2024-08-20T21:38:35.2218881Z 1. Fast convolution kernels will be used, the benefit of which could 2024-08-20T21:38:35.2219179Z outweigh overhead of permutation (if input is not in the same format). 2024-08-20T21:38:35.2219485Z 2. No unnecessary permutations are applied on layers that do not benefit 2024-08-20T21:38:35.2219613Z from memory_format conversion. 2024-08-20T21:38:35.2219696Z 2024-08-20T21:38:35.2220002Z The optimal case is that, layers between convolution layers are channels 2024-08-20T21:38:35.2220307Z last compatible. Input tensor would be permuted to channels last when it 2024-08-20T21:38:35.2220607Z encounters the first convolution layer and stay in that memory format. 2024-08-20T21:38:35.2220916Z Hence following convolutions will not need to permute its input tensor. 2024-08-20T21:38:35.2220995Z 2024-08-20T21:38:35.2221297Z In case where a channels last incompatible layer is between convolution 2024-08-20T21:38:35.2221590Z layers, we need to permute the input tensor back to contiguous format 2024-08-20T21:38:35.2221896Z for that layer. The input tensor will go through the remaining layers in 2024-08-20T21:38:35.2222191Z contiguous format and be permuted to channels last when it encounters 2024-08-20T21:38:35.2222533Z another convolution layer. There's no point in propagating that 2024-08-20T21:38:35.2222826Z permutation to an earlier layer, as most layers are quite agnostic to 2024-08-20T21:38:35.2222928Z ``memory_format``. 2024-08-20T21:38:35.2223007Z 2024-08-20T21:38:35.2223317Z This claim might change when PyTorch supports fusion of permutation, as 2024-08-20T21:38:35.2223662Z there might have been a better spot to fuse the permutation other than 2024-08-20T21:38:35.2223805Z immediately before a convolution. 2024-08-20T21:38:35.2223889Z 2024-08-20T21:38:35.2223975Z Args: 2024-08-20T21:38:35.2224268Z module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container 2024-08-20T21:38:35.2224390Z ``nn.Module`` 2024-08-20T21:38:35.2224575Z memory_format: user specified ``memory_format``, 2024-08-20T21:38:35.2224812Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2024-08-20T21:38:35.2224891Z 2024-08-20T21:38:35.2224978Z Returns: 2024-08-20T21:38:35.2225163Z The original module with updated ``nn.Conv3d`` 2024-08-20T21:38:35.2225242Z 2024-08-20T21:38:35.2225330Z Example: 2024-08-20T21:38:35.2225512Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-20T21:38:35.2225709Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2024-08-20T21:38:35.2226036Z >>> input = torch.randint(1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda") 2024-08-20T21:38:35.2226158Z >>> model = nn.Sequential( 2024-08-20T21:38:35.2226307Z >>> nn.Conv3d(8, 4, 3)).cuda().half() 2024-08-20T21:38:35.2226425Z >>> # This is identical to: 2024-08-20T21:38:35.2226759Z >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2024-08-20T21:38:35.2227115Z >>> model = nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2024-08-20T21:38:35.2227227Z >>> out = model(input) 2024-08-20T21:38:35.2227310Z 2024-08-20T21:38:35.2227706Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.2227788Z 2024-08-20T21:38:35.2227891Z warnings.warn(msg) 2024-08-20T21:38:35.2227969Z 2024-08-20T21:38:35.2228179Z --- Parse Warning: 86 / 101 --- 2024-08-20T21:38:35.2229554Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=random_structured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=936. 2024-08-20T21:38:35.2230017Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.2230319Z Prune tensor by removing random channels along the specified dimension. 2024-08-20T21:38:35.2230398Z 2024-08-20T21:38:35.2230697Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2024-08-20T21:38:35.2230986Z by removing the specified ``amount`` of (currently unpruned) channels 2024-08-20T21:38:35.2231171Z along the specified ``dim`` selected at random. 2024-08-20T21:38:35.2231445Z Modifies module in place (and also return the modified module) 2024-08-20T21:38:35.2231539Z by: 2024-08-20T21:38:35.2231628Z 2024-08-20T21:38:35.2232012Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-08-20T21:38:35.2232306Z binary mask applied to the parameter ``name`` by the pruning method. 2024-08-20T21:38:35.2232593Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-08-20T21:38:35.2232883Z original (unpruned) parameter is stored in a new parameter named 2024-08-20T21:38:35.2233024Z ``name+'_orig'``. 2024-08-20T21:38:35.2233126Z 2024-08-20T21:38:35.2233220Z Args: 2024-08-20T21:38:35.2233453Z module (nn.Module): module containing the tensor to prune 2024-08-20T21:38:35.2233711Z name (str): parameter name within ``module`` on which pruning 2024-08-20T21:38:35.2233815Z will act. 2024-08-20T21:38:35.2234046Z amount (int or float): quantity of parameters to prune. 2024-08-20T21:38:35.2234302Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-08-20T21:38:35.2234699Z fraction of parameters to prune. If ``int``, it represents the 2024-08-20T21:38:35.2234874Z absolute number of parameters to prune. 2024-08-20T21:38:35.2235173Z dim (int): index of the dim along which we define channels to prune. 2024-08-20T21:38:35.2235266Z 2024-08-20T21:38:35.2235363Z Returns: 2024-08-20T21:38:35.2235679Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-08-20T21:38:35.2235768Z 2024-08-20T21:38:35.2235882Z Examples: 2024-08-20T21:38:35.2236000Z >>> # xdoctest: +SKIP 2024-08-20T21:38:35.2236145Z >>> m = prune.random_structured( 2024-08-20T21:38:35.2236410Z ... nn.Linear(5, 3), 'weight', amount=3, dim=1 2024-08-20T21:38:35.2236504Z ... ) 2024-08-20T21:38:35.2236744Z >>> columns_pruned = int(sum(torch.sum(m.weight, dim=0) == 0)) 2024-08-20T21:38:35.2236881Z >>> print(columns_pruned) 2024-08-20T21:38:35.2236977Z 3 2024-08-20T21:38:35.2237069Z 2024-08-20T21:38:35.2237481Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.2237568Z 2024-08-20T21:38:35.2237681Z warnings.warn(msg) 2024-08-20T21:38:35.2237786Z 2024-08-20T21:38:35.2237998Z --- Parse Warning: 87 / 101 --- 2024-08-20T21:38:35.2239371Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ln_structured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=977. 2024-08-20T21:38:35.2239802Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.2240319Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension. 2024-08-20T21:38:35.2240421Z 2024-08-20T21:38:35.2240726Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2024-08-20T21:38:35.2241027Z by removing the specified ``amount`` of (currently unpruned) channels 2024-08-20T21:38:35.2241340Z along the specified ``dim`` with the lowest L\ ``n``-norm. 2024-08-20T21:38:35.2241600Z Modifies module in place (and also return the modified module) 2024-08-20T21:38:35.2241744Z by: 2024-08-20T21:38:35.2241844Z 2024-08-20T21:38:35.2242210Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-08-20T21:38:35.2242504Z binary mask applied to the parameter ``name`` by the pruning method. 2024-08-20T21:38:35.2242803Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-08-20T21:38:35.2243074Z original (unpruned) parameter is stored in a new parameter named 2024-08-20T21:38:35.2243233Z ``name+'_orig'``. 2024-08-20T21:38:35.2243321Z 2024-08-20T21:38:35.2243415Z Args: 2024-08-20T21:38:35.2243665Z module (nn.Module): module containing the tensor to prune 2024-08-20T21:38:35.2243916Z name (str): parameter name within ``module`` on which pruning 2024-08-20T21:38:35.2244022Z will act. 2024-08-20T21:38:35.2244259Z amount (int or float): quantity of parameters to prune. 2024-08-20T21:38:35.2244506Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-08-20T21:38:35.2244773Z fraction of parameters to prune. If ``int``, it represents the 2024-08-20T21:38:35.2244957Z absolute number of parameters to prune. 2024-08-20T21:38:35.2245299Z n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid 2024-08-20T21:38:35.2245503Z entries for argument ``p`` in :func:`torch.norm`. 2024-08-20T21:38:35.2245799Z dim (int): index of the dim along which we define channels to prune. 2024-08-20T21:38:35.2246101Z importance_scores (torch.Tensor): tensor of importance scores (of same 2024-08-20T21:38:35.2246424Z shape as module parameter) used to compute mask for pruning. 2024-08-20T21:38:35.2247063Z The values in this tensor indicate the importance of the corresponding 2024-08-20T21:38:35.2247237Z elements in the parameter being pruned. 2024-08-20T21:38:35.2247572Z If unspecified or None, the module parameter will be used in its place. 2024-08-20T21:38:35.2247660Z 2024-08-20T21:38:35.2247756Z Returns: 2024-08-20T21:38:35.2248070Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-08-20T21:38:35.2248158Z 2024-08-20T21:38:35.2248257Z Examples: 2024-08-20T21:38:35.2248425Z >>> from torch.nn.utils import prune 2024-08-20T21:38:35.2248557Z >>> m = prune.ln_structured( 2024-08-20T21:38:35.2248916Z ... nn.Conv2d(5, 3, 2), 'weight', amount=0.3, dim=1, n=float('-inf') 2024-08-20T21:38:35.2249021Z ... ) 2024-08-20T21:38:35.2249113Z 2024-08-20T21:38:35.2249526Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.2249613Z 2024-08-20T21:38:35.2249723Z warnings.warn(msg) 2024-08-20T21:38:35.2249821Z 2024-08-20T21:38:35.2250036Z --- Parse Warning: 88 / 101 --- 2024-08-20T21:38:35.2251443Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=global_unstructured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=1024. 2024-08-20T21:38:35.2251869Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.2251956Z 2024-08-20T21:38:35.2252517Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``. 2024-08-20T21:38:35.2252617Z 2024-08-20T21:38:35.2252744Z Modifies modules in place by: 2024-08-20T21:38:35.2252835Z 2024-08-20T21:38:35.2253209Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-08-20T21:38:35.2253503Z binary mask applied to the parameter ``name`` by the pruning method. 2024-08-20T21:38:35.2253798Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-08-20T21:38:35.2254210Z original (unpruned) parameter is stored in a new parameter named 2024-08-20T21:38:35.2254352Z ``name+'_orig'``. 2024-08-20T21:38:35.2254454Z 2024-08-20T21:38:35.2254547Z Args: 2024-08-20T21:38:35.2254804Z parameters (Iterable of (module, name) tuples): parameters of 2024-08-20T21:38:35.2255087Z the model to prune in a global fashion, i.e. by aggregating all 2024-08-20T21:38:35.2255362Z weights prior to deciding which ones to prune. module must be of 2024-08-20T21:38:35.2255561Z type :class:`nn.Module`, and name must be a string. 2024-08-20T21:38:35.2255867Z pruning_method (function): a valid pruning function from this module, 2024-08-20T21:38:35.2256117Z or a custom one implemented by the user that satisfies the 2024-08-20T21:38:35.2256499Z implementation guidelines and has ``PRUNING_TYPE='unstructured'``. 2024-08-20T21:38:35.2256798Z importance_scores (dict): a dictionary mapping (module, name) tuples to 2024-08-20T21:38:35.2257164Z the corresponding parameter's importance scores tensor. The tensor 2024-08-20T21:38:35.2257474Z should be the same shape as the parameter, and is used for computing 2024-08-20T21:38:35.2257587Z mask for pruning. 2024-08-20T21:38:35.2257872Z If unspecified or None, the parameter will be used in place of its 2024-08-20T21:38:35.2257997Z importance scores. 2024-08-20T21:38:35.2258157Z kwargs: other keyword arguments such as: 2024-08-20T21:38:35.2258429Z amount (int or float): quantity of parameters to prune across the 2024-08-20T21:38:35.2258560Z specified parameters. 2024-08-20T21:38:35.2258871Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-08-20T21:38:35.2259150Z fraction of parameters to prune. If ``int``, it represents the 2024-08-20T21:38:35.2259316Z absolute number of parameters to prune. 2024-08-20T21:38:35.2259408Z 2024-08-20T21:38:35.2259513Z Raises: 2024-08-20T21:38:35.2259769Z TypeError: if ``PRUNING_TYPE != 'unstructured'`` 2024-08-20T21:38:35.2259857Z 2024-08-20T21:38:35.2259965Z Note: 2024-08-20T21:38:35.2260316Z Since global structured pruning doesn't make much sense unless the 2024-08-20T21:38:35.2260589Z norm is normalized by the size of the parameter, we now limit the 2024-08-20T21:38:35.2260798Z scope of global pruning to unstructured methods. 2024-08-20T21:38:35.2260886Z 2024-08-20T21:38:35.2260983Z Examples: 2024-08-20T21:38:35.2261147Z >>> from torch.nn.utils import prune 2024-08-20T21:38:35.2261302Z >>> from collections import OrderedDict 2024-08-20T21:38:35.2261457Z >>> net = nn.Sequential(OrderedDict([ 2024-08-20T21:38:35.2261658Z ... ('first', nn.Linear(10, 4)), 2024-08-20T21:38:35.2261843Z ... ('second', nn.Linear(4, 1)), 2024-08-20T21:38:35.2261935Z ... ])) 2024-08-20T21:38:35.2262075Z >>> parameters_to_prune = ( 2024-08-20T21:38:35.2262242Z ... (net.first, 'weight'), 2024-08-20T21:38:35.2262409Z ... (net.second, 'weight'), 2024-08-20T21:38:35.2262512Z ... ) 2024-08-20T21:38:35.2262642Z >>> prune.global_unstructured( 2024-08-20T21:38:35.2262775Z ... parameters_to_prune, 2024-08-20T21:38:35.2262947Z ... pruning_method=prune.L1Unstructured, 2024-08-20T21:38:35.2263050Z ... amount=10, 2024-08-20T21:38:35.2263152Z ... ) 2024-08-20T21:38:35.2263450Z >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0)) 2024-08-20T21:38:35.2263547Z tensor(10) 2024-08-20T21:38:35.2263646Z 2024-08-20T21:38:35.2263736Z 2024-08-20T21:38:35.2264136Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.2264235Z 2024-08-20T21:38:35.2264345Z warnings.warn(msg) 2024-08-20T21:38:35.2264488Z 2024-08-20T21:38:35.2264712Z --- Parse Warning: 89 / 101 --- 2024-08-20T21:38:35.2266094Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=custom_from_mask in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=1143. 2024-08-20T21:38:35.2266511Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.2267166Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``. 2024-08-20T21:38:35.2267255Z 2024-08-20T21:38:35.2267553Z Modifies module in place (and also return the modified module) by: 2024-08-20T21:38:35.2267641Z 2024-08-20T21:38:35.2268010Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-08-20T21:38:35.2268316Z binary mask applied to the parameter ``name`` by the pruning method. 2024-08-20T21:38:35.2268609Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-08-20T21:38:35.2268879Z original (unpruned) parameter is stored in a new parameter named 2024-08-20T21:38:35.2269035Z ``name+'_orig'``. 2024-08-20T21:38:35.2269122Z 2024-08-20T21:38:35.2269216Z Args: 2024-08-20T21:38:35.2269464Z module (nn.Module): module containing the tensor to prune 2024-08-20T21:38:35.2269710Z name (str): parameter name within ``module`` on which pruning 2024-08-20T21:38:35.2269812Z will act. 2024-08-20T21:38:35.2270062Z mask (Tensor): binary mask to be applied to the parameter. 2024-08-20T21:38:35.2270149Z 2024-08-20T21:38:35.2270256Z Returns: 2024-08-20T21:38:35.2270610Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-08-20T21:38:35.2270698Z 2024-08-20T21:38:35.2270808Z Examples: 2024-08-20T21:38:35.2270964Z >>> from torch.nn.utils import prune 2024-08-20T21:38:35.2271106Z >>> m = prune.custom_from_mask( 2024-08-20T21:38:35.2271437Z ... nn.Linear(5, 3), name='bias', mask=torch.tensor([0, 1, 0]) 2024-08-20T21:38:35.2271531Z ... ) 2024-08-20T21:38:35.2271645Z >>> print(m.bias_mask) 2024-08-20T21:38:35.2271769Z tensor([0., 1., 0.]) 2024-08-20T21:38:35.2271857Z 2024-08-20T21:38:35.2271947Z 2024-08-20T21:38:35.2272362Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.2272450Z 2024-08-20T21:38:35.2272559Z warnings.warn(msg) 2024-08-20T21:38:35.2272657Z 2024-08-20T21:38:35.2272866Z --- Parse Warning: 90 / 101 --- 2024-08-20T21:38:35.2274247Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=AveragedModel in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py line=106. 2024-08-20T21:38:35.2274675Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.2275156Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA). 2024-08-20T21:38:35.2275256Z 2024-08-20T21:38:35.2275575Z Stochastic Weight Averaging was proposed in `Averaging Weights Leads to 2024-08-20T21:38:35.2275867Z Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii 2024-08-20T21:38:35.2276158Z Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson 2024-08-20T21:38:35.2276257Z (UAI 2018). 2024-08-20T21:38:35.2276344Z 2024-08-20T21:38:35.2276633Z Exponential Moving Average is a variation of `Polyak averaging`_, 2024-08-20T21:38:35.2276957Z but using exponential weights instead of equal weights across iterations. 2024-08-20T21:38:35.2277044Z 2024-08-20T21:38:35.2277370Z AveragedModel class creates a copy of the provided module :attr:`model` 2024-08-20T21:38:35.2277738Z on the device :attr:`device` and allows to compute running averages of the 2024-08-20T21:38:35.2277888Z parameters of the :attr:`model`. 2024-08-20T21:38:35.2277976Z 2024-08-20T21:38:35.2278070Z Args: 2024-08-20T21:38:35.2278288Z model (torch.nn.Module): model to use with SWA/EMA 2024-08-20T21:38:35.2278606Z device (torch.device, optional): if provided, the averaged model will be 2024-08-20T21:38:35.2278746Z stored on the :attr:`device` 2024-08-20T21:38:35.2279033Z avg_fn (function, optional): the averaging function used to update 2024-08-20T21:38:35.2279300Z parameters; the function must take in the current value of the 2024-08-20T21:38:35.2279595Z :class:`AveragedModel` parameter, the current value of :attr:`model` 2024-08-20T21:38:35.2279874Z parameter, and the number of models already averaged; if None, 2024-08-20T21:38:35.2280084Z an equally weighted average is used (default: None) 2024-08-20T21:38:35.2280404Z multi_avg_fn (function, optional): the averaging function used to update 2024-08-20T21:38:35.2280718Z parameters inplace; the function must take in the current values of the 2024-08-20T21:38:35.2281074Z :class:`AveragedModel` parameters as a list, the current values of :attr:`model` 2024-08-20T21:38:35.2281409Z parameters as a list, and the number of models already averaged; if None, 2024-08-20T21:38:35.2281618Z an equally weighted average is used (default: None) 2024-08-20T21:38:35.2281899Z use_buffers (bool): if ``True``, it will compute running averages for 2024-08-20T21:38:35.2282269Z both the parameters and the buffers of the model. (default: ``False``) 2024-08-20T21:38:35.2282361Z 2024-08-20T21:38:35.2282458Z Example: 2024-08-20T21:38:35.2282646Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:38:35.2282818Z >>> loader, optimizer, model, loss_fn = ... 2024-08-20T21:38:35.2283070Z >>> swa_model = torch.optim.swa_utils.AveragedModel(model) 2024-08-20T21:38:35.2283364Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 2024-08-20T21:38:35.2283519Z >>> T_max=300) 2024-08-20T21:38:35.2283644Z >>> swa_start = 160 2024-08-20T21:38:35.2283831Z >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05) 2024-08-20T21:38:35.2283950Z >>> for i in range(300): 2024-08-20T21:38:35.2284113Z >>> for input, target in loader: 2024-08-20T21:38:35.2284256Z >>> optimizer.zero_grad() 2024-08-20T21:38:35.2284438Z >>> loss_fn(model(input), target).backward() 2024-08-20T21:38:35.2284582Z >>> optimizer.step() 2024-08-20T21:38:35.2284701Z >>> if i > swa_start: 2024-08-20T21:38:35.2284871Z >>> swa_model.update_parameters(model) 2024-08-20T21:38:35.2285029Z >>> swa_scheduler.step() 2024-08-20T21:38:35.2285130Z >>> else: 2024-08-20T21:38:35.2285268Z >>> scheduler.step() 2024-08-20T21:38:35.2285362Z >>> 2024-08-20T21:38:35.2285572Z >>> # Update bn statistics for the swa_model at the end 2024-08-20T21:38:35.2285800Z >>> torch.optim.swa_utils.update_bn(loader, swa_model) 2024-08-20T21:38:35.2285888Z 2024-08-20T21:38:35.2286302Z You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters. 2024-08-20T21:38:35.2286579Z If no averaging function is provided, the default is to compute 2024-08-20T21:38:35.2286834Z equally-weighted average of the weights (SWA). 2024-08-20T21:38:35.2287042Z 2024-08-20T21:38:35.2287161Z Example: 2024-08-20T21:38:35.2287333Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:38:35.2287601Z >>> # Compute exponential moving averages of the weights and buffers 2024-08-20T21:38:35.2287913Z >>> ema_model = torch.optim.swa_utils.AveragedModel(model, 2024-08-20T21:38:35.2288209Z >>> torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True) 2024-08-20T21:38:35.2288309Z 2024-08-20T21:38:35.2288414Z .. note:: 2024-08-20T21:38:35.2288716Z When using SWA/EMA with models containing Batch Normalization you may 2024-08-20T21:38:35.2289011Z need to update the activation statistics for Batch Normalization. 2024-08-20T21:38:35.2289345Z This can be done either by using the :meth:`torch.optim.swa_utils.update_bn` 2024-08-20T21:38:35.2289666Z or by setting :attr:`use_buffers` to `True`. The first approach updates the 2024-08-20T21:38:35.2290097Z statistics in a post-training step by passing data through the model. The 2024-08-20T21:38:35.2290426Z second does it during the parameter update phase by averaging all buffers. 2024-08-20T21:38:35.2290759Z Empirical evidence has shown that updating the statistics in normalization 2024-08-20T21:38:35.2291073Z layers increases accuracy, but you may wish to empirically test which 2024-08-20T21:38:35.2291276Z approach yields the best results in your problem. 2024-08-20T21:38:35.2291375Z 2024-08-20T21:38:35.2291472Z .. note:: 2024-08-20T21:38:35.2291839Z :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model. 2024-08-20T21:38:35.2291939Z 2024-08-20T21:38:35.2292034Z .. note:: 2024-08-20T21:38:35.2292301Z When :meth:`update_parameters` is called for the first time (i.e. 2024-08-20T21:38:35.2292627Z :attr:`n_averaged` is `0`) the parameters of `model` are copied 2024-08-20T21:38:35.2292905Z to the parameters of :class:`AveragedModel`. For every subsequent 2024-08-20T21:38:35.2293158Z call of :meth:`update_parameters` the function `avg_fn` is used 2024-08-20T21:38:35.2293302Z to update the parameters. 2024-08-20T21:38:35.2293390Z 2024-08-20T21:38:35.2293682Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2024-08-20T21:38:35.2293849Z https://arxiv.org/abs/1803.05407 2024-08-20T21:38:35.2294170Z .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should 2024-08-20T21:38:35.2294284Z Average: 2024-08-20T21:38:35.2294427Z https://arxiv.org/abs/1806.05594 2024-08-20T21:38:35.2294771Z .. _SWALP: Stochastic Weight Averaging in Low-Precision Training: 2024-08-20T21:38:35.2294933Z https://arxiv.org/abs/1904.11943 2024-08-20T21:38:35.2295303Z .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That 2024-08-20T21:38:35.2295418Z Generalizes Well: 2024-08-20T21:38:35.2295573Z https://arxiv.org/abs/2001.02312 2024-08-20T21:38:35.2295684Z .. _Polyak averaging: 2024-08-20T21:38:35.2295976Z https://paperswithcode.com/method/polyak-averaging 2024-08-20T21:38:35.2296079Z 2024-08-20T21:38:35.2296479Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.2296567Z 2024-08-20T21:38:35.2296688Z warnings.warn(msg) 2024-08-20T21:38:35.2296775Z 2024-08-20T21:38:35.2296987Z --- Parse Warning: 91 / 101 --- 2024-08-20T21:38:35.2298325Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SWALR in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py line=357. 2024-08-20T21:38:35.2298745Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.2299044Z Anneals the learning rate in each parameter group to a fixed value. 2024-08-20T21:38:35.2299132Z 2024-08-20T21:38:35.2299443Z This learning rate scheduler is meant to be used with Stochastic Weight 2024-08-20T21:38:35.2299798Z Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`). 2024-08-20T21:38:35.2299884Z 2024-08-20T21:38:35.2299978Z Args: 2024-08-20T21:38:35.2300207Z optimizer (torch.optim.Optimizer): wrapped optimizer 2024-08-20T21:38:35.2300496Z swa_lrs (float or list): the learning rate value for all param groups 2024-08-20T21:38:35.2300664Z together or separately for each group. 2024-08-20T21:38:35.2300940Z annealing_epochs (int): number of epochs in the annealing phase 2024-08-20T21:38:35.2301050Z (default: 10) 2024-08-20T21:38:35.2301348Z annealing_strategy (str): "cos" or "linear"; specifies the annealing 2024-08-20T21:38:35.2301630Z strategy: "cos" for cosine annealing, "linear" for linear annealing 2024-08-20T21:38:35.2301740Z (default: "cos") 2024-08-20T21:38:35.2302065Z last_epoch (int): the index of the last epoch (default: -1) 2024-08-20T21:38:35.2302156Z 2024-08-20T21:38:35.2302398Z The :class:`SWALR` scheduler can be used together with other 2024-08-20T21:38:35.2302709Z schedulers to switch to a constant learning rate late in the training 2024-08-20T21:38:35.2302828Z as in the example below. 2024-08-20T21:38:35.2302915Z 2024-08-20T21:38:35.2303023Z Example: 2024-08-20T21:38:35.2303195Z >>> # xdoctest: +SKIP("Undefined variables") 2024-08-20T21:38:35.2303343Z >>> loader, optimizer, model = ... 2024-08-20T21:38:35.2303499Z >>> lr_lambda = lambda epoch: 0.9 2024-08-20T21:38:35.2303789Z >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, 2024-08-20T21:38:35.2303981Z >>> lr_lambda=lr_lambda) 2024-08-20T21:38:35.2304209Z >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, 2024-08-20T21:38:35.2304441Z >>> anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05) 2024-08-20T21:38:35.2304570Z >>> swa_start = 160 2024-08-20T21:38:35.2304690Z >>> for i in range(300): 2024-08-20T21:38:35.2304838Z >>> for input, target in loader: 2024-08-20T21:38:35.2304994Z >>> optimizer.zero_grad() 2024-08-20T21:38:35.2305172Z >>> loss_fn(model(input), target).backward() 2024-08-20T21:38:35.2305300Z >>> optimizer.step() 2024-08-20T21:38:35.2305435Z >>> if i > swa_start: 2024-08-20T21:38:35.2305576Z >>> swa_scheduler.step() 2024-08-20T21:38:35.2305676Z >>> else: 2024-08-20T21:38:35.2305813Z >>> scheduler.step() 2024-08-20T21:38:35.2305900Z 2024-08-20T21:38:35.2306198Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2024-08-20T21:38:35.2306356Z https://arxiv.org/abs/1803.05407 2024-08-20T21:38:35.2306447Z 2024-08-20T21:38:35.2306869Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.2306955Z 2024-08-20T21:38:35.2307064Z warnings.warn(msg) 2024-08-20T21:38:35.2307162Z 2024-08-20T21:38:35.2307371Z --- Parse Warning: 92 / 101 --- 2024-08-20T21:38:35.2308765Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_close in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_comparison.py line=1274. 2024-08-20T21:38:35.2309196Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.2309389Z Asserts that ``actual`` and ``expected`` are close. 2024-08-20T21:38:35.2309477Z 2024-08-20T21:38:35.2310103Z If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if 2024-08-20T21:38:35.2310191Z 2024-08-20T21:38:35.2310290Z .. math:: 2024-08-20T21:38:35.2310451Z 2024-08-20T21:38:35.2311095Z \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert 2024-08-20T21:38:35.2311194Z 2024-08-20T21:38:35.2311783Z Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are 2024-08-20T21:38:35.2312053Z only considered equal to each other if ``equal_nan`` is ``True``. 2024-08-20T21:38:35.2312154Z 2024-08-20T21:38:35.2312428Z In addition, they are only considered close if they have the same 2024-08-20T21:38:35.2312515Z 2024-08-20T21:38:35.2312851Z - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``), 2024-08-20T21:38:35.2313075Z - ``dtype`` (if ``check_dtype`` is ``True``), 2024-08-20T21:38:35.2313316Z - ``layout`` (if ``check_layout`` is ``True``), and 2024-08-20T21:38:35.2313538Z - stride (if ``check_stride`` is ``True``). 2024-08-20T21:38:35.2313625Z 2024-08-20T21:38:35.2314057Z If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed. 2024-08-20T21:38:35.2314157Z 2024-08-20T21:38:35.2314673Z If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are 2024-08-20T21:38:35.2315186Z checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR, 2024-08-20T21:38:35.2315499Z or ``ccol_indices`` and ``row_indices`` for CSC and BSC layouts, respectively, 2024-08-20T21:38:35.2316027Z are always checked for equality whereas the values are checked for closeness according to the definition above. 2024-08-20T21:38:35.2316128Z 2024-08-20T21:38:35.2316577Z If ``actual`` and ``expected`` are quantized, they are considered close if they have the same 2024-08-20T21:38:35.2317065Z :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the 2024-08-20T21:38:35.2317191Z definition above. 2024-08-20T21:38:35.2317277Z 2024-08-20T21:38:35.2317822Z ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which 2024-08-20T21:38:35.2318452Z :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types 2024-08-20T21:38:35.2319019Z have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s 2024-08-20T21:38:35.2319667Z or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all 2024-08-20T21:38:35.2319975Z their elements are considered close according to the above definition. 2024-08-20T21:38:35.2320063Z 2024-08-20T21:38:35.2320174Z .. note:: 2024-08-20T21:38:35.2320262Z 2024-08-20T21:38:35.2320721Z Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e. 2024-08-20T21:38:35.2321297Z :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus, 2024-08-20T21:38:35.2321677Z Python scalars of different types can be checked, but require ``check_dtype=False``. 2024-08-20T21:38:35.2321776Z 2024-08-20T21:38:35.2321870Z Args: 2024-08-20T21:38:35.2321998Z actual (Any): Actual input. 2024-08-20T21:38:35.2322152Z expected (Any): Expected input. 2024-08-20T21:38:35.2322641Z allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types 2024-08-20T21:38:35.2322855Z are allowed. Otherwise type equality is required. 2024-08-20T21:38:35.2323363Z rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default 2024-08-20T21:38:35.2323734Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2024-08-20T21:38:35.2324291Z atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default 2024-08-20T21:38:35.2324659Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2024-08-20T21:38:35.2325009Z equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal. 2024-08-20T21:38:35.2325418Z check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same 2024-08-20T21:38:35.2325755Z :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different 2024-08-20T21:38:35.2326165Z :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared. 2024-08-20T21:38:35.2326657Z check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this 2024-08-20T21:38:35.2327335Z check is disabled, tensors with different ``dtype``'s are promoted to a common ``dtype`` (according to 2024-08-20T21:38:35.2327564Z :func:`torch.promote_types`) before being compared. 2024-08-20T21:38:35.2328057Z check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this 2024-08-20T21:38:35.2328619Z check is disabled, tensors with different ``layout``'s are converted to strided tensors before being 2024-08-20T21:38:35.2328738Z compared. 2024-08-20T21:38:35.2329236Z check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride. 2024-08-20T21:38:35.2329779Z msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during 2024-08-20T21:38:35.2330297Z the comparison. Can also passed as callable in which case it will be called with the generated message and 2024-08-20T21:38:35.2330449Z should return the new message. 2024-08-20T21:38:35.2330548Z 2024-08-20T21:38:35.2330643Z Raises: 2024-08-20T21:38:35.2330958Z ValueError: If no :class:`torch.Tensor` can be constructed from an input. 2024-08-20T21:38:35.2331189Z ValueError: If only ``rtol`` or ``atol`` is specified. 2024-08-20T21:38:35.2331614Z AssertionError: If corresponding inputs are not Python scalars and are not directly related. 2024-08-20T21:38:35.2332100Z AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have 2024-08-20T21:38:35.2332230Z different types. 2024-08-20T21:38:35.2332826Z AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match. 2024-08-20T21:38:35.2333437Z AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match. 2024-08-20T21:38:35.2333858Z AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`. 2024-08-20T21:38:35.2334259Z AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same 2024-08-20T21:38:35.2334424Z :attr:`~torch.Tensor.layout`. 2024-08-20T21:38:35.2334710Z AssertionError: If only one of corresponding tensors is quantized. 2024-08-20T21:38:35.2335325Z AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s. 2024-08-20T21:38:35.2335736Z AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same 2024-08-20T21:38:35.2335888Z :attr:`~torch.Tensor.device`. 2024-08-20T21:38:35.2336352Z AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``. 2024-08-20T21:38:35.2336836Z AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride. 2024-08-20T21:38:35.2337375Z AssertionError: If the values of corresponding tensors are not close according to the definition above. 2024-08-20T21:38:35.2337476Z 2024-08-20T21:38:35.2338087Z The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching 2024-08-20T21:38:35.2338348Z ``dtype``'s, the maximum of both tolerances is used. 2024-08-20T21:38:35.2338449Z 2024-08-20T21:38:35.2338667Z +---------------------------+------------+----------+ 2024-08-20T21:38:35.2338843Z | ``dtype`` | ``rtol`` | ``atol`` | 2024-08-20T21:38:35.2339007Z +===========================+============+==========+ 2024-08-20T21:38:35.2339257Z | :attr:`~torch.float16` | ``1e-3`` | ``1e-5`` | 2024-08-20T21:38:35.2339482Z +---------------------------+------------+----------+ 2024-08-20T21:38:35.2339729Z | :attr:`~torch.bfloat16` | ``1.6e-2`` | ``1e-5`` | 2024-08-20T21:38:35.2339945Z +---------------------------+------------+----------+ 2024-08-20T21:38:35.2340201Z | :attr:`~torch.float32` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:38:35.2340412Z +---------------------------+------------+----------+ 2024-08-20T21:38:35.2340655Z | :attr:`~torch.float64` | ``1e-7`` | ``1e-7`` | 2024-08-20T21:38:35.2340875Z +---------------------------+------------+----------+ 2024-08-20T21:38:35.2341119Z | :attr:`~torch.complex32` | ``1e-3`` | ``1e-5`` | 2024-08-20T21:38:35.2341327Z +---------------------------+------------+----------+ 2024-08-20T21:38:35.2341583Z | :attr:`~torch.complex64` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:38:35.2341865Z +---------------------------+------------+----------+ 2024-08-20T21:38:35.2342131Z | :attr:`~torch.complex128` | ``1e-7`` | ``1e-7`` | 2024-08-20T21:38:35.2342338Z +---------------------------+------------+----------+ 2024-08-20T21:38:35.2342584Z | :attr:`~torch.quint8` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:38:35.2342808Z +---------------------------+------------+----------+ 2024-08-20T21:38:35.2343050Z | :attr:`~torch.quint2x4` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:38:35.2343260Z +---------------------------+------------+----------+ 2024-08-20T21:38:35.2343515Z | :attr:`~torch.quint4x2` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:38:35.2343722Z +---------------------------+------------+----------+ 2024-08-20T21:38:35.2343960Z | :attr:`~torch.qint8` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:38:35.2344182Z +---------------------------+------------+----------+ 2024-08-20T21:38:35.2344425Z | :attr:`~torch.qint32` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:38:35.2344647Z +---------------------------+------------+----------+ 2024-08-20T21:38:35.2344812Z | other | ``0.0`` | ``0.0`` | 2024-08-20T21:38:35.2345023Z +---------------------------+------------+----------+ 2024-08-20T21:38:35.2345123Z 2024-08-20T21:38:35.2345224Z .. note:: 2024-08-20T21:38:35.2345310Z 2024-08-20T21:38:35.2345836Z :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged 2024-08-20T21:38:35.2346348Z to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might 2024-08-20T21:38:35.2346918Z define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default: 2024-08-20T21:38:35.2347021Z 2024-08-20T21:38:35.2347138Z >>> import functools 2024-08-20T21:38:35.2347489Z >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0) 2024-08-20T21:38:35.2347694Z >>> assert_equal(1e-9, 1e-10) 2024-08-20T21:38:35.2347845Z Traceback (most recent call last): 2024-08-20T21:38:35.2347950Z ... 2024-08-20T21:38:35.2348224Z AssertionError: Scalars are not equal! 2024-08-20T21:38:35.2348327Z 2024-08-20T21:38:35.2348524Z Expected 1e-10 but got 1e-09. 2024-08-20T21:38:35.2348749Z Absolute difference: 9.000000000000001e-10 2024-08-20T21:38:35.2348873Z Relative difference: 9.0 2024-08-20T21:38:35.2348975Z 2024-08-20T21:38:35.2349072Z Examples: 2024-08-20T21:38:35.2349217Z >>> # tensor to tensor comparison 2024-08-20T21:38:35.2349469Z >>> expected = torch.tensor([1e0, 1e-1, 1e-2]) 2024-08-20T21:38:35.2349640Z >>> actual = torch.acos(torch.cos(expected)) 2024-08-20T21:38:35.2349836Z >>> torch.testing.assert_close(actual, expected) 2024-08-20T21:38:35.2349935Z 2024-08-20T21:38:35.2350082Z >>> # scalar to scalar comparison 2024-08-20T21:38:35.2350187Z >>> import math 2024-08-20T21:38:35.2350328Z >>> expected = math.sqrt(2.0) 2024-08-20T21:38:35.2350471Z >>> actual = 2.0 / math.sqrt(2.0) 2024-08-20T21:38:35.2350672Z >>> torch.testing.assert_close(actual, expected) 2024-08-20T21:38:35.2350759Z 2024-08-20T21:38:35.2350926Z >>> # numpy array to numpy array comparison 2024-08-20T21:38:35.2351057Z >>> import numpy as np 2024-08-20T21:38:35.2351276Z >>> expected = np.array([1e0, 1e-1, 1e-2]) 2024-08-20T21:38:35.2351436Z >>> actual = np.arccos(np.cos(expected)) 2024-08-20T21:38:35.2351634Z >>> torch.testing.assert_close(actual, expected) 2024-08-20T21:38:35.2351720Z 2024-08-20T21:38:35.2351872Z >>> # sequence to sequence comparison 2024-08-20T21:38:35.2352001Z >>> import numpy as np 2024-08-20T21:38:35.2352439Z >>> # The types of the sequences do not have to match. They only have to have the same 2024-08-20T21:38:35.2352618Z >>> # length and their elements have to match. 2024-08-20T21:38:35.2352843Z >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)] 2024-08-20T21:38:35.2352972Z >>> actual = tuple(expected) 2024-08-20T21:38:35.2353159Z >>> torch.testing.assert_close(actual, expected) 2024-08-20T21:38:35.2353257Z 2024-08-20T21:38:35.2353409Z >>> # mapping to mapping comparison 2024-08-20T21:38:35.2353579Z >>> from collections import OrderedDict 2024-08-20T21:38:35.2353696Z >>> import numpy as np 2024-08-20T21:38:35.2353820Z >>> foo = torch.tensor(1.0) 2024-08-20T21:38:35.2353936Z >>> bar = 2.0 2024-08-20T21:38:35.2354052Z >>> baz = np.array(3.0) 2024-08-20T21:38:35.2354414Z >>> # The types and a possible ordering of mappings do not have to match. They only 2024-08-20T21:38:35.2354723Z >>> # have to have the same set of keys and their elements have to match. 2024-08-20T21:38:35.2354996Z >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)]) 2024-08-20T21:38:35.2355180Z >>> actual = {"baz": baz, "bar": bar, "foo": foo} 2024-08-20T21:38:35.2355380Z >>> torch.testing.assert_close(actual, expected) 2024-08-20T21:38:35.2355466Z 2024-08-20T21:38:35.2355632Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2024-08-20T21:38:35.2355780Z >>> actual = expected.clone() 2024-08-20T21:38:35.2356012Z >>> # By default, directly related instances can be compared 2024-08-20T21:38:35.2356312Z >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected) 2024-08-20T21:38:35.2356579Z >>> # This check can be made more strict with allow_subclasses=False 2024-08-20T21:38:35.2356721Z >>> torch.testing.assert_close( 2024-08-20T21:38:35.2357004Z ... torch.nn.Parameter(actual), expected, allow_subclasses=False 2024-08-20T21:38:35.2357098Z ... ) 2024-08-20T21:38:35.2357249Z Traceback (most recent call last): 2024-08-20T21:38:35.2357407Z ... 2024-08-20T21:38:35.2357676Z TypeError: No comparison pair was able to handle inputs of type 2024-08-20T21:38:35.2358052Z and . 2024-08-20T21:38:35.2358380Z >>> # If the inputs are not directly related, they are never considered close 2024-08-20T21:38:35.2358604Z >>> torch.testing.assert_close(actual.numpy(), expected) 2024-08-20T21:38:35.2358767Z Traceback (most recent call last): 2024-08-20T21:38:35.2358860Z ... 2024-08-20T21:38:35.2359350Z TypeError: No comparison pair was able to handle inputs of type 2024-08-20T21:38:35.2359535Z and . 2024-08-20T21:38:35.2359899Z >>> # Exceptions to these rules are Python scalars. They can be checked regardless of 2024-08-20T21:38:35.2360052Z >>> # their type if check_dtype=False. 2024-08-20T21:38:35.2360289Z >>> torch.testing.assert_close(1.0, 1, check_dtype=False) 2024-08-20T21:38:35.2360381Z 2024-08-20T21:38:35.2360508Z >>> # NaN != NaN by default. 2024-08-20T21:38:35.2360687Z >>> expected = torch.tensor(float("Nan")) 2024-08-20T21:38:35.2360820Z >>> actual = expected.clone() 2024-08-20T21:38:35.2361009Z >>> torch.testing.assert_close(actual, expected) 2024-08-20T21:38:35.2361171Z Traceback (most recent call last): 2024-08-20T21:38:35.2361264Z ... 2024-08-20T21:38:35.2361426Z AssertionError: Scalars are not close! 2024-08-20T21:38:35.2361541Z 2024-08-20T21:38:35.2361667Z Expected nan but got nan. 2024-08-20T21:38:35.2361980Z Absolute difference: nan (up to 1e-05 allowed) 2024-08-20T21:38:35.2362237Z Relative difference: nan (up to 1.3e-06 allowed) 2024-08-20T21:38:35.2362498Z >>> torch.testing.assert_close(actual, expected, equal_nan=True) 2024-08-20T21:38:35.2362603Z 2024-08-20T21:38:35.2362768Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2024-08-20T21:38:35.2362926Z >>> actual = torch.tensor([1.0, 4.0, 5.0]) 2024-08-20T21:38:35.2363132Z >>> # The default error message can be overwritten. 2024-08-20T21:38:35.2363519Z >>> torch.testing.assert_close(actual, expected, msg="Argh, the tensors are not close!") 2024-08-20T21:38:35.2363671Z Traceback (most recent call last): 2024-08-20T21:38:35.2363776Z ... 2024-08-20T21:38:35.2363976Z AssertionError: Argh, the tensors are not close! 2024-08-20T21:38:35.2364304Z >>> # If msg is a callable, it can be used to augment the generated message with 2024-08-20T21:38:35.2364441Z >>> # extra information 2024-08-20T21:38:35.2364582Z >>> torch.testing.assert_close( 2024-08-20T21:38:35.2364860Z ... actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter" 2024-08-20T21:38:35.2364953Z ... ) 2024-08-20T21:38:35.2365107Z Traceback (most recent call last): 2024-08-20T21:38:35.2365213Z ... 2024-08-20T21:38:35.2365333Z AssertionError: Header 2024-08-20T21:38:35.2365435Z 2024-08-20T21:38:35.2365622Z Tensor-likes are not close! 2024-08-20T21:38:35.2365721Z 2024-08-20T21:38:35.2365868Z Mismatched elements: 2 / 3 (66.7%) 2024-08-20T21:38:35.2366261Z Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed) 2024-08-20T21:38:35.2366641Z Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed) 2024-08-20T21:38:35.2366741Z 2024-08-20T21:38:35.2366848Z Footer 2024-08-20T21:38:35.2367045Z 2024-08-20T21:38:35.2367453Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.2367553Z 2024-08-20T21:38:35.2367663Z warnings.warn(msg) 2024-08-20T21:38:35.2367812Z 2024-08-20T21:38:35.2368040Z --- Parse Warning: 93 / 101 --- 2024-08-20T21:38:35.2369459Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_pytree_node in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py line=111. 2024-08-20T21:38:35.2369891Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.2370122Z Register a container-like type as pytree node. 2024-08-20T21:38:35.2370211Z 2024-08-20T21:38:35.2370320Z Args: 2024-08-20T21:38:35.2370579Z cls (type): A Python type to treat as an internal pytree node. 2024-08-20T21:38:35.2370957Z flatten_fn (callable): A function to be used during flattening, taking an instance of 2024-08-20T21:38:35.2371326Z ``cls`` and returning a pair, with (1) an iterable for the children to be flattened 2024-08-20T21:38:35.2371728Z recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be 2024-08-20T21:38:35.2371889Z passed to the ``unflatten_fn``. 2024-08-20T21:38:35.2372258Z unflatten_fn (callable): A function taking two arguments: the auxiliary data that was 2024-08-20T21:38:35.2372625Z returned by ``flatten_fn`` and stored in the treespec, and the unflattened children. 2024-08-20T21:38:35.2372844Z The function should return an instance of ``cls``. 2024-08-20T21:38:35.2373201Z serialized_type_name (str, optional): A keyword argument used to specify the fully 2024-08-20T21:38:35.2373418Z qualified name used when serializing the tree spec. 2024-08-20T21:38:35.2373891Z to_dumpable_context (callable, optional): An optional keyword argument to custom specify how 2024-08-20T21:38:35.2374291Z to convert the context of the pytree to a custom json dumpable representation. This is 2024-08-20T21:38:35.2374678Z used for json serialization, which is being used in :mod:`torch.export` right now. 2024-08-20T21:38:35.2375072Z from_dumpable_context (callable, optional): An optional keyword argument to custom specify 2024-08-20T21:38:35.2375443Z how to convert the custom json dumpable representation of the context back to the 2024-08-20T21:38:35.2375814Z original context. This is used for json deserialization, which is being used in 2024-08-20T21:38:35.2375961Z :mod:`torch.export` right now. 2024-08-20T21:38:35.2376048Z 2024-08-20T21:38:35.2376166Z Example:: 2024-08-20T21:38:35.2376253Z 2024-08-20T21:38:35.2376371Z >>> # xdoctest: +SKIP 2024-08-20T21:38:35.2376581Z >>> # Registry a Python type with lambda functions 2024-08-20T21:38:35.2376705Z >>> register_pytree_node( 2024-08-20T21:38:35.2376803Z ... set, 2024-08-20T21:38:35.2376978Z ... lambda s: (sorted(s), None, None), 2024-08-20T21:38:35.2377146Z ... lambda children, _: set(children), 2024-08-20T21:38:35.2377251Z ... ) 2024-08-20T21:38:35.2377343Z 2024-08-20T21:38:35.2377747Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.2377846Z 2024-08-20T21:38:35.2377956Z warnings.warn(msg) 2024-08-20T21:38:35.2378043Z 2024-08-20T21:38:35.2378266Z --- Parse Warning: 94 / 101 --- 2024-08-20T21:38:35.2379758Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SelectiveCheckpointContext in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py line=1201. 2024-08-20T21:38:35.2380183Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.2380282Z 2024-08-20T21:38:35.2380563Z Context passed to policy function during selective checkpointing. 2024-08-20T21:38:35.2380719Z 2024-08-20T21:38:35.2381055Z This class is used to pass relevant metadata to the policy function during 2024-08-20T21:38:35.2381391Z selective checkpointing. The metadata includes whether the current invocation 2024-08-20T21:38:35.2381618Z of the policy function is during recomputation or not. 2024-08-20T21:38:35.2381707Z 2024-08-20T21:38:35.2381803Z Example: 2024-08-20T21:38:35.2381943Z >>> # xdoctest: +SKIP(stub) 2024-08-20T21:38:35.2382035Z >>> 2024-08-20T21:38:35.2382201Z >>> def policy_fn(ctx, op, *args, **kwargs): 2024-08-20T21:38:35.2382342Z >>> print(ctx.is_recompute) 2024-08-20T21:38:35.2382432Z >>> 2024-08-20T21:38:35.2382787Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2024-08-20T21:38:35.2382892Z >>> 2024-08-20T21:38:35.2383081Z >>> out = torch.utils.checkpoint.checkpoint( 2024-08-20T21:38:35.2383185Z >>> fn, x, y, 2024-08-20T21:38:35.2383321Z >>> use_reentrant=False, 2024-08-20T21:38:35.2383442Z >>> context_fn=context_fn, 2024-08-20T21:38:35.2383533Z >>> ) 2024-08-20T21:38:35.2383629Z 2024-08-20T21:38:35.2384034Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.2384120Z 2024-08-20T21:38:35.2384242Z warnings.warn(msg) 2024-08-20T21:38:35.2384329Z 2024-08-20T21:38:35.2384540Z --- Parse Warning: 95 / 101 --- 2024-08-20T21:38:35.2386125Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=create_selective_checkpoint_contexts in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py line=1335. 2024-08-20T21:38:35.2386545Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.2386645Z 2024-08-20T21:38:35.2386961Z Helper to avoid recomputing certain ops during activation checkpointing. 2024-08-20T21:38:35.2387057Z 2024-08-20T21:38:35.2387365Z Use this with `torch.utils.checkpoint.checkpoint` to control which 2024-08-20T21:38:35.2387568Z operations are recomputed during the backward pass. 2024-08-20T21:38:35.2387655Z 2024-08-20T21:38:35.2387758Z Args: 2024-08-20T21:38:35.2387908Z policy_fn_or_list (Callable or List): 2024-08-20T21:38:35.2388188Z - If a policy function is provided, it should accept a 2024-08-20T21:38:35.2388511Z :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and 2024-08-20T21:38:35.2388801Z kwargs to the op, and return a :class:`CheckpointPolicy` enum value 2024-08-20T21:38:35.2389128Z indicating whether the execution of the op should be recomputed or not. 2024-08-20T21:38:35.2389484Z - If a list of operations is provided, it is equivalent to a policy 2024-08-20T21:38:35.2389723Z returning `CheckpointPolicy.MUST_SAVE` for the specified 2024-08-20T21:38:35.2390025Z operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other 2024-08-20T21:38:35.2390131Z operations. 2024-08-20T21:38:35.2390411Z allow_cache_entry_mutation (bool, optional): By default, an error is 2024-08-20T21:38:35.2390714Z raised if any tensors cached by selective activation checkpoint are 2024-08-20T21:38:35.2391004Z mutated in order to ensure correctness. If set to `True`, this check 2024-08-20T21:38:35.2391109Z is disabled. 2024-08-20T21:38:35.2391218Z Returns: 2024-08-20T21:38:35.2391353Z A tuple of two context managers. 2024-08-20T21:38:35.2391441Z 2024-08-20T21:38:35.2391548Z Example: 2024-08-20T21:38:35.2391687Z >>> # xdoctest: +REQUIRES(LINUX) 2024-08-20T21:38:35.2391810Z >>> import functools 2024-08-20T21:38:35.2391902Z >>> 2024-08-20T21:38:35.2392067Z >>> x = torch.rand(10, 10, requires_grad=True) 2024-08-20T21:38:35.2392240Z >>> y = torch.rand(10, 10, requires_grad=True) 2024-08-20T21:38:35.2392386Z >>> 2024-08-20T21:38:35.2392494Z >>> ops_to_save = [ 2024-08-20T21:38:35.2392648Z >>> torch.ops.aten.mm.default, 2024-08-20T21:38:35.2392742Z >>> ] 2024-08-20T21:38:35.2392831Z >>> 2024-08-20T21:38:35.2393010Z >>> def policy_fn(ctx, op, *args, **kwargs): 2024-08-20T21:38:35.2393128Z >>> if op in ops_to_save: 2024-08-20T21:38:35.2393298Z >>> return CheckpointPolicy.MUST_SAVE 2024-08-20T21:38:35.2393405Z >>> else: 2024-08-20T21:38:35.2393594Z >>> return CheckpointPolicy.PREFER_RECOMPUTE 2024-08-20T21:38:35.2393685Z >>> 2024-08-20T21:38:35.2394053Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2024-08-20T21:38:35.2394144Z >>> 2024-08-20T21:38:35.2394255Z >>> # or equivalently 2024-08-20T21:38:35.2394629Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save) 2024-08-20T21:38:35.2394724Z >>> 2024-08-20T21:38:35.2394828Z >>> def fn(x, y): 2024-08-20T21:38:35.2395101Z >>> return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y 2024-08-20T21:38:35.2395191Z >>> 2024-08-20T21:38:35.2395392Z >>> out = torch.utils.checkpoint.checkpoint( 2024-08-20T21:38:35.2395494Z >>> fn, x, y, 2024-08-20T21:38:35.2395611Z >>> use_reentrant=False, 2024-08-20T21:38:35.2395745Z >>> context_fn=context_fn, 2024-08-20T21:38:35.2395837Z >>> ) 2024-08-20T21:38:35.2395922Z 2024-08-20T21:38:35.2396340Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.2396429Z 2024-08-20T21:38:35.2396594Z warnings.warn(msg) 2024-08-20T21:38:35.2396694Z 2024-08-20T21:38:35.2396907Z --- Parse Warning: 96 / 101 --- 2024-08-20T21:38:35.2398306Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CppExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=925. 2024-08-20T21:38:35.2398736Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.2398825Z 2024-08-20T21:38:35.2399018Z Create a :class:`setuptools.Extension` for C++. 2024-08-20T21:38:35.2399105Z 2024-08-20T21:38:35.2399418Z Convenience method that creates a :class:`setuptools.Extension` with the 2024-08-20T21:38:35.2399729Z bare minimum (but often sufficient) arguments to build a C++ extension. 2024-08-20T21:38:35.2399816Z 2024-08-20T21:38:35.2400088Z All arguments are forwarded to the :class:`setuptools.Extension` 2024-08-20T21:38:35.2400291Z constructor. Full list arguments can be found at 2024-08-20T21:38:35.2400804Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2024-08-20T21:38:35.2400891Z 2024-08-20T21:38:35.2401002Z Example: 2024-08-20T21:38:35.2401117Z >>> # xdoctest: +SKIP 2024-08-20T21:38:35.2401311Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-08-20T21:38:35.2401460Z >>> from setuptools import setup 2024-08-20T21:38:35.2401755Z >>> from torch.utils.cpp_extension import BuildExtension, CppExtension 2024-08-20T21:38:35.2401852Z >>> setup( 2024-08-20T21:38:35.2402023Z ... name='extension', 2024-08-20T21:38:35.2402129Z ... ext_modules=[ 2024-08-20T21:38:35.2402243Z ... CppExtension( 2024-08-20T21:38:35.2402427Z ... name='extension', 2024-08-20T21:38:35.2402634Z ... sources=['extension.cpp'], 2024-08-20T21:38:35.2402849Z ... extra_compile_args=['-g'], 2024-08-20T21:38:35.2403119Z ... extra_link_flags=['-Wl,--no-as-needed', '-lm']) 2024-08-20T21:38:35.2403216Z ... ], 2024-08-20T21:38:35.2403330Z ... cmdclass={ 2024-08-20T21:38:35.2403585Z ... 'build_ext': BuildExtension 2024-08-20T21:38:35.2403678Z ... }) 2024-08-20T21:38:35.2403776Z 2024-08-20T21:38:35.2404177Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.2404264Z 2024-08-20T21:38:35.2404385Z warnings.warn(msg) 2024-08-20T21:38:35.2404471Z 2024-08-20T21:38:35.2404681Z --- Parse Warning: 97 / 101 --- 2024-08-20T21:38:35.2406089Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CUDAExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=976. 2024-08-20T21:38:35.2406507Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.2406606Z 2024-08-20T21:38:35.2406813Z Create a :class:`setuptools.Extension` for CUDA/C++. 2024-08-20T21:38:35.2406899Z 2024-08-20T21:38:35.2407321Z Convenience method that creates a :class:`setuptools.Extension` with the 2024-08-20T21:38:35.2407597Z bare minimum (but often sufficient) arguments to build a CUDA/C++ 2024-08-20T21:38:35.2407907Z extension. This includes the CUDA include path, library path and runtime 2024-08-20T21:38:35.2408019Z library. 2024-08-20T21:38:35.2408106Z 2024-08-20T21:38:35.2408378Z All arguments are forwarded to the :class:`setuptools.Extension` 2024-08-20T21:38:35.2408578Z constructor. Full list arguments can be found at 2024-08-20T21:38:35.2409099Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2024-08-20T21:38:35.2409186Z 2024-08-20T21:38:35.2409295Z Example: 2024-08-20T21:38:35.2409466Z >>> # xdoctest: +SKIP 2024-08-20T21:38:35.2409663Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-08-20T21:38:35.2409811Z >>> from setuptools import setup 2024-08-20T21:38:35.2410110Z >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension 2024-08-20T21:38:35.2410223Z >>> setup( 2024-08-20T21:38:35.2410391Z ... name='cuda_extension', 2024-08-20T21:38:35.2410499Z ... ext_modules=[ 2024-08-20T21:38:35.2410628Z ... CUDAExtension( 2024-08-20T21:38:35.2410829Z ... name='cuda_extension', 2024-08-20T21:38:35.2411120Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-08-20T21:38:35.2411366Z ... extra_compile_args={'cxx': ['-g'], 2024-08-20T21:38:35.2411596Z ... 'nvcc': ['-O2']}, 2024-08-20T21:38:35.2411885Z ... extra_link_flags=['-Wl,--no-as-needed', '-lcuda']) 2024-08-20T21:38:35.2411997Z ... ], 2024-08-20T21:38:35.2412102Z ... cmdclass={ 2024-08-20T21:38:35.2412300Z ... 'build_ext': BuildExtension 2024-08-20T21:38:35.2412405Z ... }) 2024-08-20T21:38:35.2412492Z 2024-08-20T21:38:35.2412611Z Compute capabilities: 2024-08-20T21:38:35.2412708Z 2024-08-20T21:38:35.2413137Z By default the extension will be compiled to run on all archs of the cards visible during the 2024-08-20T21:38:35.2413556Z building process of the extension, plus PTX. If down the road a new card is installed the 2024-08-20T21:38:35.2414011Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's 2024-08-20T21:38:35.2414520Z newer than the newest version for which your nvcc can build fully-compiled binaries, Pytorch 2024-08-20T21:38:35.2414938Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does 2024-08-20T21:38:35.2415090Z support (see below for details on PTX). 2024-08-20T21:38:35.2415181Z 2024-08-20T21:38:35.2415622Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which 2024-08-20T21:38:35.2415768Z CCs you want the extension to support: 2024-08-20T21:38:35.2415972Z 2024-08-20T21:38:35.2416230Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py`` 2024-08-20T21:38:35.2416580Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py`` 2024-08-20T21:38:35.2416681Z 2024-08-20T21:38:35.2417114Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified 2024-08-20T21:38:35.2417632Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >= 2024-08-20T21:38:35.2418162Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with 2024-08-20T21:38:35.2418668Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to 2024-08-20T21:38:35.2419188Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on 2024-08-20T21:38:35.2419701Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better 2024-08-20T21:38:35.2420148Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6, 2024-08-20T21:38:35.2420687Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but 2024-08-20T21:38:35.2420801Z "8.0 8.6" would be better. 2024-08-20T21:38:35.2420888Z 2024-08-20T21:38:35.2421401Z Note that while it's possible to include all supported archs, the more archs get included the 2024-08-20T21:38:35.2421814Z slower the building process will be, as it will build a separate kernel image for each arch. 2024-08-20T21:38:35.2421902Z 2024-08-20T21:38:35.2422529Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows. 2024-08-20T21:38:35.2422818Z To workaround the issue, move python binding logic to pure C++ file. 2024-08-20T21:38:35.2422905Z 2024-08-20T21:38:35.2423018Z Example use: 2024-08-20T21:38:35.2423131Z #include 2024-08-20T21:38:35.2423337Z at::Tensor SigmoidAlphaBlendForwardCuda(....) 2024-08-20T21:38:35.2423437Z 2024-08-20T21:38:35.2423536Z Instead of: 2024-08-20T21:38:35.2423661Z #include 2024-08-20T21:38:35.2423875Z torch::Tensor SigmoidAlphaBlendForwardCuda(...) 2024-08-20T21:38:35.2423962Z 2024-08-20T21:38:35.2424342Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460 2024-08-20T21:38:35.2424997Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48 2024-08-20T21:38:35.2425085Z 2024-08-20T21:38:35.2425226Z Relocatable device code linking: 2024-08-20T21:38:35.2425313Z 2024-08-20T21:38:35.2425701Z If you want to reference device symbols across compilation units (across object files), 2024-08-20T21:38:35.2426164Z the object files need to be built with `relocatable device code` (-rdc=true or -dc). 2024-08-20T21:38:35.2426670Z An exception to this rule is "dynamic parallelism" (nested kernel launches) which is not used a lot anymore. 2024-08-20T21:38:35.2427132Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it. 2024-08-20T21:38:35.2427669Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step 2024-08-20T21:38:35.2427959Z help reduce the protentional perf degradation of `-rdc`. 2024-08-20T21:38:35.2428202Z Note that it needs to be used at both steps to be useful. 2024-08-20T21:38:35.2428289Z 2024-08-20T21:38:35.2428930Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step. 2024-08-20T21:38:35.2429246Z There is also a case where `-dlink` is used without `-rdc`: 2024-08-20T21:38:35.2429671Z when an extension is linked against a static lib containing rdc-compiled objects 2024-08-20T21:38:35.2429952Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem). 2024-08-20T21:38:35.2430120Z 2024-08-20T21:38:35.2430399Z Note: Ninja is required to build a CUDA Extension with RDC linking. 2024-08-20T21:38:35.2430486Z 2024-08-20T21:38:35.2430592Z Example: 2024-08-20T21:38:35.2430704Z >>> # xdoctest: +SKIP 2024-08-20T21:38:35.2430895Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-08-20T21:38:35.2431017Z >>> CUDAExtension( 2024-08-20T21:38:35.2431193Z ... name='cuda_extension', 2024-08-20T21:38:35.2431486Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-08-20T21:38:35.2431594Z ... dlink=True, 2024-08-20T21:38:35.2431743Z ... dlink_libraries=["dlink_lib"], 2024-08-20T21:38:35.2431974Z ... extra_compile_args={'cxx': ['-g'], 2024-08-20T21:38:35.2432212Z ... 'nvcc': ['-O2', '-rdc=true']}) 2024-08-20T21:38:35.2432299Z 2024-08-20T21:38:35.2432711Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.2432803Z 2024-08-20T21:38:35.2432913Z warnings.warn(msg) 2024-08-20T21:38:35.2433011Z 2024-08-20T21:38:35.2433221Z --- Parse Warning: 98 / 101 --- 2024-08-20T21:38:35.2434573Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1234. 2024-08-20T21:38:35.2435000Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.2435088Z 2024-08-20T21:38:35.2435342Z Load a PyTorch C++ extension just-in-time (JIT). 2024-08-20T21:38:35.2435429Z 2024-08-20T21:38:35.2435793Z To load an extension, a Ninja build file is emitted, which is used to 2024-08-20T21:38:35.2436081Z compile the given sources into a dynamic library. This library is 2024-08-20T21:38:35.2436368Z subsequently loaded into the current Python process as a module and 2024-08-20T21:38:35.2436538Z returned from this function, ready for use. 2024-08-20T21:38:35.2436640Z 2024-08-20T21:38:35.2436925Z By default, the directory to which the build file is emitted and the 2024-08-20T21:38:35.2437232Z resulting library compiled to is ``/torch_extensions/``, where 2024-08-20T21:38:35.2437527Z ```` is the temporary folder on the current platform and ```` 2024-08-20T21:38:35.2437826Z the name of the extension. This location can be overridden in two ways. 2024-08-20T21:38:35.2438124Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it 2024-08-20T21:38:35.2438413Z replaces ``/torch_extensions`` and all extensions will be compiled 2024-08-20T21:38:35.2438705Z into subfolders of this directory. Second, if the ``build_directory`` 2024-08-20T21:38:35.2439032Z argument to this function is supplied, it overrides the entire path, i.e. 2024-08-20T21:38:35.2439258Z the library will be compiled into that folder directly. 2024-08-20T21:38:35.2439346Z 2024-08-20T21:38:35.2439648Z To compile the sources, the default system compiler (``c++``) is used, 2024-08-20T21:38:35.2439976Z which can be overridden by setting the ``CXX`` environment variable. To pass 2024-08-20T21:38:35.2440264Z additional arguments to the compilation process, ``extra_cflags`` or 2024-08-20T21:38:35.2440576Z ``extra_ldflags`` can be provided. For example, to compile your extension 2024-08-20T21:38:35.2440917Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use 2024-08-20T21:38:35.2441128Z ``extra_cflags`` to pass further include directories. 2024-08-20T21:38:35.2441216Z 2024-08-20T21:38:35.2441537Z CUDA support with mixed compilation is provided. Simply pass CUDA source 2024-08-20T21:38:35.2441813Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be 2024-08-20T21:38:35.2442139Z detected and compiled with nvcc rather than the C++ compiler. This includes 2024-08-20T21:38:35.2442481Z passing the CUDA lib64 directory as a library directory, and linking 2024-08-20T21:38:35.2442693Z ``cudart``. You can pass additional flags to nvcc via 2024-08-20T21:38:35.2442970Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various 2024-08-20T21:38:35.2443281Z heuristics for finding the CUDA install directory are used, which usually 2024-08-20T21:38:35.2443588Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the 2024-08-20T21:38:35.2443692Z safest option. 2024-08-20T21:38:35.2443779Z 2024-08-20T21:38:35.2443883Z Args: 2024-08-20T21:38:35.2444188Z name: The name of the extension to build. This MUST be the same as the 2024-08-20T21:38:35.2444334Z name of the pybind11 module! 2024-08-20T21:38:35.2444609Z sources: A list of relative or absolute paths to C++ source files. 2024-08-20T21:38:35.2444902Z extra_cflags: optional list of compiler flags to forward to the build. 2024-08-20T21:38:35.2445205Z extra_cuda_cflags: optional list of compiler flags to forward to nvcc 2024-08-20T21:38:35.2445333Z when building CUDA sources. 2024-08-20T21:38:35.2445620Z extra_ldflags: optional list of linker flags to forward to the build. 2024-08-20T21:38:35.2445915Z extra_include_paths: optional list of include directories to forward 2024-08-20T21:38:35.2446020Z to the build. 2024-08-20T21:38:35.2446251Z build_directory: optional path to use as build workspace. 2024-08-20T21:38:35.2446503Z verbose: If ``True``, turns on verbose logging of load steps. 2024-08-20T21:38:35.2447220Z with_cuda: Determines whether CUDA headers and libraries are added to 2024-08-20T21:38:35.2447461Z the build. If set to ``None`` (default), this value is 2024-08-20T21:38:35.2447722Z automatically determined based on the existence of ``.cu`` or 2024-08-20T21:38:35.2447964Z ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers 2024-08-20T21:38:35.2448115Z and libraries to be included. 2024-08-20T21:38:35.2448391Z is_python_module: If ``True`` (default), imports the produced shared 2024-08-20T21:38:35.2448649Z library as a Python module. If ``False``, behavior depends on 2024-08-20T21:38:35.2448776Z ``is_standalone``. 2024-08-20T21:38:35.2449055Z is_standalone: If ``False`` (default) loads the constructed extension 2024-08-20T21:38:35.2449329Z into the process as a plain dynamic library. If ``True``, build a 2024-08-20T21:38:35.2449463Z standalone executable. 2024-08-20T21:38:35.2449550Z 2024-08-20T21:38:35.2449645Z Returns: 2024-08-20T21:38:35.2449803Z If ``is_python_module`` is ``True``: 2024-08-20T21:38:35.2450037Z Returns the loaded PyTorch extension as a Python module. 2024-08-20T21:38:35.2450138Z 2024-08-20T21:38:35.2450419Z If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``: 2024-08-20T21:38:35.2450705Z Returns nothing. (The shared library is loaded into the process as 2024-08-20T21:38:35.2450825Z a side effect.) 2024-08-20T21:38:35.2450912Z 2024-08-20T21:38:35.2451043Z If ``is_standalone`` is ``True``. 2024-08-20T21:38:35.2451330Z Return the path to the executable. (On Windows, TORCH_LIB_PATH is 2024-08-20T21:38:35.2451567Z added to the PATH environment variable as a side effect.) 2024-08-20T21:38:35.2451656Z 2024-08-20T21:38:35.2451764Z Example: 2024-08-20T21:38:35.2451876Z >>> # xdoctest: +SKIP 2024-08-20T21:38:35.2452056Z >>> from torch.utils.cpp_extension import load 2024-08-20T21:38:35.2452178Z >>> module = load( 2024-08-20T21:38:35.2452365Z ... name='extension', 2024-08-20T21:38:35.2452635Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-08-20T21:38:35.2452813Z ... extra_cflags=['-O2'], 2024-08-20T21:38:35.2453004Z ... verbose=True) 2024-08-20T21:38:35.2453091Z 2024-08-20T21:38:35.2453514Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.2453601Z 2024-08-20T21:38:35.2453725Z warnings.warn(msg) 2024-08-20T21:38:35.2453813Z 2024-08-20T21:38:35.2454025Z --- Parse Warning: 99 / 101 --- 2024-08-20T21:38:35.2455422Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load_inline in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1523. 2024-08-20T21:38:35.2455846Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.2455935Z 2024-08-20T21:38:35.2456303Z Load a PyTorch C++ extension just-in-time (JIT) from string sources. 2024-08-20T21:38:35.2456393Z 2024-08-20T21:38:35.2456701Z This function behaves exactly like :func:`load`, but takes its sources as 2024-08-20T21:38:35.2457020Z strings rather than filenames. These strings are stored to files in the 2024-08-20T21:38:35.2457299Z build directory, after which the behavior of :func:`load_inline` is 2024-08-20T21:38:35.2457432Z identical to :func:`load`. 2024-08-20T21:38:35.2457518Z 2024-08-20T21:38:35.2457614Z See `the 2024-08-20T21:38:35.2458054Z tests `_ 2024-08-20T21:38:35.2458214Z for good examples of using this function. 2024-08-20T21:38:35.2458302Z 2024-08-20T21:38:35.2458709Z Sources may omit two required parts of a typical non-inline C++ extension: 2024-08-20T21:38:35.2459087Z the necessary header includes, as well as the (pybind11) binding code. More 2024-08-20T21:38:35.2459406Z precisely, strings passed to ``cpp_sources`` are first concatenated into a 2024-08-20T21:38:35.2459676Z single ``.cpp`` file. This file is then prepended with ``#include 2024-08-20T21:38:35.2459794Z ``. 2024-08-20T21:38:35.2459879Z 2024-08-20T21:38:35.2460190Z Furthermore, if the ``functions`` argument is supplied, bindings will be 2024-08-20T21:38:35.2460492Z automatically generated for each function specified. ``functions`` can 2024-08-20T21:38:35.2460801Z either be a list of function names, or a dictionary mapping from function 2024-08-20T21:38:35.2461125Z names to docstrings. If a list is given, the name of each function is used 2024-08-20T21:38:35.2461232Z as its docstring. 2024-08-20T21:38:35.2461332Z 2024-08-20T21:38:35.2461629Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu`` 2024-08-20T21:38:35.2461860Z file and prepended with ``torch/types.h``, ``cuda.h`` and 2024-08-20T21:38:35.2462158Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled 2024-08-20T21:38:35.2462447Z separately, but ultimately linked into a single library. Note that no 2024-08-20T21:38:35.2462761Z bindings are generated for functions in ``cuda_sources`` per se. To bind 2024-08-20T21:38:35.2463081Z to a CUDA kernel, you must create a C++ function that calls it, and either 2024-08-20T21:38:35.2463375Z declare or define this C++ function in one of the ``cpp_sources`` (and 2024-08-20T21:38:35.2463510Z include its name in ``functions``). 2024-08-20T21:38:35.2463609Z 2024-08-20T21:38:35.2463857Z See :func:`load` for a description of arguments omitted below. 2024-08-20T21:38:35.2463956Z 2024-08-20T21:38:35.2464048Z Args: 2024-08-20T21:38:35.2464340Z cpp_sources: A string, or list of strings, containing C++ source code. 2024-08-20T21:38:35.2464656Z cuda_sources: A string, or list of strings, containing CUDA source code. 2024-08-20T21:38:35.2464931Z functions: A list of function names for which to generate function 2024-08-20T21:38:35.2465219Z bindings. If a dictionary is given, it should map function names to 2024-08-20T21:38:35.2465525Z docstrings (which are otherwise just the function names). 2024-08-20T21:38:35.2465818Z with_cuda: Determines whether CUDA headers and libraries are added to 2024-08-20T21:38:35.2466033Z the build. If set to ``None`` (default), this value is 2024-08-20T21:38:35.2466307Z automatically determined based on whether ``cuda_sources`` is 2024-08-20T21:38:35.2466508Z provided. Set it to ``True`` to force CUDA headers 2024-08-20T21:38:35.2466657Z and libraries to be included. 2024-08-20T21:38:35.2466929Z with_pytorch_error_handling: Determines whether pytorch error and 2024-08-20T21:38:35.2467197Z warning macros are handled by pytorch instead of pybind. To do 2024-08-20T21:38:35.2467509Z this, each function ``foo`` is called via an intermediary ``_safe_foo`` 2024-08-20T21:38:35.2467768Z function. This redirection might cause issues in obscure cases 2024-08-20T21:38:35.2468029Z of cpp. This flag should be set to ``False`` when this redirect 2024-08-20T21:38:35.2468153Z causes issues. 2024-08-20T21:38:35.2468241Z 2024-08-20T21:38:35.2468337Z Example: 2024-08-20T21:38:35.2468543Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-08-20T21:38:35.2468748Z >>> from torch.utils.cpp_extension import load_inline 2024-08-20T21:38:35.2468854Z >>> source = """ 2024-08-20T21:38:35.2469056Z at::Tensor sin_add(at::Tensor x, at::Tensor y) { 2024-08-20T21:38:35.2469176Z return x.sin() + y.sin(); 2024-08-20T21:38:35.2469280Z } 2024-08-20T21:38:35.2469372Z """ 2024-08-20T21:38:35.2469621Z >>> module = load_inline(name='inline_extension', 2024-08-20T21:38:35.2469861Z ... cpp_sources=[source], 2024-08-20T21:38:35.2470077Z ... functions=['sin_add']) 2024-08-20T21:38:35.2470166Z 2024-08-20T21:38:35.2470275Z .. note:: 2024-08-20T21:38:35.2470553Z By default, the Ninja backend uses #CPUS + 2 workers to build the 2024-08-20T21:38:35.2470833Z extension. This may use up too many resources on some systems. One 2024-08-20T21:38:35.2471151Z can control the number of workers by setting the `MAX_JOBS` environment 2024-08-20T21:38:35.2471343Z variable to a non-negative number. 2024-08-20T21:38:35.2471431Z 2024-08-20T21:38:35.2471847Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.2471934Z 2024-08-20T21:38:35.2472044Z warnings.warn(msg) 2024-08-20T21:38:35.2472142Z 2024-08-20T21:38:35.2472358Z --- Parse Warning: 100 / 101 --- 2024-08-20T21:38:35.2473876Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ThroughputBenchmark in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/throughput_benchmark.py line=61. 2024-08-20T21:38:35.2474290Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.2474382Z 2024-08-20T21:38:35.2474801Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark. 2024-08-20T21:38:35.2474889Z 2024-08-20T21:38:35.2475278Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible 2024-08-20T21:38:35.2475627Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference 2024-08-20T21:38:35.2475956Z server like load. It can emulate multiple calling threads to a single module 2024-08-20T21:38:35.2476307Z provided. In the future we plan to enhance this component to support inter and 2024-08-20T21:38:35.2476720Z intra-op parallelism as well as multiple models running in a single process. 2024-08-20T21:38:35.2476807Z 2024-08-20T21:38:35.2477168Z Please note that even though nn.Module is supported, it might incur an overhead 2024-08-20T21:38:35.2477485Z from the need to hold GIL every time we execute Python code or pass around 2024-08-20T21:38:35.2477873Z inputs as Python objects. As soon as you have a ScriptModule version of your 2024-08-20T21:38:35.2478202Z model for inference deployment it is better to switch to using it in this 2024-08-20T21:38:35.2478300Z benchmark. 2024-08-20T21:38:35.2478385Z 2024-08-20T21:38:35.2478496Z Example:: 2024-08-20T21:38:35.2478582Z 2024-08-20T21:38:35.2478737Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:38:35.2478933Z >>> from torch.utils import ThroughputBenchmark 2024-08-20T21:38:35.2479097Z >>> bench = ThroughputBenchmark(my_module) 2024-08-20T21:38:35.2479377Z >>> # Pre-populate benchmark's data set with the inputs 2024-08-20T21:38:35.2479513Z >>> for input in inputs: 2024-08-20T21:38:35.2479820Z ... # Both args and kwargs work, same as any PyTorch Module / ScriptModule 2024-08-20T21:38:35.2479998Z ... bench.add_input(input[0], x2=input[1]) 2024-08-20T21:38:35.2480260Z >>> # Inputs supplied above are randomly used during the execution 2024-08-20T21:38:35.2480386Z >>> stats = bench.benchmark( 2024-08-20T21:38:35.2480520Z ... num_calling_threads=4, 2024-08-20T21:38:35.2480643Z ... num_warmup_iters = 100, 2024-08-20T21:38:35.2480754Z ... num_iters = 1000, 2024-08-20T21:38:35.2480857Z ... ) 2024-08-20T21:38:35.2481093Z >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms)) 2024-08-20T21:38:35.2481325Z >>> print("Number of iterations: {}".format(stats.num_iters)) 2024-08-20T21:38:35.2481423Z 2024-08-20T21:38:35.2481822Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.2481965Z 2024-08-20T21:38:35.2482088Z warnings.warn(msg) 2024-08-20T21:38:35.2482174Z 2024-08-20T21:38:35.2482390Z --- Parse Warning: 101 / 101 --- 2024-08-20T21:38:35.2483848Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedSampler in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/distributed.py line=17. 2024-08-20T21:38:35.2484267Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:38:35.2484539Z Sampler that restricts data loading to a subset of the dataset. 2024-08-20T21:38:35.2484627Z 2024-08-20T21:38:35.2484797Z It is especially useful in conjunction with 2024-08-20T21:38:35.2485154Z :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each 2024-08-20T21:38:35.2485513Z process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a 2024-08-20T21:38:35.2485826Z :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the 2024-08-20T21:38:35.2486002Z original dataset that is exclusive to it. 2024-08-20T21:38:35.2486088Z 2024-08-20T21:38:35.2486186Z .. note:: 2024-08-20T21:38:35.2486538Z Dataset is assumed to be of constant size and that any instance of it always 2024-08-20T21:38:35.2486718Z returns the same elements in the same order. 2024-08-20T21:38:35.2486817Z 2024-08-20T21:38:35.2486984Z Args: 2024-08-20T21:38:35.2487147Z dataset: Dataset used for sampling. 2024-08-20T21:38:35.2487441Z num_replicas (int, optional): Number of processes participating in 2024-08-20T21:38:35.2487768Z distributed training. By default, :attr:`world_size` is retrieved from the 2024-08-20T21:38:35.2487903Z current distributed group. 2024-08-20T21:38:35.2488246Z rank (int, optional): Rank of the current process within :attr:`num_replicas`. 2024-08-20T21:38:35.2488524Z By default, :attr:`rank` is retrieved from the current distributed 2024-08-20T21:38:35.2488624Z group. 2024-08-20T21:38:35.2488943Z shuffle (bool, optional): If ``True`` (default), sampler will shuffle the 2024-08-20T21:38:35.2489101Z indices. 2024-08-20T21:38:35.2489377Z seed (int, optional): random seed used to shuffle the sampler if 2024-08-20T21:38:35.2489640Z :attr:`shuffle=True`. This number should be identical across all 2024-08-20T21:38:35.2489856Z processes in the distributed group. Default: ``0``. 2024-08-20T21:38:35.2490161Z drop_last (bool, optional): if ``True``, then the sampler will drop the 2024-08-20T21:38:35.2490440Z tail of the data to make it evenly divisible across the number of 2024-08-20T21:38:35.2490716Z replicas. If ``False``, the sampler will add extra indices to make 2024-08-20T21:38:35.2491014Z the data evenly divisible across the replicas. Default: ``False``. 2024-08-20T21:38:35.2491102Z 2024-08-20T21:38:35.2491203Z .. warning:: 2024-08-20T21:38:35.2491468Z In distributed mode, calling the :meth:`set_epoch` method at 2024-08-20T21:38:35.2491824Z the beginning of each epoch **before** creating the :class:`DataLoader` iterator 2024-08-20T21:38:35.2492191Z is necessary to make shuffling work properly across multiple epochs. Otherwise, 2024-08-20T21:38:35.2492349Z the same ordering will be always used. 2024-08-20T21:38:35.2492436Z 2024-08-20T21:38:35.2492548Z Example:: 2024-08-20T21:38:35.2492634Z 2024-08-20T21:38:35.2492749Z >>> # xdoctest: +SKIP 2024-08-20T21:38:35.2493049Z >>> sampler = DistributedSampler(dataset) if is_distributed else None 2024-08-20T21:38:35.2493273Z >>> loader = DataLoader(dataset, shuffle=(sampler is None), 2024-08-20T21:38:35.2493477Z ... sampler=sampler) 2024-08-20T21:38:35.2493667Z >>> for epoch in range(start_epoch, n_epochs): 2024-08-20T21:38:35.2493790Z ... if is_distributed: 2024-08-20T21:38:35.2493934Z ... sampler.set_epoch(epoch) 2024-08-20T21:38:35.2494060Z ... train(loader) 2024-08-20T21:38:35.2494151Z 2024-08-20T21:38:35.2494567Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:38:35.2494665Z 2024-08-20T21:38:35.2494775Z warnings.warn(msg) 2024-08-20T21:38:35.2494860Z 2024-08-20T21:38:35.2494981Z  2024-08-20T21:38:35.2495182Z === Found 9 run-time warnings === 2024-08-20T21:38:35.2495399Z --- Runtime Warning: 1 / 9 --- 2024-08-20T21:38:35.2495738Z example = 2024-08-20T21:38:35.2497857Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py:1250: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /var/lib/jenkins/workspace/c10/core/TensorImpl.h:1931.) 2024-08-20T21:38:35.2498013Z return super().refine_names(names) 2024-08-20T21:38:35.2498101Z 2024-08-20T21:38:35.2498310Z --- Runtime Warning: 2 / 9 --- 2024-08-20T21:38:35.2498727Z example = 2024-08-20T21:38:35.2499727Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py:250: UserWarning: Warning only once for all operators, other operators may also be overridden. 2024-08-20T21:38:35.2500145Z Overriding a previously registered kernel for the same operator and the same dispatch key 2024-08-20T21:38:35.2500478Z operator: aten::div.Tensor(Tensor self, Tensor other) -> Tensor 2024-08-20T21:38:35.2500877Z registered at /var/lib/jenkins/workspace/build/aten/src/ATen/RegisterSchema.cpp:6 2024-08-20T21:38:35.2500999Z dispatch key: CPU 2024-08-20T21:38:35.2501571Z previous kernel: registered at /var/lib/jenkins/workspace/aten/src/ATen/LegacyBatchingRegistrations.cpp:1079 2024-08-20T21:38:35.2502387Z new kernel: registered at /dev/null:811 (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/core/dispatch/OperatorEntry.cpp:162.) 2024-08-20T21:38:35.2502667Z impl_fn(self.ns, name.split("::")[-1], dispatch_key) 2024-08-20T21:38:35.2502756Z 2024-08-20T21:38:35.2502974Z --- Runtime Warning: 3 / 9 --- 2024-08-20T21:38:35.2503285Z example = 2024-08-20T21:38:35.2505009Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py:106: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/NestedTensorImpl.cpp:180.) 2024-08-20T21:38:35.2505335Z return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None) 2024-08-20T21:38:35.2505425Z 2024-08-20T21:38:35.2505632Z --- Runtime Warning: 4 / 9 --- 2024-08-20T21:38:35.2505982Z example = 2024-08-20T21:38:35.2508542Z :1: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) 2024-08-20T21:38:35.2508647Z 2024-08-20T21:38:35.2508853Z --- Runtime Warning: 5 / 9 --- 2024-08-20T21:38:35.2509322Z example = 2024-08-20T21:38:35.2511696Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/const_fold.py:252: UserWarning: Attempted to insert a get_attr Node with no underlying reference in the owning GraphModule! Call GraphModule.add_submodule to add the necessary submodule, GraphModule.add_parameter to add the necessary Parameter, or nn.Module.register_buffer to add the necessary buffer 2024-08-20T21:38:35.2511915Z new_node = root_const_gm.graph.get_attr(in_node.target) 2024-08-20T21:38:35.2512019Z 2024-08-20T21:38:35.2512228Z --- Runtime Warning: 6 / 9 --- 2024-08-20T21:38:35.2512612Z example = 2024-08-20T21:38:35.2514310Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py:379: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance) 2024-08-20T21:38:35.2514421Z warnings.warn( 2024-08-20T21:38:35.2514524Z 2024-08-20T21:38:35.2514729Z --- Runtime Warning: 7 / 9 --- 2024-08-20T21:38:35.2515164Z example = 2024-08-20T21:38:35.2516849Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py:379: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance) 2024-08-20T21:38:35.2516955Z warnings.warn( 2024-08-20T21:38:35.2517054Z 2024-08-20T21:38:35.2517259Z --- Runtime Warning: 8 / 9 --- 2024-08-20T21:38:35.2517625Z example = 2024-08-20T21:38:35.2518908Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py:143: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`. 2024-08-20T21:38:35.2519056Z WeightNorm.apply(module, name, dim) 2024-08-20T21:38:35.2519143Z 2024-08-20T21:38:35.2519359Z --- Runtime Warning: 9 / 9 --- 2024-08-20T21:38:35.2519836Z example = 2024-08-20T21:38:35.2521107Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py:143: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`. 2024-08-20T21:38:35.2521266Z WeightNorm.apply(module, name, dim) 2024-08-20T21:38:35.2521354Z 2024-08-20T21:38:35.2521738Z === 335 passed, 360 skipped, 110 warnings in 12.77 seconds === 2024-08-20T21:38:35.2522094Z Running inductor/test_max_autotune 1/1 ... [2024-08-20 21:38:34.974248] 2024-08-20T21:38:35.2522233Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:38:35.2523684Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_max_autotune.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:38:34.974642] 2024-08-20T21:38:38.2621121Z 2024-08-20T21:38:38.2623615Z inductor/test_max_autotune 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_max_autotune_1.1_065053d3f8299dea_.log 2024-08-20T21:38:38.2625337Z 2024-08-20T21:38:38.2626024Z Running inductor/test_distributed_patterns 1/1 ... [2024-08-20 21:38:38.262342] 2024-08-20T21:38:38.2627033Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:38:38.2634831Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_distributed_patterns.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:38:38.262811] 2024-08-20T21:39:18.1345200Z 2024-08-20T21:39:18.1347280Z inductor/test_distributed_patterns 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_distributed_patterns_1.1_30c8a3c1e0c95688_.log 2024-08-20T21:39:18.1359641Z Running 19 items in this shard: test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_fake_distributed_aot_eager, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_fake_distributed_inductor, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_intermediate_hook_with_closure, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_module_backward_hooks_aot, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_module_backward_hooks_eager, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_module_backward_hooks_inductor, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_module_backward_hooks_multi_layers, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_nn_param_return1, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_nn_param_return2, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_nn_param_return3, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_nn_param_return4, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_storage_resize_nonzero_cpu, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_storage_resize_nonzero_gpu, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_storage_resize_zero_cpu, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_storage_resize_zero_gpu, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_unsafe_preserve_version_counter1, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_unsafe_preserve_version_counter2, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_unsafe_set_version_counter1, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_unsafe_set_version_counter2 2024-08-20T21:39:18.1370468Z 2024-08-20T21:39:18.1371076Z Running test_utils 1/1 ... [2024-08-20 21:39:18.134902] 2024-08-20T21:39:18.1371579Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:39:18.1373213Z 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'] ... [2024-08-20 21:39:18.135280] 2024-08-20T21:41:27.6069329Z 2024-08-20T21:41:27.6070874Z PRINTING LOG FILE of test_utils 1/1 (test/test-reports/test_utils_1.1_6bb4898f3c88eb75_.log) 2024-08-20T21:41:27.6072936Z Test results will be stored in test-reports/python-pytest/test_utils/test_utils-58cee363dce3f8e5.xml 2024-08-20T21:41:27.6074416Z ============================= test session starts ============================== 2024-08-20T21:41:27.6076234Z platform linux -- Python 3.12.4, pytest-7.3.2, pluggy-1.5.0 -- /opt/conda/envs/py_3.12/bin/python 2024-08-20T21:41:27.6077805Z cachedir: .pytest_cache 2024-08-20T21:41:27.6079988Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2024-08-20T21:41:27.6082114Z rootdir: /var/lib/jenkins/workspace 2024-08-20T21:41:27.6083018Z configfile: pytest.ini 2024-08-20T21:41:27.6085100Z plugins: hypothesis-5.35.1, cpp-2.3.0, flakefinder-1.1.0, rerunfailures-14.0, xdist-3.3.1, xdoctest-1.1.0, typeguard-4.3.0 2024-08-20T21:41:27.6087408Z collecting ... collected 5947 items 2024-08-20T21:41:27.6088605Z stepcurrent: Cannot find last run test, not skipping 2024-08-20T21:41:27.9158019Z Running 5947 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, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmatmul___cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmod___cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmod___cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmod___cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmod___cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmul___cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmul___cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmul___cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmul___cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmul___cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmul___cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmul___cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmul___cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmul___cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmul___cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmul___cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmul___cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___ror___cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___ror___cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___ror___cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___ror___cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___ror___cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___ror___cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rpow___cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rpow___cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rpow___cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rpow___cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rpow___cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rpow___cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rpow___cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rpow___cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rpow___cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rpow___cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rpow___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___rxor___cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rxor___cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rxor___cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rxor___cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rxor___cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rxor___cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__batch_norm_with_update_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__batch_norm_with_update_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__batch_norm_with_update_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__batch_norm_with_update_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__chunk_cat_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__chunk_cat_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__chunk_cat_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__chunk_cat_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__chunk_cat_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__chunk_cat_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__chunk_cat_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__chunk_cat_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__chunk_cat_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__chunk_cat_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__chunk_cat_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__chunk_cat_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__chunk_cat_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__native_batch_norm_legit_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__native_batch_norm_legit_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__native_batch_norm_legit_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__native_batch_norm_legit_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__segment_reduce_lengths_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__segment_reduce_lengths_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__segment_reduce_lengths_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__segment_reduce_lengths_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__segment_reduce_offsets_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__segment_reduce_offsets_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__segment_reduce_offsets_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__segment_reduce_offsets_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__softmax_backward_data_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__softmax_backward_data_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__softmax_backward_data_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__softmax_backward_data_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_put_accumulate_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_put_accumulate_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_put_accumulate_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_put_accumulate_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_put_accumulate_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_put_accumulate_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_put_accumulate_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_put_accumulate_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_put_accumulate_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_put_accumulate_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_put_accumulate_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_put_accumulate_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__upsample_bilinear2d_aa_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__upsample_bilinear2d_aa_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__upsample_bilinear2d_aa_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_abs_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_abs_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_abs_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_abs_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_abs_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_abs_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_abs_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_abs_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_abs_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_abs_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_abs_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_abs_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acos_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acos_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acos_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acos_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acos_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acos_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acos_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acos_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acos_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acos_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acos_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acos_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcdiv_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcdiv_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcdiv_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcdiv_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcdiv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcdiv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_bool, 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_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_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_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_complex64, 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_float16, 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, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_hermite_polynomial_h_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_hermite_polynomial_h_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_hermite_polynomial_h_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_hermite_polynomial_h_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_hermite_polynomial_h_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_hermite_polynomial_he_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_hermite_polynomial_he_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_hermite_polynomial_he_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_hermite_polynomial_he_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_hermite_polynomial_he_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_hermite_polynomial_he_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_hermite_polynomial_he_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_hermite_polynomial_he_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i0e_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i0e_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i0e_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i0e_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i0e_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i0e_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i0e_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i0e_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i0e_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i0e_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i1_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i1_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i1_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i1_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i1_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i1_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i1_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i1_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i1e_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i1e_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i1e_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i1e_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i1e_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i1e_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i1e_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i1e_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_laguerre_polynomial_l_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_laguerre_polynomial_l_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_laguerre_polynomial_l_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_laguerre_polynomial_l_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_laguerre_polynomial_l_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_laguerre_polynomial_l_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_laguerre_polynomial_l_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_laguerre_polynomial_l_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_legendre_polynomial_p_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_legendre_polynomial_p_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_legendre_polynomial_p_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_legendre_polynomial_p_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_legendre_polynomial_p_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_legendre_polynomial_p_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_legendre_polynomial_p_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_legendre_polynomial_p_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_log_ndtr_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_log_ndtr_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_log_ndtr_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_log_ndtr_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_log_ndtr_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_log_ndtr_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_log_ndtr_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_log_ndtr_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_i0_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_i0_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_i0_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_i0_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_i0_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_i0_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_i0_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_i0_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_i1_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_i1_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_i1_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_i1_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_i1_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_i1_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_i1_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_i1_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_k0_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_k0_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_k0_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_k0_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_k0_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_k0_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_k0_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_k0_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_k1_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_k1_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_k1_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_k1_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_k1_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_k1_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_k1_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_k1_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_ndtr_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_ndtr_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_ndtr_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_ndtr_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_ndtr_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_ndtr_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_ndtr_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_ndtr_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_ndtr_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_ndtr_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_ndtri_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_ndtri_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_ndtri_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_ndtri_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_ndtri_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_ndtri_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_ndtri_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_ndtri_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_polygamma_special_polygamma_n_0_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_polygamma_special_polygamma_n_0_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_polygamma_special_polygamma_n_0_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_polygamma_special_polygamma_n_0_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_polygamma_special_polygamma_n_0_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_polygamma_special_polygamma_n_0_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_polygamma_special_polygamma_n_0_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_polygamma_special_polygamma_n_0_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_polygamma_special_polygamma_n_0_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_polygamma_special_polygamma_n_0_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_scaled_modified_bessel_k0_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_scaled_modified_bessel_k0_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_scaled_modified_bessel_k0_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_scaled_modified_bessel_k0_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_scaled_modified_bessel_k0_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_scaled_modified_bessel_k0_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_scaled_modified_bessel_k0_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_scaled_modified_bessel_k0_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_scaled_modified_bessel_k1_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_scaled_modified_bessel_k1_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_scaled_modified_bessel_k1_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_scaled_modified_bessel_k1_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_scaled_modified_bessel_k1_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_scaled_modified_bessel_k1_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_scaled_modified_bessel_k1_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_scaled_modified_bessel_k1_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_t_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_t_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_t_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_t_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_t_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_t_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_t_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_t_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_u_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_u_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_u_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_u_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_u_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_u_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_u_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_u_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_v_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_v_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_v_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_v_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_v_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_v_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_v_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_v_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_w_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_w_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_w_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_w_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_w_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_w_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_w_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_w_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_lowrank_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_lowrank_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_lowrank_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_lowrank_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_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_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_torch_ops_aten__safe_softmax_default_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_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_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_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_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_complex64, 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 2024-08-20T21:41:28.2163133Z 2024-08-20T21:41:28.2165012Z test_utils.py::TestCheckpoint::test_checkpoint PASSED [0.0156s] [ 0%] 2024-08-20T21:41:28.2167743Z test_utils.py::TestCheckpoint::test_checkpoint_module_list PASSED [0.0103s] [ 0%] 2024-08-20T21:41:28.2170355Z test_utils.py::TestCheckpoint::test_checkpoint_no_tensors PASSED [0.0020s] [ 0%] 2024-08-20T21:41:28.2172978Z test_utils.py::TestCheckpoint::test_checkpoint_non_tensor PASSED [0.0015s] [ 0%] 2024-08-20T21:41:28.2175765Z test_utils.py::TestCheckpoint::test_checkpoint_non_tensor_inputs_outputs PASSED [0.0036s] [ 0%] 2024-08-20T21:41:28.2179168Z test_utils.py::TestCheckpoint::test_checkpoint_not_preserve_rng_state_and_without_reentrant SKIPPED [0.0002s] (No CUDA) [ 0%] 2024-08-20T21:41:28.2182350Z test_utils.py::TestCheckpoint::test_checkpoint_partial_grad PASSED [0.0018s] [ 0%] 2024-08-20T21:41:28.2184910Z test_utils.py::TestCheckpoint::test_checkpoint_rng_cpu PASSED [0.0086s] [ 0%] 2024-08-20T21:41:28.2187637Z test_utils.py::TestCheckpoint::test_checkpoint_rng_cuda SKIPPED [0.0002s] (No CUDA) [ 0%] 2024-08-20T21:41:28.2190734Z test_utils.py::TestCheckpoint::test_checkpoint_sequential_deprecated_multiple_args PASSED [0.0012s] [ 0%] 2024-08-20T21:41:28.2194421Z test_utils.py::TestCheckpoint::test_checkpoint_sequential_deprecated_no_args PASSED [0.0012s] [ 0%] 2024-08-20T21:41:28.2197228Z test_utils.py::TestCheckpoint::test_checkpoint_trigger PASSED [0.0041s] [ 0%] 2024-08-20T21:41:28.2199757Z test_utils.py::TestCheckpoint::test_checkpoint_valid PASSED [0.0034s] [ 0%] 2024-08-20T21:41:28.2202926Z test_utils.py::TestCheckpoint::test_checkpointing_without_reentrant_early_free SKIPPED [0.0002s] (Test requires CUDA) [ 0%] 2024-08-20T21:41:28.2206551Z test_utils.py::TestCheckpoint::test_get_device_states_recursive SKIPPED [0.0002s] (multi-GPU not supported) [ 0%] 2024-08-20T21:41:28.2209692Z test_utils.py::TestCheckpoint::test_infer_device_state_recursive_meta PASSED [0.0013s] [ 0%] 2024-08-20T21:41:28.2213058Z test_utils.py::TestCheckpoint::test_infer_device_state_recursive_multi_cuda SKIPPED [0.0001s] (multi-GPU not supported) [ 0%] 2024-08-20T21:41:28.2216180Z test_utils.py::TestDataLoaderUtils::test_multi_drop PASSED [0.0763s] [ 0%] 2024-08-20T21:41:28.2219884Z test_utils.py::TestDataLoaderUtils::test_multi_keep SKIPPED [0.0003s] (FIXME: Intermittent CUDA out-of-memory error on Windows and time-out under ASAN) [ 0%] 2024-08-20T21:41:28.2223800Z test_utils.py::TestDataLoaderUtils::test_random_seed PASSED [0.1734s] [ 0%] 2024-08-20T21:41:28.2226361Z test_utils.py::TestDataLoaderUtils::test_single_drop PASSED [0.0022s] [ 0%] 2024-08-20T21:41:28.2228913Z test_utils.py::TestDataLoaderUtils::test_single_keep PASSED [0.0016s] [ 0%] 2024-08-20T21:41:28.2231653Z test_utils.py::TestBottleneck::test_bottleneck_cpu_only PASSED [7.0402s] [ 0%] 2024-08-20T21:41:28.2234311Z test_utils.py::TestBottleneck::test_bottleneck_cuda SKIPPED [0.0002s] (No CUDA) [ 0%] 2024-08-20T21:41:28.2236909Z test_utils.py::TestCollectEnv::test_smoke PASSED [0.7071s] [ 0%] 2024-08-20T21:41:28.2239422Z test_utils.py::TestONNXUtils::test_check_onnx_broadcast PASSED [0.0017s] [ 0%] 2024-08-20T21:41:28.2241964Z test_utils.py::TestONNXUtils::test_prepare_onnx_paddings PASSED [0.0013s] [ 0%] 2024-08-20T21:41:28.2244497Z test_utils.py::TestHipify::test_import_hipify PASSED [0.0011s] [ 0%] 2024-08-20T21:41:28.2247172Z test_utils.py::TestHipifyTrie::test_add_and_search_trie PASSED [0.0002s] [ 0%] 2024-08-20T21:41:28.2249810Z test_utils.py::TestHipifyTrie::test_add_multiple_and_search_trie PASSED [0.0002s] [ 0%] 2024-08-20T21:41:28.2252548Z test_utils.py::TestHipifyTrie::test_char_export_trie_to_regex PASSED [0.0003s] [ 0%] 2024-08-20T21:41:28.2255150Z test_utils.py::TestHipifyTrie::test_export_trie_to_regex PASSED [0.0014s] [ 0%] 2024-08-20T21:41:28.2257883Z test_utils.py::TestHipifyTrie::test_prefix_words_export_trie_to_regex PASSED [0.0002s] [ 0%] 2024-08-20T21:41:28.2260545Z test_utils.py::TestHipifyTrie::test_quote_escape PASSED [0.0004s] [ 0%] 2024-08-20T21:41:28.2263156Z test_utils.py::TestHipifyTrie::test_single_export_trie_to_regex PASSED [0.0003s] [ 0%] 2024-08-20T21:41:28.2265968Z test_utils.py::TestHipifyTrie::test_special_char_export_trie_to_regex PASSED [0.0002s] [ 0%] 2024-08-20T21:41:28.2268627Z test_utils.py::TestAssert::test_assert_scriptable PASSED [0.0492s] [ 0%] 2024-08-20T21:41:28.2271109Z test_utils.py::TestAssert::test_assert_true PASSED [0.0014s] [ 0%] 2024-08-20T21:41:28.2273655Z test_utils.py::TestStandaloneCPPJIT::test_load_standalone PASSED [1.7378s] [ 0%] 2024-08-20T21:41:28.2276378Z test_utils.py::TestExtensionUtils::test_external_module_register PASSED [0.0016s] [ 0%] 2024-08-20T21:41:28.2279665Z test_utils.py::TestExtensionUtils::test_external_module_register_with_renamed_backend PASSED [0.0018s] [ 0%] 2024-08-20T21:41:28.2282546Z test_utils.py::TestRenderUtils::test_basic PASSED [0.0098s] [ 0%] 2024-08-20T21:41:28.2285048Z test_utils.py::TestDeviceUtilsCPU::test_basic_cpu PASSED [0.0020s] [ 0%] 2024-08-20T21:41:28.2287634Z test_utils.py::TestDeviceUtilsCPU::test_decorator_cpu PASSED [0.0013s] [ 0%] 2024-08-20T21:41:28.2290316Z test_utils.py::TestDeviceUtilsCPU::test_decorator_generator_cpu PASSED [0.0013s] [ 0%] 2024-08-20T21:41:28.2293205Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_bfloat16 PASSED [0.0477s] [ 0%] 2024-08-20T21:41:28.2296090Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_bool PASSED [0.0058s] [ 0%] 2024-08-20T21:41:28.2298984Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_complex128 PASSED [0.0059s] [ 0%] 2024-08-20T21:41:28.2301954Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_complex32 PASSED [0.0059s] [ 0%] 2024-08-20T21:41:28.2304912Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_complex64 PASSED [0.0058s] [ 0%] 2024-08-20T21:41:28.2307818Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_float16 PASSED [0.0056s] [ 0%] 2024-08-20T21:41:28.2310614Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_float32 PASSED [0.0057s] [ 0%] 2024-08-20T21:41:28.2313736Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_float64 PASSED [0.0057s] [ 0%] 2024-08-20T21:41:28.2316609Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_int16 PASSED [0.0056s] [ 0%] 2024-08-20T21:41:28.2319457Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_int32 PASSED [0.0058s] [ 0%] 2024-08-20T21:41:28.2320693Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_int64 PASSED [0.0056s] [ 0%] 2024-08-20T21:41:28.2322093Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_int8 PASSED [0.0058s] [ 0%] 2024-08-20T21:41:28.2323292Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_uint8 PASSED [0.0056s] [ 0%] 2024-08-20T21:41:28.2324484Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_bfloat16 PASSED [0.0058s] [ 0%] 2024-08-20T21:41:28.2325697Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_bool PASSED [0.0055s] [ 1%] 2024-08-20T21:41:28.2326986Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_complex128 PASSED [0.0058s] [ 1%] 2024-08-20T21:41:28.2328233Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_complex32 PASSED [0.0056s] [ 1%] 2024-08-20T21:41:28.2329486Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_complex64 PASSED [0.0055s] [ 1%] 2024-08-20T21:41:28.2330719Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_float16 PASSED [0.0066s] [ 1%] 2024-08-20T21:41:28.2331931Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_float32 PASSED [0.0055s] [ 1%] 2024-08-20T21:41:28.2333137Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_float64 PASSED [0.0057s] [ 1%] 2024-08-20T21:41:28.2334341Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_int16 PASSED [0.0056s] [ 1%] 2024-08-20T21:41:28.2335536Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_int32 PASSED [0.0058s] [ 1%] 2024-08-20T21:41:28.2336724Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_int64 PASSED [0.0055s] [ 1%] 2024-08-20T21:41:28.2337988Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_int8 PASSED [0.0057s] [ 1%] 2024-08-20T21:41:28.2339166Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_uint8 PASSED [0.0056s] [ 1%] 2024-08-20T21:41:28.2340432Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_bfloat16 PASSED [0.0132s] [ 1%] 2024-08-20T21:41:28.2341727Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_bool PASSED [0.0129s] [ 1%] 2024-08-20T21:41:28.2343069Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_complex128 PASSED [0.0131s] [ 1%] 2024-08-20T21:41:28.2344408Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_complex32 PASSED [0.0133s] [ 1%] 2024-08-20T21:41:28.2345743Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_complex64 PASSED [0.0132s] [ 1%] 2024-08-20T21:41:28.2347280Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_float16 PASSED [0.0132s] [ 1%] 2024-08-20T21:41:28.2348613Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_float32 PASSED [0.0130s] [ 1%] 2024-08-20T21:41:28.2349923Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_float64 PASSED [0.0132s] [ 1%] 2024-08-20T21:41:28.2351212Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_int16 PASSED [0.0130s] [ 1%] 2024-08-20T21:41:28.2352476Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_int32 PASSED [0.0130s] [ 1%] 2024-08-20T21:41:28.2353910Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_int64 PASSED [0.0129s] [ 1%] 2024-08-20T21:41:28.2355199Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_int8 PASSED [0.0129s] [ 1%] 2024-08-20T21:41:28.2356475Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_uint8 PASSED [0.0132s] [ 1%] 2024-08-20T21:41:28.2357758Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_bfloat16 PASSED [0.0095s] [ 1%] 2024-08-20T21:41:28.2359022Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_bool PASSED [0.0100s] [ 1%] 2024-08-20T21:41:28.2360304Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_complex128 PASSED [0.0096s] [ 1%] 2024-08-20T21:41:28.2361611Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_complex64 PASSED [0.0098s] [ 1%] 2024-08-20T21:41:28.2362900Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_float16 PASSED [0.0094s] [ 1%] 2024-08-20T21:41:28.2364177Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_float32 PASSED [0.0096s] [ 1%] 2024-08-20T21:41:28.2365456Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_float64 PASSED [0.0095s] [ 1%] 2024-08-20T21:41:28.2366780Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_int16 PASSED [0.0094s] [ 1%] 2024-08-20T21:41:28.2368047Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_int32 PASSED [0.0096s] [ 1%] 2024-08-20T21:41:28.2369297Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_int64 PASSED [0.0095s] [ 1%] 2024-08-20T21:41:28.2370547Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_int8 PASSED [0.0096s] [ 1%] 2024-08-20T21:41:28.2371785Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_uint8 PASSED [0.0095s] [ 1%] 2024-08-20T21:41:28.2373028Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rand___cpu_bool PASSED [0.0096s] [ 1%] 2024-08-20T21:41:28.2374421Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rand___cpu_int16 PASSED [0.0094s] [ 1%] 2024-08-20T21:41:28.2375657Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rand___cpu_int32 PASSED [0.0097s] [ 1%] 2024-08-20T21:41:28.2376906Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rand___cpu_int64 PASSED [0.0095s] [ 1%] 2024-08-20T21:41:28.2378154Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rand___cpu_int8 PASSED [0.0096s] [ 1%] 2024-08-20T21:41:28.2379412Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rand___cpu_uint8 PASSED [0.0094s] [ 1%] 2024-08-20T21:41:28.2380674Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_bfloat16 PASSED [0.0100s] [ 1%] 2024-08-20T21:41:28.2381942Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_bool PASSED [0.0100s] [ 1%] 2024-08-20T21:41:28.2383226Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_complex128 PASSED [0.0100s] [ 1%] 2024-08-20T21:41:28.2384702Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_complex64 PASSED [0.0102s] [ 1%] 2024-08-20T21:41:28.2385992Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_float16 PASSED [0.0098s] [ 1%] 2024-08-20T21:41:28.2387266Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_float32 PASSED [0.0105s] [ 1%] 2024-08-20T21:41:28.2388604Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_float64 PASSED [0.0099s] [ 1%] 2024-08-20T21:41:28.2389855Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_int16 PASSED [0.0101s] [ 1%] 2024-08-20T21:41:28.2391107Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_int32 PASSED [0.0100s] [ 1%] 2024-08-20T21:41:28.2392354Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_int64 PASSED [0.0098s] [ 1%] 2024-08-20T21:41:28.2393605Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_int8 PASSED [0.0101s] [ 1%] 2024-08-20T21:41:28.2394842Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rdiv___cpu_uint8 PASSED [0.0099s] [ 1%] 2024-08-20T21:41:28.2396136Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmatmul___cpu_bfloat16 PASSED [0.0130s] [ 1%] 2024-08-20T21:41:28.2397472Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmatmul___cpu_complex128 PASSED [0.0131s] [ 1%] 2024-08-20T21:41:28.2398811Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmatmul___cpu_complex64 PASSED [0.0131s] [ 1%] 2024-08-20T21:41:28.2400127Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmatmul___cpu_float16 PASSED [0.0127s] [ 1%] 2024-08-20T21:41:28.2401442Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmatmul___cpu_float32 PASSED [0.0128s] [ 2%] 2024-08-20T21:41:28.2402751Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmatmul___cpu_float64 PASSED [0.0126s] [ 2%] 2024-08-20T21:41:28.2404029Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmatmul___cpu_int16 PASSED [0.0128s] [ 2%] 2024-08-20T21:41:28.2405315Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmatmul___cpu_int32 PASSED [0.0126s] [ 2%] 2024-08-20T21:41:28.2406607Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmatmul___cpu_int64 PASSED [0.0126s] [ 2%] 2024-08-20T21:41:28.2407965Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmatmul___cpu_int8 PASSED [0.0129s] [ 2%] 2024-08-20T21:41:28.2409313Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmatmul___cpu_uint8 PASSED [0.0126s] [ 2%] 2024-08-20T21:41:28.2410607Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmod___cpu_bfloat16 PASSED [0.0098s] [ 2%] 2024-08-20T21:41:28.2411878Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmod___cpu_float16 PASSED [0.0095s] [ 2%] 2024-08-20T21:41:28.2413152Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmod___cpu_float32 PASSED [0.0097s] [ 2%] 2024-08-20T21:41:28.2414408Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmod___cpu_float64 PASSED [0.0095s] [ 2%] 2024-08-20T21:41:28.2415692Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmul___cpu_bfloat16 PASSED [0.0101s] [ 2%] 2024-08-20T21:41:28.2416962Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmul___cpu_bool PASSED [0.0095s] [ 2%] 2024-08-20T21:41:28.2418237Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmul___cpu_complex128 PASSED [0.0096s] [ 2%] 2024-08-20T21:41:28.2419541Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmul___cpu_complex64 PASSED [0.0099s] [ 2%] 2024-08-20T21:41:28.2421031Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmul___cpu_float16 PASSED [0.0095s] [ 2%] 2024-08-20T21:41:28.2422319Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmul___cpu_float32 PASSED [0.0097s] [ 2%] 2024-08-20T21:41:28.2423572Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmul___cpu_float64 PASSED [0.0096s] [ 2%] 2024-08-20T21:41:28.2424899Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmul___cpu_int16 PASSED [0.0097s] [ 2%] 2024-08-20T21:41:28.2426157Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmul___cpu_int32 PASSED [0.0095s] [ 2%] 2024-08-20T21:41:28.2427409Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmul___cpu_int64 PASSED [0.0097s] [ 2%] 2024-08-20T21:41:28.2428636Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmul___cpu_int8 PASSED [0.0096s] [ 2%] 2024-08-20T21:41:28.2429890Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rmul___cpu_uint8 PASSED [0.0099s] [ 2%] 2024-08-20T21:41:28.2431138Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___ror___cpu_bool PASSED [0.0096s] [ 2%] 2024-08-20T21:41:28.2432362Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___ror___cpu_int16 PASSED [0.0095s] [ 2%] 2024-08-20T21:41:28.2433610Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___ror___cpu_int32 PASSED [0.0098s] [ 2%] 2024-08-20T21:41:28.2434850Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___ror___cpu_int64 PASSED [0.0095s] [ 2%] 2024-08-20T21:41:28.2436090Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___ror___cpu_int8 PASSED [0.0098s] [ 2%] 2024-08-20T21:41:28.2437312Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___ror___cpu_uint8 PASSED [0.0095s] [ 2%] 2024-08-20T21:41:28.2438585Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rpow___cpu_bfloat16 PASSED [0.0099s] [ 2%] 2024-08-20T21:41:28.2439888Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rpow___cpu_complex128 PASSED [0.0097s] [ 2%] 2024-08-20T21:41:28.2441190Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rpow___cpu_complex64 PASSED [0.0099s] [ 2%] 2024-08-20T21:41:28.2442470Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rpow___cpu_float16 PASSED [0.0096s] [ 2%] 2024-08-20T21:41:28.2443753Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rpow___cpu_float32 PASSED [0.0096s] [ 2%] 2024-08-20T21:41:28.2445097Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rpow___cpu_float64 PASSED [0.0098s] [ 2%] 2024-08-20T21:41:28.2446346Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rpow___cpu_int16 PASSED [0.0095s] [ 2%] 2024-08-20T21:41:28.2447825Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rpow___cpu_int32 PASSED [0.0097s] [ 2%] 2024-08-20T21:41:28.2449075Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rpow___cpu_int64 PASSED [0.0095s] [ 2%] 2024-08-20T21:41:28.2450329Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rpow___cpu_int8 PASSED [0.0097s] [ 2%] 2024-08-20T21:41:28.2451570Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rpow___cpu_uint8 PASSED [0.0095s] [ 2%] 2024-08-20T21:41:28.2452848Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rsub___cpu_bfloat16 PASSED [0.0097s] [ 2%] 2024-08-20T21:41:28.2454152Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rsub___cpu_complex128 PASSED [0.0097s] [ 2%] 2024-08-20T21:41:28.2455461Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rsub___cpu_complex64 PASSED [0.0098s] [ 2%] 2024-08-20T21:41:28.2456731Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rsub___cpu_float16 PASSED [0.0097s] [ 2%] 2024-08-20T21:41:28.2458008Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rsub___cpu_float32 PASSED [0.0095s] [ 2%] 2024-08-20T21:41:28.2459277Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rsub___cpu_float64 PASSED [0.0098s] [ 2%] 2024-08-20T21:41:28.2460638Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rsub___cpu_int16 PASSED [0.0096s] [ 2%] 2024-08-20T21:41:28.2461895Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rsub___cpu_int32 PASSED [0.0097s] [ 2%] 2024-08-20T21:41:28.2463155Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rsub___cpu_int64 PASSED [0.0095s] [ 2%] 2024-08-20T21:41:28.2464405Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rsub___cpu_int8 PASSED [0.0097s] [ 2%] 2024-08-20T21:41:28.2465640Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rsub___cpu_uint8 PASSED [0.0094s] [ 2%] 2024-08-20T21:41:28.2466890Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rxor___cpu_bool PASSED [0.0096s] [ 2%] 2024-08-20T21:41:28.2468137Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rxor___cpu_int16 PASSED [0.0095s] [ 2%] 2024-08-20T21:41:28.2469394Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rxor___cpu_int32 PASSED [0.0094s] [ 2%] 2024-08-20T21:41:28.2470627Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rxor___cpu_int64 PASSED [0.0101s] [ 2%] 2024-08-20T21:41:28.2471878Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rxor___cpu_int8 PASSED [0.0095s] [ 2%] 2024-08-20T21:41:28.2473126Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___rxor___cpu_uint8 PASSED [0.0097s] [ 2%] 2024-08-20T21:41:28.2474491Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__batch_norm_with_update_cpu_bfloat16 PASSED [0.0099s] [ 2%] 2024-08-20T21:41:28.2475953Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__batch_norm_with_update_cpu_float16 PASSED [0.0100s] [ 2%] 2024-08-20T21:41:28.2477393Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__batch_norm_with_update_cpu_float32 PASSED [0.0098s] [ 2%] 2024-08-20T21:41:28.2478842Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__batch_norm_with_update_cpu_float64 PASSED [0.0100s] [ 3%] 2024-08-20T21:41:28.2480202Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__chunk_cat_cpu_bfloat16 PASSED [0.0079s] [ 3%] 2024-08-20T21:41:28.2481585Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__chunk_cat_cpu_bool PASSED [0.0078s] [ 3%] 2024-08-20T21:41:28.2482885Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__chunk_cat_cpu_complex128 PASSED [0.0083s] [ 3%] 2024-08-20T21:41:28.2484226Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__chunk_cat_cpu_complex32 PASSED [0.0080s] [ 3%] 2024-08-20T21:41:28.2485538Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__chunk_cat_cpu_complex64 PASSED [0.0082s] [ 3%] 2024-08-20T21:41:28.2486966Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__chunk_cat_cpu_float16 PASSED [0.0079s] [ 3%] 2024-08-20T21:41:28.2488284Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__chunk_cat_cpu_float32 PASSED [0.0081s] [ 3%] 2024-08-20T21:41:28.2489578Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__chunk_cat_cpu_float64 PASSED [0.0079s] [ 3%] 2024-08-20T21:41:28.2490855Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__chunk_cat_cpu_int16 PASSED [0.0080s] [ 3%] 2024-08-20T21:41:28.2492140Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__chunk_cat_cpu_int32 PASSED [0.0078s] [ 3%] 2024-08-20T21:41:28.2493427Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__chunk_cat_cpu_int64 PASSED [0.0078s] [ 3%] 2024-08-20T21:41:28.2494690Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__chunk_cat_cpu_int8 PASSED [0.0081s] [ 3%] 2024-08-20T21:41:28.2496043Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__chunk_cat_cpu_uint8 PASSED [0.0079s] [ 3%] 2024-08-20T21:41:28.2501831Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__native_batch_norm_legit_cpu_bfloat16 PASSED [0.0105s] [ 3%] 2024-08-20T21:41:28.2504031Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__native_batch_norm_legit_cpu_float16 PASSED [0.0103s] [ 3%] 2024-08-20T21:41:28.2505487Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__native_batch_norm_legit_cpu_float32 PASSED [0.0109s] [ 3%] 2024-08-20T21:41:28.2506943Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__native_batch_norm_legit_cpu_float64 PASSED [0.0102s] [ 3%] 2024-08-20T21:41:28.2508401Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__segment_reduce_lengths_cpu_bfloat16 PASSED [0.0525s] [ 3%] 2024-08-20T21:41:28.2509858Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__segment_reduce_lengths_cpu_float16 PASSED [0.0526s] [ 3%] 2024-08-20T21:41:28.2511284Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__segment_reduce_lengths_cpu_float32 PASSED [0.0526s] [ 3%] 2024-08-20T21:41:28.2512781Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__segment_reduce_lengths_cpu_float64 PASSED [0.0528s] [ 3%] 2024-08-20T21:41:28.2514218Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__segment_reduce_offsets_cpu_bfloat16 PASSED [0.0555s] [ 3%] 2024-08-20T21:41:28.2515663Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__segment_reduce_offsets_cpu_float16 PASSED [0.0559s] [ 3%] 2024-08-20T21:41:28.2517103Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__segment_reduce_offsets_cpu_float32 PASSED [0.0554s] [ 3%] 2024-08-20T21:41:28.2518535Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__segment_reduce_offsets_cpu_float64 PASSED [0.0560s] [ 3%] 2024-08-20T21:41:28.2519969Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__softmax_backward_data_cpu_bfloat16 PASSED [0.0086s] [ 3%] 2024-08-20T21:41:28.2521704Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__softmax_backward_data_cpu_float16 PASSED [0.0105s] [ 3%] 2024-08-20T21:41:28.2523246Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__softmax_backward_data_cpu_float32 PASSED [0.0107s] [ 3%] 2024-08-20T21:41:28.2524666Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__softmax_backward_data_cpu_float64 PASSED [0.0105s] [ 3%] 2024-08-20T21:41:28.2526102Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_cpu_bfloat16 PASSED [0.0134s] [ 3%] 2024-08-20T21:41:28.2527576Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_cpu_bool PASSED [0.0130s] [ 3%] 2024-08-20T21:41:28.2529005Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_cpu_complex128 PASSED [0.0134s] [ 3%] 2024-08-20T21:41:28.2530432Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_cpu_complex64 PASSED [0.0098s] [ 3%] 2024-08-20T21:41:28.2531853Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_cpu_float16 PASSED [0.0084s] [ 3%] 2024-08-20T21:41:28.2533262Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_cpu_float32 PASSED [0.0082s] [ 3%] 2024-08-20T21:41:28.2534671Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_cpu_float64 PASSED [0.0082s] [ 3%] 2024-08-20T21:41:28.2536058Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_cpu_int16 PASSED [0.0088s] [ 3%] 2024-08-20T21:41:28.2537511Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_cpu_int32 PASSED [0.0084s] [ 3%] 2024-08-20T21:41:28.2538908Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_cpu_int64 PASSED [0.0087s] [ 3%] 2024-08-20T21:41:28.2540368Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_cpu_int8 PASSED [0.0084s] [ 3%] 2024-08-20T21:41:28.2541746Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_cpu_uint8 PASSED [0.0087s] [ 3%] 2024-08-20T21:41:28.2543254Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_put_accumulate_cpu_bfloat16 PASSED [0.0085s] [ 3%] 2024-08-20T21:41:28.2544823Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_put_accumulate_cpu_bool PASSED [0.0086s] [ 3%] 2024-08-20T21:41:28.2546400Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_put_accumulate_cpu_complex128 PASSED [0.0087s] [ 3%] 2024-08-20T21:41:28.2548187Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_put_accumulate_cpu_complex64 PASSED [0.0086s] [ 3%] 2024-08-20T21:41:28.2549775Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_put_accumulate_cpu_float16 PASSED [0.0088s] [ 3%] 2024-08-20T21:41:28.2551344Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_put_accumulate_cpu_float32 PASSED [0.0085s] [ 3%] 2024-08-20T21:41:28.2552909Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_put_accumulate_cpu_float64 PASSED [0.0087s] [ 3%] 2024-08-20T21:41:28.2554455Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_put_accumulate_cpu_int16 PASSED [0.0083s] [ 3%] 2024-08-20T21:41:28.2555996Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_put_accumulate_cpu_int32 PASSED [0.0085s] [ 3%] 2024-08-20T21:41:28.2557543Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_put_accumulate_cpu_int64 PASSED [0.0083s] [ 3%] 2024-08-20T21:41:28.2559084Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_put_accumulate_cpu_int8 PASSED [0.0083s] [ 3%] 2024-08-20T21:41:28.2560712Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__unsafe_masked_index_put_accumulate_cpu_uint8 PASSED [0.0085s] [ 3%] 2024-08-20T21:41:28.2562220Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__upsample_bilinear2d_aa_cpu_float32 PASSED [0.0080s] [ 3%] 2024-08-20T21:41:28.2563669Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__upsample_bilinear2d_aa_cpu_float64 PASSED [0.0082s] [ 3%] 2024-08-20T21:41:28.2565121Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops__upsample_bilinear2d_aa_cpu_uint8 PASSED [0.0080s] [ 3%] 2024-08-20T21:41:28.2566441Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_abs_cpu_bfloat16 PASSED [0.0054s] [ 3%] 2024-08-20T21:41:28.2567787Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_abs_cpu_complex128 PASSED [0.0052s] [ 3%] 2024-08-20T21:41:28.2569059Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_abs_cpu_complex32 PASSED [0.0057s] [ 4%] 2024-08-20T21:41:28.2570303Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_abs_cpu_complex64 PASSED [0.0051s] [ 4%] 2024-08-20T21:41:28.2571544Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_abs_cpu_float16 PASSED [0.0051s] [ 4%] 2024-08-20T21:41:28.2572782Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_abs_cpu_float32 PASSED [0.0052s] [ 4%] 2024-08-20T21:41:28.2574019Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_abs_cpu_float64 PASSED [0.0051s] [ 4%] 2024-08-20T21:41:28.2575311Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_abs_cpu_int16 PASSED [0.0053s] [ 4%] 2024-08-20T21:41:28.2576604Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_abs_cpu_int32 PASSED [0.0051s] [ 4%] 2024-08-20T21:41:28.2577823Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_abs_cpu_int64 PASSED [0.0053s] [ 4%] 2024-08-20T21:41:28.2579030Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_abs_cpu_int8 PASSED [0.0051s] [ 4%] 2024-08-20T21:41:28.2580227Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_abs_cpu_uint8 PASSED [0.0053s] [ 4%] 2024-08-20T21:41:28.2581469Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acos_cpu_bfloat16 PASSED [0.0062s] [ 4%] 2024-08-20T21:41:28.2582706Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acos_cpu_bool PASSED [0.0062s] [ 4%] 2024-08-20T21:41:28.2583955Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acos_cpu_complex128 PASSED [0.0062s] [ 4%] 2024-08-20T21:41:28.2585249Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acos_cpu_complex64 PASSED [0.0062s] [ 4%] 2024-08-20T21:41:28.2586518Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acos_cpu_float16 PASSED [0.0062s] [ 4%] 2024-08-20T21:41:28.2587768Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acos_cpu_float32 PASSED [0.0061s] [ 4%] 2024-08-20T21:41:28.2589008Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acos_cpu_float64 PASSED [0.0063s] [ 4%] 2024-08-20T21:41:28.2590252Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acos_cpu_int16 PASSED [0.0061s] [ 4%] 2024-08-20T21:41:28.2591484Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acos_cpu_int32 PASSED [0.0063s] [ 4%] 2024-08-20T21:41:28.2592715Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acos_cpu_int64 PASSED [0.0061s] [ 4%] 2024-08-20T21:41:28.2593925Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acos_cpu_int8 PASSED [0.0063s] [ 4%] 2024-08-20T21:41:28.2595184Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acos_cpu_uint8 PASSED [0.0061s] [ 4%] 2024-08-20T21:41:28.2596448Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_bfloat16 PASSED [0.0067s] [ 4%] 2024-08-20T21:41:28.2597684Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_bool PASSED [0.0061s] [ 4%] 2024-08-20T21:41:28.2598963Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_complex128 PASSED [0.0062s] [ 4%] 2024-08-20T21:41:28.2600255Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_complex64 PASSED [0.0063s] [ 4%] 2024-08-20T21:41:28.2601535Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_float16 PASSED [0.0062s] [ 4%] 2024-08-20T21:41:28.2602785Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_float32 PASSED [0.0063s] [ 4%] 2024-08-20T21:41:28.2604153Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_float64 PASSED [0.0062s] [ 4%] 2024-08-20T21:41:28.2605430Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_int16 PASSED [0.0064s] [ 4%] 2024-08-20T21:41:28.2606651Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_int32 PASSED [0.0061s] [ 4%] 2024-08-20T21:41:28.2607972Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_int64 PASSED [0.0063s] [ 4%] 2024-08-20T21:41:28.2609208Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_int8 PASSED [0.0061s] [ 4%] 2024-08-20T21:41:28.2610518Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_uint8 PASSED [0.0063s] [ 4%] 2024-08-20T21:41:28.2611762Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_bfloat16 PASSED [0.0105s] [ 4%] 2024-08-20T21:41:28.2613032Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_bool PASSED [0.0105s] [ 4%] 2024-08-20T21:41:28.2614286Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_complex128 PASSED [0.0109s] [ 4%] 2024-08-20T21:41:28.2615545Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_complex32 PASSED [0.0107s] [ 4%] 2024-08-20T21:41:28.2616793Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_complex64 PASSED [0.0110s] [ 4%] 2024-08-20T21:41:28.2618037Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_float16 PASSED [0.0106s] [ 4%] 2024-08-20T21:41:28.2619275Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_float32 PASSED [0.0108s] [ 4%] 2024-08-20T21:41:28.2620488Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_float64 PASSED [0.0107s] [ 4%] 2024-08-20T21:41:28.2621921Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_int16 PASSED [0.0107s] [ 4%] 2024-08-20T21:41:28.2623148Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_int32 PASSED [0.0106s] [ 4%] 2024-08-20T21:41:28.2624365Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_int64 PASSED [0.0106s] [ 4%] 2024-08-20T21:41:28.2625560Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_int8 PASSED [0.0108s] [ 4%] 2024-08-20T21:41:28.2626772Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_uint8 PASSED [0.0105s] [ 4%] 2024-08-20T21:41:28.2628024Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_bfloat16 PASSED [0.0081s] [ 4%] 2024-08-20T21:41:28.2629328Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_complex128 PASSED [0.0114s] [ 4%] 2024-08-20T21:41:28.2630619Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_complex64 PASSED [0.0115s] [ 4%] 2024-08-20T21:41:28.2631968Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_float16 PASSED [0.0079s] [ 4%] 2024-08-20T21:41:28.2633246Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_float32 PASSED [0.0080s] [ 4%] 2024-08-20T21:41:28.2634503Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_float64 PASSED [0.0079s] [ 4%] 2024-08-20T21:41:28.2635771Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_int16 PASSED [0.0079s] [ 4%] 2024-08-20T21:41:28.2637021Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_int32 PASSED [0.0080s] [ 4%] 2024-08-20T21:41:28.2638275Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_int64 PASSED [0.0079s] [ 4%] 2024-08-20T21:41:28.2639506Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_int8 PASSED [0.0082s] [ 4%] 2024-08-20T21:41:28.2640747Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_uint8 PASSED [0.0079s] [ 4%] 2024-08-20T21:41:28.2642013Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcdiv_cpu_bfloat16 PASSED [0.0125s] [ 4%] 2024-08-20T21:41:28.2643326Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcdiv_cpu_complex128 PASSED [0.0123s] [ 4%] 2024-08-20T21:41:28.2644624Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcdiv_cpu_complex64 PASSED [0.0125s] [ 5%] 2024-08-20T21:41:28.2645922Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcdiv_cpu_float16 PASSED [0.0121s] [ 5%] 2024-08-20T21:41:28.2647728Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcdiv_cpu_float32 PASSED [0.0122s] [ 5%] 2024-08-20T21:41:28.2649064Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcdiv_cpu_float64 PASSED [0.0124s] [ 5%] 2024-08-20T21:41:28.2650364Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_bfloat16 PASSED [0.0121s] [ 5%] 2024-08-20T21:41:28.2651674Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_complex128 PASSED [0.0130s] [ 5%] 2024-08-20T21:41:28.2652989Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_complex64 PASSED [0.0123s] [ 5%] 2024-08-20T21:41:28.2654272Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_float16 PASSED [0.0123s] [ 5%] 2024-08-20T21:41:28.2655548Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_float32 PASSED [0.0122s] [ 5%] 2024-08-20T21:41:28.2656821Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_float64 PASSED [0.0121s] [ 5%] 2024-08-20T21:41:28.2658089Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_int16 PASSED [0.0123s] [ 5%] 2024-08-20T21:41:28.2659564Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_int32 PASSED [0.0121s] [ 5%] 2024-08-20T21:41:28.2660844Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_int64 PASSED [0.0122s] [ 5%] 2024-08-20T21:41:28.2662114Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_int8 PASSED [0.0122s] [ 5%] 2024-08-20T21:41:28.2663367Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_uint8 PASSED [0.0123s] [ 5%] 2024-08-20T21:41:28.2664633Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_bfloat16 PASSED [0.0080s] [ 5%] 2024-08-20T21:41:28.2665922Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_complex128 PASSED [0.0082s] [ 5%] 2024-08-20T21:41:28.2667223Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_complex64 PASSED [0.0083s] [ 5%] 2024-08-20T21:41:28.2668687Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_float16 PASSED [0.0081s] [ 5%] 2024-08-20T21:41:28.2669947Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_float32 PASSED [0.0081s] [ 5%] 2024-08-20T21:41:28.2671198Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_float64 PASSED [0.0081s] [ 5%] 2024-08-20T21:41:28.2672439Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_int16 PASSED [0.0070s] [ 5%] 2024-08-20T21:41:28.2673680Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_int32 PASSED [0.0068s] [ 5%] 2024-08-20T21:41:28.2674905Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_int64 PASSED [0.0070s] [ 5%] 2024-08-20T21:41:28.2676141Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_int8 PASSED [0.0067s] [ 5%] 2024-08-20T21:41:28.2677371Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_uint8 PASSED [0.0067s] [ 5%] 2024-08-20T21:41:28.2678700Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_bfloat16 PASSED [0.0083s] [ 5%] 2024-08-20T21:41:28.2680098Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_complex128 PASSED [0.0080s] [ 5%] 2024-08-20T21:41:28.2681513Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_complex64 PASSED [0.0082s] [ 5%] 2024-08-20T21:41:28.2682976Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_float16 PASSED [0.0078s] [ 5%] 2024-08-20T21:41:28.2684354Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_float32 PASSED [0.0080s] [ 5%] 2024-08-20T21:41:28.2685790Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_float64 PASSED [0.0078s] [ 5%] 2024-08-20T21:41:28.2687267Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_int16 PASSED [0.0070s] [ 5%] 2024-08-20T21:41:28.2688637Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_int32 PASSED [0.0068s] [ 5%] 2024-08-20T21:41:28.2689990Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_int64 PASSED [0.0069s] [ 5%] 2024-08-20T21:41:28.2691354Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_int8 PASSED [0.0068s] [ 5%] 2024-08-20T21:41:28.2692712Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_uint8 PASSED [0.0068s] [ 5%] 2024-08-20T21:41:28.2694032Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_bfloat16 PASSED [0.0081s] [ 5%] 2024-08-20T21:41:28.2695307Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_complex128 PASSED [0.0079s] [ 5%] 2024-08-20T21:41:28.2696603Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_complex64 PASSED [0.0081s] [ 5%] 2024-08-20T21:41:28.2697879Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_float16 PASSED [0.0078s] [ 5%] 2024-08-20T21:41:28.2699144Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_float32 PASSED [0.0081s] [ 5%] 2024-08-20T21:41:28.2700392Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_float64 PASSED [0.0078s] [ 5%] 2024-08-20T21:41:28.2701647Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_int16 PASSED [0.0080s] [ 5%] 2024-08-20T21:41:28.2702896Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_int32 PASSED [0.0078s] [ 5%] 2024-08-20T21:41:28.2704171Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_int64 PASSED [0.0079s] [ 5%] 2024-08-20T21:41:28.2705416Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_int8 PASSED [0.0080s] [ 5%] 2024-08-20T21:41:28.2706662Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_uint8 PASSED [0.0078s] [ 5%] 2024-08-20T21:41:28.2707921Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_bfloat16 PASSED [0.0084s] [ 5%] 2024-08-20T21:41:28.2709135Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_bool PASSED [0.0067s] [ 5%] 2024-08-20T21:41:28.2710390Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_complex128 PASSED [0.0071s] [ 5%] 2024-08-20T21:41:28.2711671Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_complex64 PASSED [0.0070s] [ 5%] 2024-08-20T21:41:28.2712926Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_float16 PASSED [0.0082s] [ 5%] 2024-08-20T21:41:28.2714181Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_float32 PASSED [0.0080s] [ 5%] 2024-08-20T21:41:28.2715432Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_float64 PASSED [0.0079s] [ 5%] 2024-08-20T21:41:28.2716675Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_int16 PASSED [0.0071s] [ 5%] 2024-08-20T21:41:28.2717899Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_int32 PASSED [0.0068s] [ 5%] 2024-08-20T21:41:28.2719204Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_int64 PASSED [0.0071s] [ 5%] 2024-08-20T21:41:28.2720438Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_int8 PASSED [0.0069s] [ 5%] 2024-08-20T21:41:28.2721970Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_uint8 PASSED [0.0071s] [ 6%] 2024-08-20T21:41:28.2723260Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_bfloat16 PASSED [0.0058s] [ 6%] 2024-08-20T21:41:28.2724574Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_bool PASSED [0.0059s] [ 6%] 2024-08-20T21:41:28.2725901Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_complex128 PASSED [0.0058s] [ 6%] 2024-08-20T21:41:28.2727312Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_complex32 PASSED [0.0059s] [ 6%] 2024-08-20T21:41:28.2728669Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_complex64 PASSED [0.0056s] [ 6%] 2024-08-20T21:41:28.2730003Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_float16 PASSED [0.0056s] [ 6%] 2024-08-20T21:41:28.2731329Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_float32 PASSED [0.0058s] [ 6%] 2024-08-20T21:41:28.2732626Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_float64 PASSED [0.0055s] [ 6%] 2024-08-20T21:41:28.2733935Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int16 PASSED [0.0057s] [ 6%] 2024-08-20T21:41:28.2735226Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int32 PASSED [0.0056s] [ 6%] 2024-08-20T21:41:28.2736517Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int64 PASSED [0.0063s] [ 6%] 2024-08-20T21:41:28.2737795Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int8 PASSED [0.0056s] [ 6%] 2024-08-20T21:41:28.2739081Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_uint8 PASSED [0.0059s] [ 6%] 2024-08-20T21:41:28.2740418Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_bfloat16 PASSED [0.0145s] [ 6%] 2024-08-20T21:41:28.2741650Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_bool PASSED [0.0146s] [ 6%] 2024-08-20T21:41:28.2742886Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_complex128 PASSED [0.0145s] [ 6%] 2024-08-20T21:41:28.2744155Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_complex64 PASSED [0.0145s] [ 6%] 2024-08-20T21:41:28.2745411Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_float16 PASSED [0.0147s] [ 6%] 2024-08-20T21:41:28.2746804Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_float32 PASSED [0.0145s] [ 6%] 2024-08-20T21:41:28.2748078Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_float64 PASSED [0.0148s] [ 6%] 2024-08-20T21:41:28.2749313Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int16 PASSED [0.0146s] [ 6%] 2024-08-20T21:41:28.2750534Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int32 PASSED [0.0148s] [ 6%] 2024-08-20T21:41:28.2751742Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int64 PASSED [0.0145s] [ 6%] 2024-08-20T21:41:28.2752950Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int8 PASSED [0.0146s] [ 6%] 2024-08-20T21:41:28.2754167Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_uint8 PASSED [0.0144s] [ 6%] 2024-08-20T21:41:28.2755542Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_bfloat16 PASSED [0.0174s] [ 6%] 2024-08-20T21:41:28.2756871Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_complex128 PASSED [0.0183s] [ 6%] 2024-08-20T21:41:28.2758265Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_complex64 PASSED [0.0180s] [ 6%] 2024-08-20T21:41:28.2759663Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_float16 PASSED [0.0177s] [ 6%] 2024-08-20T21:41:28.2760951Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_float32 PASSED [0.0174s] [ 6%] 2024-08-20T21:41:28.2762248Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_float64 PASSED [0.0176s] [ 6%] 2024-08-20T21:41:28.2763530Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_bfloat16 PASSED [0.0144s] [ 6%] 2024-08-20T21:41:28.2764773Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_bool PASSED [0.0151s] [ 6%] 2024-08-20T21:41:28.2766010Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_float16 PASSED [0.0144s] [ 6%] 2024-08-20T21:41:28.2767336Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_float32 PASSED [0.0143s] [ 6%] 2024-08-20T21:41:28.2768595Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_float64 PASSED [0.0146s] [ 6%] 2024-08-20T21:41:28.2769836Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int16 PASSED [0.0144s] [ 6%] 2024-08-20T21:41:28.2771059Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int32 PASSED [0.0146s] [ 6%] 2024-08-20T21:41:28.2772281Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int64 PASSED [0.0143s] [ 6%] 2024-08-20T21:41:28.2773500Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int8 PASSED [0.0147s] [ 6%] 2024-08-20T21:41:28.2774710Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_uint8 PASSED [0.0144s] [ 6%] 2024-08-20T21:41:28.2775963Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_bfloat16 PASSED [0.0145s] [ 6%] 2024-08-20T21:41:28.2777269Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_bool PASSED [0.0144s] [ 6%] 2024-08-20T21:41:28.2778506Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_float16 PASSED [0.0143s] [ 6%] 2024-08-20T21:41:28.2779734Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_float32 PASSED [0.0145s] [ 6%] 2024-08-20T21:41:28.2780974Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_float64 PASSED [0.0143s] [ 6%] 2024-08-20T21:41:28.2782208Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int16 PASSED [0.0147s] [ 6%] 2024-08-20T21:41:28.2783420Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int32 PASSED [0.0145s] [ 6%] 2024-08-20T21:41:28.2784656Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int64 PASSED [0.0148s] [ 6%] 2024-08-20T21:41:28.2785871Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int8 PASSED [0.0144s] [ 6%] 2024-08-20T21:41:28.2787098Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_uint8 PASSED [0.0146s] [ 6%] 2024-08-20T21:41:28.2788345Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_bfloat16 PASSED [0.0084s] [ 6%] 2024-08-20T21:41:28.2789621Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_bool PASSED [0.0083s] [ 6%] 2024-08-20T21:41:28.2790885Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_float16 PASSED [0.0089s] [ 6%] 2024-08-20T21:41:28.2792223Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_float32 PASSED [0.0084s] [ 6%] 2024-08-20T21:41:28.2793532Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_float64 PASSED [0.0086s] [ 6%] 2024-08-20T21:41:28.2794795Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int16 PASSED [0.0084s] [ 6%] 2024-08-20T21:41:28.2796063Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int32 PASSED [0.0086s] [ 6%] 2024-08-20T21:41:28.2797307Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int64 PASSED [0.0083s] [ 6%] 2024-08-20T21:41:28.2798565Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int8 PASSED [0.0083s] [ 7%] 2024-08-20T21:41:28.2799816Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_uint8 PASSED [0.0085s] [ 7%] 2024-08-20T21:41:28.2801082Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_bfloat16 PASSED [0.0051s] [ 7%] 2024-08-20T21:41:28.2802320Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_bool PASSED [0.0053s] [ 7%] 2024-08-20T21:41:28.2803591Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_complex128 PASSED [0.0052s] [ 7%] 2024-08-20T21:41:28.2804880Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_complex64 PASSED [0.0053s] [ 7%] 2024-08-20T21:41:28.2806154Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_float16 PASSED [0.0051s] [ 7%] 2024-08-20T21:41:28.2807479Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_float32 PASSED [0.0054s] [ 7%] 2024-08-20T21:41:28.2808739Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_float64 PASSED [0.0052s] [ 7%] 2024-08-20T21:41:28.2809994Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int16 PASSED [0.0051s] [ 7%] 2024-08-20T21:41:28.2811225Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int32 PASSED [0.0054s] [ 7%] 2024-08-20T21:41:28.2812500Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int64 PASSED [0.0051s] [ 7%] 2024-08-20T21:41:28.2813735Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int8 PASSED [0.0053s] [ 7%] 2024-08-20T21:41:28.2814966Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_uint8 PASSED [0.0051s] [ 7%] 2024-08-20T21:41:28.2816199Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_bfloat16 PASSED [0.0146s] [ 7%] 2024-08-20T21:41:28.2817425Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_bool PASSED [0.0144s] [ 7%] 2024-08-20T21:41:28.2818677Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_complex128 PASSED [0.0150s] [ 7%] 2024-08-20T21:41:28.2819942Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_complex64 PASSED [0.0145s] [ 7%] 2024-08-20T21:41:28.2821379Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_float16 PASSED [0.0145s] [ 7%] 2024-08-20T21:41:28.2822635Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_float32 PASSED [0.0144s] [ 7%] 2024-08-20T21:41:28.2823876Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_float64 PASSED [0.0144s] [ 7%] 2024-08-20T21:41:28.2825093Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int16 PASSED [0.0145s] [ 7%] 2024-08-20T21:41:28.2826321Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int32 PASSED [0.0143s] [ 7%] 2024-08-20T21:41:28.2827615Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int64 PASSED [0.0146s] [ 7%] 2024-08-20T21:41:28.2828830Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int8 PASSED [0.0144s] [ 7%] 2024-08-20T21:41:28.2830077Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_uint8 PASSED [0.0146s] [ 7%] 2024-08-20T21:41:28.2831338Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_bfloat16 PASSED [0.0267s] [ 7%] 2024-08-20T21:41:28.2832618Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_float16 PASSED [0.0261s] [ 7%] 2024-08-20T21:41:28.2833903Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_float32 PASSED [0.0260s] [ 7%] 2024-08-20T21:41:28.2835159Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_float64 PASSED [0.0261s] [ 7%] 2024-08-20T21:41:28.2836425Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int16 PASSED [0.0207s] [ 7%] 2024-08-20T21:41:28.2837669Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int32 PASSED [0.0206s] [ 7%] 2024-08-20T21:41:28.2838905Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int64 PASSED [0.0209s] [ 7%] 2024-08-20T21:41:28.2840151Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int8 PASSED [0.0201s] [ 7%] 2024-08-20T21:41:28.2841402Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_uint8 PASSED [0.0208s] [ 7%] 2024-08-20T21:41:28.2842674Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_bfloat16 PASSED [0.0113s] [ 7%] 2024-08-20T21:41:28.2843938Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_float16 PASSED [0.0114s] [ 7%] 2024-08-20T21:41:28.2845210Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_float32 PASSED [0.0111s] [ 7%] 2024-08-20T21:41:28.2846476Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_float64 PASSED [0.0113s] [ 7%] 2024-08-20T21:41:28.2848021Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int16 PASSED [0.0111s] [ 7%] 2024-08-20T21:41:28.2849350Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int32 PASSED [0.0111s] [ 7%] 2024-08-20T21:41:28.2850595Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int64 PASSED [0.0112s] [ 7%] 2024-08-20T21:41:28.2851836Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int8 PASSED [0.0111s] [ 7%] 2024-08-20T21:41:28.2853066Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_uint8 PASSED [0.0114s] [ 7%] 2024-08-20T21:41:28.2854342Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_bfloat16 PASSED [0.0111s] [ 7%] 2024-08-20T21:41:28.2855619Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_float16 PASSED [0.0113s] [ 7%] 2024-08-20T21:41:28.2856893Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_float32 PASSED [0.0111s] [ 7%] 2024-08-20T21:41:28.2858155Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_float64 PASSED [0.0113s] [ 7%] 2024-08-20T21:41:28.2859421Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int16 PASSED [0.0111s] [ 7%] 2024-08-20T21:41:28.2860752Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int32 PASSED [0.0112s] [ 7%] 2024-08-20T21:41:28.2861998Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int64 PASSED [0.0110s] [ 7%] 2024-08-20T21:41:28.2863342Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int8 PASSED [0.0110s] [ 7%] 2024-08-20T21:41:28.2864598Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_uint8 PASSED [0.0111s] [ 7%] 2024-08-20T21:41:28.2865931Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_bfloat16 PASSED [0.0346s] [ 7%] 2024-08-20T21:41:28.2867190Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_bool PASSED [0.0349s] [ 7%] 2024-08-20T21:41:28.2868458Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_float16 PASSED [0.0343s] [ 7%] 2024-08-20T21:41:28.2869736Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_float32 PASSED [0.0354s] [ 7%] 2024-08-20T21:41:28.2871012Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_float64 PASSED [0.0347s] [ 7%] 2024-08-20T21:41:28.2872278Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int16 PASSED [0.0350s] [ 7%] 2024-08-20T21:41:28.2873543Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int32 PASSED [0.0348s] [ 8%] 2024-08-20T21:41:28.2874799Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int64 PASSED [0.0341s] [ 8%] 2024-08-20T21:41:28.2876058Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int8 PASSED [0.0344s] [ 8%] 2024-08-20T21:41:28.2877302Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_uint8 PASSED [0.0345s] [ 8%] 2024-08-20T21:41:28.2878592Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_bfloat16 PASSED [0.0082s] [ 8%] 2024-08-20T21:41:28.2879872Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_bool PASSED [0.0078s] [ 8%] 2024-08-20T21:41:28.2881168Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_complex128 PASSED [0.0080s] [ 8%] 2024-08-20T21:41:28.2882493Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_complex64 PASSED [0.0078s] [ 8%] 2024-08-20T21:41:28.2883794Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_float16 PASSED [0.0080s] [ 8%] 2024-08-20T21:41:28.2885281Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_float32 PASSED [0.0077s] [ 8%] 2024-08-20T21:41:28.2886581Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_float64 PASSED [0.0079s] [ 8%] 2024-08-20T21:41:28.2887981Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int16 PASSED [0.0077s] [ 8%] 2024-08-20T21:41:28.2889257Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int32 PASSED [0.0079s] [ 8%] 2024-08-20T21:41:28.2890532Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int64 PASSED [0.0077s] [ 8%] 2024-08-20T21:41:28.2891786Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int8 PASSED [0.0077s] [ 8%] 2024-08-20T21:41:28.2893050Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_uint8 PASSED [0.0078s] [ 8%] 2024-08-20T21:41:28.2894377Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_bfloat16 PASSED [0.0072s] [ 8%] 2024-08-20T21:41:28.2895721Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_bool PASSED [0.0074s] [ 8%] 2024-08-20T21:41:28.2897089Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_complex128 PASSED [0.0072s] [ 8%] 2024-08-20T21:41:28.2898484Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_complex32 PASSED [0.0079s] [ 8%] 2024-08-20T21:41:28.2899969Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_complex64 PASSED [0.0072s] [ 8%] 2024-08-20T21:41:28.2901334Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_float16 PASSED [0.0074s] [ 8%] 2024-08-20T21:41:28.2902734Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_float32 PASSED [0.0072s] [ 8%] 2024-08-20T21:41:28.2904085Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_float64 PASSED [0.0073s] [ 8%] 2024-08-20T21:41:28.2905443Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_int16 PASSED [0.0072s] [ 8%] 2024-08-20T21:41:28.2906769Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_int32 PASSED [0.0071s] [ 8%] 2024-08-20T21:41:28.2908103Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_int64 PASSED [0.0072s] [ 8%] 2024-08-20T21:41:28.2909450Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_int8 PASSED [0.0071s] [ 8%] 2024-08-20T21:41:28.2910788Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_uint8 PASSED [0.0073s] [ 8%] 2024-08-20T21:41:28.2912113Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_bfloat16 PASSED [0.0072s] [ 8%] 2024-08-20T21:41:28.2913417Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_bool PASSED [0.0073s] [ 8%] 2024-08-20T21:41:28.2914731Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_complex128 PASSED [0.0074s] [ 8%] 2024-08-20T21:41:28.2916058Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_complex32 PASSED [0.0074s] [ 8%] 2024-08-20T21:41:28.2917389Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_complex64 PASSED [0.0071s] [ 8%] 2024-08-20T21:41:28.2918709Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_float16 PASSED [0.0073s] [ 8%] 2024-08-20T21:41:28.2920020Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_float32 PASSED [0.0071s] [ 8%] 2024-08-20T21:41:28.2921597Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_float64 PASSED [0.0071s] [ 8%] 2024-08-20T21:41:28.2922892Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_int16 PASSED [0.0072s] [ 8%] 2024-08-20T21:41:28.2924185Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_int32 PASSED [0.0071s] [ 8%] 2024-08-20T21:41:28.2925474Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_int64 PASSED [0.0073s] [ 8%] 2024-08-20T21:41:28.2926816Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_int8 PASSED [0.0072s] [ 8%] 2024-08-20T21:41:28.2928122Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_uint8 PASSED [0.0078s] [ 8%] 2024-08-20T21:41:28.2929513Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_bfloat16 PASSED [0.0062s] [ 8%] 2024-08-20T21:41:28.2931043Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_bool PASSED [0.0063s] [ 8%] 2024-08-20T21:41:28.2932515Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_complex128 PASSED [0.0062s] [ 8%] 2024-08-20T21:41:28.2933998Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_complex32 PASSED [0.0063s] [ 8%] 2024-08-20T21:41:28.2935481Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_complex64 PASSED [0.0061s] [ 8%] 2024-08-20T21:41:28.2937016Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_float16 PASSED [0.0061s] [ 8%] 2024-08-20T21:41:28.2938512Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_float32 PASSED [0.0063s] [ 8%] 2024-08-20T21:41:28.2939973Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_float64 PASSED [0.0061s] [ 8%] 2024-08-20T21:41:28.2941428Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_int16 PASSED [0.0064s] [ 8%] 2024-08-20T21:41:28.2942846Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_int32 PASSED [0.0063s] [ 8%] 2024-08-20T21:41:28.2944280Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_int64 PASSED [0.0065s] [ 8%] 2024-08-20T21:41:28.2945719Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_int8 PASSED [0.0062s] [ 8%] 2024-08-20T21:41:28.2947344Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_uint8 PASSED [0.0064s] [ 8%] 2024-08-20T21:41:28.2948774Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_bfloat16 PASSED [0.0083s] [ 8%] 2024-08-20T21:41:28.2950160Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_bool PASSED [0.0084s] [ 8%] 2024-08-20T21:41:28.2951568Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_complex128 PASSED [0.0084s] [ 8%] 2024-08-20T21:41:28.2952991Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_complex32 PASSED [0.0083s] [ 8%] 2024-08-20T21:41:28.2954402Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_complex64 PASSED [0.0085s] [ 8%] 2024-08-20T21:41:28.2955822Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_float16 PASSED [0.0082s] [ 9%] 2024-08-20T21:41:28.2957224Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_float32 PASSED [0.0085s] [ 9%] 2024-08-20T21:41:28.2958701Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_float64 PASSED [0.0083s] [ 9%] 2024-08-20T21:41:28.2960071Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_int16 PASSED [0.0091s] [ 9%] 2024-08-20T21:41:28.2961445Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_int32 PASSED [0.0084s] [ 9%] 2024-08-20T21:41:28.2962825Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_int64 PASSED [0.0085s] [ 9%] 2024-08-20T21:41:28.2964185Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_int8 PASSED [0.0083s] [ 9%] 2024-08-20T21:41:28.2965559Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_uint8 PASSED [0.0085s] [ 9%] 2024-08-20T21:41:28.2966953Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_bfloat16 PASSED [0.0051s] [ 9%] 2024-08-20T21:41:28.2968201Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_bool PASSED [0.0051s] [ 9%] 2024-08-20T21:41:28.2969443Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_complex128 PASSED [0.0054s] [ 9%] 2024-08-20T21:41:28.2970726Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_complex64 PASSED [0.0052s] [ 9%] 2024-08-20T21:41:28.2971986Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_float16 PASSED [0.0054s] [ 9%] 2024-08-20T21:41:28.2973319Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_float32 PASSED [0.0051s] [ 9%] 2024-08-20T21:41:28.2974596Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_float64 PASSED [0.0053s] [ 9%] 2024-08-20T21:41:28.2975834Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_int16 PASSED [0.0051s] [ 9%] 2024-08-20T21:41:28.2977072Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_int32 PASSED [0.0053s] [ 9%] 2024-08-20T21:41:28.2978285Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_int64 PASSED [0.0051s] [ 9%] 2024-08-20T21:41:28.2979515Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_int8 PASSED [0.0053s] [ 9%] 2024-08-20T21:41:28.2980738Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_uint8 PASSED [0.0051s] [ 9%] 2024-08-20T21:41:28.2981987Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_bfloat16 PASSED [0.0051s] [ 9%] 2024-08-20T21:41:28.2983220Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_bool PASSED [0.0053s] [ 9%] 2024-08-20T21:41:28.2984488Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_complex128 PASSED [0.0051s] [ 9%] 2024-08-20T21:41:28.2985781Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_complex64 PASSED [0.0053s] [ 9%] 2024-08-20T21:41:28.2987047Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_float16 PASSED [0.0051s] [ 9%] 2024-08-20T21:41:28.2988302Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_float32 PASSED [0.0058s] [ 9%] 2024-08-20T21:41:28.2989559Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_float64 PASSED [0.0051s] [ 9%] 2024-08-20T21:41:28.2990809Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_int16 PASSED [0.0053s] [ 9%] 2024-08-20T21:41:28.2992035Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_int32 PASSED [0.0051s] [ 9%] 2024-08-20T21:41:28.2993265Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_int64 PASSED [0.0052s] [ 9%] 2024-08-20T21:41:28.2994534Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_int8 PASSED [0.0051s] [ 9%] 2024-08-20T21:41:28.2995770Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_uint8 PASSED [0.0051s] [ 9%] 2024-08-20T21:41:28.2997013Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_bfloat16 PASSED [0.0098s] [ 9%] 2024-08-20T21:41:28.2998260Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_bool PASSED [0.0094s] [ 9%] 2024-08-20T21:41:28.2999513Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_float16 PASSED [0.0097s] [ 9%] 2024-08-20T21:41:28.3000760Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_float32 PASSED [0.0095s] [ 9%] 2024-08-20T21:41:28.3002000Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_float64 PASSED [0.0097s] [ 9%] 2024-08-20T21:41:28.3003251Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_int16 PASSED [0.0095s] [ 9%] 2024-08-20T21:41:28.3004480Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_int32 PASSED [0.0097s] [ 9%] 2024-08-20T21:41:28.3005691Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_int64 PASSED [0.0095s] [ 9%] 2024-08-20T21:41:28.3007004Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_int8 PASSED [0.0096s] [ 9%] 2024-08-20T21:41:28.3008411Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan2_cpu_uint8 PASSED [0.0095s] [ 9%] 2024-08-20T21:41:28.3009665Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_bfloat16 PASSED [0.0051s] [ 9%] 2024-08-20T21:41:28.3010935Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_bool PASSED [0.0053s] [ 9%] 2024-08-20T21:41:28.3012192Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_complex128 PASSED [0.0051s] [ 9%] 2024-08-20T21:41:28.3013467Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_complex64 PASSED [0.0053s] [ 9%] 2024-08-20T21:41:28.3014725Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_float16 PASSED [0.0051s] [ 9%] 2024-08-20T21:41:28.3015960Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_float32 PASSED [0.0057s] [ 9%] 2024-08-20T21:41:28.3017201Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_float64 PASSED [0.0051s] [ 9%] 2024-08-20T21:41:28.3018443Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_int16 PASSED [0.0053s] [ 9%] 2024-08-20T21:41:28.3019659Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_int32 PASSED [0.0051s] [ 9%] 2024-08-20T21:41:28.3021104Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_int64 PASSED [0.0053s] [ 9%] 2024-08-20T21:41:28.3022338Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_int8 PASSED [0.0051s] [ 9%] 2024-08-20T21:41:28.3023554Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atan_cpu_uint8 PASSED [0.0051s] [ 9%] 2024-08-20T21:41:28.3024793Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_bfloat16 PASSED [0.0053s] [ 9%] 2024-08-20T21:41:28.3026041Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_bool PASSED [0.0051s] [ 9%] 2024-08-20T21:41:28.3027314Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_complex128 PASSED [0.0054s] [ 9%] 2024-08-20T21:41:28.3028599Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_complex64 PASSED [0.0051s] [ 9%] 2024-08-20T21:41:28.3029902Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_float16 PASSED [0.0053s] [ 9%] 2024-08-20T21:41:28.3031241Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_float32 PASSED [0.0051s] [ 10%] 2024-08-20T21:41:28.3032506Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_float64 PASSED [0.0053s] [ 10%] 2024-08-20T21:41:28.3033745Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_int16 PASSED [0.0051s] [ 10%] 2024-08-20T21:41:28.3034982Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_int32 PASSED [0.0053s] [ 10%] 2024-08-20T21:41:28.3036220Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_int64 PASSED [0.0051s] [ 10%] 2024-08-20T21:41:28.3037446Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_int8 PASSED [0.0051s] [ 10%] 2024-08-20T21:41:28.3038670Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atanh_cpu_uint8 PASSED [0.0053s] [ 10%] 2024-08-20T21:41:28.3039958Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_bfloat16 PASSED [0.0078s] [ 10%] 2024-08-20T21:41:28.3041256Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_bool PASSED [0.0080s] [ 10%] 2024-08-20T21:41:28.3042564Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_complex128 PASSED [0.0079s] [ 10%] 2024-08-20T21:41:28.3043913Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_complex32 PASSED [0.0085s] [ 10%] 2024-08-20T21:41:28.3045344Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_complex64 PASSED [0.0078s] [ 10%] 2024-08-20T21:41:28.3046946Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_float16 PASSED [0.0080s] [ 10%] 2024-08-20T21:41:28.3048269Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_float32 PASSED [0.0078s] [ 10%] 2024-08-20T21:41:28.3049574Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_float64 PASSED [0.0079s] [ 10%] 2024-08-20T21:41:28.3050879Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_int16 PASSED [0.0077s] [ 10%] 2024-08-20T21:41:28.3052168Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_int32 PASSED [0.0077s] [ 10%] 2024-08-20T21:41:28.3053444Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_int64 PASSED [0.0079s] [ 10%] 2024-08-20T21:41:28.3054735Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_int8 PASSED [0.0077s] [ 10%] 2024-08-20T21:41:28.3056025Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_1d_cpu_uint8 PASSED [0.0079s] [ 10%] 2024-08-20T21:41:28.3057331Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_bfloat16 PASSED [0.0077s] [ 10%] 2024-08-20T21:41:28.3058627Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_bool PASSED [0.0079s] [ 10%] 2024-08-20T21:41:28.3059943Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_complex128 PASSED [0.0078s] [ 10%] 2024-08-20T21:41:28.3061287Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_complex32 PASSED [0.0079s] [ 10%] 2024-08-20T21:41:28.3062609Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_complex64 PASSED [0.0077s] [ 10%] 2024-08-20T21:41:28.3063950Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_float16 PASSED [0.0077s] [ 10%] 2024-08-20T21:41:28.3065260Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_float32 PASSED [0.0079s] [ 10%] 2024-08-20T21:41:28.3066673Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_float64 PASSED [0.0077s] [ 10%] 2024-08-20T21:41:28.3067958Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_int16 PASSED [0.0079s] [ 10%] 2024-08-20T21:41:28.3069247Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_int32 PASSED [0.0077s] [ 10%] 2024-08-20T21:41:28.3070533Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_int64 PASSED [0.0078s] [ 10%] 2024-08-20T21:41:28.3071815Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_int8 PASSED [0.0077s] [ 10%] 2024-08-20T21:41:28.3073083Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_2d_cpu_uint8 PASSED [0.0083s] [ 10%] 2024-08-20T21:41:28.3074390Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_bfloat16 PASSED [0.0077s] [ 10%] 2024-08-20T21:41:28.3075690Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_bool PASSED [0.0076s] [ 10%] 2024-08-20T21:41:28.3076992Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_complex128 PASSED [0.0080s] [ 10%] 2024-08-20T21:41:28.3078332Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_complex32 PASSED [0.0077s] [ 10%] 2024-08-20T21:41:28.3079668Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_complex64 PASSED [0.0080s] [ 10%] 2024-08-20T21:41:28.3081081Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_float16 PASSED [0.0077s] [ 10%] 2024-08-20T21:41:28.3082376Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_float32 PASSED [0.0079s] [ 10%] 2024-08-20T21:41:28.3083733Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_float64 PASSED [0.0077s] [ 10%] 2024-08-20T21:41:28.3085033Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_int16 PASSED [0.0079s] [ 10%] 2024-08-20T21:41:28.3086323Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_int32 PASSED [0.0077s] [ 10%] 2024-08-20T21:41:28.3087712Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_int64 PASSED [0.0077s] [ 10%] 2024-08-20T21:41:28.3089000Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_int8 PASSED [0.0079s] [ 10%] 2024-08-20T21:41:28.3090290Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_atleast_3d_cpu_uint8 PASSED [0.0077s] [ 10%] 2024-08-20T21:41:28.3091579Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_baddbmm_cpu_bfloat16 PASSED [0.0081s] [ 10%] 2024-08-20T21:41:28.3092879Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_baddbmm_cpu_complex128 PASSED [0.0121s] [ 10%] 2024-08-20T21:41:28.3094193Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_baddbmm_cpu_complex64 PASSED [0.0121s] [ 10%] 2024-08-20T21:41:28.3095488Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_baddbmm_cpu_float16 PASSED [0.0078s] [ 10%] 2024-08-20T21:41:28.3096756Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_baddbmm_cpu_float32 PASSED [0.0078s] [ 10%] 2024-08-20T21:41:28.3098036Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_baddbmm_cpu_float64 PASSED [0.0081s] [ 10%] 2024-08-20T21:41:28.3099305Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_baddbmm_cpu_int16 PASSED [0.0079s] [ 10%] 2024-08-20T21:41:28.3100572Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_baddbmm_cpu_int32 PASSED [0.0084s] [ 10%] 2024-08-20T21:41:28.3101874Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_baddbmm_cpu_int64 PASSED [0.0079s] [ 10%] 2024-08-20T21:41:28.3103124Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_baddbmm_cpu_int8 PASSED [0.0082s] [ 10%] 2024-08-20T21:41:28.3104374Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_baddbmm_cpu_uint8 PASSED [0.0080s] [ 10%] 2024-08-20T21:41:28.3105672Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bernoulli_cpu_bfloat16 PASSED [0.0070s] [ 10%] 2024-08-20T21:41:28.3106972Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bernoulli_cpu_float16 PASSED [0.0066s] [ 10%] 2024-08-20T21:41:28.3108379Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bernoulli_cpu_float32 PASSED [0.0069s] [ 10%] 2024-08-20T21:41:28.3109693Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bernoulli_cpu_float64 PASSED [0.0068s] [ 11%] 2024-08-20T21:41:28.3110978Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_bfloat16 PASSED [0.0072s] [ 11%] 2024-08-20T21:41:28.3112250Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_bool PASSED [0.0073s] [ 11%] 2024-08-20T21:41:28.3113554Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_complex128 PASSED [0.0072s] [ 11%] 2024-08-20T21:41:28.3114874Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_complex32 PASSED [0.0074s] [ 11%] 2024-08-20T21:41:28.3116175Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_complex64 PASSED [0.0072s] [ 11%] 2024-08-20T21:41:28.3117550Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_float16 PASSED [0.0073s] [ 11%] 2024-08-20T21:41:28.3118891Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_float32 PASSED [0.0071s] [ 11%] 2024-08-20T21:41:28.3120185Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_float64 PASSED [0.0073s] [ 11%] 2024-08-20T21:41:28.3121664Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_int16 PASSED [0.0071s] [ 11%] 2024-08-20T21:41:28.3122942Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_int32 PASSED [0.0071s] [ 11%] 2024-08-20T21:41:28.3124214Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_int64 PASSED [0.0073s] [ 11%] 2024-08-20T21:41:28.3125466Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_int8 PASSED [0.0071s] [ 11%] 2024-08-20T21:41:28.3126806Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bfloat16_cpu_uint8 PASSED [0.0073s] [ 11%] 2024-08-20T21:41:28.3128089Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bincount_cpu_int16 PASSED [0.0123s] [ 11%] 2024-08-20T21:41:28.3129362Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bincount_cpu_int32 PASSED [0.0131s] [ 11%] 2024-08-20T21:41:28.3130625Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bincount_cpu_int64 PASSED [0.0124s] [ 11%] 2024-08-20T21:41:28.3131891Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bincount_cpu_int8 PASSED [0.0126s] [ 11%] 2024-08-20T21:41:28.3133170Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bincount_cpu_uint8 PASSED [0.0123s] [ 11%] 2024-08-20T21:41:28.3134452Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_and_cpu_bool PASSED [0.0096s] [ 11%] 2024-08-20T21:41:28.3135743Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_and_cpu_int16 PASSED [0.0095s] [ 11%] 2024-08-20T21:41:28.3137048Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_and_cpu_int32 PASSED [0.0094s] [ 11%] 2024-08-20T21:41:28.3138399Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_and_cpu_int64 PASSED [0.0097s] [ 11%] 2024-08-20T21:41:28.3139673Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_and_cpu_int8 PASSED [0.0096s] [ 11%] 2024-08-20T21:41:28.3140977Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_and_cpu_uint8 PASSED [0.0097s] [ 11%] 2024-08-20T21:41:28.3142326Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_left_shift_cpu_int16 PASSED [0.0095s] [ 11%] 2024-08-20T21:41:28.3143694Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_left_shift_cpu_int32 PASSED [0.0097s] [ 11%] 2024-08-20T21:41:28.3145060Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_left_shift_cpu_int64 PASSED [0.0095s] [ 11%] 2024-08-20T21:41:28.3146502Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_left_shift_cpu_int8 PASSED [0.0097s] [ 11%] 2024-08-20T21:41:28.3148052Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_left_shift_cpu_uint8 PASSED [0.0095s] [ 11%] 2024-08-20T21:41:28.3149382Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_not_cpu_bool PASSED [0.0062s] [ 11%] 2024-08-20T21:41:28.3150664Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_not_cpu_int16 PASSED [0.0063s] [ 11%] 2024-08-20T21:41:28.3151971Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_not_cpu_int32 PASSED [0.0061s] [ 11%] 2024-08-20T21:41:28.3153379Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_not_cpu_int64 PASSED [0.0064s] [ 11%] 2024-08-20T21:41:28.3154674Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_not_cpu_int8 PASSED [0.0062s] [ 11%] 2024-08-20T21:41:28.3156005Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_not_cpu_uint8 PASSED [0.0064s] [ 11%] 2024-08-20T21:41:28.3157298Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_or_cpu_bool PASSED [0.0095s] [ 11%] 2024-08-20T21:41:28.3158583Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_or_cpu_int16 PASSED [0.0103s] [ 11%] 2024-08-20T21:41:28.3159853Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_or_cpu_int32 PASSED [0.0096s] [ 11%] 2024-08-20T21:41:28.3161132Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_or_cpu_int64 PASSED [0.0099s] [ 11%] 2024-08-20T21:41:28.3162416Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_or_cpu_int8 PASSED [0.0096s] [ 11%] 2024-08-20T21:41:28.3163694Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_or_cpu_uint8 PASSED [0.0096s] [ 11%] 2024-08-20T21:41:28.3165021Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_right_shift_cpu_int16 PASSED [0.0098s] [ 11%] 2024-08-20T21:41:28.3166407Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_right_shift_cpu_int32 PASSED [0.0095s] [ 11%] 2024-08-20T21:41:28.3167856Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_right_shift_cpu_int64 PASSED [0.0097s] [ 11%] 2024-08-20T21:41:28.3169235Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_right_shift_cpu_int8 PASSED [0.0095s] [ 11%] 2024-08-20T21:41:28.3170598Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_right_shift_cpu_uint8 PASSED [0.0098s] [ 11%] 2024-08-20T21:41:28.3171945Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_xor_cpu_bool PASSED [0.0096s] [ 11%] 2024-08-20T21:41:28.3173246Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_xor_cpu_int16 PASSED [0.0100s] [ 11%] 2024-08-20T21:41:28.3174613Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_xor_cpu_int32 PASSED [0.0096s] [ 11%] 2024-08-20T21:41:28.3175914Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_xor_cpu_int64 PASSED [0.0098s] [ 11%] 2024-08-20T21:41:28.3177208Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_xor_cpu_int8 PASSED [0.0096s] [ 11%] 2024-08-20T21:41:28.3178500Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_xor_cpu_uint8 PASSED [0.0095s] [ 11%] 2024-08-20T21:41:28.3179799Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_bfloat16 PASSED [0.0070s] [ 11%] 2024-08-20T21:41:28.3181105Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_bool PASSED [0.0068s] [ 11%] 2024-08-20T21:41:28.3182429Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_complex128 PASSED [0.0071s] [ 11%] 2024-08-20T21:41:28.3183773Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_complex32 PASSED [0.0090s] [ 11%] 2024-08-20T21:41:28.3185098Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_complex64 PASSED [0.0092s] [ 11%] 2024-08-20T21:41:28.3186428Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_float16 PASSED [0.0068s] [ 11%] 2024-08-20T21:41:28.3187740Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_float32 PASSED [0.0073s] [ 12%] 2024-08-20T21:41:28.3189047Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_float64 PASSED [0.0068s] [ 12%] 2024-08-20T21:41:28.3190378Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_int16 PASSED [0.0068s] [ 12%] 2024-08-20T21:41:28.3191702Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_int32 PASSED [0.0070s] [ 12%] 2024-08-20T21:41:28.3192999Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_int64 PASSED [0.0067s] [ 12%] 2024-08-20T21:41:28.3194267Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_int8 PASSED [0.0069s] [ 12%] 2024-08-20T21:41:28.3195559Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_uint8 PASSED [0.0068s] [ 12%] 2024-08-20T21:41:28.3196830Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_bfloat16 PASSED [0.0054s] [ 12%] 2024-08-20T21:41:28.3198090Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_complex128 PASSED [0.0052s] [ 12%] 2024-08-20T21:41:28.3199342Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_complex64 PASSED [0.0053s] [ 12%] 2024-08-20T21:41:28.3200592Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_float16 PASSED [0.0051s] [ 12%] 2024-08-20T21:41:28.3201832Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_float32 PASSED [0.0051s] [ 12%] 2024-08-20T21:41:28.3203066Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_float64 PASSED [0.0052s] [ 12%] 2024-08-20T21:41:28.3204280Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_int16 PASSED [0.0050s] [ 12%] 2024-08-20T21:41:28.3205496Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_int32 PASSED [0.0052s] [ 12%] 2024-08-20T21:41:28.3206769Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_int64 PASSED [0.0051s] [ 12%] 2024-08-20T21:41:28.3207974Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_int8 PASSED [0.0053s] [ 12%] 2024-08-20T21:41:28.3209186Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_uint8 PASSED [0.0051s] [ 12%] 2024-08-20T21:41:28.3210639Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_bfloat16 PASSED [0.0074s] [ 12%] 2024-08-20T21:41:28.3211878Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_bool PASSED [0.0071s] [ 12%] 2024-08-20T21:41:28.3213122Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_complex128 PASSED [0.0073s] [ 12%] 2024-08-20T21:41:28.3214409Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_complex32 PASSED [0.0072s] [ 12%] 2024-08-20T21:41:28.3215675Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_complex64 PASSED [0.0072s] [ 12%] 2024-08-20T21:41:28.3216941Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_float16 PASSED [0.0073s] [ 12%] 2024-08-20T21:41:28.3218182Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_float32 PASSED [0.0071s] [ 12%] 2024-08-20T21:41:28.3219425Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_float64 PASSED [0.0073s] [ 12%] 2024-08-20T21:41:28.3220662Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_int16 PASSED [0.0071s] [ 12%] 2024-08-20T21:41:28.3222043Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_int32 PASSED [0.0074s] [ 12%] 2024-08-20T21:41:28.3223271Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_int64 PASSED [0.0071s] [ 12%] 2024-08-20T21:41:28.3224494Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_int8 PASSED [0.0073s] [ 12%] 2024-08-20T21:41:28.3225806Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_uint8 PASSED [0.0071s] [ 12%] 2024-08-20T21:41:28.3227147Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_shapes_cpu_float32 PASSED [0.0098s] [ 12%] 2024-08-20T21:41:28.3228541Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_bfloat16 PASSED [0.0051s] [ 12%] 2024-08-20T21:41:28.3229925Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_bool PASSED [0.0051s] [ 12%] 2024-08-20T21:41:28.3231322Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_complex128 PASSED [0.0053s] [ 12%] 2024-08-20T21:41:28.3232736Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_complex64 PASSED [0.0051s] [ 12%] 2024-08-20T21:41:28.3234140Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_float16 PASSED [0.0053s] [ 12%] 2024-08-20T21:41:28.3235539Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_float32 PASSED [0.0051s] [ 12%] 2024-08-20T21:41:28.3236920Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_float64 PASSED [0.0053s] [ 12%] 2024-08-20T21:41:28.3238295Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_int16 PASSED [0.0051s] [ 12%] 2024-08-20T21:41:28.3239659Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_int32 PASSED [0.0053s] [ 12%] 2024-08-20T21:41:28.3241022Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_int64 PASSED [0.0052s] [ 12%] 2024-08-20T21:41:28.3242371Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_int8 PASSED [0.0051s] [ 12%] 2024-08-20T21:41:28.3243740Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_uint8 PASSED [0.0057s] [ 12%] 2024-08-20T21:41:28.3245101Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_bfloat16 PASSED [0.0081s] [ 12%] 2024-08-20T21:41:28.3246431Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_bool PASSED [0.0083s] [ 12%] 2024-08-20T21:41:28.3248124Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_complex128 PASSED [0.0082s] [ 12%] 2024-08-20T21:41:28.3267323Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_complex64 PASSED [0.0085s] [ 12%] 2024-08-20T21:41:28.3268950Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_float16 PASSED [0.0081s] [ 12%] 2024-08-20T21:41:28.3270276Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_float32 PASSED [0.0083s] [ 12%] 2024-08-20T21:41:28.3271598Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_float64 PASSED [0.0081s] [ 12%] 2024-08-20T21:41:28.3272898Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_int16 PASSED [0.0082s] [ 12%] 2024-08-20T21:41:28.3274191Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_int32 PASSED [0.0084s] [ 12%] 2024-08-20T21:41:28.3275484Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_int64 PASSED [0.0082s] [ 12%] 2024-08-20T21:41:28.3276788Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_int8 PASSED [0.0083s] [ 12%] 2024-08-20T21:41:28.3278093Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_uint8 PASSED [0.0081s] [ 12%] 2024-08-20T21:41:28.3279391Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_bfloat16 PASSED [0.0181s] [ 12%] 2024-08-20T21:41:28.3280906Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_float16 PASSED [0.0178s] [ 12%] 2024-08-20T21:41:28.3282262Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_float32 PASSED [0.0180s] [ 12%] 2024-08-20T21:41:28.3283548Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_float64 PASSED [0.0177s] [ 12%] 2024-08-20T21:41:28.3284818Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_int16 PASSED [0.0179s] [ 13%] 2024-08-20T21:41:28.3286073Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_int32 PASSED [0.0176s] [ 13%] 2024-08-20T21:41:28.3287448Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_int64 PASSED [0.0179s] [ 13%] 2024-08-20T21:41:28.3288717Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_int8 PASSED [0.0180s] [ 13%] 2024-08-20T21:41:28.3290001Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_uint8 PASSED [0.0178s] [ 13%] 2024-08-20T21:41:28.3291284Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_bfloat16 PASSED [0.0080s] [ 13%] 2024-08-20T21:41:28.3292524Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_bool PASSED [0.0071s] [ 13%] 2024-08-20T21:41:28.3293769Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_complex128 PASSED [0.0074s] [ 13%] 2024-08-20T21:41:28.3295043Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_complex64 PASSED [0.0073s] [ 13%] 2024-08-20T21:41:28.3296305Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_float16 PASSED [0.0074s] [ 13%] 2024-08-20T21:41:28.3297534Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_float32 PASSED [0.0072s] [ 13%] 2024-08-20T21:41:28.3298789Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_float64 PASSED [0.0072s] [ 13%] 2024-08-20T21:41:28.3300223Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_int16 PASSED [0.0073s] [ 13%] 2024-08-20T21:41:28.3301532Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_int32 PASSED [0.0071s] [ 13%] 2024-08-20T21:41:28.3302746Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_int64 PASSED [0.0074s] [ 13%] 2024-08-20T21:41:28.3303972Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_int8 PASSED [0.0074s] [ 13%] 2024-08-20T21:41:28.3305197Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_uint8 PASSED [0.0074s] [ 13%] 2024-08-20T21:41:28.3306502Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_bfloat16 PASSED [0.0060s] [ 13%] 2024-08-20T21:41:28.3307848Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_bool PASSED [0.0062s] [ 13%] 2024-08-20T21:41:28.3309220Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_complex128 PASSED [0.0060s] [ 13%] 2024-08-20T21:41:28.3310606Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_complex64 PASSED [0.0062s] [ 13%] 2024-08-20T21:41:28.3311966Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_float16 PASSED [0.0060s] [ 13%] 2024-08-20T21:41:28.3313333Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_float32 PASSED [0.0060s] [ 13%] 2024-08-20T21:41:28.3314692Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_float64 PASSED [0.0061s] [ 13%] 2024-08-20T21:41:28.3316102Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_int16 PASSED [0.0060s] [ 13%] 2024-08-20T21:41:28.3317432Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_int32 PASSED [0.0062s] [ 13%] 2024-08-20T21:41:28.3318821Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_int64 PASSED [0.0060s] [ 13%] 2024-08-20T21:41:28.3320152Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_int8 PASSED [0.0066s] [ 13%] 2024-08-20T21:41:28.3321703Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_uint8 PASSED [0.0060s] [ 13%] 2024-08-20T21:41:28.3322985Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_bfloat16 PASSED [0.0096s] [ 13%] 2024-08-20T21:41:28.3324208Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_bool PASSED [0.0093s] [ 13%] 2024-08-20T21:41:28.3325457Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_complex128 PASSED [0.0097s] [ 13%] 2024-08-20T21:41:28.3326778Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_complex32 PASSED [0.0095s] [ 13%] 2024-08-20T21:41:28.3328041Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_complex64 PASSED [0.0094s] [ 13%] 2024-08-20T21:41:28.3329293Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_float16 PASSED [0.0095s] [ 13%] 2024-08-20T21:41:28.3330524Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_float32 PASSED [0.0093s] [ 13%] 2024-08-20T21:41:28.3331743Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_float64 PASSED [0.0095s] [ 13%] 2024-08-20T21:41:28.3332959Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_int16 PASSED [0.0093s] [ 13%] 2024-08-20T21:41:28.3334180Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_int32 PASSED [0.0095s] [ 13%] 2024-08-20T21:41:28.3335399Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_int64 PASSED [0.0094s] [ 13%] 2024-08-20T21:41:28.3336599Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_int8 PASSED [0.0095s] [ 13%] 2024-08-20T21:41:28.3337866Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_uint8 PASSED [0.0094s] [ 13%] 2024-08-20T21:41:28.3339117Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cauchy_cpu_bfloat16 PASSED [0.0064s] [ 13%] 2024-08-20T21:41:28.3340407Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cauchy_cpu_float16 PASSED [0.0061s] [ 13%] 2024-08-20T21:41:28.3341667Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cauchy_cpu_float32 PASSED [0.0061s] [ 13%] 2024-08-20T21:41:28.3342946Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cauchy_cpu_float64 PASSED [0.0063s] [ 13%] 2024-08-20T21:41:28.3344222Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdist_cpu_float32 PASSED [0.1377s] [ 13%] 2024-08-20T21:41:28.3345476Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdist_cpu_float64 PASSED [0.1401s] [ 13%] 2024-08-20T21:41:28.3346920Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_bfloat16 PASSED [0.0073s] [ 13%] 2024-08-20T21:41:28.3348199Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_bool PASSED [0.0080s] [ 13%] 2024-08-20T21:41:28.3349495Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_complex128 PASSED [0.0072s] [ 13%] 2024-08-20T21:41:28.3350801Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_complex32 PASSED [0.0075s] [ 13%] 2024-08-20T21:41:28.3352113Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_complex64 PASSED [0.0072s] [ 13%] 2024-08-20T21:41:28.3353505Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_float16 PASSED [0.0074s] [ 13%] 2024-08-20T21:41:28.3354832Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_float32 PASSED [0.0072s] [ 13%] 2024-08-20T21:41:28.3356107Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_float64 PASSED [0.0072s] [ 13%] 2024-08-20T21:41:28.3357370Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_int16 PASSED [0.0074s] [ 13%] 2024-08-20T21:41:28.3358627Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_int32 PASSED [0.0072s] [ 13%] 2024-08-20T21:41:28.3359879Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_int64 PASSED [0.0074s] [ 13%] 2024-08-20T21:41:28.3361137Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_int8 PASSED [0.0072s] [ 14%] 2024-08-20T21:41:28.3362403Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_uint8 PASSED [0.0074s] [ 14%] 2024-08-20T21:41:28.3363670Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_bfloat16 PASSED [0.0051s] [ 14%] 2024-08-20T21:41:28.3364913Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_float16 PASSED [0.0053s] [ 14%] 2024-08-20T21:41:28.3366149Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_float32 PASSED [0.0051s] [ 14%] 2024-08-20T21:41:28.3367461Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_float64 PASSED [0.0053s] [ 14%] 2024-08-20T21:41:28.3368698Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_int16 PASSED [0.0051s] [ 14%] 2024-08-20T21:41:28.3369919Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_int32 PASSED [0.0051s] [ 14%] 2024-08-20T21:41:28.3371149Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_int64 PASSED [0.0053s] [ 14%] 2024-08-20T21:41:28.3372370Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_int8 PASSED [0.0051s] [ 14%] 2024-08-20T21:41:28.3373632Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_uint8 PASSED [0.0053s] [ 14%] 2024-08-20T21:41:28.3374896Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_bfloat16 PASSED [0.0072s] [ 14%] 2024-08-20T21:41:28.3376156Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_bool PASSED [0.0080s] [ 14%] 2024-08-20T21:41:28.3377433Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_complex128 PASSED [0.0072s] [ 14%] 2024-08-20T21:41:28.3378719Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_complex32 PASSED [0.0075s] [ 14%] 2024-08-20T21:41:28.3380018Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_complex64 PASSED [0.0073s] [ 14%] 2024-08-20T21:41:28.3381306Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_float16 PASSED [0.0074s] [ 14%] 2024-08-20T21:41:28.3382573Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_float32 PASSED [0.0072s] [ 14%] 2024-08-20T21:41:28.3383835Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_float64 PASSED [0.0072s] [ 14%] 2024-08-20T21:41:28.3385095Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_int16 PASSED [0.0074s] [ 14%] 2024-08-20T21:41:28.3386547Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_int32 PASSED [0.0072s] [ 14%] 2024-08-20T21:41:28.3387788Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_int64 PASSED [0.0074s] [ 14%] 2024-08-20T21:41:28.3389103Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_int8 PASSED [0.0072s] [ 14%] 2024-08-20T21:41:28.3390384Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_uint8 PASSED [0.0073s] [ 14%] 2024-08-20T21:41:28.3391650Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_bfloat16 PASSED [0.0071s] [ 14%] 2024-08-20T21:41:28.3392891Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_bool PASSED [0.0073s] [ 14%] 2024-08-20T21:41:28.3394152Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_complex128 PASSED [0.0072s] [ 14%] 2024-08-20T21:41:28.3395440Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_complex32 PASSED [0.0074s] [ 14%] 2024-08-20T21:41:28.3396705Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_complex64 PASSED [0.0072s] [ 14%] 2024-08-20T21:41:28.3397985Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_float16 PASSED [0.0071s] [ 14%] 2024-08-20T21:41:28.3399244Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_float32 PASSED [0.0073s] [ 14%] 2024-08-20T21:41:28.3400497Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_float64 PASSED [0.0072s] [ 14%] 2024-08-20T21:41:28.3401730Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_int16 PASSED [0.0074s] [ 14%] 2024-08-20T21:41:28.3402969Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_int32 PASSED [0.0072s] [ 14%] 2024-08-20T21:41:28.3404200Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_int64 PASSED [0.0079s] [ 14%] 2024-08-20T21:41:28.3405428Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_int8 PASSED [0.0071s] [ 14%] 2024-08-20T21:41:28.3406656Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_uint8 PASSED [0.0073s] [ 14%] 2024-08-20T21:41:28.3407990Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_bfloat16 PASSED [0.0072s] [ 14%] 2024-08-20T21:41:28.3409269Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_bool PASSED [0.0073s] [ 14%] 2024-08-20T21:41:28.3410517Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_complex128 PASSED [0.0073s] [ 14%] 2024-08-20T21:41:28.3411800Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_complex32 PASSED [0.0073s] [ 14%] 2024-08-20T21:41:28.3413076Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_complex64 PASSED [0.0073s] [ 14%] 2024-08-20T21:41:28.3414343Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_float16 PASSED [0.0071s] [ 14%] 2024-08-20T21:41:28.3415587Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_float32 PASSED [0.0073s] [ 14%] 2024-08-20T21:41:28.3416835Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_float64 PASSED [0.0072s] [ 14%] 2024-08-20T21:41:28.3418070Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_int16 PASSED [0.0074s] [ 14%] 2024-08-20T21:41:28.3419305Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_int32 PASSED [0.0072s] [ 14%] 2024-08-20T21:41:28.3420516Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_int64 PASSED [0.0074s] [ 14%] 2024-08-20T21:41:28.3421946Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_int8 PASSED [0.0071s] [ 14%] 2024-08-20T21:41:28.3423171Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_uint8 PASSED [0.0073s] [ 14%] 2024-08-20T21:41:28.3424514Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_cpu_complex128 PASSED [0.0146s] [ 14%] 2024-08-20T21:41:28.3425846Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_cpu_complex64 PASSED [0.0143s] [ 14%] 2024-08-20T21:41:28.3427187Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_cpu_float32 PASSED [0.0140s] [ 14%] 2024-08-20T21:41:28.3428482Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_cpu_float64 PASSED [0.0138s] [ 14%] 2024-08-20T21:41:28.3429824Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_inverse_cpu_complex128 PASSED [0.0134s] [ 14%] 2024-08-20T21:41:28.3431236Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_inverse_cpu_complex64 PASSED [0.0120s] [ 14%] 2024-08-20T21:41:28.3432630Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_inverse_cpu_float32 PASSED [0.0122s] [ 14%] 2024-08-20T21:41:28.3434011Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_inverse_cpu_float64 PASSED [0.0117s] [ 14%] 2024-08-20T21:41:28.3435389Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_solve_cpu_complex128 PASSED [0.0120s] [ 14%] 2024-08-20T21:41:28.3436781Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_solve_cpu_complex64 PASSED [0.0123s] [ 14%] 2024-08-20T21:41:28.3438157Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_solve_cpu_float32 PASSED [0.0118s] [ 15%] 2024-08-20T21:41:28.3439524Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_solve_cpu_float64 PASSED [0.0121s] [ 15%] 2024-08-20T21:41:28.3440824Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_bfloat16 PASSED [0.0062s] [ 15%] 2024-08-20T21:41:28.3442070Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_bool PASSED [0.0064s] [ 15%] 2024-08-20T21:41:28.3443336Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_complex128 PASSED [0.0063s] [ 15%] 2024-08-20T21:41:28.3444611Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_complex32 PASSED [0.0062s] [ 15%] 2024-08-20T21:41:28.3445926Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_complex64 PASSED [0.0064s] [ 15%] 2024-08-20T21:41:28.3447414Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_float16 PASSED [0.0062s] [ 15%] 2024-08-20T21:41:28.3448679Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_float32 PASSED [0.0064s] [ 15%] 2024-08-20T21:41:28.3449921Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_float64 PASSED [0.0062s] [ 15%] 2024-08-20T21:41:28.3451166Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_int16 PASSED [0.0063s] [ 15%] 2024-08-20T21:41:28.3452406Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_int32 PASSED [0.0061s] [ 15%] 2024-08-20T21:41:28.3453641Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_int64 PASSED [0.0063s] [ 15%] 2024-08-20T21:41:28.3454860Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_int8 PASSED [0.0061s] [ 15%] 2024-08-20T21:41:28.3456092Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_uint8 PASSED [0.0063s] [ 15%] 2024-08-20T21:41:28.3457354Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_bfloat16 PASSED [0.0084s] [ 15%] 2024-08-20T21:41:28.3458608Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_float16 PASSED [0.0083s] [ 15%] 2024-08-20T21:41:28.3459860Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_float32 PASSED [0.0089s] [ 15%] 2024-08-20T21:41:28.3461248Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_float64 PASSED [0.0085s] [ 15%] 2024-08-20T21:41:28.3462542Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_int16 PASSED [0.0086s] [ 15%] 2024-08-20T21:41:28.3463766Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_int32 PASSED [0.0084s] [ 15%] 2024-08-20T21:41:28.3465011Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_int64 PASSED [0.0086s] [ 15%] 2024-08-20T21:41:28.3466239Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_int8 PASSED [0.0084s] [ 15%] 2024-08-20T21:41:28.3467475Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_uint8 PASSED [0.0086s] [ 15%] 2024-08-20T21:41:28.3468747Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_bfloat16 PASSED [0.0095s] [ 15%] 2024-08-20T21:41:28.3470045Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_bool PASSED [0.0096s] [ 15%] 2024-08-20T21:41:28.3471338Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_float16 PASSED [0.0095s] [ 15%] 2024-08-20T21:41:28.3472640Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_float32 PASSED [0.0094s] [ 15%] 2024-08-20T21:41:28.3473935Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_float64 PASSED [0.0097s] [ 15%] 2024-08-20T21:41:28.3475225Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_int16 PASSED [0.0094s] [ 15%] 2024-08-20T21:41:28.3476498Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_int32 PASSED [0.0097s] [ 15%] 2024-08-20T21:41:28.3477762Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_int64 PASSED [0.0095s] [ 15%] 2024-08-20T21:41:28.3479042Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_int8 PASSED [0.0097s] [ 15%] 2024-08-20T21:41:28.3480318Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_uint8 PASSED [0.0094s] [ 15%] 2024-08-20T21:41:28.3481657Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_bfloat16 PASSED [0.0097s] [ 15%] 2024-08-20T21:41:28.3482937Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_bool PASSED [0.0094s] [ 15%] 2024-08-20T21:41:28.3484230Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_float16 PASSED [0.0094s] [ 15%] 2024-08-20T21:41:28.3485523Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_float32 PASSED [0.0097s] [ 15%] 2024-08-20T21:41:28.3486916Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_float64 PASSED [0.0096s] [ 15%] 2024-08-20T21:41:28.3488216Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_int16 PASSED [0.0103s] [ 15%] 2024-08-20T21:41:28.3489505Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_int32 PASSED [0.0095s] [ 15%] 2024-08-20T21:41:28.3490790Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_int64 PASSED [0.0097s] [ 15%] 2024-08-20T21:41:28.3492047Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_int8 PASSED [0.0095s] [ 15%] 2024-08-20T21:41:28.3493322Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_uint8 PASSED [0.0097s] [ 15%] 2024-08-20T21:41:28.3494593Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_bfloat16 PASSED [0.0057s] [ 15%] 2024-08-20T21:41:28.3495840Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_bool PASSED [0.0057s] [ 15%] 2024-08-20T21:41:28.3497159Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_complex128 PASSED [0.0059s] [ 15%] 2024-08-20T21:41:28.3498480Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_complex32 PASSED [0.0058s] [ 15%] 2024-08-20T21:41:28.3499775Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_complex64 PASSED [0.0060s] [ 15%] 2024-08-20T21:41:28.3501030Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_float16 PASSED [0.0057s] [ 15%] 2024-08-20T21:41:28.3502290Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_float32 PASSED [0.0060s] [ 15%] 2024-08-20T21:41:28.3503545Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_float64 PASSED [0.0057s] [ 15%] 2024-08-20T21:41:28.3504802Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_int16 PASSED [0.0059s] [ 15%] 2024-08-20T21:41:28.3506033Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_int32 PASSED [0.0056s] [ 15%] 2024-08-20T21:41:28.3507268Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_int64 PASSED [0.0058s] [ 15%] 2024-08-20T21:41:28.3508495Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_int8 PASSED [0.0057s] [ 15%] 2024-08-20T21:41:28.3509842Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_uint8 PASSED [0.0056s] [ 15%] 2024-08-20T21:41:28.3511139Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_bfloat16 PASSED [0.0064s] [ 15%] 2024-08-20T21:41:28.3512515Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_bool PASSED [0.0062s] [ 15%] 2024-08-20T21:41:28.3513855Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_complex128 PASSED [0.0066s] [ 16%] 2024-08-20T21:41:28.3515210Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_complex32 PASSED [0.0063s] [ 16%] 2024-08-20T21:41:28.3516570Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_complex64 PASSED [0.0071s] [ 16%] 2024-08-20T21:41:28.3517956Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_float16 PASSED [0.0063s] [ 16%] 2024-08-20T21:41:28.3519289Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_float32 PASSED [0.0065s] [ 16%] 2024-08-20T21:41:28.3520609Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_float64 PASSED [0.0063s] [ 16%] 2024-08-20T21:41:28.3522175Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_int16 PASSED [0.0064s] [ 16%] 2024-08-20T21:41:28.3523489Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_int32 PASSED [0.0063s] [ 16%] 2024-08-20T21:41:28.3524802Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_int64 PASSED [0.0062s] [ 16%] 2024-08-20T21:41:28.3526098Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_int8 PASSED [0.0064s] [ 16%] 2024-08-20T21:41:28.3527502Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_uint8 PASSED [0.0062s] [ 16%] 2024-08-20T21:41:28.3528841Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_bfloat16 PASSED [0.0136s] [ 16%] 2024-08-20T21:41:28.3530161Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_bool PASSED [0.0134s] [ 16%] 2024-08-20T21:41:28.3531500Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_complex128 PASSED [0.0137s] [ 16%] 2024-08-20T21:41:28.3532956Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_complex64 PASSED [0.0134s] [ 16%] 2024-08-20T21:41:28.3534308Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_float16 PASSED [0.0136s] [ 16%] 2024-08-20T21:41:28.3535667Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_float32 PASSED [0.0134s] [ 16%] 2024-08-20T21:41:28.3537014Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_float64 PASSED [0.0137s] [ 16%] 2024-08-20T21:41:28.3538340Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_int16 PASSED [0.0134s] [ 16%] 2024-08-20T21:41:28.3539662Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_int32 PASSED [0.0134s] [ 16%] 2024-08-20T21:41:28.3540971Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_int64 PASSED [0.0135s] [ 16%] 2024-08-20T21:41:28.3542289Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_int8 PASSED [0.0134s] [ 16%] 2024-08-20T21:41:28.3543602Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_uint8 PASSED [0.0136s] [ 16%] 2024-08-20T21:41:28.3544906Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_complex_cpu_float16 PASSED [0.0096s] [ 16%] 2024-08-20T21:41:28.3546168Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_complex_cpu_float32 PASSED [0.0103s] [ 16%] 2024-08-20T21:41:28.3547652Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_complex_cpu_float64 PASSED [0.0095s] [ 16%] 2024-08-20T21:41:28.3548927Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_bfloat16 PASSED [0.0064s] [ 16%] 2024-08-20T21:41:28.3550148Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_bool PASSED [0.0061s] [ 16%] 2024-08-20T21:41:28.3551415Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_complex128 PASSED [0.0063s] [ 16%] 2024-08-20T21:41:28.3552694Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_complex32 PASSED [0.0062s] [ 16%] 2024-08-20T21:41:28.3554054Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_complex64 PASSED [0.0061s] [ 16%] 2024-08-20T21:41:28.3555304Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_float16 PASSED [0.0062s] [ 16%] 2024-08-20T21:41:28.3556553Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_float32 PASSED [0.0061s] [ 16%] 2024-08-20T21:41:28.3557802Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_float64 PASSED [0.0063s] [ 16%] 2024-08-20T21:41:28.3559038Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_int16 PASSED [0.0061s] [ 16%] 2024-08-20T21:41:28.3560259Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_int32 PASSED [0.0063s] [ 16%] 2024-08-20T21:41:28.3561486Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_int64 PASSED [0.0061s] [ 16%] 2024-08-20T21:41:28.3562704Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_int8 PASSED [0.0064s] [ 16%] 2024-08-20T21:41:28.3563916Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_uint8 PASSED [0.0061s] [ 16%] 2024-08-20T21:41:28.3565206Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_bfloat16 PASSED [0.0053s] [ 16%] 2024-08-20T21:41:28.3566539Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_bool PASSED [0.0051s] [ 16%] 2024-08-20T21:41:28.3567969Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_complex128 PASSED [0.0051s] [ 16%] 2024-08-20T21:41:28.3569426Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_complex32 PASSED [0.0053s] [ 16%] 2024-08-20T21:41:28.3570804Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_complex64 PASSED [0.0051s] [ 16%] 2024-08-20T21:41:28.3572212Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_float16 PASSED [0.0052s] [ 16%] 2024-08-20T21:41:28.3573559Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_float32 PASSED [0.0051s] [ 16%] 2024-08-20T21:41:28.3574891Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_float64 PASSED [0.0057s] [ 16%] 2024-08-20T21:41:28.3576219Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_int16 PASSED [0.0051s] [ 16%] 2024-08-20T21:41:28.3577545Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_int32 PASSED [0.0053s] [ 16%] 2024-08-20T21:41:28.3578858Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_int64 PASSED [0.0051s] [ 16%] 2024-08-20T21:41:28.3580180Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_int8 PASSED [0.0052s] [ 16%] 2024-08-20T21:41:28.3581500Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_uint8 PASSED [0.0051s] [ 16%] 2024-08-20T21:41:28.3582857Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_bfloat16 PASSED [0.0298s] [ 16%] 2024-08-20T21:41:28.3584196Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_bool PASSED [0.0300s] [ 16%] 2024-08-20T21:41:28.3585567Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_complex128 PASSED [0.0304s] [ 16%] 2024-08-20T21:41:28.3586966Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_complex64 PASSED [0.0307s] [ 16%] 2024-08-20T21:41:28.3588341Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_float16 PASSED [0.0298s] [ 16%] 2024-08-20T21:41:28.3589697Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_float32 PASSED [0.0300s] [ 16%] 2024-08-20T21:41:28.3591094Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_float64 PASSED [0.0297s] [ 16%] 2024-08-20T21:41:28.3592450Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_int16 PASSED [0.0300s] [ 17%] 2024-08-20T21:41:28.3593790Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_int32 PASSED [0.0298s] [ 17%] 2024-08-20T21:41:28.3595117Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_int64 PASSED [0.0296s] [ 17%] 2024-08-20T21:41:28.3596463Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_int8 PASSED [0.0302s] [ 17%] 2024-08-20T21:41:28.3597793Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_uint8 PASSED [0.0297s] [ 17%] 2024-08-20T21:41:28.3599128Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_bfloat16 PASSED [0.0061s] [ 17%] 2024-08-20T21:41:28.3600439Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_bool PASSED [0.0056s] [ 17%] 2024-08-20T21:41:28.3601761Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_complex128 PASSED [0.0059s] [ 17%] 2024-08-20T21:41:28.3603111Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_complex32 PASSED [0.0057s] [ 17%] 2024-08-20T21:41:28.3604442Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_complex64 PASSED [0.0062s] [ 17%] 2024-08-20T21:41:28.3605856Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_float16 PASSED [0.0056s] [ 17%] 2024-08-20T21:41:28.3607287Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_float32 PASSED [0.0056s] [ 17%] 2024-08-20T21:41:28.3608610Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_float64 PASSED [0.0057s] [ 17%] 2024-08-20T21:41:28.3609895Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_int16 PASSED [0.0056s] [ 17%] 2024-08-20T21:41:28.3611313Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_int32 PASSED [0.0061s] [ 17%] 2024-08-20T21:41:28.3612609Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_int64 PASSED [0.0058s] [ 17%] 2024-08-20T21:41:28.3613893Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_int8 PASSED [0.0061s] [ 17%] 2024-08-20T21:41:28.3615175Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_uint8 PASSED [0.0058s] [ 17%] 2024-08-20T21:41:28.3616478Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_bfloat16 PASSED [0.0100s] [ 17%] 2024-08-20T21:41:28.3617773Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_bool PASSED [0.0096s] [ 17%] 2024-08-20T21:41:28.3619043Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_float16 PASSED [0.0098s] [ 17%] 2024-08-20T21:41:28.3620342Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_float32 PASSED [0.0096s] [ 17%] 2024-08-20T21:41:28.3621861Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_float64 PASSED [0.0097s] [ 17%] 2024-08-20T21:41:28.3623145Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_int16 PASSED [0.0100s] [ 17%] 2024-08-20T21:41:28.3624411Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_int32 PASSED [0.0098s] [ 17%] 2024-08-20T21:41:28.3625683Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_int64 PASSED [0.0099s] [ 17%] 2024-08-20T21:41:28.3627013Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_int8 PASSED [0.0096s] [ 17%] 2024-08-20T21:41:28.3628292Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_uint8 PASSED [0.0099s] [ 17%] 2024-08-20T21:41:28.3629569Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_bfloat16 PASSED [0.0067s] [ 17%] 2024-08-20T21:41:28.3630889Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_complex128 PASSED [0.0069s] [ 17%] 2024-08-20T21:41:28.3632209Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_complex64 PASSED [0.0067s] [ 17%] 2024-08-20T21:41:28.3633506Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_float16 PASSED [0.0066s] [ 17%] 2024-08-20T21:41:28.3634802Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_float32 PASSED [0.0068s] [ 17%] 2024-08-20T21:41:28.3636089Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_float64 PASSED [0.0066s] [ 17%] 2024-08-20T21:41:28.3637369Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_int16 PASSED [0.0069s] [ 17%] 2024-08-20T21:41:28.3638631Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_int32 PASSED [0.0066s] [ 17%] 2024-08-20T21:41:28.3639906Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_int64 PASSED [0.0068s] [ 17%] 2024-08-20T21:41:28.3641168Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_int8 PASSED [0.0066s] [ 17%] 2024-08-20T21:41:28.3642504Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_uint8 PASSED [0.0068s] [ 17%] 2024-08-20T21:41:28.3643774Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_bfloat16 PASSED [0.0062s] [ 17%] 2024-08-20T21:41:28.3645006Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_bool PASSED [0.0063s] [ 17%] 2024-08-20T21:41:28.3646249Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_complex128 PASSED [0.0062s] [ 17%] 2024-08-20T21:41:28.3647725Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_complex64 PASSED [0.0062s] [ 17%] 2024-08-20T21:41:28.3648981Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_float16 PASSED [0.0063s] [ 17%] 2024-08-20T21:41:28.3650213Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_float32 PASSED [0.0061s] [ 17%] 2024-08-20T21:41:28.3651447Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_float64 PASSED [0.0063s] [ 17%] 2024-08-20T21:41:28.3652654Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_int16 PASSED [0.0061s] [ 17%] 2024-08-20T21:41:28.3653870Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_int32 PASSED [0.0063s] [ 17%] 2024-08-20T21:41:28.3655083Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_int64 PASSED [0.0061s] [ 17%] 2024-08-20T21:41:28.3656289Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_int8 PASSED [0.0063s] [ 17%] 2024-08-20T21:41:28.3657483Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_uint8 PASSED [0.0061s] [ 17%] 2024-08-20T21:41:28.3658725Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_bfloat16 PASSED [0.0063s] [ 17%] 2024-08-20T21:41:28.3659958Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_bool PASSED [0.0061s] [ 17%] 2024-08-20T21:41:28.3661198Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_complex128 PASSED [0.0061s] [ 17%] 2024-08-20T21:41:28.3662555Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_complex64 PASSED [0.0063s] [ 17%] 2024-08-20T21:41:28.3663821Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_float16 PASSED [0.0061s] [ 17%] 2024-08-20T21:41:28.3665066Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_float32 PASSED [0.0064s] [ 17%] 2024-08-20T21:41:28.3666295Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_float64 PASSED [0.0061s] [ 17%] 2024-08-20T21:41:28.3667532Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_int16 PASSED [0.0063s] [ 17%] 2024-08-20T21:41:28.3668766Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_int32 PASSED [0.0061s] [ 17%] 2024-08-20T21:41:28.3669986Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_int64 PASSED [0.0063s] [ 18%] 2024-08-20T21:41:28.3671200Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_int8 PASSED [0.0061s] [ 18%] 2024-08-20T21:41:28.3672425Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_uint8 PASSED [0.0063s] [ 18%] 2024-08-20T21:41:28.3673724Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_bfloat16 PASSED [0.0145s] [ 18%] 2024-08-20T21:41:28.3675048Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_bool PASSED [0.0144s] [ 18%] 2024-08-20T21:41:28.3676394Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_complex128 PASSED [0.0147s] [ 18%] 2024-08-20T21:41:28.3677847Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_complex64 PASSED [0.0145s] [ 18%] 2024-08-20T21:41:28.3679248Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_float16 PASSED [0.0150s] [ 18%] 2024-08-20T21:41:28.3680576Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_float32 PASSED [0.0145s] [ 18%] 2024-08-20T21:41:28.3681919Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_float64 PASSED [0.0148s] [ 18%] 2024-08-20T21:41:28.3683253Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_int16 PASSED [0.0145s] [ 18%] 2024-08-20T21:41:28.3684579Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_int32 PASSED [0.0148s] [ 18%] 2024-08-20T21:41:28.3685888Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_int64 PASSED [0.0146s] [ 18%] 2024-08-20T21:41:28.3687317Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_int8 PASSED [0.0145s] [ 18%] 2024-08-20T21:41:28.3688644Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_uint8 PASSED [0.0153s] [ 18%] 2024-08-20T21:41:28.3689913Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_bfloat16 PASSED [0.0248s] [ 18%] 2024-08-20T21:41:28.3691181Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_complex128 PASSED [0.0251s] [ 18%] 2024-08-20T21:41:28.3692448Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_complex64 PASSED [0.0248s] [ 18%] 2024-08-20T21:41:28.3693689Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_float16 PASSED [0.0251s] [ 18%] 2024-08-20T21:41:28.3694911Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_float32 PASSED [0.0248s] [ 18%] 2024-08-20T21:41:28.3696149Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_float64 PASSED [0.0250s] [ 18%] 2024-08-20T21:41:28.3697373Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_int16 PASSED [0.0246s] [ 18%] 2024-08-20T21:41:28.3698636Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_int32 PASSED [0.0249s] [ 18%] 2024-08-20T21:41:28.3699840Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_int64 PASSED [0.0248s] [ 18%] 2024-08-20T21:41:28.3701053Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_int8 PASSED [0.0246s] [ 18%] 2024-08-20T21:41:28.3702266Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_uint8 PASSED [0.0249s] [ 18%] 2024-08-20T21:41:28.3703502Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_bfloat16 PASSED [0.0063s] [ 18%] 2024-08-20T21:41:28.3704795Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_complex128 PASSED [0.0065s] [ 18%] 2024-08-20T21:41:28.3706090Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_complex64 PASSED [0.0063s] [ 18%] 2024-08-20T21:41:28.3707365Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_float16 PASSED [0.0063s] [ 18%] 2024-08-20T21:41:28.3708606Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_float32 PASSED [0.0062s] [ 18%] 2024-08-20T21:41:28.3709852Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_float64 PASSED [0.0061s] [ 18%] 2024-08-20T21:41:28.3711096Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_int16 PASSED [0.0063s] [ 18%] 2024-08-20T21:41:28.3712320Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_int32 PASSED [0.0062s] [ 18%] 2024-08-20T21:41:28.3713592Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_int64 PASSED [0.0064s] [ 18%] 2024-08-20T21:41:28.3714830Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_int8 PASSED [0.0061s] [ 18%] 2024-08-20T21:41:28.3716111Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_uint8 PASSED [0.0068s] [ 18%] 2024-08-20T21:41:28.3717367Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_bfloat16 PASSED [0.0061s] [ 18%] 2024-08-20T21:41:28.3718630Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_bool PASSED [0.0064s] [ 18%] 2024-08-20T21:41:28.3719887Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_float16 PASSED [0.0061s] [ 18%] 2024-08-20T21:41:28.3721350Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_float32 PASSED [0.0063s] [ 18%] 2024-08-20T21:41:28.3722620Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_float64 PASSED [0.0061s] [ 18%] 2024-08-20T21:41:28.3723891Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_int16 PASSED [0.0061s] [ 18%] 2024-08-20T21:41:28.3725147Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_int32 PASSED [0.0062s] [ 18%] 2024-08-20T21:41:28.3726399Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_int64 PASSED [0.0061s] [ 18%] 2024-08-20T21:41:28.3727695Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_int8 PASSED [0.0063s] [ 18%] 2024-08-20T21:41:28.3728947Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_uint8 PASSED [0.0061s] [ 18%] 2024-08-20T21:41:28.3730219Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_bfloat16 PASSED [0.0063s] [ 18%] 2024-08-20T21:41:28.3731470Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_bool PASSED [0.0061s] [ 18%] 2024-08-20T21:41:28.3732734Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_float16 PASSED [0.0063s] [ 18%] 2024-08-20T21:41:28.3734004Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_float32 PASSED [0.0061s] [ 18%] 2024-08-20T21:41:28.3735345Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_float64 PASSED [0.0063s] [ 18%] 2024-08-20T21:41:28.3736583Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_int16 PASSED [0.0062s] [ 18%] 2024-08-20T21:41:28.3737830Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_int32 PASSED [0.0061s] [ 18%] 2024-08-20T21:41:28.3739082Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_int64 PASSED [0.0063s] [ 18%] 2024-08-20T21:41:28.3740334Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_int8 PASSED [0.0062s] [ 18%] 2024-08-20T21:41:28.3741567Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_uint8 PASSED [0.0065s] [ 18%] 2024-08-20T21:41:28.3742845Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_bfloat16 PASSED [0.0101s] [ 18%] 2024-08-20T21:41:28.3744160Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_complex128 PASSED [0.0109s] [ 18%] 2024-08-20T21:41:28.3745463Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_complex64 PASSED [0.0101s] [ 19%] 2024-08-20T21:41:28.3746906Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_float16 PASSED [0.0102s] [ 19%] 2024-08-20T21:41:28.3748189Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_float32 PASSED [0.0101s] [ 19%] 2024-08-20T21:41:28.3749567Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_float64 PASSED [0.0101s] [ 19%] 2024-08-20T21:41:28.3750834Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_int16 PASSED [0.0100s] [ 19%] 2024-08-20T21:41:28.3752143Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_int32 PASSED [0.0100s] [ 19%] 2024-08-20T21:41:28.3753406Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_int64 PASSED [0.0104s] [ 19%] 2024-08-20T21:41:28.3754666Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_int8 PASSED [0.0101s] [ 19%] 2024-08-20T21:41:28.3755919Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_uint8 PASSED [0.0103s] [ 19%] 2024-08-20T21:41:28.3757191Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_bfloat16 PASSED [0.0068s] [ 19%] 2024-08-20T21:41:28.3758487Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_complex128 PASSED [0.0070s] [ 19%] 2024-08-20T21:41:28.3759775Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_complex64 PASSED [0.0068s] [ 19%] 2024-08-20T21:41:28.3761065Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_float16 PASSED [0.0070s] [ 19%] 2024-08-20T21:41:28.3762335Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_float32 PASSED [0.0068s] [ 19%] 2024-08-20T21:41:28.3763602Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_float64 PASSED [0.0069s] [ 19%] 2024-08-20T21:41:28.3764855Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_int16 PASSED [0.0067s] [ 19%] 2024-08-20T21:41:28.3766093Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_int32 PASSED [0.0067s] [ 19%] 2024-08-20T21:41:28.3767427Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_int64 PASSED [0.0068s] [ 19%] 2024-08-20T21:41:28.3768668Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_int8 PASSED [0.0067s] [ 19%] 2024-08-20T21:41:28.3769900Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_uint8 PASSED [0.0069s] [ 19%] 2024-08-20T21:41:28.3771309Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_bfloat16 PASSED [0.0094s] [ 19%] 2024-08-20T21:41:28.3772759Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_complex128 PASSED [0.0104s] [ 19%] 2024-08-20T21:41:28.3774195Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_complex64 PASSED [0.0095s] [ 19%] 2024-08-20T21:41:28.3775639Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_float16 PASSED [0.0096s] [ 19%] 2024-08-20T21:41:28.3777075Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_float32 PASSED [0.0094s] [ 19%] 2024-08-20T21:41:28.3778506Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_float64 PASSED [0.0096s] [ 19%] 2024-08-20T21:41:28.3779914Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_int16 PASSED [0.0095s] [ 19%] 2024-08-20T21:41:28.3781318Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_int32 PASSED [0.0094s] [ 19%] 2024-08-20T21:41:28.3782720Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_int64 PASSED [0.0095s] [ 19%] 2024-08-20T21:41:28.3784122Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_int8 PASSED [0.0094s] [ 19%] 2024-08-20T21:41:28.3785566Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_uint8 PASSED [0.0095s] [ 19%] 2024-08-20T21:41:28.3786915Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_bfloat16 PASSED [0.0052s] [ 19%] 2024-08-20T21:41:28.3788224Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_bool PASSED [0.0053s] [ 19%] 2024-08-20T21:41:28.3789498Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_float16 PASSED [0.0052s] [ 19%] 2024-08-20T21:41:28.3790764Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_float32 PASSED [0.0053s] [ 19%] 2024-08-20T21:41:28.3792042Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_float64 PASSED [0.0051s] [ 19%] 2024-08-20T21:41:28.3793311Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_int16 PASSED [0.0053s] [ 19%] 2024-08-20T21:41:28.3794564Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_int32 PASSED [0.0051s] [ 19%] 2024-08-20T21:41:28.3795818Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_int64 PASSED [0.0051s] [ 19%] 2024-08-20T21:41:28.3797078Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_int8 PASSED [0.0052s] [ 19%] 2024-08-20T21:41:28.3798334Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_uint8 PASSED [0.0051s] [ 19%] 2024-08-20T21:41:28.3799576Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_bfloat16 PASSED [0.0127s] [ 19%] 2024-08-20T21:41:28.3800814Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_bool PASSED [0.0125s] [ 19%] 2024-08-20T21:41:28.3802067Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_complex128 PASSED [0.0133s] [ 19%] 2024-08-20T21:41:28.3803355Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_complex64 PASSED [0.0125s] [ 19%] 2024-08-20T21:41:28.3804603Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_float16 PASSED [0.0128s] [ 19%] 2024-08-20T21:41:28.3805851Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_float32 PASSED [0.0125s] [ 19%] 2024-08-20T21:41:28.3807223Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_float64 PASSED [0.0127s] [ 19%] 2024-08-20T21:41:28.3808455Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_int16 PASSED [0.0126s] [ 19%] 2024-08-20T21:41:28.3809690Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_int32 PASSED [0.0126s] [ 19%] 2024-08-20T21:41:28.3810917Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_int64 PASSED [0.0128s] [ 19%] 2024-08-20T21:41:28.3812142Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_int8 PASSED [0.0125s] [ 19%] 2024-08-20T21:41:28.3813355Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_uint8 PASSED [0.0128s] [ 19%] 2024-08-20T21:41:28.3814642Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_bfloat16 PASSED [0.0124s] [ 19%] 2024-08-20T21:41:28.3815956Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_bool PASSED [0.0124s] [ 19%] 2024-08-20T21:41:28.3817272Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_complex128 PASSED [0.0124s] [ 19%] 2024-08-20T21:41:28.3818592Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_complex32 PASSED [0.0126s] [ 19%] 2024-08-20T21:41:28.3819919Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_complex64 PASSED [0.0124s] [ 19%] 2024-08-20T21:41:28.3821530Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_float16 PASSED [0.0124s] [ 19%] 2024-08-20T21:41:28.3822843Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_float32 PASSED [0.0123s] [ 19%] 2024-08-20T21:41:28.3824189Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_float64 PASSED [0.0123s] [ 20%] 2024-08-20T21:41:28.3825491Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_int16 PASSED [0.0125s] [ 20%] 2024-08-20T21:41:28.3826781Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_int32 PASSED [0.0122s] [ 20%] 2024-08-20T21:41:28.3828055Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_int64 PASSED [0.0125s] [ 20%] 2024-08-20T21:41:28.3829338Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_int8 PASSED [0.0122s] [ 20%] 2024-08-20T21:41:28.3830631Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_uint8 PASSED [0.0129s] [ 20%] 2024-08-20T21:41:28.3831933Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_bfloat16 PASSED [0.0072s] [ 20%] 2024-08-20T21:41:28.3833211Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_bool PASSED [0.0073s] [ 20%] 2024-08-20T21:41:28.3834515Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_complex128 PASSED [0.0073s] [ 20%] 2024-08-20T21:41:28.3835850Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_complex64 PASSED [0.0072s] [ 20%] 2024-08-20T21:41:28.3837154Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_float16 PASSED [0.0073s] [ 20%] 2024-08-20T21:41:28.3838449Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_float32 PASSED [0.0072s] [ 20%] 2024-08-20T21:41:28.3839751Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_float64 PASSED [0.0073s] [ 20%] 2024-08-20T21:41:28.3841035Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_int16 PASSED [0.0072s] [ 20%] 2024-08-20T21:41:28.3842298Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_int32 PASSED [0.0074s] [ 20%] 2024-08-20T21:41:28.3843742Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_int64 PASSED [0.0072s] [ 20%] 2024-08-20T21:41:28.3845015Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_int8 PASSED [0.0073s] [ 20%] 2024-08-20T21:41:28.3846280Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_uint8 PASSED [0.0071s] [ 20%] 2024-08-20T21:41:28.3847833Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_bfloat16 PASSED [0.0125s] [ 20%] 2024-08-20T21:41:28.3849183Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_bool PASSED [0.0122s] [ 20%] 2024-08-20T21:41:28.3850539Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_complex128 PASSED [0.0124s] [ 20%] 2024-08-20T21:41:28.3851916Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_complex32 PASSED [0.0127s] [ 20%] 2024-08-20T21:41:28.3853284Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_complex64 PASSED [0.0124s] [ 20%] 2024-08-20T21:41:28.3854643Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_float16 PASSED [0.0125s] [ 20%] 2024-08-20T21:41:28.3855989Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_float32 PASSED [0.0124s] [ 20%] 2024-08-20T21:41:28.3857323Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_float64 PASSED [0.0127s] [ 20%] 2024-08-20T21:41:28.3858793Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_int16 PASSED [0.0125s] [ 20%] 2024-08-20T21:41:28.3860192Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_int32 PASSED [0.0131s] [ 20%] 2024-08-20T21:41:28.3861528Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_int64 PASSED [0.0124s] [ 20%] 2024-08-20T21:41:28.3862832Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_int8 PASSED [0.0124s] [ 20%] 2024-08-20T21:41:28.3864156Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_uint8 PASSED [0.0127s] [ 20%] 2024-08-20T21:41:28.3865478Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_bfloat16 PASSED [0.0124s] [ 20%] 2024-08-20T21:41:28.3866763Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_bool PASSED [0.0127s] [ 20%] 2024-08-20T21:41:28.3868056Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_complex128 PASSED [0.0126s] [ 20%] 2024-08-20T21:41:28.3869386Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_complex32 PASSED [0.0127s] [ 20%] 2024-08-20T21:41:28.3870711Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_complex64 PASSED [0.0125s] [ 20%] 2024-08-20T21:41:28.3872005Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_float16 PASSED [0.0125s] [ 20%] 2024-08-20T21:41:28.3873300Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_float32 PASSED [0.0124s] [ 20%] 2024-08-20T21:41:28.3874593Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_float64 PASSED [0.0123s] [ 20%] 2024-08-20T21:41:28.3875876Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_int16 PASSED [0.0125s] [ 20%] 2024-08-20T21:41:28.3877145Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_int32 PASSED [0.0122s] [ 20%] 2024-08-20T21:41:28.3878417Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_int64 PASSED [0.0126s] [ 20%] 2024-08-20T21:41:28.3879733Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_int8 PASSED [0.0124s] [ 20%] 2024-08-20T21:41:28.3881008Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_uint8 PASSED [0.0126s] [ 20%] 2024-08-20T21:41:28.3882340Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_bfloat16 PASSED [0.0130s] [ 20%] 2024-08-20T21:41:28.3883707Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_bool PASSED [0.0130s] [ 20%] 2024-08-20T21:41:28.3885093Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_complex128 PASSED [0.0133s] [ 20%] 2024-08-20T21:41:28.3886517Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_complex64 PASSED [0.0134s] [ 20%] 2024-08-20T21:41:28.3888120Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_float16 PASSED [0.0138s] [ 20%] 2024-08-20T21:41:28.3889505Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_float32 PASSED [0.0130s] [ 20%] 2024-08-20T21:41:28.3890882Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_float64 PASSED [0.0133s] [ 20%] 2024-08-20T21:41:28.3892231Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_int16 PASSED [0.0131s] [ 20%] 2024-08-20T21:41:28.3893587Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_int32 PASSED [0.0134s] [ 20%] 2024-08-20T21:41:28.3895017Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_int64 PASSED [0.0128s] [ 20%] 2024-08-20T21:41:28.3896406Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_int8 PASSED [0.0130s] [ 20%] 2024-08-20T21:41:28.3897751Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_uint8 PASSED [0.0130s] [ 20%] 2024-08-20T21:41:28.3899061Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_bfloat16 PASSED [0.0386s] [ 20%] 2024-08-20T21:41:28.3900299Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_bool PASSED [0.0389s] [ 20%] 2024-08-20T21:41:28.3901558Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_complex128 PASSED [0.0393s] [ 20%] 2024-08-20T21:41:28.3902819Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_complex64 PASSED [0.0394s] [ 21%] 2024-08-20T21:41:28.3904078Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_float16 PASSED [0.0386s] [ 21%] 2024-08-20T21:41:28.3905328Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_float32 PASSED [0.0392s] [ 21%] 2024-08-20T21:41:28.3906561Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_float64 PASSED [0.0382s] [ 21%] 2024-08-20T21:41:28.3907806Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_int16 PASSED [0.0387s] [ 21%] 2024-08-20T21:41:28.3909031Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_int32 PASSED [0.0384s] [ 21%] 2024-08-20T21:41:28.3910255Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_int64 PASSED [0.0383s] [ 21%] 2024-08-20T21:41:28.3911465Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_int8 PASSED [0.0388s] [ 21%] 2024-08-20T21:41:28.3912688Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_uint8 PASSED [0.0381s] [ 21%] 2024-08-20T21:41:28.3913957Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_bfloat16 PASSED [0.0066s] [ 21%] 2024-08-20T21:41:28.3915230Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_bool PASSED [0.0062s] [ 21%] 2024-08-20T21:41:28.3916531Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_float16 PASSED [0.0068s] [ 21%] 2024-08-20T21:41:28.3917819Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_float32 PASSED [0.0061s] [ 21%] 2024-08-20T21:41:28.3919097Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_float64 PASSED [0.0064s] [ 21%] 2024-08-20T21:41:28.3920358Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_int16 PASSED [0.0061s] [ 21%] 2024-08-20T21:41:28.3921866Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_int32 PASSED [0.0061s] [ 21%] 2024-08-20T21:41:28.3923129Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_int64 PASSED [0.0062s] [ 21%] 2024-08-20T21:41:28.3924390Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_int8 PASSED [0.0061s] [ 21%] 2024-08-20T21:41:28.3925642Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_uint8 PASSED [0.0063s] [ 21%] 2024-08-20T21:41:28.3926968Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_bfloat16 PASSED [0.0310s] [ 21%] 2024-08-20T21:41:28.3928251Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_complex128 PASSED [0.0325s] [ 21%] 2024-08-20T21:41:28.3929534Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_complex64 PASSED [0.0320s] [ 21%] 2024-08-20T21:41:28.3930853Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_float16 PASSED [0.0317s] [ 21%] 2024-08-20T21:41:28.3932100Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_float32 PASSED [0.0312s] [ 21%] 2024-08-20T21:41:28.3933376Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_float64 PASSED [0.0316s] [ 21%] 2024-08-20T21:41:28.3934704Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_bfloat16 PASSED [0.0105s] [ 21%] 2024-08-20T21:41:28.3936111Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_float16 PASSED [0.0104s] [ 21%] 2024-08-20T21:41:28.3937508Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_float32 PASSED [0.0105s] [ 21%] 2024-08-20T21:41:28.3938899Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_float64 PASSED [0.0102s] [ 21%] 2024-08-20T21:41:28.3940273Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_int16 PASSED [0.0104s] [ 21%] 2024-08-20T21:41:28.3941649Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_int32 PASSED [0.0101s] [ 21%] 2024-08-20T21:41:28.3943022Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_int64 PASSED [0.0102s] [ 21%] 2024-08-20T21:41:28.3944493Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_int8 PASSED [0.0102s] [ 21%] 2024-08-20T21:41:28.3945845Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_uint8 PASSED [0.0108s] [ 21%] 2024-08-20T21:41:28.3947432Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_bfloat16 PASSED [0.0101s] [ 21%] 2024-08-20T21:41:28.3948854Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_bool PASSED [0.0101s] [ 21%] 2024-08-20T21:41:28.3950268Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_complex128 PASSED [0.0107s] [ 21%] 2024-08-20T21:41:28.3951696Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_complex64 PASSED [0.0104s] [ 21%] 2024-08-20T21:41:28.3953193Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_float16 PASSED [0.0105s] [ 21%] 2024-08-20T21:41:28.3954604Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_float32 PASSED [0.0104s] [ 21%] 2024-08-20T21:41:28.3956012Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_float64 PASSED [0.0107s] [ 21%] 2024-08-20T21:41:28.3957407Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_int16 PASSED [0.0103s] [ 21%] 2024-08-20T21:41:28.3958807Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_int32 PASSED [0.0105s] [ 21%] 2024-08-20T21:41:28.3960205Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_int64 PASSED [0.0100s] [ 21%] 2024-08-20T21:41:28.3961573Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_int8 PASSED [0.0103s] [ 21%] 2024-08-20T21:41:28.3962964Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_uint8 PASSED [0.0101s] [ 21%] 2024-08-20T21:41:28.3964363Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_bfloat16 PASSED [0.0103s] [ 21%] 2024-08-20T21:41:28.3965761Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_float16 PASSED [0.0104s] [ 21%] 2024-08-20T21:41:28.3967290Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_float32 PASSED [0.0101s] [ 21%] 2024-08-20T21:41:28.3968701Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_float64 PASSED [0.0104s] [ 21%] 2024-08-20T21:41:28.3970134Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_int16 PASSED [0.0101s] [ 21%] 2024-08-20T21:41:28.3971516Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_int32 PASSED [0.0103s] [ 21%] 2024-08-20T21:41:28.3972870Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_int64 PASSED [0.0100s] [ 21%] 2024-08-20T21:41:28.3974241Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_int8 PASSED [0.0103s] [ 21%] 2024-08-20T21:41:28.3975606Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_uint8 PASSED [0.0103s] [ 21%] 2024-08-20T21:41:28.3976923Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_bfloat16 PASSED [0.0053s] [ 21%] 2024-08-20T21:41:28.3978179Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_complex128 PASSED [0.0060s] [ 21%] 2024-08-20T21:41:28.3979452Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_complex64 PASSED [0.0057s] [ 21%] 2024-08-20T21:41:28.3980702Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_float16 PASSED [0.0054s] [ 21%] 2024-08-20T21:41:28.3981927Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_float32 PASSED [0.0051s] [ 21%] 2024-08-20T21:41:28.3983157Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_float64 PASSED [0.0053s] [ 22%] 2024-08-20T21:41:28.3984383Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_int16 PASSED [0.0052s] [ 22%] 2024-08-20T21:41:28.3985601Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_int32 PASSED [0.0054s] [ 22%] 2024-08-20T21:41:28.3986814Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_int64 PASSED [0.0052s] [ 22%] 2024-08-20T21:41:28.3988057Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_int8 PASSED [0.0054s] [ 22%] 2024-08-20T21:41:28.3989273Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_uint8 PASSED [0.0052s] [ 22%] 2024-08-20T21:41:28.3990527Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_bfloat16 PASSED [0.0072s] [ 22%] 2024-08-20T21:41:28.3991782Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_bool PASSED [0.0073s] [ 22%] 2024-08-20T21:41:28.3993058Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_complex128 PASSED [0.0072s] [ 22%] 2024-08-20T21:41:28.3994374Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_complex32 PASSED [0.0075s] [ 22%] 2024-08-20T21:41:28.3995663Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_complex64 PASSED [0.0072s] [ 22%] 2024-08-20T21:41:28.3996944Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_float16 PASSED [0.0073s] [ 22%] 2024-08-20T21:41:28.3998217Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_float32 PASSED [0.0071s] [ 22%] 2024-08-20T21:41:28.3999488Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_float64 PASSED [0.0074s] [ 22%] 2024-08-20T21:41:28.4000733Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_int16 PASSED [0.0072s] [ 22%] 2024-08-20T21:41:28.4001974Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_int32 PASSED [0.0073s] [ 22%] 2024-08-20T21:41:28.4003292Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_int64 PASSED [0.0071s] [ 22%] 2024-08-20T21:41:28.4004536Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_int8 PASSED [0.0071s] [ 22%] 2024-08-20T21:41:28.4005801Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_uint8 PASSED [0.0073s] [ 22%] 2024-08-20T21:41:28.4007228Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_bfloat16 PASSED [0.0056s] [ 22%] 2024-08-20T21:41:28.4008522Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_bool PASSED [0.0058s] [ 22%] 2024-08-20T21:41:28.4009785Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_complex128 PASSED [0.0056s] [ 22%] 2024-08-20T21:41:28.4011089Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_complex32 PASSED [0.0059s] [ 22%] 2024-08-20T21:41:28.4012386Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_complex64 PASSED [0.0056s] [ 22%] 2024-08-20T21:41:28.4013665Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_float16 PASSED [0.0058s] [ 22%] 2024-08-20T21:41:28.4014264Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_float32 PASSED [0.0056s] [ 22%] 2024-08-20T21:41:28.4014858Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_float64 PASSED [0.0058s] [ 22%] 2024-08-20T21:41:28.4015459Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_int16 PASSED [0.0056s] [ 22%] 2024-08-20T21:41:28.4016051Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_int32 PASSED [0.0056s] [ 22%] 2024-08-20T21:41:28.4016647Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_int64 PASSED [0.0058s] [ 22%] 2024-08-20T21:41:28.4017234Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_int8 PASSED [0.0055s] [ 22%] 2024-08-20T21:41:28.4017824Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_uint8 PASSED [0.0058s] [ 22%] 2024-08-20T21:41:28.4018452Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_bfloat16 PASSED [0.0057s] [ 22%] 2024-08-20T21:41:28.4019083Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_bool PASSED [0.0058s] [ 22%] 2024-08-20T21:41:28.4019700Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_complex128 PASSED [0.0057s] [ 22%] 2024-08-20T21:41:28.4020323Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_complex32 PASSED [0.0059s] [ 22%] 2024-08-20T21:41:28.4021147Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_complex64 PASSED [0.0057s] [ 22%] 2024-08-20T21:41:28.4021775Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_float16 PASSED [0.0058s] [ 22%] 2024-08-20T21:41:28.4022371Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_float32 PASSED [0.0057s] [ 22%] 2024-08-20T21:41:28.4022964Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_float64 PASSED [0.0057s] [ 22%] 2024-08-20T21:41:28.4023567Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_int16 PASSED [0.0058s] [ 22%] 2024-08-20T21:41:28.4024159Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_int32 PASSED [0.0056s] [ 22%] 2024-08-20T21:41:28.4024754Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_int64 PASSED [0.0059s] [ 22%] 2024-08-20T21:41:28.4025335Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_int8 PASSED [0.0056s] [ 22%] 2024-08-20T21:41:28.4025986Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_uint8 PASSED [0.0059s] [ 22%] 2024-08-20T21:41:28.4026609Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_bfloat16 PASSED [0.0102s] [ 22%] 2024-08-20T21:41:28.4027262Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_complex128 PASSED [0.0104s] [ 22%] 2024-08-20T21:41:28.4027891Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_complex64 PASSED [0.0102s] [ 22%] 2024-08-20T21:41:28.4028489Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_float16 PASSED [0.0101s] [ 22%] 2024-08-20T21:41:28.4029083Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_float32 PASSED [0.0102s] [ 22%] 2024-08-20T21:41:28.4029685Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_float64 PASSED [0.0102s] [ 22%] 2024-08-20T21:41:28.4030276Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_int16 PASSED [0.0103s] [ 22%] 2024-08-20T21:41:28.4030868Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_int32 PASSED [0.0101s] [ 22%] 2024-08-20T21:41:28.4031464Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_int64 PASSED [0.0104s] [ 22%] 2024-08-20T21:41:28.4032050Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_int8 PASSED [0.0101s] [ 22%] 2024-08-20T21:41:28.4032646Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_uint8 PASSED [0.0103s] [ 22%] 2024-08-20T21:41:28.4033256Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_bfloat16 PASSED [0.0077s] [ 22%] 2024-08-20T21:41:28.4033836Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_bool PASSED [0.0078s] [ 22%] 2024-08-20T21:41:28.4034468Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_complex128 PASSED [0.0079s] [ 22%] 2024-08-20T21:41:28.4035082Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_complex32 PASSED [0.0077s] [ 23%] 2024-08-20T21:41:28.4035706Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_complex64 PASSED [0.0083s] [ 23%] 2024-08-20T21:41:28.4036329Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_float16 PASSED [0.0077s] [ 23%] 2024-08-20T21:41:28.4036918Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_float32 PASSED [0.2139s] [ 23%] 2024-08-20T21:41:28.4037514Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_float64 PASSED [0.0081s] [ 23%] 2024-08-20T21:41:28.4038092Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_int16 PASSED [0.0078s] [ 23%] 2024-08-20T21:41:28.4038692Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_int32 PASSED [0.0080s] [ 23%] 2024-08-20T21:41:28.4039278Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_int64 PASSED [0.0078s] [ 23%] 2024-08-20T21:41:28.4039858Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_int8 PASSED [0.0080s] [ 23%] 2024-08-20T21:41:28.4040451Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_uint8 PASSED [0.0078s] [ 23%] 2024-08-20T21:41:28.4041083Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_bfloat16 PASSED [0.0082s] [ 23%] 2024-08-20T21:41:28.4041689Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_bool PASSED [0.0085s] [ 23%] 2024-08-20T21:41:28.4042345Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_complex128 PASSED [0.0083s] [ 23%] 2024-08-20T21:41:28.4043034Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_complex32 PASSED [0.0086s] [ 23%] 2024-08-20T21:41:28.4043685Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_complex64 PASSED [0.0084s] [ 23%] 2024-08-20T21:41:28.4044337Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_float16 PASSED [0.0086s] [ 23%] 2024-08-20T21:41:28.4044962Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_float32 PASSED [0.0083s] [ 23%] 2024-08-20T21:41:28.4045592Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_float64 PASSED [0.0087s] [ 23%] 2024-08-20T21:41:28.4046206Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_int16 PASSED [0.0085s] [ 23%] 2024-08-20T21:41:28.4047038Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_int32 PASSED [0.0085s] [ 23%] 2024-08-20T21:41:28.4047670Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_int64 PASSED [0.0087s] [ 23%] 2024-08-20T21:41:28.4048275Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_int8 PASSED [0.0085s] [ 23%] 2024-08-20T21:41:28.4048901Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_uint8 PASSED [0.0085s] [ 23%] 2024-08-20T21:41:28.4049562Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_bfloat16 PASSED [0.0254s] [ 23%] 2024-08-20T21:41:28.4050209Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_bool PASSED [0.0261s] [ 23%] 2024-08-20T21:41:28.4050874Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_complex128 PASSED [0.0258s] [ 23%] 2024-08-20T21:41:28.4051534Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_complex32 PASSED [0.0255s] [ 23%] 2024-08-20T21:41:28.4052211Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_complex64 PASSED [0.0259s] [ 23%] 2024-08-20T21:41:28.4052869Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_float16 PASSED [0.0254s] [ 23%] 2024-08-20T21:41:28.4053604Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_float32 PASSED [0.0256s] [ 23%] 2024-08-20T21:41:28.4054251Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_float64 PASSED [0.0254s] [ 23%] 2024-08-20T21:41:28.4054887Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_int16 PASSED [0.0256s] [ 23%] 2024-08-20T21:41:28.4055535Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_int32 PASSED [0.0256s] [ 23%] 2024-08-20T21:41:28.4056170Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_int64 PASSED [0.0254s] [ 23%] 2024-08-20T21:41:28.4056806Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_int8 PASSED [0.0257s] [ 23%] 2024-08-20T21:41:28.4057455Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_uint8 PASSED [0.0254s] [ 23%] 2024-08-20T21:41:28.4058103Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_bfloat16 PASSED [0.0121s] [ 23%] 2024-08-20T21:41:28.4058746Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_bool PASSED [0.0089s] [ 23%] 2024-08-20T21:41:28.4059404Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_complex128 PASSED [0.0094s] [ 23%] 2024-08-20T21:41:28.4060060Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_complex64 PASSED [0.0088s] [ 23%] 2024-08-20T21:41:28.4060794Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_float16 PASSED [0.0088s] [ 23%] 2024-08-20T21:41:28.4061438Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_float32 PASSED [0.0089s] [ 23%] 2024-08-20T21:41:28.4062133Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_float64 PASSED [0.0088s] [ 23%] 2024-08-20T21:41:28.4062764Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_int16 PASSED [0.0090s] [ 23%] 2024-08-20T21:41:28.4063396Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_int32 PASSED [0.0087s] [ 23%] 2024-08-20T21:41:28.4064034Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_int64 PASSED [0.0100s] [ 23%] 2024-08-20T21:41:28.4064662Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_int8 PASSED [0.0088s] [ 23%] 2024-08-20T21:41:28.4065312Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_uint8 PASSED [0.0089s] [ 23%] 2024-08-20T21:41:28.4065896Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_bfloat16 PASSED [0.0101s] [ 23%] 2024-08-20T21:41:28.4066461Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_bool PASSED [0.0100s] [ 23%] 2024-08-20T21:41:28.4067072Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_complex128 PASSED [0.0103s] [ 23%] 2024-08-20T21:41:28.4067663Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_complex32 PASSED [0.0102s] [ 23%] 2024-08-20T21:41:28.4068265Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_complex64 PASSED [0.0104s] [ 23%] 2024-08-20T21:41:28.4068845Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_float16 PASSED [0.0100s] [ 23%] 2024-08-20T21:41:28.4069426Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_float32 PASSED [0.0103s] [ 23%] 2024-08-20T21:41:28.4070017Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_float64 PASSED [0.0101s] [ 23%] 2024-08-20T21:41:28.4070586Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_int16 PASSED [0.0103s] [ 23%] 2024-08-20T21:41:28.4071176Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_int32 PASSED [0.0100s] [ 23%] 2024-08-20T21:41:28.4071755Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_int64 PASSED [0.0102s] [ 23%] 2024-08-20T21:41:28.4072315Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_int8 PASSED [0.0101s] [ 23%] 2024-08-20T21:41:28.4072890Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_uint8 PASSED [0.0102s] [ 24%] 2024-08-20T21:41:28.4073488Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_bfloat16 PASSED [0.0093s] [ 24%] 2024-08-20T21:41:28.4074071Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_bool PASSED [0.0090s] [ 24%] 2024-08-20T21:41:28.4074702Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_complex128 PASSED [0.0095s] [ 24%] 2024-08-20T21:41:28.4075309Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_complex64 PASSED [0.0091s] [ 24%] 2024-08-20T21:41:28.4075917Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_float16 PASSED [0.0093s] [ 24%] 2024-08-20T21:41:28.4076507Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_float32 PASSED [0.0090s] [ 24%] 2024-08-20T21:41:28.4077095Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_float64 PASSED [0.0096s] [ 24%] 2024-08-20T21:41:28.4077695Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_int16 PASSED [0.0089s] [ 24%] 2024-08-20T21:41:28.4078349Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_int32 PASSED [0.0089s] [ 24%] 2024-08-20T21:41:28.4078960Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_int64 PASSED [0.0092s] [ 24%] 2024-08-20T21:41:28.4079556Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_int8 PASSED [0.0089s] [ 24%] 2024-08-20T21:41:28.4080139Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_uint8 PASSED [0.0091s] [ 24%] 2024-08-20T21:41:28.4080738Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_bfloat16 PASSED [0.0051s] [ 24%] 2024-08-20T21:41:28.4081301Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_bool PASSED [0.0053s] [ 24%] 2024-08-20T21:41:28.4081880Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_float16 PASSED [0.0051s] [ 24%] 2024-08-20T21:41:28.4082480Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_float32 PASSED [0.0054s] [ 24%] 2024-08-20T21:41:28.4083062Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_float64 PASSED [0.0051s] [ 24%] 2024-08-20T21:41:28.4083650Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_int16 PASSED [0.0053s] [ 24%] 2024-08-20T21:41:28.4084221Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_int32 PASSED [0.0051s] [ 24%] 2024-08-20T21:41:28.4084792Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_int64 PASSED [0.0051s] [ 24%] 2024-08-20T21:41:28.4085368Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_int8 PASSED [0.0053s] [ 24%] 2024-08-20T21:41:28.4085940Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_uint8 PASSED [0.0051s] [ 24%] 2024-08-20T21:41:28.4086556Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_bfloat16 PASSED [0.0064s] [ 24%] 2024-08-20T21:41:28.4087217Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_bool PASSED [0.0061s] [ 24%] 2024-08-20T21:41:28.4087858Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_float16 PASSED [0.0064s] [ 24%] 2024-08-20T21:41:28.4088458Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_float32 PASSED [0.0061s] [ 24%] 2024-08-20T21:41:28.4089036Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_float64 PASSED [0.0063s] [ 24%] 2024-08-20T21:41:28.4089613Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_int16 PASSED [0.0061s] [ 24%] 2024-08-20T21:41:28.4090206Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_int32 PASSED [0.0068s] [ 24%] 2024-08-20T21:41:28.4090786Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_int64 PASSED [0.0062s] [ 24%] 2024-08-20T21:41:28.4091369Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_int8 PASSED [0.0062s] [ 24%] 2024-08-20T21:41:28.4091949Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_uint8 PASSED [0.0064s] [ 24%] 2024-08-20T21:41:28.4092556Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_bfloat16 PASSED [0.0051s] [ 24%] 2024-08-20T21:41:28.4093154Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_bool PASSED [0.0053s] [ 24%] 2024-08-20T21:41:28.4093755Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_float16 PASSED [0.0053s] [ 24%] 2024-08-20T21:41:28.4094367Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_float32 PASSED [0.0053s] [ 24%] 2024-08-20T21:41:28.4095024Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_float64 PASSED [0.0051s] [ 24%] 2024-08-20T21:41:28.4095614Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_int16 PASSED [0.0053s] [ 24%] 2024-08-20T21:41:28.4096247Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_int32 PASSED [0.0051s] [ 24%] 2024-08-20T21:41:28.4096829Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_int64 PASSED [0.0053s] [ 24%] 2024-08-20T21:41:28.4097411Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_int8 PASSED [0.0051s] [ 24%] 2024-08-20T21:41:28.4098010Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_uint8 PASSED [0.0051s] [ 24%] 2024-08-20T21:41:28.4098605Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_bfloat16 PASSED [0.0063s] [ 24%] 2024-08-20T21:41:28.4099195Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_bool PASSED [0.0062s] [ 24%] 2024-08-20T21:41:28.4099801Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_complex128 PASSED [0.0064s] [ 24%] 2024-08-20T21:41:28.4100408Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_complex64 PASSED [0.0062s] [ 24%] 2024-08-20T21:41:28.4101010Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_float16 PASSED [0.0064s] [ 24%] 2024-08-20T21:41:28.4101592Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_float32 PASSED [0.0062s] [ 24%] 2024-08-20T21:41:28.4102193Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_float64 PASSED [0.0063s] [ 24%] 2024-08-20T21:41:28.4102771Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_int16 PASSED [0.0062s] [ 24%] 2024-08-20T21:41:28.4103354Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_int32 PASSED [0.0068s] [ 24%] 2024-08-20T21:41:28.4103942Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_int64 PASSED [0.0061s] [ 24%] 2024-08-20T21:41:28.4104518Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_int8 PASSED [0.0062s] [ 24%] 2024-08-20T21:41:28.4105134Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_uint8 PASSED [0.0063s] [ 24%] 2024-08-20T21:41:28.4105724Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_bfloat16 PASSED [0.0062s] [ 24%] 2024-08-20T21:41:28.4106295Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_bool PASSED [0.0064s] [ 24%] 2024-08-20T21:41:28.4106914Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_complex128 PASSED [0.0062s] [ 24%] 2024-08-20T21:41:28.4107509Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_complex64 PASSED [0.0064s] [ 24%] 2024-08-20T21:41:28.4108199Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_float16 PASSED [0.0062s] [ 25%] 2024-08-20T21:41:28.4108805Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_float32 PASSED [0.0064s] [ 25%] 2024-08-20T21:41:28.4109393Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_float64 PASSED [0.0062s] [ 25%] 2024-08-20T21:41:28.4109979Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_int16 PASSED [0.0064s] [ 25%] 2024-08-20T21:41:28.4110548Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_int32 PASSED [0.0062s] [ 25%] 2024-08-20T21:41:28.4111116Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_int64 PASSED [0.0061s] [ 25%] 2024-08-20T21:41:28.4111697Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_int8 PASSED [0.0063s] [ 25%] 2024-08-20T21:41:28.4112327Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_uint8 PASSED [0.0062s] [ 25%] 2024-08-20T21:41:28.4112995Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_bfloat16 PASSED [0.0064s] [ 25%] 2024-08-20T21:41:28.4113601Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_bool PASSED [0.0062s] [ 25%] 2024-08-20T21:41:28.4114242Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_complex128 PASSED [0.0065s] [ 25%] 2024-08-20T21:41:28.4114892Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_complex64 PASSED [0.0062s] [ 25%] 2024-08-20T21:41:28.4115514Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_float16 PASSED [0.0064s] [ 25%] 2024-08-20T21:41:28.4116134Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_float32 PASSED [0.0062s] [ 25%] 2024-08-20T21:41:28.4116763Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_float64 PASSED [0.0069s] [ 25%] 2024-08-20T21:41:28.4117370Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_int16 PASSED [0.0062s] [ 25%] 2024-08-20T21:41:28.4117989Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_int32 PASSED [0.0062s] [ 25%] 2024-08-20T21:41:28.4118588Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_int64 PASSED [0.0063s] [ 25%] 2024-08-20T21:41:28.4119187Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_int8 PASSED [0.0062s] [ 25%] 2024-08-20T21:41:28.4119802Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_uint8 PASSED [0.0064s] [ 25%] 2024-08-20T21:41:28.4120442Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_bfloat16 PASSED [0.0093s] [ 25%] 2024-08-20T21:41:28.4121277Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_bool PASSED [0.0095s] [ 25%] 2024-08-20T21:41:28.4121941Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_complex128 PASSED [0.0094s] [ 25%] 2024-08-20T21:41:28.4122631Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_complex64 PASSED [0.0097s] [ 25%] 2024-08-20T21:41:28.4123276Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_float16 PASSED [0.0094s] [ 25%] 2024-08-20T21:41:28.4123905Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_float32 PASSED [0.0096s] [ 25%] 2024-08-20T21:41:28.4124544Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_float64 PASSED [0.0094s] [ 25%] 2024-08-20T21:41:28.4125167Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_int16 PASSED [0.0093s] [ 25%] 2024-08-20T21:41:28.4125788Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_int32 PASSED [0.0095s] [ 25%] 2024-08-20T21:41:28.4126427Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_int64 PASSED [0.0094s] [ 25%] 2024-08-20T21:41:28.4127101Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_int8 PASSED [0.0095s] [ 25%] 2024-08-20T21:41:28.4127739Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_uint8 PASSED [0.0093s] [ 25%] 2024-08-20T21:41:28.4128349Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_bfloat16 PASSED [0.0095s] [ 25%] 2024-08-20T21:41:28.4128935Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_bool PASSED [0.0092s] [ 25%] 2024-08-20T21:41:28.4129626Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_complex128 PASSED [0.0096s] [ 25%] 2024-08-20T21:41:28.4130251Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_complex64 PASSED [0.0093s] [ 25%] 2024-08-20T21:41:28.4130886Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_float16 PASSED [0.0099s] [ 25%] 2024-08-20T21:41:28.4131498Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_float32 PASSED [0.0092s] [ 25%] 2024-08-20T21:41:28.4132099Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_float64 PASSED [0.0092s] [ 25%] 2024-08-20T21:41:28.4132698Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_int16 PASSED [0.0094s] [ 25%] 2024-08-20T21:41:28.4133282Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_int32 PASSED [0.0092s] [ 25%] 2024-08-20T21:41:28.4133862Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_int64 PASSED [0.0095s] [ 25%] 2024-08-20T21:41:28.4134459Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_int8 PASSED [0.0092s] [ 25%] 2024-08-20T21:41:28.4135049Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_uint8 PASSED [0.0095s] [ 25%] 2024-08-20T21:41:28.4135665Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_bfloat16 PASSED [0.0052s] [ 25%] 2024-08-20T21:41:28.4136243Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_bool PASSED [0.0053s] [ 25%] 2024-08-20T21:41:28.4136858Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_complex128 PASSED [0.0051s] [ 25%] 2024-08-20T21:41:28.4137478Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_complex64 PASSED [0.0053s] [ 25%] 2024-08-20T21:41:28.4138071Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_float16 PASSED [0.0051s] [ 25%] 2024-08-20T21:41:28.4138681Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_float32 PASSED [0.0051s] [ 25%] 2024-08-20T21:41:28.4139276Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_float64 PASSED [0.0052s] [ 25%] 2024-08-20T21:41:28.4139902Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_int16 PASSED [0.0051s] [ 25%] 2024-08-20T21:41:28.4140502Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_int32 PASSED [0.0052s] [ 25%] 2024-08-20T21:41:28.4141083Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_int64 PASSED [0.0051s] [ 25%] 2024-08-20T21:41:28.4141662Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_int8 PASSED [0.0053s] [ 25%] 2024-08-20T21:41:28.4142257Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_uint8 PASSED [0.0051s] [ 25%] 2024-08-20T21:41:28.4142909Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exponential_cpu_bfloat16 PASSED [0.0064s] [ 25%] 2024-08-20T21:41:28.4143560Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exponential_cpu_float16 PASSED [0.0062s] [ 25%] 2024-08-20T21:41:28.4144191Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exponential_cpu_float32 PASSED [0.0068s] [ 25%] 2024-08-20T21:41:28.4144826Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exponential_cpu_float64 PASSED [0.0061s] [ 25%] 2024-08-20T21:41:28.4145429Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_bfloat16 PASSED [0.0897s] [ 26%] 2024-08-20T21:41:28.4145994Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_bool PASSED [0.0767s] [ 26%] 2024-08-20T21:41:28.4146611Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_complex128 PASSED [0.0898s] [ 26%] 2024-08-20T21:41:28.4147486Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_complex64 PASSED [0.0901s] [ 26%] 2024-08-20T21:41:28.4148126Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float16 PASSED [0.0894s] [ 26%] 2024-08-20T21:41:28.4148720Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float32 PASSED [0.0894s] [ 26%] 2024-08-20T21:41:28.4149302Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float64 PASSED [0.0891s] [ 26%] 2024-08-20T21:41:28.4149890Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_int16 PASSED [0.0887s] [ 26%] 2024-08-20T21:41:28.4150464Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_int32 PASSED [0.0892s] [ 26%] 2024-08-20T21:41:28.4151034Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_int64 PASSED [0.0897s] [ 26%] 2024-08-20T21:41:28.4151617Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_int8 PASSED [0.0885s] [ 26%] 2024-08-20T21:41:28.4152188Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_uint8 PASSED [0.0897s] [ 26%] 2024-08-20T21:41:28.4152789Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_bool PASSED [0.0086s] [ 26%] 2024-08-20T21:41:28.4153432Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_complex128 PASSED [0.0089s] [ 26%] 2024-08-20T21:41:28.4154059Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_complex64 PASSED [0.0085s] [ 26%] 2024-08-20T21:41:28.4154680Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_float32 PASSED [0.0087s] [ 26%] 2024-08-20T21:41:28.4155289Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_float64 PASSED [0.0087s] [ 26%] 2024-08-20T21:41:28.4155895Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_int16 PASSED [0.0085s] [ 26%] 2024-08-20T21:41:28.4156510Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_int32 PASSED [0.0086s] [ 26%] 2024-08-20T21:41:28.4157149Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_int64 PASSED [0.0084s] [ 26%] 2024-08-20T21:41:28.4157753Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_int8 PASSED [0.0086s] [ 26%] 2024-08-20T21:41:28.4158344Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_uint8 PASSED [0.0083s] [ 26%] 2024-08-20T21:41:28.4158932Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_bool PASSED [0.0091s] [ 26%] 2024-08-20T21:41:28.4159570Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_complex128 PASSED [0.0090s] [ 26%] 2024-08-20T21:41:28.4160189Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_complex64 PASSED [0.0090s] [ 26%] 2024-08-20T21:41:28.4160795Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_float32 PASSED [0.0092s] [ 26%] 2024-08-20T21:41:28.4161409Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_float64 PASSED [0.0090s] [ 26%] 2024-08-20T21:41:28.4161999Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_int16 PASSED [0.0092s] [ 26%] 2024-08-20T21:41:28.4162598Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_int32 PASSED [0.0090s] [ 26%] 2024-08-20T21:41:28.4163175Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_int64 PASSED [0.0092s] [ 26%] 2024-08-20T21:41:28.4163763Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_int8 PASSED [0.0090s] [ 26%] 2024-08-20T21:41:28.4164423Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_uint8 PASSED [0.0090s] [ 26%] 2024-08-20T21:41:28.4165045Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_bool PASSED [0.0097s] [ 26%] 2024-08-20T21:41:28.4165694Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_complex128 PASSED [0.0097s] [ 26%] 2024-08-20T21:41:28.4166319Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_complex64 PASSED [0.0099s] [ 26%] 2024-08-20T21:41:28.4167012Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_float32 PASSED [0.0096s] [ 26%] 2024-08-20T21:41:28.4167641Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_float64 PASSED [0.0097s] [ 26%] 2024-08-20T21:41:28.4168238Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_int16 PASSED [0.0095s] [ 26%] 2024-08-20T21:41:28.4168855Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_int32 PASSED [0.0094s] [ 26%] 2024-08-20T21:41:28.4169452Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_int64 PASSED [0.0097s] [ 26%] 2024-08-20T21:41:28.4170046Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_int8 PASSED [0.0095s] [ 26%] 2024-08-20T21:41:28.4170655Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_uint8 PASSED [0.0098s] [ 26%] 2024-08-20T21:41:28.4171295Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_bfloat16 PASSED [0.0073s] [ 26%] 2024-08-20T21:41:28.4171911Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_bool PASSED [0.0074s] [ 26%] 2024-08-20T21:41:28.4172584Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_complex128 PASSED [0.0073s] [ 26%] 2024-08-20T21:41:28.4173234Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_complex32 PASSED [0.0072s] [ 26%] 2024-08-20T21:41:28.4173895Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_complex64 PASSED [0.0075s] [ 26%] 2024-08-20T21:41:28.4174575Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_float16 PASSED [0.0073s] [ 26%] 2024-08-20T21:41:28.4175205Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_float32 PASSED [0.0074s] [ 26%] 2024-08-20T21:41:28.4175843Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_float64 PASSED [0.0072s] [ 26%] 2024-08-20T21:41:28.4176466Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_int16 PASSED [0.0074s] [ 26%] 2024-08-20T21:41:28.4177100Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_int32 PASSED [0.0072s] [ 26%] 2024-08-20T21:41:28.4177724Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_int64 PASSED [0.0078s] [ 26%] 2024-08-20T21:41:28.4178340Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_int8 PASSED [0.0072s] [ 26%] 2024-08-20T21:41:28.4178975Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_uint8 PASSED [0.0074s] [ 26%] 2024-08-20T21:41:28.4179574Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_bool PASSED [0.0083s] [ 26%] 2024-08-20T21:41:28.4180219Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_complex128 PASSED [0.0085s] [ 26%] 2024-08-20T21:41:28.4180850Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_complex64 PASSED [0.0086s] [ 26%] 2024-08-20T21:41:28.4181520Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_float32 PASSED [0.0084s] [ 26%] 2024-08-20T21:41:28.4182154Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_float64 PASSED [0.0086s] [ 27%] 2024-08-20T21:41:28.4182791Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_int16 PASSED [0.0084s] [ 27%] 2024-08-20T21:41:28.4183409Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_int32 PASSED [0.0085s] [ 27%] 2024-08-20T21:41:28.4184012Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_int64 PASSED [0.0083s] [ 27%] 2024-08-20T21:41:28.4184609Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_int8 PASSED [0.0083s] [ 27%] 2024-08-20T21:41:28.4185218Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_uint8 PASSED [0.0086s] [ 27%] 2024-08-20T21:41:28.4185813Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_bool PASSED [0.0088s] [ 27%] 2024-08-20T21:41:28.4186442Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_complex128 PASSED [0.0093s] [ 27%] 2024-08-20T21:41:28.4187088Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_complex64 PASSED [0.0089s] [ 27%] 2024-08-20T21:41:28.4187702Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_float32 PASSED [0.0091s] [ 27%] 2024-08-20T21:41:28.4188321Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_float64 PASSED [0.0088s] [ 27%] 2024-08-20T21:41:28.4188918Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_int16 PASSED [0.0088s] [ 27%] 2024-08-20T21:41:28.4189507Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_int32 PASSED [0.0090s] [ 27%] 2024-08-20T21:41:28.4190110Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_int64 PASSED [0.0088s] [ 27%] 2024-08-20T21:41:28.4190699Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_int8 PASSED [0.0094s] [ 27%] 2024-08-20T21:41:28.4191311Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_uint8 PASSED [0.0089s] [ 27%] 2024-08-20T21:41:28.4191934Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_bool PASSED [0.0096s] [ 27%] 2024-08-20T21:41:28.4192573Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_complex128 PASSED [0.0095s] [ 27%] 2024-08-20T21:41:28.4193220Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_complex64 PASSED [0.0094s] [ 27%] 2024-08-20T21:41:28.4193838Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_float32 PASSED [0.0096s] [ 27%] 2024-08-20T21:41:28.4194472Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_float64 PASSED [0.0093s] [ 27%] 2024-08-20T21:41:28.4195081Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_int16 PASSED [0.0097s] [ 27%] 2024-08-20T21:41:28.4195680Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_int32 PASSED [0.0093s] [ 27%] 2024-08-20T21:41:28.4196294Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_int64 PASSED [0.0095s] [ 27%] 2024-08-20T21:41:28.4196890Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_int8 PASSED [0.0093s] [ 27%] 2024-08-20T21:41:28.4197490Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_uint8 PASSED [0.0093s] [ 27%] 2024-08-20T21:41:28.4198101Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_bool PASSED [0.0085s] [ 27%] 2024-08-20T21:41:28.4198832Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_complex128 PASSED [0.0084s] [ 27%] 2024-08-20T21:41:28.4199524Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_complex64 PASSED [0.0086s] [ 27%] 2024-08-20T21:41:28.4200143Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_float32 PASSED [0.0083s] [ 27%] 2024-08-20T21:41:28.4200759Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_float64 PASSED [0.0085s] [ 27%] 2024-08-20T21:41:28.4201381Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_int16 PASSED [0.0083s] [ 27%] 2024-08-20T21:41:28.4201989Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_int32 PASSED [0.0083s] [ 27%] 2024-08-20T21:41:28.4202597Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_int64 PASSED [0.0084s] [ 27%] 2024-08-20T21:41:28.4203199Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_int8 PASSED [0.0083s] [ 27%] 2024-08-20T21:41:28.4203802Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_uint8 PASSED [0.0088s] [ 27%] 2024-08-20T21:41:28.4204408Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_bool PASSED [0.0087s] [ 27%] 2024-08-20T21:41:28.4205041Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_complex128 PASSED [0.0091s] [ 27%] 2024-08-20T21:41:28.4205677Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_complex64 PASSED [0.0089s] [ 27%] 2024-08-20T21:41:28.4206284Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_float32 PASSED [0.0088s] [ 27%] 2024-08-20T21:41:28.4206986Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_float64 PASSED [0.0091s] [ 27%] 2024-08-20T21:41:28.4207608Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_int16 PASSED [0.0088s] [ 27%] 2024-08-20T21:41:28.4208206Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_int32 PASSED [0.0091s] [ 27%] 2024-08-20T21:41:28.4208845Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_int64 PASSED [0.0088s] [ 27%] 2024-08-20T21:41:28.4209451Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_int8 PASSED [0.0090s] [ 27%] 2024-08-20T21:41:28.4210044Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_uint8 PASSED [0.0089s] [ 27%] 2024-08-20T21:41:28.4210644Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_bool PASSED [0.0094s] [ 27%] 2024-08-20T21:41:28.4211285Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_complex128 PASSED [0.0098s] [ 27%] 2024-08-20T21:41:28.4211916Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_complex64 PASSED [0.0095s] [ 27%] 2024-08-20T21:41:28.4212545Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_float32 PASSED [0.0096s] [ 27%] 2024-08-20T21:41:28.4213167Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_float64 PASSED [0.0095s] [ 27%] 2024-08-20T21:41:28.4213786Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_int16 PASSED [0.0097s] [ 27%] 2024-08-20T21:41:28.4214389Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_int32 PASSED [0.0095s] [ 27%] 2024-08-20T21:41:28.4214987Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_int64 PASSED [0.0094s] [ 27%] 2024-08-20T21:41:28.4215599Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_int8 PASSED [0.0096s] [ 27%] 2024-08-20T21:41:28.4216253Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_uint8 PASSED [0.0094s] [ 27%] 2024-08-20T21:41:28.4216943Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_bfloat16 PASSED [0.0077s] [ 27%] 2024-08-20T21:41:28.4217570Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_bool PASSED [0.0072s] [ 27%] 2024-08-20T21:41:28.4218230Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_complex128 PASSED [0.0075s] [ 27%] 2024-08-20T21:41:28.4218899Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_complex32 PASSED [0.0073s] [ 27%] 2024-08-20T21:41:28.4219550Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_complex64 PASSED [0.0075s] [ 28%] 2024-08-20T21:41:28.4220207Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_float16 PASSED [0.0072s] [ 28%] 2024-08-20T21:41:28.4221052Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_float32 PASSED [0.0072s] [ 28%] 2024-08-20T21:41:28.4221708Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_float64 PASSED [0.0074s] [ 28%] 2024-08-20T21:41:28.4222355Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_int16 PASSED [0.0072s] [ 28%] 2024-08-20T21:41:28.4222984Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_int32 PASSED [0.0074s] [ 28%] 2024-08-20T21:41:28.4223612Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_int64 PASSED [0.0072s] [ 28%] 2024-08-20T21:41:28.4224247Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_int8 PASSED [0.0076s] [ 28%] 2024-08-20T21:41:28.4224881Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_uint8 PASSED [0.0073s] [ 28%] 2024-08-20T21:41:28.4225498Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_bool PASSED [0.0087s] [ 28%] 2024-08-20T21:41:28.4226121Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_float32 PASSED [0.0084s] [ 28%] 2024-08-20T21:41:28.4226777Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_float64 PASSED [0.0085s] [ 28%] 2024-08-20T21:41:28.4227401Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_int16 PASSED [0.0084s] [ 28%] 2024-08-20T21:41:28.4228013Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_int32 PASSED [0.0085s] [ 28%] 2024-08-20T21:41:28.4228637Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_int64 PASSED [0.0087s] [ 28%] 2024-08-20T21:41:28.4229247Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_int8 PASSED [0.0084s] [ 28%] 2024-08-20T21:41:28.4229855Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_uint8 PASSED [0.0086s] [ 28%] 2024-08-20T21:41:28.4230471Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_bool PASSED [0.0090s] [ 28%] 2024-08-20T21:41:28.4231090Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_float32 PASSED [0.0097s] [ 28%] 2024-08-20T21:41:28.4231708Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_float64 PASSED [0.0090s] [ 28%] 2024-08-20T21:41:28.4232313Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_int16 PASSED [0.0090s] [ 28%] 2024-08-20T21:41:28.4232911Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_int32 PASSED [0.0091s] [ 28%] 2024-08-20T21:41:28.4233574Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_int64 PASSED [0.0089s] [ 28%] 2024-08-20T21:41:28.4234198Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_int8 PASSED [0.0092s] [ 28%] 2024-08-20T21:41:28.4234798Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_uint8 PASSED [0.0089s] [ 28%] 2024-08-20T21:41:28.4235408Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_bool PASSED [0.0096s] [ 28%] 2024-08-20T21:41:28.4236029Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_float32 PASSED [0.0095s] [ 28%] 2024-08-20T21:41:28.4236654Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_float64 PASSED [0.0094s] [ 28%] 2024-08-20T21:41:28.4237262Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_int16 PASSED [0.0097s] [ 28%] 2024-08-20T21:41:28.4237877Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_int32 PASSED [0.0095s] [ 28%] 2024-08-20T21:41:28.4238497Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_int64 PASSED [0.0096s] [ 28%] 2024-08-20T21:41:28.4239103Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_int8 PASSED [0.0094s] [ 28%] 2024-08-20T21:41:28.4239725Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_uint8 PASSED [0.0096s] [ 28%] 2024-08-20T21:41:28.4240324Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_bool PASSED [0.0083s] [ 28%] 2024-08-20T21:41:28.4240963Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_complex128 PASSED [0.0084s] [ 28%] 2024-08-20T21:41:28.4241609Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_complex64 PASSED [0.0085s] [ 28%] 2024-08-20T21:41:28.4242243Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_float32 PASSED [0.0083s] [ 28%] 2024-08-20T21:41:28.4242878Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_float64 PASSED [0.0085s] [ 28%] 2024-08-20T21:41:28.4243519Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_int16 PASSED [0.0083s] [ 28%] 2024-08-20T21:41:28.4244122Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_int32 PASSED [0.0088s] [ 28%] 2024-08-20T21:41:28.4244743Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_int64 PASSED [0.0083s] [ 28%] 2024-08-20T21:41:28.4245346Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_int8 PASSED [0.0084s] [ 28%] 2024-08-20T21:41:28.4245965Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_uint8 PASSED [0.0085s] [ 28%] 2024-08-20T21:41:28.4246571Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_bool PASSED [0.0087s] [ 28%] 2024-08-20T21:41:28.4247439Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_complex128 PASSED [0.0092s] [ 28%] 2024-08-20T21:41:28.4248095Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_complex64 PASSED [0.0088s] [ 28%] 2024-08-20T21:41:28.4248713Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_float32 PASSED [0.0090s] [ 28%] 2024-08-20T21:41:28.4249322Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_float64 PASSED [0.0088s] [ 28%] 2024-08-20T21:41:28.4249939Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_int16 PASSED [0.0088s] [ 28%] 2024-08-20T21:41:28.4250534Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_int32 PASSED [0.0091s] [ 28%] 2024-08-20T21:41:28.4251240Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_int64 PASSED [0.0088s] [ 28%] 2024-08-20T21:41:28.4251878Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_int8 PASSED [0.0090s] [ 28%] 2024-08-20T21:41:28.4252481Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_uint8 PASSED [0.0088s] [ 28%] 2024-08-20T21:41:28.4253098Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_bool PASSED [0.0094s] [ 28%] 2024-08-20T21:41:28.4256152Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_complex128 PASSED [0.0095s] [ 28%] 2024-08-20T21:41:28.4256842Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_complex64 PASSED [0.0094s] [ 28%] 2024-08-20T21:41:28.4257486Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_float32 PASSED [0.0095s] [ 28%] 2024-08-20T21:41:28.4258113Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_float64 PASSED [0.0094s] [ 28%] 2024-08-20T21:41:28.4258740Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_int16 PASSED [0.0096s] [ 28%] 2024-08-20T21:41:28.4259352Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_int32 PASSED [0.0093s] [ 29%] 2024-08-20T21:41:28.4259963Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_int64 PASSED [0.0098s] [ 29%] 2024-08-20T21:41:28.4262310Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_int8 PASSED [0.0093s] [ 29%] 2024-08-20T21:41:28.4263011Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_uint8 PASSED [0.0093s] [ 29%] 2024-08-20T21:41:28.4263616Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_bool PASSED [0.0084s] [ 29%] 2024-08-20T21:41:28.4264254Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_float32 PASSED [0.0083s] [ 29%] 2024-08-20T21:41:28.4264876Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_float64 PASSED [0.0086s] [ 29%] 2024-08-20T21:41:28.4265611Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_int16 PASSED [0.0083s] [ 29%] 2024-08-20T21:41:28.4266215Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_int32 PASSED [0.0085s] [ 29%] 2024-08-20T21:41:28.4266815Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_int64 PASSED [0.0083s] [ 29%] 2024-08-20T21:41:28.4267427Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_int8 PASSED [0.0083s] [ 29%] 2024-08-20T21:41:28.4268025Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_uint8 PASSED [0.0085s] [ 29%] 2024-08-20T21:41:28.4268633Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_bool PASSED [0.0088s] [ 29%] 2024-08-20T21:41:28.4269246Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_float32 PASSED [0.0091s] [ 29%] 2024-08-20T21:41:28.4269859Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_float64 PASSED [0.0088s] [ 29%] 2024-08-20T21:41:28.4270475Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_int16 PASSED [0.0090s] [ 29%] 2024-08-20T21:41:28.4271074Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_int32 PASSED [0.0088s] [ 29%] 2024-08-20T21:41:28.4271660Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_int64 PASSED [0.0088s] [ 29%] 2024-08-20T21:41:28.4272297Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_int8 PASSED [0.0090s] [ 29%] 2024-08-20T21:41:28.4272893Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_uint8 PASSED [0.0088s] [ 29%] 2024-08-20T21:41:28.4273545Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_bool PASSED [0.0096s] [ 29%] 2024-08-20T21:41:28.4274165Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_float32 PASSED [0.0094s] [ 29%] 2024-08-20T21:41:28.4274781Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_float64 PASSED [0.0098s] [ 29%] 2024-08-20T21:41:28.4275474Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_int16 PASSED [0.0094s] [ 29%] 2024-08-20T21:41:28.4276077Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_int32 PASSED [0.0094s] [ 29%] 2024-08-20T21:41:28.4276689Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_int64 PASSED [0.0096s] [ 29%] 2024-08-20T21:41:28.4277290Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_int8 PASSED [0.0094s] [ 29%] 2024-08-20T21:41:28.4277888Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_uint8 PASSED [0.0096s] [ 29%] 2024-08-20T21:41:28.4278500Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_bfloat16 PASSED [0.0061s] [ 29%] 2024-08-20T21:41:28.4279075Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_bool PASSED [0.0063s] [ 29%] 2024-08-20T21:41:28.4279687Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_complex128 PASSED [0.0062s] [ 29%] 2024-08-20T21:41:28.4280300Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_complex32 PASSED [0.0061s] [ 29%] 2024-08-20T21:41:28.4280907Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_complex64 PASSED [0.0063s] [ 29%] 2024-08-20T21:41:28.4281512Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_float16 PASSED [0.0061s] [ 29%] 2024-08-20T21:41:28.4282099Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_float32 PASSED [0.0063s] [ 29%] 2024-08-20T21:41:28.4282715Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_float64 PASSED [0.0061s] [ 29%] 2024-08-20T21:41:28.4283307Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_int16 PASSED [0.0064s] [ 29%] 2024-08-20T21:41:28.4283890Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_int32 PASSED [0.0061s] [ 29%] 2024-08-20T21:41:28.4284481Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_int64 PASSED [0.0063s] [ 29%] 2024-08-20T21:41:28.4285056Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_int8 PASSED [0.0061s] [ 29%] 2024-08-20T21:41:28.4285634Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_uint8 PASSED [0.0063s] [ 29%] 2024-08-20T21:41:28.4286265Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_bfloat16 PASSED [0.0078s] [ 29%] 2024-08-20T21:41:28.4286973Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_bool PASSED [0.0077s] [ 29%] 2024-08-20T21:41:28.4287624Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_complex128 PASSED [0.0083s] [ 29%] 2024-08-20T21:41:28.4288250Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_complex32 PASSED [0.0077s] [ 29%] 2024-08-20T21:41:28.4288875Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_complex64 PASSED [0.0079s] [ 29%] 2024-08-20T21:41:28.4289537Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_float16 PASSED [0.0077s] [ 29%] 2024-08-20T21:41:28.4290148Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_float32 PASSED [0.0079s] [ 29%] 2024-08-20T21:41:28.4290782Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_float64 PASSED [0.0077s] [ 29%] 2024-08-20T21:41:28.4291390Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_int16 PASSED [0.0079s] [ 29%] 2024-08-20T21:41:28.4291979Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_int32 PASSED [0.0077s] [ 29%] 2024-08-20T21:41:28.4292627Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_int64 PASSED [0.0078s] [ 29%] 2024-08-20T21:41:28.4293223Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_int8 PASSED [0.0077s] [ 29%] 2024-08-20T21:41:28.4293819Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_uint8 PASSED [0.0076s] [ 29%] 2024-08-20T21:41:28.4294421Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_bfloat16 PASSED [0.0098s] [ 29%] 2024-08-20T21:41:28.4294992Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_bool PASSED [0.0096s] [ 29%] 2024-08-20T21:41:28.4295611Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_complex128 PASSED [0.0100s] [ 29%] 2024-08-20T21:41:28.4296217Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_complex64 PASSED [0.0098s] [ 29%] 2024-08-20T21:41:28.4296801Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_float16 PASSED [0.0099s] [ 29%] 2024-08-20T21:41:28.4297396Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_float32 PASSED [0.0098s] [ 29%] 2024-08-20T21:41:28.4297981Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_float64 PASSED [0.0100s] [ 30%] 2024-08-20T21:41:28.4298576Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_int16 PASSED [0.0097s] [ 30%] 2024-08-20T21:41:28.4299158Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_int32 PASSED [0.0099s] [ 30%] 2024-08-20T21:41:28.4299775Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_int64 PASSED [0.0098s] [ 30%] 2024-08-20T21:41:28.4300354Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_int8 PASSED [0.0097s] [ 30%] 2024-08-20T21:41:28.4300926Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_uint8 PASSED [0.0105s] [ 30%] 2024-08-20T21:41:28.4301534Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_bfloat16 PASSED [0.0057s] [ 30%] 2024-08-20T21:41:28.4302135Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_bool PASSED [0.0059s] [ 30%] 2024-08-20T21:41:28.4302754Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_complex128 PASSED [0.0057s] [ 30%] 2024-08-20T21:41:28.4303442Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_complex64 PASSED [0.0060s] [ 30%] 2024-08-20T21:41:28.4304087Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_float16 PASSED [0.0057s] [ 30%] 2024-08-20T21:41:28.4304724Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_float32 PASSED [0.0059s] [ 30%] 2024-08-20T21:41:28.4305532Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_float64 PASSED [0.0056s] [ 30%] 2024-08-20T21:41:28.4306167Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_int16 PASSED [0.0058s] [ 30%] 2024-08-20T21:41:28.4306791Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_int32 PASSED [0.0057s] [ 30%] 2024-08-20T21:41:28.4307487Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_int64 PASSED [0.0057s] [ 30%] 2024-08-20T21:41:28.4308152Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_int8 PASSED [0.0060s] [ 30%] 2024-08-20T21:41:28.4308868Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_uint8 PASSED [0.0057s] [ 30%] 2024-08-20T21:41:28.4309519Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_bfloat16 PASSED [0.0059s] [ 30%] 2024-08-20T21:41:28.4310140Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_bool PASSED [0.0056s] [ 30%] 2024-08-20T21:41:28.4310877Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_complex128 PASSED [0.0059s] [ 30%] 2024-08-20T21:41:28.4311546Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_complex64 PASSED [0.0057s] [ 30%] 2024-08-20T21:41:28.4312228Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_float16 PASSED [0.0058s] [ 30%] 2024-08-20T21:41:28.4312905Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_float32 PASSED [0.0057s] [ 30%] 2024-08-20T21:41:28.4313543Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_float64 PASSED [0.0059s] [ 30%] 2024-08-20T21:41:28.4314215Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_int16 PASSED [0.0057s] [ 30%] 2024-08-20T21:41:28.4314832Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_int32 PASSED [0.0056s] [ 30%] 2024-08-20T21:41:28.4315503Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_int64 PASSED [0.0062s] [ 30%] 2024-08-20T21:41:28.4316125Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_int8 PASSED [0.0057s] [ 30%] 2024-08-20T21:41:28.4316787Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_uint8 PASSED [0.0058s] [ 30%] 2024-08-20T21:41:28.4317478Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_bfloat16 PASSED [0.0073s] [ 30%] 2024-08-20T21:41:28.4318123Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_bool PASSED [0.0074s] [ 30%] 2024-08-20T21:41:28.4318779Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_complex128 PASSED [0.0073s] [ 30%] 2024-08-20T21:41:28.4319469Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_complex32 PASSED [0.0075s] [ 30%] 2024-08-20T21:41:28.4320118Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_complex64 PASSED [0.0074s] [ 30%] 2024-08-20T21:41:28.4321077Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_float16 PASSED [0.0074s] [ 30%] 2024-08-20T21:41:28.4321727Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_float32 PASSED [0.0072s] [ 30%] 2024-08-20T21:41:28.4322372Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_float64 PASSED [0.0072s] [ 30%] 2024-08-20T21:41:28.4323042Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_int16 PASSED [0.0074s] [ 30%] 2024-08-20T21:41:28.4323668Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_int32 PASSED [0.0072s] [ 30%] 2024-08-20T21:41:28.4324382Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_int64 PASSED [0.0074s] [ 30%] 2024-08-20T21:41:28.4325000Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_int8 PASSED [0.0072s] [ 30%] 2024-08-20T21:41:28.4325615Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_uint8 PASSED [0.0074s] [ 30%] 2024-08-20T21:41:28.4326389Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_bfloat16 PASSED [0.0095s] [ 30%] 2024-08-20T21:41:28.4327169Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_bool PASSED [0.0097s] [ 30%] 2024-08-20T21:41:28.4327914Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_complex128 PASSED [0.0096s] [ 30%] 2024-08-20T21:41:28.4328651Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_complex64 PASSED [0.0098s] [ 30%] 2024-08-20T21:41:28.4329332Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_float16 PASSED [0.0095s] [ 30%] 2024-08-20T21:41:28.4330077Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_float32 PASSED [0.0095s] [ 30%] 2024-08-20T21:41:28.4330749Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_float64 PASSED [0.0101s] [ 30%] 2024-08-20T21:41:28.4331405Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_int16 PASSED [0.0095s] [ 30%] 2024-08-20T21:41:28.4332112Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_int32 PASSED [0.0098s] [ 30%] 2024-08-20T21:41:28.4332807Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_int64 PASSED [0.0095s] [ 30%] 2024-08-20T21:41:28.4333506Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_int8 PASSED [0.0097s] [ 30%] 2024-08-20T21:41:28.4334163Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_uint8 PASSED [0.0095s] [ 30%] 2024-08-20T21:41:28.4334807Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_bfloat16 PASSED [0.0054s] [ 30%] 2024-08-20T21:41:28.4335484Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_float16 PASSED [0.0052s] [ 30%] 2024-08-20T21:41:28.4336114Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_float32 PASSED [0.0052s] [ 30%] 2024-08-20T21:41:28.4336833Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_float64 PASSED [0.0052s] [ 30%] 2024-08-20T21:41:28.4337485Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_int16 PASSED [0.0051s] [ 31%] 2024-08-20T21:41:28.4338104Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_int32 PASSED [0.0052s] [ 31%] 2024-08-20T21:41:28.4338766Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_int64 PASSED [0.0051s] [ 31%] 2024-08-20T21:41:28.4339383Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_int8 PASSED [0.0053s] [ 31%] 2024-08-20T21:41:28.4340119Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_uint8 PASSED [0.0051s] [ 31%] 2024-08-20T21:41:28.4340800Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_bfloat16 PASSED [0.0104s] [ 31%] 2024-08-20T21:41:28.4341475Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_float16 PASSED [0.0100s] [ 31%] 2024-08-20T21:41:28.4342191Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_float32 PASSED [0.0103s] [ 31%] 2024-08-20T21:41:28.4342859Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_float64 PASSED [0.0100s] [ 31%] 2024-08-20T21:41:28.4343528Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_int16 PASSED [0.0102s] [ 31%] 2024-08-20T21:41:28.4352178Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_int32 PASSED [0.0100s] [ 31%] 2024-08-20T21:41:28.4353014Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_int64 PASSED [0.0099s] [ 31%] 2024-08-20T21:41:28.4353788Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_int8 PASSED [0.0106s] [ 31%] 2024-08-20T21:41:28.4354471Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_uint8 PASSED [0.0100s] [ 31%] 2024-08-20T21:41:28.4355094Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_bfloat16 PASSED [0.0097s] [ 31%] 2024-08-20T21:41:28.4355673Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_bool PASSED [0.0094s] [ 31%] 2024-08-20T21:41:28.4356264Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_float16 PASSED [0.0097s] [ 31%] 2024-08-20T21:41:28.4356932Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_float32 PASSED [0.0095s] [ 31%] 2024-08-20T21:41:28.4357523Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_float64 PASSED [0.0097s] [ 31%] 2024-08-20T21:41:28.4358101Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_int16 PASSED [0.0094s] [ 31%] 2024-08-20T21:41:28.4358690Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_int32 PASSED [0.0096s] [ 31%] 2024-08-20T21:41:28.4359267Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_int64 PASSED [0.0095s] [ 31%] 2024-08-20T21:41:28.4359855Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_int8 PASSED [0.0094s] [ 31%] 2024-08-20T21:41:28.4360431Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_uint8 PASSED [0.0095s] [ 31%] 2024-08-20T21:41:28.4361030Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_bfloat16 PASSED [0.0095s] [ 31%] 2024-08-20T21:41:28.4361616Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_bool PASSED [0.0096s] [ 31%] 2024-08-20T21:41:28.4362208Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_float16 PASSED [0.0095s] [ 31%] 2024-08-20T21:41:28.4362810Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_float32 PASSED [0.0097s] [ 31%] 2024-08-20T21:41:28.4363448Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_float64 PASSED [0.0095s] [ 31%] 2024-08-20T21:41:28.4364023Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_int16 PASSED [0.0097s] [ 31%] 2024-08-20T21:41:28.4364615Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_int32 PASSED [0.0095s] [ 31%] 2024-08-20T21:41:28.4365198Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_int64 PASSED [0.0097s] [ 31%] 2024-08-20T21:41:28.4365994Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_int8 PASSED [0.0094s] [ 31%] 2024-08-20T21:41:28.4366608Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_uint8 PASSED [0.0094s] [ 31%] 2024-08-20T21:41:28.4367291Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_bfloat16 PASSED [0.0107s] [ 31%] 2024-08-20T21:41:28.4367902Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_float16 PASSED [0.0100s] [ 31%] 2024-08-20T21:41:28.4368482Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_float32 PASSED [0.0103s] [ 31%] 2024-08-20T21:41:28.4369067Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_float64 PASSED [0.0100s] [ 31%] 2024-08-20T21:41:28.4369663Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_int16 PASSED [0.0103s] [ 31%] 2024-08-20T21:41:28.4370238Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_int32 PASSED [0.0100s] [ 31%] 2024-08-20T21:41:28.4370872Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_int64 PASSED [0.0102s] [ 31%] 2024-08-20T21:41:28.4371444Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_int8 PASSED [0.0100s] [ 31%] 2024-08-20T21:41:28.4372045Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_uint8 PASSED [0.0102s] [ 31%] 2024-08-20T21:41:28.4372658Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frac_cpu_bfloat16 PASSED [0.0051s] [ 31%] 2024-08-20T21:41:28.4373246Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frac_cpu_float16 PASSED [0.0051s] [ 31%] 2024-08-20T21:41:28.4373881Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frac_cpu_float32 PASSED [0.0052s] [ 31%] 2024-08-20T21:41:28.4374465Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frac_cpu_float64 PASSED [0.0051s] [ 31%] 2024-08-20T21:41:28.4375072Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frexp_cpu_bfloat16 PASSED [0.0063s] [ 31%] 2024-08-20T21:41:28.4375682Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frexp_cpu_float16 PASSED [0.0061s] [ 31%] 2024-08-20T21:41:28.4376280Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frexp_cpu_float32 PASSED [0.0063s] [ 31%] 2024-08-20T21:41:28.4376877Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frexp_cpu_float64 PASSED [0.0061s] [ 31%] 2024-08-20T21:41:28.4377477Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_bfloat16 PASSED [0.0070s] [ 31%] 2024-08-20T21:41:28.4378049Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_bool PASSED [0.0068s] [ 31%] 2024-08-20T21:41:28.4378672Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_complex128 PASSED [0.0070s] [ 31%] 2024-08-20T21:41:28.4379276Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_complex32 PASSED [0.0070s] [ 31%] 2024-08-20T21:41:28.4379874Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_complex64 PASSED [0.0068s] [ 31%] 2024-08-20T21:41:28.4380476Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_float16 PASSED [0.0076s] [ 31%] 2024-08-20T21:41:28.4381099Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_float32 PASSED [0.0068s] [ 31%] 2024-08-20T21:41:28.4381691Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_float64 PASSED [0.0071s] [ 31%] 2024-08-20T21:41:28.4382274Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_int16 PASSED [0.0070s] [ 31%] 2024-08-20T21:41:28.4382854Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_int32 PASSED [0.0073s] [ 32%] 2024-08-20T21:41:28.4383447Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_int64 PASSED [0.0070s] [ 32%] 2024-08-20T21:41:28.4384019Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_int8 PASSED [0.0071s] [ 32%] 2024-08-20T21:41:28.4384597Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_uint8 PASSED [0.0069s] [ 32%] 2024-08-20T21:41:28.4385236Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_bfloat16 PASSED [0.0084s] [ 32%] 2024-08-20T21:41:28.4385835Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_bool PASSED [0.0085s] [ 32%] 2024-08-20T21:41:28.4386485Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_complex128 PASSED [0.0084s] [ 32%] 2024-08-20T21:41:28.4387116Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_complex64 PASSED [0.0086s] [ 32%] 2024-08-20T21:41:28.4387909Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_float16 PASSED [0.0083s] [ 32%] 2024-08-20T21:41:28.4388586Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_float32 PASSED [0.0085s] [ 32%] 2024-08-20T21:41:28.4389238Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_float64 PASSED [0.0083s] [ 32%] 2024-08-20T21:41:28.4389861Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_int16 PASSED [0.0085s] [ 32%] 2024-08-20T21:41:28.4390466Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_int32 PASSED [0.0083s] [ 32%] 2024-08-20T21:41:28.4391104Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_int64 PASSED [0.0085s] [ 32%] 2024-08-20T21:41:28.4391716Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_int8 PASSED [0.0083s] [ 32%] 2024-08-20T21:41:28.4392318Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_uint8 PASSED [0.0082s] [ 32%] 2024-08-20T21:41:28.4392927Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_bfloat16 PASSED [0.0083s] [ 32%] 2024-08-20T21:41:28.4393522Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_bool PASSED [0.0079s] [ 32%] 2024-08-20T21:41:28.4394145Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_complex128 PASSED [0.0087s] [ 32%] 2024-08-20T21:41:28.4394770Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_complex64 PASSED [0.0080s] [ 32%] 2024-08-20T21:41:28.4395370Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_float16 PASSED [0.0082s] [ 32%] 2024-08-20T21:41:28.4395972Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_float32 PASSED [0.0080s] [ 32%] 2024-08-20T21:41:28.4396582Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_float64 PASSED [0.0083s] [ 32%] 2024-08-20T21:41:28.4397175Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_int16 PASSED [0.0081s] [ 32%] 2024-08-20T21:41:28.4397780Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_int32 PASSED [0.0083s] [ 32%] 2024-08-20T21:41:28.4398398Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_int64 PASSED [0.0080s] [ 32%] 2024-08-20T21:41:28.4398983Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_int8 PASSED [0.0080s] [ 32%] 2024-08-20T21:41:28.4399585Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_uint8 PASSED [0.0082s] [ 32%] 2024-08-20T21:41:28.4400159Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_int16 PASSED [0.0095s] [ 32%] 2024-08-20T21:41:28.4400748Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_int32 PASSED [0.0098s] [ 32%] 2024-08-20T21:41:28.4401327Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_int64 PASSED [0.0095s] [ 32%] 2024-08-20T21:41:28.4401898Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_int8 PASSED [0.0097s] [ 32%] 2024-08-20T21:41:28.4402485Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_uint8 PASSED [0.0095s] [ 32%] 2024-08-20T21:41:28.4403064Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_bfloat16 PASSED [0.0097s] [ 32%] 2024-08-20T21:41:28.4403639Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_bool PASSED [0.0095s] [ 32%] 2024-08-20T21:41:28.4404215Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_float16 PASSED [0.0097s] [ 32%] 2024-08-20T21:41:28.4404795Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_float32 PASSED [0.0096s] [ 32%] 2024-08-20T21:41:28.4405422Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_float64 PASSED [0.0097s] [ 32%] 2024-08-20T21:41:28.4406019Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_int16 PASSED [0.0100s] [ 32%] 2024-08-20T21:41:28.4406590Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_int32 PASSED [0.0097s] [ 32%] 2024-08-20T21:41:28.4407257Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_int64 PASSED [0.0104s] [ 32%] 2024-08-20T21:41:28.4407821Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_int8 PASSED [0.0099s] [ 32%] 2024-08-20T21:41:28.4408433Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_uint8 PASSED [0.0100s] [ 32%] 2024-08-20T21:41:28.4409068Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_bfloat16 PASSED [0.0064s] [ 32%] 2024-08-20T21:41:28.4409697Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_float16 PASSED [0.0066s] [ 32%] 2024-08-20T21:41:28.4410334Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_float32 PASSED [0.0063s] [ 32%] 2024-08-20T21:41:28.4410960Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_float64 PASSED [0.0065s] [ 32%] 2024-08-20T21:41:28.4411584Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_int16 PASSED [0.0062s] [ 32%] 2024-08-20T21:41:28.4412189Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_int32 PASSED [0.0062s] [ 32%] 2024-08-20T21:41:28.4412795Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_int64 PASSED [0.0063s] [ 32%] 2024-08-20T21:41:28.4413415Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_int8 PASSED [0.0061s] [ 32%] 2024-08-20T21:41:28.4414023Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_uint8 PASSED [0.0063s] [ 32%] 2024-08-20T21:41:28.4414637Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geqrf_cpu_complex128 PASSED [0.0306s] [ 32%] 2024-08-20T21:41:28.4415279Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geqrf_cpu_complex64 PASSED [0.0310s] [ 32%] 2024-08-20T21:41:28.4415869Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geqrf_cpu_float32 PASSED [0.0299s] [ 32%] 2024-08-20T21:41:28.4416471Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geqrf_cpu_float64 PASSED [0.0299s] [ 32%] 2024-08-20T21:41:28.4417091Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_bfloat16 PASSED [0.0091s] [ 32%] 2024-08-20T21:41:28.4417722Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_complex128 PASSED [0.0090s] [ 32%] 2024-08-20T21:41:28.4418368Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_complex64 PASSED [0.0091s] [ 32%] 2024-08-20T21:41:28.4418984Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_float16 PASSED [0.0089s] [ 33%] 2024-08-20T21:41:28.4419618Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_float32 PASSED [0.0090s] [ 33%] 2024-08-20T21:41:28.4420232Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_float64 PASSED [0.0090s] [ 33%] 2024-08-20T21:41:28.4421084Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_int16 PASSED [0.0095s] [ 33%] 2024-08-20T21:41:28.4421708Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_int32 PASSED [0.0088s] [ 33%] 2024-08-20T21:41:28.4422304Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_int64 PASSED [0.0090s] [ 33%] 2024-08-20T21:41:28.4422963Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_int8 PASSED [0.0088s] [ 33%] 2024-08-20T21:41:28.4423643Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_2d_cpu_float32 PASSED [0.0144s] [ 33%] 2024-08-20T21:41:28.4424300Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_2d_cpu_float64 PASSED [0.0147s] [ 33%] 2024-08-20T21:41:28.4424895Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_bfloat16 PASSED [0.0096s] [ 33%] 2024-08-20T21:41:28.4425457Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_bool PASSED [0.0098s] [ 33%] 2024-08-20T21:41:28.4426082Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_float16 PASSED [0.0097s] [ 33%] 2024-08-20T21:41:28.4426665Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_float32 PASSED [0.0099s] [ 33%] 2024-08-20T21:41:28.4427240Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_float64 PASSED [0.0095s] [ 33%] 2024-08-20T21:41:28.4427825Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_int16 PASSED [0.0098s] [ 33%] 2024-08-20T21:41:28.4428393Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_int32 PASSED [0.0095s] [ 33%] 2024-08-20T21:41:28.4428955Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_int64 PASSED [0.0097s] [ 33%] 2024-08-20T21:41:28.4429523Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_int8 PASSED [0.0095s] [ 33%] 2024-08-20T21:41:28.4430083Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_uint8 PASSED [0.0095s] [ 33%] 2024-08-20T21:41:28.4430692Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_bfloat16 PASSED [0.0073s] [ 33%] 2024-08-20T21:41:28.4431262Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_bool PASSED [0.0071s] [ 33%] 2024-08-20T21:41:28.4431872Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_complex128 PASSED [0.0074s] [ 33%] 2024-08-20T21:41:28.4432490Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_complex64 PASSED [0.0072s] [ 33%] 2024-08-20T21:41:28.4433109Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_float16 PASSED [0.0073s] [ 33%] 2024-08-20T21:41:28.4433707Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_float32 PASSED [0.0071s] [ 33%] 2024-08-20T21:41:28.4434288Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_float64 PASSED [0.0077s] [ 33%] 2024-08-20T21:41:28.4434866Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_int16 PASSED [0.0072s] [ 33%] 2024-08-20T21:41:28.4435457Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_int32 PASSED [0.0074s] [ 33%] 2024-08-20T21:41:28.4436033Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_int64 PASSED [0.0072s] [ 33%] 2024-08-20T21:41:28.4436606Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_int8 PASSED [0.0071s] [ 33%] 2024-08-20T21:41:28.4437192Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_uint8 PASSED [0.0073s] [ 33%] 2024-08-20T21:41:28.4437821Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_bfloat16 PASSED [0.0095s] [ 33%] 2024-08-20T21:41:28.4438437Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_bool PASSED [0.0097s] [ 33%] 2024-08-20T21:41:28.4439053Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_float16 PASSED [0.0095s] [ 33%] 2024-08-20T21:41:28.4439697Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_float32 PASSED [0.0099s] [ 33%] 2024-08-20T21:41:28.4440335Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_float64 PASSED [0.0096s] [ 33%] 2024-08-20T21:41:28.4441004Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_int16 PASSED [0.0098s] [ 33%] 2024-08-20T21:41:28.4441623Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_int32 PASSED [0.0095s] [ 33%] 2024-08-20T21:41:28.4442234Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_int64 PASSED [0.0097s] [ 33%] 2024-08-20T21:41:28.4442866Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_int8 PASSED [0.0095s] [ 33%] 2024-08-20T21:41:28.4443492Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_uint8 PASSED [0.0095s] [ 33%] 2024-08-20T21:41:28.4444096Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histc_cpu_bfloat16 PASSED [0.0549s] [ 33%] 2024-08-20T21:41:28.4444708Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histc_cpu_float16 PASSED [0.0543s] [ 33%] 2024-08-20T21:41:28.4445297Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histc_cpu_float32 PASSED [0.0546s] [ 33%] 2024-08-20T21:41:28.4445885Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histc_cpu_float64 PASSED [0.0540s] [ 33%] 2024-08-20T21:41:28.4446519Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histogram_cpu_float32 PASSED [0.1080s] [ 33%] 2024-08-20T21:41:28.4447352Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histogram_cpu_float64 PASSED [0.1077s] [ 33%] 2024-08-20T21:41:28.4447990Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histogramdd_cpu_float32 PASSED [0.0569s] [ 33%] 2024-08-20T21:41:28.4448645Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histogramdd_cpu_float64 PASSED [0.0577s] [ 33%] 2024-08-20T21:41:28.4449256Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_bfloat16 PASSED [0.0059s] [ 33%] 2024-08-20T21:41:28.4449857Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_bool PASSED [0.0059s] [ 33%] 2024-08-20T21:41:28.4450551Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_complex128 PASSED [0.0057s] [ 33%] 2024-08-20T21:41:28.4451174Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_complex32 PASSED [0.0059s] [ 33%] 2024-08-20T21:41:28.4451810Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_complex64 PASSED [0.0056s] [ 33%] 2024-08-20T21:41:28.4452412Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_float16 PASSED [0.0058s] [ 33%] 2024-08-20T21:41:28.4453031Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_float32 PASSED [0.0057s] [ 33%] 2024-08-20T21:41:28.4453628Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_float64 PASSED [0.0057s] [ 33%] 2024-08-20T21:41:28.4454224Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_int16 PASSED [0.0058s] [ 33%] 2024-08-20T21:41:28.4454832Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_int32 PASSED [0.0056s] [ 33%] 2024-08-20T21:41:28.4455413Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_int64 PASSED [0.0058s] [ 34%] 2024-08-20T21:41:28.4456015Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_int8 PASSED [0.0056s] [ 34%] 2024-08-20T21:41:28.4456606Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_uint8 PASSED [0.0059s] [ 34%] 2024-08-20T21:41:28.4457254Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_bfloat16 PASSED [0.0057s] [ 34%] 2024-08-20T21:41:28.4457857Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_bool PASSED [0.0058s] [ 34%] 2024-08-20T21:41:28.4458520Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_complex128 PASSED [0.0058s] [ 34%] 2024-08-20T21:41:28.4459136Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_complex32 PASSED [0.0060s] [ 34%] 2024-08-20T21:41:28.4459758Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_complex64 PASSED [0.0057s] [ 34%] 2024-08-20T21:41:28.4460402Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_float16 PASSED [0.0057s] [ 34%] 2024-08-20T21:41:28.4461017Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_float32 PASSED [0.0064s] [ 34%] 2024-08-20T21:41:28.4461615Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_float64 PASSED [0.0057s] [ 34%] 2024-08-20T21:41:28.4462205Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_int16 PASSED [0.0059s] [ 34%] 2024-08-20T21:41:28.4462807Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_int32 PASSED [0.0057s] [ 34%] 2024-08-20T21:41:28.4463391Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_int64 PASSED [0.0059s] [ 34%] 2024-08-20T21:41:28.4463989Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_int8 PASSED [0.0056s] [ 34%] 2024-08-20T21:41:28.4464574Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_uint8 PASSED [0.0058s] [ 34%] 2024-08-20T21:41:28.4465174Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hypot_cpu_bfloat16 PASSED [0.0096s] [ 34%] 2024-08-20T21:41:28.4465776Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hypot_cpu_float16 PASSED [0.0096s] [ 34%] 2024-08-20T21:41:28.4466360Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hypot_cpu_float32 PASSED [0.0097s] [ 34%] 2024-08-20T21:41:28.4466970Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hypot_cpu_float64 PASSED [0.0095s] [ 34%] 2024-08-20T21:41:28.4467600Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_bfloat16 PASSED [0.0059s] [ 34%] 2024-08-20T21:41:28.4468158Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_bool PASSED [0.0056s] [ 34%] 2024-08-20T21:41:28.4468748Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_float16 PASSED [0.0058s] [ 34%] 2024-08-20T21:41:28.4469321Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_float32 PASSED [0.0056s] [ 34%] 2024-08-20T21:41:28.4469899Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_float64 PASSED [0.0058s] [ 34%] 2024-08-20T21:41:28.4470472Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_int16 PASSED [0.0056s] [ 34%] 2024-08-20T21:41:28.4471041Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_int32 PASSED [0.0058s] [ 34%] 2024-08-20T21:41:28.4471618Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_int64 PASSED [0.0056s] [ 34%] 2024-08-20T21:41:28.4472175Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_int8 PASSED [0.0055s] [ 34%] 2024-08-20T21:41:28.4472738Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_uint8 PASSED [0.0058s] [ 34%] 2024-08-20T21:41:28.4473363Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igamma_cpu_bfloat16 PASSED [0.0095s] [ 34%] 2024-08-20T21:41:28.4473962Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igamma_cpu_float16 PASSED [0.0102s] [ 34%] 2024-08-20T21:41:28.4474600Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igamma_cpu_float32 PASSED [0.0095s] [ 34%] 2024-08-20T21:41:28.4475216Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igamma_cpu_float64 PASSED [0.0097s] [ 34%] 2024-08-20T21:41:28.4475829Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igammac_cpu_bfloat16 PASSED [0.0098s] [ 34%] 2024-08-20T21:41:28.4476444Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igammac_cpu_float16 PASSED [0.0098s] [ 34%] 2024-08-20T21:41:28.4477048Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igammac_cpu_float32 PASSED [0.0096s] [ 34%] 2024-08-20T21:41:28.4477671Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igammac_cpu_float64 PASSED [0.0098s] [ 34%] 2024-08-20T21:41:28.4478282Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_imag_cpu_complex128 PASSED [0.0064s] [ 34%] 2024-08-20T21:41:28.4478887Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_imag_cpu_complex32 PASSED [0.0063s] [ 34%] 2024-08-20T21:41:28.4479497Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_imag_cpu_complex64 PASSED [0.0065s] [ 34%] 2024-08-20T21:41:28.4480122Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_bfloat16 PASSED [0.0097s] [ 34%] 2024-08-20T21:41:28.4480721Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_bool PASSED [0.0082s] [ 34%] 2024-08-20T21:41:28.4481368Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_complex128 PASSED [0.0098s] [ 34%] 2024-08-20T21:41:28.4482002Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_complex32 PASSED [0.0100s] [ 34%] 2024-08-20T21:41:28.4482641Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_complex64 PASSED [0.0097s] [ 34%] 2024-08-20T21:41:28.4483259Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_float16 PASSED [0.0098s] [ 34%] 2024-08-20T21:41:28.4483874Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_float32 PASSED [0.0095s] [ 34%] 2024-08-20T21:41:28.4484531Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_float64 PASSED [0.0097s] [ 34%] 2024-08-20T21:41:28.4485135Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_int16 PASSED [0.0095s] [ 34%] 2024-08-20T21:41:28.4485747Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_int32 PASSED [0.0095s] [ 34%] 2024-08-20T21:41:28.4486351Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_int64 PASSED [0.0097s] [ 34%] 2024-08-20T21:41:28.4487030Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_int8 PASSED [0.0094s] [ 34%] 2024-08-20T21:41:28.4487646Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_uint8 PASSED [0.0102s] [ 34%] 2024-08-20T21:41:28.4488282Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_bfloat16 PASSED [0.0063s] [ 34%] 2024-08-20T21:41:28.4488898Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_bool PASSED [0.0064s] [ 34%] 2024-08-20T21:41:28.4489537Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_complex128 PASSED [0.0063s] [ 34%] 2024-08-20T21:41:28.4490177Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_complex32 PASSED [0.0065s] [ 34%] 2024-08-20T21:41:28.4490822Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_complex64 PASSED [0.0063s] [ 34%] 2024-08-20T21:41:28.4491483Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_float16 PASSED [0.0064s] [ 34%] 2024-08-20T21:41:28.4492107Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_float32 PASSED [0.0063s] [ 35%] 2024-08-20T21:41:28.4492780Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_float64 PASSED [0.0063s] [ 35%] 2024-08-20T21:41:28.4493395Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_int16 PASSED [0.0064s] [ 35%] 2024-08-20T21:41:28.4494010Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_int32 PASSED [0.0062s] [ 35%] 2024-08-20T21:41:28.4494654Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_int64 PASSED [0.0064s] [ 35%] 2024-08-20T21:41:28.4495261Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_int8 PASSED [0.0062s] [ 35%] 2024-08-20T21:41:28.4495883Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_uint8 PASSED [0.0064s] [ 35%] 2024-08-20T21:41:28.4496511Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_bfloat16 PASSED [0.0079s] [ 35%] 2024-08-20T21:41:28.4497131Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_bool PASSED [0.0080s] [ 35%] 2024-08-20T21:41:28.4497774Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_complex128 PASSED [0.0079s] [ 35%] 2024-08-20T21:41:28.4498408Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_complex32 PASSED [0.0081s] [ 35%] 2024-08-20T21:41:28.4499053Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_complex64 PASSED [0.0079s] [ 35%] 2024-08-20T21:41:28.4499676Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_float16 PASSED [0.0079s] [ 35%] 2024-08-20T21:41:28.4500314Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_float32 PASSED [0.0080s] [ 35%] 2024-08-20T21:41:28.4500937Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_float64 PASSED [0.0078s] [ 35%] 2024-08-20T21:41:28.4501589Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_int16 PASSED [0.0085s] [ 35%] 2024-08-20T21:41:28.4502208Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_int32 PASSED [0.0077s] [ 35%] 2024-08-20T21:41:28.4502814Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_int64 PASSED [0.0080s] [ 35%] 2024-08-20T21:41:28.4503441Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_int8 PASSED [0.0078s] [ 35%] 2024-08-20T21:41:28.4504158Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_uint8 PASSED [0.0079s] [ 35%] 2024-08-20T21:41:28.4504790Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_bfloat16 PASSED [0.0069s] [ 35%] 2024-08-20T21:41:28.4505406Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_bool PASSED [0.0070s] [ 35%] 2024-08-20T21:41:28.4506043Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_complex128 PASSED [0.0069s] [ 35%] 2024-08-20T21:41:28.4506672Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_complex32 PASSED [0.0069s] [ 35%] 2024-08-20T21:41:28.4507311Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_complex64 PASSED [0.0070s] [ 35%] 2024-08-20T21:41:28.4507931Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_float16 PASSED [0.0068s] [ 35%] 2024-08-20T21:41:28.4508557Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_float32 PASSED [0.0071s] [ 35%] 2024-08-20T21:41:28.4509213Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_float64 PASSED [0.0069s] [ 35%] 2024-08-20T21:41:28.4509847Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_int16 PASSED [0.0071s] [ 35%] 2024-08-20T21:41:28.4510461Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_int32 PASSED [0.0069s] [ 35%] 2024-08-20T21:41:28.4511057Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_int64 PASSED [0.0071s] [ 35%] 2024-08-20T21:41:28.4511673Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_int8 PASSED [0.0069s] [ 35%] 2024-08-20T21:41:28.4512311Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_uint8 PASSED [0.0070s] [ 35%] 2024-08-20T21:41:28.4512984Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_bfloat16 PASSED [0.0090s] [ 35%] 2024-08-20T21:41:28.4513666Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_float16 PASSED [0.0089s] [ 35%] 2024-08-20T21:41:28.4514329Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_float32 PASSED [0.0091s] [ 35%] 2024-08-20T21:41:28.4515009Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_float64 PASSED [0.0090s] [ 35%] 2024-08-20T21:41:28.4515663Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_int16 PASSED [0.0096s] [ 35%] 2024-08-20T21:41:28.4516316Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_int32 PASSED [0.0089s] [ 35%] 2024-08-20T21:41:28.4516975Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_int64 PASSED [0.0091s] [ 35%] 2024-08-20T21:41:28.4517625Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_int8 PASSED [0.0089s] [ 35%] 2024-08-20T21:41:28.4518276Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_uint8 PASSED [0.0092s] [ 35%] 2024-08-20T21:41:28.4518955Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_bfloat16 PASSED [0.0090s] [ 35%] 2024-08-20T21:41:28.4519660Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_float16 PASSED [0.0092s] [ 35%] 2024-08-20T21:41:28.4520336Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_float32 PASSED [0.0091s] [ 35%] 2024-08-20T21:41:28.4521243Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_float64 PASSED [0.0090s] [ 35%] 2024-08-20T21:41:28.4521901Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_int16 PASSED [0.0091s] [ 35%] 2024-08-20T21:41:28.4522562Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_int32 PASSED [0.0090s] [ 35%] 2024-08-20T21:41:28.4523205Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_int64 PASSED [0.0091s] [ 35%] 2024-08-20T21:41:28.4523865Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_int8 PASSED [0.0089s] [ 35%] 2024-08-20T21:41:28.4524511Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_uint8 PASSED [0.0091s] [ 35%] 2024-08-20T21:41:28.4525183Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_bfloat16 PASSED [0.0090s] [ 35%] 2024-08-20T21:41:28.4525855Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_float16 PASSED [0.0092s] [ 35%] 2024-08-20T21:41:28.4526558Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_float32 PASSED [0.0092s] [ 35%] 2024-08-20T21:41:28.4527308Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_float64 PASSED [0.0092s] [ 35%] 2024-08-20T21:41:28.4527997Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_int16 PASSED [0.0089s] [ 35%] 2024-08-20T21:41:28.4528641Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_int32 PASSED [0.0089s] [ 35%] 2024-08-20T21:41:28.4529295Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_int64 PASSED [0.0092s] [ 35%] 2024-08-20T21:41:28.4529986Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_int8 PASSED [0.0090s] [ 35%] 2024-08-20T21:41:28.4530646Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_uint8 PASSED [0.0098s] [ 36%] 2024-08-20T21:41:28.4531318Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_bfloat16 PASSED [0.0091s] [ 36%] 2024-08-20T21:41:28.4531982Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_float16 PASSED [0.0094s] [ 36%] 2024-08-20T21:41:28.4532658Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_float32 PASSED [0.0092s] [ 36%] 2024-08-20T21:41:28.4533324Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_float64 PASSED [0.0093s] [ 36%] 2024-08-20T21:41:28.4533981Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_int16 PASSED [0.0090s] [ 36%] 2024-08-20T21:41:28.4534629Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_int32 PASSED [0.0090s] [ 36%] 2024-08-20T21:41:28.4535274Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_int64 PASSED [0.0093s] [ 36%] 2024-08-20T21:41:28.4535942Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_int8 PASSED [0.0091s] [ 36%] 2024-08-20T21:41:28.4536593Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_uint8 PASSED [0.0093s] [ 36%] 2024-08-20T21:41:28.4537274Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_bfloat16 PASSED [0.0063s] [ 36%] 2024-08-20T21:41:28.4537905Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_bool PASSED [0.0064s] [ 36%] 2024-08-20T21:41:28.4538558Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_complex128 PASSED [0.0063s] [ 36%] 2024-08-20T21:41:28.4539215Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_complex32 PASSED [0.0065s] [ 36%] 2024-08-20T21:41:28.4539864Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_complex64 PASSED [0.0062s] [ 36%] 2024-08-20T21:41:28.4540499Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_float16 PASSED [0.0064s] [ 36%] 2024-08-20T21:41:28.4541151Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_float32 PASSED [0.0062s] [ 36%] 2024-08-20T21:41:28.4541786Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_float64 PASSED [0.0062s] [ 36%] 2024-08-20T21:41:28.4542418Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_int16 PASSED [0.0063s] [ 36%] 2024-08-20T21:41:28.4543044Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_int32 PASSED [0.0061s] [ 36%] 2024-08-20T21:41:28.4543661Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_int64 PASSED [0.0064s] [ 36%] 2024-08-20T21:41:28.4544325Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_int8 PASSED [0.0062s] [ 36%] 2024-08-20T21:41:28.4544976Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_uint8 PASSED [0.0069s] [ 36%] 2024-08-20T21:41:28.4545589Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_bfloat16 PASSED [0.0057s] [ 36%] 2024-08-20T21:41:28.4546201Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_complex128 PASSED [0.0059s] [ 36%] 2024-08-20T21:41:28.4547027Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_complex64 PASSED [0.0057s] [ 36%] 2024-08-20T21:41:28.4547726Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_float16 PASSED [0.0059s] [ 36%] 2024-08-20T21:41:28.4548321Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_float32 PASSED [0.0057s] [ 36%] 2024-08-20T21:41:28.4548932Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_float64 PASSED [0.0057s] [ 36%] 2024-08-20T21:41:28.4549520Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_int16 PASSED [0.0058s] [ 36%] 2024-08-20T21:41:28.4550103Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_int32 PASSED [0.0058s] [ 36%] 2024-08-20T21:41:28.4550699Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_int64 PASSED [0.0059s] [ 36%] 2024-08-20T21:41:28.4551280Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_int8 PASSED [0.0056s] [ 36%] 2024-08-20T21:41:28.4551861Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_uint8 PASSED [0.0059s] [ 36%] 2024-08-20T21:41:28.4552470Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_bfloat16 PASSED [0.0072s] [ 36%] 2024-08-20T21:41:28.4553040Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_bool PASSED [0.0074s] [ 36%] 2024-08-20T21:41:28.4553659Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_complex128 PASSED [0.0073s] [ 36%] 2024-08-20T21:41:28.4554264Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_complex64 PASSED [0.0074s] [ 36%] 2024-08-20T21:41:28.4554904Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_float16 PASSED [0.0072s] [ 36%] 2024-08-20T21:41:28.4555492Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_float32 PASSED [0.0071s] [ 36%] 2024-08-20T21:41:28.4556078Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_float64 PASSED [0.0073s] [ 36%] 2024-08-20T21:41:28.4556662Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_int16 PASSED [0.0072s] [ 36%] 2024-08-20T21:41:28.4557229Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_int32 PASSED [0.0074s] [ 36%] 2024-08-20T21:41:28.4557798Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_int64 PASSED [0.0071s] [ 36%] 2024-08-20T21:41:28.4558378Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_int8 PASSED [0.0078s] [ 36%] 2024-08-20T21:41:28.4558950Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_uint8 PASSED [0.0072s] [ 36%] 2024-08-20T21:41:28.4559570Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_bfloat16 PASSED [0.0141s] [ 36%] 2024-08-20T21:41:28.4560171Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_bool PASSED [0.0137s] [ 36%] 2024-08-20T21:41:28.4560798Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_complex128 PASSED [0.0143s] [ 36%] 2024-08-20T21:41:28.4561510Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_complex64 PASSED [0.0140s] [ 36%] 2024-08-20T21:41:28.4562118Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_float16 PASSED [0.0139s] [ 36%] 2024-08-20T21:41:28.4562760Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_float32 PASSED [0.0141s] [ 36%] 2024-08-20T21:41:28.4563384Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_float64 PASSED [0.0141s] [ 36%] 2024-08-20T21:41:28.4563980Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_int16 PASSED [0.0141s] [ 36%] 2024-08-20T21:41:28.4564645Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_int32 PASSED [0.0138s] [ 36%] 2024-08-20T21:41:28.4565233Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_int64 PASSED [0.0140s] [ 36%] 2024-08-20T21:41:28.4565834Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_int8 PASSED [0.0138s] [ 36%] 2024-08-20T21:41:28.4566446Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_uint8 PASSED [0.0139s] [ 36%] 2024-08-20T21:41:28.4567142Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_bfloat16 PASSED [0.0062s] [ 36%] 2024-08-20T21:41:28.4567755Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_bool PASSED [0.0061s] [ 36%] 2024-08-20T21:41:28.4568388Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_complex128 PASSED [0.0063s] [ 37%] 2024-08-20T21:41:28.4569017Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_complex32 PASSED [0.0061s] [ 37%] 2024-08-20T21:41:28.4569661Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_complex64 PASSED [0.0063s] [ 37%] 2024-08-20T21:41:28.4570276Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_float16 PASSED [0.0061s] [ 37%] 2024-08-20T21:41:28.4570882Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_float32 PASSED [0.0063s] [ 37%] 2024-08-20T21:41:28.4571511Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_float64 PASSED [0.0061s] [ 37%] 2024-08-20T21:41:28.4572156Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_int16 PASSED [0.0067s] [ 37%] 2024-08-20T21:41:28.4572764Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_int32 PASSED [0.0061s] [ 37%] 2024-08-20T21:41:28.4573366Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_int64 PASSED [0.0063s] [ 37%] 2024-08-20T21:41:28.4573966Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_int8 PASSED [0.0061s] [ 37%] 2024-08-20T21:41:28.4574586Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_uint8 PASSED [0.0061s] [ 37%] 2024-08-20T21:41:28.4575176Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_float32 PASSED [0.0058s] [ 37%] 2024-08-20T21:41:28.4575787Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_float64 PASSED [0.0056s] [ 37%] 2024-08-20T21:41:28.4576366Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_int16 PASSED [0.0059s] [ 37%] 2024-08-20T21:41:28.4576944Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_int32 PASSED [0.0056s] [ 37%] 2024-08-20T21:41:28.4577535Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_int64 PASSED [0.0059s] [ 37%] 2024-08-20T21:41:28.4578107Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_int8 PASSED [0.0056s] [ 37%] 2024-08-20T21:41:28.4578729Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_uint8 PASSED [0.0058s] [ 37%] 2024-08-20T21:41:28.4579337Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_bfloat16 PASSED [0.0051s] [ 37%] 2024-08-20T21:41:28.4579936Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_bool PASSED [0.0053s] [ 37%] 2024-08-20T21:41:28.4580564Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_complex128 PASSED [0.0051s] [ 37%] 2024-08-20T21:41:28.4581171Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_complex32 PASSED [0.0051s] [ 37%] 2024-08-20T21:41:28.4581805Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_complex64 PASSED [0.0052s] [ 37%] 2024-08-20T21:41:28.4582415Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_float16 PASSED [0.0051s] [ 37%] 2024-08-20T21:41:28.4583010Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_float32 PASSED [0.0053s] [ 37%] 2024-08-20T21:41:28.4583608Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_float64 PASSED [0.0051s] [ 37%] 2024-08-20T21:41:28.4584191Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_int16 PASSED [0.0052s] [ 37%] 2024-08-20T21:41:28.4584776Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_int32 PASSED [0.0051s] [ 37%] 2024-08-20T21:41:28.4585373Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_int64 PASSED [0.0056s] [ 37%] 2024-08-20T21:41:28.4585949Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_int8 PASSED [0.0051s] [ 37%] 2024-08-20T21:41:28.4586541Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_uint8 PASSED [0.0052s] [ 37%] 2024-08-20T21:41:28.4587139Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_bfloat16 PASSED [0.0050s] [ 37%] 2024-08-20T21:41:28.4587720Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_bool PASSED [0.0051s] [ 37%] 2024-08-20T21:41:28.4588348Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_complex128 PASSED [0.0052s] [ 37%] 2024-08-20T21:41:28.4588997Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_complex64 PASSED [0.0050s] [ 37%] 2024-08-20T21:41:28.4589602Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_float16 PASSED [0.0053s] [ 37%] 2024-08-20T21:41:28.4590190Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_float32 PASSED [0.0050s] [ 37%] 2024-08-20T21:41:28.4590776Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_float64 PASSED [0.0053s] [ 37%] 2024-08-20T21:41:28.4591369Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_int16 PASSED [0.0051s] [ 37%] 2024-08-20T21:41:28.4591948Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_int32 PASSED [0.0053s] [ 37%] 2024-08-20T21:41:28.4592530Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_int64 PASSED [0.0051s] [ 37%] 2024-08-20T21:41:28.4593123Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_int8 PASSED [0.0052s] [ 37%] 2024-08-20T21:41:28.4593700Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_uint8 PASSED [0.0051s] [ 37%] 2024-08-20T21:41:28.4594333Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_bfloat16 PASSED [0.0051s] [ 37%] 2024-08-20T21:41:28.4594931Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_bool PASSED [0.0052s] [ 37%] 2024-08-20T21:41:28.4595539Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_float16 PASSED [0.0051s] [ 37%] 2024-08-20T21:41:28.4596189Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_float32 PASSED [0.0053s] [ 37%] 2024-08-20T21:41:28.4596836Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_float64 PASSED [0.0051s] [ 37%] 2024-08-20T21:41:28.4597453Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_int16 PASSED [0.0053s] [ 37%] 2024-08-20T21:41:28.4598047Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_int32 PASSED [0.0051s] [ 37%] 2024-08-20T21:41:28.4598640Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_int64 PASSED [0.0058s] [ 37%] 2024-08-20T21:41:28.4599271Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_int8 PASSED [0.0051s] [ 37%] 2024-08-20T21:41:28.4599872Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_uint8 PASSED [0.0053s] [ 37%] 2024-08-20T21:41:28.4600503Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_bfloat16 PASSED [0.0051s] [ 37%] 2024-08-20T21:41:28.4601104Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_bool PASSED [0.0051s] [ 37%] 2024-08-20T21:41:28.4601716Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_float16 PASSED [0.0053s] [ 37%] 2024-08-20T21:41:28.4602340Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_float32 PASSED [0.0051s] [ 37%] 2024-08-20T21:41:28.4602951Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_float64 PASSED [0.0052s] [ 37%] 2024-08-20T21:41:28.4603553Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_int16 PASSED [0.0051s] [ 37%] 2024-08-20T21:41:28.4604165Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_int32 PASSED [0.0053s] [ 38%] 2024-08-20T21:41:28.4604887Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_int64 PASSED [0.0051s] [ 38%] 2024-08-20T21:41:28.4605499Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_int8 PASSED [0.0053s] [ 38%] 2024-08-20T21:41:28.4606137Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_uint8 PASSED [0.0051s] [ 38%] 2024-08-20T21:41:28.4606836Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_bfloat16 PASSED [0.0063s] [ 38%] 2024-08-20T21:41:28.4607439Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_bool PASSED [0.0062s] [ 38%] 2024-08-20T21:41:28.4608062Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_complex128 PASSED [0.0062s] [ 38%] 2024-08-20T21:41:28.4608690Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_complex32 PASSED [0.0063s] [ 38%] 2024-08-20T21:41:28.4609304Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_complex64 PASSED [0.0061s] [ 38%] 2024-08-20T21:41:28.4609902Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_float16 PASSED [0.0063s] [ 38%] 2024-08-20T21:41:28.4610510Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_float32 PASSED [0.0062s] [ 38%] 2024-08-20T21:41:28.4611102Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_float64 PASSED [0.0063s] [ 38%] 2024-08-20T21:41:28.4611690Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_int16 PASSED [0.0062s] [ 38%] 2024-08-20T21:41:28.4612295Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_int32 PASSED [0.0067s] [ 38%] 2024-08-20T21:41:28.4612878Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_int64 PASSED [0.0061s] [ 38%] 2024-08-20T21:41:28.4613500Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_int8 PASSED [0.0063s] [ 38%] 2024-08-20T21:41:28.4614123Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_uint8 PASSED [0.0061s] [ 38%] 2024-08-20T21:41:28.4614735Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_istft_cpu_complex128 PASSED [0.0102s] [ 38%] 2024-08-20T21:41:28.4615358Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_istft_cpu_complex64 PASSED [0.0103s] [ 38%] 2024-08-20T21:41:28.4615954Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_bfloat16 PASSED [0.0067s] [ 38%] 2024-08-20T21:41:28.4616565Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_bool PASSED [0.0068s] [ 38%] 2024-08-20T21:41:28.4617174Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_complex128 PASSED [0.0067s] [ 38%] 2024-08-20T21:41:28.4617774Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_complex32 PASSED [0.0068s] [ 38%] 2024-08-20T21:41:28.4618382Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_complex64 PASSED [0.0067s] [ 38%] 2024-08-20T21:41:28.4618970Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_float16 PASSED [0.0068s] [ 38%] 2024-08-20T21:41:28.4619568Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_float32 PASSED [0.0069s] [ 38%] 2024-08-20T21:41:28.4620148Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_float64 PASSED [0.0066s] [ 38%] 2024-08-20T21:41:28.4620726Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_int16 PASSED [0.0068s] [ 38%] 2024-08-20T21:41:28.4621605Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_int32 PASSED [0.0066s] [ 38%] 2024-08-20T21:41:28.4622187Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_int64 PASSED [0.0068s] [ 38%] 2024-08-20T21:41:28.4622763Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_int8 PASSED [0.0066s] [ 38%] 2024-08-20T21:41:28.4623403Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_uint8 PASSED [0.0068s] [ 38%] 2024-08-20T21:41:28.4624262Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_bfloat16 SKIPPED [0.0012s] (Only runs on cuda) [ 38%] 2024-08-20T21:41:28.4625104Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_bool SKIPPED [0.0011s] (Only runs on cuda) [ 38%] 2024-08-20T21:41:28.4625962Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_complex128 SKIPPED [0.0011s] (Only runs on cuda) [ 38%] 2024-08-20T21:41:28.4626821Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_complex64 SKIPPED [0.0011s] (Only runs on cuda) [ 38%] 2024-08-20T21:41:28.4627680Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_float16 SKIPPED [0.0015s] (Only runs on cuda) [ 38%] 2024-08-20T21:41:28.4628518Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_float32 SKIPPED [0.0011s] (Only runs on cuda) [ 38%] 2024-08-20T21:41:28.4629368Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_float64 SKIPPED [0.0011s] (Only runs on cuda) [ 38%] 2024-08-20T21:41:28.4630201Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_int16 SKIPPED [0.0011s] (Only runs on cuda) [ 38%] 2024-08-20T21:41:28.4631065Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_int32 SKIPPED [0.0011s] (Only runs on cuda) [ 38%] 2024-08-20T21:41:28.4631880Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_int64 SKIPPED [0.0011s] (Only runs on cuda) [ 38%] 2024-08-20T21:41:28.4632728Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_int8 SKIPPED [0.0017s] (Only runs on cuda) [ 38%] 2024-08-20T21:41:28.4633572Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_uint8 SKIPPED [0.0011s] (Only runs on cuda) [ 38%] 2024-08-20T21:41:28.4634478Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_bfloat16 SKIPPED [0.0011s] (Only runs on cuda) [ 38%] 2024-08-20T21:41:28.4635351Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_bool SKIPPED [0.0011s] (Only runs on cuda) [ 38%] 2024-08-20T21:41:28.4636249Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_complex128 SKIPPED [0.0011s] (Only runs on cuda) [ 38%] 2024-08-20T21:41:28.4637136Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_complex64 SKIPPED [0.0011s] (Only runs on cuda) [ 38%] 2024-08-20T21:41:28.4638021Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_float16 SKIPPED [0.0013s] (Only runs on cuda) [ 38%] 2024-08-20T21:41:28.4638887Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_float32 SKIPPED [0.0011s] (Only runs on cuda) [ 38%] 2024-08-20T21:41:28.4639764Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_float64 SKIPPED [0.0011s] (Only runs on cuda) [ 38%] 2024-08-20T21:41:28.4640632Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_int16 SKIPPED [0.0011s] (Only runs on cuda) [ 38%] 2024-08-20T21:41:28.4641489Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_int32 SKIPPED [0.0011s] (Only runs on cuda) [ 38%] 2024-08-20T21:41:28.4642388Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_int64 SKIPPED [0.0012s] (Only runs on cuda) [ 38%] 2024-08-20T21:41:28.4643245Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_int8 SKIPPED [0.0013s] (Only runs on cuda) [ 38%] 2024-08-20T21:41:28.4644117Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_uint8 SKIPPED [0.0011s] (Only runs on cuda) [ 38%] 2024-08-20T21:41:28.4644906Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_bfloat16 SKIPPED [0.0011s] (Only runs on cuda) [ 38%] 2024-08-20T21:41:28.4645685Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_bool SKIPPED [0.0011s] (Only runs on cuda) [ 38%] 2024-08-20T21:41:28.4646485Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_complex128 SKIPPED [0.0011s] (Only runs on cuda) [ 38%] 2024-08-20T21:41:28.4647500Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_complex64 SKIPPED [0.0011s] (Only runs on cuda) [ 38%] 2024-08-20T21:41:28.4648303Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_float16 SKIPPED [0.0014s] (Only runs on cuda) [ 39%] 2024-08-20T21:41:28.4649094Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_float32 SKIPPED [0.0011s] (Only runs on cuda) [ 39%] 2024-08-20T21:41:28.4649954Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_float64 SKIPPED [0.0011s] (Only runs on cuda) [ 39%] 2024-08-20T21:41:28.4650729Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_int16 SKIPPED [0.0012s] (Only runs on cuda) [ 39%] 2024-08-20T21:41:28.4651540Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_int32 SKIPPED [0.0011s] (Only runs on cuda) [ 39%] 2024-08-20T21:41:28.4652325Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_int64 SKIPPED [0.0011s] (Only runs on cuda) [ 39%] 2024-08-20T21:41:28.4653087Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_int8 SKIPPED [0.0013s] (Only runs on cuda) [ 39%] 2024-08-20T21:41:28.4653902Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_uint8 SKIPPED [0.0011s] (Only runs on cuda) [ 39%] 2024-08-20T21:41:28.4654773Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_bfloat16 SKIPPED [0.0011s] (Only runs on cuda) [ 39%] 2024-08-20T21:41:28.4655613Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_bool SKIPPED [0.0011s] (Only runs on cuda) [ 39%] 2024-08-20T21:41:28.4656505Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_complex128 SKIPPED [0.0012s] (Only runs on cuda) [ 39%] 2024-08-20T21:41:28.4657372Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_complex64 SKIPPED [0.0011s] (Only runs on cuda) [ 39%] 2024-08-20T21:41:28.4658240Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_float16 SKIPPED [0.0013s] (Only runs on cuda) [ 39%] 2024-08-20T21:41:28.4659095Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_float32 SKIPPED [0.0011s] (Only runs on cuda) [ 39%] 2024-08-20T21:41:28.4659944Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_float64 SKIPPED [0.0011s] (Only runs on cuda) [ 39%] 2024-08-20T21:41:28.4660795Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_int16 SKIPPED [0.0011s] (Only runs on cuda) [ 39%] 2024-08-20T21:41:28.4661681Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_int32 SKIPPED [0.0011s] (Only runs on cuda) [ 39%] 2024-08-20T21:41:28.4662541Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_int64 SKIPPED [0.0011s] (Only runs on cuda) [ 39%] 2024-08-20T21:41:28.4663375Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_int8 SKIPPED [0.0013s] (Only runs on cuda) [ 39%] 2024-08-20T21:41:28.4664217Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_uint8 SKIPPED [0.0011s] (Only runs on cuda) [ 39%] 2024-08-20T21:41:28.4665006Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_bfloat16 SKIPPED [0.0011s] (Only runs on cuda) [ 39%] 2024-08-20T21:41:28.4665768Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_bool SKIPPED [0.0012s] (Only runs on cuda) [ 39%] 2024-08-20T21:41:28.4666568Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_complex128 SKIPPED [0.0011s] (Only runs on cuda) [ 39%] 2024-08-20T21:41:28.4667353Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_complex64 SKIPPED [0.0011s] (Only runs on cuda) [ 39%] 2024-08-20T21:41:28.4668118Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_float16 SKIPPED [0.0013s] (Only runs on cuda) [ 39%] 2024-08-20T21:41:28.4668947Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_float32 SKIPPED [0.0011s] (Only runs on cuda) [ 39%] 2024-08-20T21:41:28.4669752Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_float64 SKIPPED [0.0011s] (Only runs on cuda) [ 39%] 2024-08-20T21:41:28.4670531Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_int16 SKIPPED [0.0012s] (Only runs on cuda) [ 39%] 2024-08-20T21:41:28.4671291Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_int32 SKIPPED [0.0011s] (Only runs on cuda) [ 39%] 2024-08-20T21:41:28.4672077Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_int64 SKIPPED [0.0011s] (Only runs on cuda) [ 39%] 2024-08-20T21:41:28.4672846Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_int8 SKIPPED [0.0013s] (Only runs on cuda) [ 39%] 2024-08-20T21:41:28.4673608Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_uint8 SKIPPED [0.0012s] (Only runs on cuda) [ 39%] 2024-08-20T21:41:28.4674226Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_bfloat16 PASSED [0.0052s] [ 39%] 2024-08-20T21:41:28.4674801Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_bool PASSED [0.0054s] [ 39%] 2024-08-20T21:41:28.4675411Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_complex128 PASSED [0.0052s] [ 39%] 2024-08-20T21:41:28.4676032Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_complex64 PASSED [0.0053s] [ 39%] 2024-08-20T21:41:28.4676625Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_float16 PASSED [0.0051s] [ 39%] 2024-08-20T21:41:28.4677229Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_float32 PASSED [0.0053s] [ 39%] 2024-08-20T21:41:28.4677813Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_float64 PASSED [0.0051s] [ 39%] 2024-08-20T21:41:28.4678395Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_int16 PASSED [0.0051s] [ 39%] 2024-08-20T21:41:28.4679024Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_int32 PASSED [0.0052s] [ 39%] 2024-08-20T21:41:28.4679600Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_int64 PASSED [0.0051s] [ 39%] 2024-08-20T21:41:28.4680183Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_int8 PASSED [0.0052s] [ 39%] 2024-08-20T21:41:28.4680763Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_uint8 PASSED [0.0051s] [ 39%] 2024-08-20T21:41:28.4681378Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_bfloat16 PASSED [0.0100s] [ 39%] 2024-08-20T21:41:28.4682003Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_float16 PASSED [0.0097s] [ 39%] 2024-08-20T21:41:28.4682619Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_float32 PASSED [0.0099s] [ 39%] 2024-08-20T21:41:28.4683252Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_float64 PASSED [0.0097s] [ 39%] 2024-08-20T21:41:28.4683857Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_int16 PASSED [0.0099s] [ 39%] 2024-08-20T21:41:28.4684459Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_int32 PASSED [0.0097s] [ 39%] 2024-08-20T21:41:28.4685076Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_int64 PASSED [0.0097s] [ 39%] 2024-08-20T21:41:28.4685671Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_int8 PASSED [0.0099s] [ 39%] 2024-08-20T21:41:28.4686296Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_uint8 PASSED [0.0097s] [ 39%] 2024-08-20T21:41:28.4687023Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lcm_cpu_int16 PASSED [0.0097s] [ 39%] 2024-08-20T21:41:28.4687606Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lcm_cpu_int32 PASSED [0.0095s] [ 39%] 2024-08-20T21:41:28.4688191Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lcm_cpu_int64 PASSED [0.0098s] [ 39%] 2024-08-20T21:41:28.4688757Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lcm_cpu_int8 PASSED [0.0095s] [ 39%] 2024-08-20T21:41:28.4689353Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lcm_cpu_uint8 PASSED [0.0097s] [ 39%] 2024-08-20T21:41:28.4689979Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_bfloat16 PASSED [0.0095s] [ 39%] 2024-08-20T21:41:28.4690561Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_bool PASSED [0.0096s] [ 40%] 2024-08-20T21:41:28.4691189Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_complex128 PASSED [0.0096s] [ 40%] 2024-08-20T21:41:28.4691801Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_complex64 PASSED [0.0096s] [ 40%] 2024-08-20T21:41:28.4692397Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_float16 PASSED [0.0096s] [ 40%] 2024-08-20T21:41:28.4693005Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_float32 PASSED [0.0094s] [ 40%] 2024-08-20T21:41:28.4693600Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_float64 PASSED [0.0097s] [ 40%] 2024-08-20T21:41:28.4694188Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_int16 PASSED [0.0095s] [ 40%] 2024-08-20T21:41:28.4694791Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_int32 PASSED [0.0097s] [ 40%] 2024-08-20T21:41:28.4695385Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_int64 PASSED [0.0094s] [ 40%] 2024-08-20T21:41:28.4696017Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_int8 PASSED [0.0097s] [ 40%] 2024-08-20T21:41:28.4696603Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_uint8 PASSED [0.0095s] [ 40%] 2024-08-20T21:41:28.4697185Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_bfloat16 PASSED [0.0097s] [ 40%] 2024-08-20T21:41:28.4697767Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_bool PASSED [0.0095s] [ 40%] 2024-08-20T21:41:28.4698343Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_float16 PASSED [0.0095s] [ 40%] 2024-08-20T21:41:28.4698941Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_float32 PASSED [0.0096s] [ 40%] 2024-08-20T21:41:28.4699519Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_float64 PASSED [0.0096s] [ 40%] 2024-08-20T21:41:28.4700084Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_int16 PASSED [0.0098s] [ 40%] 2024-08-20T21:41:28.4700670Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_int32 PASSED [0.0095s] [ 40%] 2024-08-20T21:41:28.4701235Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_int64 PASSED [0.0097s] [ 40%] 2024-08-20T21:41:28.4701809Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_int8 PASSED [0.0095s] [ 40%] 2024-08-20T21:41:28.4702375Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_uint8 PASSED [0.0096s] [ 40%] 2024-08-20T21:41:28.4703004Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_bfloat16 PASSED [0.0120s] [ 40%] 2024-08-20T21:41:28.4703629Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_complex128 PASSED [0.0167s] [ 40%] 2024-08-20T21:41:28.4704264Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_complex64 PASSED [0.0165s] [ 40%] 2024-08-20T21:41:28.4704857Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_float16 PASSED [0.0121s] [ 40%] 2024-08-20T21:41:28.4705446Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_float32 PASSED [0.0123s] [ 40%] 2024-08-20T21:41:28.4706069Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_float64 PASSED [0.0121s] [ 40%] 2024-08-20T21:41:28.4706690Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_bfloat16 PASSED [0.0063s] [ 40%] 2024-08-20T21:41:28.4707278Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_bool PASSED [0.0061s] [ 40%] 2024-08-20T21:41:28.4707876Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_float16 PASSED [0.0063s] [ 40%] 2024-08-20T21:41:28.4708492Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_float32 PASSED [0.0061s] [ 40%] 2024-08-20T21:41:28.4709090Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_float64 PASSED [0.0063s] [ 40%] 2024-08-20T21:41:28.4709695Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_int16 PASSED [0.0061s] [ 40%] 2024-08-20T21:41:28.4710279Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_int32 PASSED [0.0063s] [ 40%] 2024-08-20T21:41:28.4710860Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_int64 PASSED [0.0061s] [ 40%] 2024-08-20T21:41:28.4711453Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_int8 PASSED [0.0060s] [ 40%] 2024-08-20T21:41:28.4712040Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_uint8 PASSED [0.0062s] [ 40%] 2024-08-20T21:41:28.4712721Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_cpu_complex128 PASSED [0.0143s] [ 40%] 2024-08-20T21:41:28.4713432Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_cpu_complex64 PASSED [0.0144s] [ 40%] 2024-08-20T21:41:28.4714086Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_cpu_float32 PASSED [0.0138s] [ 40%] 2024-08-20T21:41:28.4714753Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_cpu_float64 PASSED [0.0139s] [ 40%] 2024-08-20T21:41:28.4715442Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_ex_cpu_complex128 PASSED [0.0142s] [ 40%] 2024-08-20T21:41:28.4716127Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_ex_cpu_complex64 PASSED [0.0142s] [ 40%] 2024-08-20T21:41:28.4716915Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_ex_cpu_float32 PASSED [0.0137s] [ 40%] 2024-08-20T21:41:28.4717593Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_ex_cpu_float64 PASSED [0.0134s] [ 40%] 2024-08-20T21:41:28.4718257Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cond_cpu_complex128 PASSED [0.0071s] [ 40%] 2024-08-20T21:41:28.4718898Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cond_cpu_complex64 PASSED [0.0064s] [ 40%] 2024-08-20T21:41:28.4719529Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cond_cpu_float32 PASSED [0.0066s] [ 40%] 2024-08-20T21:41:28.4720170Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cond_cpu_float64 PASSED [0.0063s] [ 40%] 2024-08-20T21:41:28.4721107Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_bfloat16 PASSED [0.0063s] [ 40%] 2024-08-20T21:41:28.4721826Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_complex128 PASSED [0.0062s] [ 40%] 2024-08-20T21:41:28.4722485Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_complex64 PASSED [0.0062s] [ 40%] 2024-08-20T21:41:28.4723124Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_float16 PASSED [0.0063s] [ 40%] 2024-08-20T21:41:28.4723807Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_float32 PASSED [0.0061s] [ 40%] 2024-08-20T21:41:28.4724441Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_float64 PASSED [0.0064s] [ 40%] 2024-08-20T21:41:28.4725075Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_int16 PASSED [0.0062s] [ 40%] 2024-08-20T21:41:28.4725701Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_int32 PASSED [0.0064s] [ 40%] 2024-08-20T21:41:28.4726325Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_int64 PASSED [0.0062s] [ 40%] 2024-08-20T21:41:28.4727041Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_int8 PASSED [0.0064s] [ 40%] 2024-08-20T21:41:28.4727674Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_uint8 PASSED [0.0061s] [ 40%] 2024-08-20T21:41:28.4728332Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_cpu_complex128 PASSED [0.0118s] [ 41%] 2024-08-20T21:41:28.4728966Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_cpu_complex64 PASSED [0.0115s] [ 41%] 2024-08-20T21:41:28.4729593Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_cpu_float32 PASSED [0.0112s] [ 41%] 2024-08-20T21:41:28.4730223Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_cpu_float64 PASSED [0.0113s] [ 41%] 2024-08-20T21:41:28.4730923Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_singular_cpu_complex128 PASSED [0.0304s] [ 41%] 2024-08-20T21:41:28.4731664Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_singular_cpu_complex64 PASSED [0.0295s] [ 41%] 2024-08-20T21:41:28.4732346Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_singular_cpu_float32 PASSED [0.0281s] [ 41%] 2024-08-20T21:41:28.4733027Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_singular_cpu_float64 PASSED [0.0287s] [ 41%] 2024-08-20T21:41:28.4733696Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_bfloat16 PASSED [0.0126s] [ 41%] 2024-08-20T21:41:28.4734343Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_bool PASSED [0.0124s] [ 41%] 2024-08-20T21:41:28.4735018Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_complex128 PASSED [0.0127s] [ 41%] 2024-08-20T21:41:28.4735704Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_complex32 PASSED [0.0125s] [ 41%] 2024-08-20T21:41:28.4736371Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_complex64 PASSED [0.0128s] [ 41%] 2024-08-20T21:41:28.4737040Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_float16 PASSED [0.0124s] [ 41%] 2024-08-20T21:41:28.4737692Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_float32 PASSED [0.0126s] [ 41%] 2024-08-20T21:41:28.4738372Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_float64 PASSED [0.0124s] [ 41%] 2024-08-20T21:41:28.4739025Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_int16 PASSED [0.0127s] [ 41%] 2024-08-20T21:41:28.4739699Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_int32 PASSED [0.0125s] [ 41%] 2024-08-20T21:41:28.4740355Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_int64 PASSED [0.0124s] [ 41%] 2024-08-20T21:41:28.4740991Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_int8 PASSED [0.0129s] [ 41%] 2024-08-20T21:41:28.4741660Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_uint8 PASSED [0.0125s] [ 41%] 2024-08-20T21:41:28.4742314Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eig_cpu_complex128 PASSED [0.0116s] [ 41%] 2024-08-20T21:41:28.4742948Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eig_cpu_complex64 PASSED [0.0111s] [ 41%] 2024-08-20T21:41:28.4743587Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eig_cpu_float32 PASSED [0.0110s] [ 41%] 2024-08-20T21:41:28.4744211Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eig_cpu_float64 PASSED [0.0109s] [ 41%] 2024-08-20T21:41:28.4744857Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigh_cpu_complex128 PASSED [0.0110s] [ 41%] 2024-08-20T21:41:28.4745510Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigh_cpu_complex64 PASSED [0.0112s] [ 41%] 2024-08-20T21:41:28.4746142Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigh_cpu_float32 PASSED [0.0109s] [ 41%] 2024-08-20T21:41:28.4747001Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigh_cpu_float64 PASSED [0.0115s] [ 41%] 2024-08-20T21:41:28.4747687Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvals_cpu_complex128 PASSED [0.0108s] [ 41%] 2024-08-20T21:41:28.4748355Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvals_cpu_complex64 PASSED [0.0110s] [ 41%] 2024-08-20T21:41:28.4749083Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvals_cpu_float32 PASSED [0.0109s] [ 41%] 2024-08-20T21:41:28.4749734Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvals_cpu_float64 PASSED [0.0107s] [ 41%] 2024-08-20T21:41:28.4750408Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvalsh_cpu_complex128 PASSED [0.0110s] [ 41%] 2024-08-20T21:41:28.4751090Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvalsh_cpu_complex64 PASSED [0.0108s] [ 41%] 2024-08-20T21:41:28.4751751Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvalsh_cpu_float32 PASSED [0.0108s] [ 41%] 2024-08-20T21:41:28.4752418Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvalsh_cpu_float64 PASSED [0.0106s] [ 41%] 2024-08-20T21:41:28.4753159Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_householder_product_cpu_complex128 PASSED [0.0094s] [ 41%] 2024-08-20T21:41:28.4753895Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_householder_product_cpu_complex64 PASSED [0.0094s] [ 41%] 2024-08-20T21:41:28.4754629Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_householder_product_cpu_float32 PASSED [0.0091s] [ 41%] 2024-08-20T21:41:28.4755351Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_householder_product_cpu_float64 PASSED [0.0093s] [ 41%] 2024-08-20T21:41:28.4756001Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_cpu_complex128 PASSED [0.0107s] [ 41%] 2024-08-20T21:41:28.4756676Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_cpu_complex64 PASSED [0.0109s] [ 41%] 2024-08-20T21:41:28.4757337Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_cpu_float32 PASSED [0.0104s] [ 41%] 2024-08-20T21:41:28.4757977Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_cpu_float64 PASSED [0.0106s] [ 41%] 2024-08-20T21:41:28.4758633Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_ex_cpu_complex128 PASSED [0.0106s] [ 41%] 2024-08-20T21:41:28.4759333Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_ex_cpu_complex64 PASSED [0.0107s] [ 41%] 2024-08-20T21:41:28.4759975Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_ex_cpu_float32 PASSED [0.0106s] [ 41%] 2024-08-20T21:41:28.4760615Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_ex_cpu_float64 PASSED [0.0103s] [ 41%] 2024-08-20T21:41:28.4761306Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_cpu_complex128 PASSED [0.0090s] [ 41%] 2024-08-20T21:41:28.4761989Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_cpu_complex64 PASSED [0.0083s] [ 41%] 2024-08-20T21:41:28.4762664Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_cpu_float32 PASSED [0.0071s] [ 41%] 2024-08-20T21:41:28.4763326Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_cpu_float64 PASSED [0.0069s] [ 41%] 2024-08-20T21:41:28.4764029Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_ex_cpu_complex128 PASSED [0.0083s] [ 41%] 2024-08-20T21:41:28.4764821Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_ex_cpu_complex64 PASSED [0.0084s] [ 41%] 2024-08-20T21:41:28.4765512Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_ex_cpu_float32 PASSED [0.0070s] [ 41%] 2024-08-20T21:41:28.4766194Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_ex_cpu_float64 PASSED [0.0071s] [ 41%] 2024-08-20T21:41:28.4766972Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_solve_cpu_complex128 PASSED [0.0190s] [ 41%] 2024-08-20T21:41:28.4767650Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_solve_cpu_complex64 PASSED [0.0191s] [ 41%] 2024-08-20T21:41:28.4768328Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_solve_cpu_float32 PASSED [0.0144s] [ 42%] 2024-08-20T21:41:28.4768980Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_solve_cpu_float64 PASSED [0.0142s] [ 42%] 2024-08-20T21:41:28.4769646Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_cpu_complex128 PASSED [0.0360s] [ 42%] 2024-08-20T21:41:28.4770291Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_cpu_complex64 PASSED [0.0356s] [ 42%] 2024-08-20T21:41:28.4770927Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_cpu_float32 PASSED [0.0353s] [ 42%] 2024-08-20T21:41:28.4771579Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_cpu_float64 PASSED [0.0345s] [ 42%] 2024-08-20T21:41:28.4772308Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_grad_oriented_cpu_complex128 PASSED [0.0356s] [ 42%] 2024-08-20T21:41:28.4773044Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_grad_oriented_cpu_complex64 PASSED [0.0358s] [ 42%] 2024-08-20T21:41:28.4773770Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_grad_oriented_cpu_float32 PASSED [0.0346s] [ 42%] 2024-08-20T21:41:28.4774517Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_grad_oriented_cpu_float64 PASSED [0.0351s] [ 42%] 2024-08-20T21:41:28.4775191Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_cpu_complex128 PASSED [0.0128s] [ 42%] 2024-08-20T21:41:28.4775819Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_cpu_complex64 PASSED [0.0136s] [ 42%] 2024-08-20T21:41:28.4776435Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_cpu_float32 PASSED [0.0125s] [ 42%] 2024-08-20T21:41:28.4777090Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_cpu_float64 PASSED [0.0127s] [ 42%] 2024-08-20T21:41:28.4777762Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_cpu_complex128 PASSED [0.0181s] [ 42%] 2024-08-20T21:41:28.4778447Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_cpu_complex64 PASSED [0.0180s] [ 42%] 2024-08-20T21:41:28.4779099Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_cpu_float32 PASSED [0.0178s] [ 42%] 2024-08-20T21:41:28.4779754Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_cpu_float64 PASSED [0.0173s] [ 42%] 2024-08-20T21:41:28.4780455Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_ex_cpu_complex128 PASSED [0.0129s] [ 42%] 2024-08-20T21:41:28.4781141Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_ex_cpu_complex64 PASSED [0.0127s] [ 42%] 2024-08-20T21:41:28.4781828Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_ex_cpu_float32 PASSED [0.0127s] [ 42%] 2024-08-20T21:41:28.4782497Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_ex_cpu_float64 PASSED [0.0126s] [ 42%] 2024-08-20T21:41:28.4783172Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_solve_cpu_complex128 PASSED [0.0671s] [ 42%] 2024-08-20T21:41:28.4783849Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_solve_cpu_complex64 PASSED [0.0677s] [ 42%] 2024-08-20T21:41:28.4784526Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_solve_cpu_float32 PASSED [0.0665s] [ 42%] 2024-08-20T21:41:28.4785180Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_solve_cpu_float64 PASSED [0.0666s] [ 42%] 2024-08-20T21:41:28.4785853Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_bfloat16 PASSED [0.0261s] [ 42%] 2024-08-20T21:41:28.4786538Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_complex128 PASSED [0.0400s] [ 42%] 2024-08-20T21:41:28.4787234Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_complex64 PASSED [0.0396s] [ 42%] 2024-08-20T21:41:28.4787898Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_float16 PASSED [0.0262s] [ 42%] 2024-08-20T21:41:28.4788587Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_float32 PASSED [0.0395s] [ 42%] 2024-08-20T21:41:28.4789254Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_float64 PASSED [0.0389s] [ 42%] 2024-08-20T21:41:28.4789948Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_power_cpu_complex128 PASSED [0.0181s] [ 42%] 2024-08-20T21:41:28.4790647Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_power_cpu_complex64 PASSED [0.0174s] [ 42%] 2024-08-20T21:41:28.4791319Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_power_cpu_float32 PASSED [0.0172s] [ 42%] 2024-08-20T21:41:28.4792030Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_power_cpu_float64 PASSED [0.0170s] [ 42%] 2024-08-20T21:41:28.4792747Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_cpu_complex128 PASSED [0.0573s] [ 42%] 2024-08-20T21:41:28.4793431Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_cpu_complex64 PASSED [0.0585s] [ 42%] 2024-08-20T21:41:28.4794109Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_cpu_float32 PASSED [0.0564s] [ 42%] 2024-08-20T21:41:28.4794814Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_cpu_float64 PASSED [0.0562s] [ 42%] 2024-08-20T21:41:28.4795569Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_hermitian_cpu_complex128 PASSED [0.0108s] [ 42%] 2024-08-20T21:41:28.4796306Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_hermitian_cpu_complex64 PASSED [0.0112s] [ 42%] 2024-08-20T21:41:28.4797032Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_hermitian_cpu_float32 PASSED [0.0106s] [ 42%] 2024-08-20T21:41:28.4797772Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_hermitian_cpu_float64 PASSED [0.0107s] [ 42%] 2024-08-20T21:41:28.4798435Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_bfloat16 PASSED [0.0088s] [ 42%] 2024-08-20T21:41:28.4799120Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_complex128 PASSED [0.0086s] [ 42%] 2024-08-20T21:41:28.4799785Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_complex64 PASSED [0.0088s] [ 42%] 2024-08-20T21:41:28.4800440Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_float16 PASSED [0.0084s] [ 42%] 2024-08-20T21:41:28.4801110Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_float32 PASSED [0.0086s] [ 42%] 2024-08-20T21:41:28.4801769Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_float64 PASSED [0.0083s] [ 42%] 2024-08-20T21:41:28.4802448Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_int16 PASSED [0.0086s] [ 42%] 2024-08-20T21:41:28.4803102Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_int32 PASSED [0.0085s] [ 42%] 2024-08-20T21:41:28.4803748Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_int64 PASSED [0.0083s] [ 42%] 2024-08-20T21:41:28.4804394Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_int8 PASSED [0.0089s] [ 42%] 2024-08-20T21:41:28.4805035Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_uint8 PASSED [0.0084s] [ 42%] 2024-08-20T21:41:28.4805674Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_bfloat16 PASSED [0.0473s] [ 42%] 2024-08-20T21:41:28.4806339Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_complex128 PASSED [0.0599s] [ 42%] 2024-08-20T21:41:28.4807042Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_complex64 PASSED [0.0600s] [ 42%] 2024-08-20T21:41:28.4807690Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_float16 PASSED [0.0463s] [ 42%] 2024-08-20T21:41:28.4808314Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_float32 PASSED [0.0588s] [ 42%] 2024-08-20T21:41:28.4808943Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_float64 PASSED [0.0598s] [ 43%] 2024-08-20T21:41:28.4809750Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_bfloat16 PASSED [0.0352s] [ 43%] 2024-08-20T21:41:28.4810545Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_complex128 PASSED [0.0440s] [ 43%] 2024-08-20T21:41:28.4811322Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_complex64 PASSED [0.0430s] [ 43%] 2024-08-20T21:41:28.4812071Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_float16 PASSED [0.0354s] [ 43%] 2024-08-20T21:41:28.4812850Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_float32 PASSED [0.0433s] [ 43%] 2024-08-20T21:41:28.4813617Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_float64 PASSED [0.0431s] [ 43%] 2024-08-20T21:41:28.4814262Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_cpu_complex128 PASSED [0.0200s] [ 43%] 2024-08-20T21:41:28.4814922Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_cpu_complex64 PASSED [0.0195s] [ 43%] 2024-08-20T21:41:28.4815554Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_cpu_float32 PASSED [0.0194s] [ 43%] 2024-08-20T21:41:28.4816185Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_cpu_float64 PASSED [0.0192s] [ 43%] 2024-08-20T21:41:28.4816909Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_hermitian_cpu_complex128 PASSED [0.0109s] [ 43%] 2024-08-20T21:41:28.4817695Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_hermitian_cpu_complex64 PASSED [0.0110s] [ 43%] 2024-08-20T21:41:28.4818405Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_hermitian_cpu_float32 PASSED [0.0106s] [ 43%] 2024-08-20T21:41:28.4819092Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_hermitian_cpu_float64 PASSED [0.0112s] [ 43%] 2024-08-20T21:41:28.4820208Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_singular_cpu_complex128 SKIPPED [0.0012s] (test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test) [ 43%] 2024-08-20T21:41:28.4821487Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_singular_cpu_complex64 SKIPPED [0.0012s] (test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test) [ 43%] 2024-08-20T21:41:28.4822550Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_singular_cpu_float32 SKIPPED [0.0012s] (test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test) [ 43%] 2024-08-20T21:41:28.4823610Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_singular_cpu_float64 SKIPPED [0.0011s] (test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test) [ 43%] 2024-08-20T21:41:28.4824249Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_qr_cpu_complex128 PASSED [0.0309s] [ 43%] 2024-08-20T21:41:28.4824884Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_qr_cpu_complex64 PASSED [0.0316s] [ 43%] 2024-08-20T21:41:28.4825517Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_qr_cpu_float32 PASSED [0.0303s] [ 43%] 2024-08-20T21:41:28.4826132Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_qr_cpu_float64 PASSED [0.0306s] [ 43%] 2024-08-20T21:41:28.4826816Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_slogdet_cpu_complex128 PASSED [0.0121s] [ 43%] 2024-08-20T21:41:28.4827511Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_slogdet_cpu_complex64 PASSED [0.0123s] [ 43%] 2024-08-20T21:41:28.4828165Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_slogdet_cpu_float32 PASSED [0.0118s] [ 43%] 2024-08-20T21:41:28.4828853Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_slogdet_cpu_float64 PASSED [0.0117s] [ 43%] 2024-08-20T21:41:28.4829506Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_cpu_complex128 PASSED [0.0188s] [ 43%] 2024-08-20T21:41:28.4830165Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_cpu_complex64 PASSED [0.0185s] [ 43%] 2024-08-20T21:41:28.4830829Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_cpu_float32 PASSED [0.0183s] [ 43%] 2024-08-20T21:41:28.4831468Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_cpu_float64 PASSED [0.0179s] [ 43%] 2024-08-20T21:41:28.4832156Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_ex_cpu_complex128 PASSED [0.0188s] [ 43%] 2024-08-20T21:41:28.4832822Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_ex_cpu_complex64 PASSED [0.0188s] [ 43%] 2024-08-20T21:41:28.4833486Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_ex_cpu_float32 PASSED [0.0181s] [ 43%] 2024-08-20T21:41:28.4834137Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_ex_cpu_float64 PASSED [0.0184s] [ 43%] 2024-08-20T21:41:28.4834851Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_triangular_cpu_complex128 PASSED [0.0894s] [ 43%] 2024-08-20T21:41:28.4835575Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_triangular_cpu_complex64 PASSED [0.0899s] [ 43%] 2024-08-20T21:41:28.4836275Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_triangular_cpu_float32 PASSED [0.0877s] [ 43%] 2024-08-20T21:41:28.4836984Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_triangular_cpu_float64 PASSED [0.0879s] [ 43%] 2024-08-20T21:41:28.4837630Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svd_cpu_complex128 PASSED [0.1627s] [ 43%] 2024-08-20T21:41:28.4838325Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svd_cpu_complex64 PASSED [0.1625s] [ 43%] 2024-08-20T21:41:28.4838961Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svd_cpu_float32 PASSED [0.1601s] [ 43%] 2024-08-20T21:41:28.4839578Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svd_cpu_float64 PASSED [0.1568s] [ 43%] 2024-08-20T21:41:28.4840249Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svdvals_cpu_complex128 PASSED [0.0248s] [ 43%] 2024-08-20T21:41:28.4840920Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svdvals_cpu_complex64 PASSED [0.0244s] [ 43%] 2024-08-20T21:41:28.4841565Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svdvals_cpu_float32 PASSED [0.0242s] [ 43%] 2024-08-20T21:41:28.4842222Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svdvals_cpu_float64 PASSED [0.0243s] [ 43%] 2024-08-20T21:41:28.4842907Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorinv_cpu_complex128 PASSED [0.0072s] [ 43%] 2024-08-20T21:41:28.4843579Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorinv_cpu_complex64 PASSED [0.0071s] [ 43%] 2024-08-20T21:41:28.4844252Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorinv_cpu_float32 PASSED [0.0065s] [ 43%] 2024-08-20T21:41:28.4844910Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorinv_cpu_float64 PASSED [0.0070s] [ 43%] 2024-08-20T21:41:28.4845640Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorsolve_cpu_complex128 PASSED [0.0070s] [ 43%] 2024-08-20T21:41:28.4846355Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorsolve_cpu_complex64 PASSED [0.0072s] [ 43%] 2024-08-20T21:41:28.4847292Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorsolve_cpu_float32 PASSED [0.0069s] [ 43%] 2024-08-20T21:41:28.4847994Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorsolve_cpu_float64 PASSED [0.0069s] [ 43%] 2024-08-20T21:41:28.4848738Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_complex128 PASSED [0.0101s] [ 43%] 2024-08-20T21:41:28.4849412Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_complex64 PASSED [0.0099s] [ 43%] 2024-08-20T21:41:28.4850056Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_float32 PASSED [0.0103s] [ 43%] 2024-08-20T21:41:28.4850698Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_float64 PASSED [0.0099s] [ 43%] 2024-08-20T21:41:28.4851345Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_int16 PASSED [0.0106s] [ 44%] 2024-08-20T21:41:28.4851977Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_int32 PASSED [0.0099s] [ 44%] 2024-08-20T21:41:28.4852617Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_int64 PASSED [0.0100s] [ 44%] 2024-08-20T21:41:28.4853245Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_int8 PASSED [0.0099s] [ 44%] 2024-08-20T21:41:28.4853882Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_uint8 PASSED [0.0098s] [ 44%] 2024-08-20T21:41:28.4854544Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_bfloat16 PASSED [0.0285s] [ 44%] 2024-08-20T21:41:28.4855205Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_complex128 PASSED [0.0285s] [ 44%] 2024-08-20T21:41:28.4855916Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_complex64 PASSED [0.0289s] [ 44%] 2024-08-20T21:41:28.4856552Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_float16 PASSED [0.0280s] [ 44%] 2024-08-20T21:41:28.4857195Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_float32 PASSED [0.0284s] [ 44%] 2024-08-20T21:41:28.4857846Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_float64 PASSED [0.0278s] [ 44%] 2024-08-20T21:41:28.4858524Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_bfloat16 PASSED [0.0919s] [ 44%] 2024-08-20T21:41:28.4859209Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_complex128 PASSED [0.0921s] [ 44%] 2024-08-20T21:41:28.4859904Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_complex64 PASSED [0.0919s] [ 44%] 2024-08-20T21:41:28.4860575Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_float16 PASSED [0.0918s] [ 44%] 2024-08-20T21:41:28.4861253Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_float32 PASSED [0.0908s] [ 44%] 2024-08-20T21:41:28.4861925Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_float64 PASSED [0.0911s] [ 44%] 2024-08-20T21:41:28.4862549Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_bfloat16 PASSED [0.0389s] [ 44%] 2024-08-20T21:41:28.4863232Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_complex128 PASSED [0.0387s] [ 44%] 2024-08-20T21:41:28.4863863Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_complex64 PASSED [0.0390s] [ 44%] 2024-08-20T21:41:28.4864544Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_float16 PASSED [0.0382s] [ 44%] 2024-08-20T21:41:28.4865248Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_float32 PASSED [0.0394s] [ 44%] 2024-08-20T21:41:28.4865869Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_float64 PASSED [0.0385s] [ 44%] 2024-08-20T21:41:28.4866520Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_int16 PASSED [0.0390s] [ 44%] 2024-08-20T21:41:28.4867119Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_int32 PASSED [0.0386s] [ 44%] 2024-08-20T21:41:28.4867725Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_int64 PASSED [0.0386s] [ 44%] 2024-08-20T21:41:28.4868320Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_int8 PASSED [0.0389s] [ 44%] 2024-08-20T21:41:28.4868926Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_uint8 PASSED [0.0254s] [ 44%] 2024-08-20T21:41:28.4869653Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_bfloat16 PASSED [0.0961s] [ 44%] 2024-08-20T21:41:28.4870376Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_complex128 PASSED [0.0956s] [ 44%] 2024-08-20T21:41:28.4871114Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_complex64 PASSED [0.0947s] [ 44%] 2024-08-20T21:41:28.4871823Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_float16 PASSED [0.0954s] [ 44%] 2024-08-20T21:41:28.4872533Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_float32 PASSED [0.0944s] [ 44%] 2024-08-20T21:41:28.4873253Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_float64 PASSED [0.0955s] [ 44%] 2024-08-20T21:41:28.4873992Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_int16 PASSED [0.0943s] [ 44%] 2024-08-20T21:41:28.4874698Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_int32 PASSED [0.0948s] [ 44%] 2024-08-20T21:41:28.4875388Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_int64 PASSED [0.0937s] [ 44%] 2024-08-20T21:41:28.4876075Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_int8 PASSED [0.0940s] [ 44%] 2024-08-20T21:41:28.4876782Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_uint8 PASSED [0.0598s] [ 44%] 2024-08-20T21:41:28.4877387Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_bfloat16 PASSED [0.0064s] [ 44%] 2024-08-20T21:41:28.4877966Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_bool PASSED [0.0066s] [ 44%] 2024-08-20T21:41:28.4878598Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_complex128 PASSED [0.0063s] [ 44%] 2024-08-20T21:41:28.4879202Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_complex64 PASSED [0.0070s] [ 44%] 2024-08-20T21:41:28.4879814Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_float16 PASSED [0.0062s] [ 44%] 2024-08-20T21:41:28.4880406Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_float32 PASSED [0.0064s] [ 44%] 2024-08-20T21:41:28.4881026Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_float64 PASSED [0.0062s] [ 44%] 2024-08-20T21:41:28.4881648Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_int16 PASSED [0.0062s] [ 44%] 2024-08-20T21:41:28.4882233Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_int32 PASSED [0.0065s] [ 44%] 2024-08-20T21:41:28.4882824Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_int64 PASSED [0.0063s] [ 44%] 2024-08-20T21:41:28.4883401Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_int8 PASSED [0.0064s] [ 44%] 2024-08-20T21:41:28.4884006Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_uint8 PASSED [0.0062s] [ 44%] 2024-08-20T21:41:28.4884617Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_bfloat16 PASSED [0.0055s] [ 44%] 2024-08-20T21:41:28.4885195Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_bool PASSED [0.0052s] [ 44%] 2024-08-20T21:41:28.4885821Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_complex128 PASSED [0.0055s] [ 44%] 2024-08-20T21:41:28.4886430Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_complex64 PASSED [0.0052s] [ 44%] 2024-08-20T21:41:28.4887118Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_float16 PASSED [0.0054s] [ 44%] 2024-08-20T21:41:28.4887733Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_float32 PASSED [0.0053s] [ 44%] 2024-08-20T21:41:28.4888323Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_float64 PASSED [0.0052s] [ 44%] 2024-08-20T21:41:28.4889070Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_int16 PASSED [0.0053s] [ 44%] 2024-08-20T21:41:28.4889679Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_int32 PASSED [0.0051s] [ 44%] 2024-08-20T21:41:28.4890259Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_int64 PASSED [0.0054s] [ 45%] 2024-08-20T21:41:28.4890848Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_int8 PASSED [0.0051s] [ 45%] 2024-08-20T21:41:28.4891476Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_uint8 PASSED [0.0053s] [ 45%] 2024-08-20T21:41:28.4892073Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_bfloat16 PASSED [0.0061s] [ 45%] 2024-08-20T21:41:28.4892658Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_bool PASSED [0.0067s] [ 45%] 2024-08-20T21:41:28.4893265Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_complex128 PASSED [0.0061s] [ 45%] 2024-08-20T21:41:28.4893886Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_complex64 PASSED [0.0063s] [ 45%] 2024-08-20T21:41:28.4894474Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_float16 PASSED [0.0063s] [ 45%] 2024-08-20T21:41:28.4895059Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_float32 PASSED [0.0061s] [ 45%] 2024-08-20T21:41:28.4895654Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_float64 PASSED [0.0063s] [ 45%] 2024-08-20T21:41:28.4896229Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_int16 PASSED [0.0062s] [ 45%] 2024-08-20T21:41:28.4896822Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_int32 PASSED [0.0064s] [ 45%] 2024-08-20T21:41:28.4897396Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_int64 PASSED [0.0061s] [ 45%] 2024-08-20T21:41:28.4897994Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_int8 PASSED [0.0063s] [ 45%] 2024-08-20T21:41:28.4898586Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_uint8 PASSED [0.0062s] [ 45%] 2024-08-20T21:41:28.4899210Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_bfloat16 PASSED [0.0064s] [ 45%] 2024-08-20T21:41:28.4899779Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_bool PASSED [0.0061s] [ 45%] 2024-08-20T21:41:28.4900392Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_complex128 PASSED [0.0063s] [ 45%] 2024-08-20T21:41:28.4901016Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_complex64 PASSED [0.0062s] [ 45%] 2024-08-20T21:41:28.4901612Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_float16 PASSED [0.0061s] [ 45%] 2024-08-20T21:41:28.4902193Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_float32 PASSED [0.0062s] [ 45%] 2024-08-20T21:41:28.4902771Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_float64 PASSED [0.0061s] [ 45%] 2024-08-20T21:41:28.4903361Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_int16 PASSED [0.0063s] [ 45%] 2024-08-20T21:41:28.4903932Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_int32 PASSED [0.0061s] [ 45%] 2024-08-20T21:41:28.4904512Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_int64 PASSED [0.0063s] [ 45%] 2024-08-20T21:41:28.4905076Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_int8 PASSED [0.0061s] [ 45%] 2024-08-20T21:41:28.4905644Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_uint8 PASSED [0.0068s] [ 45%] 2024-08-20T21:41:28.4906288Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_normal_cpu_bfloat16 PASSED [0.0061s] [ 45%] 2024-08-20T21:41:28.4906913Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_normal_cpu_float16 PASSED [0.0063s] [ 45%] 2024-08-20T21:41:28.4907539Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_normal_cpu_float32 PASSED [0.0061s] [ 45%] 2024-08-20T21:41:28.4908199Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_normal_cpu_float64 PASSED [0.0061s] [ 45%] 2024-08-20T21:41:28.4908834Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_cpu_bfloat16 PASSED [0.0082s] [ 45%] 2024-08-20T21:41:28.4909472Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_cpu_float16 PASSED [0.0081s] [ 45%] 2024-08-20T21:41:28.4910102Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_cpu_float32 PASSED [0.0084s] [ 45%] 2024-08-20T21:41:28.4910731Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_cpu_float64 PASSED [0.0081s] [ 45%] 2024-08-20T21:41:28.4911439Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_bfloat16 PASSED [0.0083s] [ 45%] 2024-08-20T21:41:28.4912113Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_bool PASSED [0.0081s] [ 45%] 2024-08-20T21:41:28.4912841Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_complex128 PASSED [0.0084s] [ 45%] 2024-08-20T21:41:28.4913544Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_complex32 PASSED [0.0082s] [ 45%] 2024-08-20T21:41:28.4914237Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_complex64 PASSED [0.0084s] [ 45%] 2024-08-20T21:41:28.4914943Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_float16 PASSED [0.0081s] [ 45%] 2024-08-20T21:41:28.4915658Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_float32 PASSED [0.0081s] [ 45%] 2024-08-20T21:41:28.4916377Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_float64 PASSED [0.0083s] [ 45%] 2024-08-20T21:41:28.4917056Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_int16 PASSED [0.0081s] [ 45%] 2024-08-20T21:41:28.4917726Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_int32 PASSED [0.0083s] [ 45%] 2024-08-20T21:41:28.4918430Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_int64 PASSED [0.0081s] [ 45%] 2024-08-20T21:41:28.4919099Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_int8 PASSED [0.0083s] [ 45%] 2024-08-20T21:41:28.4919791Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_uint8 PASSED [0.0081s] [ 45%] 2024-08-20T21:41:28.4920426Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp2_cpu_bfloat16 PASSED [0.0059s] [ 45%] 2024-08-20T21:41:28.4921309Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp2_cpu_float16 PASSED [0.0052s] [ 45%] 2024-08-20T21:41:28.4921959Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp2_cpu_float32 PASSED [0.0053s] [ 45%] 2024-08-20T21:41:28.4922581Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp2_cpu_float64 PASSED [0.0052s] [ 45%] 2024-08-20T21:41:28.4923228Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_bfloat16 PASSED [0.0096s] [ 45%] 2024-08-20T21:41:28.4923868Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_complex128 PASSED [0.0100s] [ 45%] 2024-08-20T21:41:28.4924503Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_complex64 PASSED [0.0097s] [ 45%] 2024-08-20T21:41:28.4925142Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_float16 PASSED [0.0098s] [ 45%] 2024-08-20T21:41:28.4925806Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_float32 PASSED [0.0096s] [ 45%] 2024-08-20T21:41:28.4926426Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_float64 PASSED [0.0098s] [ 45%] 2024-08-20T21:41:28.4927151Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_bfloat16 PASSED [0.0078s] [ 45%] 2024-08-20T21:41:28.4927815Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_complex128 PASSED [0.0080s] [ 46%] 2024-08-20T21:41:28.4928484Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_complex64 PASSED [0.0077s] [ 46%] 2024-08-20T21:41:28.4929124Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_float16 PASSED [0.0079s] [ 46%] 2024-08-20T21:41:28.4929767Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_float32 PASSED [0.0077s] [ 46%] 2024-08-20T21:41:28.4930424Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_float64 PASSED [0.0077s] [ 46%] 2024-08-20T21:41:28.4931046Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logdet_cpu_complex128 PASSED [0.0118s] [ 46%] 2024-08-20T21:41:28.4931677Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logdet_cpu_complex64 PASSED [0.0116s] [ 46%] 2024-08-20T21:41:28.4932286Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logdet_cpu_float32 PASSED [0.0122s] [ 46%] 2024-08-20T21:41:28.4932892Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logdet_cpu_float64 PASSED [0.0120s] [ 46%] 2024-08-20T21:41:28.4933594Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_bfloat16 PASSED [0.0099s] [ 46%] 2024-08-20T21:41:28.4934240Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_bool PASSED [0.0095s] [ 46%] 2024-08-20T21:41:28.4934908Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_complex128 PASSED [0.0097s] [ 46%] 2024-08-20T21:41:28.4935554Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_complex64 PASSED [0.0098s] [ 46%] 2024-08-20T21:41:28.4936211Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_float16 PASSED [0.0095s] [ 46%] 2024-08-20T21:41:28.4936861Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_float32 PASSED [0.0099s] [ 46%] 2024-08-20T21:41:28.4937492Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_float64 PASSED [0.0096s] [ 46%] 2024-08-20T21:41:28.4938128Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_int16 PASSED [0.0099s] [ 46%] 2024-08-20T21:41:28.4938751Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_int32 PASSED [0.0097s] [ 46%] 2024-08-20T21:41:28.4939369Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_int64 PASSED [0.0099s] [ 46%] 2024-08-20T21:41:28.4939999Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_int8 PASSED [0.0096s] [ 46%] 2024-08-20T21:41:28.4940618Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_uint8 PASSED [0.0096s] [ 46%] 2024-08-20T21:41:28.4941255Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_bfloat16 PASSED [0.0065s] [ 46%] 2024-08-20T21:41:28.4941889Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_bool PASSED [0.0062s] [ 46%] 2024-08-20T21:41:28.4942538Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_complex128 PASSED [0.0064s] [ 46%] 2024-08-20T21:41:28.4943195Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_complex64 PASSED [0.0062s] [ 46%] 2024-08-20T21:41:28.4943856Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_float16 PASSED [0.0064s] [ 46%] 2024-08-20T21:41:28.4944484Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_float32 PASSED [0.0061s] [ 46%] 2024-08-20T21:41:28.4945133Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_float64 PASSED [0.0063s] [ 46%] 2024-08-20T21:41:28.4945750Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_int16 PASSED [0.0062s] [ 46%] 2024-08-20T21:41:28.4946386Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_int32 PASSED [0.0063s] [ 46%] 2024-08-20T21:41:28.4947178Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_int64 PASSED [0.0061s] [ 46%] 2024-08-20T21:41:28.4947799Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_int8 PASSED [0.0061s] [ 46%] 2024-08-20T21:41:28.4948434Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_uint8 PASSED [0.0067s] [ 46%] 2024-08-20T21:41:28.4949064Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_bfloat16 PASSED [0.0096s] [ 46%] 2024-08-20T21:41:28.4949685Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_bool PASSED [0.0098s] [ 46%] 2024-08-20T21:41:28.4950329Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_complex128 PASSED [0.0097s] [ 46%] 2024-08-20T21:41:28.4951030Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_complex64 PASSED [0.0100s] [ 46%] 2024-08-20T21:41:28.4951704Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_float16 PASSED [0.0096s] [ 46%] 2024-08-20T21:41:28.4952328Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_float32 PASSED [0.0098s] [ 46%] 2024-08-20T21:41:28.4952961Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_float64 PASSED [0.0095s] [ 46%] 2024-08-20T21:41:28.4953570Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_int16 PASSED [0.0097s] [ 46%] 2024-08-20T21:41:28.4954222Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_int32 PASSED [0.0095s] [ 46%] 2024-08-20T21:41:28.4954856Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_int64 PASSED [0.0095s] [ 46%] 2024-08-20T21:41:28.4955465Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_int8 PASSED [0.0096s] [ 46%] 2024-08-20T21:41:28.4956069Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_uint8 PASSED [0.0094s] [ 46%] 2024-08-20T21:41:28.4956723Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_bfloat16 PASSED [0.0097s] [ 46%] 2024-08-20T21:41:28.4957336Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_bool PASSED [0.0094s] [ 46%] 2024-08-20T21:41:28.4957992Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_complex128 PASSED [0.0098s] [ 46%] 2024-08-20T21:41:28.4958639Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_complex64 PASSED [0.0096s] [ 46%] 2024-08-20T21:41:28.4959268Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_float16 PASSED [0.0097s] [ 46%] 2024-08-20T21:41:28.4959905Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_float32 PASSED [0.0095s] [ 46%] 2024-08-20T21:41:28.4960536Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_float64 PASSED [0.0096s] [ 46%] 2024-08-20T21:41:28.4961209Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_int16 PASSED [0.0095s] [ 46%] 2024-08-20T21:41:28.4961824Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_int32 PASSED [0.0094s] [ 46%] 2024-08-20T21:41:28.4962442Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_int64 PASSED [0.0100s] [ 46%] 2024-08-20T21:41:28.4963066Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_int8 PASSED [0.0094s] [ 46%] 2024-08-20T21:41:28.4963678Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_uint8 PASSED [0.0097s] [ 46%] 2024-08-20T21:41:28.4964291Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_bfloat16 PASSED [0.0066s] [ 46%] 2024-08-20T21:41:28.4964872Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_bool PASSED [0.0068s] [ 46%] 2024-08-20T21:41:28.4965467Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_float16 PASSED [0.0066s] [ 46%] 2024-08-20T21:41:28.4966071Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_float32 PASSED [0.0069s] [ 47%] 2024-08-20T21:41:28.4966664Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_float64 PASSED [0.0069s] [ 47%] 2024-08-20T21:41:28.4967317Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_int16 PASSED [0.0068s] [ 47%] 2024-08-20T21:41:28.4967949Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_int32 PASSED [0.0066s] [ 47%] 2024-08-20T21:41:28.4968534Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_int64 PASSED [0.0066s] [ 47%] 2024-08-20T21:41:28.4969160Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_int8 PASSED [0.0068s] [ 47%] 2024-08-20T21:41:28.4969738Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_uint8 PASSED [0.0066s] [ 47%] 2024-08-20T21:41:28.4970360Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_bfloat16 PASSED [0.2238s] [ 47%] 2024-08-20T21:41:28.4971034Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_complex128 PASSED [0.2215s] [ 47%] 2024-08-20T21:41:28.4971664Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_complex64 PASSED [0.2232s] [ 47%] 2024-08-20T21:41:28.4972295Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_float16 PASSED [0.2227s] [ 47%] 2024-08-20T21:41:28.4972905Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_float32 PASSED [0.2245s] [ 47%] 2024-08-20T21:41:28.4973513Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_float64 PASSED [0.2216s] [ 47%] 2024-08-20T21:41:28.4974133Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_int16 PASSED [0.2078s] [ 47%] 2024-08-20T21:41:28.4974728Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_int32 PASSED [0.2095s] [ 47%] 2024-08-20T21:41:28.4975334Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_int64 PASSED [0.2116s] [ 47%] 2024-08-20T21:41:28.4975927Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_int8 PASSED [0.0878s] [ 47%] 2024-08-20T21:41:28.4976531Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_uint8 PASSED [0.0708s] [ 47%] 2024-08-20T21:41:28.4977253Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_bfloat16 PASSED [0.6019s] [ 47%] 2024-08-20T21:41:28.4977981Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_complex128 PASSED [0.5973s] [ 47%] 2024-08-20T21:41:28.4978744Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_complex64 PASSED [0.6003s] [ 47%] 2024-08-20T21:41:28.4979449Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_float16 PASSED [0.6024s] [ 47%] 2024-08-20T21:41:28.4980154Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_float32 PASSED [0.5971s] [ 47%] 2024-08-20T21:41:28.4980864Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_float64 PASSED [0.6025s] [ 47%] 2024-08-20T21:41:28.4981558Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_int16 PASSED [0.5622s] [ 47%] 2024-08-20T21:41:28.4982243Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_int32 PASSED [0.5560s] [ 47%] 2024-08-20T21:41:28.4982939Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_int64 PASSED [0.5617s] [ 47%] 2024-08-20T21:41:28.4983624Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_int8 PASSED [0.2275s] [ 47%] 2024-08-20T21:41:28.4984341Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_uint8 PASSED [0.1846s] [ 47%] 2024-08-20T21:41:28.4984965Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_bfloat16 PASSED [0.0074s] [ 47%] 2024-08-20T21:41:28.4985598Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_bool PASSED [0.0074s] [ 47%] 2024-08-20T21:41:28.4986258Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_float16 PASSED [0.0072s] [ 47%] 2024-08-20T21:41:28.4986878Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_float32 PASSED [0.0126s] [ 47%] 2024-08-20T21:41:28.4987509Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_float64 PASSED [0.0122s] [ 47%] 2024-08-20T21:41:28.4988119Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_int16 PASSED [0.0073s] [ 47%] 2024-08-20T21:41:28.4988746Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_int32 PASSED [0.0071s] [ 47%] 2024-08-20T21:41:28.4989366Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_int64 PASSED [0.0071s] [ 47%] 2024-08-20T21:41:28.4989972Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_int8 PASSED [0.0077s] [ 47%] 2024-08-20T21:41:28.4990595Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_uint8 PASSED [0.0071s] [ 47%] 2024-08-20T21:41:28.4991195Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_bfloat16 PASSED [0.0074s] [ 47%] 2024-08-20T21:41:28.4991769Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_bool PASSED [0.0071s] [ 47%] 2024-08-20T21:41:28.4992386Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_complex128 PASSED [0.0076s] [ 47%] 2024-08-20T21:41:28.4992991Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_complex32 PASSED [0.0072s] [ 47%] 2024-08-20T21:41:28.4993605Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_complex64 PASSED [0.0074s] [ 47%] 2024-08-20T21:41:28.4994193Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_float16 PASSED [0.0072s] [ 47%] 2024-08-20T21:41:28.4994777Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_float32 PASSED [0.0073s] [ 47%] 2024-08-20T21:41:28.4995412Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_float64 PASSED [0.0071s] [ 47%] 2024-08-20T21:41:28.4995987Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_int16 PASSED [0.0072s] [ 47%] 2024-08-20T21:41:28.4996564Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_int32 PASSED [0.0073s] [ 47%] 2024-08-20T21:41:28.4997154Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_int64 PASSED [0.0071s] [ 47%] 2024-08-20T21:41:28.4997720Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_int8 PASSED [0.0073s] [ 47%] 2024-08-20T21:41:28.4998316Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_uint8 PASSED [0.0071s] [ 47%] 2024-08-20T21:41:28.4998894Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_bfloat16 PASSED [0.0097s] [ 47%] 2024-08-20T21:41:28.4999457Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_bool PASSED [0.0094s] [ 47%] 2024-08-20T21:41:28.5000055Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_float16 PASSED [0.0097s] [ 47%] 2024-08-20T21:41:28.5000628Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_float32 PASSED [0.0095s] [ 47%] 2024-08-20T21:41:28.5001215Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_float64 PASSED [0.0097s] [ 47%] 2024-08-20T21:41:28.5001780Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_int16 PASSED [0.0094s] [ 47%] 2024-08-20T21:41:28.5002367Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_int32 PASSED [0.0094s] [ 47%] 2024-08-20T21:41:28.5002947Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_int64 PASSED [0.0100s] [ 48%] 2024-08-20T21:41:28.5003548Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_int8 PASSED [0.0094s] [ 48%] 2024-08-20T21:41:28.5004112Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_uint8 PASSED [0.0097s] [ 48%] 2024-08-20T21:41:28.5004721Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_cpu_complex128 PASSED [0.0311s] [ 48%] 2024-08-20T21:41:28.5005309Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_cpu_complex64 PASSED [0.0314s] [ 48%] 2024-08-20T21:41:28.5005920Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_cpu_float32 PASSED [0.0302s] [ 48%] 2024-08-20T21:41:28.5006493Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_cpu_float64 PASSED [0.0302s] [ 48%] 2024-08-20T21:41:28.5007295Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_solve_cpu_complex128 PASSED [0.0259s] [ 48%] 2024-08-20T21:41:28.5007959Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_solve_cpu_complex64 PASSED [0.0261s] [ 48%] 2024-08-20T21:41:28.5008579Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_solve_cpu_float32 PASSED [0.0257s] [ 48%] 2024-08-20T21:41:28.5009199Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_solve_cpu_float64 PASSED [0.0256s] [ 48%] 2024-08-20T21:41:28.5009836Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_unpack_cpu_complex128 PASSED [0.0150s] [ 48%] 2024-08-20T21:41:28.5010473Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_unpack_cpu_complex64 PASSED [0.0144s] [ 48%] 2024-08-20T21:41:28.5011106Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_unpack_cpu_float32 PASSED [0.0145s] [ 48%] 2024-08-20T21:41:28.5011722Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_unpack_cpu_float64 PASSED [0.0141s] [ 48%] 2024-08-20T21:41:28.5012329Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_bfloat16 PASSED [0.0072s] [ 48%] 2024-08-20T21:41:28.5012933Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_bool PASSED [0.0074s] [ 48%] 2024-08-20T21:41:28.5013533Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_complex128 PASSED [0.0072s] [ 48%] 2024-08-20T21:41:28.5014134Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_complex32 PASSED [0.0074s] [ 48%] 2024-08-20T21:41:28.5014723Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_complex64 PASSED [0.0071s] [ 48%] 2024-08-20T21:41:28.5015302Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_float16 PASSED [0.0074s] [ 48%] 2024-08-20T21:41:28.5015888Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_float32 PASSED [0.0072s] [ 48%] 2024-08-20T21:41:28.5016464Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_float64 PASSED [0.0079s] [ 48%] 2024-08-20T21:41:28.5017040Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_int16 PASSED [0.0073s] [ 48%] 2024-08-20T21:41:28.5017604Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_int32 PASSED [0.0075s] [ 48%] 2024-08-20T21:41:28.5018166Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_int64 PASSED [0.0072s] [ 48%] 2024-08-20T21:41:28.5018741Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_int8 PASSED [0.0071s] [ 48%] 2024-08-20T21:41:28.5019299Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_uint8 PASSED [0.0073s] [ 48%] 2024-08-20T21:41:28.5019918Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_bfloat16 PASSED [0.0072s] [ 48%] 2024-08-20T21:41:28.5020503Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_bool PASSED [0.0073s] [ 48%] 2024-08-20T21:41:28.5021327Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_complex128 PASSED [0.0072s] [ 48%] 2024-08-20T21:41:28.5021942Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_complex32 PASSED [0.0074s] [ 48%] 2024-08-20T21:41:28.5022526Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_complex64 PASSED [0.0072s] [ 48%] 2024-08-20T21:41:28.5023139Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_float16 PASSED [0.0075s] [ 48%] 2024-08-20T21:41:28.5023733Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_float32 PASSED [0.0072s] [ 48%] 2024-08-20T21:41:28.5024310Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_float64 PASSED [0.0074s] [ 48%] 2024-08-20T21:41:28.5024887Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_int16 PASSED [0.0072s] [ 48%] 2024-08-20T21:41:28.5025453Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_int32 PASSED [0.0071s] [ 48%] 2024-08-20T21:41:28.5026018Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_int64 PASSED [0.0073s] [ 48%] 2024-08-20T21:41:28.5026588Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_int8 PASSED [0.0071s] [ 48%] 2024-08-20T21:41:28.5027153Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_uint8 PASSED [0.0074s] [ 48%] 2024-08-20T21:41:28.5027805Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_bfloat16 PASSED [0.0509s] [ 48%] 2024-08-20T21:41:28.5028441Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_float16 PASSED [0.0510s] [ 48%] 2024-08-20T21:41:28.5029072Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_float32 PASSED [0.0507s] [ 48%] 2024-08-20T21:41:28.5029713Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_float64 PASSED [0.0514s] [ 48%] 2024-08-20T21:41:28.5030357Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_int16 PASSED [0.0439s] [ 48%] 2024-08-20T21:41:28.5030984Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_int32 PASSED [0.0430s] [ 48%] 2024-08-20T21:41:28.5031606Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_int64 PASSED [0.0438s] [ 48%] 2024-08-20T21:41:28.5032219Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_int8 PASSED [0.0432s] [ 48%] 2024-08-20T21:41:28.5032852Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_uint8 PASSED [0.0435s] [ 48%] 2024-08-20T21:41:28.5033492Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_bfloat16 PASSED [0.0511s] [ 48%] 2024-08-20T21:41:28.5034120Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_float16 PASSED [0.0507s] [ 48%] 2024-08-20T21:41:28.5034762Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_float32 PASSED [0.0516s] [ 48%] 2024-08-20T21:41:28.5035386Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_float64 PASSED [0.0505s] [ 48%] 2024-08-20T21:41:28.5036012Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_int16 PASSED [0.0434s] [ 48%] 2024-08-20T21:41:28.5036626Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_int32 PASSED [0.0436s] [ 48%] 2024-08-20T21:41:28.5037270Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_int64 PASSED [0.0431s] [ 48%] 2024-08-20T21:41:28.5037915Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_int8 PASSED [0.0437s] [ 48%] 2024-08-20T21:41:28.5038538Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_uint8 PASSED [0.0432s] [ 48%] 2024-08-20T21:41:28.5039199Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_bfloat16 PASSED [0.0333s] [ 48%] 2024-08-20T21:41:28.5039836Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_float16 PASSED [0.0329s] [ 49%] 2024-08-20T21:41:28.5040505Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_float32 PASSED [0.0329s] [ 49%] 2024-08-20T21:41:28.5041158Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_float64 PASSED [0.0334s] [ 49%] 2024-08-20T21:41:28.5041790Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_int16 PASSED [0.0284s] [ 49%] 2024-08-20T21:41:28.5042431Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_int32 PASSED [0.0292s] [ 49%] 2024-08-20T21:41:28.5043056Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_int64 PASSED [0.0285s] [ 49%] 2024-08-20T21:41:28.5043684Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_int8 PASSED [0.0288s] [ 49%] 2024-08-20T21:41:28.5044332Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_uint8 PASSED [0.0285s] [ 49%] 2024-08-20T21:41:28.5044986Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_bfloat16 PASSED [0.0334s] [ 49%] 2024-08-20T21:41:28.5045644Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_float16 PASSED [0.0331s] [ 49%] 2024-08-20T21:41:28.5046284Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_float32 PASSED [0.0328s] [ 49%] 2024-08-20T21:41:28.5047140Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_float64 PASSED [0.0333s] [ 49%] 2024-08-20T21:41:28.5047867Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_int16 PASSED [0.0284s] [ 49%] 2024-08-20T21:41:28.5048488Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_int32 PASSED [0.0286s] [ 49%] 2024-08-20T21:41:28.5049120Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_int64 PASSED [0.0284s] [ 49%] 2024-08-20T21:41:28.5049760Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_int8 PASSED [0.0287s] [ 49%] 2024-08-20T21:41:28.5050395Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_uint8 PASSED [0.0284s] [ 49%] 2024-08-20T21:41:28.5051069Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_bfloat16 PASSED [0.0142s] [ 49%] 2024-08-20T21:41:28.5051741Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_complex128 PASSED [0.0140s] [ 49%] 2024-08-20T21:41:28.5052411Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_complex64 PASSED [0.0139s] [ 49%] 2024-08-20T21:41:28.5053075Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_float16 PASSED [0.0141s] [ 49%] 2024-08-20T21:41:28.5053725Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_float32 PASSED [0.0139s] [ 49%] 2024-08-20T21:41:28.5054390Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_float64 PASSED [0.0143s] [ 49%] 2024-08-20T21:41:28.5055066Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_int16 PASSED [0.0138s] [ 49%] 2024-08-20T21:41:28.5055742Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_int32 PASSED [0.0141s] [ 49%] 2024-08-20T21:41:28.5056396Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_int64 PASSED [0.0138s] [ 49%] 2024-08-20T21:41:28.5057028Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_int8 PASSED [0.0144s] [ 49%] 2024-08-20T21:41:28.5057677Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_uint8 PASSED [0.0138s] [ 49%] 2024-08-20T21:41:28.5058372Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_bfloat16 PASSED [0.0137s] [ 49%] 2024-08-20T21:41:28.5059037Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_complex128 PASSED [0.0142s] [ 49%] 2024-08-20T21:41:28.5059703Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_complex64 PASSED [0.0138s] [ 49%] 2024-08-20T21:41:28.5060350Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_float16 PASSED [0.0140s] [ 49%] 2024-08-20T21:41:28.5061007Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_float32 PASSED [0.0137s] [ 49%] 2024-08-20T21:41:28.5061652Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_float64 PASSED [0.0140s] [ 49%] 2024-08-20T21:41:28.5062285Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_int16 PASSED [0.0137s] [ 49%] 2024-08-20T21:41:28.5062925Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_int32 PASSED [0.0138s] [ 49%] 2024-08-20T21:41:28.5063553Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_int64 PASSED [0.0137s] [ 49%] 2024-08-20T21:41:28.5064193Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_int8 PASSED [0.0137s] [ 49%] 2024-08-20T21:41:28.5064822Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_uint8 PASSED [0.0139s] [ 49%] 2024-08-20T21:41:28.5065493Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_bfloat16 PASSED [0.0091s] [ 49%] 2024-08-20T21:41:28.5066123Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_bool PASSED [0.0092s] [ 49%] 2024-08-20T21:41:28.5066774Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_complex128 PASSED [0.0091s] [ 49%] 2024-08-20T21:41:28.5067419Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_complex32 PASSED [0.0093s] [ 49%] 2024-08-20T21:41:28.5068074Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_complex64 PASSED [0.0091s] [ 49%] 2024-08-20T21:41:28.5068706Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_float16 PASSED [0.0092s] [ 49%] 2024-08-20T21:41:28.5069349Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_float32 PASSED [0.0090s] [ 49%] 2024-08-20T21:41:28.5069972Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_float64 PASSED [0.0090s] [ 49%] 2024-08-20T21:41:28.5070586Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_int16 PASSED [0.0098s] [ 49%] 2024-08-20T21:41:28.5071219Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_int32 PASSED [0.0092s] [ 49%] 2024-08-20T21:41:28.5071832Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_int64 PASSED [0.0093s] [ 49%] 2024-08-20T21:41:28.5072490Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_int8 PASSED [0.0091s] [ 49%] 2024-08-20T21:41:28.5073132Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_uint8 PASSED [0.0093s] [ 49%] 2024-08-20T21:41:28.5073813Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_log_softmax_cpu_bfloat16 PASSED [0.0172s] [ 49%] 2024-08-20T21:41:28.5074504Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_log_softmax_cpu_float16 PASSED [0.0175s] [ 49%] 2024-08-20T21:41:28.5075169Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_log_softmax_cpu_float32 PASSED [0.0174s] [ 49%] 2024-08-20T21:41:28.5075881Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_log_softmax_cpu_float64 PASSED [0.0176s] [ 49%] 2024-08-20T21:41:28.5076551Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logaddexp_cpu_bfloat16 PASSED [0.0108s] [ 49%] 2024-08-20T21:41:28.5077212Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logaddexp_cpu_float16 PASSED [0.0107s] [ 49%] 2024-08-20T21:41:28.5077885Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logaddexp_cpu_float32 PASSED [0.0110s] [ 49%] 2024-08-20T21:41:28.5078548Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logaddexp_cpu_float64 PASSED [0.0109s] [ 50%] 2024-08-20T21:41:28.5079227Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_bfloat16 PASSED [0.0511s] [ 50%] 2024-08-20T21:41:28.5079884Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_float16 PASSED [0.0509s] [ 50%] 2024-08-20T21:41:28.5080541Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_float32 PASSED [0.0515s] [ 50%] 2024-08-20T21:41:28.5081214Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_float64 PASSED [0.0516s] [ 50%] 2024-08-20T21:41:28.5081867Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_int16 PASSED [0.0435s] [ 50%] 2024-08-20T21:41:28.5082543Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_int32 PASSED [0.0436s] [ 50%] 2024-08-20T21:41:28.5083204Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_int64 PASSED [0.0434s] [ 50%] 2024-08-20T21:41:28.5083844Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_int8 PASSED [0.0437s] [ 50%] 2024-08-20T21:41:28.5084500Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_uint8 PASSED [0.0440s] [ 50%] 2024-08-20T21:41:28.5085137Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_bfloat16 PASSED [0.0510s] [ 50%] 2024-08-20T21:41:28.5085751Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_bool PASSED [0.0438s] [ 50%] 2024-08-20T21:41:28.5086413Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_complex128 PASSED [0.0436s] [ 50%] 2024-08-20T21:41:28.5087139Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_complex64 PASSED [0.0437s] [ 50%] 2024-08-20T21:41:28.5087788Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_float16 PASSED [0.0513s] [ 50%] 2024-08-20T21:41:28.5088419Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_float32 PASSED [0.0507s] [ 50%] 2024-08-20T21:41:28.5089045Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_float64 PASSED [0.0510s] [ 50%] 2024-08-20T21:41:28.5089714Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_int16 PASSED [0.0434s] [ 50%] 2024-08-20T21:41:28.5090333Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_int32 PASSED [0.0438s] [ 50%] 2024-08-20T21:41:28.5090987Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_int64 PASSED [0.0438s] [ 50%] 2024-08-20T21:41:28.5091602Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_int8 PASSED [0.0431s] [ 50%] 2024-08-20T21:41:28.5092217Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_uint8 PASSED [0.0440s] [ 50%] 2024-08-20T21:41:28.5092906Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_median_cpu_bfloat16 PASSED [0.0139s] [ 50%] 2024-08-20T21:41:28.5093547Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_median_cpu_float16 PASSED [0.0142s] [ 50%] 2024-08-20T21:41:28.5094201Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_median_cpu_float32 PASSED [0.0139s] [ 50%] 2024-08-20T21:41:28.5094835Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_median_cpu_float64 PASSED [0.0141s] [ 50%] 2024-08-20T21:41:28.5095473Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_norm_cpu_bfloat16 PASSED [0.2445s] [ 50%] 2024-08-20T21:41:28.5096114Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_norm_cpu_float16 PASSED [0.2413s] [ 50%] 2024-08-20T21:41:28.5096745Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_norm_cpu_float32 PASSED [0.2424s] [ 50%] 2024-08-20T21:41:28.5097375Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_norm_cpu_float64 PASSED [0.2402s] [ 50%] 2024-08-20T21:41:28.5098055Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_normalize_cpu_bfloat16 PASSED [0.0214s] [ 50%] 2024-08-20T21:41:28.5098732Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_normalize_cpu_complex128 PASSED [0.0210s] [ 50%] 2024-08-20T21:41:28.5099420Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_normalize_cpu_complex64 PASSED [0.0208s] [ 50%] 2024-08-20T21:41:28.5100112Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_normalize_cpu_float16 PASSED [0.0210s] [ 50%] 2024-08-20T21:41:28.5100769Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_normalize_cpu_float32 PASSED [0.0205s] [ 50%] 2024-08-20T21:41:28.5101439Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_normalize_cpu_float64 PASSED [0.0211s] [ 50%] 2024-08-20T21:41:28.5102073Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_bfloat16 PASSED [0.0509s] [ 50%] 2024-08-20T21:41:28.5102696Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_bool PASSED [0.0437s] [ 50%] 2024-08-20T21:41:28.5103339Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_complex128 PASSED [0.0440s] [ 50%] 2024-08-20T21:41:28.5103983Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_complex64 PASSED [0.0439s] [ 50%] 2024-08-20T21:41:28.5104626Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_float16 PASSED [0.0515s] [ 50%] 2024-08-20T21:41:28.5105256Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_float32 PASSED [0.0512s] [ 50%] 2024-08-20T21:41:28.5105893Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_float64 PASSED [0.0515s] [ 50%] 2024-08-20T21:41:28.5106510Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_int16 PASSED [0.0439s] [ 50%] 2024-08-20T21:41:28.5107154Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_int32 PASSED [0.0437s] [ 50%] 2024-08-20T21:41:28.5107808Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_int64 PASSED [0.0439s] [ 50%] 2024-08-20T21:41:28.5108516Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_int8 PASSED [0.0434s] [ 50%] 2024-08-20T21:41:28.5109154Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_uint8 PASSED [0.0436s] [ 50%] 2024-08-20T21:41:28.5109802Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_bfloat16 PASSED [0.0072s] [ 50%] 2024-08-20T21:41:28.5110501Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_bool PASSED [0.0070s] [ 50%] 2024-08-20T21:41:28.5111185Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_complex128 PASSED [0.0075s] [ 50%] 2024-08-20T21:41:28.5111848Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_complex64 PASSED [0.0071s] [ 50%] 2024-08-20T21:41:28.5112506Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_float16 PASSED [0.0072s] [ 50%] 2024-08-20T21:41:28.5113153Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_float32 PASSED [0.0069s] [ 50%] 2024-08-20T21:41:28.5113799Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_float64 PASSED [0.0072s] [ 50%] 2024-08-20T21:41:28.5114450Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_int16 PASSED [0.0070s] [ 50%] 2024-08-20T21:41:28.5115092Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_int32 PASSED [0.0071s] [ 50%] 2024-08-20T21:41:28.5115725Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_int64 PASSED [0.0069s] [ 50%] 2024-08-20T21:41:28.5116369Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_int8 PASSED [0.0071s] [ 50%] 2024-08-20T21:41:28.5117003Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_uint8 PASSED [0.0069s] [ 51%] 2024-08-20T21:41:28.5117692Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_bfloat16 PASSED [0.0089s] [ 51%] 2024-08-20T21:41:28.5118319Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_bool PASSED [0.0089s] [ 51%] 2024-08-20T21:41:28.5118975Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_complex128 PASSED [0.0088s] [ 51%] 2024-08-20T21:41:28.5119641Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_complex64 PASSED [0.0090s] [ 51%] 2024-08-20T21:41:28.5120282Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_float16 PASSED [0.0088s] [ 51%] 2024-08-20T21:41:28.5121161Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_float32 PASSED [0.0090s] [ 51%] 2024-08-20T21:41:28.5121819Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_float64 PASSED [0.0088s] [ 51%] 2024-08-20T21:41:28.5122450Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_int16 PASSED [0.0089s] [ 51%] 2024-08-20T21:41:28.5123089Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_int32 PASSED [0.0089s] [ 51%] 2024-08-20T21:41:28.5123725Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_int64 PASSED [0.0090s] [ 51%] 2024-08-20T21:41:28.5124369Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_int8 PASSED [0.0088s] [ 51%] 2024-08-20T21:41:28.5125044Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_uint8 PASSED [0.0087s] [ 51%] 2024-08-20T21:41:28.5125705Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmax_cpu_bfloat16 PASSED [0.0180s] [ 51%] 2024-08-20T21:41:28.5126413Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmax_cpu_float16 PASSED [0.0171s] [ 51%] 2024-08-20T21:41:28.5127150Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmax_cpu_float32 PASSED [0.0174s] [ 51%] 2024-08-20T21:41:28.5127814Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmax_cpu_float64 PASSED [0.0172s] [ 51%] 2024-08-20T21:41:28.5128512Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmin_cpu_bfloat16 PASSED [0.0174s] [ 51%] 2024-08-20T21:41:28.5129155Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmin_cpu_float16 PASSED [0.0172s] [ 51%] 2024-08-20T21:41:28.5129816Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmin_cpu_float32 PASSED [0.0174s] [ 51%] 2024-08-20T21:41:28.5130467Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmin_cpu_float64 PASSED [0.0173s] [ 51%] 2024-08-20T21:41:28.5131098Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_bfloat16 PASSED [0.0344s] [ 51%] 2024-08-20T21:41:28.5131757Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_complex128 PASSED [0.0340s] [ 51%] 2024-08-20T21:41:28.5132395Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_complex64 PASSED [0.0338s] [ 51%] 2024-08-20T21:41:28.5133030Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_float16 PASSED [0.0344s] [ 51%] 2024-08-20T21:41:28.5133652Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_float32 PASSED [0.0344s] [ 51%] 2024-08-20T21:41:28.5134278Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_float64 PASSED [0.0342s] [ 51%] 2024-08-20T21:41:28.5134902Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_int16 PASSED [0.0344s] [ 51%] 2024-08-20T21:41:28.5135542Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_int32 PASSED [0.0344s] [ 51%] 2024-08-20T21:41:28.5136166Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_int64 PASSED [0.0341s] [ 51%] 2024-08-20T21:41:28.5136776Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_int8 PASSED [0.0347s] [ 51%] 2024-08-20T21:41:28.5137387Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_uint8 PASSED [0.0345s] [ 51%] 2024-08-20T21:41:28.5138032Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_bfloat16 PASSED [0.0519s] [ 51%] 2024-08-20T21:41:28.5138636Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_bool PASSED [0.0429s] [ 51%] 2024-08-20T21:41:28.5139297Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_complex128 PASSED [0.0439s] [ 51%] 2024-08-20T21:41:28.5139931Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_complex64 PASSED [0.0434s] [ 51%] 2024-08-20T21:41:28.5140554Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_float16 PASSED [0.0512s] [ 51%] 2024-08-20T21:41:28.5141190Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_float32 PASSED [0.0512s] [ 51%] 2024-08-20T21:41:28.5141811Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_float64 PASSED [0.0510s] [ 51%] 2024-08-20T21:41:28.5142463Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_int16 PASSED [0.0438s] [ 51%] 2024-08-20T21:41:28.5143077Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_int32 PASSED [0.0432s] [ 51%] 2024-08-20T21:41:28.5143714Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_int64 PASSED [0.0436s] [ 51%] 2024-08-20T21:41:28.5144404Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_int8 PASSED [0.0437s] [ 51%] 2024-08-20T21:41:28.5145195Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_uint8 PASSED [0.0431s] [ 51%] 2024-08-20T21:41:28.5145886Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_bfloat16 PASSED [0.0349s] [ 51%] 2024-08-20T21:41:28.5146544Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_complex128 PASSED [0.0345s] [ 51%] 2024-08-20T21:41:28.5147344Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_complex64 PASSED [0.0343s] [ 51%] 2024-08-20T21:41:28.5147988Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_float16 PASSED [0.0340s] [ 51%] 2024-08-20T21:41:28.5148620Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_float32 PASSED [0.0342s] [ 51%] 2024-08-20T21:41:28.5149251Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_float64 PASSED [0.0340s] [ 51%] 2024-08-20T21:41:28.5149878Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_int16 PASSED [0.0343s] [ 51%] 2024-08-20T21:41:28.5150485Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_int32 PASSED [0.0344s] [ 51%] 2024-08-20T21:41:28.5151107Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_int64 PASSED [0.0341s] [ 51%] 2024-08-20T21:41:28.5151713Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_int8 PASSED [0.0341s] [ 51%] 2024-08-20T21:41:28.5152327Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_uint8 PASSED [0.0341s] [ 51%] 2024-08-20T21:41:28.5153010Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_bfloat16 PASSED [0.0130s] [ 51%] 2024-08-20T21:41:28.5153632Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_complex128 PASSED [0.0131s] [ 51%] 2024-08-20T21:41:28.5154267Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_complex64 PASSED [0.0132s] [ 51%] 2024-08-20T21:41:28.5154871Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_float16 PASSED [0.0129s] [ 51%] 2024-08-20T21:41:28.5155476Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_float32 PASSED [0.0129s] [ 51%] 2024-08-20T21:41:28.5156092Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_float64 PASSED [0.0131s] [ 52%] 2024-08-20T21:41:28.5156686Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_int16 PASSED [0.0127s] [ 52%] 2024-08-20T21:41:28.5157274Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_int32 PASSED [0.0131s] [ 52%] 2024-08-20T21:41:28.5157874Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_int64 PASSED [0.0127s] [ 52%] 2024-08-20T21:41:28.5158454Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_int8 PASSED [0.0130s] [ 52%] 2024-08-20T21:41:28.5159062Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_uint8 PASSED [0.0127s] [ 52%] 2024-08-20T21:41:28.5159690Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_bfloat16 PASSED [0.0060s] [ 52%] 2024-08-20T21:41:28.5160383Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_complex128 PASSED [0.0058s] [ 52%] 2024-08-20T21:41:28.5161073Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_complex64 PASSED [0.0058s] [ 52%] 2024-08-20T21:41:28.5161700Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_float16 PASSED [0.0057s] [ 52%] 2024-08-20T21:41:28.5162335Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_float32 PASSED [0.0056s] [ 52%] 2024-08-20T21:41:28.5162950Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_float64 PASSED [0.0058s] [ 52%] 2024-08-20T21:41:28.5163628Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_bfloat16 PASSED [0.0095s] [ 52%] 2024-08-20T21:41:28.5164251Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_bool PASSED [0.0097s] [ 52%] 2024-08-20T21:41:28.5164880Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_float16 PASSED [0.0095s] [ 52%] 2024-08-20T21:41:28.5165516Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_float32 PASSED [0.0098s] [ 52%] 2024-08-20T21:41:28.5166140Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_float64 PASSED [0.0095s] [ 52%] 2024-08-20T21:41:28.5166838Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_int16 PASSED [0.0097s] [ 52%] 2024-08-20T21:41:28.5167474Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_int32 PASSED [0.0095s] [ 52%] 2024-08-20T21:41:28.5168089Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_int64 PASSED [0.0096s] [ 52%] 2024-08-20T21:41:28.5168696Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_int8 PASSED [0.0096s] [ 52%] 2024-08-20T21:41:28.5169323Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_uint8 PASSED [0.0095s] [ 52%] 2024-08-20T21:41:28.5170088Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_pool2d_with_indices_backward_cpu_bfloat16 PASSED [0.7552s] [ 52%] 2024-08-20T21:41:28.5170896Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_pool2d_with_indices_backward_cpu_float16 PASSED [0.7544s] [ 52%] 2024-08-20T21:41:28.5171642Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_pool2d_with_indices_backward_cpu_float32 PASSED [0.7610s] [ 52%] 2024-08-20T21:41:28.5172396Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_pool2d_with_indices_backward_cpu_float64 PASSED [0.7571s] [ 52%] 2024-08-20T21:41:28.5173102Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_bfloat16 PASSED [0.0060s] [ 52%] 2024-08-20T21:41:28.5173773Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_bool PASSED [0.0057s] [ 52%] 2024-08-20T21:41:28.5174473Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_float16 PASSED [0.0057s] [ 52%] 2024-08-20T21:41:28.5175153Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_float32 PASSED [0.0058s] [ 52%] 2024-08-20T21:41:28.5175826Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_float64 PASSED [0.0056s] [ 52%] 2024-08-20T21:41:28.5176513Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_int16 PASSED [0.0059s] [ 52%] 2024-08-20T21:41:28.5177183Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_int32 PASSED [0.0057s] [ 52%] 2024-08-20T21:41:28.5177898Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_int64 PASSED [0.0059s] [ 52%] 2024-08-20T21:41:28.5178566Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_int8 PASSED [0.0056s] [ 52%] 2024-08-20T21:41:28.5179259Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_uint8 PASSED [0.0059s] [ 52%] 2024-08-20T21:41:28.5179972Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_bfloat16 PASSED [0.0066s] [ 52%] 2024-08-20T21:41:28.5180649Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_bool PASSED [0.0067s] [ 52%] 2024-08-20T21:41:28.5181384Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_float16 PASSED [0.0066s] [ 52%] 2024-08-20T21:41:28.5182076Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_float32 PASSED [0.0066s] [ 52%] 2024-08-20T21:41:28.5182763Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_float64 PASSED [0.0068s] [ 52%] 2024-08-20T21:41:28.5183462Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_int16 PASSED [0.0066s] [ 52%] 2024-08-20T21:41:28.5184139Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_int32 PASSED [0.0072s] [ 52%] 2024-08-20T21:41:28.5184833Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_int64 PASSED [0.0067s] [ 52%] 2024-08-20T21:41:28.5185510Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_int8 PASSED [0.0069s] [ 52%] 2024-08-20T21:41:28.5186185Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_uint8 PASSED [0.0066s] [ 52%] 2024-08-20T21:41:28.5186818Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_bfloat16 PASSED [0.0097s] [ 52%] 2024-08-20T21:41:28.5187409Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_bool PASSED [0.0094s] [ 52%] 2024-08-20T21:41:28.5188033Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_float16 PASSED [0.0097s] [ 52%] 2024-08-20T21:41:28.5188666Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_float32 PASSED [0.0095s] [ 52%] 2024-08-20T21:41:28.5189263Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_float64 PASSED [0.0095s] [ 52%] 2024-08-20T21:41:28.5189871Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_int16 PASSED [0.0097s] [ 52%] 2024-08-20T21:41:28.5190461Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_int32 PASSED [0.0095s] [ 52%] 2024-08-20T21:41:28.5191047Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_int64 PASSED [0.0097s] [ 52%] 2024-08-20T21:41:28.5191647Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_int8 PASSED [0.0095s] [ 52%] 2024-08-20T21:41:28.5192245Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_uint8 PASSED [0.0097s] [ 52%] 2024-08-20T21:41:28.5192850Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_bfloat16 PASSED [0.0145s] [ 52%] 2024-08-20T21:41:28.5193452Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_complex128 PASSED [0.0147s] [ 52%] 2024-08-20T21:41:28.5194057Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_complex64 PASSED [0.0145s] [ 52%] 2024-08-20T21:41:28.5194657Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_float16 PASSED [0.0146s] [ 53%] 2024-08-20T21:41:28.5195275Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_float32 PASSED [0.0144s] [ 53%] 2024-08-20T21:41:28.5195870Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_float64 PASSED [0.0144s] [ 53%] 2024-08-20T21:41:28.5196502Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_bfloat16 PASSED [0.0111s] [ 53%] 2024-08-20T21:41:28.5197108Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_float16 PASSED [0.0110s] [ 53%] 2024-08-20T21:41:28.5197716Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_float32 PASSED [0.0119s] [ 53%] 2024-08-20T21:41:28.5198335Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_float64 PASSED [0.0110s] [ 53%] 2024-08-20T21:41:28.5198941Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_int16 PASSED [0.0112s] [ 53%] 2024-08-20T21:41:28.5199533Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_int32 PASSED [0.0112s] [ 53%] 2024-08-20T21:41:28.5200115Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_int64 PASSED [0.0113s] [ 53%] 2024-08-20T21:41:28.5200712Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_int8 PASSED [0.0111s] [ 53%] 2024-08-20T21:41:28.5201306Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_uint8 PASSED [0.0113s] [ 53%] 2024-08-20T21:41:28.5202019Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_bfloat16 PASSED [0.0102s] [ 53%] 2024-08-20T21:41:28.5202721Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_bool PASSED [0.0100s] [ 53%] 2024-08-20T21:41:28.5203441Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_complex128 PASSED [0.0104s] [ 53%] 2024-08-20T21:41:28.5204165Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_complex64 PASSED [0.0102s] [ 53%] 2024-08-20T21:41:28.5204867Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_float16 PASSED [0.0103s] [ 53%] 2024-08-20T21:41:28.5205601Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_float32 PASSED [0.0100s] [ 53%] 2024-08-20T21:41:28.5206311Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_float64 PASSED [0.0104s] [ 53%] 2024-08-20T21:41:28.5207068Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_int16 PASSED [0.0100s] [ 53%] 2024-08-20T21:41:28.5207780Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_int32 PASSED [0.0102s] [ 53%] 2024-08-20T21:41:28.5208470Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_int64 PASSED [0.0099s] [ 53%] 2024-08-20T21:41:28.5209155Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_int8 PASSED [0.0099s] [ 53%] 2024-08-20T21:41:28.5209853Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_uint8 PASSED [0.0102s] [ 53%] 2024-08-20T21:41:28.5210568Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_bfloat16 PASSED [0.0100s] [ 53%] 2024-08-20T21:41:28.5211274Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_bool PASSED [0.0102s] [ 53%] 2024-08-20T21:41:28.5212005Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_complex128 PASSED [0.0101s] [ 53%] 2024-08-20T21:41:28.5212758Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_complex64 PASSED [0.0110s] [ 53%] 2024-08-20T21:41:28.5213479Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_float16 PASSED [0.0101s] [ 53%] 2024-08-20T21:41:28.5214220Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_float32 PASSED [0.0103s] [ 53%] 2024-08-20T21:41:28.5214945Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_float64 PASSED [0.0100s] [ 53%] 2024-08-20T21:41:28.5215646Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_int16 PASSED [0.0100s] [ 53%] 2024-08-20T21:41:28.5216374Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_int32 PASSED [0.0101s] [ 53%] 2024-08-20T21:41:28.5217081Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_int64 PASSED [0.0100s] [ 53%] 2024-08-20T21:41:28.5217775Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_int8 PASSED [0.0102s] [ 53%] 2024-08-20T21:41:28.5218477Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_uint8 PASSED [0.0100s] [ 53%] 2024-08-20T21:41:28.5219114Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_bfloat16 PASSED [0.0099s] [ 53%] 2024-08-20T21:41:28.5219719Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_bool PASSED [0.0098s] [ 53%] 2024-08-20T21:41:28.5220354Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_float16 PASSED [0.0098s] [ 53%] 2024-08-20T21:41:28.5221202Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_float32 PASSED [0.0095s] [ 53%] 2024-08-20T21:41:28.5221854Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_float64 PASSED [0.0098s] [ 53%] 2024-08-20T21:41:28.5222465Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_int16 PASSED [0.0097s] [ 53%] 2024-08-20T21:41:28.5223075Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_int32 PASSED [0.0096s] [ 53%] 2024-08-20T21:41:28.5223741Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_int64 PASSED [0.0098s] [ 53%] 2024-08-20T21:41:28.5224346Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_int8 PASSED [0.0096s] [ 53%] 2024-08-20T21:41:28.5224969Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_uint8 PASSED [0.0098s] [ 53%] 2024-08-20T21:41:28.5225658Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_bfloat16 PASSED [0.0058s] [ 53%] 2024-08-20T21:41:28.5226321Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_bool PASSED [0.0059s] [ 53%] 2024-08-20T21:41:28.5227014Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_float16 PASSED [0.0056s] [ 53%] 2024-08-20T21:41:28.5227692Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_float32 PASSED [0.0063s] [ 53%] 2024-08-20T21:41:28.5228372Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_float64 PASSED [0.0056s] [ 53%] 2024-08-20T21:41:28.5229038Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_int16 PASSED [0.0058s] [ 53%] 2024-08-20T21:41:28.5229707Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_int32 PASSED [0.0056s] [ 53%] 2024-08-20T21:41:28.5230384Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_int64 PASSED [0.0056s] [ 53%] 2024-08-20T21:41:28.5231074Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_int8 PASSED [0.0058s] [ 53%] 2024-08-20T21:41:28.5231764Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_uint8 PASSED [0.0056s] [ 53%] 2024-08-20T21:41:28.5232476Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_bfloat16 PASSED [0.0068s] [ 53%] 2024-08-20T21:41:28.5233145Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_bool PASSED [0.0066s] [ 53%] 2024-08-20T21:41:28.5233877Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_float16 PASSED [0.0069s] [ 53%] 2024-08-20T21:41:28.5234566Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_float32 PASSED [0.0066s] [ 53%] 2024-08-20T21:41:28.5235254Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_float64 PASSED [0.0069s] [ 54%] 2024-08-20T21:41:28.5235943Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_int16 PASSED [0.0066s] [ 54%] 2024-08-20T21:41:28.5236619Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_int32 PASSED [0.0067s] [ 54%] 2024-08-20T21:41:28.5237303Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_int64 PASSED [0.0066s] [ 54%] 2024-08-20T21:41:28.5237975Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_int8 PASSED [0.0066s] [ 54%] 2024-08-20T21:41:28.5238653Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_uint8 PASSED [0.0067s] [ 54%] 2024-08-20T21:41:28.5239280Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_bfloat16 PASSED [0.0095s] [ 54%] 2024-08-20T21:41:28.5239871Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_bool PASSED [0.0096s] [ 54%] 2024-08-20T21:41:28.5240490Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_float16 PASSED [0.0095s] [ 54%] 2024-08-20T21:41:28.5241120Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_float32 PASSED [0.0096s] [ 54%] 2024-08-20T21:41:28.5241716Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_float64 PASSED [0.0095s] [ 54%] 2024-08-20T21:41:28.5242324Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_int16 PASSED [0.0102s] [ 54%] 2024-08-20T21:41:28.5242922Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_int32 PASSED [0.0095s] [ 54%] 2024-08-20T21:41:28.5243528Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_int64 PASSED [0.0097s] [ 54%] 2024-08-20T21:41:28.5244118Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_int8 PASSED [0.0094s] [ 54%] 2024-08-20T21:41:28.5244712Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_uint8 PASSED [0.0094s] [ 54%] 2024-08-20T21:41:28.5245309Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_bfloat16 PASSED [0.0063s] [ 54%] 2024-08-20T21:41:28.5245903Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_complex128 PASSED [0.0068s] [ 54%] 2024-08-20T21:41:28.5246491Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_complex64 PASSED [0.0069s] [ 54%] 2024-08-20T21:41:28.5247378Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_float16 PASSED [0.0061s] [ 54%] 2024-08-20T21:41:28.5247967Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_float32 PASSED [0.0064s] [ 54%] 2024-08-20T21:41:28.5248626Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_float64 PASSED [0.0062s] [ 54%] 2024-08-20T21:41:28.5249237Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_int16 PASSED [0.0064s] [ 54%] 2024-08-20T21:41:28.5249805Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_int32 PASSED [0.0062s] [ 54%] 2024-08-20T21:41:28.5250382Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_int64 PASSED [0.0064s] [ 54%] 2024-08-20T21:41:28.5250941Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_int8 PASSED [0.0062s] [ 54%] 2024-08-20T21:41:28.5251557Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_uint8 PASSED [0.0061s] [ 54%] 2024-08-20T21:41:28.5252155Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_bfloat16 PASSED [0.0082s] [ 54%] 2024-08-20T21:41:28.5252729Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_bool PASSED [0.0081s] [ 54%] 2024-08-20T21:41:28.5253333Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_float16 PASSED [0.0085s] [ 54%] 2024-08-20T21:41:28.5253920Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_float32 PASSED [0.0082s] [ 54%] 2024-08-20T21:41:28.5254510Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_float64 PASSED [0.0084s] [ 54%] 2024-08-20T21:41:28.5255085Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_int16 PASSED [0.0082s] [ 54%] 2024-08-20T21:41:28.5255665Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_int32 PASSED [0.0088s] [ 54%] 2024-08-20T21:41:28.5256255Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_int64 PASSED [0.0081s] [ 54%] 2024-08-20T21:41:28.5256831Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_int8 PASSED [0.0083s] [ 54%] 2024-08-20T21:41:28.5257404Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_uint8 PASSED [0.0082s] [ 54%] 2024-08-20T21:41:28.5258038Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_bfloat16 PASSED [0.0056s] [ 54%] 2024-08-20T21:41:28.5258678Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_bool PASSED [0.0057s] [ 54%] 2024-08-20T21:41:28.5259317Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_complex128 PASSED [0.0056s] [ 54%] 2024-08-20T21:41:28.5259936Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_complex32 PASSED [0.0059s] [ 54%] 2024-08-20T21:41:28.5260555Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_complex64 PASSED [0.0056s] [ 54%] 2024-08-20T21:41:28.5261177Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_float16 PASSED [0.0058s] [ 54%] 2024-08-20T21:41:28.5261777Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_float32 PASSED [0.0056s] [ 54%] 2024-08-20T21:41:28.5262401Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_float64 PASSED [0.0058s] [ 54%] 2024-08-20T21:41:28.5262997Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_int16 PASSED [0.0056s] [ 54%] 2024-08-20T21:41:28.5263595Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_int32 PASSED [0.0058s] [ 54%] 2024-08-20T21:41:28.5264199Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_int64 PASSED [0.0056s] [ 54%] 2024-08-20T21:41:28.5264788Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_int8 PASSED [0.0056s] [ 54%] 2024-08-20T21:41:28.5265412Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_uint8 PASSED [0.0057s] [ 54%] 2024-08-20T21:41:28.5266026Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_bfloat16 PASSED [0.0058s] [ 54%] 2024-08-20T21:41:28.5266631Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_bool PASSED [0.0059s] [ 54%] 2024-08-20T21:41:28.5267237Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_float16 PASSED [0.0057s] [ 54%] 2024-08-20T21:41:28.5267828Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_float32 PASSED [0.0060s] [ 54%] 2024-08-20T21:41:28.5268441Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_float64 PASSED [0.0058s] [ 54%] 2024-08-20T21:41:28.5269035Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_int16 PASSED [0.0063s] [ 54%] 2024-08-20T21:41:28.5269616Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_int32 PASSED [0.0057s] [ 54%] 2024-08-20T21:41:28.5270209Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_int64 PASSED [0.0058s] [ 54%] 2024-08-20T21:41:28.5270793Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_int8 PASSED [0.0057s] [ 54%] 2024-08-20T21:41:28.5271378Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_uint8 PASSED [0.0057s] [ 55%] 2024-08-20T21:41:28.5271981Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_bfloat16 PASSED [0.0097s] [ 55%] 2024-08-20T21:41:28.5272548Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_bool PASSED [0.0097s] [ 55%] 2024-08-20T21:41:28.5273161Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_complex128 PASSED [0.0098s] [ 55%] 2024-08-20T21:41:28.5273753Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_complex32 PASSED [0.0096s] [ 55%] 2024-08-20T21:41:28.5274463Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_complex64 PASSED [0.0098s] [ 55%] 2024-08-20T21:41:28.5275070Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_float16 PASSED [0.0095s] [ 55%] 2024-08-20T21:41:28.5275686Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_float32 PASSED [0.0097s] [ 55%] 2024-08-20T21:41:28.5276269Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_float64 PASSED [0.0095s] [ 55%] 2024-08-20T21:41:28.5276854Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_int16 PASSED [0.0097s] [ 55%] 2024-08-20T21:41:28.5277424Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_int32 PASSED [0.0095s] [ 55%] 2024-08-20T21:41:28.5278014Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_int64 PASSED [0.0096s] [ 55%] 2024-08-20T21:41:28.5278581Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_int8 PASSED [0.0098s] [ 55%] 2024-08-20T21:41:28.5279152Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_uint8 PASSED [0.0095s] [ 55%] 2024-08-20T21:41:28.5279806Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_multinomial_cpu_bfloat16 PASSED [0.0086s] [ 55%] 2024-08-20T21:41:28.5280441Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_multinomial_cpu_float16 PASSED [0.0083s] [ 55%] 2024-08-20T21:41:28.5281086Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_multinomial_cpu_float32 PASSED [0.0085s] [ 55%] 2024-08-20T21:41:28.5281714Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_multinomial_cpu_float64 PASSED [0.0083s] [ 55%] 2024-08-20T21:41:28.5282324Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_bfloat16 PASSED [0.0059s] [ 55%] 2024-08-20T21:41:28.5282940Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_complex128 PASSED [0.0053s] [ 55%] 2024-08-20T21:41:28.5283572Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_complex64 PASSED [0.0054s] [ 55%] 2024-08-20T21:41:28.5284168Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_float16 PASSED [0.0052s] [ 55%] 2024-08-20T21:41:28.5284747Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_float32 PASSED [0.0051s] [ 55%] 2024-08-20T21:41:28.5285325Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_float64 PASSED [0.0053s] [ 55%] 2024-08-20T21:41:28.5285936Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_int16 PASSED [0.0051s] [ 55%] 2024-08-20T21:41:28.5286506Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_int32 PASSED [0.0053s] [ 55%] 2024-08-20T21:41:28.5287173Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_int64 PASSED [0.0052s] [ 55%] 2024-08-20T21:41:28.5287759Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_int8 PASSED [0.0053s] [ 55%] 2024-08-20T21:41:28.5288327Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_uint8 PASSED [0.0051s] [ 55%] 2024-08-20T21:41:28.5289043Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_bfloat16 PASSED [0.0101s] [ 55%] 2024-08-20T21:41:28.5289736Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_float16 PASSED [0.0098s] [ 55%] 2024-08-20T21:41:28.5290424Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_float32 PASSED [0.0099s] [ 55%] 2024-08-20T21:41:28.5291115Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_float64 PASSED [0.0098s] [ 55%] 2024-08-20T21:41:28.5291789Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_int16 PASSED [0.0100s] [ 55%] 2024-08-20T21:41:28.5292481Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_int32 PASSED [0.0101s] [ 55%] 2024-08-20T21:41:28.5293189Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_int64 PASSED [0.0099s] [ 55%] 2024-08-20T21:41:28.5293860Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_int8 PASSED [0.0101s] [ 55%] 2024-08-20T21:41:28.5294545Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_uint8 PASSED [0.0098s] [ 55%] 2024-08-20T21:41:28.5295239Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_bfloat16 PASSED [0.0101s] [ 55%] 2024-08-20T21:41:28.5295945Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_float16 PASSED [0.0098s] [ 55%] 2024-08-20T21:41:28.5296628Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_float32 PASSED [0.0105s] [ 55%] 2024-08-20T21:41:28.5297317Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_float64 PASSED [0.0098s] [ 55%] 2024-08-20T21:41:28.5298007Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_int16 PASSED [0.0100s] [ 55%] 2024-08-20T21:41:28.5298683Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_int32 PASSED [0.0098s] [ 55%] 2024-08-20T21:41:28.5299371Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_int64 PASSED [0.0098s] [ 55%] 2024-08-20T21:41:28.5300070Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_int8 PASSED [0.0099s] [ 55%] 2024-08-20T21:41:28.5300742Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_uint8 PASSED [0.0098s] [ 55%] 2024-08-20T21:41:28.5301487Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_bfloat16 PASSED [0.0101s] [ 55%] 2024-08-20T21:41:28.5302176Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_float16 PASSED [0.0098s] [ 55%] 2024-08-20T21:41:28.5302875Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_float32 PASSED [0.0101s] [ 55%] 2024-08-20T21:41:28.5303583Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_float64 PASSED [0.0099s] [ 55%] 2024-08-20T21:41:28.5304263Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_int16 PASSED [0.0100s] [ 55%] 2024-08-20T21:41:28.5304952Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_int32 PASSED [0.0098s] [ 55%] 2024-08-20T21:41:28.5305622Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_int64 PASSED [0.0099s] [ 55%] 2024-08-20T21:41:28.5306293Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_int8 PASSED [0.0097s] [ 55%] 2024-08-20T21:41:28.5306976Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_uint8 PASSED [0.0098s] [ 55%] 2024-08-20T21:41:28.5307607Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_bfloat16 PASSED [0.0062s] [ 55%] 2024-08-20T21:41:28.5308224Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_bool PASSED [0.0061s] [ 55%] 2024-08-20T21:41:28.5308850Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_float16 PASSED [0.0063s] [ 55%] 2024-08-20T21:41:28.5309469Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_float32 PASSED [0.0061s] [ 55%] 2024-08-20T21:41:28.5310102Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_float64 PASSED [0.0063s] [ 56%] 2024-08-20T21:41:28.5310740Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_int16 PASSED [0.0061s] [ 56%] 2024-08-20T21:41:28.5311355Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_int32 PASSED [0.0067s] [ 56%] 2024-08-20T21:41:28.5311963Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_int64 PASSED [0.0061s] [ 56%] 2024-08-20T21:41:28.5312563Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_int8 PASSED [0.0063s] [ 56%] 2024-08-20T21:41:28.5313188Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_uint8 PASSED [0.0061s] [ 56%] 2024-08-20T21:41:28.5313803Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmean_cpu_bfloat16 PASSED [0.0219s] [ 56%] 2024-08-20T21:41:28.5314427Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmean_cpu_float16 PASSED [0.0222s] [ 56%] 2024-08-20T21:41:28.5315033Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmean_cpu_float32 PASSED [0.0219s] [ 56%] 2024-08-20T21:41:28.5315638Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmean_cpu_float64 PASSED [0.0223s] [ 56%] 2024-08-20T21:41:28.5316280Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_bfloat16 PASSED [0.0112s] [ 56%] 2024-08-20T21:41:28.5316901Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_float16 PASSED [0.0115s] [ 56%] 2024-08-20T21:41:28.5317556Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_float32 PASSED [0.0112s] [ 56%] 2024-08-20T21:41:28.5318199Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_float64 PASSED [0.0114s] [ 56%] 2024-08-20T21:41:28.5318811Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_int16 PASSED [0.0110s] [ 56%] 2024-08-20T21:41:28.5319427Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_int32 PASSED [0.0110s] [ 56%] 2024-08-20T21:41:28.5320027Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_int64 PASSED [0.0112s] [ 56%] 2024-08-20T21:41:28.5320668Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_int8 PASSED [0.0110s] [ 56%] 2024-08-20T21:41:28.5321510Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_uint8 PASSED [0.0113s] [ 56%] 2024-08-20T21:41:28.5322152Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanquantile_cpu_float32 PASSED [0.0340s] [ 56%] 2024-08-20T21:41:28.5322808Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanquantile_cpu_float64 PASSED [0.0345s] [ 56%] 2024-08-20T21:41:28.5323420Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_bfloat16 PASSED [0.0218s] [ 56%] 2024-08-20T21:41:28.5324005Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_bool PASSED [0.0167s] [ 56%] 2024-08-20T21:41:28.5324618Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_float16 PASSED [0.0218s] [ 56%] 2024-08-20T21:41:28.5325215Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_float32 PASSED [0.0229s] [ 56%] 2024-08-20T21:41:28.5325830Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_float64 PASSED [0.0218s] [ 56%] 2024-08-20T21:41:28.5326420Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_int16 PASSED [0.0165s] [ 56%] 2024-08-20T21:41:28.5327069Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_int32 PASSED [0.0166s] [ 56%] 2024-08-20T21:41:28.5327713Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_int64 PASSED [0.0165s] [ 56%] 2024-08-20T21:41:28.5328293Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_int8 PASSED [0.0168s] [ 56%] 2024-08-20T21:41:28.5328896Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_uint8 PASSED [0.0166s] [ 56%] 2024-08-20T21:41:28.5329536Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_bfloat16 PASSED [0.0076s] [ 56%] 2024-08-20T21:41:28.5330149Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_bool PASSED [0.0074s] [ 56%] 2024-08-20T21:41:28.5330813Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_complex128 PASSED [0.0076s] [ 56%] 2024-08-20T21:41:28.5331458Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_complex32 PASSED [0.0073s] [ 56%] 2024-08-20T21:41:28.5332106Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_complex64 PASSED [0.0076s] [ 56%] 2024-08-20T21:41:28.5332750Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_float16 PASSED [0.0072s] [ 56%] 2024-08-20T21:41:28.5333374Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_float32 PASSED [0.0072s] [ 56%] 2024-08-20T21:41:28.5334023Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_float64 PASSED [0.0073s] [ 56%] 2024-08-20T21:41:28.5334636Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_int16 PASSED [0.0072s] [ 56%] 2024-08-20T21:41:28.5335302Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_int32 PASSED [0.0074s] [ 56%] 2024-08-20T21:41:28.5335961Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_int64 PASSED [0.0072s] [ 56%] 2024-08-20T21:41:28.5336576Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_int8 PASSED [0.0075s] [ 56%] 2024-08-20T21:41:28.5337207Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_uint8 PASSED [0.0072s] [ 56%] 2024-08-20T21:41:28.5337819Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_bfloat16 PASSED [0.0099s] [ 56%] 2024-08-20T21:41:28.5338432Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_bool PASSED [0.0096s] [ 56%] 2024-08-20T21:41:28.5339072Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_complex128 PASSED [0.0103s] [ 56%] 2024-08-20T21:41:28.5339689Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_complex32 PASSED [0.0096s] [ 56%] 2024-08-20T21:41:28.5340319Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_complex64 PASSED [0.0097s] [ 56%] 2024-08-20T21:41:28.5340921Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_float16 PASSED [0.0097s] [ 56%] 2024-08-20T21:41:28.5341513Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_float32 PASSED [0.0095s] [ 56%] 2024-08-20T21:41:28.5342117Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_float64 PASSED [0.0099s] [ 56%] 2024-08-20T21:41:28.5342705Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_int16 PASSED [0.0097s] [ 56%] 2024-08-20T21:41:28.5343289Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_int32 PASSED [0.0098s] [ 56%] 2024-08-20T21:41:28.5343883Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_int64 PASSED [0.0098s] [ 56%] 2024-08-20T21:41:28.5344466Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_int8 PASSED [0.0100s] [ 56%] 2024-08-20T21:41:28.5345098Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_uint8 PASSED [0.0098s] [ 56%] 2024-08-20T21:41:28.5345769Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_batch_norm_cpu_bfloat16 PASSED [0.0106s] [ 56%] 2024-08-20T21:41:28.5346434Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_batch_norm_cpu_float16 PASSED [0.0104s] [ 56%] 2024-08-20T21:41:28.5347294Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_batch_norm_cpu_float32 PASSED [0.0104s] [ 57%] 2024-08-20T21:41:28.5347972Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_batch_norm_cpu_float64 PASSED [0.0106s] [ 57%] 2024-08-20T21:41:28.5348696Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_bfloat16 PASSED [0.0095s] [ 57%] 2024-08-20T21:41:28.5349383Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_bool PASSED [0.0096s] [ 57%] 2024-08-20T21:41:28.5350084Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_float16 PASSED [0.0095s] [ 57%] 2024-08-20T21:41:28.5350785Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_float32 PASSED [0.0097s] [ 57%] 2024-08-20T21:41:28.5351491Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_float64 PASSED [0.0095s] [ 57%] 2024-08-20T21:41:28.5352195Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_int16 PASSED [0.0096s] [ 57%] 2024-08-20T21:41:28.5352946Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_int32 PASSED [0.0093s] [ 57%] 2024-08-20T21:41:28.5353661Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_int64 PASSED [0.0095s] [ 57%] 2024-08-20T21:41:28.5354361Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_int8 PASSED [0.0096s] [ 57%] 2024-08-20T21:41:28.5355046Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_uint8 PASSED [0.0094s] [ 57%] 2024-08-20T21:41:28.5355764Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_layer_norm_cpu_bfloat16 PASSED [0.0154s] [ 57%] 2024-08-20T21:41:28.5356432Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_layer_norm_cpu_float16 PASSED [0.0149s] [ 57%] 2024-08-20T21:41:28.5357097Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_layer_norm_cpu_float32 PASSED [0.0154s] [ 57%] 2024-08-20T21:41:28.5357775Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_layer_norm_cpu_float64 PASSED [0.0149s] [ 57%] 2024-08-20T21:41:28.5358360Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_bfloat16 PASSED [0.0098s] [ 57%] 2024-08-20T21:41:28.5358936Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_bool PASSED [0.0094s] [ 57%] 2024-08-20T21:41:28.5359532Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_complex128 PASSED [0.0098s] [ 57%] 2024-08-20T21:41:28.5360119Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_complex64 PASSED [0.0095s] [ 57%] 2024-08-20T21:41:28.5360705Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_float16 PASSED [0.0095s] [ 57%] 2024-08-20T21:41:28.5361284Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_float32 PASSED [0.0097s] [ 57%] 2024-08-20T21:41:28.5361856Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_float64 PASSED [0.0094s] [ 57%] 2024-08-20T21:41:28.5362436Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_int16 PASSED [0.0097s] [ 57%] 2024-08-20T21:41:28.5363035Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_int32 PASSED [0.0095s] [ 57%] 2024-08-20T21:41:28.5363611Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_int64 PASSED [0.0097s] [ 57%] 2024-08-20T21:41:28.5364172Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_int8 PASSED [0.0095s] [ 57%] 2024-08-20T21:41:28.5364733Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_uint8 PASSED [0.0097s] [ 57%] 2024-08-20T21:41:28.5365337Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_bfloat16 PASSED [0.0051s] [ 57%] 2024-08-20T21:41:28.5365934Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_complex128 PASSED [0.0053s] [ 57%] 2024-08-20T21:41:28.5366547Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_complex32 PASSED [0.0051s] [ 57%] 2024-08-20T21:41:28.5367232Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_complex64 PASSED [0.0051s] [ 57%] 2024-08-20T21:41:28.5367820Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_float16 PASSED [0.0053s] [ 57%] 2024-08-20T21:41:28.5368416Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_float32 PASSED [0.0051s] [ 57%] 2024-08-20T21:41:28.5368995Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_float64 PASSED [0.0053s] [ 57%] 2024-08-20T21:41:28.5369581Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_int16 PASSED [0.0052s] [ 57%] 2024-08-20T21:41:28.5370185Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_int32 PASSED [0.0053s] [ 57%] 2024-08-20T21:41:28.5370787Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_int64 PASSED [0.0052s] [ 57%] 2024-08-20T21:41:28.5371368Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_int8 PASSED [0.0053s] [ 57%] 2024-08-20T21:41:28.5371935Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_uint8 PASSED [0.0051s] [ 57%] 2024-08-20T21:41:28.5372562Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_bfloat16 PASSED [0.0084s] [ 57%] 2024-08-20T21:41:28.5373199Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_bool PASSED [0.0082s] [ 57%] 2024-08-20T21:41:28.5373837Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_complex128 PASSED [0.0083s] [ 57%] 2024-08-20T21:41:28.5374485Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_complex32 PASSED [0.0084s] [ 57%] 2024-08-20T21:41:28.5375117Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_complex64 PASSED [0.0082s] [ 57%] 2024-08-20T21:41:28.5375737Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_float16 PASSED [0.0084s] [ 57%] 2024-08-20T21:41:28.5376360Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_float32 PASSED [0.0082s] [ 57%] 2024-08-20T21:41:28.5376977Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_float64 PASSED [0.0084s] [ 57%] 2024-08-20T21:41:28.5377594Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_int16 PASSED [0.0082s] [ 57%] 2024-08-20T21:41:28.5378193Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_int32 PASSED [0.0085s] [ 57%] 2024-08-20T21:41:28.5378797Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_int64 PASSED [0.0082s] [ 57%] 2024-08-20T21:41:28.5379410Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_int8 PASSED [0.0084s] [ 57%] 2024-08-20T21:41:28.5380042Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_uint8 PASSED [0.0082s] [ 57%] 2024-08-20T21:41:28.5380722Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_bfloat16 PASSED [0.0082s] [ 57%] 2024-08-20T21:41:28.5381367Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_bool PASSED [0.0085s] [ 57%] 2024-08-20T21:41:28.5382045Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_complex128 PASSED [0.0083s] [ 57%] 2024-08-20T21:41:28.5382731Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_complex32 PASSED [0.0086s] [ 57%] 2024-08-20T21:41:28.5383403Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_complex64 PASSED [0.0084s] [ 57%] 2024-08-20T21:41:28.5384067Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_float16 PASSED [0.0084s] [ 57%] 2024-08-20T21:41:28.5384744Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_float32 PASSED [0.0082s] [ 57%] 2024-08-20T21:41:28.5385403Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_float64 PASSED [0.0084s] [ 58%] 2024-08-20T21:41:28.5386065Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_int16 PASSED [0.0082s] [ 58%] 2024-08-20T21:41:28.5386715Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_int32 PASSED [0.0082s] [ 58%] 2024-08-20T21:41:28.5387395Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_int64 PASSED [0.0083s] [ 58%] 2024-08-20T21:41:28.5388123Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_int8 PASSED [0.0081s] [ 58%] 2024-08-20T21:41:28.5388773Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_uint8 PASSED [0.0085s] [ 58%] 2024-08-20T21:41:28.5389472Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_bfloat16 PASSED [0.0084s] [ 58%] 2024-08-20T21:41:28.5390188Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_bool PASSED [0.0085s] [ 58%] 2024-08-20T21:41:28.5390869Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_complex128 PASSED [0.0084s] [ 58%] 2024-08-20T21:41:28.5391513Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_complex32 PASSED [0.0086s] [ 58%] 2024-08-20T21:41:28.5392134Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_complex64 PASSED [0.0084s] [ 58%] 2024-08-20T21:41:28.5392763Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_float16 PASSED [0.0085s] [ 58%] 2024-08-20T21:41:28.5393377Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_float32 PASSED [0.0084s] [ 58%] 2024-08-20T21:41:28.5393986Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_float64 PASSED [0.0083s] [ 58%] 2024-08-20T21:41:28.5394596Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_int16 PASSED [0.0089s] [ 58%] 2024-08-20T21:41:28.5395189Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_int32 PASSED [0.0084s] [ 58%] 2024-08-20T21:41:28.5395790Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_int64 PASSED [0.0087s] [ 58%] 2024-08-20T21:41:28.5396386Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_int8 PASSED [0.0084s] [ 58%] 2024-08-20T21:41:28.5396982Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_uint8 PASSED [0.0086s] [ 58%] 2024-08-20T21:41:28.5397638Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_bfloat16 PASSED [0.0082s] [ 58%] 2024-08-20T21:41:28.5398226Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_bool PASSED [0.0084s] [ 58%] 2024-08-20T21:41:28.5398855Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_complex128 PASSED [0.0083s] [ 58%] 2024-08-20T21:41:28.5399493Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_complex32 PASSED [0.0084s] [ 58%] 2024-08-20T21:41:28.5400121Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_complex64 PASSED [0.0082s] [ 58%] 2024-08-20T21:41:28.5400745Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_float16 PASSED [0.0082s] [ 58%] 2024-08-20T21:41:28.5401357Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_float32 PASSED [0.0083s] [ 58%] 2024-08-20T21:41:28.5401964Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_float64 PASSED [0.0081s] [ 58%] 2024-08-20T21:41:28.5402581Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_int16 PASSED [0.0084s] [ 58%] 2024-08-20T21:41:28.5403178Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_int32 PASSED [0.0081s] [ 58%] 2024-08-20T21:41:28.5403781Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_int64 PASSED [0.0084s] [ 58%] 2024-08-20T21:41:28.5404402Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_int8 PASSED [0.0081s] [ 58%] 2024-08-20T21:41:28.5405001Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_uint8 PASSED [0.0083s] [ 58%] 2024-08-20T21:41:28.5405666Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_bfloat16 PASSED [0.0081s] [ 58%] 2024-08-20T21:41:28.5406259Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_bool PASSED [0.0083s] [ 58%] 2024-08-20T21:41:28.5406985Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_complex128 PASSED [0.0082s] [ 58%] 2024-08-20T21:41:28.5407684Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_complex32 PASSED [0.0082s] [ 58%] 2024-08-20T21:41:28.5408322Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_complex64 PASSED [0.0091s] [ 58%] 2024-08-20T21:41:28.5408954Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_float16 PASSED [0.0083s] [ 58%] 2024-08-20T21:41:28.5409566Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_float32 PASSED [0.0085s] [ 58%] 2024-08-20T21:41:28.5410185Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_float64 PASSED [0.0082s] [ 58%] 2024-08-20T21:41:28.5410810Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_int16 PASSED [0.0085s] [ 58%] 2024-08-20T21:41:28.5411406Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_int32 PASSED [0.0083s] [ 58%] 2024-08-20T21:41:28.5412027Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_int64 PASSED [0.0085s] [ 58%] 2024-08-20T21:41:28.5412620Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_int8 PASSED [0.0082s] [ 58%] 2024-08-20T21:41:28.5413223Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_uint8 PASSED [0.0081s] [ 58%] 2024-08-20T21:41:28.5414081Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nextafter_cpu_bfloat16 PASSED [0.0097s] [ 58%] 2024-08-20T21:41:28.5414746Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nextafter_cpu_float16 PASSED [0.0094s] [ 58%] 2024-08-20T21:41:28.5415486Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nextafter_cpu_float32 PASSED [0.0097s] [ 58%] 2024-08-20T21:41:28.5416154Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nextafter_cpu_float64 PASSED [0.0094s] [ 58%] 2024-08-20T21:41:28.5424038Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool1d_cpu_bfloat16 PASSED [0.0079s] [ 58%] 2024-08-20T21:41:28.5424975Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool1d_cpu_float16 PASSED [0.0076s] [ 58%] 2024-08-20T21:41:28.5425754Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool1d_cpu_float32 PASSED [0.0078s] [ 58%] 2024-08-20T21:41:28.5426532Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool1d_cpu_float64 PASSED [0.0076s] [ 58%] 2024-08-20T21:41:28.5427313Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool2d_cpu_bfloat16 PASSED [0.0098s] [ 58%] 2024-08-20T21:41:28.5428071Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool2d_cpu_float16 PASSED [0.0097s] [ 58%] 2024-08-20T21:41:28.5428832Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool2d_cpu_float32 PASSED [0.0097s] [ 58%] 2024-08-20T21:41:28.5429587Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool2d_cpu_float64 PASSED [0.0099s] [ 58%] 2024-08-20T21:41:28.5430456Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool3d_cpu_bfloat16 PASSED [0.0108s] [ 58%] 2024-08-20T21:41:28.5431245Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool3d_cpu_float16 PASSED [0.0116s] [ 58%] 2024-08-20T21:41:28.5431999Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool3d_cpu_float32 PASSED [0.0109s] [ 59%] 2024-08-20T21:41:28.5432766Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool3d_cpu_float64 PASSED [0.0113s] [ 59%] 2024-08-20T21:41:28.5433565Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool1d_cpu_bfloat16 PASSED [0.0088s] [ 59%] 2024-08-20T21:41:28.5434336Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool1d_cpu_float16 PASSED [0.0091s] [ 59%] 2024-08-20T21:41:28.5435089Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool1d_cpu_float32 PASSED [0.0088s] [ 59%] 2024-08-20T21:41:28.5435848Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool1d_cpu_float64 PASSED [0.0090s] [ 59%] 2024-08-20T21:41:28.5436633Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool2d_cpu_bfloat16 PASSED [0.0169s] [ 59%] 2024-08-20T21:41:28.5437381Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool2d_cpu_float16 PASSED [0.0170s] [ 59%] 2024-08-20T21:41:28.5438141Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool2d_cpu_float32 PASSED [0.0172s] [ 59%] 2024-08-20T21:41:28.5438890Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool2d_cpu_float64 PASSED [0.0171s] [ 59%] 2024-08-20T21:41:28.5439648Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool3d_cpu_bfloat16 PASSED [0.0154s] [ 59%] 2024-08-20T21:41:28.5440416Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool3d_cpu_float32 PASSED [0.0151s] [ 59%] 2024-08-20T21:41:28.5441201Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool3d_cpu_float64 PASSED [0.0153s] [ 59%] 2024-08-20T21:41:28.5441944Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_alpha_dropout_cpu_bfloat16 PASSED [0.0144s] [ 59%] 2024-08-20T21:41:28.5442669Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_alpha_dropout_cpu_float16 PASSED [0.0147s] [ 59%] 2024-08-20T21:41:28.5443389Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_alpha_dropout_cpu_float32 PASSED [0.0144s] [ 59%] 2024-08-20T21:41:28.5444121Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_alpha_dropout_cpu_float64 PASSED [0.0144s] [ 59%] 2024-08-20T21:41:28.5444835Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool1d_cpu_bfloat16 PASSED [0.0095s] [ 59%] 2024-08-20T21:41:28.5445552Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool1d_cpu_float16 PASSED [0.0093s] [ 59%] 2024-08-20T21:41:28.5446246Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool1d_cpu_float32 PASSED [0.0095s] [ 59%] 2024-08-20T21:41:28.5447206Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool1d_cpu_float64 PASSED [0.0093s] [ 59%] 2024-08-20T21:41:28.5447928Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool1d_cpu_int64 PASSED [0.0100s] [ 59%] 2024-08-20T21:41:28.5448715Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool2d_cpu_bfloat16 PASSED [0.0083s] [ 59%] 2024-08-20T21:41:28.5449477Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool2d_cpu_float16 PASSED [0.0085s] [ 59%] 2024-08-20T21:41:28.5450183Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool2d_cpu_float32 PASSED [0.0083s] [ 59%] 2024-08-20T21:41:28.5450880Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool2d_cpu_float64 PASSED [0.0083s] [ 59%] 2024-08-20T21:41:28.5451629Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool2d_cpu_int64 PASSED [0.0084s] [ 59%] 2024-08-20T21:41:28.5452334Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool3d_cpu_float32 PASSED [0.0087s] [ 59%] 2024-08-20T21:41:28.5453050Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool3d_cpu_float64 PASSED [0.0090s] [ 59%] 2024-08-20T21:41:28.5453746Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool3d_cpu_int64 PASSED [0.0087s] [ 59%] 2024-08-20T21:41:28.5454459Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_batch_norm_cpu_bfloat16 PASSED [0.0115s] [ 59%] 2024-08-20T21:41:28.5455177Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_batch_norm_cpu_float16 PASSED [0.0112s] [ 59%] 2024-08-20T21:41:28.5455884Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_batch_norm_cpu_float32 PASSED [0.0114s] [ 59%] 2024-08-20T21:41:28.5456593Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_batch_norm_cpu_float64 PASSED [0.0112s] [ 59%] 2024-08-20T21:41:28.5457292Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_bfloat16 PASSED [0.0136s] [ 59%] 2024-08-20T21:41:28.5457986Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_float16 PASSED [0.0133s] [ 59%] 2024-08-20T21:41:28.5458683Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_float32 PASSED [0.0133s] [ 59%] 2024-08-20T21:41:28.5459408Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_float64 PASSED [0.0136s] [ 59%] 2024-08-20T21:41:28.5460096Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_int16 PASSED [0.0132s] [ 59%] 2024-08-20T21:41:28.5460778Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_int32 PASSED [0.0136s] [ 59%] 2024-08-20T21:41:28.5461451Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_int64 PASSED [0.0132s] [ 59%] 2024-08-20T21:41:28.5462140Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_int8 PASSED [0.0135s] [ 59%] 2024-08-20T21:41:28.5462823Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_uint8 PASSED [0.0132s] [ 59%] 2024-08-20T21:41:28.5463601Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_cpu_bfloat16 PASSED [0.0111s] [ 59%] 2024-08-20T21:41:28.5464364Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_cpu_float16 PASSED [0.0104s] [ 59%] 2024-08-20T21:41:28.5465134Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_cpu_float32 PASSED [0.0104s] [ 59%] 2024-08-20T21:41:28.5465901Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_cpu_float64 PASSED [0.0106s] [ 59%] 2024-08-20T21:41:28.5466765Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_with_logits_cpu_bfloat16 PASSED [0.0186s] [ 59%] 2024-08-20T21:41:28.5467625Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_with_logits_cpu_float16 PASSED [0.0188s] [ 59%] 2024-08-20T21:41:28.5468451Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_with_logits_cpu_float32 PASSED [0.0186s] [ 59%] 2024-08-20T21:41:28.5469276Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_with_logits_cpu_float64 PASSED [0.0189s] [ 59%] 2024-08-20T21:41:28.5469989Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_celu_cpu_bfloat16 PASSED [0.0062s] [ 59%] 2024-08-20T21:41:28.5470660Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_celu_cpu_float16 PASSED [0.0063s] [ 59%] 2024-08-20T21:41:28.5471341Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_celu_cpu_float32 PASSED [0.0061s] [ 59%] 2024-08-20T21:41:28.5472014Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_celu_cpu_float64 PASSED [0.0061s] [ 59%] 2024-08-20T21:41:28.5472756Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_bfloat16 PASSED [0.0063s] [ 59%] 2024-08-20T21:41:28.5473482Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_bool PASSED [0.0061s] [ 59%] 2024-08-20T21:41:28.5474238Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_complex128 PASSED [0.0064s] [ 59%] 2024-08-20T21:41:28.5474990Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_complex64 PASSED [0.0062s] [ 59%] 2024-08-20T21:41:28.5475723Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_float16 PASSED [0.0063s] [ 59%] 2024-08-20T21:41:28.5476454Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_float32 PASSED [0.0061s] [ 60%] 2024-08-20T21:41:28.5477228Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_float64 PASSED [0.0064s] [ 60%] 2024-08-20T21:41:28.5477950Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_int16 PASSED [0.0062s] [ 60%] 2024-08-20T21:41:28.5478682Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_int32 PASSED [0.0063s] [ 60%] 2024-08-20T21:41:28.5479400Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_int64 PASSED [0.0061s] [ 60%] 2024-08-20T21:41:28.5480118Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_int8 PASSED [0.0062s] [ 60%] 2024-08-20T21:41:28.5480850Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_uint8 PASSED [0.0063s] [ 60%] 2024-08-20T21:41:28.5481541Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_bfloat16 PASSED [0.0100s] [ 60%] 2024-08-20T21:41:28.5482247Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_complex128 PASSED [0.0105s] [ 60%] 2024-08-20T21:41:28.5482940Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_complex64 PASSED [0.0103s] [ 60%] 2024-08-20T21:41:28.5483621Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_float16 PASSED [0.0104s] [ 60%] 2024-08-20T21:41:28.5484351Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_float32 PASSED [0.0101s] [ 60%] 2024-08-20T21:41:28.5485053Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_float64 PASSED [0.0103s] [ 60%] 2024-08-20T21:41:28.5485735Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_int64 PASSED [0.0100s] [ 60%] 2024-08-20T21:41:28.5486429Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_bfloat16 PASSED [0.0210s] [ 60%] 2024-08-20T21:41:28.5487216Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_complex128 PASSED [0.0212s] [ 60%] 2024-08-20T21:41:28.5487974Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_complex64 PASSED [0.0211s] [ 60%] 2024-08-20T21:41:28.5488660Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_float16 PASSED [0.0211s] [ 60%] 2024-08-20T21:41:28.5489333Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_float32 PASSED [0.0207s] [ 60%] 2024-08-20T21:41:28.5490026Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_float64 PASSED [0.0210s] [ 60%] 2024-08-20T21:41:28.5490693Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_int64 PASSED [0.0208s] [ 60%] 2024-08-20T21:41:28.5491396Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_bfloat16 PASSED [0.0156s] [ 60%] 2024-08-20T21:41:28.5492093Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_complex128 PASSED [0.0157s] [ 60%] 2024-08-20T21:41:28.5492788Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_complex64 PASSED [0.0158s] [ 60%] 2024-08-20T21:41:28.5493481Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_float16 PASSED [0.0153s] [ 60%] 2024-08-20T21:41:28.5494160Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_float32 PASSED [0.0153s] [ 60%] 2024-08-20T21:41:28.5494902Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_float64 PASSED [0.0162s] [ 60%] 2024-08-20T21:41:28.5495575Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_int64 PASSED [0.0153s] [ 60%] 2024-08-20T21:41:28.5496321Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_bfloat16 PASSED [0.0101s] [ 60%] 2024-08-20T21:41:28.5497091Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_complex128 PASSED [0.0101s] [ 60%] 2024-08-20T21:41:28.5497844Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_complex64 PASSED [0.0104s] [ 60%] 2024-08-20T21:41:28.5498604Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_float16 PASSED [0.0100s] [ 60%] 2024-08-20T21:41:28.5499346Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_float32 PASSED [0.0102s] [ 60%] 2024-08-20T21:41:28.5500086Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_float64 PASSED [0.0100s] [ 60%] 2024-08-20T21:41:28.5500832Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_int64 PASSED [0.0099s] [ 60%] 2024-08-20T21:41:28.5501574Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_bfloat16 PASSED [0.0113s] [ 60%] 2024-08-20T21:41:28.5502372Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_complex128 PASSED [0.0112s] [ 60%] 2024-08-20T21:41:28.5503156Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_complex64 PASSED [0.0116s] [ 60%] 2024-08-20T21:41:28.5503891Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_float16 PASSED [0.0113s] [ 60%] 2024-08-20T21:41:28.5504641Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_float32 PASSED [0.0115s] [ 60%] 2024-08-20T21:41:28.5505404Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_float64 PASSED [0.0112s] [ 60%] 2024-08-20T21:41:28.5506144Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_int64 PASSED [0.0114s] [ 60%] 2024-08-20T21:41:28.5506885Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_bfloat16 PASSED [0.0103s] [ 60%] 2024-08-20T21:41:28.5507642Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_complex128 PASSED [0.0106s] [ 60%] 2024-08-20T21:41:28.5508409Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_complex64 PASSED [0.0105s] [ 60%] 2024-08-20T21:41:28.5509146Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_float16 PASSED [0.0102s] [ 60%] 2024-08-20T21:41:28.5509894Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_float32 PASSED [0.0105s] [ 60%] 2024-08-20T21:41:28.5510631Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_float64 PASSED [0.0103s] [ 60%] 2024-08-20T21:41:28.5511363Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_int64 PASSED [0.0109s] [ 60%] 2024-08-20T21:41:28.5512146Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_bfloat16 PASSED [0.0083s] [ 60%] 2024-08-20T21:41:28.5512921Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_bool PASSED [0.0083s] [ 60%] 2024-08-20T21:41:28.5513699Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_float16 PASSED [0.0081s] [ 60%] 2024-08-20T21:41:28.5514462Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_float32 PASSED [0.0084s] [ 60%] 2024-08-20T21:41:28.5515225Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_float64 PASSED [0.0081s] [ 60%] 2024-08-20T21:41:28.5515993Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_int16 PASSED [0.0083s] [ 60%] 2024-08-20T21:41:28.5516746Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_int32 PASSED [0.0081s] [ 60%] 2024-08-20T21:41:28.5517514Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_int64 PASSED [0.0080s] [ 60%] 2024-08-20T21:41:28.5518263Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_int8 PASSED [0.0084s] [ 60%] 2024-08-20T21:41:28.5519013Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_uint8 PASSED [0.0081s] [ 60%] 2024-08-20T21:41:28.5519773Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_similarity_cpu_bfloat16 PASSED [0.0096s] [ 61%] 2024-08-20T21:41:28.5520674Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_similarity_cpu_float16 PASSED [0.0094s] [ 61%] 2024-08-20T21:41:28.5521695Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_similarity_cpu_float32 PASSED [0.0097s] [ 61%] 2024-08-20T21:41:28.5522450Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_similarity_cpu_float64 PASSED [0.0095s] [ 61%] 2024-08-20T21:41:28.5523181Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cross_entropy_cpu_bfloat16 PASSED [0.0164s] [ 61%] 2024-08-20T21:41:28.5523953Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cross_entropy_cpu_float16 PASSED [0.0161s] [ 61%] 2024-08-20T21:41:28.5524676Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cross_entropy_cpu_float32 PASSED [0.0162s] [ 61%] 2024-08-20T21:41:28.5525403Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cross_entropy_cpu_float64 PASSED [0.0163s] [ 61%] 2024-08-20T21:41:28.5526098Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_ctc_loss_cpu_float32 PASSED [0.0144s] [ 61%] 2024-08-20T21:41:28.5526834Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_ctc_loss_cpu_float64 PASSED [0.0153s] [ 61%] 2024-08-20T21:41:28.5527563Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout2d_cpu_bfloat16 PASSED [0.0114s] [ 61%] 2024-08-20T21:41:28.5528266Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout2d_cpu_float16 PASSED [0.0125s] [ 61%] 2024-08-20T21:41:28.5528972Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout2d_cpu_float32 PASSED [0.0115s] [ 61%] 2024-08-20T21:41:28.5529663Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout2d_cpu_float64 PASSED [0.0118s] [ 61%] 2024-08-20T21:41:28.5530371Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout3d_cpu_bfloat16 PASSED [0.0116s] [ 61%] 2024-08-20T21:41:28.5531110Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout3d_cpu_float16 PASSED [0.0118s] [ 61%] 2024-08-20T21:41:28.5531793Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout3d_cpu_float32 PASSED [0.0115s] [ 61%] 2024-08-20T21:41:28.5532495Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout3d_cpu_float64 PASSED [0.0115s] [ 61%] 2024-08-20T21:41:28.5533192Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout_cpu_bfloat16 PASSED [0.0145s] [ 61%] 2024-08-20T21:41:28.5533884Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout_cpu_float16 PASSED [0.0144s] [ 61%] 2024-08-20T21:41:28.5534586Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout_cpu_float32 PASSED [0.0147s] [ 61%] 2024-08-20T21:41:28.5535271Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout_cpu_float64 PASSED [0.0144s] [ 61%] 2024-08-20T21:41:28.5535957Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_elu_cpu_bfloat16 PASSED [0.0065s] [ 61%] 2024-08-20T21:41:28.5536621Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_elu_cpu_float16 PASSED [0.0062s] [ 61%] 2024-08-20T21:41:28.5537294Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_elu_cpu_float32 PASSED [0.0064s] [ 61%] 2024-08-20T21:41:28.5537971Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_elu_cpu_float64 PASSED [0.0061s] [ 61%] 2024-08-20T21:41:28.5538736Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_bag_cpu_bfloat16 PASSED [0.0331s] [ 61%] 2024-08-20T21:41:28.5539508Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_bag_cpu_float16 PASSED [0.0325s] [ 61%] 2024-08-20T21:41:28.5540239Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_bag_cpu_float32 PASSED [0.0326s] [ 61%] 2024-08-20T21:41:28.5540961Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_bag_cpu_float64 PASSED [0.0334s] [ 61%] 2024-08-20T21:41:28.5541716Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_cpu_bfloat16 PASSED [0.0103s] [ 61%] 2024-08-20T21:41:28.5542421Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_cpu_float16 PASSED [0.0105s] [ 61%] 2024-08-20T21:41:28.5543132Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_cpu_float32 PASSED [0.0102s] [ 61%] 2024-08-20T21:41:28.5543828Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_cpu_float64 PASSED [0.0111s] [ 61%] 2024-08-20T21:41:28.5544665Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_with_train_cpu_bfloat16 PASSED [0.0084s] [ 61%] 2024-08-20T21:41:28.5545502Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_with_train_cpu_float16 PASSED [0.0086s] [ 61%] 2024-08-20T21:41:28.5546331Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_with_train_cpu_float32 PASSED [0.0083s] [ 61%] 2024-08-20T21:41:28.5547349Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_with_train_cpu_float64 PASSED [0.0084s] [ 61%] 2024-08-20T21:41:28.5548228Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_bfloat16 PASSED [0.0099s] [ 61%] 2024-08-20T21:41:28.5549055Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_bool PASSED [0.0097s] [ 61%] 2024-08-20T21:41:28.5550022Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_complex128 PASSED [0.0101s] [ 61%] 2024-08-20T21:41:28.5550876Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_complex64 PASSED [0.0099s] [ 61%] 2024-08-20T21:41:28.5551740Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_float16 PASSED [0.0100s] [ 61%] 2024-08-20T21:41:28.5552587Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_float32 PASSED [0.0097s] [ 61%] 2024-08-20T21:41:28.5553447Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_float64 PASSED [0.0099s] [ 61%] 2024-08-20T21:41:28.5554279Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_int16 PASSED [0.0098s] [ 61%] 2024-08-20T21:41:28.5555107Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_int32 PASSED [0.0100s] [ 61%] 2024-08-20T21:41:28.5555950Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_int64 PASSED [0.0098s] [ 61%] 2024-08-20T21:41:28.5556777Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_int8 PASSED [0.0097s] [ 61%] 2024-08-20T21:41:28.5557657Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_uint8 PASSED [0.0099s] [ 61%] 2024-08-20T21:41:28.5558469Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool2d_cpu_bfloat16 PASSED [0.0232s] [ 61%] 2024-08-20T21:41:28.5559236Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool2d_cpu_float16 PASSED [0.0237s] [ 61%] 2024-08-20T21:41:28.5560018Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool2d_cpu_float32 PASSED [0.0235s] [ 61%] 2024-08-20T21:41:28.5560819Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool2d_cpu_float64 PASSED [0.0236s] [ 61%] 2024-08-20T21:41:28.5561606Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool3d_cpu_bfloat16 PASSED [0.0297s] [ 61%] 2024-08-20T21:41:28.5562367Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool3d_cpu_float16 PASSED [0.0304s] [ 61%] 2024-08-20T21:41:28.5563130Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool3d_cpu_float32 PASSED [0.0298s] [ 61%] 2024-08-20T21:41:28.5563906Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool3d_cpu_float64 PASSED [0.0300s] [ 61%] 2024-08-20T21:41:28.5564654Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gaussian_nll_loss_cpu_bfloat16 PASSED [0.1834s] [ 61%] 2024-08-20T21:41:28.5565410Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gaussian_nll_loss_cpu_float16 PASSED [0.1814s] [ 61%] 2024-08-20T21:41:28.5566153Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gaussian_nll_loss_cpu_float32 PASSED [0.1814s] [ 62%] 2024-08-20T21:41:28.5566951Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gaussian_nll_loss_cpu_float64 PASSED [0.1823s] [ 62%] 2024-08-20T21:41:28.5567687Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gelu_cpu_bfloat16 PASSED [0.0092s] [ 62%] 2024-08-20T21:41:28.5568364Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gelu_cpu_float16 PASSED [0.0088s] [ 62%] 2024-08-20T21:41:28.5569049Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gelu_cpu_float32 PASSED [0.0087s] [ 62%] 2024-08-20T21:41:28.5569723Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gelu_cpu_float64 PASSED [0.0089s] [ 62%] 2024-08-20T21:41:28.5570391Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_glu_cpu_bfloat16 PASSED [0.0287s] [ 62%] 2024-08-20T21:41:28.5571074Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_glu_cpu_float16 PASSED [0.0292s] [ 62%] 2024-08-20T21:41:28.5571739Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_glu_cpu_float32 PASSED [0.0289s] [ 62%] 2024-08-20T21:41:28.5572417Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_glu_cpu_float64 PASSED [0.0293s] [ 62%] 2024-08-20T21:41:28.5573126Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_grid_sample_cpu_float32 PASSED [0.0208s] [ 62%] 2024-08-20T21:41:28.5573841Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_grid_sample_cpu_float64 PASSED [0.0211s] [ 62%] 2024-08-20T21:41:28.5574562Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_group_norm_cpu_bfloat16 PASSED [0.0159s] [ 62%] 2024-08-20T21:41:28.5575295Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_group_norm_cpu_float16 PASSED [0.0158s] [ 62%] 2024-08-20T21:41:28.5576060Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_group_norm_cpu_float32 PASSED [0.0159s] [ 62%] 2024-08-20T21:41:28.5576767Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_group_norm_cpu_float64 PASSED [0.0156s] [ 62%] 2024-08-20T21:41:28.5577479Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardshrink_cpu_bfloat16 PASSED [0.0084s] [ 62%] 2024-08-20T21:41:28.5578226Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardshrink_cpu_float16 PASSED [0.0078s] [ 62%] 2024-08-20T21:41:28.5578927Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardshrink_cpu_float32 PASSED [0.0079s] [ 62%] 2024-08-20T21:41:28.5579648Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardshrink_cpu_float64 PASSED [0.0077s] [ 62%] 2024-08-20T21:41:28.5580371Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardsigmoid_cpu_bfloat16 PASSED [0.0063s] [ 62%] 2024-08-20T21:41:28.5581085Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardsigmoid_cpu_float16 PASSED [0.0062s] [ 62%] 2024-08-20T21:41:28.5581806Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardsigmoid_cpu_float32 PASSED [0.0064s] [ 62%] 2024-08-20T21:41:28.5582515Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardsigmoid_cpu_float64 PASSED [0.0064s] [ 62%] 2024-08-20T21:41:28.5583233Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardswish_cpu_bfloat16 PASSED [0.0067s] [ 62%] 2024-08-20T21:41:28.5583931Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardswish_cpu_float16 PASSED [0.0069s] [ 62%] 2024-08-20T21:41:28.5584628Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardswish_cpu_float32 PASSED [0.0068s] [ 62%] 2024-08-20T21:41:28.5585331Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardswish_cpu_float64 PASSED [0.0070s] [ 62%] 2024-08-20T21:41:28.5586053Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_bfloat16 PASSED [0.0073s] [ 62%] 2024-08-20T21:41:28.5586739Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_float16 PASSED [0.0075s] [ 62%] 2024-08-20T21:41:28.5587452Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_float32 PASSED [0.0073s] [ 62%] 2024-08-20T21:41:28.5588139Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_float64 PASSED [0.0075s] [ 62%] 2024-08-20T21:41:28.5588831Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_int16 PASSED [0.0073s] [ 62%] 2024-08-20T21:41:28.5589509Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_int32 PASSED [0.0073s] [ 62%] 2024-08-20T21:41:28.5590192Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_int64 PASSED [0.0074s] [ 62%] 2024-08-20T21:41:28.5590884Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_int8 PASSED [0.0072s] [ 62%] 2024-08-20T21:41:28.5591657Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hinge_embedding_loss_cpu_bfloat16 PASSED [0.0108s] [ 62%] 2024-08-20T21:41:28.5592430Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hinge_embedding_loss_cpu_float16 PASSED [0.0105s] [ 62%] 2024-08-20T21:41:28.5593217Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hinge_embedding_loss_cpu_float32 PASSED [0.0112s] [ 62%] 2024-08-20T21:41:28.5594002Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hinge_embedding_loss_cpu_float64 PASSED [0.0104s] [ 62%] 2024-08-20T21:41:28.5594723Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_huber_loss_cpu_bfloat16 PASSED [0.0098s] [ 62%] 2024-08-20T21:41:28.5595426Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_huber_loss_cpu_float16 PASSED [0.0096s] [ 62%] 2024-08-20T21:41:28.5596157Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_huber_loss_cpu_float32 PASSED [0.0098s] [ 62%] 2024-08-20T21:41:28.5596858Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_huber_loss_cpu_float64 PASSED [0.0096s] [ 62%] 2024-08-20T21:41:28.5597585Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_instance_norm_cpu_bfloat16 PASSED [0.0098s] [ 62%] 2024-08-20T21:41:28.5598320Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_instance_norm_cpu_float16 PASSED [0.0100s] [ 62%] 2024-08-20T21:41:28.5599038Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_instance_norm_cpu_float32 PASSED [0.0097s] [ 62%] 2024-08-20T21:41:28.5599771Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_instance_norm_cpu_float64 PASSED [0.0100s] [ 62%] 2024-08-20T21:41:28.5600514Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_area_cpu_bfloat16 PASSED [0.0139s] [ 62%] 2024-08-20T21:41:28.5601254Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_area_cpu_float16 PASSED [0.0142s] [ 62%] 2024-08-20T21:41:28.5602007Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_area_cpu_float32 PASSED [0.0140s] [ 62%] 2024-08-20T21:41:28.5602745Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_area_cpu_float64 PASSED [0.0143s] [ 62%] 2024-08-20T21:41:28.5603557Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bicubic_cpu_bfloat16 PASSED [0.0139s] [ 62%] 2024-08-20T21:41:28.5604310Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bicubic_cpu_float16 PASSED [0.0140s] [ 62%] 2024-08-20T21:41:28.5605074Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bicubic_cpu_float32 PASSED [0.0142s] [ 62%] 2024-08-20T21:41:28.5605843Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bicubic_cpu_float64 PASSED [0.0140s] [ 62%] 2024-08-20T21:41:28.5606588Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bicubic_cpu_uint8 PASSED [0.0249s] [ 62%] 2024-08-20T21:41:28.5607430Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bilinear_cpu_bfloat16 PASSED [0.0140s] [ 62%] 2024-08-20T21:41:28.5608194Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bilinear_cpu_float16 PASSED [0.0142s] [ 62%] 2024-08-20T21:41:28.5608961Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bilinear_cpu_float32 PASSED [0.0140s] [ 63%] 2024-08-20T21:41:28.5609744Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bilinear_cpu_float64 PASSED [0.0146s] [ 63%] 2024-08-20T21:41:28.5610498Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bilinear_cpu_uint8 PASSED [0.0247s] [ 63%] 2024-08-20T21:41:28.5611306Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_linear_cpu_bfloat16 PASSED [0.0141s] [ 63%] 2024-08-20T21:41:28.5612077Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_linear_cpu_float16 PASSED [0.0142s] [ 63%] 2024-08-20T21:41:28.5612825Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_linear_cpu_float32 PASSED [0.0139s] [ 63%] 2024-08-20T21:41:28.5613587Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_linear_cpu_float64 PASSED [0.0143s] [ 63%] 2024-08-20T21:41:28.5614426Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest-exact_cpu_bfloat16 PASSED [0.0140s] [ 63%] 2024-08-20T21:41:28.5615240Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest-exact_cpu_float16 PASSED [0.0142s] [ 63%] 2024-08-20T21:41:28.5616038Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest-exact_cpu_float32 PASSED [0.0140s] [ 63%] 2024-08-20T21:41:28.5616839Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest-exact_cpu_float64 PASSED [0.0142s] [ 63%] 2024-08-20T21:41:28.5617627Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest-exact_cpu_uint8 PASSED [0.0139s] [ 63%] 2024-08-20T21:41:28.5618388Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest_cpu_bfloat16 PASSED [0.0141s] [ 63%] 2024-08-20T21:41:28.5619158Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest_cpu_float16 PASSED [0.0140s] [ 63%] 2024-08-20T21:41:28.5619915Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest_cpu_float32 PASSED [0.0139s] [ 63%] 2024-08-20T21:41:28.5620683Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest_cpu_float64 PASSED [0.0142s] [ 63%] 2024-08-20T21:41:28.5621663Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest_cpu_uint8 PASSED [0.0140s] [ 63%] 2024-08-20T21:41:28.5622875Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_trilinear_cpu_bfloat16 PASSED [0.0143s] [ 63%] 2024-08-20T21:41:28.5623660Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_trilinear_cpu_float16 PASSED [0.0140s] [ 63%] 2024-08-20T21:41:28.5624435Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_trilinear_cpu_float32 PASSED [0.0143s] [ 63%] 2024-08-20T21:41:28.5625217Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_trilinear_cpu_float64 PASSED [0.0140s] [ 63%] 2024-08-20T21:41:28.5625909Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_kl_div_cpu_bfloat16 PASSED [0.0162s] [ 63%] 2024-08-20T21:41:28.5626592Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_kl_div_cpu_float16 PASSED [0.0158s] [ 63%] 2024-08-20T21:41:28.5627280Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_kl_div_cpu_float32 PASSED [0.0150s] [ 63%] 2024-08-20T21:41:28.5627955Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_kl_div_cpu_float64 PASSED [0.0152s] [ 63%] 2024-08-20T21:41:28.5628657Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_l1_loss_cpu_bfloat16 PASSED [0.0079s] [ 63%] 2024-08-20T21:41:28.5629360Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_l1_loss_cpu_complex128 PASSED [0.0092s] [ 63%] 2024-08-20T21:41:28.5630084Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_l1_loss_cpu_complex64 PASSED [0.0090s] [ 63%] 2024-08-20T21:41:28.5630837Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_l1_loss_cpu_float16 PASSED [0.0080s] [ 63%] 2024-08-20T21:41:28.5631518Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_l1_loss_cpu_float32 PASSED [0.0078s] [ 63%] 2024-08-20T21:41:28.5632187Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_l1_loss_cpu_float64 PASSED [0.0081s] [ 63%] 2024-08-20T21:41:28.5632939Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_layer_norm_cpu_bfloat16 PASSED [0.0079s] [ 63%] 2024-08-20T21:41:28.5633647Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_layer_norm_cpu_float16 PASSED [0.0078s] [ 63%] 2024-08-20T21:41:28.5634366Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_layer_norm_cpu_float32 PASSED [0.0081s] [ 63%] 2024-08-20T21:41:28.5635070Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_layer_norm_cpu_float64 PASSED [0.0079s] [ 63%] 2024-08-20T21:41:28.5635778Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_leaky_relu_cpu_bfloat16 PASSED [0.0094s] [ 63%] 2024-08-20T21:41:28.5636494Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_leaky_relu_cpu_float16 PASSED [0.0093s] [ 63%] 2024-08-20T21:41:28.5637189Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_leaky_relu_cpu_float32 PASSED [0.0095s] [ 63%] 2024-08-20T21:41:28.5637900Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_leaky_relu_cpu_float64 PASSED [0.0092s] [ 63%] 2024-08-20T21:41:28.5638590Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_bfloat16 PASSED [0.0145s] [ 63%] 2024-08-20T21:41:28.5639290Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_complex128 PASSED [0.0146s] [ 63%] 2024-08-20T21:41:28.5640000Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_complex64 PASSED [0.0145s] [ 63%] 2024-08-20T21:41:28.5640706Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_float16 PASSED [0.0146s] [ 63%] 2024-08-20T21:41:28.5641395Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_float32 PASSED [0.0143s] [ 63%] 2024-08-20T21:41:28.5642074Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_float64 PASSED [0.0151s] [ 63%] 2024-08-20T21:41:28.5642746Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_int16 PASSED [0.0144s] [ 63%] 2024-08-20T21:41:28.5643427Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_int32 PASSED [0.0148s] [ 63%] 2024-08-20T21:41:28.5644099Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_int64 PASSED [0.0145s] [ 63%] 2024-08-20T21:41:28.5644775Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_int8 PASSED [0.0146s] [ 63%] 2024-08-20T21:41:28.5645443Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_uint8 PASSED [0.0143s] [ 63%] 2024-08-20T21:41:28.5646201Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_local_response_norm_cpu_bfloat16 PASSED [0.0104s] [ 63%] 2024-08-20T21:41:28.5647191Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_local_response_norm_cpu_float16 PASSED [0.0106s] [ 63%] 2024-08-20T21:41:28.5648036Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_local_response_norm_cpu_float32 PASSED [0.0103s] [ 63%] 2024-08-20T21:41:28.5648841Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_local_response_norm_cpu_float64 PASSED [0.0106s] [ 63%] 2024-08-20T21:41:28.5649584Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_local_response_norm_cpu_int64 PASSED [0.0104s] [ 63%] 2024-08-20T21:41:28.5650293Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_logsigmoid_cpu_bfloat16 PASSED [0.0064s] [ 63%] 2024-08-20T21:41:28.5651054Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_logsigmoid_cpu_float16 PASSED [0.0061s] [ 63%] 2024-08-20T21:41:28.5651840Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_logsigmoid_cpu_float32 PASSED [0.0063s] [ 63%] 2024-08-20T21:41:28.5652574Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_logsigmoid_cpu_float64 PASSED [0.0062s] [ 63%] 2024-08-20T21:41:28.5653337Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_bfloat16 PASSED [0.0177s] [ 63%] 2024-08-20T21:41:28.5654094Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_float16 PASSED [0.0181s] [ 64%] 2024-08-20T21:41:28.5654867Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_float32 PASSED [0.0178s] [ 64%] 2024-08-20T21:41:28.5655617Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_float64 PASSED [0.0183s] [ 64%] 2024-08-20T21:41:28.5656371Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_int16 PASSED [0.0179s] [ 64%] 2024-08-20T21:41:28.5657114Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_int32 PASSED [0.0180s] [ 64%] 2024-08-20T21:41:28.5657849Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_int64 PASSED [0.0178s] [ 64%] 2024-08-20T21:41:28.5658651Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_int8 PASSED [0.0186s] [ 64%] 2024-08-20T21:41:28.5659396Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_uint8 PASSED [0.0180s] [ 64%] 2024-08-20T21:41:28.5660120Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool1d_cpu_bfloat16 PASSED [0.4519s] [ 64%] 2024-08-20T21:41:28.5660827Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool1d_cpu_float16 PASSED [0.4594s] [ 64%] 2024-08-20T21:41:28.5661537Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool1d_cpu_float32 PASSED [0.4539s] [ 64%] 2024-08-20T21:41:28.5662248Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool1d_cpu_float64 PASSED [0.4562s] [ 64%] 2024-08-20T21:41:28.5662959Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_bfloat16 PASSED [0.7558s] [ 64%] 2024-08-20T21:41:28.5663672Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_float16 PASSED [0.7570s] [ 64%] 2024-08-20T21:41:28.5664368Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_float32 PASSED [0.7627s] [ 64%] 2024-08-20T21:41:28.5665060Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_float64 PASSED [0.7547s] [ 64%] 2024-08-20T21:41:28.5665797Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_int16 PASSED [0.7579s] [ 64%] 2024-08-20T21:41:28.5666491Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_int32 PASSED [0.7529s] [ 64%] 2024-08-20T21:41:28.5667218Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_int64 PASSED [0.7525s] [ 64%] 2024-08-20T21:41:28.5667903Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_int8 PASSED [0.7581s] [ 64%] 2024-08-20T21:41:28.5668593Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_uint8 PASSED [0.7512s] [ 64%] 2024-08-20T21:41:28.5669339Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_bfloat16 PASSED [0.3043s] [ 64%] 2024-08-20T21:41:28.5670042Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_float16 PASSED [0.3047s] [ 64%] 2024-08-20T21:41:28.5670762Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_float32 PASSED [0.3031s] [ 64%] 2024-08-20T21:41:28.5671465Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_float64 PASSED [0.3066s] [ 64%] 2024-08-20T21:41:28.5672158Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_int16 PASSED [0.3031s] [ 64%] 2024-08-20T21:41:28.5672868Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_int32 PASSED [0.3063s] [ 64%] 2024-08-20T21:41:28.5673566Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_int64 PASSED [0.3059s] [ 64%] 2024-08-20T21:41:28.5674268Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_int8 PASSED [0.3069s] [ 64%] 2024-08-20T21:41:28.5674959Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_uint8 PASSED [0.3090s] [ 64%] 2024-08-20T21:41:28.5675679Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_cpu_float32 PASSED [0.0975s] [ 64%] 2024-08-20T21:41:28.5676436Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_cpu_float64 PASSED [0.0981s] [ 64%] 2024-08-20T21:41:28.5677185Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_grad_cpu_float32 PASSED [0.0278s] [ 64%] 2024-08-20T21:41:28.5677950Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_grad_cpu_float64 PASSED [0.0270s] [ 64%] 2024-08-20T21:41:28.5678662Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_cpu_float32 PASSED [0.1891s] [ 64%] 2024-08-20T21:41:28.5679367Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_cpu_float64 PASSED [0.1873s] [ 64%] 2024-08-20T21:41:28.5680124Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_grad_cpu_float32 PASSED [0.0535s] [ 64%] 2024-08-20T21:41:28.5680868Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_grad_cpu_float64 PASSED [0.0509s] [ 64%] 2024-08-20T21:41:28.5681595Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_cpu_float32 PASSED [0.0674s] [ 64%] 2024-08-20T21:41:28.5682302Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_cpu_float64 PASSED [0.0679s] [ 64%] 2024-08-20T21:41:28.5683043Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_grad_cpu_float32 PASSED [0.0228s] [ 64%] 2024-08-20T21:41:28.5683828Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_grad_cpu_float64 PASSED [0.0218s] [ 64%] 2024-08-20T21:41:28.5684513Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mish_cpu_bfloat16 PASSED [0.0062s] [ 64%] 2024-08-20T21:41:28.5685237Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mish_cpu_float16 PASSED [0.0064s] [ 64%] 2024-08-20T21:41:28.5685910Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mish_cpu_float32 PASSED [0.0062s] [ 64%] 2024-08-20T21:41:28.5686667Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mish_cpu_float64 PASSED [0.0063s] [ 64%] 2024-08-20T21:41:28.5687486Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mse_loss_cpu_float16 PASSED [0.0078s] [ 64%] 2024-08-20T21:41:28.5688184Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mse_loss_cpu_float32 PASSED [0.0078s] [ 64%] 2024-08-20T21:41:28.5688887Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mse_loss_cpu_float64 PASSED [0.0081s] [ 64%] 2024-08-20T21:41:28.5689702Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multi_head_attention_forward_cpu_bfloat16 PASSED [0.1229s] [ 64%] 2024-08-20T21:41:28.5690508Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multi_head_attention_forward_cpu_float16 PASSED [0.1250s] [ 64%] 2024-08-20T21:41:28.5691320Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multi_head_attention_forward_cpu_float32 PASSED [0.1230s] [ 64%] 2024-08-20T21:41:28.5692125Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multi_head_attention_forward_cpu_float64 PASSED [0.1248s] [ 64%] 2024-08-20T21:41:28.5692876Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multi_margin_loss_cpu_float32 PASSED [0.0102s] [ 64%] 2024-08-20T21:41:28.5693618Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multi_margin_loss_cpu_float64 PASSED [0.0100s] [ 64%] 2024-08-20T21:41:28.5694392Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multilabel_margin_loss_cpu_float32 PASSED [0.0081s] [ 64%] 2024-08-20T21:41:28.5695209Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multilabel_margin_loss_cpu_float64 PASSED [0.0078s] [ 64%] 2024-08-20T21:41:28.5696010Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multilabel_soft_margin_loss_cpu_bfloat16 PASSED [0.0071s] [ 64%] 2024-08-20T21:41:28.5696828Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multilabel_soft_margin_loss_cpu_float16 PASSED [0.0069s] [ 64%] 2024-08-20T21:41:28.5697618Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multilabel_soft_margin_loss_cpu_float32 PASSED [0.0071s] [ 65%] 2024-08-20T21:41:28.5698413Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multilabel_soft_margin_loss_cpu_float64 PASSED [0.0069s] [ 65%] 2024-08-20T21:41:28.5699122Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_nll_loss_cpu_bfloat16 PASSED [0.0387s] [ 65%] 2024-08-20T21:41:28.5699810Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_nll_loss_cpu_float16 PASSED [0.0385s] [ 65%] 2024-08-20T21:41:28.5700514Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_nll_loss_cpu_float32 PASSED [0.0380s] [ 65%] 2024-08-20T21:41:28.5701202Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_nll_loss_cpu_float64 PASSED [0.0386s] [ 65%] 2024-08-20T21:41:28.5701937Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_normalize_cpu_bfloat16 PASSED [0.0089s] [ 65%] 2024-08-20T21:41:28.5702670Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_normalize_cpu_complex128 PASSED [0.0091s] [ 65%] 2024-08-20T21:41:28.5703401Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_normalize_cpu_complex64 PASSED [0.0089s] [ 65%] 2024-08-20T21:41:28.5704117Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_normalize_cpu_float16 PASSED [0.0095s] [ 65%] 2024-08-20T21:41:28.5704807Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_normalize_cpu_float32 PASSED [0.0087s] [ 65%] 2024-08-20T21:41:28.5705534Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_normalize_cpu_float64 PASSED [0.0089s] [ 65%] 2024-08-20T21:41:28.5706217Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_one_hot_cpu_int64 PASSED [0.0081s] [ 65%] 2024-08-20T21:41:28.5706943Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_bfloat16 PASSED [0.0090s] [ 65%] 2024-08-20T21:41:28.5707648Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_bool PASSED [0.0060s] [ 65%] 2024-08-20T21:41:28.5708377Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_complex128 PASSED [0.0089s] [ 65%] 2024-08-20T21:41:28.5709108Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_complex64 PASSED [0.0091s] [ 65%] 2024-08-20T21:41:28.5709838Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_float16 PASSED [0.0087s] [ 65%] 2024-08-20T21:41:28.5710551Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_float32 PASSED [0.0090s] [ 65%] 2024-08-20T21:41:28.5711278Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_float64 PASSED [0.0087s] [ 65%] 2024-08-20T21:41:28.5711986Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_int16 PASSED [0.0089s] [ 65%] 2024-08-20T21:41:28.5712713Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_int32 PASSED [0.0087s] [ 65%] 2024-08-20T21:41:28.5713427Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_int64 PASSED [0.0088s] [ 65%] 2024-08-20T21:41:28.5714126Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_int8 PASSED [0.0089s] [ 65%] 2024-08-20T21:41:28.5714842Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_uint8 PASSED [0.0087s] [ 65%] 2024-08-20T21:41:28.5715566Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_bfloat16 PASSED [0.0304s] [ 65%] 2024-08-20T21:41:28.5716265Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_bool PASSED [0.0298s] [ 65%] 2024-08-20T21:41:28.5717013Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_complex128 PASSED [0.0309s] [ 65%] 2024-08-20T21:41:28.5717743Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_complex64 PASSED [0.0302s] [ 65%] 2024-08-20T21:41:28.5718467Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_float16 PASSED [0.0300s] [ 65%] 2024-08-20T21:41:28.5719181Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_float32 PASSED [0.0296s] [ 65%] 2024-08-20T21:41:28.5719920Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_float64 PASSED [0.0304s] [ 65%] 2024-08-20T21:41:28.5720636Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_int16 PASSED [0.0297s] [ 65%] 2024-08-20T21:41:28.5721576Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_int32 PASSED [0.0298s] [ 65%] 2024-08-20T21:41:28.5722302Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_int64 PASSED [0.0299s] [ 65%] 2024-08-20T21:41:28.5722996Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_int8 PASSED [0.0295s] [ 65%] 2024-08-20T21:41:28.5723731Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_uint8 PASSED [0.0303s] [ 65%] 2024-08-20T21:41:28.5724470Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_bfloat16 PASSED [0.0108s] [ 65%] 2024-08-20T21:41:28.5725194Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_complex128 PASSED [0.0113s] [ 65%] 2024-08-20T21:41:28.5725921Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_complex64 PASSED [0.0109s] [ 65%] 2024-08-20T21:41:28.5726647Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_float32 PASSED [0.0110s] [ 65%] 2024-08-20T21:41:28.5727426Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_float64 PASSED [0.0109s] [ 65%] 2024-08-20T21:41:28.5728140Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_int16 PASSED [0.0108s] [ 65%] 2024-08-20T21:41:28.5728830Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_int32 PASSED [0.0110s] [ 65%] 2024-08-20T21:41:28.5729532Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_int64 PASSED [0.0108s] [ 65%] 2024-08-20T21:41:28.5730239Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_int8 PASSED [0.0110s] [ 65%] 2024-08-20T21:41:28.5730979Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_uint8 PASSED [0.0108s] [ 65%] 2024-08-20T21:41:28.5731718Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_bfloat16 PASSED [0.0110s] [ 65%] 2024-08-20T21:41:28.5732461Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_complex128 PASSED [0.0108s] [ 65%] 2024-08-20T21:41:28.5733191Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_complex64 PASSED [0.0110s] [ 65%] 2024-08-20T21:41:28.5733920Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_float32 PASSED [0.0109s] [ 65%] 2024-08-20T21:41:28.5734645Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_float64 PASSED [0.0110s] [ 65%] 2024-08-20T21:41:28.5735370Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_int16 PASSED [0.0107s] [ 65%] 2024-08-20T21:41:28.5736077Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_int32 PASSED [0.0107s] [ 65%] 2024-08-20T21:41:28.5736793Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_int64 PASSED [0.0110s] [ 65%] 2024-08-20T21:41:28.5737506Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_int8 PASSED [0.0107s] [ 65%] 2024-08-20T21:41:28.5738246Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_uint8 PASSED [0.0110s] [ 65%] 2024-08-20T21:41:28.5739071Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_bfloat16 PASSED [0.0077s] [ 65%] 2024-08-20T21:41:28.5739864Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_complex128 PASSED [0.0081s] [ 65%] 2024-08-20T21:41:28.5740645Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_complex64 PASSED [0.0079s] [ 65%] 2024-08-20T21:41:28.5741458Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_float32 PASSED [0.0081s] [ 66%] 2024-08-20T21:41:28.5742225Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_float64 PASSED [0.0079s] [ 66%] 2024-08-20T21:41:28.5743000Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_int16 PASSED [0.0081s] [ 66%] 2024-08-20T21:41:28.5743769Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_int32 PASSED [0.0078s] [ 66%] 2024-08-20T21:41:28.5744532Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_int64 PASSED [0.0077s] [ 66%] 2024-08-20T21:41:28.5745298Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_int8 PASSED [0.0080s] [ 66%] 2024-08-20T21:41:28.5746062Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_uint8 PASSED [0.0078s] [ 66%] 2024-08-20T21:41:28.5747020Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_bfloat16 PASSED [0.0086s] [ 66%] 2024-08-20T21:41:28.5747796Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_complex128 PASSED [0.0083s] [ 66%] 2024-08-20T21:41:28.5748552Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_complex64 PASSED [0.0086s] [ 66%] 2024-08-20T21:41:28.5749396Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_float16 PASSED [0.0083s] [ 66%] 2024-08-20T21:41:28.5750138Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_float32 PASSED [0.0084s] [ 66%] 2024-08-20T21:41:28.5750901Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_float64 PASSED [0.0083s] [ 66%] 2024-08-20T21:41:28.5751636Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_int16 PASSED [0.0084s] [ 66%] 2024-08-20T21:41:28.5752469Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_int32 PASSED [0.0084s] [ 66%] 2024-08-20T21:41:28.5753223Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_int64 PASSED [0.0083s] [ 66%] 2024-08-20T21:41:28.5753953Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_int8 PASSED [0.0085s] [ 66%] 2024-08-20T21:41:28.5754697Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_uint8 PASSED [0.0084s] [ 66%] 2024-08-20T21:41:28.5755376Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pdist_cpu_float32 PASSED [0.0097s] [ 66%] 2024-08-20T21:41:28.5756050Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pdist_cpu_float64 PASSED [0.0094s] [ 66%] 2024-08-20T21:41:28.5756868Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_bfloat16 PASSED [0.0075s] [ 66%] 2024-08-20T21:41:28.5757614Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_bool PASSED [0.0072s] [ 66%] 2024-08-20T21:41:28.5758368Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_complex128 PASSED [0.0074s] [ 66%] 2024-08-20T21:41:28.5759098Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_complex64 PASSED [0.0073s] [ 66%] 2024-08-20T21:41:28.5759861Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_float16 PASSED [0.0073s] [ 66%] 2024-08-20T21:41:28.5760597Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_float32 PASSED [0.0077s] [ 66%] 2024-08-20T21:41:28.5761314Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_float64 PASSED [0.0076s] [ 66%] 2024-08-20T21:41:28.5762036Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_int16 PASSED [0.0077s] [ 66%] 2024-08-20T21:41:28.5762742Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_int32 PASSED [0.0074s] [ 66%] 2024-08-20T21:41:28.5763450Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_int64 PASSED [0.0076s] [ 66%] 2024-08-20T21:41:28.5764162Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_int8 PASSED [0.0073s] [ 66%] 2024-08-20T21:41:28.5764869Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_uint8 PASSED [0.0076s] [ 66%] 2024-08-20T21:41:28.5765626Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_bfloat16 PASSED [0.0073s] [ 66%] 2024-08-20T21:41:28.5766347Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_bool PASSED [0.0075s] [ 66%] 2024-08-20T21:41:28.5767234Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_complex128 PASSED [0.0073s] [ 66%] 2024-08-20T21:41:28.5768001Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_complex64 PASSED [0.0072s] [ 66%] 2024-08-20T21:41:28.5768733Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_float16 PASSED [0.0078s] [ 66%] 2024-08-20T21:41:28.5769479Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_float32 PASSED [0.0072s] [ 66%] 2024-08-20T21:41:28.5770202Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_float64 PASSED [0.0074s] [ 66%] 2024-08-20T21:41:28.5770917Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_int16 PASSED [0.0072s] [ 66%] 2024-08-20T21:41:28.5771650Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_int32 PASSED [0.0074s] [ 66%] 2024-08-20T21:41:28.5772371Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_int64 PASSED [0.0072s] [ 66%] 2024-08-20T21:41:28.5773099Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_int8 PASSED [0.0074s] [ 66%] 2024-08-20T21:41:28.5773822Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_uint8 PASSED [0.0072s] [ 66%] 2024-08-20T21:41:28.5774604Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_bfloat16 PASSED [0.0427s] [ 66%] 2024-08-20T21:41:28.5775356Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_float16 PASSED [0.0429s] [ 66%] 2024-08-20T21:41:28.5776119Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_float32 PASSED [0.0426s] [ 66%] 2024-08-20T21:41:28.5776863Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_float64 PASSED [0.0429s] [ 66%] 2024-08-20T21:41:28.5777584Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_int16 PASSED [0.0424s] [ 66%] 2024-08-20T21:41:28.5778332Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_int32 PASSED [0.0429s] [ 66%] 2024-08-20T21:41:28.5779069Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_int64 PASSED [0.0427s] [ 66%] 2024-08-20T21:41:28.5779778Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_int8 PASSED [0.0424s] [ 66%] 2024-08-20T21:41:28.5780514Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_uint8 PASSED [0.0430s] [ 66%] 2024-08-20T21:41:28.5781201Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_prelu_cpu_bfloat16 PASSED [0.0181s] [ 66%] 2024-08-20T21:41:28.5781875Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_prelu_cpu_float16 PASSED [0.0182s] [ 66%] 2024-08-20T21:41:28.5782568Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_prelu_cpu_float32 PASSED [0.0179s] [ 66%] 2024-08-20T21:41:28.5783244Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_prelu_cpu_float64 PASSED [0.0180s] [ 66%] 2024-08-20T21:41:28.5783941Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_bfloat16 PASSED [0.0062s] [ 66%] 2024-08-20T21:41:28.5784615Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_float16 PASSED [0.0061s] [ 66%] 2024-08-20T21:41:28.5785318Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_float32 PASSED [0.0063s] [ 67%] 2024-08-20T21:41:28.5786001Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_float64 PASSED [0.0061s] [ 67%] 2024-08-20T21:41:28.5786681Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_int16 PASSED [0.0063s] [ 67%] 2024-08-20T21:41:28.5787438Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_int32 PASSED [0.0061s] [ 67%] 2024-08-20T21:41:28.5788107Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_int64 PASSED [0.0063s] [ 67%] 2024-08-20T21:41:28.5788764Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_int8 PASSED [0.0061s] [ 67%] 2024-08-20T21:41:28.5789439Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_uint8 PASSED [0.0063s] [ 67%] 2024-08-20T21:41:28.5790113Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_bfloat16 PASSED [0.0067s] [ 67%] 2024-08-20T21:41:28.5790793Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_float16 PASSED [0.0068s] [ 67%] 2024-08-20T21:41:28.5791464Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_float32 PASSED [0.0066s] [ 67%] 2024-08-20T21:41:28.5792132Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_float64 PASSED [0.0066s] [ 67%] 2024-08-20T21:41:28.5792842Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_int16 PASSED [0.0068s] [ 67%] 2024-08-20T21:41:28.5793524Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_int32 PASSED [0.0066s] [ 67%] 2024-08-20T21:41:28.5794197Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_int64 PASSED [0.0069s] [ 67%] 2024-08-20T21:41:28.5794846Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_int8 PASSED [0.0067s] [ 67%] 2024-08-20T21:41:28.5795529Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_uint8 PASSED [0.0069s] [ 67%] 2024-08-20T21:41:28.5796236Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rms_norm_cpu_bfloat16 PASSED [0.0078s] [ 67%] 2024-08-20T21:41:28.5796949Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rms_norm_cpu_complex128 PASSED [0.0081s] [ 67%] 2024-08-20T21:41:28.5797666Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rms_norm_cpu_complex64 PASSED [0.0079s] [ 67%] 2024-08-20T21:41:28.5798362Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rms_norm_cpu_float16 PASSED [0.0080s] [ 67%] 2024-08-20T21:41:28.5799048Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rms_norm_cpu_float32 PASSED [0.0078s] [ 67%] 2024-08-20T21:41:28.5799742Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rms_norm_cpu_float64 PASSED [0.0078s] [ 67%] 2024-08-20T21:41:28.5800420Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rrelu_cpu_bfloat16 PASSED [0.0075s] [ 67%] 2024-08-20T21:41:28.5801107Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rrelu_cpu_float32 PASSED [0.0072s] [ 67%] 2024-08-20T21:41:28.5801782Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rrelu_cpu_float64 PASSED [0.0074s] [ 67%] 2024-08-20T21:41:28.5802594Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_scaled_dot_product_attention_cpu_bfloat16 PASSED [0.0180s] [ 67%] 2024-08-20T21:41:28.5803448Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_scaled_dot_product_attention_cpu_float16 PASSED [0.0183s] [ 67%] 2024-08-20T21:41:28.5804259Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_scaled_dot_product_attention_cpu_float32 PASSED [0.0180s] [ 67%] 2024-08-20T21:41:28.5805077Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_scaled_dot_product_attention_cpu_float64 PASSED [0.0183s] [ 67%] 2024-08-20T21:41:28.5805756Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_selu_cpu_bfloat16 PASSED [0.0061s] [ 67%] 2024-08-20T21:41:28.5806428Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_selu_cpu_float16 PASSED [0.0063s] [ 67%] 2024-08-20T21:41:28.5807181Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_selu_cpu_float32 PASSED [0.0061s] [ 67%] 2024-08-20T21:41:28.5807859Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_selu_cpu_float64 PASSED [0.0061s] [ 67%] 2024-08-20T21:41:28.5808605Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_silu_complex_cpu_complex128 PASSED [0.0064s] [ 67%] 2024-08-20T21:41:28.5809342Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_silu_complex_cpu_complex64 PASSED [0.0061s] [ 67%] 2024-08-20T21:41:28.5810018Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_silu_cpu_bfloat16 PASSED [0.0063s] [ 67%] 2024-08-20T21:41:28.5810741Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_silu_cpu_float16 PASSED [0.0061s] [ 67%] 2024-08-20T21:41:28.5811437Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_silu_cpu_float32 PASSED [0.0063s] [ 67%] 2024-08-20T21:41:28.5812103Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_silu_cpu_float64 PASSED [0.0061s] [ 67%] 2024-08-20T21:41:28.5812850Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_smooth_l1_loss_cpu_bfloat16 PASSED [0.0090s] [ 67%] 2024-08-20T21:41:28.5813605Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_smooth_l1_loss_cpu_float16 PASSED [0.0088s] [ 67%] 2024-08-20T21:41:28.5814343Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_smooth_l1_loss_cpu_float32 PASSED [0.0090s] [ 67%] 2024-08-20T21:41:28.5815064Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_smooth_l1_loss_cpu_float64 PASSED [0.0088s] [ 67%] 2024-08-20T21:41:28.5815811Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_soft_margin_loss_cpu_bfloat16 PASSED [0.0078s] [ 67%] 2024-08-20T21:41:28.5816566Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_soft_margin_loss_cpu_float16 PASSED [0.0080s] [ 67%] 2024-08-20T21:41:28.5817301Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_soft_margin_loss_cpu_float32 PASSED [0.0077s] [ 67%] 2024-08-20T21:41:28.5818052Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_soft_margin_loss_cpu_float64 PASSED [0.0080s] [ 67%] 2024-08-20T21:41:28.5818746Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_cpu_bfloat16 PASSED [0.0081s] [ 67%] 2024-08-20T21:41:28.5819442Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_cpu_float16 PASSED [0.0083s] [ 67%] 2024-08-20T21:41:28.5820147Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_cpu_float32 PASSED [0.0081s] [ 67%] 2024-08-20T21:41:28.5821166Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_cpu_float64 PASSED [0.0083s] [ 67%] 2024-08-20T21:41:28.5821946Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_bfloat16 PASSED [0.0082s] [ 67%] 2024-08-20T21:41:28.5822717Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_complex128 PASSED [0.0081s] [ 67%] 2024-08-20T21:41:28.5823479Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_complex64 PASSED [0.0083s] [ 67%] 2024-08-20T21:41:28.5824242Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_float16 PASSED [0.0081s] [ 67%] 2024-08-20T21:41:28.5824991Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_float32 PASSED [0.0083s] [ 67%] 2024-08-20T21:41:28.5825749Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_float64 PASSED [0.0081s] [ 67%] 2024-08-20T21:41:28.5826489Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_int16 PASSED [0.0083s] [ 67%] 2024-08-20T21:41:28.5827232Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_int32 PASSED [0.0081s] [ 67%] 2024-08-20T21:41:28.5827979Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_int64 PASSED [0.0083s] [ 68%] 2024-08-20T21:41:28.5828744Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_int8 PASSED [0.0081s] [ 68%] 2024-08-20T21:41:28.5829517Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_uint8 PASSED [0.0084s] [ 68%] 2024-08-20T21:41:28.5830215Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softplus_cpu_bfloat16 PASSED [0.0063s] [ 68%] 2024-08-20T21:41:28.5830903Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softplus_cpu_float16 PASSED [0.0063s] [ 68%] 2024-08-20T21:41:28.5831602Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softplus_cpu_float32 PASSED [0.0070s] [ 68%] 2024-08-20T21:41:28.5832319Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softplus_cpu_float64 PASSED [0.0062s] [ 68%] 2024-08-20T21:41:28.5833049Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softshrink_cpu_bfloat16 PASSED [0.0075s] [ 68%] 2024-08-20T21:41:28.5833751Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softshrink_cpu_float16 PASSED [0.0073s] [ 68%] 2024-08-20T21:41:28.5834451Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softshrink_cpu_float32 PASSED [0.0075s] [ 68%] 2024-08-20T21:41:28.5835172Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softshrink_cpu_float64 PASSED [0.0073s] [ 68%] 2024-08-20T21:41:28.5835867Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_bfloat16 PASSED [0.0065s] [ 68%] 2024-08-20T21:41:28.5836592Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_complex128 PASSED [0.0063s] [ 68%] 2024-08-20T21:41:28.5837304Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_complex64 PASSED [0.0065s] [ 68%] 2024-08-20T21:41:28.5837999Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_float16 PASSED [0.0063s] [ 68%] 2024-08-20T21:41:28.5838698Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_float32 PASSED [0.0062s] [ 68%] 2024-08-20T21:41:28.5839417Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_float64 PASSED [0.0064s] [ 68%] 2024-08-20T21:41:28.5840114Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_int16 PASSED [0.0062s] [ 68%] 2024-08-20T21:41:28.5840788Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_int32 PASSED [0.0064s] [ 68%] 2024-08-20T21:41:28.5841459Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_int64 PASSED [0.0061s] [ 68%] 2024-08-20T21:41:28.5842146Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_int8 PASSED [0.0063s] [ 68%] 2024-08-20T21:41:28.5842829Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_uint8 PASSED [0.0062s] [ 68%] 2024-08-20T21:41:28.5843552Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_bfloat16 PASSED [0.0065s] [ 68%] 2024-08-20T21:41:28.5844280Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_complex128 PASSED [0.0062s] [ 68%] 2024-08-20T21:41:28.5844998Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_complex64 PASSED [0.0063s] [ 68%] 2024-08-20T21:41:28.5845717Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_float16 PASSED [0.0062s] [ 68%] 2024-08-20T21:41:28.5846455Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_float32 PASSED [0.0061s] [ 68%] 2024-08-20T21:41:28.5847445Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_float64 PASSED [0.0067s] [ 68%] 2024-08-20T21:41:28.5848242Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_int16 PASSED [0.0061s] [ 68%] 2024-08-20T21:41:28.5848930Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_int32 PASSED [0.0063s] [ 68%] 2024-08-20T21:41:28.5849641Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_int64 PASSED [0.0061s] [ 68%] 2024-08-20T21:41:28.5850369Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_int8 PASSED [0.0063s] [ 68%] 2024-08-20T21:41:28.5851084Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_uint8 PASSED [0.0061s] [ 68%] 2024-08-20T21:41:28.5851788Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_bfloat16 PASSED [0.0070s] [ 68%] 2024-08-20T21:41:28.5852494Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_float16 PASSED [0.0068s] [ 68%] 2024-08-20T21:41:28.5853206Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_float32 PASSED [0.0069s] [ 68%] 2024-08-20T21:41:28.5853892Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_float64 PASSED [0.0068s] [ 68%] 2024-08-20T21:41:28.5854597Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_int16 PASSED [0.0068s] [ 68%] 2024-08-20T21:41:28.5855286Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_int32 PASSED [0.0070s] [ 68%] 2024-08-20T21:41:28.5855967Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_int64 PASSED [0.0067s] [ 68%] 2024-08-20T21:41:28.5856660Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_int8 PASSED [0.0069s] [ 68%] 2024-08-20T21:41:28.5857388Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_uint8 PASSED [0.0068s] [ 68%] 2024-08-20T21:41:28.5858163Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_bfloat16 PASSED [0.0086s] [ 68%] 2024-08-20T21:41:28.5858938Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_complex128 PASSED [0.0086s] [ 68%] 2024-08-20T21:41:28.5859703Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_complex64 PASSED [0.0087s] [ 68%] 2024-08-20T21:41:28.5860477Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_float16 PASSED [0.0084s] [ 68%] 2024-08-20T21:41:28.5861238Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_float32 PASSED [0.0087s] [ 68%] 2024-08-20T21:41:28.5862007Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_float64 PASSED [0.0085s] [ 68%] 2024-08-20T21:41:28.5862748Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_int16 PASSED [0.0084s] [ 68%] 2024-08-20T21:41:28.5863487Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_int32 PASSED [0.0092s] [ 68%] 2024-08-20T21:41:28.5864237Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_int64 PASSED [0.0086s] [ 68%] 2024-08-20T21:41:28.5865014Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_int8 PASSED [0.0087s] [ 68%] 2024-08-20T21:41:28.5865791Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_uint8 PASSED [0.0085s] [ 68%] 2024-08-20T21:41:28.5866632Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_bfloat16 PASSED [0.0090s] [ 68%] 2024-08-20T21:41:28.5867481Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_complex128 PASSED [0.0087s] [ 68%] 2024-08-20T21:41:28.5868367Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_complex64 PASSED [0.0089s] [ 68%] 2024-08-20T21:41:28.5869198Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_float16 PASSED [0.0088s] [ 68%] 2024-08-20T21:41:28.5870043Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_float32 PASSED [0.0086s] [ 68%] 2024-08-20T21:41:28.5870870Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_float64 PASSED [0.0088s] [ 68%] 2024-08-20T21:41:28.5871700Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_int16 PASSED [0.0087s] [ 68%] 2024-08-20T21:41:28.5872537Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_int32 PASSED [0.0087s] [ 69%] 2024-08-20T21:41:28.5873360Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_int64 PASSED [0.0087s] [ 69%] 2024-08-20T21:41:28.5874184Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_int8 PASSED [0.0085s] [ 69%] 2024-08-20T21:41:28.5875010Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_uint8 PASSED [0.0088s] [ 69%] 2024-08-20T21:41:28.5875728Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_bfloat16 PASSED [0.0874s] [ 69%] 2024-08-20T21:41:28.5876434Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_complex128 PASSED [0.0889s] [ 69%] 2024-08-20T21:41:28.5877137Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_complex64 PASSED [0.0898s] [ 69%] 2024-08-20T21:41:28.5877837Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_float16 PASSED [0.0874s] [ 69%] 2024-08-20T21:41:28.5878521Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_float32 PASSED [0.0886s] [ 69%] 2024-08-20T21:41:28.5879197Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_float64 PASSED [0.0876s] [ 69%] 2024-08-20T21:41:28.5879960Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_bilinear_cpu_bfloat16 PASSED [0.0076s] [ 69%] 2024-08-20T21:41:28.5880712Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_bilinear_cpu_float16 PASSED [0.0068s] [ 69%] 2024-08-20T21:41:28.5881470Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_bilinear_cpu_float32 PASSED [0.0069s] [ 69%] 2024-08-20T21:41:28.5882207Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_bilinear_cpu_float64 PASSED [0.0068s] [ 69%] 2024-08-20T21:41:28.5882965Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_bilinear_cpu_uint8 PASSED [0.0069s] [ 69%] 2024-08-20T21:41:28.5883719Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_nearest_cpu_bfloat16 PASSED [0.0110s] [ 69%] 2024-08-20T21:41:28.5884490Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_nearest_cpu_float16 PASSED [0.0109s] [ 69%] 2024-08-20T21:41:28.5885236Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_nearest_cpu_float32 PASSED [0.0111s] [ 69%] 2024-08-20T21:41:28.5886017Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_nearest_cpu_float64 PASSED [0.0110s] [ 69%] 2024-08-20T21:41:28.5886845Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_nearest_cpu_uint8 PASSED [0.0112s] [ 69%] 2024-08-20T21:41:28.5887487Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_bfloat16 PASSED [0.0167s] [ 69%] 2024-08-20T21:41:28.5888079Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_bool PASSED [0.0169s] [ 69%] 2024-08-20T21:41:28.5888723Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_complex128 PASSED [0.0167s] [ 69%] 2024-08-20T21:41:28.5889345Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_complex32 PASSED [0.0168s] [ 69%] 2024-08-20T21:41:28.5889972Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_complex64 PASSED [0.0167s] [ 69%] 2024-08-20T21:41:28.5890738Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_float16 PASSED [0.0168s] [ 69%] 2024-08-20T21:41:28.5891392Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_float32 PASSED [0.0168s] [ 69%] 2024-08-20T21:41:28.5892011Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_float64 PASSED [0.0169s] [ 69%] 2024-08-20T21:41:28.5892609Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_int16 PASSED [0.0170s] [ 69%] 2024-08-20T21:41:28.5893203Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_int32 PASSED [0.0168s] [ 69%] 2024-08-20T21:41:28.5893866Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_int64 PASSED [0.0171s] [ 69%] 2024-08-20T21:41:28.5894456Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_int8 PASSED [0.0169s] [ 69%] 2024-08-20T21:41:28.5895063Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_uint8 PASSED [0.0177s] [ 69%] 2024-08-20T21:41:28.5895722Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_bfloat16 PASSED [0.0348s] [ 69%] 2024-08-20T21:41:28.5896356Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_bool PASSED [0.0351s] [ 69%] 2024-08-20T21:41:28.5897038Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_complex128 PASSED [0.0353s] [ 69%] 2024-08-20T21:41:28.5897701Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_complex32 PASSED [0.0349s] [ 69%] 2024-08-20T21:41:28.5898356Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_complex64 PASSED [0.0351s] [ 69%] 2024-08-20T21:41:28.5899020Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_float16 PASSED [0.0348s] [ 69%] 2024-08-20T21:41:28.5899669Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_float32 PASSED [0.0350s] [ 69%] 2024-08-20T21:41:28.5900318Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_float64 PASSED [0.0352s] [ 69%] 2024-08-20T21:41:28.5900984Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_int16 PASSED [0.0346s] [ 69%] 2024-08-20T21:41:28.5901647Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_int32 PASSED [0.0348s] [ 69%] 2024-08-20T21:41:28.5902298Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_int64 PASSED [0.0347s] [ 69%] 2024-08-20T21:41:28.5902926Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_int8 PASSED [0.0347s] [ 69%] 2024-08-20T21:41:28.5903577Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_uint8 PASSED [0.0350s] [ 69%] 2024-08-20T21:41:28.5904203Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_bfloat16 PASSED [0.0269s] [ 69%] 2024-08-20T21:41:28.5904812Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_complex128 PASSED [0.0274s] [ 69%] 2024-08-20T21:41:28.5905428Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_complex64 PASSED [0.0269s] [ 69%] 2024-08-20T21:41:28.5906018Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_float16 PASSED [0.0268s] [ 69%] 2024-08-20T21:41:28.5906622Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_float32 PASSED [0.0266s] [ 69%] 2024-08-20T21:41:28.5907204Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_float64 PASSED [0.0265s] [ 69%] 2024-08-20T21:41:28.5907819Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_fro_cpu_bfloat16 PASSED [0.0067s] [ 69%] 2024-08-20T21:41:28.5908463Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_fro_cpu_complex128 PASSED [0.0062s] [ 69%] 2024-08-20T21:41:28.5909087Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_fro_cpu_complex64 PASSED [0.0064s] [ 69%] 2024-08-20T21:41:28.5909692Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_fro_cpu_float16 PASSED [0.0061s] [ 69%] 2024-08-20T21:41:28.5910315Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_fro_cpu_float32 PASSED [0.0063s] [ 69%] 2024-08-20T21:41:28.5910953Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_fro_cpu_float64 PASSED [0.0062s] [ 69%] 2024-08-20T21:41:28.5911581Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_bfloat16 PASSED [0.0069s] [ 69%] 2024-08-20T21:41:28.5912206Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_complex128 PASSED [0.0067s] [ 70%] 2024-08-20T21:41:28.5912832Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_complex64 PASSED [0.0068s] [ 70%] 2024-08-20T21:41:28.5913458Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_float16 PASSED [0.0067s] [ 70%] 2024-08-20T21:41:28.5914060Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_float32 PASSED [0.0066s] [ 70%] 2024-08-20T21:41:28.5914682Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_float64 PASSED [0.0068s] [ 70%] 2024-08-20T21:41:28.5915312Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_nuc_cpu_complex128 PASSED [0.0057s] [ 70%] 2024-08-20T21:41:28.5915936Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_nuc_cpu_complex64 PASSED [0.0059s] [ 70%] 2024-08-20T21:41:28.5916562Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_nuc_cpu_float32 PASSED [0.0057s] [ 70%] 2024-08-20T21:41:28.5917172Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_nuc_cpu_float64 PASSED [0.0059s] [ 70%] 2024-08-20T21:41:28.5917846Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_cpu_bfloat16 PASSED [0.0073s] [ 70%] 2024-08-20T21:41:28.5918448Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_cpu_float16 PASSED [0.0073s] [ 70%] 2024-08-20T21:41:28.5919080Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_cpu_float32 PASSED [0.0072s] [ 70%] 2024-08-20T21:41:28.5919691Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_cpu_float64 PASSED [0.0071s] [ 70%] 2024-08-20T21:41:28.5920346Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_in_place_cpu_bfloat16 PASSED [0.0057s] [ 70%] 2024-08-20T21:41:28.5921291Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_in_place_cpu_complex128 PASSED [0.0056s] [ 70%] 2024-08-20T21:41:28.5921971Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_in_place_cpu_complex64 PASSED [0.0062s] [ 70%] 2024-08-20T21:41:28.5922624Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_in_place_cpu_float16 PASSED [0.0056s] [ 70%] 2024-08-20T21:41:28.5923289Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_in_place_cpu_float32 PASSED [0.0058s] [ 70%] 2024-08-20T21:41:28.5923944Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_in_place_cpu_float64 PASSED [0.0056s] [ 70%] 2024-08-20T21:41:28.5924699Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_number_mean_cpu_bfloat16 SKIPPED [0.0002s] (Skipped!) [ 70%] 2024-08-20T21:41:28.5925456Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_number_mean_cpu_float16 SKIPPED [0.0004s] (Skipped!) [ 70%] 2024-08-20T21:41:28.5926198Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_number_mean_cpu_float32 SKIPPED [0.0002s] (Skipped!) [ 70%] 2024-08-20T21:41:28.5927011Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_number_mean_cpu_float64 SKIPPED [0.0001s] (Skipped!) [ 70%] 2024-08-20T21:41:28.5927618Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_bfloat16 PASSED [0.0059s] [ 70%] 2024-08-20T21:41:28.5928196Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_bool PASSED [0.0060s] [ 70%] 2024-08-20T21:41:28.5928854Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_complex128 PASSED [0.0058s] [ 70%] 2024-08-20T21:41:28.5929456Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_complex32 PASSED [0.0058s] [ 70%] 2024-08-20T21:41:28.5930075Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_complex64 PASSED [0.0061s] [ 70%] 2024-08-20T21:41:28.5930666Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_float16 PASSED [0.0058s] [ 70%] 2024-08-20T21:41:28.5931255Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_float32 PASSED [0.0061s] [ 70%] 2024-08-20T21:41:28.5931853Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_float64 PASSED [0.0058s] [ 70%] 2024-08-20T21:41:28.5932435Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_int16 PASSED [0.0060s] [ 70%] 2024-08-20T21:41:28.5933035Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_int32 PASSED [0.0057s] [ 70%] 2024-08-20T21:41:28.5933610Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_int64 PASSED [0.0059s] [ 70%] 2024-08-20T21:41:28.5934182Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_int8 PASSED [0.0057s] [ 70%] 2024-08-20T21:41:28.5934776Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_uint8 PASSED [0.0059s] [ 70%] 2024-08-20T21:41:28.5935402Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_bfloat16 PASSED [0.0082s] [ 70%] 2024-08-20T21:41:28.5936052Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_bool PASSED [0.0082s] [ 70%] 2024-08-20T21:41:28.5936715Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_complex128 PASSED [0.0084s] [ 70%] 2024-08-20T21:41:28.5937353Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_complex32 PASSED [0.0082s] [ 70%] 2024-08-20T21:41:28.5937998Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_complex64 PASSED [0.0090s] [ 70%] 2024-08-20T21:41:28.5938643Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_float16 PASSED [0.0082s] [ 70%] 2024-08-20T21:41:28.5939265Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_float32 PASSED [0.0084s] [ 70%] 2024-08-20T21:41:28.5939900Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_float64 PASSED [0.0082s] [ 70%] 2024-08-20T21:41:28.5940506Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_int16 PASSED [0.0084s] [ 70%] 2024-08-20T21:41:28.5941122Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_int32 PASSED [0.0082s] [ 70%] 2024-08-20T21:41:28.5941725Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_int64 PASSED [0.0082s] [ 70%] 2024-08-20T21:41:28.5942328Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_int8 PASSED [0.0084s] [ 70%] 2024-08-20T21:41:28.5942954Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_uint8 PASSED [0.0082s] [ 70%] 2024-08-20T21:41:28.5943573Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ormqr_cpu_complex128 PASSED [0.0922s] [ 70%] 2024-08-20T21:41:28.5944196Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ormqr_cpu_complex64 PASSED [0.0914s] [ 70%] 2024-08-20T21:41:28.5944794Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ormqr_cpu_float32 PASSED [0.0887s] [ 70%] 2024-08-20T21:41:28.5945387Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ormqr_cpu_float64 PASSED [0.0902s] [ 70%] 2024-08-20T21:41:28.5946032Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_bfloat16 PASSED [0.0055s] [ 70%] 2024-08-20T21:41:28.5946613Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_bool PASSED [0.0054s] [ 70%] 2024-08-20T21:41:28.5947473Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_complex128 PASSED [0.0051s] [ 70%] 2024-08-20T21:41:28.5948085Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_complex64 PASSED [0.0054s] [ 70%] 2024-08-20T21:41:28.5948680Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_float16 PASSED [0.0051s] [ 70%] 2024-08-20T21:41:28.5949283Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_float32 PASSED [0.0053s] [ 70%] 2024-08-20T21:41:28.5949884Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_float64 PASSED [0.0051s] [ 70%] 2024-08-20T21:41:28.5950470Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_int16 PASSED [0.0054s] [ 71%] 2024-08-20T21:41:28.5951063Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_int32 PASSED [0.0051s] [ 71%] 2024-08-20T21:41:28.5951646Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_int64 PASSED [0.0058s] [ 71%] 2024-08-20T21:41:28.5952240Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_int8 PASSED [0.0051s] [ 71%] 2024-08-20T21:41:28.5952819Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_uint8 PASSED [0.0051s] [ 71%] 2024-08-20T21:41:28.5953531Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pca_lowrank_cpu_complex128 PASSED [0.0144s] [ 71%] 2024-08-20T21:41:28.5954248Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pca_lowrank_cpu_complex64 PASSED [0.0139s] [ 71%] 2024-08-20T21:41:28.5954878Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pca_lowrank_cpu_float32 PASSED [0.0141s] [ 71%] 2024-08-20T21:41:28.5955520Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pca_lowrank_cpu_float64 PASSED [0.0139s] [ 71%] 2024-08-20T21:41:28.5956170Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_bfloat16 PASSED [0.0069s] [ 71%] 2024-08-20T21:41:28.5956773Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_bool PASSED [0.0067s] [ 71%] 2024-08-20T21:41:28.5957415Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_complex128 PASSED [0.0067s] [ 71%] 2024-08-20T21:41:28.5958038Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_complex32 PASSED [0.0069s] [ 71%] 2024-08-20T21:41:28.5958670Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_complex64 PASSED [0.0067s] [ 71%] 2024-08-20T21:41:28.5959281Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_float16 PASSED [0.0069s] [ 71%] 2024-08-20T21:41:28.5959885Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_float32 PASSED [0.0067s] [ 71%] 2024-08-20T21:41:28.5960503Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_float64 PASSED [0.0068s] [ 71%] 2024-08-20T21:41:28.5961096Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_int16 PASSED [0.0066s] [ 71%] 2024-08-20T21:41:28.5961687Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_int32 PASSED [0.0068s] [ 71%] 2024-08-20T21:41:28.5962295Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_int64 PASSED [0.0066s] [ 71%] 2024-08-20T21:41:28.5962891Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_int8 PASSED [0.0068s] [ 71%] 2024-08-20T21:41:28.5963541Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_uint8 PASSED [0.0066s] [ 71%] 2024-08-20T21:41:28.5964175Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pinverse_cpu_complex128 PASSED [0.0107s] [ 71%] 2024-08-20T21:41:28.5964808Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pinverse_cpu_complex64 PASSED [0.0111s] [ 71%] 2024-08-20T21:41:28.5965429Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pinverse_cpu_float32 PASSED [0.0104s] [ 71%] 2024-08-20T21:41:28.5966050Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pinverse_cpu_float64 PASSED [0.0106s] [ 71%] 2024-08-20T21:41:28.5966659Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polar_cpu_float32 PASSED [0.0097s] [ 71%] 2024-08-20T21:41:28.5967344Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polar_cpu_float64 PASSED [0.0097s] [ 71%] 2024-08-20T21:41:28.5968055Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_bfloat16 PASSED [0.0097s] [ 71%] 2024-08-20T21:41:28.5968753Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_bool PASSED [0.0097s] [ 71%] 2024-08-20T21:41:28.5969455Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_float16 PASSED [0.0099s] [ 71%] 2024-08-20T21:41:28.5970168Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_float32 PASSED [0.0097s] [ 71%] 2024-08-20T21:41:28.5970902Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_float64 PASSED [0.0100s] [ 71%] 2024-08-20T21:41:28.5971621Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_int16 PASSED [0.0097s] [ 71%] 2024-08-20T21:41:28.5972317Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_int32 PASSED [0.0101s] [ 71%] 2024-08-20T21:41:28.5973003Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_int64 PASSED [0.0097s] [ 71%] 2024-08-20T21:41:28.5973718Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_int8 PASSED [0.0099s] [ 71%] 2024-08-20T21:41:28.5974404Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_uint8 PASSED [0.0097s] [ 71%] 2024-08-20T21:41:28.5975110Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_bfloat16 PASSED [0.0099s] [ 71%] 2024-08-20T21:41:28.5975800Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_bool PASSED [0.0096s] [ 71%] 2024-08-20T21:41:28.5976497Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_float32 PASSED [0.0097s] [ 71%] 2024-08-20T21:41:28.5977197Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_float64 PASSED [0.0098s] [ 71%] 2024-08-20T21:41:28.5977878Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_int16 PASSED [0.0096s] [ 71%] 2024-08-20T21:41:28.5978566Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_int32 PASSED [0.0099s] [ 71%] 2024-08-20T21:41:28.5979265Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_int64 PASSED [0.0097s] [ 71%] 2024-08-20T21:41:28.5979946Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_int8 PASSED [0.0104s] [ 71%] 2024-08-20T21:41:28.5980629Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_uint8 PASSED [0.0097s] [ 71%] 2024-08-20T21:41:28.5981379Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_bfloat16 PASSED [0.0100s] [ 71%] 2024-08-20T21:41:28.5982061Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_bool PASSED [0.0098s] [ 71%] 2024-08-20T21:41:28.5982771Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_float32 PASSED [0.0101s] [ 71%] 2024-08-20T21:41:28.5983471Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_float64 PASSED [0.0099s] [ 71%] 2024-08-20T21:41:28.5984155Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_int16 PASSED [0.0098s] [ 71%] 2024-08-20T21:41:28.5984852Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_int32 PASSED [0.0100s] [ 71%] 2024-08-20T21:41:28.5985535Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_int64 PASSED [0.0098s] [ 71%] 2024-08-20T21:41:28.5986225Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_int8 PASSED [0.0100s] [ 71%] 2024-08-20T21:41:28.5986919Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_uint8 PASSED [0.0098s] [ 71%] 2024-08-20T21:41:28.5987625Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_bfloat16 PASSED [0.0101s] [ 71%] 2024-08-20T21:41:28.5988340Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_bool PASSED [0.0097s] [ 71%] 2024-08-20T21:41:28.5989038Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_float32 PASSED [0.0099s] [ 71%] 2024-08-20T21:41:28.5989779Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_float64 PASSED [0.0098s] [ 72%] 2024-08-20T21:41:28.5990465Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_int16 PASSED [0.0099s] [ 72%] 2024-08-20T21:41:28.5991144Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_int32 PASSED [0.0097s] [ 72%] 2024-08-20T21:41:28.5991882Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_int64 PASSED [0.0097s] [ 72%] 2024-08-20T21:41:28.5992565Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_int8 PASSED [0.0099s] [ 72%] 2024-08-20T21:41:28.5993260Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_uint8 PASSED [0.0097s] [ 72%] 2024-08-20T21:41:28.5993965Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_bfloat16 PASSED [0.0100s] [ 72%] 2024-08-20T21:41:28.5994649Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_bool PASSED [0.0097s] [ 72%] 2024-08-20T21:41:28.5995355Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_float32 PASSED [0.0105s] [ 72%] 2024-08-20T21:41:28.5996045Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_float64 PASSED [0.0098s] [ 72%] 2024-08-20T21:41:28.5996737Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_int16 PASSED [0.0100s] [ 72%] 2024-08-20T21:41:28.5997415Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_int32 PASSED [0.0098s] [ 72%] 2024-08-20T21:41:28.5998107Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_int64 PASSED [0.0099s] [ 72%] 2024-08-20T21:41:28.5998834Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_int8 PASSED [0.0098s] [ 72%] 2024-08-20T21:41:28.5999519Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_uint8 PASSED [0.0097s] [ 72%] 2024-08-20T21:41:28.6000157Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_bfloat16 PASSED [0.0053s] [ 72%] 2024-08-20T21:41:28.6000790Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_complex128 PASSED [0.0051s] [ 72%] 2024-08-20T21:41:28.6001418Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_complex32 PASSED [0.0053s] [ 72%] 2024-08-20T21:41:28.6002061Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_complex64 PASSED [0.0051s] [ 72%] 2024-08-20T21:41:28.6002678Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_float16 PASSED [0.0053s] [ 72%] 2024-08-20T21:41:28.6003298Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_float32 PASSED [0.0051s] [ 72%] 2024-08-20T21:41:28.6003928Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_float64 PASSED [0.0053s] [ 72%] 2024-08-20T21:41:28.6004636Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_int16 PASSED [0.0051s] [ 72%] 2024-08-20T21:41:28.6005247Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_int32 PASSED [0.0052s] [ 72%] 2024-08-20T21:41:28.6005887Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_int64 PASSED [0.0051s] [ 72%] 2024-08-20T21:41:28.6006481Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_int8 PASSED [0.0051s] [ 72%] 2024-08-20T21:41:28.6007179Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_uint8 PASSED [0.0054s] [ 72%] 2024-08-20T21:41:28.6007777Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_bfloat16 PASSED [0.0096s] [ 72%] 2024-08-20T21:41:28.6008391Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_complex128 PASSED [0.0099s] [ 72%] 2024-08-20T21:41:28.6009019Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_complex64 PASSED [0.0097s] [ 72%] 2024-08-20T21:41:28.6009600Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_float16 PASSED [0.0103s] [ 72%] 2024-08-20T21:41:28.6010200Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_float32 PASSED [0.0098s] [ 72%] 2024-08-20T21:41:28.6010781Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_float64 PASSED [0.0099s] [ 72%] 2024-08-20T21:41:28.6011371Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_int16 PASSED [0.0096s] [ 72%] 2024-08-20T21:41:28.6011951Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_int32 PASSED [0.0098s] [ 72%] 2024-08-20T21:41:28.6012517Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_int64 PASSED [0.0095s] [ 72%] 2024-08-20T21:41:28.6013092Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_int8 PASSED [0.0095s] [ 72%] 2024-08-20T21:41:28.6013659Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_uint8 PASSED [0.0097s] [ 72%] 2024-08-20T21:41:28.6014264Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_bfloat16 PASSED [0.0249s] [ 72%] 2024-08-20T21:41:28.6014838Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_bool PASSED [0.0252s] [ 72%] 2024-08-20T21:41:28.6015445Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_complex128 PASSED [0.0251s] [ 72%] 2024-08-20T21:41:28.6016091Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_complex64 PASSED [0.0255s] [ 72%] 2024-08-20T21:41:28.6016678Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_float16 PASSED [0.0248s] [ 72%] 2024-08-20T21:41:28.6017258Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_float32 PASSED [0.0250s] [ 72%] 2024-08-20T21:41:28.6017859Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_float64 PASSED [0.0247s] [ 72%] 2024-08-20T21:41:28.6018438Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_int16 PASSED [0.0251s] [ 72%] 2024-08-20T21:41:28.6019031Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_int32 PASSED [0.0246s] [ 72%] 2024-08-20T21:41:28.6019610Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_int64 PASSED [0.0248s] [ 72%] 2024-08-20T21:41:28.6020182Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_int8 PASSED [0.0252s] [ 72%] 2024-08-20T21:41:28.6020768Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_uint8 PASSED [0.0248s] [ 72%] 2024-08-20T21:41:28.6021661Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_bfloat16 PASSED [0.0197s] [ 72%] 2024-08-20T21:41:28.6022249Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_bool PASSED [0.0190s] [ 72%] 2024-08-20T21:41:28.6022856Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_complex128 PASSED [0.0200s] [ 72%] 2024-08-20T21:41:28.6023500Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_complex64 PASSED [0.0193s] [ 72%] 2024-08-20T21:41:28.6024126Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_float16 PASSED [0.0192s] [ 72%] 2024-08-20T21:41:28.6024711Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_float32 PASSED [0.0190s] [ 72%] 2024-08-20T21:41:28.6025294Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_float64 PASSED [0.0189s] [ 72%] 2024-08-20T21:41:28.6025877Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_int16 PASSED [0.0192s] [ 72%] 2024-08-20T21:41:28.6026474Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_int32 PASSED [0.0189s] [ 72%] 2024-08-20T21:41:28.6027060Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_int64 PASSED [0.0192s] [ 72%] 2024-08-20T21:41:28.6027632Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_int8 PASSED [0.0190s] [ 73%] 2024-08-20T21:41:28.6028200Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_uint8 PASSED [0.0193s] [ 73%] 2024-08-20T21:41:28.6028819Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_qr_cpu_complex128 PASSED [0.0309s] [ 73%] 2024-08-20T21:41:28.6029410Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_qr_cpu_complex64 PASSED [0.0313s] [ 73%] 2024-08-20T21:41:28.6030001Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_qr_cpu_float32 PASSED [0.0304s] [ 73%] 2024-08-20T21:41:28.6030577Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_qr_cpu_float64 PASSED [0.0299s] [ 73%] 2024-08-20T21:41:28.6031192Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_quantile_cpu_float32 PASSED [0.0349s] [ 73%] 2024-08-20T21:41:28.6031819Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_quantile_cpu_float64 PASSED [0.0344s] [ 73%] 2024-08-20T21:41:28.6032430Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_bfloat16 PASSED [0.0056s] [ 73%] 2024-08-20T21:41:28.6033036Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_bool PASSED [0.0052s] [ 73%] 2024-08-20T21:41:28.6033677Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_float16 PASSED [0.0053s] [ 73%] 2024-08-20T21:41:28.6034271Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_float32 PASSED [0.0051s] [ 73%] 2024-08-20T21:41:28.6034894Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_float64 PASSED [0.0053s] [ 73%] 2024-08-20T21:41:28.6035491Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_int16 PASSED [0.0051s] [ 73%] 2024-08-20T21:41:28.6036087Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_int32 PASSED [0.0051s] [ 73%] 2024-08-20T21:41:28.6036686Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_int64 PASSED [0.0053s] [ 73%] 2024-08-20T21:41:28.6037276Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_int8 PASSED [0.0051s] [ 73%] 2024-08-20T21:41:28.6037882Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_uint8 PASSED [0.0053s] [ 73%] 2024-08-20T21:41:28.6038507Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_bfloat16 PASSED [0.0082s] [ 73%] 2024-08-20T21:41:28.6039150Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_complex128 PASSED [0.0085s] [ 73%] 2024-08-20T21:41:28.6039790Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_complex32 PASSED [0.0082s] [ 73%] 2024-08-20T21:41:28.6040450Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_complex64 PASSED [0.0085s] [ 73%] 2024-08-20T21:41:28.6041081Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_float16 PASSED [0.0082s] [ 73%] 2024-08-20T21:41:28.6041728Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_float32 PASSED [0.0083s] [ 73%] 2024-08-20T21:41:28.6042342Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_float64 PASSED [0.0082s] [ 73%] 2024-08-20T21:41:28.6042971Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_bfloat16 PASSED [0.0198s] [ 73%] 2024-08-20T21:41:28.6043604Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_float16 PASSED [0.0199s] [ 73%] 2024-08-20T21:41:28.6044222Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_float32 PASSED [0.0197s] [ 73%] 2024-08-20T21:41:28.6044826Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_float64 PASSED [0.0201s] [ 73%] 2024-08-20T21:41:28.6045417Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_int16 PASSED [0.0198s] [ 73%] 2024-08-20T21:41:28.6046026Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_int32 PASSED [0.0200s] [ 73%] 2024-08-20T21:41:28.6046615Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_int64 PASSED [0.0200s] [ 73%] 2024-08-20T21:41:28.6047535Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_int8 PASSED [0.0198s] [ 73%] 2024-08-20T21:41:28.6048151Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_uint8 PASSED [0.0201s] [ 73%] 2024-08-20T21:41:28.6048797Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_bfloat16 PASSED [0.0117s] [ 73%] 2024-08-20T21:41:28.6049454Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_float16 PASSED [0.0125s] [ 73%] 2024-08-20T21:41:28.6050094Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_float32 PASSED [0.0117s] [ 73%] 2024-08-20T21:41:28.6050817Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_float64 PASSED [0.0121s] [ 73%] 2024-08-20T21:41:28.6051456Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_int16 PASSED [0.0118s] [ 73%] 2024-08-20T21:41:28.6052081Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_int32 PASSED [0.0120s] [ 73%] 2024-08-20T21:41:28.6052719Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_int64 PASSED [0.0118s] [ 73%] 2024-08-20T21:41:28.6053337Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_int8 PASSED [0.0118s] [ 73%] 2024-08-20T21:41:28.6053969Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_uint8 PASSED [0.0121s] [ 73%] 2024-08-20T21:41:28.6054588Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_bfloat16 PASSED [0.0072s] [ 73%] 2024-08-20T21:41:28.6055203Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_complex128 PASSED [0.0072s] [ 73%] 2024-08-20T21:41:28.6055823Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_complex32 PASSED [0.0069s] [ 73%] 2024-08-20T21:41:28.6056427Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_complex64 PASSED [0.0071s] [ 73%] 2024-08-20T21:41:28.6057027Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_float16 PASSED [0.0069s] [ 73%] 2024-08-20T21:41:28.6057638Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_float32 PASSED [0.0070s] [ 73%] 2024-08-20T21:41:28.6058264Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_float64 PASSED [0.0069s] [ 73%] 2024-08-20T21:41:28.6058935Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_bfloat16 PASSED [0.0083s] [ 73%] 2024-08-20T21:41:28.6059593Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_complex128 PASSED [0.0085s] [ 73%] 2024-08-20T21:41:28.6060241Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_complex32 PASSED [0.0084s] [ 73%] 2024-08-20T21:41:28.6060894Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_complex64 PASSED [0.0085s] [ 73%] 2024-08-20T21:41:28.6061555Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_float16 PASSED [0.0082s] [ 73%] 2024-08-20T21:41:28.6062183Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_float32 PASSED [0.0085s] [ 73%] 2024-08-20T21:41:28.6062816Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_float64 PASSED [0.0082s] [ 73%] 2024-08-20T21:41:28.6063426Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_bfloat16 PASSED [0.0069s] [ 73%] 2024-08-20T21:41:28.6064029Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_bool PASSED [0.0063s] [ 73%] 2024-08-20T21:41:28.6064643Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_complex128 PASSED [0.0063s] [ 74%] 2024-08-20T21:41:28.6065250Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_complex32 PASSED [0.0064s] [ 74%] 2024-08-20T21:41:28.6065869Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_complex64 PASSED [0.0062s] [ 74%] 2024-08-20T21:41:28.6066462Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_float16 PASSED [0.0065s] [ 74%] 2024-08-20T21:41:28.6067067Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_float32 PASSED [0.0063s] [ 74%] 2024-08-20T21:41:28.6067665Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_float64 PASSED [0.0064s] [ 74%] 2024-08-20T21:41:28.6068281Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_int16 PASSED [0.0062s] [ 74%] 2024-08-20T21:41:28.6068879Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_int32 PASSED [0.0064s] [ 74%] 2024-08-20T21:41:28.6069463Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_int64 PASSED [0.0062s] [ 74%] 2024-08-20T21:41:28.6070048Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_int8 PASSED [0.0063s] [ 74%] 2024-08-20T21:41:28.6070643Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_uint8 PASSED [0.0062s] [ 74%] 2024-08-20T21:41:28.6071243Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_bfloat16 PASSED [0.0062s] [ 74%] 2024-08-20T21:41:28.6071829Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_bool PASSED [0.0063s] [ 74%] 2024-08-20T21:41:28.6072440Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_complex128 PASSED [0.0062s] [ 74%] 2024-08-20T21:41:28.6073043Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_complex32 PASSED [0.0064s] [ 74%] 2024-08-20T21:41:28.6073655Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_complex64 PASSED [0.0063s] [ 74%] 2024-08-20T21:41:28.6074246Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_float16 PASSED [0.0064s] [ 74%] 2024-08-20T21:41:28.6074840Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_float32 PASSED [0.0061s] [ 74%] 2024-08-20T21:41:28.6075449Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_float64 PASSED [0.0064s] [ 74%] 2024-08-20T21:41:28.6076055Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_int16 PASSED [0.0062s] [ 74%] 2024-08-20T21:41:28.6076647Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_int32 PASSED [0.0067s] [ 74%] 2024-08-20T21:41:28.6077227Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_int64 PASSED [0.0061s] [ 74%] 2024-08-20T21:41:28.6077812Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_int8 PASSED [0.0061s] [ 74%] 2024-08-20T21:41:28.6078415Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_uint8 PASSED [0.0063s] [ 74%] 2024-08-20T21:41:28.6079050Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_bfloat16 PASSED [0.0061s] [ 74%] 2024-08-20T21:41:28.6079671Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_bool PASSED [0.0063s] [ 74%] 2024-08-20T21:41:28.6080317Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_complex128 PASSED [0.0062s] [ 74%] 2024-08-20T21:41:28.6080957Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_complex64 PASSED [0.0064s] [ 74%] 2024-08-20T21:41:28.6081599Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_float16 PASSED [0.0062s] [ 74%] 2024-08-20T21:41:28.6082221Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_float32 PASSED [0.0064s] [ 74%] 2024-08-20T21:41:28.6082862Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_float64 PASSED [0.0062s] [ 74%] 2024-08-20T21:41:28.6083483Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_int16 PASSED [0.0063s] [ 74%] 2024-08-20T21:41:28.6084099Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_int32 PASSED [0.0062s] [ 74%] 2024-08-20T21:41:28.6084718Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_int64 PASSED [0.0062s] [ 74%] 2024-08-20T21:41:28.6085366Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_int8 PASSED [0.0064s] [ 74%] 2024-08-20T21:41:28.6085988Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_uint8 PASSED [0.0063s] [ 74%] 2024-08-20T21:41:28.6086614Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_bfloat16 PASSED [0.0106s] [ 74%] 2024-08-20T21:41:28.6087354Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_float16 PASSED [0.0102s] [ 74%] 2024-08-20T21:41:28.6087994Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_float32 PASSED [0.0105s] [ 74%] 2024-08-20T21:41:28.6088615Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_float64 PASSED [0.0103s] [ 74%] 2024-08-20T21:41:28.6089236Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_int16 PASSED [0.0105s] [ 74%] 2024-08-20T21:41:28.6089849Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_int32 PASSED [0.0102s] [ 74%] 2024-08-20T21:41:28.6090450Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_int64 PASSED [0.0107s] [ 74%] 2024-08-20T21:41:28.6091069Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_int8 PASSED [0.0101s] [ 74%] 2024-08-20T21:41:28.6091674Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_uint8 PASSED [0.0101s] [ 74%] 2024-08-20T21:41:28.6092278Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_renorm_cpu_bfloat16 PASSED [0.0069s] [ 74%] 2024-08-20T21:41:28.6092941Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_renorm_cpu_complex128 PASSED [0.0067s] [ 74%] 2024-08-20T21:41:28.6093590Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_renorm_cpu_complex64 PASSED [0.0069s] [ 74%] 2024-08-20T21:41:28.6094200Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_renorm_cpu_float16 PASSED [0.0067s] [ 74%] 2024-08-20T21:41:28.6094800Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_renorm_cpu_float32 PASSED [0.0069s] [ 74%] 2024-08-20T21:41:28.6095392Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_renorm_cpu_float64 PASSED [0.0068s] [ 74%] 2024-08-20T21:41:28.6096038Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_bfloat16 PASSED [0.0254s] [ 74%] 2024-08-20T21:41:28.6096621Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_bool PASSED [0.0251s] [ 74%] 2024-08-20T21:41:28.6097259Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_complex128 PASSED [0.0258s] [ 74%] 2024-08-20T21:41:28.6097874Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_complex64 PASSED [0.0255s] [ 74%] 2024-08-20T21:41:28.6098472Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_float16 PASSED [0.0252s] [ 74%] 2024-08-20T21:41:28.6099077Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_float32 PASSED [0.0253s] [ 74%] 2024-08-20T21:41:28.6099676Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_float64 PASSED [0.0252s] [ 74%] 2024-08-20T21:41:28.6100279Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_int16 PASSED [0.0256s] [ 74%] 2024-08-20T21:41:28.6100867Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_int32 PASSED [0.0253s] [ 74%] 2024-08-20T21:41:28.6101448Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_int64 PASSED [0.0257s] [ 75%] 2024-08-20T21:41:28.6102046Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_int8 PASSED [0.0253s] [ 75%] 2024-08-20T21:41:28.6102665Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_uint8 PASSED [0.0254s] [ 75%] 2024-08-20T21:41:28.6103341Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_bfloat16 PASSED [0.0069s] [ 75%] 2024-08-20T21:41:28.6104009Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_bool PASSED [0.0067s] [ 75%] 2024-08-20T21:41:28.6104724Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_complex128 PASSED [0.0069s] [ 75%] 2024-08-20T21:41:28.6105478Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_complex32 PASSED [0.0068s] [ 75%] 2024-08-20T21:41:28.6106160Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_complex64 PASSED [0.0070s] [ 75%] 2024-08-20T21:41:28.6106834Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_float16 PASSED [0.0067s] [ 75%] 2024-08-20T21:41:28.6107511Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_float32 PASSED [0.0069s] [ 75%] 2024-08-20T21:41:28.6108175Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_float64 PASSED [0.0067s] [ 75%] 2024-08-20T21:41:28.6108838Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_int16 PASSED [0.0070s] [ 75%] 2024-08-20T21:41:28.6109488Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_int32 PASSED [0.0068s] [ 75%] 2024-08-20T21:41:28.6110180Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_int64 PASSED [0.0069s] [ 75%] 2024-08-20T21:41:28.6110872Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_int8 PASSED [0.0067s] [ 75%] 2024-08-20T21:41:28.6111527Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_uint8 PASSED [0.0067s] [ 75%] 2024-08-20T21:41:28.6112171Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_bfloat16 PASSED [0.0070s] [ 75%] 2024-08-20T21:41:28.6112778Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_bool PASSED [0.0067s] [ 75%] 2024-08-20T21:41:28.6113447Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_complex128 PASSED [0.0070s] [ 75%] 2024-08-20T21:41:28.6114098Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_complex32 PASSED [0.0068s] [ 75%] 2024-08-20T21:41:28.6114737Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_complex64 PASSED [0.0070s] [ 75%] 2024-08-20T21:41:28.6115376Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_float16 PASSED [0.0068s] [ 75%] 2024-08-20T21:41:28.6116003Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_float32 PASSED [0.0069s] [ 75%] 2024-08-20T21:41:28.6116628Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_float64 PASSED [0.0067s] [ 75%] 2024-08-20T21:41:28.6117251Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_int16 PASSED [0.0069s] [ 75%] 2024-08-20T21:41:28.6117861Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_int32 PASSED [0.0068s] [ 75%] 2024-08-20T21:41:28.6118483Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_int64 PASSED [0.0067s] [ 75%] 2024-08-20T21:41:28.6119094Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_int8 PASSED [0.0069s] [ 75%] 2024-08-20T21:41:28.6119707Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_uint8 PASSED [0.0067s] [ 75%] 2024-08-20T21:41:28.6120361Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_bfloat16 PASSED [0.0084s] [ 75%] 2024-08-20T21:41:28.6121221Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_bool PASSED [0.0083s] [ 75%] 2024-08-20T21:41:28.6121854Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_complex128 PASSED [0.0086s] [ 75%] 2024-08-20T21:41:28.6122494Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_complex32 PASSED [0.0084s] [ 75%] 2024-08-20T21:41:28.6123113Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_complex64 PASSED [0.0087s] [ 75%] 2024-08-20T21:41:28.6123733Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_float16 PASSED [0.0085s] [ 75%] 2024-08-20T21:41:28.6124338Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_float32 PASSED [0.0085s] [ 75%] 2024-08-20T21:41:28.6124936Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_float64 PASSED [0.0083s] [ 75%] 2024-08-20T21:41:28.6125547Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_int16 PASSED [0.0083s] [ 75%] 2024-08-20T21:41:28.6126141Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_int32 PASSED [0.0084s] [ 75%] 2024-08-20T21:41:28.6126803Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_int64 PASSED [0.0083s] [ 75%] 2024-08-20T21:41:28.6127440Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_int8 PASSED [0.0085s] [ 75%] 2024-08-20T21:41:28.6128039Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_uint8 PASSED [0.0083s] [ 75%] 2024-08-20T21:41:28.6128689Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_bfloat16 PASSED [0.0064s] [ 75%] 2024-08-20T21:41:28.6129277Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_bool PASSED [0.0062s] [ 75%] 2024-08-20T21:41:28.6129911Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_complex128 PASSED [0.0064s] [ 75%] 2024-08-20T21:41:28.6130555Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_complex64 PASSED [0.0062s] [ 75%] 2024-08-20T21:41:28.6131156Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_float16 PASSED [0.0063s] [ 75%] 2024-08-20T21:41:28.6131767Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_float32 PASSED [0.0062s] [ 75%] 2024-08-20T21:41:28.6132359Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_float64 PASSED [0.0062s] [ 75%] 2024-08-20T21:41:28.6132952Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_int16 PASSED [0.0066s] [ 75%] 2024-08-20T21:41:28.6133552Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_int32 PASSED [0.0062s] [ 75%] 2024-08-20T21:41:28.6134137Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_int64 PASSED [0.0063s] [ 75%] 2024-08-20T21:41:28.6134736Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_int8 PASSED [0.0063s] [ 75%] 2024-08-20T21:41:28.6135327Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_uint8 PASSED [0.0065s] [ 75%] 2024-08-20T21:41:28.6135952Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_bfloat16 PASSED [0.0063s] [ 75%] 2024-08-20T21:41:28.6136567Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_bool PASSED [0.0064s] [ 75%] 2024-08-20T21:41:28.6137207Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_complex128 PASSED [0.0064s] [ 75%] 2024-08-20T21:41:28.6137887Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_complex64 PASSED [0.0066s] [ 75%] 2024-08-20T21:41:28.6138505Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_float16 PASSED [0.0062s] [ 75%] 2024-08-20T21:41:28.6139131Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_float32 PASSED [0.0062s] [ 76%] 2024-08-20T21:41:28.6139759Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_float64 PASSED [0.0064s] [ 76%] 2024-08-20T21:41:28.6140368Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_int16 PASSED [0.0063s] [ 76%] 2024-08-20T21:41:28.6140991Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_int32 PASSED [0.0064s] [ 76%] 2024-08-20T21:41:28.6141599Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_int64 PASSED [0.0062s] [ 76%] 2024-08-20T21:41:28.6142208Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_int8 PASSED [0.0064s] [ 76%] 2024-08-20T21:41:28.6142826Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_uint8 PASSED [0.0062s] [ 76%] 2024-08-20T21:41:28.6143472Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_bfloat16 PASSED [0.0058s] [ 76%] 2024-08-20T21:41:28.6144091Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_bool PASSED [0.0057s] [ 76%] 2024-08-20T21:41:28.6144782Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_complex128 PASSED [0.0058s] [ 76%] 2024-08-20T21:41:28.6145432Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_complex64 PASSED [0.0056s] [ 76%] 2024-08-20T21:41:28.6146110Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_float16 PASSED [0.0056s] [ 76%] 2024-08-20T21:41:28.6146940Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_float32 PASSED [0.0058s] [ 76%] 2024-08-20T21:41:28.6147589Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_float64 PASSED [0.0056s] [ 76%] 2024-08-20T21:41:28.6148305Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_int16 PASSED [0.0058s] [ 76%] 2024-08-20T21:41:28.6148934Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_int32 PASSED [0.0057s] [ 76%] 2024-08-20T21:41:28.6149573Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_int64 PASSED [0.0059s] [ 76%] 2024-08-20T21:41:28.6150195Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_int8 PASSED [0.0056s] [ 76%] 2024-08-20T21:41:28.6150825Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_uint8 PASSED [0.0059s] [ 76%] 2024-08-20T21:41:28.6151477Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_bfloat16 PASSED [0.0057s] [ 76%] 2024-08-20T21:41:28.6152088Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_bool PASSED [0.0059s] [ 76%] 2024-08-20T21:41:28.6152759Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_complex128 PASSED [0.0058s] [ 76%] 2024-08-20T21:41:28.6153399Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_complex32 PASSED [0.0058s] [ 76%] 2024-08-20T21:41:28.6154039Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_complex64 PASSED [0.0059s] [ 76%] 2024-08-20T21:41:28.6154680Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_float16 PASSED [0.0057s] [ 76%] 2024-08-20T21:41:28.6155358Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_float32 PASSED [0.0059s] [ 76%] 2024-08-20T21:41:28.6156000Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_float64 PASSED [0.0057s] [ 76%] 2024-08-20T21:41:28.6156615Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_int16 PASSED [0.0060s] [ 76%] 2024-08-20T21:41:28.6157234Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_int32 PASSED [0.0057s] [ 76%] 2024-08-20T21:41:28.6157866Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_int64 PASSED [0.0059s] [ 76%] 2024-08-20T21:41:28.6158550Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_int8 PASSED [0.0056s] [ 76%] 2024-08-20T21:41:28.6159174Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_uint8 PASSED [0.0059s] [ 76%] 2024-08-20T21:41:28.6159781Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_bfloat16 PASSED [0.0135s] [ 76%] 2024-08-20T21:41:28.6160352Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_bool PASSED [0.0135s] [ 76%] 2024-08-20T21:41:28.6160972Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_complex128 PASSED [0.0141s] [ 76%] 2024-08-20T21:41:28.6161579Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_complex32 PASSED [0.0138s] [ 76%] 2024-08-20T21:41:28.6162222Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_complex64 PASSED [0.0141s] [ 76%] 2024-08-20T21:41:28.6162827Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_float16 PASSED [0.0136s] [ 76%] 2024-08-20T21:41:28.6163463Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_float32 PASSED [0.0137s] [ 76%] 2024-08-20T21:41:28.6164057Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_float64 PASSED [0.0135s] [ 76%] 2024-08-20T21:41:28.6164635Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_int16 PASSED [0.0135s] [ 76%] 2024-08-20T21:41:28.6165214Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_int32 PASSED [0.0137s] [ 76%] 2024-08-20T21:41:28.6165832Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_int64 PASSED [0.0134s] [ 76%] 2024-08-20T21:41:28.6166412Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_int8 PASSED [0.0137s] [ 76%] 2024-08-20T21:41:28.6167074Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_uint8 PASSED [0.0135s] [ 76%] 2024-08-20T21:41:28.6167699Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_bfloat16 PASSED [0.0226s] [ 76%] 2024-08-20T21:41:28.6168278Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_bool PASSED [0.0219s] [ 76%] 2024-08-20T21:41:28.6168911Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_complex128 PASSED [0.0225s] [ 76%] 2024-08-20T21:41:28.6169682Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_complex64 PASSED [0.0232s] [ 76%] 2024-08-20T21:41:28.6170293Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_float16 PASSED [0.0222s] [ 76%] 2024-08-20T21:41:28.6170908Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_float32 PASSED [0.0224s] [ 76%] 2024-08-20T21:41:28.6171508Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_float64 PASSED [0.0222s] [ 76%] 2024-08-20T21:41:28.6172115Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_int16 PASSED [0.0225s] [ 76%] 2024-08-20T21:41:28.6172753Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_int32 PASSED [0.0222s] [ 76%] 2024-08-20T21:41:28.6173336Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_int64 PASSED [0.0231s] [ 76%] 2024-08-20T21:41:28.6173929Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_int8 PASSED [0.0220s] [ 76%] 2024-08-20T21:41:28.6174514Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_uint8 PASSED [0.0221s] [ 76%] 2024-08-20T21:41:28.6175142Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_bfloat16 PASSED [0.0054s] [ 76%] 2024-08-20T21:41:28.6175744Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_float16 PASSED [0.0051s] [ 76%] 2024-08-20T21:41:28.6176336Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_float32 PASSED [0.0054s] [ 76%] 2024-08-20T21:41:28.6176941Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_float64 PASSED [0.0051s] [ 77%] 2024-08-20T21:41:28.6177526Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_int16 PASSED [0.0053s] [ 77%] 2024-08-20T21:41:28.6178107Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_int32 PASSED [0.0052s] [ 77%] 2024-08-20T21:41:28.6178710Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_int64 PASSED [0.0053s] [ 77%] 2024-08-20T21:41:28.6179293Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_int8 PASSED [0.0052s] [ 77%] 2024-08-20T21:41:28.6179923Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_uint8 PASSED [0.0053s] [ 77%] 2024-08-20T21:41:28.6180622Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_0_cpu_bfloat16 PASSED [0.0061s] [ 77%] 2024-08-20T21:41:28.6181282Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_0_cpu_float16 PASSED [0.0062s] [ 77%] 2024-08-20T21:41:28.6181955Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_0_cpu_float32 PASSED [0.0063s] [ 77%] 2024-08-20T21:41:28.6182616Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_0_cpu_float64 PASSED [0.0061s] [ 77%] 2024-08-20T21:41:28.6183324Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_3_cpu_bfloat16 PASSED [0.0064s] [ 77%] 2024-08-20T21:41:28.6183985Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_3_cpu_float32 PASSED [0.0061s] [ 77%] 2024-08-20T21:41:28.6184642Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_3_cpu_float64 PASSED [0.0063s] [ 77%] 2024-08-20T21:41:28.6185357Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_neg_3_cpu_bfloat16 PASSED [0.0061s] [ 77%] 2024-08-20T21:41:28.6186036Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_neg_3_cpu_float32 PASSED [0.0063s] [ 77%] 2024-08-20T21:41:28.6186724Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_neg_3_cpu_float64 PASSED [0.0062s] [ 77%] 2024-08-20T21:41:28.6187330Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_bfloat16 PASSED [0.0067s] [ 77%] 2024-08-20T21:41:28.6187903Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_bool PASSED [0.0062s] [ 77%] 2024-08-20T21:41:28.6188532Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_complex128 PASSED [0.0062s] [ 77%] 2024-08-20T21:41:28.6189142Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_complex64 PASSED [0.0063s] [ 77%] 2024-08-20T21:41:28.6189753Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_float16 PASSED [0.0061s] [ 77%] 2024-08-20T21:41:28.6190413Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_float32 PASSED [0.0063s] [ 77%] 2024-08-20T21:41:28.6191000Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_float64 PASSED [0.0061s] [ 77%] 2024-08-20T21:41:28.6191595Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_int16 PASSED [0.0064s] [ 77%] 2024-08-20T21:41:28.6192174Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_int32 PASSED [0.0062s] [ 77%] 2024-08-20T21:41:28.6192755Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_int64 PASSED [0.0063s] [ 77%] 2024-08-20T21:41:28.6193345Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_int8 PASSED [0.0061s] [ 77%] 2024-08-20T21:41:28.6193926Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_uint8 PASSED [0.0064s] [ 77%] 2024-08-20T21:41:28.6194538Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_bfloat16 PASSED [0.0106s] [ 77%] 2024-08-20T21:41:28.6195140Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_complex128 PASSED [0.0107s] [ 77%] 2024-08-20T21:41:28.6195746Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_complex64 PASSED [0.0111s] [ 77%] 2024-08-20T21:41:28.6196353Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_float16 PASSED [0.0106s] [ 77%] 2024-08-20T21:41:28.6196970Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_float32 PASSED [0.0110s] [ 77%] 2024-08-20T21:41:28.6197562Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_float64 PASSED [0.0108s] [ 77%] 2024-08-20T21:41:28.6198170Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_int16 PASSED [0.0111s] [ 77%] 2024-08-20T21:41:28.6198751Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_int32 PASSED [0.0108s] [ 77%] 2024-08-20T21:41:28.6199337Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_int64 PASSED [0.0110s] [ 77%] 2024-08-20T21:41:28.6199913Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_int8 PASSED [0.0107s] [ 77%] 2024-08-20T21:41:28.6200518Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_uint8 PASSED [0.0107s] [ 77%] 2024-08-20T21:41:28.6201180Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_bfloat16 PASSED [0.0066s] [ 77%] 2024-08-20T21:41:28.6201805Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_bool PASSED [0.0062s] [ 77%] 2024-08-20T21:41:28.6202483Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_complex128 PASSED [0.0065s] [ 77%] 2024-08-20T21:41:28.6203138Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_complex32 PASSED [0.0063s] [ 77%] 2024-08-20T21:41:28.6203797Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_complex64 PASSED [0.0065s] [ 77%] 2024-08-20T21:41:28.6204456Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_float16 PASSED [0.0063s] [ 77%] 2024-08-20T21:41:28.6205096Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_float32 PASSED [0.0064s] [ 77%] 2024-08-20T21:41:28.6205745Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_float64 PASSED [0.0063s] [ 77%] 2024-08-20T21:41:28.6206373Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_int16 PASSED [0.0063s] [ 77%] 2024-08-20T21:41:28.6207104Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_int32 PASSED [0.0065s] [ 77%] 2024-08-20T21:41:28.6207795Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_int64 PASSED [0.0063s] [ 77%] 2024-08-20T21:41:28.6208419Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_int8 PASSED [0.0065s] [ 77%] 2024-08-20T21:41:28.6209063Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_uint8 PASSED [0.0062s] [ 77%] 2024-08-20T21:41:28.6209705Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_bfloat16 PASSED [0.0094s] [ 77%] 2024-08-20T21:41:28.6210318Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_bool PASSED [0.0090s] [ 77%] 2024-08-20T21:41:28.6210982Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_complex128 PASSED [0.0094s] [ 77%] 2024-08-20T21:41:28.6211627Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_complex64 PASSED [0.0091s] [ 77%] 2024-08-20T21:41:28.6212267Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_float16 PASSED [0.0093s] [ 77%] 2024-08-20T21:41:28.6212898Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_float32 PASSED [0.0091s] [ 77%] 2024-08-20T21:41:28.6213523Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_float64 PASSED [0.0091s] [ 77%] 2024-08-20T21:41:28.6214151Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_int16 PASSED [0.0099s] [ 78%] 2024-08-20T21:41:28.6214794Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_int32 PASSED [0.0091s] [ 78%] 2024-08-20T21:41:28.6215465Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_int64 PASSED [0.0095s] [ 78%] 2024-08-20T21:41:28.6216079Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_int8 PASSED [0.0091s] [ 78%] 2024-08-20T21:41:28.6216699Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_uint8 PASSED [0.0093s] [ 78%] 2024-08-20T21:41:28.6217323Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_bfloat16 PASSED [0.0171s] [ 78%] 2024-08-20T21:41:28.6217939Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_bool PASSED [0.0135s] [ 78%] 2024-08-20T21:41:28.6218569Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_complex128 PASSED [0.0133s] [ 78%] 2024-08-20T21:41:28.6219210Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_complex64 PASSED [0.0131s] [ 78%] 2024-08-20T21:41:28.6219819Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_float16 PASSED [0.0174s] [ 78%] 2024-08-20T21:41:28.6220440Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_float32 PASSED [0.0172s] [ 78%] 2024-08-20T21:41:28.6221298Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_float64 PASSED [0.0175s] [ 78%] 2024-08-20T21:41:28.6221904Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_int16 PASSED [0.0132s] [ 78%] 2024-08-20T21:41:28.6222519Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_int32 PASSED [0.0135s] [ 78%] 2024-08-20T21:41:28.6223109Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_int64 PASSED [0.0132s] [ 78%] 2024-08-20T21:41:28.6223718Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_int8 PASSED [0.0133s] [ 78%] 2024-08-20T21:41:28.6224316Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_uint8 PASSED [0.0132s] [ 78%] 2024-08-20T21:41:28.6225040Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_bfloat16 PASSED [0.0164s] [ 78%] 2024-08-20T21:41:28.6225712Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_bool PASSED [0.0164s] [ 78%] 2024-08-20T21:41:28.6226391Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_float16 PASSED [0.0163s] [ 78%] 2024-08-20T21:41:28.6227082Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_float32 PASSED [0.0165s] [ 78%] 2024-08-20T21:41:28.6227760Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_float64 PASSED [0.0163s] [ 78%] 2024-08-20T21:41:28.6228423Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_int16 PASSED [0.0170s] [ 78%] 2024-08-20T21:41:28.6229103Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_int32 PASSED [0.0164s] [ 78%] 2024-08-20T21:41:28.6229768Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_int64 PASSED [0.0166s] [ 78%] 2024-08-20T21:41:28.6230424Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_int8 PASSED [0.0165s] [ 78%] 2024-08-20T21:41:28.6231105Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_uint8 PASSED [0.0164s] [ 78%] 2024-08-20T21:41:28.6231785Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_bfloat16 PASSED [0.0168s] [ 78%] 2024-08-20T21:41:28.6232480Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_bool PASSED [0.0163s] [ 78%] 2024-08-20T21:41:28.6233182Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_float16 PASSED [0.0168s] [ 78%] 2024-08-20T21:41:28.6233855Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_float32 PASSED [0.0165s] [ 78%] 2024-08-20T21:41:28.6234535Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_float64 PASSED [0.0167s] [ 78%] 2024-08-20T21:41:28.6235224Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_int16 PASSED [0.0164s] [ 78%] 2024-08-20T21:41:28.6235898Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_int32 PASSED [0.0165s] [ 78%] 2024-08-20T21:41:28.6236563Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_int64 PASSED [0.0163s] [ 78%] 2024-08-20T21:41:28.6237221Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_int8 PASSED [0.0165s] [ 78%] 2024-08-20T21:41:28.6237896Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_uint8 PASSED [0.0167s] [ 78%] 2024-08-20T21:41:28.6238588Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_bfloat16 PASSED [0.0165s] [ 78%] 2024-08-20T21:41:28.6239278Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_float16 PASSED [0.0166s] [ 78%] 2024-08-20T21:41:28.6239958Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_float32 PASSED [0.0163s] [ 78%] 2024-08-20T21:41:28.6240630Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_float64 PASSED [0.0165s] [ 78%] 2024-08-20T21:41:28.6241307Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_int16 PASSED [0.0163s] [ 78%] 2024-08-20T21:41:28.6241971Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_int32 PASSED [0.0164s] [ 78%] 2024-08-20T21:41:28.6242673Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_int64 PASSED [0.0162s] [ 78%] 2024-08-20T21:41:28.6243329Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_int8 PASSED [0.0163s] [ 78%] 2024-08-20T21:41:28.6243989Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_uint8 PASSED [0.0165s] [ 78%] 2024-08-20T21:41:28.6244686Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_bfloat16 PASSED [0.0163s] [ 78%] 2024-08-20T21:41:28.6245345Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_bool PASSED [0.0165s] [ 78%] 2024-08-20T21:41:28.6246031Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_float16 PASSED [0.0164s] [ 78%] 2024-08-20T21:41:28.6246956Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_float32 PASSED [0.0167s] [ 78%] 2024-08-20T21:41:28.6247658Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_float64 PASSED [0.0165s] [ 78%] 2024-08-20T21:41:28.6248335Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_int16 PASSED [0.0166s] [ 78%] 2024-08-20T21:41:28.6248996Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_int32 PASSED [0.0163s] [ 78%] 2024-08-20T21:41:28.6249675Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_int64 PASSED [0.0163s] [ 78%] 2024-08-20T21:41:28.6250404Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_int8 PASSED [0.0165s] [ 78%] 2024-08-20T21:41:28.6251106Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_uint8 PASSED [0.0162s] [ 78%] 2024-08-20T21:41:28.6251792Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_bfloat16 PASSED [0.0167s] [ 78%] 2024-08-20T21:41:28.6252445Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_bool PASSED [0.0162s] [ 78%] 2024-08-20T21:41:28.6253165Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_float16 PASSED [0.0166s] [ 78%] 2024-08-20T21:41:28.6253840Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_float32 PASSED [0.0164s] [ 78%] 2024-08-20T21:41:28.6254514Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_float64 PASSED [0.0167s] [ 79%] 2024-08-20T21:41:28.6255187Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_int16 PASSED [0.0164s] [ 79%] 2024-08-20T21:41:28.6255847Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_int32 PASSED [0.0163s] [ 79%] 2024-08-20T21:41:28.6256504Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_int64 PASSED [0.0168s] [ 79%] 2024-08-20T21:41:28.6257164Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_int8 PASSED [0.0165s] [ 79%] 2024-08-20T21:41:28.6257822Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_uint8 PASSED [0.0172s] [ 79%] 2024-08-20T21:41:28.6258479Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_bfloat16 PASSED [0.1522s] [ 79%] 2024-08-20T21:41:28.6259213Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_float16 PASSED [0.1522s] [ 79%] 2024-08-20T21:41:28.6259866Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_float32 PASSED [0.1506s] [ 79%] 2024-08-20T21:41:28.6260564Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_float64 PASSED [0.1519s] [ 79%] 2024-08-20T21:41:28.6261191Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_int16 PASSED [0.1519s] [ 79%] 2024-08-20T21:41:28.6261830Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_int32 PASSED [0.1511s] [ 79%] 2024-08-20T21:41:28.6262461Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_int64 PASSED [0.1517s] [ 79%] 2024-08-20T21:41:28.6263085Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_int8 PASSED [0.1507s] [ 79%] 2024-08-20T21:41:28.6263727Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_uint8 PASSED [0.1515s] [ 79%] 2024-08-20T21:41:28.6264335Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_bfloat16 PASSED [0.0074s] [ 79%] 2024-08-20T21:41:28.6264935Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_bool PASSED [0.0074s] [ 79%] 2024-08-20T21:41:28.6265554Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_complex128 PASSED [0.0073s] [ 79%] 2024-08-20T21:41:28.6266166Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_complex32 PASSED [0.0074s] [ 79%] 2024-08-20T21:41:28.6266789Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_complex64 PASSED [0.0072s] [ 79%] 2024-08-20T21:41:28.6267390Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_float16 PASSED [0.0071s] [ 79%] 2024-08-20T21:41:28.6268026Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_float32 PASSED [0.0073s] [ 79%] 2024-08-20T21:41:28.6268647Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_float64 PASSED [0.0072s] [ 79%] 2024-08-20T21:41:28.6269239Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_int16 PASSED [0.0074s] [ 79%] 2024-08-20T21:41:28.6269836Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_int32 PASSED [0.0072s] [ 79%] 2024-08-20T21:41:28.6270418Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_int64 PASSED [0.0073s] [ 79%] 2024-08-20T21:41:28.6271046Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_int8 PASSED [0.0071s] [ 79%] 2024-08-20T21:41:28.6271652Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_uint8 PASSED [0.0079s] [ 79%] 2024-08-20T21:41:28.6272308Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_bfloat16 PASSED [0.0073s] [ 79%] 2024-08-20T21:41:28.6272956Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_bool PASSED [0.0075s] [ 79%] 2024-08-20T21:41:28.6273604Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_float16 PASSED [0.0073s] [ 79%] 2024-08-20T21:41:28.6274250Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_float32 PASSED [0.0072s] [ 79%] 2024-08-20T21:41:28.6274910Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_float64 PASSED [0.0074s] [ 79%] 2024-08-20T21:41:28.6275550Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_int16 PASSED [0.0072s] [ 79%] 2024-08-20T21:41:28.6276194Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_int32 PASSED [0.0074s] [ 79%] 2024-08-20T21:41:28.6276826Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_int64 PASSED [0.0072s] [ 79%] 2024-08-20T21:41:28.6277456Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_int8 PASSED [0.0074s] [ 79%] 2024-08-20T21:41:28.6278129Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_uint8 PASSED [0.0073s] [ 79%] 2024-08-20T21:41:28.6278724Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_bfloat16 PASSED [0.0053s] [ 79%] 2024-08-20T21:41:28.6279299Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_bool PASSED [0.0051s] [ 79%] 2024-08-20T21:41:28.6279904Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_complex128 PASSED [0.0053s] [ 79%] 2024-08-20T21:41:28.6280501Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_complex32 PASSED [0.0051s] [ 79%] 2024-08-20T21:41:28.6281107Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_complex64 PASSED [0.0051s] [ 79%] 2024-08-20T21:41:28.6281692Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_float16 PASSED [0.0052s] [ 79%] 2024-08-20T21:41:28.6282278Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_float32 PASSED [0.0051s] [ 79%] 2024-08-20T21:41:28.6282867Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_float64 PASSED [0.0053s] [ 79%] 2024-08-20T21:41:28.6283440Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_int16 PASSED [0.0051s] [ 79%] 2024-08-20T21:41:28.6284026Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_int32 PASSED [0.0053s] [ 79%] 2024-08-20T21:41:28.6284602Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_int64 PASSED [0.0051s] [ 79%] 2024-08-20T21:41:28.6285213Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_int8 PASSED [0.0057s] [ 79%] 2024-08-20T21:41:28.6285833Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_uint8 PASSED [0.0051s] [ 79%] 2024-08-20T21:41:28.6286439Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_bfloat16 PASSED [0.0073s] [ 79%] 2024-08-20T21:41:28.6287116Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_bool PASSED [0.0071s] [ 79%] 2024-08-20T21:41:28.6287738Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_complex128 PASSED [0.0072s] [ 79%] 2024-08-20T21:41:28.6288380Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_complex64 PASSED [0.0073s] [ 79%] 2024-08-20T21:41:28.6288997Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_float16 PASSED [0.0072s] [ 79%] 2024-08-20T21:41:28.6289588Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_float32 PASSED [0.0074s] [ 79%] 2024-08-20T21:41:28.6290190Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_float64 PASSED [0.0071s] [ 79%] 2024-08-20T21:41:28.6290782Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_int16 PASSED [0.0074s] [ 79%] 2024-08-20T21:41:28.6291368Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_int32 PASSED [0.0071s] [ 80%] 2024-08-20T21:41:28.6291963Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_int64 PASSED [0.0074s] [ 80%] 2024-08-20T21:41:28.6292546Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_int8 PASSED [0.0072s] [ 80%] 2024-08-20T21:41:28.6293129Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_uint8 PASSED [0.0074s] [ 80%] 2024-08-20T21:41:28.6293756Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_bfloat16 PASSED [0.0061s] [ 80%] 2024-08-20T21:41:28.6294353Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_bool PASSED [0.0061s] [ 80%] 2024-08-20T21:41:28.6295026Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_complex128 PASSED [0.0064s] [ 80%] 2024-08-20T21:41:28.6295644Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_complex64 PASSED [0.0062s] [ 80%] 2024-08-20T21:41:28.6296251Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_float16 PASSED [0.0064s] [ 80%] 2024-08-20T21:41:28.6296873Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_float32 PASSED [0.0061s] [ 80%] 2024-08-20T21:41:28.6297474Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_float64 PASSED [0.0064s] [ 80%] 2024-08-20T21:41:28.6298091Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_int16 PASSED [0.0061s] [ 80%] 2024-08-20T21:41:28.6298690Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_int32 PASSED [0.0067s] [ 80%] 2024-08-20T21:41:28.6299288Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_int64 PASSED [0.0061s] [ 80%] 2024-08-20T21:41:28.6299899Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_int8 PASSED [0.0063s] [ 80%] 2024-08-20T21:41:28.6300498Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_uint8 PASSED [0.0063s] [ 80%] 2024-08-20T21:41:28.6301095Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_bfloat16 PASSED [0.0051s] [ 80%] 2024-08-20T21:41:28.6301680Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_bool PASSED [0.0052s] [ 80%] 2024-08-20T21:41:28.6302300Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_float16 PASSED [0.0051s] [ 80%] 2024-08-20T21:41:28.6302925Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_float32 PASSED [0.0053s] [ 80%] 2024-08-20T21:41:28.6303518Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_float64 PASSED [0.0051s] [ 80%] 2024-08-20T21:41:28.6304096Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_int16 PASSED [0.0053s] [ 80%] 2024-08-20T21:41:28.6304685Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_int32 PASSED [0.0052s] [ 80%] 2024-08-20T21:41:28.6305286Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_int64 PASSED [0.0054s] [ 80%] 2024-08-20T21:41:28.6305873Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_int8 PASSED [0.0051s] [ 80%] 2024-08-20T21:41:28.6306453Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_uint8 PASSED [0.0053s] [ 80%] 2024-08-20T21:41:28.6307156Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_bartlett_cpu_float32 PASSED [0.0136s] [ 80%] 2024-08-20T21:41:28.6307865Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_bartlett_cpu_float64 PASSED [0.0132s] [ 80%] 2024-08-20T21:41:28.6308569Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_blackman_cpu_float32 PASSED [0.0170s] [ 80%] 2024-08-20T21:41:28.6309273Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_blackman_cpu_float64 PASSED [0.0166s] [ 80%] 2024-08-20T21:41:28.6309968Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_cosine_cpu_float32 PASSED [0.0123s] [ 80%] 2024-08-20T21:41:28.6310650Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_cosine_cpu_float64 PASSED [0.0124s] [ 80%] 2024-08-20T21:41:28.6311384Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_exponential_cpu_float32 PASSED [0.0138s] [ 80%] 2024-08-20T21:41:28.6312110Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_exponential_cpu_float64 PASSED [0.0133s] [ 80%] 2024-08-20T21:41:28.6312843Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_gaussian_cpu_float32 PASSED [0.0145s] [ 80%] 2024-08-20T21:41:28.6313530Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_gaussian_cpu_float64 PASSED [0.0145s] [ 80%] 2024-08-20T21:41:28.6314265Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_general_cosine_cpu_float32 PASSED [0.0164s] [ 80%] 2024-08-20T21:41:28.6315012Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_general_cosine_cpu_float64 PASSED [0.0166s] [ 80%] 2024-08-20T21:41:28.6315752Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_general_hamming_cpu_float32 PASSED [0.0166s] [ 80%] 2024-08-20T21:41:28.6316502Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_general_hamming_cpu_float64 PASSED [0.0163s] [ 80%] 2024-08-20T21:41:28.6317197Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_hamming_cpu_float32 PASSED [0.0166s] [ 80%] 2024-08-20T21:41:28.6317882Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_hamming_cpu_float64 PASSED [0.0166s] [ 80%] 2024-08-20T21:41:28.6318569Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_hann_cpu_float32 PASSED [0.0163s] [ 80%] 2024-08-20T21:41:28.6319244Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_hann_cpu_float64 PASSED [0.0166s] [ 80%] 2024-08-20T21:41:28.6319969Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_kaiser_cpu_float32 PASSED [0.0201s] [ 80%] 2024-08-20T21:41:28.6320684Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_kaiser_cpu_float64 PASSED [0.0197s] [ 80%] 2024-08-20T21:41:28.6321604Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_nuttall_cpu_float32 PASSED [0.0164s] [ 80%] 2024-08-20T21:41:28.6322311Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_nuttall_cpu_float64 PASSED [0.0166s] [ 80%] 2024-08-20T21:41:28.6322964Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_bfloat16 PASSED [0.0054s] [ 80%] 2024-08-20T21:41:28.6323563Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_bool PASSED [0.0052s] [ 80%] 2024-08-20T21:41:28.6324188Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_float16 PASSED [0.0056s] [ 80%] 2024-08-20T21:41:28.6324792Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_float32 PASSED [0.0052s] [ 80%] 2024-08-20T21:41:28.6325419Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_float64 PASSED [0.0055s] [ 80%] 2024-08-20T21:41:28.6326013Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_int16 PASSED [0.0052s] [ 80%] 2024-08-20T21:41:28.6326610Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_int32 PASSED [0.0055s] [ 80%] 2024-08-20T21:41:28.6327281Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_int64 PASSED [0.0052s] [ 80%] 2024-08-20T21:41:28.6327881Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_int8 PASSED [0.0054s] [ 80%] 2024-08-20T21:41:28.6328494Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_uint8 PASSED [0.0052s] [ 80%] 2024-08-20T21:41:28.6329086Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_bfloat16 PASSED [0.0052s] [ 80%] 2024-08-20T21:41:28.6329657Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_bool PASSED [0.0054s] [ 80%] 2024-08-20T21:41:28.6330314Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_complex128 PASSED [0.0052s] [ 81%] 2024-08-20T21:41:28.6330912Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_complex64 PASSED [0.0054s] [ 81%] 2024-08-20T21:41:28.6331508Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_float16 PASSED [0.0052s] [ 81%] 2024-08-20T21:41:28.6332094Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_float32 PASSED [0.0054s] [ 81%] 2024-08-20T21:41:28.6332675Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_float64 PASSED [0.0052s] [ 81%] 2024-08-20T21:41:28.6333262Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_int16 PASSED [0.0054s] [ 81%] 2024-08-20T21:41:28.6333838Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_int32 PASSED [0.0051s] [ 81%] 2024-08-20T21:41:28.6334408Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_int64 PASSED [0.0053s] [ 81%] 2024-08-20T21:41:28.6334985Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_int8 PASSED [0.0051s] [ 81%] 2024-08-20T21:41:28.6335553Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_uint8 PASSED [0.0051s] [ 81%] 2024-08-20T21:41:28.6336165Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_bfloat16 PASSED [0.0065s] [ 81%] 2024-08-20T21:41:28.6336737Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_bool PASSED [0.0062s] [ 81%] 2024-08-20T21:41:28.6337375Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_complex128 PASSED [0.0070s] [ 81%] 2024-08-20T21:41:28.6338017Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_complex64 PASSED [0.0064s] [ 81%] 2024-08-20T21:41:28.6338610Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_float16 PASSED [0.0065s] [ 81%] 2024-08-20T21:41:28.6339200Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_float32 PASSED [0.0062s] [ 81%] 2024-08-20T21:41:28.6339786Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_float64 PASSED [0.0065s] [ 81%] 2024-08-20T21:41:28.6340387Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_int16 PASSED [0.0063s] [ 81%] 2024-08-20T21:41:28.6340978Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_int32 PASSED [0.0065s] [ 81%] 2024-08-20T21:41:28.6341557Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_int64 PASSED [0.0063s] [ 81%] 2024-08-20T21:41:28.6342141Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_int8 PASSED [0.0061s] [ 81%] 2024-08-20T21:41:28.6342718Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_uint8 PASSED [0.0063s] [ 81%] 2024-08-20T21:41:28.6343312Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_bfloat16 PASSED [0.0052s] [ 81%] 2024-08-20T21:41:28.6343894Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_bool PASSED [0.0053s] [ 81%] 2024-08-20T21:41:28.6344501Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_complex128 PASSED [0.0052s] [ 81%] 2024-08-20T21:41:28.6345110Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_complex64 PASSED [0.0054s] [ 81%] 2024-08-20T21:41:28.6345709Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_float16 PASSED [0.0051s] [ 81%] 2024-08-20T21:41:28.6346286Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_float32 PASSED [0.0053s] [ 81%] 2024-08-20T21:41:28.6347048Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_float64 PASSED [0.0051s] [ 81%] 2024-08-20T21:41:28.6347708Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_int16 PASSED [0.0053s] [ 81%] 2024-08-20T21:41:28.6348282Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_int32 PASSED [0.0051s] [ 81%] 2024-08-20T21:41:28.6348879Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_int64 PASSED [0.0050s] [ 81%] 2024-08-20T21:41:28.6349449Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_int8 PASSED [0.0053s] [ 81%] 2024-08-20T21:41:28.6350044Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_uint8 PASSED [0.0051s] [ 81%] 2024-08-20T21:41:28.6350645Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_bfloat16 PASSED [0.0080s] [ 81%] 2024-08-20T21:41:28.6351221Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_bool PASSED [0.0075s] [ 81%] 2024-08-20T21:41:28.6351857Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_complex128 PASSED [0.0089s] [ 81%] 2024-08-20T21:41:28.6352465Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_complex32 PASSED [0.0078s] [ 81%] 2024-08-20T21:41:28.6353070Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_complex64 PASSED [0.0078s] [ 81%] 2024-08-20T21:41:28.6353681Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_float16 PASSED [0.0073s] [ 81%] 2024-08-20T21:41:28.6354319Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_float32 PASSED [0.0073s] [ 81%] 2024-08-20T21:41:28.6354932Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_float64 PASSED [0.0075s] [ 81%] 2024-08-20T21:41:28.6355560Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_int16 PASSED [0.0075s] [ 81%] 2024-08-20T21:41:28.6356147Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_int32 PASSED [0.0078s] [ 81%] 2024-08-20T21:41:28.6356740Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_int64 PASSED [0.0072s] [ 81%] 2024-08-20T21:41:28.6357359Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_int8 PASSED [0.0077s] [ 81%] 2024-08-20T21:41:28.6357952Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_uint8 PASSED [0.0075s] [ 81%] 2024-08-20T21:41:28.6358609Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_bfloat16 PASSED [0.0105s] [ 81%] 2024-08-20T21:41:28.6359238Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_bool PASSED [0.0107s] [ 81%] 2024-08-20T21:41:28.6359900Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_float16 PASSED [0.0104s] [ 81%] 2024-08-20T21:41:28.6360548Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_float32 PASSED [0.0100s] [ 81%] 2024-08-20T21:41:28.6361198Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_float64 PASSED [0.0104s] [ 81%] 2024-08-20T21:41:28.6361832Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_int16 PASSED [0.0107s] [ 81%] 2024-08-20T21:41:28.6362461Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_int32 PASSED [0.0107s] [ 81%] 2024-08-20T21:41:28.6363103Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_int64 PASSED [0.0105s] [ 81%] 2024-08-20T21:41:28.6363732Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_int8 PASSED [0.0106s] [ 81%] 2024-08-20T21:41:28.6364361Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_uint8 PASSED [0.0114s] [ 81%] 2024-08-20T21:41:28.6365019Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_cpu_bfloat16 PASSED [0.0083s] [ 81%] 2024-08-20T21:41:28.6365628Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_cpu_float16 PASSED [0.0085s] [ 81%] 2024-08-20T21:41:28.6366247Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_cpu_float32 PASSED [0.0082s] [ 82%] 2024-08-20T21:41:28.6366947Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_cpu_float64 PASSED [0.0084s] [ 82%] 2024-08-20T21:41:28.6367629Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_bfloat16 PASSED [0.0081s] [ 82%] 2024-08-20T21:41:28.6368300Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_bool PASSED [0.0081s] [ 82%] 2024-08-20T21:41:28.6368995Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_complex128 PASSED [0.0084s] [ 82%] 2024-08-20T21:41:28.6369690Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_complex64 PASSED [0.0083s] [ 82%] 2024-08-20T21:41:28.6370359Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_float16 PASSED [0.0084s] [ 82%] 2024-08-20T21:41:28.6371031Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_float32 PASSED [0.0082s] [ 82%] 2024-08-20T21:41:28.6371752Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_float64 PASSED [0.0085s] [ 82%] 2024-08-20T21:41:28.6372407Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_int16 PASSED [0.0083s] [ 82%] 2024-08-20T21:41:28.6373106Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_int32 PASSED [0.0085s] [ 82%] 2024-08-20T21:41:28.6373763Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_int64 PASSED [0.0083s] [ 82%] 2024-08-20T21:41:28.6374412Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_int8 PASSED [0.0084s] [ 82%] 2024-08-20T21:41:28.6375100Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_uint8 PASSED [0.0082s] [ 82%] 2024-08-20T21:41:28.6375700Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_bfloat16 PASSED [0.0349s] [ 82%] 2024-08-20T21:41:28.6376360Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_bool PASSED [0.0353s] [ 82%] 2024-08-20T21:41:28.6376967Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_float16 PASSED [0.0347s] [ 82%] 2024-08-20T21:41:28.6377554Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_float32 PASSED [0.0356s] [ 82%] 2024-08-20T21:41:28.6378153Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_float64 PASSED [0.0351s] [ 82%] 2024-08-20T21:41:28.6378736Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_int16 PASSED [0.0361s] [ 82%] 2024-08-20T21:41:28.6379311Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_int32 PASSED [0.0347s] [ 82%] 2024-08-20T21:41:28.6379897Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_int64 PASSED [0.0344s] [ 82%] 2024-08-20T21:41:28.6380472Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_int8 PASSED [0.0351s] [ 82%] 2024-08-20T21:41:28.6381056Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_uint8 PASSED [0.0353s] [ 82%] 2024-08-20T21:41:28.6383770Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_mm_reduce_cpu_bfloat16 SKIPPED [0.0006s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/132494 for platform(s) asan, linux, mac, macos, win, windows. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests.) [ 82%] 2024-08-20T21:41:28.6386505Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_mm_reduce_cpu_float16 SKIPPED [0.0005s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/133827 for platform(s) asan, linux, mac, macos, win, windows. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests.) [ 82%] 2024-08-20T21:41:28.6389156Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_mm_reduce_cpu_float32 SKIPPED [0.0007s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/132552 for platform(s) asan, linux, mac, macos, win, windows. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests.) [ 82%] 2024-08-20T21:41:28.6391996Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_mm_reduce_cpu_float64 SKIPPED [0.0005s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/132619 for platform(s) asan, linux, mac, macos, win, windows. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests.) [ 82%] 2024-08-20T21:41:28.6394577Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_sampled_addmm_cpu_complex128 SKIPPED [0.0004s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/132643 for platform(s) asan, linux. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests.) [ 82%] 2024-08-20T21:41:28.6397180Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_sampled_addmm_cpu_complex64 SKIPPED [0.0005s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/132686 for platform(s) asan, linux. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests.) [ 82%] 2024-08-20T21:41:28.6399727Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_sampled_addmm_cpu_float32 SKIPPED [0.0005s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/132720 for platform(s) asan, linux. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests.) [ 82%] 2024-08-20T21:41:28.6400507Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_sampled_addmm_cpu_float64 ('RERUN', {'yellow': True}) [0.0071s] [ 82%] 2024-08-20T21:41:28.6401259Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_sampled_addmm_cpu_float64 ('RERUN', {'yellow': True}) [0.0068s] [ 82%] 2024-08-20T21:41:28.6401956Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_sampled_addmm_cpu_float64 FAILED [0.0068s] [ 82%] 2024-08-20T21:41:28.6401971Z 2024-08-20T21:41:28.6402151Z ==================================== RERUNS ==================================== 2024-08-20T21:41:28.6402636Z ___ TestDeviceUtilsCPU.test_device_mode_ops_sparse_sampled_addmm_cpu_float64 ___ 2024-08-20T21:41:28.6402781Z Traceback (most recent call last): 2024-08-20T21:41:28.6403226Z File "/var/lib/jenkins/workspace/test/test_utils.py", line 1164, in test_device_mode_ops 2024-08-20T21:41:28.6403357Z for sample in samples: 2024-08-20T21:41:28.6404081Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py", line 388, in __next__ 2024-08-20T21:41:28.6404246Z input_idx, input_val = next(self.child_iter) 2024-08-20T21:41:28.6404383Z ^^^^^^^^^^^^^^^^^^^^^ 2024-08-20T21:41:28.6405069Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 36, in generator_context 2024-08-20T21:41:28.6405188Z response = gen.send(None) 2024-08-20T21:41:28.6405299Z ^^^^^^^^^^^^^^ 2024-08-20T21:41:28.6406271Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_methods_invocations.py", line 1213, in sample_inputs_sparse_sampled_addmm 2024-08-20T21:41:28.6406391Z .to_sparse_csr() 2024-08-20T21:41:28.6406485Z ^^^^^^^^^^^^^^^ 2024-08-20T21:41:28.6407063Z RuntimeError: Please register PrivateUse1HooksInterface by `RegisterPrivateUse1HooksInterface` first. 2024-08-20T21:41:28.6407071Z 2024-08-20T21:41:28.6407339Z To execute this test, run the following from the base repo dir: 2024-08-20T21:41:28.6407797Z python test/test_utils.py TestDeviceUtilsCPU.test_device_mode_ops_sparse_sampled_addmm_cpu_float64 2024-08-20T21:41:28.6407802Z 2024-08-20T21:41:28.6408121Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2024-08-20T21:41:28.6408624Z ___ TestDeviceUtilsCPU.test_device_mode_ops_sparse_sampled_addmm_cpu_float64 ___ 2024-08-20T21:41:28.6408757Z Traceback (most recent call last): 2024-08-20T21:41:28.6409213Z File "/var/lib/jenkins/workspace/test/test_utils.py", line 1164, in test_device_mode_ops 2024-08-20T21:41:28.6409333Z for sample in samples: 2024-08-20T21:41:28.6410079Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py", line 388, in __next__ 2024-08-20T21:41:28.6410263Z input_idx, input_val = next(self.child_iter) 2024-08-20T21:41:28.6410391Z ^^^^^^^^^^^^^^^^^^^^^ 2024-08-20T21:41:28.6411075Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 36, in generator_context 2024-08-20T21:41:28.6411213Z response = gen.send(None) 2024-08-20T21:41:28.6411314Z ^^^^^^^^^^^^^^ 2024-08-20T21:41:28.6412320Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_methods_invocations.py", line 1213, in sample_inputs_sparse_sampled_addmm 2024-08-20T21:41:28.6412443Z .to_sparse_csr() 2024-08-20T21:41:28.6412541Z ^^^^^^^^^^^^^^^ 2024-08-20T21:41:28.6413057Z RuntimeError: Please register PrivateUse1HooksInterface by `RegisterPrivateUse1HooksInterface` first. 2024-08-20T21:41:28.6413066Z 2024-08-20T21:41:28.6413320Z To execute this test, run the following from the base repo dir: 2024-08-20T21:41:28.6413781Z python test/test_utils.py TestDeviceUtilsCPU.test_device_mode_ops_sparse_sampled_addmm_cpu_float64 2024-08-20T21:41:28.6413787Z 2024-08-20T21:41:28.6414122Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2024-08-20T21:41:28.6414309Z =================================== FAILURES =================================== 2024-08-20T21:41:28.6414794Z ___ TestDeviceUtilsCPU.test_device_mode_ops_sparse_sampled_addmm_cpu_float64 ___ 2024-08-20T21:41:28.6414943Z Traceback (most recent call last): 2024-08-20T21:41:28.6415352Z File "/var/lib/jenkins/workspace/test/test_utils.py", line 1164, in test_device_mode_ops 2024-08-20T21:41:28.6415484Z for sample in samples: 2024-08-20T21:41:28.6416208Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py", line 388, in __next__ 2024-08-20T21:41:28.6416376Z input_idx, input_val = next(self.child_iter) 2024-08-20T21:41:28.6416516Z ^^^^^^^^^^^^^^^^^^^^^ 2024-08-20T21:41:28.6417228Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 36, in generator_context 2024-08-20T21:41:28.6417352Z response = gen.send(None) 2024-08-20T21:41:28.6417468Z ^^^^^^^^^^^^^^ 2024-08-20T21:41:28.6418428Z File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_methods_invocations.py", line 1213, in sample_inputs_sparse_sampled_addmm 2024-08-20T21:41:28.6418550Z .to_sparse_csr() 2024-08-20T21:41:28.6418649Z ^^^^^^^^^^^^^^^ 2024-08-20T21:41:28.6419149Z RuntimeError: Please register PrivateUse1HooksInterface by `RegisterPrivateUse1HooksInterface` first. 2024-08-20T21:41:28.6419155Z 2024-08-20T21:41:28.6419430Z To execute this test, run the following from the base repo dir: 2024-08-20T21:41:28.6419887Z python test/test_utils.py TestDeviceUtilsCPU.test_device_mode_ops_sparse_sampled_addmm_cpu_float64 2024-08-20T21:41:28.6419895Z 2024-08-20T21:41:28.6420211Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2024-08-20T21:41:28.6421187Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_utils/test_utils-58cee363dce3f8e5.xml - 2024-08-20T21:41:28.6421541Z =========================== short test summary info ============================ 2024-08-20T21:41:28.6423175Z FAILED [0.0068s] test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_sampled_addmm_cpu_float64 - RuntimeError: Please register PrivateUse1HooksInterface by `RegisterPrivateUse1HooksInterface` first. 2024-08-20T21:41:28.6423183Z 2024-08-20T21:41:28.6423435Z To execute this test, run the following from the base repo dir: 2024-08-20T21:41:28.6423938Z python test/test_utils.py TestDeviceUtilsCPU.test_device_mode_ops_sparse_sampled_addmm_cpu_float64 2024-08-20T21:41:28.6423973Z 2024-08-20T21:41:28.6424304Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2024-08-20T21:41:28.6424642Z !!!!!!!!!!!!!!!!!!!!!!!!!! stopping after 1 failures !!!!!!!!!!!!!!!!!!!!!!!!!!! 2024-08-20T21:41:28.6425311Z ======== 1 failed, 4825 passed, 82 skipped, 2 rerun in 99.17s (0:01:39) ======== 2024-08-20T21:41:28.6425415Z Got exit code 1 2024-08-20T21:41:28.6425526Z Retrying single test... 2024-08-20T21:41:28.6426100Z Test results will be stored in test-reports/python-pytest/test_utils/test_utils-201b7b6a5e6e09f4.xml 2024-08-20T21:41:28.6426435Z ============================= test session starts ============================== 2024-08-20T21:41:28.6426954Z platform linux -- Python 3.12.4, pytest-7.3.2, pluggy-1.5.0 -- /opt/conda/envs/py_3.12/bin/python 2024-08-20T21:41:28.6427077Z cachedir: .pytest_cache 2024-08-20T21:41:28.6427779Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2024-08-20T21:41:28.6427923Z rootdir: /var/lib/jenkins/workspace 2024-08-20T21:41:28.6428036Z configfile: pytest.ini 2024-08-20T21:41:28.6428718Z plugins: hypothesis-5.35.1, cpp-2.3.0, flakefinder-1.1.0, rerunfailures-14.0, xdist-3.3.1, xdoctest-1.1.0, typeguard-4.3.0 2024-08-20T21:41:28.6429085Z collecting ... collected 5947 items / 5946 deselected / 1 selected 2024-08-20T21:41:28.6429818Z stepcurrent: skipping 4907 already run items. Running only test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_sampled_addmm_cpu_float64 2024-08-20T21:41:28.6429942Z Running 1 items in this shard 2024-08-20T21:41:28.6429949Z 2024-08-20T21:41:28.6430652Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_sampled_addmm_cpu_float64 PASSED [0.0547s] [100%] 2024-08-20T21:41:28.6430659Z 2024-08-20T21:41:28.6431407Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_utils/test_utils-201b7b6a5e6e09f4.xml - 2024-08-20T21:41:28.6431933Z ====================== 1 passed, 5946 deselected in 0.32s ====================== 2024-08-20T21:41:28.6432066Z Got exit code 0 2024-08-20T21:41:28.6432356Z Test succeeeded in new process, continuing with the rest of the tests 2024-08-20T21:41:28.6432924Z Test results will be stored in test-reports/python-pytest/test_utils/test_utils-0a537fe028a4479e.xml 2024-08-20T21:41:28.6433230Z ============================= test session starts ============================== 2024-08-20T21:41:28.6433738Z platform linux -- Python 3.12.4, pytest-7.3.2, pluggy-1.5.0 -- /opt/conda/envs/py_3.12/bin/python 2024-08-20T21:41:28.6433863Z cachedir: .pytest_cache 2024-08-20T21:41:28.6434564Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2024-08-20T21:41:28.6434708Z rootdir: /var/lib/jenkins/workspace 2024-08-20T21:41:28.6434820Z configfile: pytest.ini 2024-08-20T21:41:28.6435502Z plugins: hypothesis-5.35.1, cpp-2.3.0, flakefinder-1.1.0, rerunfailures-14.0, xdist-3.3.1, xdoctest-1.1.0, typeguard-4.3.0 2024-08-20T21:41:28.6435882Z collecting ... collected 5947 items / 4908 deselected / 1039 selected 2024-08-20T21:41:28.6436047Z stepcurrent: skipping 4908 already run items. 2024-08-20T21:41:28.6436168Z Running 1039 items in this shard 2024-08-20T21:41:28.6436174Z 2024-08-20T21:41:28.6436837Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_bool PASSED [0.0474s] [ 0%] 2024-08-20T21:41:28.6437494Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_float32 PASSED [0.0066s] [ 0%] 2024-08-20T21:41:28.6438159Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_float64 PASSED [0.0065s] [ 0%] 2024-08-20T21:41:28.6438826Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_int16 PASSED [0.0063s] [ 0%] 2024-08-20T21:41:28.6439490Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_int32 PASSED [0.0064s] [ 0%] 2024-08-20T21:41:28.6440146Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_int64 PASSED [0.0062s] [ 0%] 2024-08-20T21:41:28.6440783Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_int8 PASSED [0.0062s] [ 0%] 2024-08-20T21:41:28.6441427Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_uint8 PASSED [0.0071s] [ 0%] 2024-08-20T21:41:28.6442104Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_bool PASSED [0.0061s] [ 0%] 2024-08-20T21:41:28.6442773Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_float32 PASSED [0.0063s] [ 0%] 2024-08-20T21:41:28.6443444Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_float64 PASSED [0.0062s] [ 1%] 2024-08-20T21:41:28.6444095Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_int16 PASSED [0.0064s] [ 1%] 2024-08-20T21:41:28.6444742Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_int32 PASSED [0.0116s] [ 1%] 2024-08-20T21:41:28.6445406Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_int64 PASSED [0.0067s] [ 1%] 2024-08-20T21:41:28.6446053Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_int8 PASSED [0.0063s] [ 1%] 2024-08-20T21:41:28.6446948Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_uint8 PASSED [0.0063s] [ 1%] 2024-08-20T21:41:28.6447616Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_bool PASSED [0.0061s] [ 1%] 2024-08-20T21:41:28.6448281Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_float32 PASSED [0.0062s] [ 1%] 2024-08-20T21:41:28.6449033Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_float64 PASSED [0.0063s] [ 1%] 2024-08-20T21:41:28.6449681Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_int16 PASSED [0.0061s] [ 1%] 2024-08-20T21:41:28.6450342Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_int32 PASSED [0.0063s] [ 2%] 2024-08-20T21:41:28.6450992Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_int64 PASSED [0.0062s] [ 2%] 2024-08-20T21:41:28.6451637Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_int8 PASSED [0.0064s] [ 2%] 2024-08-20T21:41:28.6452301Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_uint8 PASSED [0.0061s] [ 2%] 2024-08-20T21:41:28.6452946Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y0_cpu_bool PASSED [0.0064s] [ 2%] 2024-08-20T21:41:28.6453625Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y0_cpu_float32 PASSED [0.0062s] [ 2%] 2024-08-20T21:41:28.6454285Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y0_cpu_float64 PASSED [0.0063s] [ 2%] 2024-08-20T21:41:28.6454933Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y0_cpu_int16 PASSED [0.0062s] [ 2%] 2024-08-20T21:41:28.6455594Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y0_cpu_int32 PASSED [0.0062s] [ 2%] 2024-08-20T21:41:28.6456281Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y0_cpu_int64 PASSED [0.0067s] [ 2%] 2024-08-20T21:41:28.6456944Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y0_cpu_int8 PASSED [0.0061s] [ 2%] 2024-08-20T21:41:28.6457622Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y0_cpu_uint8 PASSED [0.0063s] [ 3%] 2024-08-20T21:41:28.6458268Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_bool PASSED [0.0062s] [ 3%] 2024-08-20T21:41:28.6458940Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_float32 PASSED [0.0064s] [ 3%] 2024-08-20T21:41:28.6459662Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_float64 PASSED [0.0062s] [ 3%] 2024-08-20T21:41:28.6460325Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_int16 PASSED [0.0063s] [ 3%] 2024-08-20T21:41:28.6460973Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_int32 PASSED [0.0062s] [ 3%] 2024-08-20T21:41:28.6461624Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_int64 PASSED [0.0063s] [ 3%] 2024-08-20T21:41:28.6462281Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_int8 PASSED [0.0062s] [ 3%] 2024-08-20T21:41:28.6462926Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_uint8 PASSED [0.0061s] [ 3%] 2024-08-20T21:41:28.6463665Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_t_cpu_bool PASSED [0.0097s] [ 3%] 2024-08-20T21:41:28.6464403Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_t_cpu_float32 PASSED [0.0096s] [ 4%] 2024-08-20T21:41:28.6465145Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_t_cpu_float64 PASSED [0.0098s] [ 4%] 2024-08-20T21:41:28.6465892Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_t_cpu_int16 PASSED [0.0096s] [ 4%] 2024-08-20T21:41:28.6466655Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_t_cpu_int32 PASSED [0.0098s] [ 4%] 2024-08-20T21:41:28.6467398Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_t_cpu_int64 PASSED [0.0096s] [ 4%] 2024-08-20T21:41:28.6468123Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_t_cpu_int8 PASSED [0.0098s] [ 4%] 2024-08-20T21:41:28.6468849Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_t_cpu_uint8 PASSED [0.0096s] [ 4%] 2024-08-20T21:41:28.6469587Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_u_cpu_bool PASSED [0.0098s] [ 4%] 2024-08-20T21:41:28.6470324Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_u_cpu_float32 PASSED [0.0098s] [ 4%] 2024-08-20T21:41:28.6471081Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_u_cpu_float64 PASSED [0.0096s] [ 4%] 2024-08-20T21:41:28.6471808Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_u_cpu_int16 PASSED [0.0102s] [ 5%] 2024-08-20T21:41:28.6472532Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_u_cpu_int32 PASSED [0.0096s] [ 5%] 2024-08-20T21:41:28.6473270Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_u_cpu_int64 PASSED [0.0098s] [ 5%] 2024-08-20T21:41:28.6474035Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_u_cpu_int8 PASSED [0.0096s] [ 5%] 2024-08-20T21:41:28.6474759Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_u_cpu_uint8 PASSED [0.0097s] [ 5%] 2024-08-20T21:41:28.6475894Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_v_cpu_bool SKIPPED [0.0002s] (Skipping - testing takes an unreasonably long time, #79528) [ 5%] 2024-08-20T21:41:28.6477075Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_v_cpu_float32 SKIPPED [0.0002s] (Skipping - testing takes an unreasonably long time, #79528) [ 5%] 2024-08-20T21:41:28.6478227Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_v_cpu_float64 SKIPPED [0.0001s] (Skipping - testing takes an unreasonably long time, #79528) [ 5%] 2024-08-20T21:41:28.6479311Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_v_cpu_int16 SKIPPED [0.0002s] (Skipping - testing takes an unreasonably long time, #79528) [ 5%] 2024-08-20T21:41:28.6480397Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_v_cpu_int32 SKIPPED [0.0002s] (Skipping - testing takes an unreasonably long time, #79528) [ 5%] 2024-08-20T21:41:28.6481478Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_v_cpu_int64 SKIPPED [0.0001s] (Skipping - testing takes an unreasonably long time, #79528) [ 5%] 2024-08-20T21:41:28.6482556Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_v_cpu_int8 SKIPPED [0.0002s] (Skipping - testing takes an unreasonably long time, #79528) [ 6%] 2024-08-20T21:41:28.6483646Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_v_cpu_uint8 SKIPPED [0.0001s] (Skipping - testing takes an unreasonably long time, #79528) [ 6%] 2024-08-20T21:41:28.6484719Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_w_cpu_bool SKIPPED [0.0001s] (Skipping - testing takes an unreasonably long time, #79528) [ 6%] 2024-08-20T21:41:28.6485853Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_w_cpu_float32 SKIPPED [0.0002s] (Skipping - testing takes an unreasonably long time, #79528) [ 6%] 2024-08-20T21:41:28.6487034Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_w_cpu_float64 SKIPPED [0.0002s] (Skipping - testing takes an unreasonably long time, #79528) [ 6%] 2024-08-20T21:41:28.6488142Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_w_cpu_int16 SKIPPED [0.0003s] (Skipping - testing takes an unreasonably long time, #79528) [ 6%] 2024-08-20T21:41:28.6489221Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_w_cpu_int32 SKIPPED [0.0001s] (Skipping - testing takes an unreasonably long time, #79528) [ 6%] 2024-08-20T21:41:28.6490297Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_w_cpu_int64 SKIPPED [0.0001s] (Skipping - testing takes an unreasonably long time, #79528) [ 6%] 2024-08-20T21:41:28.6491385Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_w_cpu_int8 SKIPPED [0.0001s] (Skipping - testing takes an unreasonably long time, #79528) [ 6%] 2024-08-20T21:41:28.6492471Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_w_cpu_uint8 SKIPPED [0.0002s] (Skipping - testing takes an unreasonably long time, #79528) [ 6%] 2024-08-20T21:41:28.6493131Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_entr_cpu_bfloat16 PASSED [0.0060s] [ 7%] 2024-08-20T21:41:28.6493789Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_entr_cpu_bool PASSED [0.0056s] [ 7%] 2024-08-20T21:41:28.6494475Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_entr_cpu_float16 PASSED [0.0059s] [ 7%] 2024-08-20T21:41:28.6495116Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_entr_cpu_float32 PASSED [0.0056s] [ 7%] 2024-08-20T21:41:28.6495752Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_entr_cpu_float64 PASSED [0.0058s] [ 7%] 2024-08-20T21:41:28.6496390Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_entr_cpu_int16 PASSED [0.0057s] [ 7%] 2024-08-20T21:41:28.6497041Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_entr_cpu_int32 PASSED [0.0058s] [ 7%] 2024-08-20T21:41:28.6497682Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_entr_cpu_int64 PASSED [0.0056s] [ 7%] 2024-08-20T21:41:28.6498305Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_entr_cpu_int8 PASSED [0.0056s] [ 7%] 2024-08-20T21:41:28.6498931Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_entr_cpu_uint8 PASSED [0.0058s] [ 7%] 2024-08-20T21:41:28.6499569Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_erfcx_cpu_bool PASSED [0.0061s] [ 7%] 2024-08-20T21:41:28.6500212Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_erfcx_cpu_float32 PASSED [0.0063s] [ 8%] 2024-08-20T21:41:28.6500852Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_erfcx_cpu_float64 PASSED [0.0061s] [ 8%] 2024-08-20T21:41:28.6501497Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_erfcx_cpu_int16 PASSED [0.0068s] [ 8%] 2024-08-20T21:41:28.6502126Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_erfcx_cpu_int32 PASSED [0.0061s] [ 8%] 2024-08-20T21:41:28.6502765Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_erfcx_cpu_int64 PASSED [0.0064s] [ 8%] 2024-08-20T21:41:28.6503387Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_erfcx_cpu_int8 PASSED [0.0062s] [ 8%] 2024-08-20T21:41:28.6504046Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_erfcx_cpu_uint8 PASSED [0.0063s] [ 8%] 2024-08-20T21:41:28.6504774Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_hermite_polynomial_h_cpu_bool PASSED [0.0095s] [ 8%] 2024-08-20T21:41:28.6505502Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_hermite_polynomial_h_cpu_float32 PASSED [0.0095s] [ 8%] 2024-08-20T21:41:28.6506235Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_hermite_polynomial_h_cpu_float64 PASSED [0.0097s] [ 8%] 2024-08-20T21:41:28.6506954Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_hermite_polynomial_h_cpu_int16 PASSED [0.0095s] [ 9%] 2024-08-20T21:41:28.6507674Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_hermite_polynomial_h_cpu_int32 PASSED [0.0098s] [ 9%] 2024-08-20T21:41:28.6508403Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_hermite_polynomial_h_cpu_int64 PASSED [0.0095s] [ 9%] 2024-08-20T21:41:28.6509108Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_hermite_polynomial_h_cpu_int8 PASSED [0.0098s] [ 9%] 2024-08-20T21:41:28.6509833Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_hermite_polynomial_h_cpu_uint8 PASSED [0.0095s] [ 9%] 2024-08-20T21:41:28.6510547Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_hermite_polynomial_he_cpu_bool PASSED [0.0098s] [ 9%] 2024-08-20T21:41:28.6511299Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_hermite_polynomial_he_cpu_float32 PASSED [0.0095s] [ 9%] 2024-08-20T21:41:28.6512071Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_hermite_polynomial_he_cpu_float64 PASSED [0.0097s] [ 9%] 2024-08-20T21:41:28.6512796Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_hermite_polynomial_he_cpu_int16 PASSED [0.0095s] [ 9%] 2024-08-20T21:41:28.6513529Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_hermite_polynomial_he_cpu_int32 PASSED [0.0095s] [ 9%] 2024-08-20T21:41:28.6514274Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_hermite_polynomial_he_cpu_int64 PASSED [0.0097s] [ 10%] 2024-08-20T21:41:28.6514985Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_hermite_polynomial_he_cpu_int8 PASSED [0.0095s] [ 10%] 2024-08-20T21:41:28.6515714Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_hermite_polynomial_he_cpu_uint8 PASSED [0.0097s] [ 10%] 2024-08-20T21:41:28.6516355Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i0e_cpu_bfloat16 PASSED [0.0057s] [ 10%] 2024-08-20T21:41:28.6516983Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i0e_cpu_bool PASSED [0.0063s] [ 10%] 2024-08-20T21:41:28.6517615Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i0e_cpu_float16 PASSED [0.0057s] [ 10%] 2024-08-20T21:41:28.6518241Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i0e_cpu_float32 PASSED [0.0058s] [ 10%] 2024-08-20T21:41:28.6518883Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i0e_cpu_float64 PASSED [0.0057s] [ 10%] 2024-08-20T21:41:28.6519501Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i0e_cpu_int16 PASSED [0.0056s] [ 10%] 2024-08-20T21:41:28.6520134Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i0e_cpu_int32 PASSED [0.0058s] [ 10%] 2024-08-20T21:41:28.6520752Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i0e_cpu_int64 PASSED [0.0056s] [ 10%] 2024-08-20T21:41:28.6521600Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i0e_cpu_int8 PASSED [0.0059s] [ 11%] 2024-08-20T21:41:28.6522237Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i0e_cpu_uint8 PASSED [0.0056s] [ 11%] 2024-08-20T21:41:28.6522853Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i1_cpu_bool PASSED [0.0059s] [ 11%] 2024-08-20T21:41:28.6523486Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i1_cpu_float32 PASSED [0.0056s] [ 11%] 2024-08-20T21:41:28.6524125Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i1_cpu_float64 PASSED [0.0058s] [ 11%] 2024-08-20T21:41:28.6524737Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i1_cpu_int16 PASSED [0.0057s] [ 11%] 2024-08-20T21:41:28.6525432Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i1_cpu_int32 PASSED [0.0058s] [ 11%] 2024-08-20T21:41:28.6526055Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i1_cpu_int64 PASSED [0.0056s] [ 11%] 2024-08-20T21:41:28.6526659Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i1_cpu_int8 PASSED [0.0056s] [ 11%] 2024-08-20T21:41:28.6527389Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i1_cpu_uint8 PASSED [0.0058s] [ 11%] 2024-08-20T21:41:28.6528008Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i1e_cpu_bool PASSED [0.0056s] [ 12%] 2024-08-20T21:41:28.6528658Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i1e_cpu_float32 PASSED [0.0059s] [ 12%] 2024-08-20T21:41:28.6529345Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i1e_cpu_float64 PASSED [0.0057s] [ 12%] 2024-08-20T21:41:28.6529994Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i1e_cpu_int16 PASSED [0.0058s] [ 12%] 2024-08-20T21:41:28.6530620Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i1e_cpu_int32 PASSED [0.0057s] [ 12%] 2024-08-20T21:41:28.6531235Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i1e_cpu_int64 PASSED [0.0063s] [ 12%] 2024-08-20T21:41:28.6531864Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i1e_cpu_int8 PASSED [0.0056s] [ 12%] 2024-08-20T21:41:28.6532509Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_i1e_cpu_uint8 PASSED [0.0058s] [ 12%] 2024-08-20T21:41:28.6533232Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_laguerre_polynomial_l_cpu_bool PASSED [0.0095s] [ 12%] 2024-08-20T21:41:28.6533979Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_laguerre_polynomial_l_cpu_float32 PASSED [0.0096s] [ 12%] 2024-08-20T21:41:28.6534714Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_laguerre_polynomial_l_cpu_float64 PASSED [0.0098s] [ 12%] 2024-08-20T21:41:28.6535451Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_laguerre_polynomial_l_cpu_int16 PASSED [0.0096s] [ 13%] 2024-08-20T21:41:28.6536167Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_laguerre_polynomial_l_cpu_int32 PASSED [0.0098s] [ 13%] 2024-08-20T21:41:28.6536894Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_laguerre_polynomial_l_cpu_int64 PASSED [0.0096s] [ 13%] 2024-08-20T21:41:28.6544339Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_laguerre_polynomial_l_cpu_int8 PASSED [0.0098s] [ 13%] 2024-08-20T21:41:28.6545181Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_laguerre_polynomial_l_cpu_uint8 PASSED [0.0095s] [ 13%] 2024-08-20T21:41:28.6546290Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_legendre_polynomial_p_cpu_bool SKIPPED [0.0002s] (Skipping - testing takes an unreasonably long time, #79528) [ 13%] 2024-08-20T21:41:28.6547711Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_legendre_polynomial_p_cpu_float32 SKIPPED [0.0004s] (Skipping - testing takes an unreasonably long time, #79528) [ 13%] 2024-08-20T21:41:28.6548825Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_legendre_polynomial_p_cpu_float64 SKIPPED [0.0002s] (Skipping - testing takes an unreasonably long time, #79528) [ 13%] 2024-08-20T21:41:28.6549922Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_legendre_polynomial_p_cpu_int16 SKIPPED [0.0002s] (Skipping - testing takes an unreasonably long time, #79528) [ 13%] 2024-08-20T21:41:28.6551004Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_legendre_polynomial_p_cpu_int32 SKIPPED [0.0001s] (Skipping - testing takes an unreasonably long time, #79528) [ 13%] 2024-08-20T21:41:28.6552094Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_legendre_polynomial_p_cpu_int64 SKIPPED [0.0001s] (Skipping - testing takes an unreasonably long time, #79528) [ 14%] 2024-08-20T21:41:28.6553158Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_legendre_polynomial_p_cpu_int8 SKIPPED [0.0002s] (Skipping - testing takes an unreasonably long time, #79528) [ 14%] 2024-08-20T21:41:28.6554245Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_legendre_polynomial_p_cpu_uint8 SKIPPED [0.0002s] (Skipping - testing takes an unreasonably long time, #79528) [ 14%] 2024-08-20T21:41:28.6554988Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_log_ndtr_cpu_bool PASSED [0.0065s] [ 14%] 2024-08-20T21:41:28.6555692Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_log_ndtr_cpu_float32 PASSED [0.0065s] [ 14%] 2024-08-20T21:41:28.6556368Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_log_ndtr_cpu_float64 PASSED [0.0063s] [ 14%] 2024-08-20T21:41:28.6557017Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_log_ndtr_cpu_int16 PASSED [0.0065s] [ 14%] 2024-08-20T21:41:28.6557679Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_log_ndtr_cpu_int32 PASSED [0.0118s] [ 14%] 2024-08-20T21:41:28.6558404Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_log_ndtr_cpu_int64 PASSED [0.0065s] [ 14%] 2024-08-20T21:41:28.6559051Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_log_ndtr_cpu_int8 PASSED [0.0063s] [ 14%] 2024-08-20T21:41:28.6559713Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_log_ndtr_cpu_uint8 PASSED [0.0065s] [ 15%] 2024-08-20T21:41:28.6560423Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_i0_cpu_bool PASSED [0.0062s] [ 15%] 2024-08-20T21:41:28.6561147Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_i0_cpu_float32 PASSED [0.0069s] [ 15%] 2024-08-20T21:41:28.6561883Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_i0_cpu_float64 PASSED [0.0063s] [ 15%] 2024-08-20T21:41:28.6562593Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_i0_cpu_int16 PASSED [0.0061s] [ 15%] 2024-08-20T21:41:28.6563311Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_i0_cpu_int32 PASSED [0.0063s] [ 15%] 2024-08-20T21:41:28.6564012Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_i0_cpu_int64 PASSED [0.0062s] [ 15%] 2024-08-20T21:41:28.6564709Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_i0_cpu_int8 PASSED [0.0064s] [ 15%] 2024-08-20T21:41:28.6565475Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_i0_cpu_uint8 PASSED [0.0062s] [ 15%] 2024-08-20T21:41:28.6566176Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_i1_cpu_bool PASSED [0.0064s] [ 15%] 2024-08-20T21:41:28.6566995Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_i1_cpu_float32 PASSED [0.0063s] [ 15%] 2024-08-20T21:41:28.6567715Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_i1_cpu_float64 PASSED [0.0065s] [ 16%] 2024-08-20T21:41:28.6568415Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_i1_cpu_int16 PASSED [0.0062s] [ 16%] 2024-08-20T21:41:28.6569133Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_i1_cpu_int32 PASSED [0.0066s] [ 16%] 2024-08-20T21:41:28.6569833Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_i1_cpu_int64 PASSED [0.0063s] [ 16%] 2024-08-20T21:41:28.6570547Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_i1_cpu_int8 PASSED [0.0062s] [ 16%] 2024-08-20T21:41:28.6571246Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_i1_cpu_uint8 PASSED [0.0064s] [ 16%] 2024-08-20T21:41:28.6571945Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_k0_cpu_bool PASSED [0.0062s] [ 16%] 2024-08-20T21:41:28.6572703Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_k0_cpu_float32 PASSED [0.0064s] [ 16%] 2024-08-20T21:41:28.6573415Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_k0_cpu_float64 PASSED [0.0062s] [ 16%] 2024-08-20T21:41:28.6574160Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_k0_cpu_int16 PASSED [0.0064s] [ 16%] 2024-08-20T21:41:28.6574863Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_k0_cpu_int32 PASSED [0.0062s] [ 17%] 2024-08-20T21:41:28.6575556Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_k0_cpu_int64 PASSED [0.0064s] [ 17%] 2024-08-20T21:41:28.6576289Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_k0_cpu_int8 PASSED [0.0062s] [ 17%] 2024-08-20T21:41:28.6576992Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_k0_cpu_uint8 PASSED [0.0068s] [ 17%] 2024-08-20T21:41:28.6577701Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_k1_cpu_bool PASSED [0.0062s] [ 17%] 2024-08-20T21:41:28.6578414Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_k1_cpu_float32 PASSED [0.0062s] [ 17%] 2024-08-20T21:41:28.6579133Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_k1_cpu_float64 PASSED [0.0064s] [ 17%] 2024-08-20T21:41:28.6579844Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_k1_cpu_int16 PASSED [0.0061s] [ 17%] 2024-08-20T21:41:28.6580542Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_k1_cpu_int32 PASSED [0.0064s] [ 17%] 2024-08-20T21:41:28.6581253Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_k1_cpu_int64 PASSED [0.0062s] [ 17%] 2024-08-20T21:41:28.6581943Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_k1_cpu_int8 PASSED [0.0064s] [ 17%] 2024-08-20T21:41:28.6582641Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_modified_bessel_k1_cpu_uint8 PASSED [0.0062s] [ 18%] 2024-08-20T21:41:28.6583326Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_ndtr_cpu_bfloat16 PASSED [0.0064s] [ 18%] 2024-08-20T21:41:28.6583947Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_ndtr_cpu_bool PASSED [0.0062s] [ 18%] 2024-08-20T21:41:28.6584597Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_ndtr_cpu_float16 PASSED [0.0063s] [ 18%] 2024-08-20T21:41:28.6585237Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_ndtr_cpu_float32 PASSED [0.0062s] [ 18%] 2024-08-20T21:41:28.6585869Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_ndtr_cpu_float64 PASSED [0.0062s] [ 18%] 2024-08-20T21:41:28.6586507Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_ndtr_cpu_int16 PASSED [0.0064s] [ 18%] 2024-08-20T21:41:28.6587132Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_ndtr_cpu_int32 PASSED [0.0062s] [ 18%] 2024-08-20T21:41:28.6587772Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_ndtr_cpu_int64 PASSED [0.0063s] [ 18%] 2024-08-20T21:41:28.6588389Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_ndtr_cpu_int8 PASSED [0.0062s] [ 18%] 2024-08-20T21:41:28.6589010Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_ndtr_cpu_uint8 PASSED [0.0064s] [ 19%] 2024-08-20T21:41:28.6589646Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_ndtri_cpu_bool PASSED [0.0062s] [ 19%] 2024-08-20T21:41:28.6590318Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_ndtri_cpu_float32 PASSED [0.0064s] [ 19%] 2024-08-20T21:41:28.6590977Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_ndtri_cpu_float64 PASSED [0.0062s] [ 19%] 2024-08-20T21:41:28.6591637Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_ndtri_cpu_int16 PASSED [0.0068s] [ 19%] 2024-08-20T21:41:28.6592272Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_ndtri_cpu_int32 PASSED [0.0061s] [ 19%] 2024-08-20T21:41:28.6592909Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_ndtri_cpu_int64 PASSED [0.0061s] [ 19%] 2024-08-20T21:41:28.6593560Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_ndtri_cpu_int8 PASSED [0.0064s] [ 19%] 2024-08-20T21:41:28.6594190Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_ndtri_cpu_uint8 PASSED [0.0062s] [ 19%] 2024-08-20T21:41:28.6595000Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_polygamma_special_polygamma_n_0_cpu_bfloat16 PASSED [0.0101s] [ 19%] 2024-08-20T21:41:28.6595783Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_polygamma_special_polygamma_n_0_cpu_bool PASSED [0.0098s] [ 20%] 2024-08-20T21:41:28.6596588Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_polygamma_special_polygamma_n_0_cpu_float16 PASSED [0.0101s] [ 20%] 2024-08-20T21:41:28.6597373Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_polygamma_special_polygamma_n_0_cpu_float32 PASSED [0.0099s] [ 20%] 2024-08-20T21:41:28.6598155Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_polygamma_special_polygamma_n_0_cpu_float64 PASSED [0.0101s] [ 20%] 2024-08-20T21:41:28.6598940Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_polygamma_special_polygamma_n_0_cpu_int16 PASSED [0.0098s] [ 20%] 2024-08-20T21:41:28.6599708Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_polygamma_special_polygamma_n_0_cpu_int32 PASSED [0.0100s] [ 20%] 2024-08-20T21:41:28.6600506Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_polygamma_special_polygamma_n_0_cpu_int64 PASSED [0.0099s] [ 20%] 2024-08-20T21:41:28.6601307Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_polygamma_special_polygamma_n_0_cpu_int8 PASSED [0.0097s] [ 20%] 2024-08-20T21:41:28.6602085Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_polygamma_special_polygamma_n_0_cpu_uint8 PASSED [0.0099s] [ 20%] 2024-08-20T21:41:28.6602841Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_scaled_modified_bessel_k0_cpu_bool PASSED [0.0061s] [ 20%] 2024-08-20T21:41:28.6603591Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_scaled_modified_bessel_k0_cpu_float32 PASSED [0.0064s] [ 20%] 2024-08-20T21:41:28.6604355Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_scaled_modified_bessel_k0_cpu_float64 PASSED [0.0062s] [ 21%] 2024-08-20T21:41:28.6605098Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_scaled_modified_bessel_k0_cpu_int16 PASSED [0.0064s] [ 21%] 2024-08-20T21:41:28.6605847Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_scaled_modified_bessel_k0_cpu_int32 PASSED [0.0061s] [ 21%] 2024-08-20T21:41:28.6606589Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_scaled_modified_bessel_k0_cpu_int64 PASSED [0.0064s] [ 21%] 2024-08-20T21:41:28.6607414Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_scaled_modified_bessel_k0_cpu_int8 PASSED [0.0062s] [ 21%] 2024-08-20T21:41:28.6608278Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_scaled_modified_bessel_k0_cpu_uint8 PASSED [0.0067s] [ 21%] 2024-08-20T21:41:28.6609063Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_scaled_modified_bessel_k1_cpu_bool PASSED [0.0062s] [ 21%] 2024-08-20T21:41:28.6609856Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_scaled_modified_bessel_k1_cpu_float32 PASSED [0.0062s] [ 21%] 2024-08-20T21:41:28.6610616Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_scaled_modified_bessel_k1_cpu_float64 PASSED [0.0063s] [ 21%] 2024-08-20T21:41:28.6611350Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_scaled_modified_bessel_k1_cpu_int16 PASSED [0.0061s] [ 21%] 2024-08-20T21:41:28.6612132Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_scaled_modified_bessel_k1_cpu_int32 PASSED [0.0064s] [ 22%] 2024-08-20T21:41:28.6612876Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_scaled_modified_bessel_k1_cpu_int64 PASSED [0.0061s] [ 22%] 2024-08-20T21:41:28.6613622Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_scaled_modified_bessel_k1_cpu_int8 PASSED [0.0063s] [ 22%] 2024-08-20T21:41:28.6614363Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_scaled_modified_bessel_k1_cpu_uint8 PASSED [0.0062s] [ 22%] 2024-08-20T21:41:28.6615491Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_t_cpu_bool SKIPPED [0.0002s] (Skipping - testing takes an unreasonably long time, #79528) [ 22%] 2024-08-20T21:41:28.6616640Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_t_cpu_float32 SKIPPED [0.0004s] (Skipping - testing takes an unreasonably long time, #79528) [ 22%] 2024-08-20T21:41:28.6617779Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_t_cpu_float64 SKIPPED [0.0001s] (Skipping - testing takes an unreasonably long time, #79528) [ 22%] 2024-08-20T21:41:28.6618912Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_t_cpu_int16 SKIPPED [0.0002s] (Skipping - testing takes an unreasonably long time, #79528) [ 22%] 2024-08-20T21:41:28.6620085Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_t_cpu_int32 SKIPPED [0.0001s] (Skipping - testing takes an unreasonably long time, #79528) [ 22%] 2024-08-20T21:41:28.6621420Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_t_cpu_int64 SKIPPED [0.0002s] (Skipping - testing takes an unreasonably long time, #79528) [ 22%] 2024-08-20T21:41:28.6622566Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_t_cpu_int8 SKIPPED [0.0002s] (Skipping - testing takes an unreasonably long time, #79528) [ 23%] 2024-08-20T21:41:28.6623690Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_t_cpu_uint8 SKIPPED [0.0002s] (Skipping - testing takes an unreasonably long time, #79528) [ 23%] 2024-08-20T21:41:28.6624821Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_u_cpu_bool SKIPPED [0.0003s] (Skipping - testing takes an unreasonably long time, #79528) [ 23%] 2024-08-20T21:41:28.6626035Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_u_cpu_float32 SKIPPED [0.0002s] (Skipping - testing takes an unreasonably long time, #79528) [ 23%] 2024-08-20T21:41:28.6627195Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_u_cpu_float64 SKIPPED [0.0001s] (Skipping - testing takes an unreasonably long time, #79528) [ 23%] 2024-08-20T21:41:28.6628367Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_u_cpu_int16 SKIPPED [0.0001s] (Skipping - testing takes an unreasonably long time, #79528) [ 23%] 2024-08-20T21:41:28.6629536Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_u_cpu_int32 SKIPPED [0.0002s] (Skipping - testing takes an unreasonably long time, #79528) [ 23%] 2024-08-20T21:41:28.6630664Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_u_cpu_int64 SKIPPED [0.0001s] (Skipping - testing takes an unreasonably long time, #79528) [ 23%] 2024-08-20T21:41:28.6631807Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_u_cpu_int8 SKIPPED [0.0002s] (Skipping - testing takes an unreasonably long time, #79528) [ 23%] 2024-08-20T21:41:28.6632941Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_u_cpu_uint8 SKIPPED [0.0003s] (Skipping - testing takes an unreasonably long time, #79528) [ 23%] 2024-08-20T21:41:28.6634062Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_v_cpu_bool SKIPPED [0.0002s] (Skipping - testing takes an unreasonably long time, #79528) [ 23%] 2024-08-20T21:41:28.6635214Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_v_cpu_float32 SKIPPED [0.0002s] (Skipping - testing takes an unreasonably long time, #79528) [ 24%] 2024-08-20T21:41:28.6636347Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_v_cpu_float64 SKIPPED [0.0001s] (Skipping - testing takes an unreasonably long time, #79528) [ 24%] 2024-08-20T21:41:28.6637482Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_v_cpu_int16 SKIPPED [0.0001s] (Skipping - testing takes an unreasonably long time, #79528) [ 24%] 2024-08-20T21:41:28.6638605Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_v_cpu_int32 SKIPPED [0.0001s] (Skipping - testing takes an unreasonably long time, #79528) [ 24%] 2024-08-20T21:41:28.6639772Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_v_cpu_int64 SKIPPED [0.0002s] (Skipping - testing takes an unreasonably long time, #79528) [ 24%] 2024-08-20T21:41:28.6640904Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_v_cpu_int8 SKIPPED [0.0003s] (Skipping - testing takes an unreasonably long time, #79528) [ 24%] 2024-08-20T21:41:28.6642025Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_v_cpu_uint8 SKIPPED [0.0001s] (Skipping - testing takes an unreasonably long time, #79528) [ 24%] 2024-08-20T21:41:28.6643167Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_w_cpu_bool SKIPPED [0.0002s] (Skipping - testing takes an unreasonably long time, #79528) [ 24%] 2024-08-20T21:41:28.6644309Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_w_cpu_float32 SKIPPED [0.0002s] (Skipping - testing takes an unreasonably long time, #79528) [ 24%] 2024-08-20T21:41:28.6645453Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_w_cpu_float64 SKIPPED [0.0002s] (Skipping - testing takes an unreasonably long time, #79528) [ 24%] 2024-08-20T21:41:28.6646575Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_w_cpu_int16 SKIPPED [0.0001s] (Skipping - testing takes an unreasonably long time, #79528) [ 25%] 2024-08-20T21:41:28.6648022Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_w_cpu_int32 SKIPPED [0.0002s] (Skipping - testing takes an unreasonably long time, #79528) [ 25%] 2024-08-20T21:41:28.6649202Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_w_cpu_int64 SKIPPED [0.0003s] (Skipping - testing takes an unreasonably long time, #79528) [ 25%] 2024-08-20T21:41:28.6650322Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_w_cpu_int8 SKIPPED [0.0001s] (Skipping - testing takes an unreasonably long time, #79528) [ 25%] 2024-08-20T21:41:28.6651492Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_w_cpu_uint8 SKIPPED [0.0001s] (Skipping - testing takes an unreasonably long time, #79528) [ 25%] 2024-08-20T21:41:28.6652205Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_bool PASSED [0.0065s] [ 25%] 2024-08-20T21:41:28.6652946Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_float32 PASSED [0.0064s] [ 25%] 2024-08-20T21:41:28.6653670Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_float64 PASSED [0.0063s] [ 25%] 2024-08-20T21:41:28.6654382Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_int16 PASSED [0.0062s] [ 25%] 2024-08-20T21:41:28.6655103Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_int32 PASSED [0.0067s] [ 25%] 2024-08-20T21:41:28.6655816Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_int64 PASSED [0.0062s] [ 25%] 2024-08-20T21:41:28.6656534Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_int8 PASSED [0.0064s] [ 26%] 2024-08-20T21:41:28.6657252Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_uint8 PASSED [0.0062s] [ 26%] 2024-08-20T21:41:28.6657918Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_bfloat16 PASSED [0.0099s] [ 26%] 2024-08-20T21:41:28.6658623Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_bool PASSED [0.0096s] [ 26%] 2024-08-20T21:41:28.6659275Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_float16 PASSED [0.0098s] [ 26%] 2024-08-20T21:41:28.6659939Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_float32 PASSED [0.0096s] [ 26%] 2024-08-20T21:41:28.6660586Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_float64 PASSED [0.0098s] [ 26%] 2024-08-20T21:41:28.6661223Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_int16 PASSED [0.0097s] [ 26%] 2024-08-20T21:41:28.6661886Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_int32 PASSED [0.0096s] [ 26%] 2024-08-20T21:41:28.6662530Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_int64 PASSED [0.0098s] [ 26%] 2024-08-20T21:41:28.6663181Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_int8 PASSED [0.0095s] [ 27%] 2024-08-20T21:41:28.6663819Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_uint8 PASSED [0.0099s] [ 27%] 2024-08-20T21:41:28.6664434Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_bool PASSED [0.0097s] [ 27%] 2024-08-20T21:41:28.6665090Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_float32 PASSED [0.0100s] [ 27%] 2024-08-20T21:41:28.6665755Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_float64 PASSED [0.0097s] [ 27%] 2024-08-20T21:41:28.6666389Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_int16 PASSED [0.0099s] [ 27%] 2024-08-20T21:41:28.6667053Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_int32 PASSED [0.0096s] [ 27%] 2024-08-20T21:41:28.6667680Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_int64 PASSED [0.0096s] [ 27%] 2024-08-20T21:41:28.6668315Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_int8 PASSED [0.0099s] [ 27%] 2024-08-20T21:41:28.6668968Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_uint8 PASSED [0.0097s] [ 27%] 2024-08-20T21:41:28.6669574Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_bfloat16 PASSED [0.0064s] [ 28%] 2024-08-20T21:41:28.6670172Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_bool PASSED [0.0057s] [ 28%] 2024-08-20T21:41:28.6670786Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_complex128 PASSED [0.0060s] [ 28%] 2024-08-20T21:41:28.6671412Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_complex32 PASSED [0.0059s] [ 28%] 2024-08-20T21:41:28.6672026Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_complex64 PASSED [0.0059s] [ 28%] 2024-08-20T21:41:28.6672621Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_float16 PASSED [0.0057s] [ 28%] 2024-08-20T21:41:28.6673231Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_float32 PASSED [0.0057s] [ 28%] 2024-08-20T21:41:28.6673819Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_float64 PASSED [0.0058s] [ 28%] 2024-08-20T21:41:28.6674424Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_int16 PASSED [0.0057s] [ 28%] 2024-08-20T21:41:28.6675010Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_int32 PASSED [0.0059s] [ 28%] 2024-08-20T21:41:28.6675593Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_int64 PASSED [0.0057s] [ 28%] 2024-08-20T21:41:28.6676220Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_int8 PASSED [0.0059s] [ 29%] 2024-08-20T21:41:28.6676803Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_uint8 PASSED [0.0108s] [ 29%] 2024-08-20T21:41:28.6677468Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_bfloat16 PASSED [0.0067s] [ 29%] 2024-08-20T21:41:28.6678125Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_bool PASSED [0.0062s] [ 29%] 2024-08-20T21:41:28.6678800Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_complex128 PASSED [0.0065s] [ 29%] 2024-08-20T21:41:28.6679484Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_complex64 PASSED [0.0063s] [ 29%] 2024-08-20T21:41:28.6680138Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_float16 PASSED [0.0062s] [ 29%] 2024-08-20T21:41:28.6680791Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_float32 PASSED [0.0063s] [ 29%] 2024-08-20T21:41:28.6681451Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_float64 PASSED [0.0063s] [ 29%] 2024-08-20T21:41:28.6682095Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_int16 PASSED [0.0064s] [ 29%] 2024-08-20T21:41:28.6682753Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_int32 PASSED [0.0062s] [ 30%] 2024-08-20T21:41:28.6683418Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_int64 PASSED [0.0069s] [ 30%] 2024-08-20T21:41:28.6684079Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_int8 PASSED [0.0061s] [ 30%] 2024-08-20T21:41:28.6684742Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_uint8 PASSED [0.0064s] [ 30%] 2024-08-20T21:41:28.6685434Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_bfloat16 PASSED [0.0067s] [ 30%] 2024-08-20T21:41:28.6686115Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_bool PASSED [0.0069s] [ 30%] 2024-08-20T21:41:28.6686962Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_complex128 PASSED [0.0068s] [ 30%] 2024-08-20T21:41:28.6687675Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_complex32 PASSED [0.0068s] [ 30%] 2024-08-20T21:41:28.6688377Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_complex64 PASSED [0.0069s] [ 30%] 2024-08-20T21:41:28.6689063Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_float16 PASSED [0.0067s] [ 30%] 2024-08-20T21:41:28.6689758Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_float32 PASSED [0.0069s] [ 30%] 2024-08-20T21:41:28.6690439Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_float64 PASSED [0.0068s] [ 31%] 2024-08-20T21:41:28.6691107Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_int16 PASSED [0.0071s] [ 31%] 2024-08-20T21:41:28.6691793Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_int32 PASSED [0.0068s] [ 31%] 2024-08-20T21:41:28.6692465Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_int64 PASSED [0.0070s] [ 31%] 2024-08-20T21:41:28.6693148Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_int8 PASSED [0.0068s] [ 31%] 2024-08-20T21:41:28.6693844Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_uint8 PASSED [0.0070s] [ 31%] 2024-08-20T21:41:28.6694508Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_bfloat16 PASSED [0.0068s] [ 31%] 2024-08-20T21:41:28.6695165Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_bool PASSED [0.0068s] [ 31%] 2024-08-20T21:41:28.6695833Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_complex128 PASSED [0.0071s] [ 31%] 2024-08-20T21:41:28.6696505Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_complex32 PASSED [0.0069s] [ 31%] 2024-08-20T21:41:28.6697181Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_complex64 PASSED [0.0071s] [ 32%] 2024-08-20T21:41:28.6697840Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_float16 PASSED [0.0068s] [ 32%] 2024-08-20T21:41:28.6698512Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_float32 PASSED [0.0074s] [ 32%] 2024-08-20T21:41:28.6699165Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_float64 PASSED [0.0068s] [ 32%] 2024-08-20T21:41:28.6699813Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_int16 PASSED [0.0070s] [ 32%] 2024-08-20T21:41:28.6700465Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_int32 PASSED [0.0068s] [ 32%] 2024-08-20T21:41:28.6701138Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_int64 PASSED [0.0067s] [ 32%] 2024-08-20T21:41:28.6701817Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_int8 PASSED [0.0069s] [ 32%] 2024-08-20T21:41:28.6702457Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_uint8 PASSED [0.0068s] [ 32%] 2024-08-20T21:41:28.6703051Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_bfloat16 PASSED [0.0054s] [ 32%] 2024-08-20T21:41:28.6703638Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_bool PASSED [0.0052s] [ 33%] 2024-08-20T21:41:28.6704274Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_complex128 PASSED [0.0054s] [ 33%] 2024-08-20T21:41:28.6704884Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_complex64 PASSED [0.0052s] [ 33%] 2024-08-20T21:41:28.6705473Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_float16 PASSED [0.0054s] [ 33%] 2024-08-20T21:41:28.6706061Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_float32 PASSED [0.0052s] [ 33%] 2024-08-20T21:41:28.6706659Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_float64 PASSED [0.0053s] [ 33%] 2024-08-20T21:41:28.6707234Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_int16 PASSED [0.0052s] [ 33%] 2024-08-20T21:41:28.6707821Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_int32 PASSED [0.0051s] [ 33%] 2024-08-20T21:41:28.6708399Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_int64 PASSED [0.0053s] [ 33%] 2024-08-20T21:41:28.6709081Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_int8 PASSED [0.0052s] [ 33%] 2024-08-20T21:41:28.6709679Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_uint8 PASSED [0.0054s] [ 33%] 2024-08-20T21:41:28.6710293Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_bfloat16 PASSED [0.0062s] [ 34%] 2024-08-20T21:41:28.6710919Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_bool PASSED [0.0064s] [ 34%] 2024-08-20T21:41:28.6711549Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_complex128 PASSED [0.0062s] [ 34%] 2024-08-20T21:41:28.6712164Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_complex64 PASSED [0.0068s] [ 34%] 2024-08-20T21:41:28.6712780Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_float16 PASSED [0.0062s] [ 34%] 2024-08-20T21:41:28.6713381Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_float32 PASSED [0.0064s] [ 34%] 2024-08-20T21:41:28.6713979Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_float64 PASSED [0.0062s] [ 34%] 2024-08-20T21:41:28.6714577Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_int16 PASSED [0.0061s] [ 34%] 2024-08-20T21:41:28.6715157Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_int32 PASSED [0.0063s] [ 34%] 2024-08-20T21:41:28.6715758Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_int64 PASSED [0.0062s] [ 34%] 2024-08-20T21:41:28.6716342Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_int8 PASSED [0.0064s] [ 35%] 2024-08-20T21:41:28.6716930Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_uint8 PASSED [0.0062s] [ 35%] 2024-08-20T21:41:28.6717557Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_bfloat16 PASSED [0.0091s] [ 35%] 2024-08-20T21:41:28.6718170Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_bool PASSED [0.0087s] [ 35%] 2024-08-20T21:41:28.6718847Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_complex128 PASSED [0.0092s] [ 35%] 2024-08-20T21:41:28.6719475Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_complex32 PASSED [0.0088s] [ 35%] 2024-08-20T21:41:28.6720093Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_complex64 PASSED [0.0090s] [ 35%] 2024-08-20T21:41:28.6720705Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_float16 PASSED [0.0087s] [ 35%] 2024-08-20T21:41:28.6721539Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_float32 PASSED [0.0087s] [ 35%] 2024-08-20T21:41:28.6722146Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_float64 PASSED [0.0090s] [ 35%] 2024-08-20T21:41:28.6722756Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_int16 PASSED [0.0088s] [ 35%] 2024-08-20T21:41:28.6723360Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_int32 PASSED [0.0090s] [ 36%] 2024-08-20T21:41:28.6723965Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_int64 PASSED [0.0088s] [ 36%] 2024-08-20T21:41:28.6724555Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_int8 PASSED [0.0090s] [ 36%] 2024-08-20T21:41:28.6725143Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_uint8 PASSED [0.0087s] [ 36%] 2024-08-20T21:41:28.6725823Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_bfloat16 PASSED [0.0084s] [ 36%] 2024-08-20T21:41:28.6726470Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_bool PASSED [0.0077s] [ 36%] 2024-08-20T21:41:28.6727229Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_complex128 PASSED [0.0080s] [ 36%] 2024-08-20T21:41:28.6727910Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_complex32 PASSED [0.0078s] [ 36%] 2024-08-20T21:41:28.6728618Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_complex64 PASSED [0.0078s] [ 36%] 2024-08-20T21:41:28.6729290Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_float16 PASSED [0.0079s] [ 36%] 2024-08-20T21:41:28.6729950Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_float32 PASSED [0.0078s] [ 37%] 2024-08-20T21:41:28.6730625Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_float64 PASSED [0.0079s] [ 37%] 2024-08-20T21:41:28.6731273Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_int16 PASSED [0.0078s] [ 37%] 2024-08-20T21:41:28.6731920Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_int32 PASSED [0.0080s] [ 37%] 2024-08-20T21:41:28.6732579Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_int64 PASSED [0.0078s] [ 37%] 2024-08-20T21:41:28.6733226Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_int8 PASSED [0.0080s] [ 37%] 2024-08-20T21:41:28.6733886Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_uint8 PASSED [0.0078s] [ 37%] 2024-08-20T21:41:28.6734490Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_bfloat16 PASSED [0.0093s] [ 37%] 2024-08-20T21:41:28.6735065Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_bool PASSED [0.0091s] [ 37%] 2024-08-20T21:41:28.6735717Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_complex128 PASSED [0.0092s] [ 37%] 2024-08-20T21:41:28.6736328Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_complex32 PASSED [0.0094s] [ 38%] 2024-08-20T21:41:28.6736956Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_complex64 PASSED [0.0091s] [ 38%] 2024-08-20T21:41:28.6737562Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_float16 PASSED [0.0094s] [ 38%] 2024-08-20T21:41:28.6738150Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_float32 PASSED [0.0091s] [ 38%] 2024-08-20T21:41:28.6738784Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_float64 PASSED [0.0092s] [ 38%] 2024-08-20T21:41:28.6739369Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_int16 PASSED [0.0091s] [ 38%] 2024-08-20T21:41:28.6739953Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_int32 PASSED [0.0096s] [ 38%] 2024-08-20T21:41:28.6740542Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_int64 PASSED [0.0091s] [ 38%] 2024-08-20T21:41:28.6741122Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_int8 PASSED [0.0090s] [ 38%] 2024-08-20T21:41:28.6741716Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_uint8 PASSED [0.0092s] [ 38%] 2024-08-20T21:41:28.6742303Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_bfloat16 PASSED [0.0104s] [ 38%] 2024-08-20T21:41:28.6742902Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_complex128 PASSED [0.0107s] [ 39%] 2024-08-20T21:41:28.6743510Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_complex64 PASSED [0.0105s] [ 39%] 2024-08-20T21:41:28.6744093Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_float16 PASSED [0.0106s] [ 39%] 2024-08-20T21:41:28.6744672Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_float32 PASSED [0.0104s] [ 39%] 2024-08-20T21:41:28.6745266Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_float64 PASSED [0.0106s] [ 39%] 2024-08-20T21:41:28.6745911Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_bfloat16 PASSED [0.0104s] [ 39%] 2024-08-20T21:41:28.6746550Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_complex128 PASSED [0.0106s] [ 39%] 2024-08-20T21:41:28.6747339Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_complex64 PASSED [0.0105s] [ 39%] 2024-08-20T21:41:28.6747951Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_float16 PASSED [0.0103s] [ 39%] 2024-08-20T21:41:28.6748573Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_float32 PASSED [0.0104s] [ 39%] 2024-08-20T21:41:28.6749184Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_float64 PASSED [0.0103s] [ 40%] 2024-08-20T21:41:28.6749872Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_bfloat16 PASSED [0.0059s] [ 40%] 2024-08-20T21:41:28.6750558Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_complex128 PASSED [0.0057s] [ 40%] 2024-08-20T21:41:28.6751238Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_complex64 PASSED [0.0058s] [ 40%] 2024-08-20T21:41:28.6751917Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_float16 PASSED [0.0084s] [ 40%] 2024-08-20T21:41:28.6752575Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_float32 PASSED [0.0088s] [ 40%] 2024-08-20T21:41:28.6753312Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_float64 PASSED [0.0057s] [ 40%] 2024-08-20T21:41:28.6754046Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_bfloat16 PASSED [0.0063s] [ 40%] 2024-08-20T21:41:28.6754705Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_complex128 PASSED [0.0057s] [ 40%] 2024-08-20T21:41:28.6755369Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_complex64 PASSED [0.0057s] [ 40%] 2024-08-20T21:41:28.6756005Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_float16 PASSED [0.0059s] [ 41%] 2024-08-20T21:41:28.6756684Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_float32 PASSED [0.0056s] [ 41%] 2024-08-20T21:41:28.6757324Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_float64 PASSED [0.0059s] [ 41%] 2024-08-20T21:41:28.6757936Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stft_cpu_complex128 PASSED [0.0097s] [ 41%] 2024-08-20T21:41:28.6758546Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stft_cpu_complex64 PASSED [0.0099s] [ 41%] 2024-08-20T21:41:28.6759138Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stft_cpu_float32 PASSED [0.0096s] [ 41%] 2024-08-20T21:41:28.6759719Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stft_cpu_float64 PASSED [0.0097s] [ 41%] 2024-08-20T21:41:28.6760314Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_bfloat16 PASSED [0.0106s] [ 41%] 2024-08-20T21:41:28.6760916Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_complex128 PASSED [0.0108s] [ 41%] 2024-08-20T21:41:28.6761527Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_complex32 PASSED [0.0110s] [ 41%] 2024-08-20T21:41:28.6762118Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_complex64 PASSED [0.0108s] [ 41%] 2024-08-20T21:41:28.6762702Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_float16 PASSED [0.0109s] [ 42%] 2024-08-20T21:41:28.6763343Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_float32 PASSED [0.0106s] [ 42%] 2024-08-20T21:41:28.6763921Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_float64 PASSED [0.0108s] [ 42%] 2024-08-20T21:41:28.6764501Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_int16 PASSED [0.0106s] [ 42%] 2024-08-20T21:41:28.6765076Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_int32 PASSED [0.0109s] [ 42%] 2024-08-20T21:41:28.6765643Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_int64 PASSED [0.0107s] [ 42%] 2024-08-20T21:41:28.6766219Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_int8 PASSED [0.0106s] [ 42%] 2024-08-20T21:41:28.6766850Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_uint8 PASSED [0.0113s] [ 42%] 2024-08-20T21:41:28.6767463Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_bfloat16 PASSED [0.0147s] [ 42%] 2024-08-20T21:41:28.6768030Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_bool PASSED [0.0149s] [ 42%] 2024-08-20T21:41:28.6768629Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_complex128 PASSED [0.0146s] [ 43%] 2024-08-20T21:41:28.6769241Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_complex64 PASSED [0.0149s] [ 43%] 2024-08-20T21:41:28.6769820Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_float16 PASSED [0.0149s] [ 43%] 2024-08-20T21:41:28.6770437Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_float32 PASSED [0.0150s] [ 43%] 2024-08-20T21:41:28.6771058Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_float64 PASSED [0.0148s] [ 43%] 2024-08-20T21:41:28.6771633Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_int16 PASSED [0.0147s] [ 43%] 2024-08-20T21:41:28.6772214Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_int32 PASSED [0.0149s] [ 43%] 2024-08-20T21:41:28.6772782Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_int64 PASSED [0.0147s] [ 43%] 2024-08-20T21:41:28.6773371Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_int8 PASSED [0.0149s] [ 43%] 2024-08-20T21:41:28.6773953Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_uint8 PASSED [0.0146s] [ 43%] 2024-08-20T21:41:28.6774589Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_bfloat16 PASSED [0.0132s] [ 43%] 2024-08-20T21:41:28.6775212Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_bool PASSED [0.0130s] [ 44%] 2024-08-20T21:41:28.6775859Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_complex128 PASSED [0.0134s] [ 44%] 2024-08-20T21:41:28.6776500Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_complex64 PASSED [0.0131s] [ 44%] 2024-08-20T21:41:28.6777141Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_float16 PASSED [0.0129s] [ 44%] 2024-08-20T21:41:28.6777769Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_float32 PASSED [0.0132s] [ 44%] 2024-08-20T21:41:28.6778402Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_float64 PASSED [0.0130s] [ 44%] 2024-08-20T21:41:28.6779016Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_int16 PASSED [0.0133s] [ 44%] 2024-08-20T21:41:28.6779632Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_int32 PASSED [0.0130s] [ 44%] 2024-08-20T21:41:28.6780291Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_int64 PASSED [0.0137s] [ 44%] 2024-08-20T21:41:28.6780897Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_int8 PASSED [0.0131s] [ 44%] 2024-08-20T21:41:28.6781513Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_uint8 PASSED [0.0132s] [ 45%] 2024-08-20T21:41:28.6782130Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_cpu_complex128 PASSED [0.1669s] [ 45%] 2024-08-20T21:41:28.6782732Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_cpu_complex64 PASSED [0.1673s] [ 45%] 2024-08-20T21:41:28.6783334Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_cpu_float32 PASSED [0.1677s] [ 45%] 2024-08-20T21:41:28.6783917Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_cpu_float64 PASSED [0.1596s] [ 45%] 2024-08-20T21:41:28.6784569Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_lowrank_cpu_complex128 PASSED [0.0149s] [ 45%] 2024-08-20T21:41:28.6785227Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_lowrank_cpu_complex64 PASSED [0.0145s] [ 45%] 2024-08-20T21:41:28.6785858Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_lowrank_cpu_float32 PASSED [0.0146s] [ 45%] 2024-08-20T21:41:28.6786503Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_lowrank_cpu_float64 PASSED [0.0147s] [ 45%] 2024-08-20T21:41:28.6787108Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_bfloat16 PASSED [0.0065s] [ 45%] 2024-08-20T21:41:28.6787722Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_bool PASSED [0.0066s] [ 46%] 2024-08-20T21:41:28.6788386Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_complex128 PASSED [0.0064s] [ 46%] 2024-08-20T21:41:28.6789004Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_complex64 PASSED [0.0065s] [ 46%] 2024-08-20T21:41:28.6789619Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_float16 PASSED [0.0063s] [ 46%] 2024-08-20T21:41:28.6790214Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_float32 PASSED [0.0065s] [ 46%] 2024-08-20T21:41:28.6790842Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_float64 PASSED [0.0063s] [ 46%] 2024-08-20T21:41:28.6791448Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_int16 PASSED [0.0064s] [ 46%] 2024-08-20T21:41:28.6792034Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_int32 PASSED [0.0062s] [ 46%] 2024-08-20T21:41:28.6792619Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_int64 PASSED [0.0062s] [ 46%] 2024-08-20T21:41:28.6793219Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_int8 PASSED [0.0068s] [ 46%] 2024-08-20T21:41:28.6793813Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_uint8 PASSED [0.0062s] [ 46%] 2024-08-20T21:41:28.6794408Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_bfloat16 PASSED [0.0064s] [ 47%] 2024-08-20T21:41:28.6794968Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_bool PASSED [0.0062s] [ 47%] 2024-08-20T21:41:28.6795559Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_complex128 PASSED [0.0066s] [ 47%] 2024-08-20T21:41:28.6796165Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_complex64 PASSED [0.0063s] [ 47%] 2024-08-20T21:41:28.6796739Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_float16 PASSED [0.0064s] [ 47%] 2024-08-20T21:41:28.6797351Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_float32 PASSED [0.0062s] [ 47%] 2024-08-20T21:41:28.6797923Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_float64 PASSED [0.0064s] [ 47%] 2024-08-20T21:41:28.6798484Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_int16 PASSED [0.0062s] [ 47%] 2024-08-20T21:41:28.6799058Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_int32 PASSED [0.0062s] [ 47%] 2024-08-20T21:41:28.6799609Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_int64 PASSED [0.0064s] [ 47%] 2024-08-20T21:41:28.6800166Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_int8 PASSED [0.0062s] [ 48%] 2024-08-20T21:41:28.6800740Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_uint8 PASSED [0.0063s] [ 48%] 2024-08-20T21:41:28.6801394Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_bfloat16 PASSED [0.0073s] [ 48%] 2024-08-20T21:41:28.6802041Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_bool PASSED [0.0073s] [ 48%] 2024-08-20T21:41:28.6802705Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_complex128 PASSED [0.0072s] [ 48%] 2024-08-20T21:41:28.6803369Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_complex64 PASSED [0.0074s] [ 48%] 2024-08-20T21:41:28.6804030Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_float16 PASSED [0.0071s] [ 48%] 2024-08-20T21:41:28.6804705Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_float32 PASSED [0.0073s] [ 48%] 2024-08-20T21:41:28.6805383Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_float64 PASSED [0.0071s] [ 48%] 2024-08-20T21:41:28.6806017Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_int16 PASSED [0.0071s] [ 48%] 2024-08-20T21:41:28.6806651Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_int32 PASSED [0.0078s] [ 48%] 2024-08-20T21:41:28.6807393Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_int64 PASSED [0.0072s] [ 49%] 2024-08-20T21:41:28.6808058Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_int8 PASSED [0.0073s] [ 49%] 2024-08-20T21:41:28.6808711Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_uint8 PASSED [0.0071s] [ 49%] 2024-08-20T21:41:28.6809308Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_bfloat16 PASSED [0.0104s] [ 49%] 2024-08-20T21:41:28.6809880Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_bool PASSED [0.0100s] [ 49%] 2024-08-20T21:41:28.6810513Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_complex128 PASSED [0.0103s] [ 49%] 2024-08-20T21:41:28.6811115Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_complex64 PASSED [0.0101s] [ 49%] 2024-08-20T21:41:28.6811714Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_float16 PASSED [0.0102s] [ 49%] 2024-08-20T21:41:28.6812297Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_float32 PASSED [0.0101s] [ 49%] 2024-08-20T21:41:28.6812878Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_float64 PASSED [0.0100s] [ 49%] 2024-08-20T21:41:28.6813468Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_int16 PASSED [0.0102s] [ 50%] 2024-08-20T21:41:28.6814045Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_int32 PASSED [0.0100s] [ 50%] 2024-08-20T21:41:28.6814661Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_int64 PASSED [0.0102s] [ 50%] 2024-08-20T21:41:28.6815244Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_int8 PASSED [0.0100s] [ 50%] 2024-08-20T21:41:28.6815813Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_uint8 PASSED [0.0102s] [ 50%] 2024-08-20T21:41:28.6816416Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_bfloat16 PASSED [0.0051s] [ 50%] 2024-08-20T21:41:28.6816981Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_bool PASSED [0.0053s] [ 50%] 2024-08-20T21:41:28.6817583Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_complex128 PASSED [0.0052s] [ 50%] 2024-08-20T21:41:28.6818191Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_complex64 PASSED [0.0051s] [ 50%] 2024-08-20T21:41:28.6818773Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_float16 PASSED [0.0053s] [ 50%] 2024-08-20T21:41:28.6819365Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_float32 PASSED [0.0051s] [ 51%] 2024-08-20T21:41:28.6819944Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_float64 PASSED [0.0057s] [ 51%] 2024-08-20T21:41:28.6820516Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_int16 PASSED [0.0052s] [ 51%] 2024-08-20T21:41:28.6821282Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_int32 PASSED [0.0053s] [ 51%] 2024-08-20T21:41:28.6821913Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_int64 PASSED [0.0051s] [ 51%] 2024-08-20T21:41:28.6822480Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_int8 PASSED [0.0054s] [ 51%] 2024-08-20T21:41:28.6823091Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_uint8 PASSED [0.0051s] [ 51%] 2024-08-20T21:41:28.6823691Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_bfloat16 PASSED [0.0053s] [ 51%] 2024-08-20T21:41:28.6824274Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_bool PASSED [0.0052s] [ 51%] 2024-08-20T21:41:28.6824906Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_complex128 PASSED [0.0051s] [ 51%] 2024-08-20T21:41:28.6825512Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_complex64 PASSED [0.0054s] [ 51%] 2024-08-20T21:41:28.6826115Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_float16 PASSED [0.0051s] [ 52%] 2024-08-20T21:41:28.6826696Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_float32 PASSED [0.0053s] [ 52%] 2024-08-20T21:41:28.6827294Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_float64 PASSED [0.0052s] [ 52%] 2024-08-20T21:41:28.6827874Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_int16 PASSED [0.0054s] [ 52%] 2024-08-20T21:41:28.6828450Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_int32 PASSED [0.0052s] [ 52%] 2024-08-20T21:41:28.6829039Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_int64 PASSED [0.0053s] [ 52%] 2024-08-20T21:41:28.6829609Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_int8 PASSED [0.0051s] [ 52%] 2024-08-20T21:41:28.6830194Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_uint8 PASSED [0.0053s] [ 52%] 2024-08-20T21:41:28.6830839Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_bfloat16 PASSED [0.0099s] [ 52%] 2024-08-20T21:41:28.6831457Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_bool PASSED [0.0098s] [ 52%] 2024-08-20T21:41:28.6832157Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_complex128 PASSED [0.0101s] [ 53%] 2024-08-20T21:41:28.6832809Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_complex64 PASSED [0.0099s] [ 53%] 2024-08-20T21:41:28.6833449Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_float16 PASSED [0.0106s] [ 53%] 2024-08-20T21:41:28.6834094Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_float32 PASSED [0.0098s] [ 53%] 2024-08-20T21:41:28.6834725Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_float64 PASSED [0.0100s] [ 53%] 2024-08-20T21:41:28.6835361Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_int16 PASSED [0.0099s] [ 53%] 2024-08-20T21:41:28.6835986Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_int32 PASSED [0.0101s] [ 53%] 2024-08-20T21:41:28.6836611Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_int64 PASSED [0.0100s] [ 53%] 2024-08-20T21:41:28.6837236Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_int8 PASSED [0.0121s] [ 53%] 2024-08-20T21:41:28.6837861Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_uint8 PASSED [0.0132s] [ 53%] 2024-08-20T21:41:28.6838503Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_bfloat16 PASSED [0.0057s] [ 53%] 2024-08-20T21:41:28.6839166Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_complex128 PASSED [0.0059s] [ 54%] 2024-08-20T21:41:28.6839799Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_complex64 PASSED [0.0058s] [ 54%] 2024-08-20T21:41:28.6840463Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_float16 PASSED [0.0059s] [ 54%] 2024-08-20T21:41:28.6841081Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_float32 PASSED [0.0057s] [ 54%] 2024-08-20T21:41:28.6841709Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_float64 PASSED [0.0059s] [ 54%] 2024-08-20T21:41:28.6842344Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_int16 PASSED [0.0057s] [ 54%] 2024-08-20T21:41:28.6842946Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_int32 PASSED [0.0059s] [ 54%] 2024-08-20T21:41:28.6843558Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_int64 PASSED [0.0057s] [ 54%] 2024-08-20T21:41:28.6844164Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_int8 PASSED [0.0058s] [ 54%] 2024-08-20T21:41:28.6844762Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_uint8 PASSED [0.0057s] [ 54%] 2024-08-20T21:41:28.6845370Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_bfloat16 PASSED [0.0324s] [ 55%] 2024-08-20T21:41:28.6845939Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_bool PASSED [0.0328s] [ 55%] 2024-08-20T21:41:28.6846558Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_complex128 PASSED [0.0330s] [ 55%] 2024-08-20T21:41:28.6847396Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_complex64 PASSED [0.0338s] [ 55%] 2024-08-20T21:41:28.6847996Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_float16 PASSED [0.0326s] [ 55%] 2024-08-20T21:41:28.6848597Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_float32 PASSED [0.0333s] [ 55%] 2024-08-20T21:41:28.6849248Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_float64 PASSED [0.0325s] [ 55%] 2024-08-20T21:41:28.6849839Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_int16 PASSED [0.0328s] [ 55%] 2024-08-20T21:41:28.6850414Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_int32 PASSED [0.0324s] [ 55%] 2024-08-20T21:41:28.6850990Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_int64 PASSED [0.0326s] [ 55%] 2024-08-20T21:41:28.6851576Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_int8 PASSED [0.0331s] [ 56%] 2024-08-20T21:41:28.6852156Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_uint8 PASSED [0.0327s] [ 56%] 2024-08-20T21:41:28.6852751Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_bfloat16 PASSED [0.0182s] [ 56%] 2024-08-20T21:41:28.6853316Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_bool PASSED [0.0177s] [ 56%] 2024-08-20T21:41:28.6853915Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_complex128 PASSED [0.0182s] [ 56%] 2024-08-20T21:41:28.6854520Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_complex64 PASSED [0.0179s] [ 56%] 2024-08-20T21:41:28.6855100Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_float16 PASSED [0.0177s] [ 56%] 2024-08-20T21:41:28.6855675Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_float32 PASSED [0.0174s] [ 56%] 2024-08-20T21:41:28.6856302Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_float64 PASSED [0.0175s] [ 56%] 2024-08-20T21:41:28.6856875Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_int16 PASSED [0.0181s] [ 56%] 2024-08-20T21:41:28.6857495Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_int32 PASSED [0.0177s] [ 56%] 2024-08-20T21:41:28.6858059Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_int64 PASSED [0.0179s] [ 57%] 2024-08-20T21:41:28.6858619Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_int8 PASSED [0.0177s] [ 57%] 2024-08-20T21:41:28.6859193Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_uint8 PASSED [0.0179s] [ 57%] 2024-08-20T21:41:28.6859857Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_bfloat16 PASSED [0.0058s] [ 57%] 2024-08-20T21:41:28.6860474Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_bool PASSED [0.0063s] [ 57%] 2024-08-20T21:41:28.6861108Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_complex128 PASSED [0.0057s] [ 57%] 2024-08-20T21:41:28.6861742Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_complex64 PASSED [0.0056s] [ 57%] 2024-08-20T21:41:28.6862375Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_float16 PASSED [0.0059s] [ 57%] 2024-08-20T21:41:28.6862996Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_float32 PASSED [0.0056s] [ 57%] 2024-08-20T21:41:28.6863626Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_float64 PASSED [0.0058s] [ 57%] 2024-08-20T21:41:28.6864231Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_int16 PASSED [0.0057s] [ 58%] 2024-08-20T21:41:28.6864835Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_int32 PASSED [0.0059s] [ 58%] 2024-08-20T21:41:28.6865448Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_int64 PASSED [0.0056s] [ 58%] 2024-08-20T21:41:28.6866050Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_int8 PASSED [0.0059s] [ 58%] 2024-08-20T21:41:28.6866678Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_uint8 PASSED [0.0057s] [ 58%] 2024-08-20T21:41:28.6867285Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_bfloat16 PASSED [0.0120s] [ 58%] 2024-08-20T21:41:28.6867873Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_float16 PASSED [0.0119s] [ 58%] 2024-08-20T21:41:28.6868470Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_float32 PASSED [0.0119s] [ 58%] 2024-08-20T21:41:28.6869056Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_float64 PASSED [0.0121s] [ 58%] 2024-08-20T21:41:28.6869640Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_int16 PASSED [0.0119s] [ 58%] 2024-08-20T21:41:28.6870230Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_int32 PASSED [0.0123s] [ 58%] 2024-08-20T21:41:28.6870809Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_int64 PASSED [0.0120s] [ 59%] 2024-08-20T21:41:28.6871395Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_int8 PASSED [0.0122s] [ 59%] 2024-08-20T21:41:28.6871968Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_uint8 PASSED [0.0120s] [ 59%] 2024-08-20T21:41:28.6872743Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_bfloat16 PASSED [0.0094s] [ 59%] 2024-08-20T21:41:28.6873534Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_bool PASSED [0.0090s] [ 59%] 2024-08-20T21:41:28.6874309Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_float16 PASSED [0.0097s] [ 59%] 2024-08-20T21:41:28.6875116Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_float32 PASSED [0.0088s] [ 59%] 2024-08-20T21:41:28.6875874Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_float64 PASSED [0.0090s] [ 59%] 2024-08-20T21:41:28.6876653Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_int16 PASSED [0.0091s] [ 59%] 2024-08-20T21:41:28.6877423Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_int32 PASSED [0.0089s] [ 59%] 2024-08-20T21:41:28.6878180Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_int64 PASSED [0.0129s] [ 60%] 2024-08-20T21:41:28.6878933Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_int8 PASSED [0.0108s] [ 60%] 2024-08-20T21:41:28.6879690Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_uint8 PASSED [0.0091s] [ 60%] 2024-08-20T21:41:28.6880307Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_complex128 PASSED [0.0052s] [ 60%] 2024-08-20T21:41:28.6880920Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_complex64 PASSED [0.0053s] [ 60%] 2024-08-20T21:41:28.6881516Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_float32 PASSED [0.0051s] [ 60%] 2024-08-20T21:41:28.6882116Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_float64 PASSED [0.0051s] [ 60%] 2024-08-20T21:41:28.6882704Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_int16 PASSED [0.0053s] [ 60%] 2024-08-20T21:41:28.6883288Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_int32 PASSED [0.0051s] [ 60%] 2024-08-20T21:41:28.6883928Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_int64 PASSED [0.0053s] [ 60%] 2024-08-20T21:41:28.6884506Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_int8 PASSED [0.0051s] [ 61%] 2024-08-20T21:41:28.6885087Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_uint8 PASSED [0.0054s] [ 61%] 2024-08-20T21:41:28.6885727Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_bfloat16 PASSED [0.0088s] [ 61%] 2024-08-20T21:41:28.6886334Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_bool PASSED [0.0090s] [ 61%] 2024-08-20T21:41:28.6887057Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_complex128 PASSED [0.0089s] [ 61%] 2024-08-20T21:41:28.6887705Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_complex32 PASSED [0.0090s] [ 61%] 2024-08-20T21:41:28.6888335Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_complex64 PASSED [0.0089s] [ 61%] 2024-08-20T21:41:28.6888973Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_float16 PASSED [0.0088s] [ 61%] 2024-08-20T21:41:28.6889590Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_float32 PASSED [0.0090s] [ 61%] 2024-08-20T21:41:28.6890226Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_float64 PASSED [0.0089s] [ 61%] 2024-08-20T21:41:28.6890835Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_int16 PASSED [0.0091s] [ 61%] 2024-08-20T21:41:28.6891472Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_int32 PASSED [0.0088s] [ 62%] 2024-08-20T21:41:28.6892141Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_int64 PASSED [0.0090s] [ 62%] 2024-08-20T21:41:28.6892913Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_int8 PASSED [0.0088s] [ 62%] 2024-08-20T21:41:28.6893542Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_uint8 PASSED [0.0090s] [ 62%] 2024-08-20T21:41:28.6894167Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_bfloat16 PASSED [0.0094s] [ 62%] 2024-08-20T21:41:28.6894845Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_complex128 PASSED [0.0099s] [ 62%] 2024-08-20T21:41:28.6895504Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_complex64 PASSED [0.0095s] [ 62%] 2024-08-20T21:41:28.6896130Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_float16 PASSED [0.0094s] [ 62%] 2024-08-20T21:41:28.6896754Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_float32 PASSED [0.0095s] [ 62%] 2024-08-20T21:41:28.6897392Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_float64 PASSED [0.0094s] [ 62%] 2024-08-20T21:41:28.6898001Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_int16 PASSED [0.0096s] [ 63%] 2024-08-20T21:41:28.6898625Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_int32 PASSED [0.0095s] [ 63%] 2024-08-20T21:41:28.6899232Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_int64 PASSED [0.0096s] [ 63%] 2024-08-20T21:41:28.6899840Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_int8 PASSED [0.0095s] [ 63%] 2024-08-20T21:41:28.6900461Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_uint8 PASSED [0.0097s] [ 63%] 2024-08-20T21:41:28.6901070Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_bfloat16 PASSED [0.0095s] [ 63%] 2024-08-20T21:41:28.6901730Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_complex128 PASSED [0.0098s] [ 63%] 2024-08-20T21:41:28.6902340Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_complex64 PASSED [0.0095s] [ 63%] 2024-08-20T21:41:28.6902944Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_float16 PASSED [0.0094s] [ 63%] 2024-08-20T21:41:28.6903552Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_float32 PASSED [0.0096s] [ 63%] 2024-08-20T21:41:28.6904147Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_float64 PASSED [0.0094s] [ 64%] 2024-08-20T21:41:28.6904751Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_int16 PASSED [0.0096s] [ 64%] 2024-08-20T21:41:28.6905339Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_int32 PASSED [0.0094s] [ 64%] 2024-08-20T21:41:28.6905925Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_int64 PASSED [0.0097s] [ 64%] 2024-08-20T21:41:28.6906519Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_int8 PASSED [0.0094s] [ 64%] 2024-08-20T21:41:28.6907100Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_uint8 PASSED [0.0096s] [ 64%] 2024-08-20T21:41:28.6907784Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triangular_solve_cpu_complex128 PASSED [0.0143s] [ 64%] 2024-08-20T21:41:28.6908499Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triangular_solve_cpu_complex64 PASSED [0.0144s] [ 64%] 2024-08-20T21:41:28.6909164Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triangular_solve_cpu_float32 PASSED [0.0139s] [ 64%] 2024-08-20T21:41:28.6909865Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triangular_solve_cpu_float64 PASSED [0.0136s] [ 64%] 2024-08-20T21:41:28.6910464Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_bfloat16 PASSED [0.0090s] [ 64%] 2024-08-20T21:41:28.6911039Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_bool PASSED [0.0088s] [ 65%] 2024-08-20T21:41:28.6911687Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_complex128 PASSED [0.0091s] [ 65%] 2024-08-20T21:41:28.6912293Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_complex32 PASSED [0.0089s] [ 65%] 2024-08-20T21:41:28.6912906Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_complex64 PASSED [0.0090s] [ 65%] 2024-08-20T21:41:28.6913494Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_float16 PASSED [0.0088s] [ 65%] 2024-08-20T21:41:28.6914082Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_float32 PASSED [0.0089s] [ 65%] 2024-08-20T21:41:28.6914682Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_float64 PASSED [0.0088s] [ 65%] 2024-08-20T21:41:28.6915261Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_int16 PASSED [0.0087s] [ 65%] 2024-08-20T21:41:28.6915849Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_int32 PASSED [0.0094s] [ 65%] 2024-08-20T21:41:28.6916429Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_int64 PASSED [0.0087s] [ 65%] 2024-08-20T21:41:28.6917001Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_int8 PASSED [0.0089s] [ 66%] 2024-08-20T21:41:28.6917590Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_uint8 PASSED [0.0088s] [ 66%] 2024-08-20T21:41:28.6918215Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_indices_cpu_int32 PASSED [0.0216s] [ 66%] 2024-08-20T21:41:28.6918868Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_indices_cpu_int64 PASSED [0.0203s] [ 66%] 2024-08-20T21:41:28.6919477Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_bfloat16 PASSED [0.0091s] [ 66%] 2024-08-20T21:41:28.6920050Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_bool PASSED [0.0093s] [ 66%] 2024-08-20T21:41:28.6920669Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_complex128 PASSED [0.0091s] [ 66%] 2024-08-20T21:41:28.6921481Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_complex32 PASSED [0.0092s] [ 66%] 2024-08-20T21:41:28.6922085Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_complex64 PASSED [0.0090s] [ 66%] 2024-08-20T21:41:28.6922693Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_float16 PASSED [0.0092s] [ 66%] 2024-08-20T21:41:28.6923280Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_float32 PASSED [0.0089s] [ 66%] 2024-08-20T21:41:28.6923872Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_float64 PASSED [0.0092s] [ 67%] 2024-08-20T21:41:28.6924455Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_int16 PASSED [0.0089s] [ 67%] 2024-08-20T21:41:28.6925038Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_int32 PASSED [0.0091s] [ 67%] 2024-08-20T21:41:28.6925623Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_int64 PASSED [0.0089s] [ 67%] 2024-08-20T21:41:28.6926229Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_int8 PASSED [0.0088s] [ 67%] 2024-08-20T21:41:28.6926903Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_uint8 PASSED [0.0094s] [ 67%] 2024-08-20T21:41:28.6927548Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_indices_cpu_int32 PASSED [0.0208s] [ 67%] 2024-08-20T21:41:28.6928171Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_indices_cpu_int64 PASSED [0.0203s] [ 67%] 2024-08-20T21:41:28.6928820Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_bfloat16 PASSED [0.0107s] [ 67%] 2024-08-20T21:41:28.6929464Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_bool PASSED [0.0103s] [ 67%] 2024-08-20T21:41:28.6930114Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_complex128 PASSED [0.0104s] [ 68%] 2024-08-20T21:41:28.6930774Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_complex64 PASSED [0.0106s] [ 68%] 2024-08-20T21:41:28.6931405Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_float16 PASSED [0.0104s] [ 68%] 2024-08-20T21:41:28.6932047Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_float32 PASSED [0.0107s] [ 68%] 2024-08-20T21:41:28.6932676Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_float64 PASSED [0.0104s] [ 68%] 2024-08-20T21:41:28.6933295Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_int16 PASSED [0.0107s] [ 68%] 2024-08-20T21:41:28.6933924Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_int32 PASSED [0.0103s] [ 68%] 2024-08-20T21:41:28.6934539Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_int64 PASSED [0.0104s] [ 68%] 2024-08-20T21:41:28.6935159Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_int8 PASSED [0.0103s] [ 68%] 2024-08-20T21:41:28.6935774Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_uint8 PASSED [0.0105s] [ 68%] 2024-08-20T21:41:28.6936406Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_bfloat16 PASSED [0.0052s] [ 69%] 2024-08-20T21:41:28.6937012Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_float16 PASSED [0.0065s] [ 69%] 2024-08-20T21:41:28.6937604Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_float32 PASSED [0.0098s] [ 69%] 2024-08-20T21:41:28.6938210Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_float64 PASSED [0.0053s] [ 69%] 2024-08-20T21:41:28.6938789Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_int16 PASSED [0.0054s] [ 69%] 2024-08-20T21:41:28.6939372Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_int32 PASSED [0.0052s] [ 69%] 2024-08-20T21:41:28.6939966Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_int64 PASSED [0.0058s] [ 69%] 2024-08-20T21:41:28.6940541Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_int8 PASSED [0.0051s] [ 69%] 2024-08-20T21:41:28.6941120Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_uint8 PASSED [0.0054s] [ 69%] 2024-08-20T21:41:28.6941740Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_bfloat16 PASSED [0.0078s] [ 69%] 2024-08-20T21:41:28.6942324Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_bool PASSED [0.0079s] [ 69%] 2024-08-20T21:41:28.6942982Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_complex128 PASSED [0.0079s] [ 70%] 2024-08-20T21:41:28.6943597Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_complex32 PASSED [0.0078s] [ 70%] 2024-08-20T21:41:28.6944243Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_complex64 PASSED [0.0080s] [ 70%] 2024-08-20T21:41:28.6944863Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_float16 PASSED [0.0077s] [ 70%] 2024-08-20T21:41:28.6945464Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_float32 PASSED [0.0080s] [ 70%] 2024-08-20T21:41:28.6946071Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_float64 PASSED [0.0078s] [ 70%] 2024-08-20T21:41:28.6946886Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_int16 PASSED [0.0082s] [ 70%] 2024-08-20T21:41:28.6947484Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_int32 PASSED [0.0079s] [ 70%] 2024-08-20T21:41:28.6948089Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_int64 PASSED [0.0081s] [ 70%] 2024-08-20T21:41:28.6948675Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_int8 PASSED [0.0079s] [ 70%] 2024-08-20T21:41:28.6949277Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_uint8 PASSED [0.0080s] [ 71%] 2024-08-20T21:41:28.6949907Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_bfloat16 PASSED [0.0099s] [ 71%] 2024-08-20T21:41:28.6950511Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_bool PASSED [0.0097s] [ 71%] 2024-08-20T21:41:28.6951164Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_complex128 PASSED [0.0102s] [ 71%] 2024-08-20T21:41:28.6951803Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_complex32 PASSED [0.0099s] [ 71%] 2024-08-20T21:41:28.6952435Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_complex64 PASSED [0.0102s] [ 71%] 2024-08-20T21:41:28.6953072Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_float16 PASSED [0.0098s] [ 71%] 2024-08-20T21:41:28.6953752Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_float32 PASSED [0.0105s] [ 71%] 2024-08-20T21:41:28.6954381Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_float64 PASSED [0.0098s] [ 71%] 2024-08-20T21:41:28.6954988Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_int16 PASSED [0.0101s] [ 71%] 2024-08-20T21:41:28.6955597Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_int32 PASSED [0.0098s] [ 71%] 2024-08-20T21:41:28.6956217Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_int64 PASSED [0.0101s] [ 72%] 2024-08-20T21:41:28.6956819Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_int8 PASSED [0.0099s] [ 72%] 2024-08-20T21:41:28.6957433Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_uint8 PASSED [0.0098s] [ 72%] 2024-08-20T21:41:28.6958072Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_bfloat16 PASSED [0.0164s] [ 72%] 2024-08-20T21:41:28.6958682Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_bool PASSED [0.0161s] [ 72%] 2024-08-20T21:41:28.6959346Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_complex128 PASSED [0.0167s] [ 72%] 2024-08-20T21:41:28.6959991Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_complex32 PASSED [0.0163s] [ 72%] 2024-08-20T21:41:28.6960686Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_complex64 PASSED [0.0166s] [ 72%] 2024-08-20T21:41:28.6961317Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_float16 PASSED [0.0161s] [ 72%] 2024-08-20T21:41:28.6961984Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_float32 PASSED [0.0162s] [ 72%] 2024-08-20T21:41:28.6962629Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_float64 PASSED [0.0166s] [ 73%] 2024-08-20T21:41:28.6963244Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_int16 PASSED [0.0160s] [ 73%] 2024-08-20T21:41:28.6963888Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_int32 PASSED [0.0164s] [ 73%] 2024-08-20T21:41:28.6964520Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_int64 PASSED [0.0161s] [ 73%] 2024-08-20T21:41:28.6965133Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_int8 PASSED [0.0166s] [ 73%] 2024-08-20T21:41:28.6965766Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_uint8 PASSED [0.0164s] [ 73%] 2024-08-20T21:41:28.6966373Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_bfloat16 PASSED [0.0167s] [ 73%] 2024-08-20T21:41:28.6967038Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_bool PASSED [0.0162s] [ 73%] 2024-08-20T21:41:28.6967679Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_complex128 PASSED [0.0173s] [ 73%] 2024-08-20T21:41:28.6968296Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_complex32 PASSED [0.0165s] [ 73%] 2024-08-20T21:41:28.6968924Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_complex64 PASSED [0.0166s] [ 74%] 2024-08-20T21:41:28.6969529Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_float16 PASSED [0.0161s] [ 74%] 2024-08-20T21:41:28.6970126Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_float32 PASSED [0.0161s] [ 74%] 2024-08-20T21:41:28.6970772Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_float64 PASSED [0.0164s] [ 74%] 2024-08-20T21:41:28.6971363Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_int16 PASSED [0.0162s] [ 74%] 2024-08-20T21:41:28.6971963Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_int32 PASSED [0.0164s] [ 74%] 2024-08-20T21:41:28.6972544Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_int64 PASSED [0.0161s] [ 74%] 2024-08-20T21:41:28.6973129Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_int8 PASSED [0.0165s] [ 74%] 2024-08-20T21:41:28.6973729Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_uint8 PASSED [0.0162s] [ 74%] 2024-08-20T21:41:28.6974340Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_bfloat16 PASSED [0.0065s] [ 74%] 2024-08-20T21:41:28.6974969Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_complex128 PASSED [0.0063s] [ 74%] 2024-08-20T21:41:28.6975598Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_complex64 PASSED [0.0064s] [ 75%] 2024-08-20T21:41:28.6976202Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_float16 PASSED [0.0062s] [ 75%] 2024-08-20T21:41:28.6976816Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_float32 PASSED [0.0062s] [ 75%] 2024-08-20T21:41:28.6977415Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_float64 PASSED [0.0064s] [ 75%] 2024-08-20T21:41:28.6978125Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_bfloat16 PASSED [0.1453s] [ 75%] 2024-08-20T21:41:28.6978815Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_bool PASSED [0.1501s] [ 75%] 2024-08-20T21:41:28.6979484Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_float16 PASSED [0.1461s] [ 75%] 2024-08-20T21:41:28.6980165Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_float32 PASSED [0.1468s] [ 75%] 2024-08-20T21:41:28.6980832Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_float64 PASSED [0.1464s] [ 75%] 2024-08-20T21:41:28.6981521Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_int16 PASSED [0.1460s] [ 75%] 2024-08-20T21:41:28.6982196Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_int32 PASSED [0.1461s] [ 76%] 2024-08-20T21:41:28.6982855Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_int64 PASSED [0.1464s] [ 76%] 2024-08-20T21:41:28.6983525Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_int8 PASSED [0.1529s] [ 76%] 2024-08-20T21:41:28.6984181Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_uint8 PASSED [0.1460s] [ 76%] 2024-08-20T21:41:28.6984790Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_bfloat16 PASSED [0.2820s] [ 76%] 2024-08-20T21:41:28.6985393Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_bool PASSED [0.2785s] [ 76%] 2024-08-20T21:41:28.6985995Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_float16 PASSED [0.2856s] [ 76%] 2024-08-20T21:41:28.6986609Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_float32 PASSED [0.2827s] [ 76%] 2024-08-20T21:41:28.6987200Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_float64 PASSED [0.2792s] [ 76%] 2024-08-20T21:41:28.6987789Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_int16 PASSED [0.2790s] [ 76%] 2024-08-20T21:41:28.6988418Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_int32 PASSED [0.2859s] [ 76%] 2024-08-20T21:41:28.6989003Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_int64 PASSED [0.2793s] [ 77%] 2024-08-20T21:41:28.6989584Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_int8 PASSED [0.2806s] [ 77%] 2024-08-20T21:41:28.6990191Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_uint16 PASSED [0.2858s] [ 77%] 2024-08-20T21:41:28.6990786Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_uint32 PASSED [0.2802s] [ 77%] 2024-08-20T21:41:28.6991384Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_uint64 PASSED [0.2790s] [ 77%] 2024-08-20T21:41:28.6991975Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_uint8 PASSED [0.2784s] [ 77%] 2024-08-20T21:41:28.6992605Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_int16 PASSED [0.0129s] [ 77%] 2024-08-20T21:41:28.6993239Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_int32 PASSED [0.0123s] [ 77%] 2024-08-20T21:41:28.6993870Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_int64 PASSED [0.0130s] [ 77%] 2024-08-20T21:41:28.6994510Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_int8 PASSED [0.0123s] [ 77%] 2024-08-20T21:41:28.6995170Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_uint8 PASSED [0.0125s] [ 78%] 2024-08-20T21:41:28.6995818Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_bfloat16 PASSED [0.0063s] [ 78%] 2024-08-20T21:41:28.6996478Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_bool PASSED [0.0062s] [ 78%] 2024-08-20T21:41:28.6997131Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_complex128 PASSED [0.0064s] [ 78%] 2024-08-20T21:41:28.6997787Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_complex32 PASSED [0.0062s] [ 78%] 2024-08-20T21:41:28.6998457Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_complex64 PASSED [0.0064s] [ 78%] 2024-08-20T21:41:28.6999093Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_float16 PASSED [0.0064s] [ 78%] 2024-08-20T21:41:28.6999741Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_float32 PASSED [0.0118s] [ 78%] 2024-08-20T21:41:28.7000375Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_float64 PASSED [0.0062s] [ 78%] 2024-08-20T21:41:28.7001011Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_int16 PASSED [0.0064s] [ 78%] 2024-08-20T21:41:28.7001632Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_int32 PASSED [0.0062s] [ 79%] 2024-08-20T21:41:28.7002251Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_int64 PASSED [0.0064s] [ 79%] 2024-08-20T21:41:28.7002886Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_int8 PASSED [0.0062s] [ 79%] 2024-08-20T21:41:28.7003510Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_uint8 PASSED [0.0061s] [ 79%] 2024-08-20T21:41:28.7004151Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_bfloat16 PASSED [0.0058s] [ 79%] 2024-08-20T21:41:28.7004784Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_bool PASSED [0.0057s] [ 79%] 2024-08-20T21:41:28.7005464Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_complex128 PASSED [0.0058s] [ 79%] 2024-08-20T21:41:28.7006234Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_complex32 PASSED [0.0056s] [ 79%] 2024-08-20T21:41:28.7006968Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_complex64 PASSED [0.0059s] [ 79%] 2024-08-20T21:41:28.7007610Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_float16 PASSED [0.0056s] [ 79%] 2024-08-20T21:41:28.7008258Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_float32 PASSED [0.0062s] [ 79%] 2024-08-20T21:41:28.7008896Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_float64 PASSED [0.0057s] [ 80%] 2024-08-20T21:41:28.7009536Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_int16 PASSED [0.0059s] [ 80%] 2024-08-20T21:41:28.7010158Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_int32 PASSED [0.0057s] [ 80%] 2024-08-20T21:41:28.7010785Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_int64 PASSED [0.0057s] [ 80%] 2024-08-20T21:41:28.7011418Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_int8 PASSED [0.0058s] [ 80%] 2024-08-20T21:41:28.7012042Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_uint8 PASSED [0.0056s] [ 80%] 2024-08-20T21:41:28.7012753Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_bfloat16 PASSED [0.0095s] [ 80%] 2024-08-20T21:41:28.7013394Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_bool PASSED [0.0093s] [ 80%] 2024-08-20T21:41:28.7014105Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_complex128 PASSED [0.0096s] [ 80%] 2024-08-20T21:41:28.7014785Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_complex32 PASSED [0.0095s] [ 80%] 2024-08-20T21:41:28.7015449Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_complex64 PASSED [0.0095s] [ 81%] 2024-08-20T21:41:28.7016140Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_float16 PASSED [0.0093s] [ 81%] 2024-08-20T21:41:28.7016792Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_float32 PASSED [0.0095s] [ 81%] 2024-08-20T21:41:28.7017441Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_float64 PASSED [0.0093s] [ 81%] 2024-08-20T21:41:28.7018099Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_int16 PASSED [0.0092s] [ 81%] 2024-08-20T21:41:28.7018740Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_int32 PASSED [0.0095s] [ 81%] 2024-08-20T21:41:28.7019379Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_int64 PASSED [0.0093s] [ 81%] 2024-08-20T21:41:28.7020028Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_int8 PASSED [0.0095s] [ 81%] 2024-08-20T21:41:28.7020668Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_uint8 PASSED [0.0092s] [ 81%] 2024-08-20T21:41:28.7021503Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_bfloat16 PASSED [0.0095s] [ 81%] 2024-08-20T21:41:28.7022117Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_bool PASSED [0.0092s] [ 82%] 2024-08-20T21:41:28.7022758Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_complex128 PASSED [0.0100s] [ 82%] 2024-08-20T21:41:28.7023455Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_complex32 PASSED [0.0094s] [ 82%] 2024-08-20T21:41:28.7024089Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_complex64 PASSED [0.0096s] [ 82%] 2024-08-20T21:41:28.7024724Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_float16 PASSED [0.0094s] [ 82%] 2024-08-20T21:41:28.7025348Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_float32 PASSED [0.0094s] [ 82%] 2024-08-20T21:41:28.7025969Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_float64 PASSED [0.0095s] [ 82%] 2024-08-20T21:41:28.7026594Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_int16 PASSED [0.0093s] [ 82%] 2024-08-20T21:41:28.7027207Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_int32 PASSED [0.0095s] [ 82%] 2024-08-20T21:41:28.7027825Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_int64 PASSED [0.0093s] [ 82%] 2024-08-20T21:41:28.7028430Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_int8 PASSED [0.0095s] [ 82%] 2024-08-20T21:41:28.7029033Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_uint8 PASSED [0.0093s] [ 83%] 2024-08-20T21:41:28.7029637Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_bfloat16 PASSED [0.0105s] [ 83%] 2024-08-20T21:41:28.7030300Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_complex128 PASSED [0.0105s] [ 83%] 2024-08-20T21:41:28.7030904Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_complex64 PASSED [0.0106s] [ 83%] 2024-08-20T21:41:28.7031529Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_float16 PASSED [0.0103s] [ 83%] 2024-08-20T21:41:28.7032109Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_float32 PASSED [0.0103s] [ 83%] 2024-08-20T21:41:28.7032705Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_float64 PASSED [0.0104s] [ 83%] 2024-08-20T21:41:28.7033322Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_bfloat16 PASSED [0.0103s] [ 83%] 2024-08-20T21:41:28.7033983Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_complex128 PASSED [0.0106s] [ 83%] 2024-08-20T21:41:28.7034623Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_complex64 PASSED [0.0104s] [ 83%] 2024-08-20T21:41:28.7035236Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_float16 PASSED [0.0106s] [ 84%] 2024-08-20T21:41:28.7035863Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_float32 PASSED [0.0103s] [ 84%] 2024-08-20T21:41:28.7036479Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_float64 PASSED [0.0110s] [ 84%] 2024-08-20T21:41:28.7037151Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_bfloat16 PASSED [0.0056s] [ 84%] 2024-08-20T21:41:28.7037849Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_complex128 PASSED [0.0059s] [ 84%] 2024-08-20T21:41:28.7038525Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_complex64 PASSED [0.0057s] [ 84%] 2024-08-20T21:41:28.7039205Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_float16 PASSED [0.0056s] [ 84%] 2024-08-20T21:41:28.7039862Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_float32 PASSED [0.0058s] [ 84%] 2024-08-20T21:41:28.7040556Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_float64 PASSED [0.0057s] [ 84%] 2024-08-20T21:41:28.7041215Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_bfloat16 PASSED [0.0058s] [ 84%] 2024-08-20T21:41:28.7041873Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_complex128 PASSED [0.0057s] [ 84%] 2024-08-20T21:41:28.7042537Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_complex64 PASSED [0.0059s] [ 85%] 2024-08-20T21:41:28.7043167Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_float16 PASSED [0.0056s] [ 85%] 2024-08-20T21:41:28.7043801Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_float32 PASSED [0.0059s] [ 85%] 2024-08-20T21:41:28.7044450Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_float64 PASSED [0.0057s] [ 85%] 2024-08-20T21:41:28.7045045Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_bfloat16 PASSED [0.0053s] [ 85%] 2024-08-20T21:41:28.7045660Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_complex128 PASSED [0.0058s] [ 85%] 2024-08-20T21:41:28.7046274Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_complex64 PASSED [0.0057s] [ 85%] 2024-08-20T21:41:28.7047112Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_float16 PASSED [0.0053s] [ 85%] 2024-08-20T21:41:28.7047735Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_float32 PASSED [0.0051s] [ 85%] 2024-08-20T21:41:28.7048396Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_float64 PASSED [0.0053s] [ 85%] 2024-08-20T21:41:28.7049014Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_int16 PASSED [0.0051s] [ 86%] 2024-08-20T21:41:28.7049609Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_int32 PASSED [0.0053s] [ 86%] 2024-08-20T21:41:28.7050189Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_int64 PASSED [0.0051s] [ 86%] 2024-08-20T21:41:28.7050777Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_int8 PASSED [0.0059s] [ 86%] 2024-08-20T21:41:28.7051390Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_uint8 PASSED [0.0054s] [ 86%] 2024-08-20T21:41:28.7052047Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_complex_cpu_float16 PASSED [0.0055s] [ 86%] 2024-08-20T21:41:28.7052715Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_complex_cpu_float32 PASSED [0.0054s] [ 86%] 2024-08-20T21:41:28.7053368Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_complex_cpu_float64 PASSED [0.0053s] [ 86%] 2024-08-20T21:41:28.7053993Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_bfloat16 PASSED [0.0072s] [ 86%] 2024-08-20T21:41:28.7054579Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_bool PASSED [0.0069s] [ 86%] 2024-08-20T21:41:28.7055207Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_complex128 PASSED [0.0072s] [ 87%] 2024-08-20T21:41:28.7055838Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_complex32 PASSED [0.0070s] [ 87%] 2024-08-20T21:41:28.7056455Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_complex64 PASSED [0.0073s] [ 87%] 2024-08-20T21:41:28.7057060Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_float16 PASSED [0.0070s] [ 87%] 2024-08-20T21:41:28.7057675Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_float32 PASSED [0.0072s] [ 87%] 2024-08-20T21:41:28.7058315Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_float64 PASSED [0.0069s] [ 87%] 2024-08-20T21:41:28.7058920Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_int16 PASSED [0.0071s] [ 87%] 2024-08-20T21:41:28.7059509Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_int32 PASSED [0.0069s] [ 87%] 2024-08-20T21:41:28.7060099Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_int64 PASSED [0.0069s] [ 87%] 2024-08-20T21:41:28.7060694Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_int8 PASSED [0.0070s] [ 87%] 2024-08-20T21:41:28.7061285Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_uint8 PASSED [0.0068s] [ 87%] 2024-08-20T21:41:28.7061947Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_real_cpu_complex128 PASSED [0.0059s] [ 88%] 2024-08-20T21:41:28.7062595Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_real_cpu_complex64 PASSED [0.0057s] [ 88%] 2024-08-20T21:41:28.7063216Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_bfloat16 PASSED [0.0085s] [ 88%] 2024-08-20T21:41:28.7063832Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_bool PASSED [0.0083s] [ 88%] 2024-08-20T21:41:28.7064464Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_complex128 PASSED [0.0090s] [ 88%] 2024-08-20T21:41:28.7065145Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_complex64 PASSED [0.0084s] [ 88%] 2024-08-20T21:41:28.7065767Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_float16 PASSED [0.0085s] [ 88%] 2024-08-20T21:41:28.7066416Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_float32 PASSED [0.0083s] [ 88%] 2024-08-20T21:41:28.7067043Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_float64 PASSED [0.0083s] [ 88%] 2024-08-20T21:41:28.7067645Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_int16 PASSED [0.0085s] [ 88%] 2024-08-20T21:41:28.7068273Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_int32 PASSED [0.0083s] [ 89%] 2024-08-20T21:41:28.7068882Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_int64 PASSED [0.0085s] [ 89%] 2024-08-20T21:41:28.7069484Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_int8 PASSED [0.0083s] [ 89%] 2024-08-20T21:41:28.7070098Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_uint8 PASSED [0.0085s] [ 89%] 2024-08-20T21:41:28.7070695Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_bfloat16 PASSED [0.0084s] [ 89%] 2024-08-20T21:41:28.7071265Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_bool PASSED [0.0085s] [ 89%] 2024-08-20T21:41:28.7071882Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_complex128 PASSED [0.0084s] [ 89%] 2024-08-20T21:41:28.7072490Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_complex32 PASSED [0.0085s] [ 89%] 2024-08-20T21:41:28.7073099Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_complex64 PASSED [0.0084s] [ 89%] 2024-08-20T21:41:28.7073690Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_float16 PASSED [0.0143s] [ 89%] 2024-08-20T21:41:28.7074273Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_float32 PASSED [0.0084s] [ 89%] 2024-08-20T21:41:28.7074873Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_float64 PASSED [0.0083s] [ 90%] 2024-08-20T21:41:28.7075476Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_int16 PASSED [0.0085s] [ 90%] 2024-08-20T21:41:28.7076062Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_int32 PASSED [0.0082s] [ 90%] 2024-08-20T21:41:28.7076640Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_int64 PASSED [0.0085s] [ 90%] 2024-08-20T21:41:28.7077212Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_int8 PASSED [0.0082s] [ 90%] 2024-08-20T21:41:28.7077796Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_uint8 PASSED [0.0089s] [ 90%] 2024-08-20T21:41:28.7078402Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_bfloat16 PASSED [0.0057s] [ 90%] 2024-08-20T21:41:28.7078988Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_bool PASSED [0.0058s] [ 90%] 2024-08-20T21:41:28.7079622Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_complex128 PASSED [0.0057s] [ 90%] 2024-08-20T21:41:28.7080237Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_complex32 PASSED [0.0057s] [ 90%] 2024-08-20T21:41:28.7080860Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_complex64 PASSED [0.0059s] [ 91%] 2024-08-20T21:41:28.7081466Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_float16 PASSED [0.0056s] [ 91%] 2024-08-20T21:41:28.7082065Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_float32 PASSED [0.0059s] [ 91%] 2024-08-20T21:41:28.7082718Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_float64 PASSED [0.0058s] [ 91%] 2024-08-20T21:41:28.7083338Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_int16 PASSED [0.0059s] [ 91%] 2024-08-20T21:41:28.7083935Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_int32 PASSED [0.0057s] [ 91%] 2024-08-20T21:41:28.7084525Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_int64 PASSED [0.0060s] [ 91%] 2024-08-20T21:41:28.7085111Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_int8 PASSED [0.0057s] [ 91%] 2024-08-20T21:41:28.7085739Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_uint8 PASSED [0.0058s] [ 91%] 2024-08-20T21:41:28.7086348Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_bfloat16 PASSED [0.0058s] [ 91%] 2024-08-20T21:41:28.7087043Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_bool PASSED [0.0058s] [ 92%] 2024-08-20T21:41:28.7087672Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_complex128 PASSED [0.0060s] [ 92%] 2024-08-20T21:41:28.7088289Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_complex32 PASSED [0.0058s] [ 92%] 2024-08-20T21:41:28.7088922Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_complex64 PASSED [0.0060s] [ 92%] 2024-08-20T21:41:28.7089523Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_float16 PASSED [0.0057s] [ 92%] 2024-08-20T21:41:28.7090119Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_float32 PASSED [0.0060s] [ 92%] 2024-08-20T21:41:28.7090724Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_float64 PASSED [0.0058s] [ 92%] 2024-08-20T21:41:28.7091314Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_int16 PASSED [0.0064s] [ 92%] 2024-08-20T21:41:28.7091917Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_int32 PASSED [0.0057s] [ 92%] 2024-08-20T21:41:28.7092541Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_int64 PASSED [0.0059s] [ 92%] 2024-08-20T21:41:28.7093118Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_int8 PASSED [0.0057s] [ 92%] 2024-08-20T21:41:28.7093716Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_uint8 PASSED [0.0057s] [ 93%] 2024-08-20T21:41:28.7094321Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_bfloat16 PASSED [0.0085s] [ 93%] 2024-08-20T21:41:28.7094912Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_bool PASSED [0.0081s] [ 93%] 2024-08-20T21:41:28.7095525Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_complex128 PASSED [0.0085s] [ 93%] 2024-08-20T21:41:28.7096137Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_complex32 PASSED [0.0082s] [ 93%] 2024-08-20T21:41:28.7096759Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_complex64 PASSED [0.0085s] [ 93%] 2024-08-20T21:41:28.7097351Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_float16 PASSED [0.0081s] [ 93%] 2024-08-20T21:41:28.7097952Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_float32 PASSED [0.0084s] [ 93%] 2024-08-20T21:41:28.7098537Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_float64 PASSED [0.0082s] [ 93%] 2024-08-20T21:41:28.7099119Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_int16 PASSED [0.0083s] [ 93%] 2024-08-20T21:41:28.7099738Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_int32 PASSED [0.0081s] [ 94%] 2024-08-20T21:41:28.7100345Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_int64 PASSED [0.0081s] [ 94%] 2024-08-20T21:41:28.7100925Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_int8 PASSED [0.0083s] [ 94%] 2024-08-20T21:41:28.7101513Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_uint8 PASSED [0.0080s] [ 94%] 2024-08-20T21:41:28.7102118Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_bfloat16 PASSED [0.0098s] [ 94%] 2024-08-20T21:41:28.7102732Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_bool PASSED [0.0095s] [ 94%] 2024-08-20T21:41:28.7103327Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_float16 PASSED [0.0098s] [ 94%] 2024-08-20T21:41:28.7103924Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_float32 PASSED [0.0096s] [ 94%] 2024-08-20T21:41:28.7104531Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_float64 PASSED [0.0102s] [ 94%] 2024-08-20T21:41:28.7105114Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_int16 PASSED [0.0096s] [ 94%] 2024-08-20T21:41:28.7105701Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_int32 PASSED [0.0098s] [ 94%] 2024-08-20T21:41:28.7106278Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_int64 PASSED [0.0096s] [ 95%] 2024-08-20T21:41:28.7106962Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_int8 PASSED [0.0096s] [ 95%] 2024-08-20T21:41:28.7107562Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_uint8 PASSED [0.0098s] [ 95%] 2024-08-20T21:41:28.7108164Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_bfloat16 PASSED [0.0062s] [ 95%] 2024-08-20T21:41:28.7108742Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_bool PASSED [0.0063s] [ 95%] 2024-08-20T21:41:28.7109369Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_complex128 PASSED [0.0062s] [ 95%] 2024-08-20T21:41:28.7110016Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_complex64 PASSED [0.0065s] [ 95%] 2024-08-20T21:41:28.7110618Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_float16 PASSED [0.0062s] [ 95%] 2024-08-20T21:41:28.7111211Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_float32 PASSED [0.0064s] [ 95%] 2024-08-20T21:41:28.7111794Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_float64 PASSED [0.0062s] [ 95%] 2024-08-20T21:41:28.7112386Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_int16 PASSED [0.0064s] [ 96%] 2024-08-20T21:41:28.7112963Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_int32 PASSED [0.0062s] [ 96%] 2024-08-20T21:41:28.7113557Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_int64 PASSED [0.0061s] [ 96%] 2024-08-20T21:41:28.7114134Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_int8 PASSED [0.0064s] [ 96%] 2024-08-20T21:41:28.7114706Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_uint8 PASSED [0.0062s] [ 96%] 2024-08-20T21:41:28.7115322Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_bfloat16 PASSED [0.0061s] [ 96%] 2024-08-20T21:41:28.7115903Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_bool PASSED [0.0058s] [ 96%] 2024-08-20T21:41:28.7116561Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_complex128 PASSED [0.0060s] [ 96%] 2024-08-20T21:41:28.7117171Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_complex32 PASSED [0.0058s] [ 96%] 2024-08-20T21:41:28.7117800Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_complex64 PASSED [0.0064s] [ 96%] 2024-08-20T21:41:28.7118413Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_float16 PASSED [0.0058s] [ 97%] 2024-08-20T21:41:28.7118999Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_float32 PASSED [0.0060s] [ 97%] 2024-08-20T21:41:28.7119611Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_float64 PASSED [0.0058s] [ 97%] 2024-08-20T21:41:28.7120202Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_int16 PASSED [0.0058s] [ 97%] 2024-08-20T21:41:28.7120787Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_int32 PASSED [0.0059s] [ 97%] 2024-08-20T21:41:28.7121686Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_int64 PASSED [0.0058s] [ 97%] 2024-08-20T21:41:28.7122271Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_int8 PASSED [0.0059s] [ 97%] 2024-08-20T21:41:28.7122852Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_uint8 PASSED [0.0057s] [ 97%] 2024-08-20T21:41:28.7123494Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_bfloat16 PASSED [0.0086s] [ 97%] 2024-08-20T21:41:28.7124102Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_bool PASSED [0.0082s] [ 97%] 2024-08-20T21:41:28.7124764Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_complex128 PASSED [0.0086s] [ 97%] 2024-08-20T21:41:28.7125404Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_complex32 PASSED [0.0083s] [ 98%] 2024-08-20T21:41:28.7126040Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_complex64 PASSED [0.0085s] [ 98%] 2024-08-20T21:41:28.7126683Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_float16 PASSED [0.0082s] [ 98%] 2024-08-20T21:41:28.7127422Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_float32 PASSED [0.0082s] [ 98%] 2024-08-20T21:41:28.7128060Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_float64 PASSED [0.0084s] [ 98%] 2024-08-20T21:41:28.7128676Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_int16 PASSED [0.0082s] [ 98%] 2024-08-20T21:41:28.7129289Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_int32 PASSED [0.0084s] [ 98%] 2024-08-20T21:41:28.7129915Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_int64 PASSED [0.0082s] [ 98%] 2024-08-20T21:41:28.7130523Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_int8 PASSED [0.0084s] [ 98%] 2024-08-20T21:41:28.7131135Z test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_uint8 PASSED [0.0082s] [ 98%] 2024-08-20T21:41:28.7131696Z test_utils.py::TestDeviceUtilsCPU::test_get_default_device_cpu PASSED [0.0020s] [ 99%] 2024-08-20T21:41:28.7132429Z test_utils.py::TestDeviceUtilsCPU::test_get_default_device_more_cpu SKIPPED [0.0002s] (multi-GPU not supported) [ 99%] 2024-08-20T21:41:28.7132947Z test_utils.py::TestDeviceUtilsCPU::test_nn_module_cpu PASSED [0.0016s] [ 99%] 2024-08-20T21:41:28.7133486Z test_utils.py::TestDeviceUtilsCPU::test_set_default_device_cpu PASSED [0.0012s] [ 99%] 2024-08-20T21:41:28.7134052Z test_utils.py::TestCppExtensionUtils::test_cc_compiler_is_ok PASSED [0.0423s] [ 99%] 2024-08-20T21:41:28.7134612Z test_utils.py::TestCppExtensionUtils::test_cpp_compiler_is_ok PASSED [0.0234s] [ 99%] 2024-08-20T21:41:28.7135129Z test_utils.py::TestTraceback::test_basic PASSED [0.0021s] [ 99%] 2024-08-20T21:41:28.7135639Z test_utils.py::TestTraceback::test_captured_traceback PASSED [0.0014s] [ 99%] 2024-08-20T21:41:28.7136186Z test_utils.py::TestTraceback::test_captured_traceback_format_all PASSED [0.0017s] [ 99%] 2024-08-20T21:41:28.7136778Z test_utils.py::TestTraceback::test_captured_traceback_format_all_cached PASSED [0.0017s] [ 99%] 2024-08-20T21:41:28.7137327Z test_utils.py::TestTraceback::test_format_traceback_short PASSED [0.0013s] [100%] 2024-08-20T21:41:28.7137341Z 2024-08-20T21:41:28.7138095Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_utils/test_utils-0a537fe028a4479e.xml - 2024-08-20T21:41:28.7138713Z ============== 982 passed, 57 skipped, 4908 deselected in 15.49s =============== 2024-08-20T21:41:28.7139785Z The following tests failed and then succeeded when run in a new process['test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_sampled_addmm_cpu_float64'] 2024-08-20T21:41:28.7139794Z 2024-08-20T21:41:28.7140346Z FINISHED PRINTING LOG FILE of test_utils 1/1 (test/test-reports/test_utils_1.1_6bb4898f3c88eb75_.log) 2024-08-20T21:41:28.7140352Z 2024-08-20T21:41:28.7140609Z Running test_nn 1/1 ... [2024-08-20 21:41:27.622925] 2024-08-20T21:41:28.7140750Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:41:28.7142104Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_nn.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:41:27.623373] 2024-08-20T21:43:53.0188879Z 2024-08-20T21:43:53.0190203Z test_nn 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_nn_1.1_0f17624421021c3b_.log 2024-08-20T21:43:53.1863501Z Running 2172 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, 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_float, 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_float, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_sum_cuda_double, 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, test/test_nn.py::TestNN::test_BCELoss_no_reduce_cuda, test/test_nn.py::TestNN::test_BCELoss_no_reduce_scalar, test/test_nn.py::TestNN::test_BCELoss_no_reduce_scalar_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, test/test_nn.py::TestNN::test_BCELoss_weights_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_legacy_enum, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_legacy_enum_cuda, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean, 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, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_reduce, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_reduce_scalar, 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_critical_target_len, test/test_nn.py::TestNN::test_CTCLoss_lengthchecks_cpu, test/test_nn.py::TestNN::test_CTCLoss_lengthchecks_cuda, test/test_nn.py::TestNN::test_CTCLoss_long_targets, test/test_nn.py::TestNN::test_CTCLoss_typechecks, test/test_nn.py::TestNN::test_CTCLoss_zero_infinity, 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, test/test_nn.py::TestNN::test_Conv1d_circular_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv1d_cuda, test/test_nn.py::TestNN::test_Conv1d_dilated, test/test_nn.py::TestNN::test_Conv1d_dilated_cuda, test/test_nn.py::TestNN::test_Conv1d_groups, test/test_nn.py::TestNN::test_Conv1d_groups_cuda, test/test_nn.py::TestNN::test_Conv1d_pad1, test/test_nn.py::TestNN::test_Conv1d_pad1_cuda, test/test_nn.py::TestNN::test_Conv1d_pad1size1, test/test_nn.py::TestNN::test_Conv1d_pad1size1_cuda, test/test_nn.py::TestNN::test_Conv1d_pad2, test/test_nn.py::TestNN::test_Conv1d_pad2_cuda, test/test_nn.py::TestNN::test_Conv1d_pad2size1, test/test_nn.py::TestNN::test_Conv1d_pad2size1_cuda, test/test_nn.py::TestNN::test_Conv1d_pad_same, test/test_nn.py::TestNN::test_Conv1d_pad_same2, test/test_nn.py::TestNN::test_Conv1d_pad_same2_cuda, test/test_nn.py::TestNN::test_Conv1d_pad_same_cuda, test/test_nn.py::TestNN::test_Conv1d_pad_same_dilated, test/test_nn.py::TestNN::test_Conv1d_pad_same_dilated_cuda, test/test_nn.py::TestNN::test_Conv1d_pad_valid, test/test_nn.py::TestNN::test_Conv1d_pad_valid_cuda, test/test_nn.py::TestNN::test_Conv1d_reflect_stride2_pad2, test/test_nn.py::TestNN::test_Conv1d_reflect_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv1d_replicate_stride2_pad2, test/test_nn.py::TestNN::test_Conv1d_replicate_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv1d_stride, test/test_nn.py::TestNN::test_Conv1d_stride_cuda, test/test_nn.py::TestNN::test_Conv1d_zero_batch, test/test_nn.py::TestNN::test_Conv1d_zero_batch_cuda, test/test_nn.py::TestNN::test_Conv1d_zeros_stride2_pad2, test/test_nn.py::TestNN::test_Conv1d_zeros_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv2d, test/test_nn.py::TestNN::test_Conv2d_circular_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_circular_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv2d_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise, test/test_nn.py::TestNN::test_Conv2d_depthwise_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise_dilated, test/test_nn.py::TestNN::test_Conv2d_depthwise_dilated_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise_padded, test/test_nn.py::TestNN::test_Conv2d_depthwise_padded_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise_strided, test/test_nn.py::TestNN::test_Conv2d_depthwise_strided_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise_with_multiplier, test/test_nn.py::TestNN::test_Conv2d_depthwise_with_multiplier_cuda, test/test_nn.py::TestNN::test_Conv2d_dilated, test/test_nn.py::TestNN::test_Conv2d_dilated_cuda, test/test_nn.py::TestNN::test_Conv2d_dilated_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_dilated_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_groups, test/test_nn.py::TestNN::test_Conv2d_groups_cuda, test/test_nn.py::TestNN::test_Conv2d_groups_thnn, test/test_nn.py::TestNN::test_Conv2d_groups_thnn_cuda, test/test_nn.py::TestNN::test_Conv2d_groups_thnn_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_groups_thnn_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_groups_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_groups_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_no_bias, test/test_nn.py::TestNN::test_Conv2d_no_bias_cuda, test/test_nn.py::TestNN::test_Conv2d_no_bias_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_no_bias_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_pad_same, test/test_nn.py::TestNN::test_Conv2d_pad_same_cuda, test/test_nn.py::TestNN::test_Conv2d_pad_same_dilated, test/test_nn.py::TestNN::test_Conv2d_pad_same_dilated_cuda, test/test_nn.py::TestNN::test_Conv2d_pad_valid, test/test_nn.py::TestNN::test_Conv2d_pad_valid_cuda, test/test_nn.py::TestNN::test_Conv2d_padding, test/test_nn.py::TestNN::test_Conv2d_padding_cuda, test/test_nn.py::TestNN::test_Conv2d_padding_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_padding_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_reflect_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_reflect_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv2d_replicate_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_replicate_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv2d_strided, test/test_nn.py::TestNN::test_Conv2d_strided_cuda, test/test_nn.py::TestNN::test_Conv2d_strided_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_strided_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_zero_batch, test/test_nn.py::TestNN::test_Conv2d_zero_batch_cuda, test/test_nn.py::TestNN::test_Conv2d_zero_batch_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_zero_batch_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_zeros_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_zeros_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv3d, test/test_nn.py::TestNN::test_Conv3d_1x1x1_no_bias, test/test_nn.py::TestNN::test_Conv3d_1x1x1_no_bias_cuda, test/test_nn.py::TestNN::test_Conv3d_1x1x1_no_bias_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_1x1x1_no_bias_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_circular_stride2_pad2, test/test_nn.py::TestNN::test_Conv3d_circular_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv3d_cuda, test/test_nn.py::TestNN::test_Conv3d_dilated, test/test_nn.py::TestNN::test_Conv3d_dilated_cuda, test/test_nn.py::TestNN::test_Conv3d_dilated_strided, test/test_nn.py::TestNN::test_Conv3d_dilated_strided_cuda, test/test_nn.py::TestNN::test_Conv3d_groups, test/test_nn.py::TestNN::test_Conv3d_groups_cuda, test/test_nn.py::TestNN::test_Conv3d_groups_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_groups_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_no_bias, test/test_nn.py::TestNN::test_Conv3d_no_bias_cuda, test/test_nn.py::TestNN::test_Conv3d_no_bias_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_no_bias_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_pad_same, test/test_nn.py::TestNN::test_Conv3d_pad_same_cuda, test/test_nn.py::TestNN::test_Conv3d_pad_same_dilated, test/test_nn.py::TestNN::test_Conv3d_pad_same_dilated_cuda, test/test_nn.py::TestNN::test_Conv3d_pad_valid, test/test_nn.py::TestNN::test_Conv3d_pad_valid_cuda, test/test_nn.py::TestNN::test_Conv3d_replicate_stride2_pad2, test/test_nn.py::TestNN::test_Conv3d_replicate_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv3d_stride, test/test_nn.py::TestNN::test_Conv3d_stride_cuda, test/test_nn.py::TestNN::test_Conv3d_stride_padding, test/test_nn.py::TestNN::test_Conv3d_stride_padding_cuda, test/test_nn.py::TestNN::test_Conv3d_stride_padding_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_stride_padding_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_stride_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_stride_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_zero_batch, test/test_nn.py::TestNN::test_Conv3d_zero_batch_cuda, test/test_nn.py::TestNN::test_Conv3d_zero_batch_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_zero_batch_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_zeros_stride2_pad2, test/test_nn.py::TestNN::test_Conv3d_zeros_stride2_pad2_cuda, test/test_nn.py::TestNN::test_ConvTranspose1d, test/test_nn.py::TestNN::test_ConvTranspose1d_cuda, test/test_nn.py::TestNN::test_ConvTranspose1d_dilated, test/test_nn.py::TestNN::test_ConvTranspose1d_dilated_cuda, test/test_nn.py::TestNN::test_ConvTranspose1d_groups, test/test_nn.py::TestNN::test_ConvTranspose1d_groups_cuda, test/test_nn.py::TestNN::test_ConvTranspose1d_no_bias, test/test_nn.py::TestNN::test_ConvTranspose1d_no_bias_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d, test/test_nn.py::TestNN::test_ConvTranspose2d_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_dilated, test/test_nn.py::TestNN::test_ConvTranspose2d_dilated_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_dilated_with_long_tensor, test/test_nn.py::TestNN::test_ConvTranspose2d_dilated_with_long_tensor_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_groups, test/test_nn.py::TestNN::test_ConvTranspose2d_groups_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_groups_with_long_tensor, test/test_nn.py::TestNN::test_ConvTranspose2d_groups_with_long_tensor_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias_with_long_tensor, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias_with_long_tensor_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_with_long_tensor, test/test_nn.py::TestNN::test_ConvTranspose2d_with_long_tensor_cuda, test/test_nn.py::TestNN::test_ConvTranspose3d, test/test_nn.py::TestNN::test_ConvTranspose3d_cuda, test/test_nn.py::TestNN::test_ConvTranspose3d_dilated, test/test_nn.py::TestNN::test_ConvTranspose3d_dilated_cuda, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_CrossMapLRN2d, test/test_nn.py::TestNN::test_CrossMapLRN2d_cuda, test/test_nn.py::TestNN::test_ELU_no_batch_dim, test/test_nn.py::TestNN::test_ELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Embedding, test/test_nn.py::TestNN::test_EmbeddingBag_discontiguous, test/test_nn.py::TestNN::test_EmbeddingBag_discontiguous_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_max, test/test_nn.py::TestNN::test_EmbeddingBag_max_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_max_padding_idx, test/test_nn.py::TestNN::test_EmbeddingBag_max_padding_idx_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_mean, test/test_nn.py::TestNN::test_EmbeddingBag_mean_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_mean_padding_idx, test/test_nn.py::TestNN::test_EmbeddingBag_mean_padding_idx_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_sparse, test/test_nn.py::TestNN::test_EmbeddingBag_sparse_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_sum, test/test_nn.py::TestNN::test_EmbeddingBag_sum_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_sum_padding_idx, test/test_nn.py::TestNN::test_EmbeddingBag_sum_padding_idx_cuda, test/test_nn.py::TestNN::test_Embedding_cuda, test/test_nn.py::TestNN::test_Embedding_discontiguous, test/test_nn.py::TestNN::test_Embedding_discontiguous_cuda, test/test_nn.py::TestNN::test_Embedding_sparse, test/test_nn.py::TestNN::test_Embedding_sparse_cuda, test/test_nn.py::TestNN::test_Flatten, test/test_nn.py::TestNN::test_Flatten_cuda, test/test_nn.py::TestNN::test_Flatten_no_batch_dim, test/test_nn.py::TestNN::test_Flatten_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Fold, test/test_nn.py::TestNN::test_Fold_cuda, test/test_nn.py::TestNN::test_Fold_int_input, test/test_nn.py::TestNN::test_Fold_int_input_cuda, test/test_nn.py::TestNN::test_Fold_no_batch_dim_input, test/test_nn.py::TestNN::test_Fold_no_batch_dim_input_cuda, test/test_nn.py::TestNN::test_Fold_no_batch_dim_int_input, test/test_nn.py::TestNN::test_Fold_no_batch_dim_int_input_cuda, test/test_nn.py::TestNN::test_GELU_no_batch_dim, test/test_nn.py::TestNN::test_GELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_GLU_no_batch_dim, test/test_nn.py::TestNN::test_GLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Hardshrink_no_batch_dim, test/test_nn.py::TestNN::test_Hardshrink_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Hardsigmoid_no_batch_dim, test/test_nn.py::TestNN::test_Hardsigmoid_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Hardswish_no_batch_dim, test/test_nn.py::TestNN::test_Hardswish_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Hardtanh_no_batch_dim, test/test_nn.py::TestNN::test_Hardtanh_no_batch_dim_cuda, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_margin_no_reduce, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_margin_no_reduce_cuda, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_reduce, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_HuberLoss_delta, test/test_nn.py::TestNN::test_HuberLoss_delta_cuda, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_KLDivLoss_batch_mean, test/test_nn.py::TestNN::test_KLDivLoss_batch_mean_log_target, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_log_target, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_log_target_cuda, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_scalar, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_scalar_log_target, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_scalar_log_target_cuda, test/test_nn.py::TestNN::test_KLDivLoss_with_log_target_no_reduce, test/test_nn.py::TestNN::test_KLDivLoss_with_log_target_no_reduce_cuda, test/test_nn.py::TestNN::test_KLDivLoss_with_target_no_reduce, test/test_nn.py::TestNN::test_KLDivLoss_with_target_no_reduce_cuda, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_mean, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_sum, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_L1Loss_no_reduce, test/test_nn.py::TestNN::test_L1Loss_no_reduce_complex, test/test_nn.py::TestNN::test_L1Loss_no_reduce_complex_cuda, test/test_nn.py::TestNN::test_L1Loss_no_reduce_cuda, test/test_nn.py::TestNN::test_L1Loss_no_reduce_scalar, test/test_nn.py::TestNN::test_L1Loss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_LSTM_cell, test/test_nn.py::TestNN::test_LSTM_cell_forward_hidden_size, test/test_nn.py::TestNN::test_LSTM_cell_forward_input_size, test/test_nn.py::TestNN::test_LayerNorm_3d_no_affine_large_feature, test/test_nn.py::TestNN::test_LayerNorm_3d_no_affine_large_feature_cuda, test/test_nn.py::TestNN::test_LayerNorm_3d_no_affine_large_feature_eval, test/test_nn.py::TestNN::test_LayerNorm_3d_no_affine_large_feature_eval_cuda, test/test_nn.py::TestNN::test_LeakyReLU_no_batch_dim, test/test_nn.py::TestNN::test_LeakyReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Linear, test/test_nn.py::TestNN::test_Linear_cuda, test/test_nn.py::TestNN::test_Linear_no_batch_dim, test/test_nn.py::TestNN::test_Linear_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Linear_no_bias, test/test_nn.py::TestNN::test_Linear_no_bias_cuda, test/test_nn.py::TestNN::test_LogSigmoid_no_batch_dim, test/test_nn.py::TestNN::test_LogSigmoid_no_batch_dim_cuda, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_none, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_MSELoss_no_reduce, test/test_nn.py::TestNN::test_MSELoss_no_reduce_cuda, test/test_nn.py::TestNN::test_MSELoss_no_reduce_scalar, test/test_nn.py::TestNN::test_MSELoss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_MaxUnpool1d_net, test/test_nn.py::TestNN::test_MaxUnpool1d_net_cuda, test/test_nn.py::TestNN::test_MaxUnpool1d_net_no_batch_dim, test/test_nn.py::TestNN::test_MaxUnpool1d_net_no_batch_dim_cuda, test/test_nn.py::TestNN::test_MaxUnpool2d_net, test/test_nn.py::TestNN::test_MaxUnpool2d_net_cuda, test/test_nn.py::TestNN::test_MaxUnpool2d_net_no_batch_dim, test/test_nn.py::TestNN::test_MaxUnpool2d_net_no_batch_dim_cuda, test/test_nn.py::TestNN::test_MaxUnpool3d_net, test/test_nn.py::TestNN::test_MaxUnpool3d_net_cuda, test/test_nn.py::TestNN::test_MaxUnpool3d_net_no_batch_dim, test/test_nn.py::TestNN::test_MaxUnpool3d_net_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Mish_no_batch_dim, test/test_nn.py::TestNN::test_Mish_no_batch_dim_cuda, test/test_nn.py::TestNN::test_ModuleDict, test/test_nn.py::TestNN::test_ModuleList, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_0d_no_reduce, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_0d_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_1d_no_reduce, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_1d_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_index_neg, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_index_neg_cuda, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_reduce, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_reduce, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_weights_no_reduce, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_weights_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiMarginLoss_1d_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_1d_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiMarginLoss_margin_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_margin_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiMarginLoss_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiMarginLoss_p_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_p_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiMarginLoss_weights_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_weights_no_reduce_cuda, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_cuda, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_ignore_index, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_ignore_index_cuda, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_weights, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_weights_cuda, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_cuda, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_ignore_index, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_ignore_index_cuda, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_weights, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_weights_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_NLLLoss_no_reduce, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_ignore_index, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_ignore_index_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_ignore_index, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_ignore_index_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_ignore_index_neg, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_ignore_index_neg_cuda, test/test_nn.py::TestNN::test_PReLU_backward_requires_grad_false, test/test_nn.py::TestNN::test_PReLU_no_batch_dim, test/test_nn.py::TestNN::test_PReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_PairwiseDistance, test/test_nn.py::TestNN::test_PairwiseDistance_broadcast_lhs, test/test_nn.py::TestNN::test_PairwiseDistance_broadcast_lhs_cuda, test/test_nn.py::TestNN::test_PairwiseDistance_broadcast_rhs, test/test_nn.py::TestNN::test_PairwiseDistance_broadcast_rhs_cuda, test/test_nn.py::TestNN::test_PairwiseDistance_cuda, test/test_nn.py::TestNN::test_PairwiseDistance_no_batch_dim, test/test_nn.py::TestNN::test_PairwiseDistance_no_batch_dim_cuda, test/test_nn.py::TestNN::test_PairwiseDistance_with_non_default_args, test/test_nn.py::TestNN::test_PairwiseDistance_with_non_default_args_cuda, test/test_nn.py::TestNN::test_ParameterDict, test/test_nn.py::TestNN::test_ParameterDict_replication, test/test_nn.py::TestNN::test_ParameterList, test/test_nn.py::TestNN::test_ParameterList_meta, test/test_nn.py::TestNN::test_ParameterList_replication, test/test_nn.py::TestNN::test_PixelShuffle, test/test_nn.py::TestNN::test_PixelShuffle_cuda, test/test_nn.py::TestNN::test_PixelUnshuffle, test/test_nn.py::TestNN::test_PixelUnshuffle_cuda, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_reduce, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_RNN_cell, test/test_nn.py::TestNN::test_RNN_cell_forward_zero_hidden_size, test/test_nn.py::TestNN::test_RNN_cell_no_broadcasting, test/test_nn.py::TestNN::test_RNN_change_dropout, test/test_nn.py::TestNN::test_RNN_cpu_vs_cudnn_no_dropout, test/test_nn.py::TestNN::test_RNN_cpu_vs_cudnn_with_dropout, test/test_nn.py::TestNN::test_RNN_cudnn_weight_norm, test/test_nn.py::TestNN::test_RNN_dropout, test/test_nn.py::TestNN::test_RNN_dropout_state, test/test_nn.py::TestNN::test_RNN_input_size_zero, test/test_nn.py::TestNN::test_RNN_nonlinearity, test/test_nn.py::TestNN::test_RNN_nonlinearity_passed_as_arg, test/test_nn.py::TestNN::test_RReLU, test/test_nn.py::TestNN::test_RReLU_cuda, test/test_nn.py::TestNN::test_RReLU_no_batch_dim, test/test_nn.py::TestNN::test_RReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_RReLU_with_up_down, test/test_nn.py::TestNN::test_RReLU_with_up_down_cuda, test/test_nn.py::TestNN::test_RReLU_with_up_down_scalar, test/test_nn.py::TestNN::test_RReLU_with_up_down_scalar_cuda, test/test_nn.py::TestNN::test_ReLU6_no_batch_dim, test/test_nn.py::TestNN::test_ReLU6_no_batch_dim_cuda, test/test_nn.py::TestNN::test_ReLU_no_batch_dim, test/test_nn.py::TestNN::test_ReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_ReplicationPad3d, test/test_nn.py::TestNN::test_ReplicationPad3d_complex, test/test_nn.py::TestNN::test_ReplicationPad3d_complex_cuda, test/test_nn.py::TestNN::test_ReplicationPad3d_cuda, test/test_nn.py::TestNN::test_ReplicationPad3d_no_batch_dim, test/test_nn.py::TestNN::test_ReplicationPad3d_no_batch_dim_cuda, test/test_nn.py::TestNN::test_SELU_no_batch_dim, test/test_nn.py::TestNN::test_SELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Sequential_add, test/test_nn.py::TestNN::test_Sequential_append, test/test_nn.py::TestNN::test_Sequential_delitem, 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_imul, test/test_nn.py::TestNN::test_Sequential_insert, test/test_nn.py::TestNN::test_Sequential_insert_fail_case, test/test_nn.py::TestNN::test_Sequential_mul, test/test_nn.py::TestNN::test_Sequential_pop, test/test_nn.py::TestNN::test_Sequential_rmul, test/test_nn.py::TestNN::test_Sequential_setitem, test/test_nn.py::TestNN::test_Sequential_setitem_named, test/test_nn.py::TestNN::test_SiLU_no_batch_dim, test/test_nn.py::TestNN::test_SiLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Sigmoid_no_batch_dim, 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, 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_none_cuda_double, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_sum, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_sum_cuda_half, 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, test/test_nn.py::TestNN::test_SmoothL1Loss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_SmoothL1Loss_zero_beta, test/test_nn.py::TestNN::test_SmoothL1Loss_zero_beta_cuda, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_mean, 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_mean_cuda_half, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_SoftMarginLoss_no_reduce, test/test_nn.py::TestNN::test_SoftMarginLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_Softplus_no_batch_dim, 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_Softshrink_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Softsign_no_batch_dim, test/test_nn.py::TestNN::test_Softsign_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Tanh_no_batch_dim, test/test_nn.py::TestNN::test_Tanh_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Tanhshrink_no_batch_dim, test/test_nn.py::TestNN::test_Tanhshrink_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Threshold_no_batch_dim, test/test_nn.py::TestNN::test_Threshold_no_batch_dim_cuda, test/test_nn.py::TestNN::test_TransformerDecoderLayer_gelu_activation, test/test_nn.py::TestNN::test_TransformerDecoderLayer_gelu_activation_cuda, test/test_nn.py::TestNN::test_TransformerDecoderLayer_relu_activation, 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_gelu_activation_cuda, test/test_nn.py::TestNN::test_TransformerEncoderLayer_relu_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_float, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_none_cuda_double, 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_TripletMarginLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_Unflatten_no_batch_dim, test/test_nn.py::TestNN::test_Unflatten_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Unfold, test/test_nn.py::TestNN::test_Unfold_cuda, test/test_nn.py::TestNN::test_Unfold_int_input, test/test_nn.py::TestNN::test_Unfold_int_input_cuda, test/test_nn.py::TestNN::test_adaptive_log_softmax, test/test_nn.py::TestNN::test_add_module, test/test_nn.py::TestNN::test_add_module_raises_error_if_attr_exists, test/test_nn.py::TestNN::test_affine_grid, 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_backward_cl_cf_consistency_device_cpu_nd_3, 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_non_contig_cpu_SyncBatchNorm, 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_batchnorm_raises_error_if_running_mean_is_not_same_size_as_input, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_running_var_is_not_same_size_as_input, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_running_var_or_running_mean_have_forward_grad, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_weight_is_not_same_size_as_input, test/test_nn.py::TestNN::test_bce_loss_always_nonnegative, test/test_nn.py::TestNN::test_bce_loss_broadcasts_weights, test/test_nn.py::TestNN::test_bce_loss_input_range, test/test_nn.py::TestNN::test_bce_loss_size_mismatch, test/test_nn.py::TestNN::test_bce_with_logits_broadcasts_pos_weights, 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_has_correct_grad_at_zero, 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_raises_if_target_and_input_are_different_size, test/test_nn.py::TestNN::test_bce_with_logits_stability, 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_bilinear_broadcasting, test/test_nn.py::TestNN::test_bilinear_no_bias, test/test_nn.py::TestNN::test_bilinear_non_contiguous, 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_broadcast_not_requiring_grad, test/test_nn.py::TestNN::test_buffer_bad_module_subclass, test/test_nn.py::TestNN::test_buffer_not_persistent, test/test_nn.py::TestNN::test_buffer_not_persistent_assign, test/test_nn.py::TestNN::test_buffer_not_persistent_del, test/test_nn.py::TestNN::test_buffer_not_persistent_load, test/test_nn.py::TestNN::test_buffer_not_persistent_overwrite, test/test_nn.py::TestNN::test_buffers_and_named_buffers, test/test_nn.py::TestNN::test_call_supports_python_dict_output, test/test_nn.py::TestNN::test_channel_shuffle_return_alias_of_self, test/test_nn.py::TestNN::test_children, test/test_nn.py::TestNN::test_container_copy, test/test_nn.py::TestNN::test_convert_sync_batchnorm, test/test_nn.py::TestNN::test_cosine_embedding_loss_error_on_diff_shapes, test/test_nn.py::TestNN::test_cosine_embedding_loss_error_on_nonexpandable_shapes, test/test_nn.py::TestNN::test_cosine_embedding_loss_invalid_shape, 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_cosine_similarity, test/test_nn.py::TestNN::test_cross_entropy_loss, test/test_nn.py::TestNN::test_cross_entropy_loss_precision, test/test_nn.py::TestNN::test_cross_entropy_loss_zero_div, test/test_nn.py::TestNN::test_cudnn_forward_exception, test/test_nn.py::TestNN::test_cudnn_rnn_dropout_states_device, test/test_nn.py::TestNN::test_cudnn_weight_format, test/test_nn.py::TestNN::test_cudnn_weight_tying, test/test_nn.py::TestNN::test_dir, test/test_nn.py::TestNN::test_dir_digit, test/test_nn.py::TestNN::test_elu_inplace_gradgrad, test/test_nn.py::TestNN::test_elu_inplace_on_view, test/test_nn.py::TestNN::test_error_RNN_seq_len_zero, test/test_nn.py::TestNN::test_extra_state, test/test_nn.py::TestNN::test_extra_state_missing_get_extra_state, test/test_nn.py::TestNN::test_extra_state_missing_set_extra_state, test/test_nn.py::TestNN::test_extra_state_non_dict, test/test_nn.py::TestNN::test_fb_fc_packed, test/test_nn.py::TestNN::test_flatten, test/test_nn.py::TestNN::test_fold_invalid_arg, 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_get_buffer_from_submodules, test/test_nn.py::TestNN::test_getattr_with_property, 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_grid_sample_nearest_neighbor_rounding_mode_consistency, 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, test/test_nn.py::TestNN::test_interpolate_bicubic_2d, 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_2d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_2d_cuda, 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, 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_2d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_2d, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_shared_2d, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_shared_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_skewed_2d, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_skewed_2d_align_corners, 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, test/test_nn.py::TestNN::test_interpolate_bilinear_tuple_2d_align_corners, test/test_nn.py::TestNN::test_interpolate_bilinear_tuple_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_tuple_2d_cuda, test/test_nn.py::TestNN::test_interpolate_buffer_overflow, test/test_nn.py::TestNN::test_interpolate_illegal_memory_access, test/test_nn.py::TestNN::test_interpolate_linear_1d, test/test_nn.py::TestNN::test_interpolate_linear_1d_align_corners, test/test_nn.py::TestNN::test_interpolate_linear_1d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_linear_1d_cuda, test/test_nn.py::TestNN::test_interpolate_linear_1d_zero_dim, test/test_nn.py::TestNN::test_interpolate_linear_1d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_linear_scale_1d, 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_scale_1d_cuda, test/test_nn.py::TestNN::test_interpolate_linear_tuple_1d, test/test_nn.py::TestNN::test_interpolate_linear_tuple_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_1d, 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_1d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_2d, test/test_nn.py::TestNN::test_interpolate_nearest_2d_cuda, 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_2d_zero_dim_cuda, 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, 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_2d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_3d, 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_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_3d_zero_dim, test/test_nn.py::TestNN::test_interpolate_trilinear_3d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_scale_3d, test/test_nn.py::TestNN::test_interpolate_trilinear_scale_3d_align_corners, test/test_nn.py::TestNN::test_interpolate_trilinear_scale_3d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_scale_3d_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_tuple_3d, 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_kl_div_log_softmax_target, test/test_nn.py::TestNN::test_kl_div_with_diff_type, test/test_nn.py::TestNN::test_kl_div_with_diff_type_log_target, test/test_nn.py::TestNN::test_l1_loss_correct, test/test_nn.py::TestNN::test_layer_norm_eps, test/test_nn.py::TestNN::test_layer_norm_grads_with_create_graph_flag, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_bias_weightCOO, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_bias_weightCSC, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_bias_weightCSR, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_bias_weightStrided, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_nobias_weightCOO, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_nobias_weightCSC, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_nobias_weightCSR, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_nobias_weightStrided, test/test_nn.py::TestNN::test_linear_broadcasting, test/test_nn.py::TestNN::test_linear_raise_on_scalar_input, test/test_nn.py::TestNN::test_log_softmax_dim0, test/test_nn.py::TestNN::test_log_softmax_dim0_cuda, test/test_nn.py::TestNN::test_log_softmax_dim3, test/test_nn.py::TestNN::test_log_softmax_dim3_cuda, test/test_nn.py::TestNN::test_log_softmax_lastdim, test/test_nn.py::TestNN::test_log_softmax_lastdim_cuda, test/test_nn.py::TestNN::test_log_softmax_scalar, test/test_nn.py::TestNN::test_log_softmax_scalar_cuda, test/test_nn.py::TestNN::test_log_softmax_spatial, test/test_nn.py::TestNN::test_log_softmax_spatial_cuda, test/test_nn.py::TestNN::test_log_softmax_spatial_special, 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_margin_no_reduce, test/test_nn.py::TestNN::test_margin_ranking_loss_no_reduce, test/test_nn.py::TestNN::test_max_pool1d_invalid_output_size, test/test_nn.py::TestNN::test_module_apply_inplace_op, test/test_nn.py::TestNN::test_module_backcompat, test/test_nn.py::TestNN::test_module_super_init, test/test_nn.py::TestNN::test_module_to_argparse, test/test_nn.py::TestNN::test_modules, test/test_nn.py::TestNN::test_mse_loss_size_warning, test/test_nn.py::TestNN::test_multimarginloss_1d_input_0d_target_no_reduce, test/test_nn.py::TestNN::test_multimarginloss_1d_input_0d_target_no_reduce_cuda, test/test_nn.py::TestNN::test_named_children, test/test_nn.py::TestNN::test_named_modules, test/test_nn.py::TestNN::test_named_parameters_remove_duplicate, test/test_nn.py::TestNN::test_native_channel_shuffle_return_alias_of_self, test/test_nn.py::TestNN::test_nested_tensor_from_mask, test/test_nn.py::TestNN::test_nested_tensor_from_mask_error, test/test_nn.py::TestNN::test_no_grad, test/test_nn.py::TestNN::test_non_leaf_parameters, test/test_nn.py::TestNN::test_normalize, 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_pad_scalar_error, test/test_nn.py::TestNN::test_padding_list, test/test_nn.py::TestNN::test_pairwise_distance, test/test_nn.py::TestNN::test_parameter_assignment, test/test_nn.py::TestNN::test_parameterlistdict_pickle, test/test_nn.py::TestNN::test_parameterlistdict_setting_attributes, test/test_nn.py::TestNN::test_parameters_and_named_parameters, test/test_nn.py::TestNN::test_parameters_to_vector, test/test_nn.py::TestNN::test_parse_to, test/test_nn.py::TestNN::test_partial_flat_weights, test/test_nn.py::TestNN::test_pdist, test/test_nn.py::TestNN::test_pdist_cpu_gradgrad_unimplemented, test/test_nn.py::TestNN::test_pdist_cuda_gradgrad_unimplemented, test/test_nn.py::TestNN::test_pdist_empty_col, test/test_nn.py::TestNN::test_pdist_empty_row, test/test_nn.py::TestNN::test_pdist_large, test/test_nn.py::TestNN::test_pdist_zeros, test/test_nn.py::TestNN::test_pickle_module_no_weights_only_warning, test/test_nn.py::TestNN::test_pixel_shuffle_nhwc_cpu, test/test_nn.py::TestNN::test_pixel_shuffle_unshuffle, test/test_nn.py::TestNN::test_pointwise_loss_broadcast, test/test_nn.py::TestNN::test_pointwise_loss_target_grad_none_reduction, 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_check_device, 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_buffer_raises_error_if_name_is_not_string, test/test_nn.py::TestNN::test_register_buffer_raises_error_if_not_tensor, test/test_nn.py::TestNN::test_register_parameter_allows_overwriting_with_same_name, test/test_nn.py::TestNN::test_register_parameter_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_repr, test/test_nn.py::TestNN::test_requires_grad_, test/test_nn.py::TestNN::test_rnn_args_check, test/test_nn.py::TestNN::test_rnn_check_device, test/test_nn.py::TestNN::test_rnn_initial_hidden_state, test/test_nn.py::TestNN::test_rnn_weight_norm, test/test_nn.py::TestNN::test_set_submodule, test/test_nn.py::TestNN::test_share_memory, test/test_nn.py::TestNN::test_smoothl1loss_intergral_target, test/test_nn.py::TestNN::test_smoothl1loss_negative_beta_not_supported, 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_functional_dim3_cuda, test/test_nn.py::TestNN::test_softmax_functional_scalar, test/test_nn.py::TestNN::test_softmax_functional_scalar_cuda, test/test_nn.py::TestNN::test_softmax_lastdim, test/test_nn.py::TestNN::test_softmax_lastdim_cuda, test/test_nn.py::TestNN::test_softmax_lastdim_dtype, test/test_nn.py::TestNN::test_softmax_lastdim_dtype_cuda, test/test_nn.py::TestNN::test_softmax_spatial, test/test_nn.py::TestNN::test_softmax_spatial_cuda, test/test_nn.py::TestNN::test_softmax_spatial_dtype, 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_softmax_spatial_special_cuda, 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_sync_batchnorm_accuracy_cuda, test/test_nn.py::TestNN::test_sync_batchnorm_backward_elemt, test/test_nn.py::TestNN::test_threshold_bfloat16_half, test/test_nn.py::TestNN::test_threshold_int, test/test_nn.py::TestNN::test_to, test/test_nn.py::TestNN::test_train_errors_for_invalid_mode, test/test_nn.py::TestNN::test_transformer_args_check, test/test_nn.py::TestNN::test_transformer_layer_args_check, test/test_nn.py::TestNN::test_transformerdecoder, test/test_nn.py::TestNN::test_transformerdecoderlayer, test/test_nn.py::TestNN::test_transformerdecoderlayer_gelu, test/test_nn.py::TestNN::test_triplet_margin_loss, 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_triplet_margin_loss_swap_no_reduce, test/test_nn.py::TestNN::test_type, test/test_nn.py::TestNN::test_unflatten, test/test_nn.py::TestNN::test_unflatten_invalid_arg, 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, test/test_nn.py::TestNN::test_upsamplingLinear1d_spatial_invariance, test/test_nn.py::TestNN::test_upsamplingTrilinear3d_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::TestNN::test_vector_to_parameters, test/test_nn.py::TestNN::test_weight_norm, test/test_nn.py::TestNN::test_weight_norm_pickle, test/test_nn.py::TestNN::test_zero_grad, test/test_nn.py::TestFusionEval::test_fuse_module_eval_numerics, test/test_nn.py::TestConstantPadNd::test_constant_pad_nd, test/test_nn.py::TestConstantPadNd::test_preserves_memory_format, test/test_nn.py::TestAddRelu::test_add_relu, test/test_nn.py::TestAddRelu::test_add_relu_broadcasting, test/test_nn.py::TestFunctionalPickle::test_pickle_softsign, test/test_nn.py::TestFusionUtils::test_fuse_conv_bn_requires_grad, 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_BatchNorm_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_Bilinear_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_cudnn_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_empty_target_cpu, 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_mean_use_module_form_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_none_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_GRU_grad_and_gradgrad_cpu_float64, 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_GroupNorm_raises_error_if_one_value_per_group_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_differentiable_backward_using_oneDNN_cpu_float32, 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_LayerNorm_numeric_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_LocalResponseNorm_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_MarginLoss_empty_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_MarginLoss_empty_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_MarginLoss_warnings_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad2d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad3d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad_empty_cpu_complex64, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad_empty_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad1d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad2d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad3d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad_empty_cpu_complex128, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad_empty_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_TransformerDecoderLayer_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_TransformerDecoder_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_TransformerEncoderLayer_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_TransformerEncoder_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_Transformer_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_Unfold_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_activations_bfloat16_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_2d_rotate45_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_affine_2d_rotate90_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_affine_2d_rotateRandom_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_affine_3d_rotateRandom_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_avg_pool_large_tensor2_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_avg_pool_large_tensor_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_affine_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_affine_mixed_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_affine_mixed_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_eval_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_eval_mixed_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_eval_mixed_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_grad_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_large_batch_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_large_batch_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_simple_average_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_simple_average_mixed_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_simple_average_mixed_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_update_stats_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_channel_shuffle_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_error_if_nonfinite_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_0_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_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_4_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_1_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_foreach_True_norm_type_4_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_True_norm_type_inf_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_multi_device_foreach_False_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_conv_empty_input_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_conv_empty_input_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_64bit_reduction_mean_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_64bit_reduction_none_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_64bit_reduction_sum_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_label_smoothing_consistent_index_target_and_probs_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_label_smoothing_with_probs_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_large_tensor_reduction_mean_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_2d_out_of_bounds_class_index_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_2d_out_of_bounds_class_index_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_index_target_unit_weights_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_all_reductions_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_mean_weighted_False_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_none_weighted_True_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_cross_entropy_loss_prob_target_unit_weights_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_ctc_loss_cudnn_tensor_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_device_mask_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_fold_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_glu_bfloat16_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_bfloat16_precision_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_half_precision_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_large_index_2d_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_large_index_2d_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_large_index_3d_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_large_index_3d_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_nan_inf_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_nan_inf_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_groupnorm_nhwc_cpu_bfloat16, 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_hardsigmoid_grad_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_hardswish_grad_cpu, 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_InstanceNorm1d_no_batch_dim_False_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm1d_no_batch_dim_False_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm1d_no_batch_dim_True_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm1d_no_batch_dim_True_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm2d_no_batch_dim_False_affine_False_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_InstanceNorm2d_no_batch_dim_True_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm2d_no_batch_dim_True_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_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_True_affine_True_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_layernorm_half_precision_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_layernorm_weight_bias_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_log_softmax_cpu_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_logsigmoid_out_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_lstmcell_backward_only_one_output_grad_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_TxT_layout_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_devices_parity_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_forward_with_nans_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_grad_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_lowp_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_lowp_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_mask_types_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_transformer_layout_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_mish_inplace_overlap_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_module_to_empty_non_recursive_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_all_ignored_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_byte_target_matches_long_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_empty_tensor_reduction_mean_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_empty_tensor_reduction_none_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_empty_tensor_reduction_sum_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_invalid_target_dim_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_large_tensor_reduction_sum_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_mismatched_batch_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_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nn_scalars_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_pad_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_prelu_backward_32bit_indexing_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_replicatepad_64bit_indexing_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_rnn_fused_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_rnn_fused_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_rnn_retain_variables_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_save_lstm_compatibility_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_silu_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_skip_init_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_smooth_l1_loss_bfloat16_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_smooth_l1_loss_vs_huber_loss_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_smoothl1loss_backward_zero_beta_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_64bit_indexing_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_backward_64bit_indexing_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_bfloat16_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_cpu_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_cpu_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_forward_64bit_indexing_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_results_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_softplus_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softplus_low_threshold_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softshrink_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softshrink_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softshrink_negative_cpu, 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_transformerencoderlayer_gelu_cpu_float32, 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_399_output_size_437_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_format0_align_corners_True_input_size_399_output_size_437_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format0_align_corners_True_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_False_input_size_403_output_size_377_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format1_align_corners_True_input_size_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_False_mode_bicubic_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_False_mode_bilinear_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_False_mode_bilinear_memory_format1_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_bicubic_memory_format1_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_False_align_corners_True_mode_bilinear_memory_format1_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_bicubic_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_False_mode_bilinear_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_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_True_mode_bicubic_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_True_mode_bilinear_memory_format0_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_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_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_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_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_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_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_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_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_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_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_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_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_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_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_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_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_False_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_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_False_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_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_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_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_False_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_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_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_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_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_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_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_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_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_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_False_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_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_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_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_format0_mode_bicubic_antialias_False_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_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_format0_mode_bicubic_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_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_format0_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_format0_mode_bicubic_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_bicubic_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_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_format0_mode_bicubic_antialias_False_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_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_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_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_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_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_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_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_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_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_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_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_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_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_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_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_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_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_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_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_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_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_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_True_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_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_1_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_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_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_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_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_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_1_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_format0_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_format0_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_format0_mode_bicubic_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_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_format0_mode_bicubic_antialias_True_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_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_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_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_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_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_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_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_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_format0_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_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_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_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_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_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_format0_mode_bicubic_antialias_True_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_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_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_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_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_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_bilinear_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_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_format0_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_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_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_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_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_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_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_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_bilinear_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_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_format0_mode_bilinear_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_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_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_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_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_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_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_format0_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_format0_mode_bilinear_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_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_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_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_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_restrided_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_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_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_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_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_bilinear_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_format0_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_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_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_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_False_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_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_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_False_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_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_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_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_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_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_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_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_format0_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_format0_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_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_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_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_bilinear_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_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_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_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_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_format0_mode_bilinear_antialias_False_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_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_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_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_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_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_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_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_format0_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_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_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_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_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_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_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_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_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_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_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_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_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_bilinear_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_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_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_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_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_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_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_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_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_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_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_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_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_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_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_format0_mode_bilinear_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_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_format0_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_format0_mode_bilinear_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_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_format0_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_format0_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_format0_mode_bilinear_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_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_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_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_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_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_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_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_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_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_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_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_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_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_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_bilinear_antialias_True_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_bilinear_antialias_True_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_format0_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_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_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_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_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_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_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_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_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_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_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_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_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_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_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_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_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_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_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_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_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_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_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_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_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_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_bicubic_antialias_False_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_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_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_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_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_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_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_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_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_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_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_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_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_False_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_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_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_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_bicubic_antialias_False_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_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_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_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_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_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_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_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_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_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_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_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_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_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_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_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_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_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_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_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_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_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_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_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_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_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_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_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_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_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_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_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_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_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_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_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_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_format1_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_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_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_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_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_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_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_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_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_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_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_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_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_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_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_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_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_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_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_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_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_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_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_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_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_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_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_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_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_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_False_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_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_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_True_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_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_True_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_bicubic_antialias_True_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_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_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_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_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_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_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_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_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_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_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_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_1_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_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_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_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_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_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_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_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_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_1_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_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_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_False_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_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_bilinear_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_format1_mode_bilinear_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_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_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_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_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_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_format1_mode_bilinear_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_format1_mode_bilinear_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_bilinear_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_bilinear_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_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_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_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_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_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_False_non_contig_restrided_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_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_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_False_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_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_restrided_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_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_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_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_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_False_non_contig_restrided_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_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_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_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_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_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_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_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_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_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_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_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_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_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_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_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_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_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_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_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_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_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_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_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_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_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_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_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_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_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_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_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_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_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_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_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_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_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_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_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_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_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_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_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_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_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_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_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_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_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_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_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_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_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_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_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_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_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_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_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_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_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_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_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_False_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_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_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_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_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_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_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_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_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_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_bilinear_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_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_format1_mode_bilinear_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_format1_mode_bilinear_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_int16_cpu_int16, 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_int8_cpu_int8, 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_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_int32_cpu_int32, 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_bilinear_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_float32_cpu_float32, 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_int64_cpu_int64, 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-exact_uint8_cpu_uint8, 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_float64_cpu_float64, 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_int8_cpu_int8, 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_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_int16_cpu_int16, 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_int64_cpu_int64, 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_bicubic_uint8_cpu_uint8, 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_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_int16_cpu_int16, 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_int8_cpu_int8, 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_float32_cpu_float32, 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_int16_cpu_int16, 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-exact_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_uint8_cpu_uint8, 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_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_int8_cpu_int8, 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_float64_cpu_float64, 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_int64_cpu_int64, 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_float32_cpu_float32, 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_int32_cpu_int32, 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_int8_cpu_int8, 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_float64_cpu_float64, 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_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_int8_cpu_int8, 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_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_int32_cpu_int32, 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_int8_cpu_int8, 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_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_float64_cpu_float64, 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_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_int8_cpu_int8, 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_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_float64_cpu_float64, 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_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_int64_cpu_int64, 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_bilinear_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_int16_cpu_int16, 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_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_int8_cpu_int8, 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_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBicubic2d_aa_correctness_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBicubic2d_aa_correctness_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBicubic2d_correctness_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBilinear2d_aa_correctness_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBilinear2d_aa_correctness_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_correctness_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_correctness_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_launch_config_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_mode_nearest-exact_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_mode_nearest_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_format0_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_correctness_memory_format1_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_config_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-exact_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_correctness_memory_format1_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_launch_config_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_memory_format0_mode_nearest-exact_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_memory_format0_mode_nearest_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_memory_format1_mode_nearest-exact_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_memory_format1_mode_nearest_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_upsamplingNearestExact1d_rescale_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact2d_correctness_memory_format0_isize_10_osize_15_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_upsamplingNearestExact2d_correctness_memory_format1_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact3d_correctness_memory_format0_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact3d_correctness_memory_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_upsamplingTrilinear3d_align_corners_False_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingTrilinear3d_align_corners_False_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingTrilinear3d_align_corners_True_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingTrilinear3d_align_corners_True_memory_format1_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, test/test_nn.py::TestNNDeviceTypeCPU::test_warp_softmax_64bit_indexing_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_warp_softmax_64bit_indexing_cpu_float32 2024-08-20T21:43:53.3807382Z 2024-08-20T21:43:53.3808004Z Running inductor/test_torchinductor 3/4 ... [2024-08-20 21:43:53.022584] 2024-08-20T21:43:53.3808616Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:43:53.3810679Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_torchinductor.py', '-m', 'serial', '--shard-id=3', '--num-shards=4', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:43:53.025372] 2024-08-20T21:44:00.1034899Z 2024-08-20T21:44:00.1036956Z inductor/test_torchinductor 3/4 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_3.4_981815e81d615304_.log 2024-08-20T21:44:00.1038241Z Running 0 items in this shard: 2024-08-20T21:44:00.1038501Z 2024-08-20T21:44:00.1039200Z Running inductor/test_torchinductor_opinfo 9/39 ... [2024-08-20 21:44:00.103487] 2024-08-20T21:44:00.1039995Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:44:00.1042032Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'serial', '--shard-id=9', '--num-shards=39', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:44:00.103879] 2024-08-20T21:44:08.3811768Z 2024-08-20T21:44:08.3813617Z inductor/test_torchinductor_opinfo 9/39 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_9.39_48e4427ed6613f27_.log 2024-08-20T21:44:08.3815070Z Running 0 items in this shard: 2024-08-20T21:44:08.3815350Z 2024-08-20T21:44:08.3815776Z Running inductor/test_torchinductor_opinfo 10/39 ... [2024-08-20 21:44:08.381339] 2024-08-20T21:44:08.3816417Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:44:08.3819717Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'serial', '--shard-id=10', '--num-shards=39', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:44:08.381655] 2024-08-20T21:44:16.3066969Z 2024-08-20T21:44:16.3068975Z inductor/test_torchinductor_opinfo 10/39 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_10.39_ebad1739ac29331c_.log 2024-08-20T21:44:16.3070611Z Running 0 items in this shard: 2024-08-20T21:44:16.3071093Z 2024-08-20T21:44:16.3071572Z Running inductor/test_torchinductor_opinfo 11/39 ... [2024-08-20 21:44:16.306878] 2024-08-20T21:44:16.3072213Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:44:16.3075132Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'serial', '--shard-id=11', '--num-shards=39', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:44:16.307205] 2024-08-20T21:44:24.2838026Z 2024-08-20T21:44:24.2840142Z inductor/test_torchinductor_opinfo 11/39 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_11.39_66539e414fa4e6e6_.log 2024-08-20T21:44:24.2841466Z Running 0 items in this shard: 2024-08-20T21:44:24.2841726Z 2024-08-20T21:44:24.2842176Z Running inductor/test_torchinductor_opinfo 21/39 ... [2024-08-20 21:44:24.283924] 2024-08-20T21:44:24.2842810Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:44:24.2845654Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'serial', '--shard-id=21', '--num-shards=39', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:44:24.284230] 2024-08-20T21:44:32.2599445Z 2024-08-20T21:44:32.2601403Z inductor/test_torchinductor_opinfo 21/39 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_21.39_c47656872eb0923e_.log 2024-08-20T21:44:32.2602840Z Running 0 items in this shard: 2024-08-20T21:44:32.2603137Z 2024-08-20T21:44:32.2603797Z Running inductor/test_torchinductor_opinfo 22/39 ... [2024-08-20 21:44:32.260094] 2024-08-20T21:44:32.2604441Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:44:32.2607661Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'serial', '--shard-id=22', '--num-shards=39', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:44:32.260436] 2024-08-20T21:44:40.1861004Z 2024-08-20T21:44:40.1863190Z inductor/test_torchinductor_opinfo 22/39 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_22.39_ac65fa055e1601a7_.log 2024-08-20T21:44:40.1865101Z Running 0 items in this shard: 2024-08-20T21:44:40.1865378Z 2024-08-20T21:44:40.1865827Z Running inductor/test_torchinductor_opinfo 23/39 ... [2024-08-20 21:44:40.186241] 2024-08-20T21:44:40.1866511Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:44:40.1869168Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'serial', '--shard-id=23', '--num-shards=39', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:44:40.186578] 2024-08-20T21:44:48.1629718Z 2024-08-20T21:44:48.1631705Z inductor/test_torchinductor_opinfo 23/39 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_23.39_2c404fda42b4c5be_.log 2024-08-20T21:44:48.1633401Z Running 0 items in this shard: 2024-08-20T21:44:48.1633666Z 2024-08-20T21:44:48.1634114Z Running inductor/test_torchinductor_opinfo 33/39 ... [2024-08-20 21:44:48.163129] 2024-08-20T21:44:48.1634769Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:44:48.1637359Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'serial', '--shard-id=33', '--num-shards=39', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:44:48.163445] 2024-08-20T21:44:56.0887879Z 2024-08-20T21:44:56.0890029Z inductor/test_torchinductor_opinfo 33/39 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_33.39_b7516d18cca7b60f_.log 2024-08-20T21:44:56.0891742Z Running 0 items in this shard: 2024-08-20T21:44:56.0892006Z 2024-08-20T21:44:56.0892469Z Running inductor/test_torchinductor_opinfo 34/39 ... [2024-08-20 21:44:56.088928] 2024-08-20T21:44:56.0893152Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:44:56.0895836Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'serial', '--shard-id=34', '--num-shards=39', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:44:56.089280] 2024-08-20T21:45:04.2655570Z 2024-08-20T21:45:04.2657548Z inductor/test_torchinductor_opinfo 34/39 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_34.39_c73a538749dad314_.log 2024-08-20T21:45:04.2658881Z Running 0 items in this shard: 2024-08-20T21:45:04.2659200Z 2024-08-20T21:45:04.2659691Z Running inductor/test_torchinductor_opinfo 35/39 ... [2024-08-20 21:45:04.265734] 2024-08-20T21:45:04.2660329Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:45:04.2664011Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'serial', '--shard-id=35', '--num-shards=39', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:45:04.266081] 2024-08-20T21:45:12.2433921Z 2024-08-20T21:45:12.2436438Z inductor/test_torchinductor_opinfo 35/39 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_35.39_e52f852de0821d98_.log 2024-08-20T21:45:12.2438944Z Running 0 items in this shard: 2024-08-20T21:45:12.2439409Z 2024-08-20T21:45:12.2440409Z Running inductor/test_torchinductor_codegen_dynamic_shapes 2/3 ... [2024-08-20 21:45:12.243618] 2024-08-20T21:45:12.2441514Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:45:12.2444602Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_torchinductor_codegen_dynamic_shapes.py', '-m', 'serial', '--shard-id=2', '--num-shards=3', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:45:12.244050] 2024-08-20T21:45:19.0177939Z 2024-08-20T21:45:19.0180420Z inductor/test_torchinductor_codegen_dynamic_shapes 2/3 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_codegen_dynamic_shapes_2.3_5dfa75bf8653ed6d_.log 2024-08-20T21:45:19.0182269Z Running 0 items in this shard: 2024-08-20T21:45:19.0182550Z 2024-08-20T21:45:19.0183100Z Running inductor/test_torchinductor_codegen_dynamic_shapes 3/3 ... [2024-08-20 21:45:19.017923] 2024-08-20T21:45:19.0184259Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:45:19.0186759Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_torchinductor_codegen_dynamic_shapes.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'] ... [2024-08-20 21:45:19.018247] 2024-08-20T21:45:25.6921060Z 2024-08-20T21:45:25.6923123Z inductor/test_torchinductor_codegen_dynamic_shapes 3/3 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_codegen_dynamic_shapes_3.3_d74033d66f78ea2a_.log 2024-08-20T21:45:25.6924587Z Running 0 items in this shard: 2024-08-20T21:45:25.6924847Z 2024-08-20T21:45:25.6925210Z Running inductor/test_mmdecomp 1/1 ... [2024-08-20 21:45:25.692240] 2024-08-20T21:45:25.6925773Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:45:25.6928789Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_mmdecomp.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:45:25.692569] 2024-08-20T21:45:28.3604969Z 2024-08-20T21:45:28.3606999Z inductor/test_mmdecomp 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_mmdecomp_1.1_33b439996fc8e461_.log 2024-08-20T21:45:28.3608163Z Running 0 items in this shard: 2024-08-20T21:45:28.3608417Z 2024-08-20T21:45:28.3609934Z Running dynamo/test_interop 1/1 ... [2024-08-20 21:45:28.360738] 2024-08-20T21:45:28.3610659Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:45:28.3614780Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_interop.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:45:28.361158] 2024-08-20T21:45:30.9793264Z 2024-08-20T21:45:30.9794836Z dynamo/test_interop 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_interop_1.1_53a51a845bf8522e_.log 2024-08-20T21:45:30.9796143Z Running 0 items in this shard: 2024-08-20T21:45:30.9796473Z 2024-08-20T21:45:30.9797109Z Running dynamo/test_logging 1/1 ... [2024-08-20 21:45:30.979490] 2024-08-20T21:45:30.9797650Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:45:30.9801278Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_logging.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:45:30.979838] 2024-08-20T21:45:33.5978595Z 2024-08-20T21:45:33.5980342Z dynamo/test_logging 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_logging_1.1_8c0485e03a553c47_.log 2024-08-20T21:45:33.5981470Z Running 0 items in this shard: 2024-08-20T21:45:33.5981804Z 2024-08-20T21:45:33.5982134Z Running dynamo/test_exc 1/1 ... [2024-08-20 21:45:33.598040] 2024-08-20T21:45:33.5982654Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:45:33.5995823Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_exc.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:45:33.599274] 2024-08-20T21:45:36.1671533Z 2024-08-20T21:45:36.1673497Z dynamo/test_exc 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_exc_1.1_7d0b16acef5bd912_.log 2024-08-20T21:45:36.1674597Z Running 0 items in this shard: 2024-08-20T21:45:36.1674984Z 2024-08-20T21:45:36.1675389Z Running dynamo/test_global 1/1 ... [2024-08-20 21:45:36.167364] 2024-08-20T21:45:36.1675938Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:45:36.1679970Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_global.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:45:36.167716] 2024-08-20T21:45:38.7357555Z 2024-08-20T21:45:38.7359272Z dynamo/test_global 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_global_1.1_7ca2269c39d76dc5_.log 2024-08-20T21:45:38.7360429Z Running 0 items in this shard: 2024-08-20T21:45:38.7360772Z 2024-08-20T21:45:38.7361514Z Running dynamo/test_unspec 1/1 ... [2024-08-20 21:45:38.735971] 2024-08-20T21:45:38.7362053Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:45:38.7365880Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_unspec.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:45:38.736305] 2024-08-20T21:45:41.3042368Z 2024-08-20T21:45:41.3043898Z dynamo/test_unspec 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_unspec_1.1_ba9bb451c648dab2_.log 2024-08-20T21:45:41.3045808Z Running 0 items in this shard: 2024-08-20T21:45:41.3046075Z 2024-08-20T21:45:41.3046576Z Running inductor/test_cudagraph_trees 1/1 ... [2024-08-20 21:45:41.304388] 2024-08-20T21:45:41.3047361Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:45:41.3050343Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_cudagraph_trees.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:45:41.304728] 2024-08-20T21:45:44.6900799Z 2024-08-20T21:45:44.6902695Z inductor/test_cudagraph_trees 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cudagraph_trees_1.1_4281314fe2a5e2cf_.log 2024-08-20T21:45:44.6903868Z 2024-08-20T21:45:44.6904237Z Running dynamo/test_ctx_manager 1/1 ... [2024-08-20 21:45:44.690209] 2024-08-20T21:45:44.6904827Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:45:44.6908033Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_ctx_manager.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:45:44.690532] 2024-08-20T21:45:47.3083901Z 2024-08-20T21:45:47.3085745Z dynamo/test_ctx_manager 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_ctx_manager_1.1_3fcfa8f593b5fac7_.log 2024-08-20T21:45:47.3087057Z Running 0 items in this shard: 2024-08-20T21:45:47.3087323Z 2024-08-20T21:45:47.3087943Z Running dynamo/test_subgraphs 1/1 ... [2024-08-20 21:45:47.308602] 2024-08-20T21:45:47.3088506Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:45:47.3092341Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_subgraphs.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:45:47.308941] 2024-08-20T21:45:49.8268399Z 2024-08-20T21:45:49.8270147Z dynamo/test_subgraphs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_subgraphs_1.1_d9a22598ae6118d8_.log 2024-08-20T21:45:49.8271424Z Running 0 items in this shard: 2024-08-20T21:45:49.8271683Z 2024-08-20T21:45:49.8272313Z Running dynamo/test_autograd_function 1/1 ... [2024-08-20 21:45:49.826982] 2024-08-20T21:45:49.8272911Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:45:49.8275865Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_autograd_function.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:45:49.827309] 2024-08-20T21:45:52.4453792Z 2024-08-20T21:45:52.4455529Z dynamo/test_autograd_function 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_autograd_function_1.1_dd2786a46876a16a_.log 2024-08-20T21:45:52.4456722Z Running 0 items in this shard: 2024-08-20T21:45:52.4457074Z 2024-08-20T21:45:52.4457512Z Running dynamo/test_activation_checkpointing 1/1 ... [2024-08-20 21:45:52.445549] 2024-08-20T21:45:52.4458160Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:45:52.4461651Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_activation_checkpointing.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:45:52.445876] 2024-08-20T21:45:55.1139464Z 2024-08-20T21:45:55.1141683Z 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_69901fcf9b3ac032_.log 2024-08-20T21:45:55.1143562Z Running 0 items in this shard: 2024-08-20T21:45:55.1143840Z 2024-08-20T21:45:55.1144301Z Running inductor/test_inductor_freezing 1/1 ... [2024-08-20 21:45:55.114052] 2024-08-20T21:45:55.1145072Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:45:55.1147659Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_inductor_freezing.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:45:55.114394] 2024-08-20T21:46:01.6877100Z 2024-08-20T21:46:01.6878819Z inductor/test_inductor_freezing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_inductor_freezing_1.1_714f08d6949c7e60_.log 2024-08-20T21:46:01.6880258Z Running 0 items in this shard: 2024-08-20T21:46:01.6880529Z 2024-08-20T21:46:01.6880969Z Running inductor/test_mkldnn_pattern_matcher 1/2 ... [2024-08-20 21:46:01.687875] 2024-08-20T21:46:01.6881622Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:46:01.6885321Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_mkldnn_pattern_matcher.py', '-m', 'serial', '--shard-id=1', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:46:01.688219] 2024-08-20T21:46:08.3623506Z 2024-08-20T21:46:08.3625960Z inductor/test_mkldnn_pattern_matcher 1/2 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_mkldnn_pattern_matcher_1.2_82352819c36cedc5_.log 2024-08-20T21:46:08.3628067Z Running 0 items in this shard: 2024-08-20T21:46:08.3628470Z 2024-08-20T21:46:08.3629364Z Running inductor/test_cuda_repro 1/1 ... [2024-08-20 21:46:08.362578] 2024-08-20T21:46:08.3630367Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:46:08.3633867Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_cuda_repro.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:46:08.363005] 2024-08-20T21:46:11.6809934Z 2024-08-20T21:46:11.6811591Z inductor/test_cuda_repro 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cuda_repro_1.1_dc31c456540ba31e_.log 2024-08-20T21:46:11.6812804Z 2024-08-20T21:46:11.6813490Z Running inductor/test_kernel_benchmark 1/1 ... [2024-08-20 21:46:11.681113] 2024-08-20T21:46:11.6814120Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:46:11.6817744Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_kernel_benchmark.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:46:11.681465] 2024-08-20T21:46:14.0768562Z 2024-08-20T21:46:14.0770462Z inductor/test_kernel_benchmark 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_kernel_benchmark_1.1_701eb82d554bdf2a_.log 2024-08-20T21:46:14.0771690Z 2024-08-20T21:46:14.0772934Z Running inductor/test_triton_heuristics 1/1 ... [2024-08-20 21:46:14.077022] 2024-08-20T21:46:14.0773896Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:46:14.0777893Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_triton_heuristics.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:46:14.077388] 2024-08-20T21:46:16.5277499Z 2024-08-20T21:46:16.5279758Z inductor/test_triton_heuristics 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_triton_heuristics_1.1_577632b2e95e1526_.log 2024-08-20T21:46:16.5281537Z 2024-08-20T21:46:16.5282252Z Running inductor/test_cudacodecache 1/1 ... [2024-08-20 21:46:16.527897] 2024-08-20T21:46:16.5283448Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:46:16.5287171Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_cudacodecache.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:46:16.528312] 2024-08-20T21:46:18.9404220Z 2024-08-20T21:46:18.9405856Z inductor/test_cudacodecache 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cudacodecache_1.1_dbdda3d01aaf443d_.log 2024-08-20T21:46:18.9407044Z 2024-08-20T21:46:18.9472309Z Running inductor/test_torchinductor 3/4 ... [2024-08-20 21:46:18.946931] 2024-08-20T21:46:18.9472929Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:46:18.9476361Z Running inductor/test_torchinductor_opinfo 9/39 ... [2024-08-20 21:46:18.947330] 2024-08-20T21:46:18.9477258Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:46:18.9479489Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_torchinductor.py', '-m', 'not serial', '--shard-id=3', '--num-shards=4', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:46:18.947411] 2024-08-20T21:46:18.9481839Z Running inductor/test_torchinductor_opinfo 10/39 ... [2024-08-20 21:46:18.947504] 2024-08-20T21:46:18.9482496Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:46:18.9485059Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'not serial', '--shard-id=9', '--num-shards=39', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:46:18.947814] 2024-08-20T21:46:18.9488317Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'not serial', '--shard-id=10', '--num-shards=39', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:46:18.947930] 2024-08-20T21:53:35.7568373Z 2024-08-20T21:53:35.7570421Z inductor/test_torchinductor 3/4 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_3.4_6687661e4569e7c8_.log 2024-08-20T21:53:35.7652163Z Running 176 items in this shard: test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_broadcast1_broadcast1, test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_broadcast1_broadcast2, test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_broadcast1_int, test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_broadcast2_broadcast2, test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_broadcast2_broadcast3, test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_broadcast3_strided, test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_double_broadcast3, test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_double_transposed, test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_strided_int, test/inductor/test_torchinductor.py::CpuTests::test__unsafe_masked_index_cpu, test/inductor/test_torchinductor.py::CpuTests::test_abs_cpu, test/inductor/test_torchinductor.py::CpuTests::test_adaptive_avg_pool2d1_cpu, test/inductor/test_torchinductor.py::CpuTests::test_adaptive_max_pool2d2_cpu, test/inductor/test_torchinductor.py::CpuTests::test_add_complex5_cpu, test/inductor/test_torchinductor.py::CpuTests::test_add_complex6_cpu, test/inductor/test_torchinductor.py::CpuTests::test_add_const_int_cpu, test/inductor/test_torchinductor.py::CpuTests::test_add_inplace_permuted_cpu, test/inductor/test_torchinductor.py::CpuTests::test_arange1_cpu, test/inductor/test_torchinductor.py::CpuTests::test_arange5_cpu, test/inductor/test_torchinductor.py::CpuTests::test_argmax_argmin1_cpu, test/inductor/test_torchinductor.py::CpuTests::test_argmax_argmin2_cpu, test/inductor/test_torchinductor.py::CpuTests::test_argmax_to_float_cpu, test/inductor/test_torchinductor.py::CpuTests::test_as_strided_scatter_cpu, test/inductor/test_torchinductor.py::CpuTests::test_avg_pool2d5_cpu, test/inductor/test_torchinductor.py::CpuTests::test_avg_pool2d6_cpu, test/inductor/test_torchinductor.py::CpuTests::test_avg_pool2d_backward3_cpu, test/inductor/test_torchinductor.py::CpuTests::test_avg_pool3d_backward2_cpu, test/inductor/test_torchinductor.py::CpuTests::test_avg_pool3d_backward4_cpu, test/inductor/test_torchinductor.py::CpuTests::test_batch_norm_2d_cpu, test/inductor/test_torchinductor.py::CpuTests::test_both_scalars_cpu, test/inductor/test_torchinductor.py::CpuTests::test_bucketize_cpu, test/inductor/test_torchinductor.py::CpuTests::test_bucketize_int_cpu, test/inductor/test_torchinductor.py::CpuTests::test_buffer_batch_norm_cpu, test/inductor/test_torchinductor.py::CpuTests::test_buffer_copied_in_graph_with_different_shapes_cpu, test/inductor/test_torchinductor.py::CpuTests::test_builtins_round_float_ndigits_neg_cpu, test/inductor/test_torchinductor.py::CpuTests::test_builtins_round_int_ndigits_zero_cpu, test/inductor/test_torchinductor.py::CpuTests::test_cat_negative_dim_cpu, test/inductor/test_torchinductor.py::CpuTests::test_cat_of_loops_and_extern_kernel_cpu, test/inductor/test_torchinductor.py::CpuTests::test_cat_unbacked_legacy_empty_cpu, test/inductor/test_torchinductor.py::CpuTests::test_cat_upcasting_cpu, test/inductor/test_torchinductor.py::CpuTests::test_cauchy_cpu, test/inductor/test_torchinductor.py::CpuTests::test_config_option_dont_assume_alignment_cpu, test/inductor/test_torchinductor.py::CpuTests::test_consecutive_split_cumsum_cpu, test/inductor/test_torchinductor.py::CpuTests::test_const_int32_to_float_cpu, test/inductor/test_torchinductor.py::CpuTests::test_constant_pad_2d_cpu, test/inductor/test_torchinductor.py::CpuTests::test_conv_with_as_strided_cpu, test/inductor/test_torchinductor.py::CpuTests::test_convolution3_cpu, test/inductor/test_torchinductor.py::CpuTests::test_convolution4_cpu, test/inductor/test_torchinductor.py::CpuTests::test_cumsum_inf_cpu, test/inductor/test_torchinductor.py::CpuTests::test_cumsum_no_mask_cpu, test/inductor/test_torchinductor.py::CpuTests::test_custom_op_3_cpu, test/inductor/test_torchinductor.py::CpuTests::test_div1_cpu, test/inductor/test_torchinductor.py::CpuTests::test_div2_cpu, test/inductor/test_torchinductor.py::CpuTests::test_div4_cpu, test/inductor/test_torchinductor.py::CpuTests::test_div5_cpu, test/inductor/test_torchinductor.py::CpuTests::test_div_prim_cpu, test/inductor/test_torchinductor.py::CpuTests::test_dropout_cpu, test/inductor/test_torchinductor.py::CpuTests::test_dropout_trivial_0_cpu, test/inductor/test_torchinductor.py::CpuTests::test_dropout_trivial_1_cpu, test/inductor/test_torchinductor.py::CpuTests::test_embedding_bag_cpu, test/inductor/test_torchinductor.py::CpuTests::test_empty2_cpu, test/inductor/test_torchinductor.py::CpuTests::test_erfc_cpu, test/inductor/test_torchinductor.py::CpuTests::test_fallback_mutable_op_with_return_cpu, test/inductor/test_torchinductor.py::CpuTests::test_fft_real_input_cpu, test/inductor/test_torchinductor.py::CpuTests::test_fill1_cpu, test/inductor/test_torchinductor.py::CpuTests::test_float_index_expression_cpu, test/inductor/test_torchinductor.py::CpuTests::test_float_index_expression_type_promotion_cpu, test/inductor/test_torchinductor.py::CpuTests::test_forced_buffer_realize_cpu, test/inductor/test_torchinductor.py::CpuTests::test_full_boolean_cpu, test/inductor/test_torchinductor.py::CpuTests::test_fuse_tiled_cpu, test/inductor/test_torchinductor.py::CpuTests::test_fusing_write_into_disjoint_read_cpu, test/inductor/test_torchinductor.py::CpuTests::test_gather3_cpu, test/inductor/test_torchinductor.py::CpuTests::test_gelu_cpu, test/inductor/test_torchinductor.py::CpuTests::test_generate_rand_fp8_cpu, test/inductor/test_torchinductor.py::CpuTests::test_hardswish_cpu, test/inductor/test_torchinductor.py::CpuTests::test_horizonal_fusion2_cpu, test/inductor/test_torchinductor.py::CpuTests::test_index3_cpu, test/inductor/test_torchinductor.py::CpuTests::test_index_dynamic_shapes_cpu, test/inductor/test_torchinductor.py::CpuTests::test_index_propagation_device_assert_masked_cpu, test/inductor/test_torchinductor.py::CpuTests::test_index_propagation_flip_cpu, test/inductor/test_torchinductor.py::CpuTests::test_index_propagation_floordiv_cpu, test/inductor/test_torchinductor.py::CpuTests::test_index_put1_cpu, test/inductor/test_torchinductor.py::CpuTests::test_index_put4_cpu, test/inductor/test_torchinductor.py::CpuTests::test_index_put_failed_reinplace_cpu, test/inductor/test_torchinductor.py::CpuTests::test_index_put_fallback2_cpu, test/inductor/test_torchinductor.py::CpuTests::test_index_select_cpu, test/inductor/test_torchinductor.py::CpuTests::test_inductor_assert_cpu, test/inductor/test_torchinductor.py::CpuTests::test_inplace_activations_cpu, test/inductor/test_torchinductor.py::CpuTests::test_inplace_add_cpu, test/inductor/test_torchinductor.py::CpuTests::test_input_mutation2_cpu, test/inductor/test_torchinductor.py::CpuTests::test_issue102546_cpu, test/inductor/test_torchinductor.py::CpuTests::test_l1_loss_cpu, test/inductor/test_torchinductor.py::CpuTests::test_large_broadcast_reduction_cpu, test/inductor/test_torchinductor.py::CpuTests::test_lgamma_cpu, test/inductor/test_torchinductor.py::CpuTests::test_like_rands2_cpu, test/inductor/test_torchinductor.py::CpuTests::test_linspace3_cpu, test/inductor/test_torchinductor.py::CpuTests::test_masked_scatter_cpu, test/inductor/test_torchinductor.py::CpuTests::test_max_pool2d1_cpu, test/inductor/test_torchinductor.py::CpuTests::test_max_pool2d2_cpu, test/inductor/test_torchinductor.py::CpuTests::test_max_pool2d6_cpu, test/inductor/test_torchinductor.py::CpuTests::test_mean_cpu, test/inductor/test_torchinductor.py::CpuTests::test_mixed_mm2_cpu, test/inductor/test_torchinductor.py::CpuTests::test_multi_gpu_recompile_on_index_cpu, test/inductor/test_torchinductor.py::CpuTests::test_multi_threading_cpu, test/inductor/test_torchinductor.py::CpuTests::test_mutations_loop_fusion_cpu, test/inductor/test_torchinductor.py::CpuTests::test_neg_index_cpu, test/inductor/test_torchinductor.py::CpuTests::test_new_empty_strided_cpu, test/inductor/test_torchinductor.py::CpuTests::test_output_strides_cpu, test/inductor/test_torchinductor.py::CpuTests::test_pad_cast_cpu, test/inductor/test_torchinductor.py::CpuTests::test_pixel_shuffle_channels_last_cpu, test/inductor/test_torchinductor.py::CpuTests::test_pointwise_bessel_j1_cpu, test/inductor/test_torchinductor.py::CpuTests::test_pointwise_bessel_y1_cpu, test/inductor/test_torchinductor.py::CpuTests::test_pointwise_chebyshev_polynomial_u_cpu, test/inductor/test_torchinductor.py::CpuTests::test_pointwise_digamma_cpu, test/inductor/test_torchinductor.py::CpuTests::test_pointwise_gammainc_cpu, test/inductor/test_torchinductor.py::CpuTests::test_pointwise_gammaln_cpu, test/inductor/test_torchinductor.py::CpuTests::test_pointwise_legendre_polynomial_p_cpu, test/inductor/test_torchinductor.py::CpuTests::test_pointwise_log_ndtr_cpu, test/inductor/test_torchinductor.py::CpuTests::test_pointwise_logit_cpu, test/inductor/test_torchinductor.py::CpuTests::test_pointwise_modified_bessel_i1_cpu, test/inductor/test_torchinductor.py::CpuTests::test_pointwise_modified_bessel_k0_cpu, test/inductor/test_torchinductor.py::CpuTests::test_pointwise_round_cpu, test/inductor/test_torchinductor.py::CpuTests::test_pointwise_scaled_modified_bessel_k0_cpu, test/inductor/test_torchinductor.py::CpuTests::test_pointwise_shifted_chebyshev_polynomial_v_cpu, test/inductor/test_torchinductor.py::CpuTests::test_pointwise_xlog1py_cpu, test/inductor/test_torchinductor.py::CpuTests::test_polar_cpu, test/inductor/test_torchinductor.py::CpuTests::test_pow3_cpu, test/inductor/test_torchinductor.py::CpuTests::test_rand_like_deterministic_cpu, test/inductor/test_torchinductor.py::CpuTests::test_randn_generator_cpu, test/inductor/test_torchinductor.py::CpuTests::test_reduction2_cpu, test/inductor/test_torchinductor.py::CpuTests::test_reduction3_cpu, test/inductor/test_torchinductor.py::CpuTests::test_reduction4_cpu, test/inductor/test_torchinductor.py::CpuTests::test_reflection_pad2d_backward_cpu, test/inductor/test_torchinductor.py::CpuTests::test_reinterpret_dtypeview_cpu, test/inductor/test_torchinductor.py::CpuTests::test_relu_cpu, test/inductor/test_torchinductor.py::CpuTests::test_remove_no_ops_cpu, test/inductor/test_torchinductor.py::CpuTests::test_remove_noop_clone_cpu, test/inductor/test_torchinductor.py::CpuTests::test_resize_cpu, test/inductor/test_torchinductor.py::CpuTests::test_roll_cpu, test/inductor/test_torchinductor.py::CpuTests::test_round_cpu, test/inductor/test_torchinductor.py::CpuTests::test_rsqrt_cpu, test/inductor/test_torchinductor.py::CpuTests::test_rsqrt_dynamic_shapes_cpu, test/inductor/test_torchinductor.py::CpuTests::test_scalar_output_cpu, test/inductor/test_torchinductor.py::CpuTests::test_scatter_bf16_cpu, test/inductor/test_torchinductor.py::CpuTests::test_scatter_reduce1_cpu, test/inductor/test_torchinductor.py::CpuTests::test_scheduler_vertical_fusion1_cpu, test/inductor/test_torchinductor.py::CpuTests::test_sdpa_prefer_nd_tiling_False_use_block_ptr_True_cpu, test/inductor/test_torchinductor.py::CpuTests::test_sdpa_prefer_nd_tiling_True_use_block_ptr_True_cpu, test/inductor/test_torchinductor.py::CpuTests::test_sdpa_unaligned_mask_cpu, test/inductor/test_torchinductor.py::CpuTests::test_setitem_with_int_parameter_cpu, test/inductor/test_torchinductor.py::CpuTests::test_shape_padding_cpu, test/inductor/test_torchinductor.py::CpuTests::test_shape_prop_torch_ones_cpu, test/inductor/test_torchinductor.py::CpuTests::test_slice2_cpu, test/inductor/test_torchinductor.py::CpuTests::test_slice_mutation1_cpu, test/inductor/test_torchinductor.py::CpuTests::test_slice_scatter2_cpu, test/inductor/test_torchinductor.py::CpuTests::test_sort_stable_cpu, test/inductor/test_torchinductor.py::CpuTests::test_split_cumsum_cpu, test/inductor/test_torchinductor.py::CpuTests::test_split_cumsum_low_prec_cpu, test/inductor/test_torchinductor.py::CpuTests::test_split_with_sizes_with_unbacked_symints_cpu, test/inductor/test_torchinductor.py::CpuTests::test_sqrt_dynamic_shapes_cpu, test/inductor/test_torchinductor.py::CpuTests::test_squeeze1_cpu, test/inductor/test_torchinductor.py::CpuTests::test_squeeze2_cpu, test/inductor/test_torchinductor.py::CpuTests::test_squeeze_varargs_cpu, test/inductor/test_torchinductor.py::CpuTests::test_stack_cpu, test/inductor/test_torchinductor.py::CpuTests::test_tensor1_cpu, test/inductor/test_torchinductor.py::CpuTests::test_to_device_constant_cpu, test/inductor/test_torchinductor.py::CpuTests::test_to_dtype_cpu, test/inductor/test_torchinductor.py::CpuTests::test_transpose_add_cpu, test/inductor/test_torchinductor.py::CpuTests::test_unspec_inputs_cpu, test/inductor/test_torchinductor.py::CpuTests::test_upsample_nearest2d_cpu, test/inductor/test_torchinductor.py::CpuTests::test_upsample_nearest3d_cpu, test/inductor/test_torchinductor.py::CpuTests::test_var_correction_cpu, test/inductor/test_torchinductor.py::CpuTests::test_vectorized_ops_masked_var_novec_cpu, test/inductor/test_torchinductor.py::CpuTests::test_view_as_complex_cpu, test/inductor/test_torchinductor.py::CpuTests::test_views1_cpu, test/inductor/test_torchinductor.py::TestFull::test_full_dtype 2024-08-20T21:53:35.7720848Z 2024-08-20T21:53:38.2770220Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:53:38.3991987Z Running inductor/test_torchinductor_opinfo 11/39 ... [2024-08-20 21:53:38.398755] 2024-08-20T21:53:38.3992763Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:38.3995469Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'not serial', '--shard-id=11', '--num-shards=39', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:53:38.399182] 2024-08-20T21:54:19.7763723Z 2024-08-20T21:54:19.7766381Z inductor/test_torchinductor_opinfo 10/39 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_10.39_52013efaee73e542_.log 2024-08-20T21:54:19.7828746Z Running 100 items in this shard: test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive__unsafe_masked_index_put_accumulate_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_acosh_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_add_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_add_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_addcdiv_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_addmm_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_all_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_all_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_amin_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_angle_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_argmax_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_as_strided_copy_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_as_strided_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_asinh_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atanh_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atleast_1d_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atleast_3d_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bitwise_and_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bitwise_not_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_byte_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cat_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cat_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_char_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_corrcoef_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_corrcoef_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cov_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cumulative_trapezoid_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_diag_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_diag_embed_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_diagonal_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_diagonal_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_fftn_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ifftn_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ihfftn_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fliplr_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_flipud_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fmin_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_gather_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_gather_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_copy_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_reduce_prod_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_inner_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_int_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isinf_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isreal_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_jiterator_4inputs_with_extra_args_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_kthvalue_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_lcm_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_cholesky_ex_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_multi_dot_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_pinv_singular_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_pinv_singular_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_vector_norm_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_vector_norm_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log1p_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log_softmax_with_dtype_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logcumsumexp_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logical_not_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_select_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_max_binary_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_max_pool2d_with_indices_backward_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_meshgrid_variadic_tensors_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mv_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mvlgamma_mvlgamma_p_3_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_narrow_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_avg_pool3d_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_avg_pool3d_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_grid_sample_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_nll_loss_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pad_constant_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pad_reflect_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pixel_shuffle_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pixel_unshuffle_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pixel_unshuffle_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_relu6_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_relu_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nonzero_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_permute_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_repeat_interleave_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_resolve_neg_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sinh_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_slice_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_softmax_with_dtype_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sort_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_scaled_modified_bessel_k0_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_shifted_chebyshev_polynomial_v_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sum_to_size_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_svd_lowrank_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_t_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tan_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tensordot_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tensordot_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_to_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_trace_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_triu_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_triu_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unsafe_split_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_view_as_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_where_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_zeros_cpu_float32 2024-08-20T21:54:19.7887139Z 2024-08-20T21:54:22.3232047Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:54:22.3804793Z Running inductor/test_torchinductor_opinfo 21/39 ... [2024-08-20 21:54:22.379989] 2024-08-20T21:54:22.3806012Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:22.3809649Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'not serial', '--shard-id=21', '--num-shards=39', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:54:22.380377] 2024-08-20T21:54:48.2232577Z 2024-08-20T21:54:48.2235384Z inductor/test_torchinductor_opinfo 9/39 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_9.39_ffc05fcc1e48f661_.log 2024-08-20T21:54:48.2332805Z Running 92 items in this shard: test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_H_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_H_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___getitem___cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___getitem___cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___rpow___cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive__batch_norm_with_update_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive__chunk_cat_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_addbmm_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_alias_copy_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_aminmax_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_as_strided_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_as_strided_scatter_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_as_strided_scatter_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_asin_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atanh_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_baddbmm_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_chalf_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_clamp_max_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_combinations_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_constant_pad_nd_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_corrcoef_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cummin_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_diag_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_dsplit_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_empty_permuted_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_erfinv_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_expm1_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_rfft_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_rfftn_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_gather_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_gather_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_geometric_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_grid_sampler_2d_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_reduce_amax_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_select_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_inner_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isnan_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isposinf_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_item_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_jiterator_2inputs_2outputs_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_jiterator_4inputs_with_extra_args_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_cross_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_matrix_power_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_norm_subgradients_at_zero_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log2_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logical_and_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logical_not_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logspace_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logspace_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logsumexp_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mH_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_logsumexp_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_norm_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_min_reduction_with_dim_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_movedim_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mv_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mvlgamma_mvlgamma_p_5_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_native_batch_norm_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_new_zeros_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_adaptive_avg_pool1d_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_avg_pool1d_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_channel_shuffle_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_channel_shuffle_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_grid_sample_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_l1_loss_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_softmin_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_tanhshrink_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_norm_fro_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_ones_like_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_polygamma_polygamma_n_0_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_polygamma_polygamma_n_3_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_randint_like_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_resize__cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_roll_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_rsqrt_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scatter_add_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scatter_add_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scatter_reduce_prod_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sgn_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sign_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_softmax_with_dtype_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_chebyshev_polynomial_u_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_erfcx_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_i1e_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_log_ndtr_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_ndtr_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_spherical_bessel_j0_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_split_list_args_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sqrt_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_std_mean_unbiased_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_take_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_zeros_cpu_float64 2024-08-20T21:54:48.2433118Z 2024-08-20T21:54:50.7810696Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:54:50.8376871Z Running inductor/test_torchinductor_opinfo 22/39 ... [2024-08-20 21:54:50.837199] 2024-08-20T21:54:50.8378072Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:50.8381785Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'not serial', '--shard-id=22', '--num-shards=39', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:54:50.837579] 2024-08-20T22:02:47.3609392Z 2024-08-20T22:02:47.3614150Z inductor/test_torchinductor_opinfo 11/39 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_11.39_6293ab6bae3481ea_.log 2024-08-20T22:02:47.3684782Z Running 97 items in this shard: test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_addr_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_all_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_amax_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_amax_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_any_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_argmax_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_argwhere_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atleast_2d_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_baddbmm_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bitwise_or_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_char_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_constant_pad_nd_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_copysign_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cummin_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_dsplit_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_equal_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_expand_as_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_expand_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_exponential_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_eye_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ifft2_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_float_power_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_frexp_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_full_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_geometric_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_hsplit_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_put_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_reduce_prod_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_inner_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_int_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isclose_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isfinite_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_item_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_item_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_jiterator_binary_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_jiterator_binary_return_by_ref_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_det_singular_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_multi_dot_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_slogdet_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logaddexp2_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_max_reduction_no_dim_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mode_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mul_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_multinomial_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nanmean_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_narrow_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_new_ones_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_alpha_dropout_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_dropout2d_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_dropout3d_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_dropout_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_instance_norm_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_interpolate_nearest-exact_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_interpolate_nearest_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_multilabel_soft_margin_loss_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_normalize_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pad_circular_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pad_circular_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pixel_shuffle_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_silu_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_silu_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_normal_number_mean_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_ones_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_pca_lowrank_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_permute_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_pinverse_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_pinverse_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_put_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_randint_like_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scatter_add_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scatter_reduce_mean_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scatter_reduce_sum_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_slice_scatter_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_softmax_with_dtype_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sort_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sparse_mm_reduce_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sparse_mm_reduce_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_airy_ai_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_bessel_j0_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_chebyshev_polynomial_v_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_entr_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_i0e_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_modified_bessel_k0_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_scaled_modified_bessel_k1_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_shifted_chebyshev_polynomial_t_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_shifted_chebyshev_polynomial_t_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sum_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_t_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_trapezoid_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_trunc_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unfold_copy_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unique_consecutive_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unique_cpu_uint64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unsqueeze_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_view_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_zeros_cpu_int32 2024-08-20T22:02:47.3742563Z 2024-08-20T22:02:49.9444241Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:02:50.0015318Z Running inductor/test_torchinductor_opinfo 23/39 ... [2024-08-20 22:02:50.001083] 2024-08-20T22:02:50.0016430Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:02:50.0018764Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'not serial', '--shard-id=23', '--num-shards=39', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:02:50.001467] 2024-08-20T22:04:58.9807024Z 2024-08-20T22:04:58.9811203Z inductor/test_torchinductor_opinfo 22/39 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_22.39_d1a40eb3e369f1af_.log 2024-08-20T22:04:58.9941754Z Running 92 items in this shard: test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___rmatmul___cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___rmul___cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___rsub___cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive__chunk_cat_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive__softmax_backward_data_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_asin_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atan_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atan_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atleast_2d_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_baddbmm_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bincount_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_byte_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_chalf_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cholesky_solve_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_combinations_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_copysign_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cross_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_diagonal_copy_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_digamma_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_div_no_rounding_mode_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_div_no_rounding_mode_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_div_trunc_rounding_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_dstack_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_empty_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_eq_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_erf_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_erfc_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_expand_as_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ifft_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ihfft2_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_floor_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_ge_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_gradient_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_gt_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_gt_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_half_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_hsplit_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_i0_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_add_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isinf_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isnan_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isreal_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_jiterator_binary_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_eig_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logical_not_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logical_or_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logspace_tensor_overload_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logsumexp_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_long_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_amax_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_argmax_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_cumsum_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_log_softmax_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_logsumexp_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_select_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_ne_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_avg_pool2d_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_batch_norm_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_dropout2d_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_interpolate_bicubic_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_local_response_norm_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_max_pool2d_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_max_unpool2d_grad_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pad_circular_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_relu6_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_outer_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_polygamma_polygamma_n_4_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_resize_as__cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sign_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_signbit_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_slice_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sort_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sort_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sparse_sampled_addmm_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_chebyshev_polynomial_t_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_chebyshev_polynomial_u_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_laguerre_polynomial_l_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_ndtr_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_split_with_sizes_copy_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sqrt_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_std_mean_unbiased_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sum_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_svd_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_t_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tan_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tanh_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_trapezoid_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_trunc_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unfold_copy_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unique_cpu_uint16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_zero__cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_zeros_like_cpu_uint8 2024-08-20T22:04:59.0022968Z 2024-08-20T22:05:01.5093948Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:05:01.5666089Z Running inductor/test_torchinductor_opinfo 33/39 ... [2024-08-20 22:05:01.566125] 2024-08-20T22:05:01.5667247Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:05:01.5669798Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'not serial', '--shard-id=33', '--num-shards=39', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:05:01.566508] 2024-08-20T22:05:12.9023070Z 2024-08-20T22:05:12.9025389Z inductor/test_torchinductor_opinfo 21/39 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_21.39_dc1319bc6cb18637_.log 2024-08-20T22:05:12.9090356Z Running 92 items in this shard: test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_T_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___radd___cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_addmm_decomposed_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_amin_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_argmax_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bitwise_or_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_clamp_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cumulative_trapezoid_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_diagflat_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_diff_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_div_floor_rounding_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_dot_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_double_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_eq_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_fft_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_fftn_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_hfft2_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_hfft2_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_hfft2_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ihfftn_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fill_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_floor_divide_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fmax_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fmod_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_frexp_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_gt_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_fill_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_cross_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linspace_tensor_overload_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log10_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logical_xor_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logit_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logspace_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_long_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_lu_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_scatter_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_scatter_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_softmax_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_std_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_median_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_meshgrid_list_of_tensors_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_meshgrid_list_of_tensors_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mm_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_movedim_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_msort_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_msort_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mv_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mvlgamma_mvlgamma_p_1_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nanmedian_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_narrow_copy_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_narrow_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_channel_shuffle_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_hardswish_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_interpolate_nearest-exact_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_l1_loss_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pixel_unshuffle_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_relu_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_smooth_l1_loss_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_softmin_with_dtype_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_triplet_margin_loss_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_norm_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_norm_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_normal_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_normal_in_place_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_ones_like_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_permute_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_polygamma_polygamma_n_1_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_resize_as__cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scatter_reduce_prod_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_select_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sgn_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_signal_windows_exponential_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_slice_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_softmax_with_dtype_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_bessel_y0_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_bessel_y1_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_hermite_polynomial_h_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_hermite_polynomial_he_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_modified_bessel_k1_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_modified_bessel_k1_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_scaled_modified_bessel_k1_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_shifted_chebyshev_polynomial_v_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_xlog1py_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_t_copy_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_torch_ops_aten__safe_softmax_default_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_trapezoid_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_uniform_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unique_consecutive_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unsafe_chunk_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_where_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_xlogy_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_zero__cpu_float32 2024-08-20T22:05:12.9144552Z 2024-08-20T22:05:15.4295290Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:05:15.4871482Z Running inductor/test_torchinductor_opinfo 34/39 ... [2024-08-20 22:05:15.486693] 2024-08-20T22:05:15.4872620Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:05:15.4875312Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'not serial', '--shard-id=34', '--num-shards=39', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:05:15.487070] 2024-08-20T22:12:57.5675612Z 2024-08-20T22:12:57.5678119Z inductor/test_torchinductor_opinfo 23/39 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_23.39_4a8366a92531ada5_.log 2024-08-20T22:12:57.5750091Z Running 101 items in this shard: test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___rand___cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive__upsample_bilinear2d_aa_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_abs_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_acos_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_addmv_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_amax_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_any_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_argmin_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_argwhere_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_baddbmm_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cfloat_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cholesky_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_clamp_min_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_conj_physical_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cummax_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_deg2rad_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_dstack_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_einsum_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_empty_strided_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_exp_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_expm1_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_fft_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ifftshift_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fmin_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_full_like_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_put_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_reduce_mean_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isnan_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isposinf_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_jiterator_binary_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_jiterator_binary_return_by_ref_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_jiterator_unary_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_diagonal_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log10_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log_softmax_with_dtype_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logical_not_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logical_or_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_lt_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_prod_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_prod_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_select_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_sum_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_var_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_max_binary_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_maximum_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mode_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mode_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mvlgamma_mvlgamma_p_3_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_ne_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_new_empty_strided_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_new_ones_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_conv2d_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_ctc_loss_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_dropout2d_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_gaussian_nll_loss_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_interpolate_bicubic_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_max_pool2d_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pixel_unshuffle_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_relu6_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_relu_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_selu_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_tanhshrink_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_triplet_margin_with_distance_loss_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_permute_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_permute_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_polygamma_polygamma_n_2_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_polygamma_polygamma_n_4_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_put_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_repeat_interleave_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_reshape_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_resolve_conj_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_roll_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_round_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_rsqrt_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scatter_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scatter_reduce_amin_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sigmoid_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_signal_windows_blackman_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_signal_windows_nuttall_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_signbit_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_signbit_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sin_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sinc_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sinc_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_bessel_j1_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_bessel_y0_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_laguerre_polynomial_l_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_modified_bessel_i1_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_shifted_chebyshev_polynomial_w_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_zeta_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_split_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_split_with_sizes_copy_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sub_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_take_along_dim_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tanh_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tile_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_trapz_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_view_as_complex_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_view_copy_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_view_copy_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_vsplit_cpu_float64 2024-08-20T22:12:57.5809403Z 2024-08-20T22:13:00.0966195Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:13:00.1542519Z Running inductor/test_torchinductor_opinfo 35/39 ... [2024-08-20 22:13:00.153778] 2024-08-20T22:13:00.1543864Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:13:00.1547580Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'not serial', '--shard-id=35', '--num-shards=39', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:13:00.154203] 2024-08-20T22:15:01.5105696Z 2024-08-20T22:15:01.5108304Z inductor/test_torchinductor_opinfo 33/39 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_33.39_bf3cba02aefd04be_.log 2024-08-20T22:15:01.5175212Z Running 104 items in this shard: test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___rmul___cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive__unsafe_masked_index_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive__unsafe_masked_index_put_accumulate_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_allclose_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_arange_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_argsort_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_as_strided_scatter_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atan_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atleast_3d_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_baddbmm_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bernoulli_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bincount_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bitwise_and_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bucketize_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_ceil_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_clamp_max_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_conj_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_corrcoef_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cummax_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_diag_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_diagonal_copy_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_diagonal_copy_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_digamma_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_eq_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_erfinv_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_fft2_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ifft2_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ihfft_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fmod_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_ge_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_add_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_fill_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_fill_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_reduce_amin_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_reduce_prod_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isinf_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isnan_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_le_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_lgamma_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log2_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log2_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log_softmax_with_dtype_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logaddexp2_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logspace_tensor_overload_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logsumexp_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mT_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_amin_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_cumsum_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_fill_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_std_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_max_binary_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_median_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_minimum_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_msort_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nanmedian_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nansum_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nansum_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_narrow_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_avg_pool3d_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_binary_cross_entropy_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_celu_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_conv_transpose2d_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_cosine_embedding_loss_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_cross_entropy_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_embedding_bag_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_mse_loss_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_softsign_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_polygamma_polygamma_n_3_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_polygamma_polygamma_n_4_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_positive_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_ravel_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_real_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_repeat_interleave_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_resolve_conj_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_roll_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_rsqrt_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_rsub_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scalar_tensor_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scatter_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scatter_reduce_prod_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sgn_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sigmoid_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sin_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_entr_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_erfcx_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_i0e_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_spherical_bessel_j0_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_zeta_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_split_with_sizes_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_split_with_sizes_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_square_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_svd_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tan_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_trapezoid_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_trapz_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unravel_index_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unsqueeze_copy_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unsqueeze_copy_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_var_mean_unbiased_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_view_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_view_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_vstack_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_zeros_like_cpu_int64 2024-08-20T22:15:01.5235671Z 2024-08-20T22:15:04.1503553Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:15:04.2082894Z Running inductor/test_torchinductor_codegen_dynamic_shapes 2/3 ... [2024-08-20 22:15:04.207809] 2024-08-20T22:15:04.2084186Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:15:04.2087078Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_torchinductor_codegen_dynamic_shapes.py', '-m', 'not serial', '--shard-id=2', '--num-shards=3', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:15:04.208216] 2024-08-20T22:16:27.4570255Z 2024-08-20T22:16:27.4572407Z inductor/test_torchinductor_opinfo 34/39 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_34.39_fb0a849ff42e78b8_.log 2024-08-20T22:16:27.4628683Z Running 90 items in this shard: test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___getitem___cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___rand___cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___rmatmul___cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_addbmm_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_allclose_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_amin_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_as_strided_partial_views_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atan2_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atan_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bernoulli_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cfloat_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_clamp_min_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cos_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_diagflat_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_diagonal_scatter_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_diagonal_scatter_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_div_no_rounding_mode_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_div_trunc_rounding_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_einsum_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_empty_permuted_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_empty_strided_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_exp2_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_fft2_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_fftshift_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ifft2_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_irfft_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_rfft_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_rfftn_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_flip_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_float_power_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_geqrf_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_half_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_heaviside_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_add_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_copy_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_put_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isnan_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_jiterator_unary_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_jiterator_unary_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_ldl_factor_ex_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_qr_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linspace_tensor_overload_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log10_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logical_or_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mT_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_amin_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_argmax_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_logsumexp_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_scatter_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_std_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_max_pool2d_with_indices_backward_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_min_binary_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_movedim_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nan_to_num_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nextafter_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_adaptive_max_pool3d_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_conv1d_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_conv_transpose2d_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_gaussian_nll_loss_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_interpolate_area_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_interpolate_bilinear_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_interpolate_nearest_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_max_pool1d_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_max_pool3d_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_poisson_nll_loss_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_triplet_margin_loss_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_triplet_margin_with_distance_loss_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_normal_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_polygamma_polygamma_n_2_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_reshape_as_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_reshape_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_select_scatter_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_slice_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_bessel_j0_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_chebyshev_polynomial_t_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_erfcx_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_scaled_modified_bessel_k0_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_scaled_modified_bessel_k1_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_split_with_sizes_copy_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_std_unbiased_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sum_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_t_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_take_along_dim_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tile_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_transpose_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_transpose_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tril_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_triu_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unravel_index_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_vdot_cpu_int32 2024-08-20T22:16:27.4682427Z 2024-08-20T22:16:30.0229358Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:16:30.0813589Z Running inductor/test_torchinductor_codegen_dynamic_shapes 3/3 ... [2024-08-20 22:16:30.080839] 2024-08-20T22:16:30.0814813Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:16:30.0817747Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_torchinductor_codegen_dynamic_shapes.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'] ... [2024-08-20 22:16:30.081281] 2024-08-20T22:20:48.6727285Z 2024-08-20T22:20:48.6729539Z inductor/test_torchinductor_codegen_dynamic_shapes 2/3 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_codegen_dynamic_shapes_2.3_9a84b9d9021c4143_.log 2024-08-20T22:20:48.6945473Z Running 194 items in this shard: test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_adaptive_avg_pool_with_output_size_0_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_add_complex3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_add_complex6_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_add_complex_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_addmm_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_aoti_eager_support_out_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_aoti_eager_with_scalar_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_arange1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_arange4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_arange5_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_argmax_argmin3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_argmax_argmin_with_nan_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_avg_pool2d4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_avg_pool2d5_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_avg_pool2d8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_avg_pool2d_backward2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_avg_pool2d_backward_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_baddbmm_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_batch_norm_2d_2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bernoulli2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bfloat16_to_int16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bitwise3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bucketize_computed_offsets_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bucketize_int_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_buffer_copied_in_graph_with_different_shapes_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_builtins_round_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_builtins_round_float_ndigits_pos_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_builtins_round_int_ndigits_zero_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cat_empty_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cat_empty_index_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cat_inplace_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cat_unbacked_empty_1d_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cat_upcasting_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_clamp_type_promotion_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_compar_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_complex_memory_overlap_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_config_option_dont_assume_alignment_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_constant_pad_fill_dtype_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_conv2d_backward_channels_last_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_conv_bn_fuse_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_conv_functional_bn_fuse_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_convolution1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cos_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cumsum_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cumsum_no_mask_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cumsum_pattern_matcher_issue_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_custom_op_2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_custom_op_fixed_layout_sequential_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_custom_scan_op_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_custom_scan_would_split_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_data_type_propogation_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_device_assert_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_diagonal_copy_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_div1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_div5_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_div7_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_div8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dropout2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dropout3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dropout_deterministic_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_elu_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_embedding_bag_byte_unpack_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_embedding_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_empty1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_empty2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_empty_strided_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_expanded_reduction_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fallback_mutable_op_list_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fallback_mutable_op_no_mutated_tensors_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_flip_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fmod_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fmod_zero_dim_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fractional_max_pool2d3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fractional_max_pool2d4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_full_boolean_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fuse_large_params_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_gelu_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_generate_rand_fp8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_getitem_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_hardswish_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_horizonal_fusion1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_dynamic_shapes_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_put2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_put_fallback1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_input_mutation1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_input_mutation5_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_int_input_dynamic_shapes_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_invalid_operand_issue1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_kwargs_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_large_offset_pointwise_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_like_channels_last_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_like_rands3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_like_rands_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_linear_float64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_linspace1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_linspace2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_log1p_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_log_softmax_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_low_memory_max_pool_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_masked_fill_promotion_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_max_min_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_max_pool2d2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_max_pool2d7_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_max_pool2d_with_indices_backward2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_max_pool2d_with_indices_backward5_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_max_pool2d_with_indices_backward_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_min_max_reduction_nan_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_mixed_mm2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_mixed_mm_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_move_arange_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_mul_index_expr_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_multi_gpu_recompile_on_index_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_multi_threading_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_multilayer_sum_low_prec_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_multilayer_var_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_mutations_loop_fusion_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_nan_to_num_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_new_empty_strided_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_no_op_reduction_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_nonzero_unbacked_refinement_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pad_cast_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_permute2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_airy_ai_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_bessel_j0_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_chebyshev_polynomial_w_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_entr_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_erf_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_expit_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_expm1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_gammaln_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_hermite_polynomial_h_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_hermite_polynomial_he_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_i0_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_i1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_i1e_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_logit_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_modified_bessel_k0_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_ndtr_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_polygamma_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_round_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_shifted_chebyshev_polynomial_u_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_shifted_chebyshev_polynomial_v_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_spherical_bessel_j0_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_xlog1py_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pow1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pow_symfloat_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_prod_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_rand_like_deterministic_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_randint_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_reduction2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_reduction4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_reinterpret_dtypeview_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_rsqrt_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_scaled_dot_product_attention_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_scaled_dot_product_efficient_attention_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_scatter4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_scatter5_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_scatter_add2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_scatter_add3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_scatter_reduce1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sdpa_prefer_nd_tiling_False_use_block_ptr_True_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sdpa_prefer_nd_tiling_True_use_block_ptr_True_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sdpa_unaligned_mask_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sgn_extremal_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sizehint_issue1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_slice_view_with_graph_break_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_softmax_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_softmax_one_kernel_loop_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_softmax_one_kernel_persist_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sort_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sort_transpose_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sqrt_dynamic_shapes_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_strided_inputs_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sum1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sum2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sum_dtype_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_tanh_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_tensor1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_tmp_not_defined_issue2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_to_dtype_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_to_memory_format_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_transpose_add_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_unbacked_floordiv_simplify_errors_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_unbind_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_unsqueeze_inplace_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_upsample_cat_conv_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_var_correction_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_vectorized_ops_masked_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_vectorized_ops_masked_var_novec_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_view_as_real_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_view_detach_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_views4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_views7_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_where_with_logical_op_dynamic_shapes_cpu 2024-08-20T22:20:48.7081640Z 2024-08-20T22:20:51.3122496Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:20:51.3697557Z Running inductor/test_mmdecomp 1/1 ... [2024-08-20 22:20:51.369288] 2024-08-20T22:20:51.3698642Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:20:51.3701937Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_mmdecomp.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:20:51.369730] 2024-08-20T22:20:54.1888821Z 2024-08-20T22:20:54.1891389Z inductor/test_mmdecomp 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_mmdecomp_1.1_2f96aaf16045af96_.log 2024-08-20T22:20:54.1911376Z Running 22 items in this shard: test/inductor/test_mmdecomp.py::TestDecompCPU::test_batched_mm_float32_bs_10_cpu_float32, test/inductor/test_mmdecomp.py::TestDecompCPU::test_batched_mm_float32_bs_1_cpu_float32, test/inductor/test_mmdecomp.py::TestDecompCPU::test_batched_mm_float32_bs_2_cpu_float32, test/inductor/test_mmdecomp.py::TestDecompCPU::test_batched_mm_float32_bs_4_cpu_float32, test/inductor/test_mmdecomp.py::TestDecompCPU::test_bmm_batch2_last_dim_size_is_one_cpu, test/inductor/test_mmdecomp.py::TestDecompCPU::test_simple_mm_bfloat16_cpu_bfloat16, test/inductor/test_mmdecomp.py::TestDecompCPU::test_simple_mm_float32_cpu_float32, test/inductor/test_mmdecomp.py::TestDecompCPU::test_some_batched_bfloat16_bs_10_cpu_bfloat16, test/inductor/test_mmdecomp.py::TestDecompCPU::test_some_batched_bfloat16_bs_1_cpu_bfloat16, test/inductor/test_mmdecomp.py::TestDecompCPU::test_some_batched_bfloat16_bs_2_cpu_bfloat16, test/inductor/test_mmdecomp.py::TestDecompCPU::test_some_batched_bfloat16_bs_4_cpu_bfloat16, test/inductor/test_mmdecomp.py::TestDecompCPU::test_some_batched_float32_bs_10_cpu_float32, test/inductor/test_mmdecomp.py::TestDecompCPU::test_some_batched_float32_bs_1_cpu_float32, test/inductor/test_mmdecomp.py::TestDecompCPU::test_some_batched_float32_bs_2_cpu_float32, test/inductor/test_mmdecomp.py::TestDecompCPU::test_some_batched_float32_bs_4_cpu_float32, test/inductor/test_mmdecomp.py::TestDecompCPU::test_some_batched_int32_bs_10_cpu_int32, test/inductor/test_mmdecomp.py::TestDecompCPU::test_some_batched_int32_bs_1_cpu_int32, test/inductor/test_mmdecomp.py::TestDecompCPU::test_some_batched_int32_bs_2_cpu_int32, test/inductor/test_mmdecomp.py::TestDecompCPU::test_some_batched_int32_bs_4_cpu_int32, test/inductor/test_mmdecomp.py::TestDecompCPU::test_some_bfloat16_cpu_bfloat16, test/inductor/test_mmdecomp.py::TestDecompCPU::test_some_float32_cpu_float32, test/inductor/test_mmdecomp.py::TestDecompCPU::test_some_int32_cpu_int32 2024-08-20T22:20:54.1930640Z 2024-08-20T22:20:56.6614259Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:20:56.7183331Z Running dynamo/test_interop 1/1 ... [2024-08-20 22:20:56.717864] 2024-08-20T22:20:56.7184420Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:20:56.7187706Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_interop.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:20:56.718232] 2024-08-20T22:20:59.7372573Z 2024-08-20T22:20:59.7374328Z dynamo/test_interop 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_interop_1.1_bbc12aa6a52e730f_.log 2024-08-20T22:20:59.7377539Z Running 4 items in this shard: test/dynamo/test_interop.py::InteropTests::test_fx_fn, test/dynamo/test_interop.py::InteropTests::test_script_fn, test/dynamo/test_interop.py::InteropTests::test_trace_fn, test/dynamo/test_interop.py::InteropTests::test_vmap_in_graph 2024-08-20T22:20:59.7379143Z 2024-08-20T22:21:02.2883440Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:21:02.3483663Z Running dynamo/test_logging 1/1 ... [2024-08-20 22:21:02.347911] 2024-08-20T22:21:02.3484733Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:21:02.3488185Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_logging.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:21:02.348304] 2024-08-20T22:21:25.7029770Z 2024-08-20T22:21:25.7032021Z dynamo/test_logging 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_logging_1.1_d0fbfde556f44c46_.log 2024-08-20T22:21:25.7143935Z Running 41 items in this shard: test/dynamo/test_logging.py::LoggingTests::test_all, test/dynamo/test_logging.py::LoggingTests::test_aot, test/dynamo/test_logging.py::LoggingTests::test_aot_graphs, test/dynamo/test_logging.py::LoggingTests::test_aot_joint_graph, test/dynamo/test_logging.py::LoggingTests::test_bytecode, test/dynamo/test_logging.py::LoggingTests::test_cudagraph_static_inputs, test/dynamo/test_logging.py::LoggingTests::test_cudagraphs, test/dynamo/test_logging.py::LoggingTests::test_custom_format, test/dynamo/test_logging.py::LoggingTests::test_custom_format_exc, test/dynamo/test_logging.py::LoggingTests::test_ddp_graphs, test/dynamo/test_logging.py::LoggingTests::test_default_logging, test/dynamo/test_logging.py::LoggingTests::test_distributed_rank_logging, test/dynamo/test_logging.py::LoggingTests::test_dump_compile_times, test/dynamo/test_logging.py::LoggingTests::test_dynamo_debug, test/dynamo/test_logging.py::LoggingTests::test_dynamo_debug_default_off_artifacts, test/dynamo/test_logging.py::LoggingTests::test_dynamo_error, test/dynamo/test_logging.py::LoggingTests::test_dynamo_info, test/dynamo/test_logging.py::LoggingTests::test_fusion, test/dynamo/test_logging.py::LoggingTests::test_graph_breaks, test/dynamo/test_logging.py::LoggingTests::test_guards_recompiles, test/dynamo/test_logging.py::LoggingTests::test_inductor_debug, test/dynamo/test_logging.py::LoggingTests::test_inductor_error, test/dynamo/test_logging.py::LoggingTests::test_inductor_info, test/dynamo/test_logging.py::LoggingTests::test_invalid_artifact_flag, test/dynamo/test_logging.py::LoggingTests::test_kernel_code, test/dynamo/test_logging.py::LoggingTests::test_logs_out, test/dynamo/test_logging.py::LoggingTests::test_multiline_format, test/dynamo/test_logging.py::LoggingTests::test_open_registration, test/dynamo/test_logging.py::LoggingTests::test_open_registration_python_api, test/dynamo/test_logging.py::LoggingTests::test_open_registration_with_registered_parent, test/dynamo/test_logging.py::LoggingTests::test_output_code, test/dynamo/test_logging.py::LoggingTests::test_recompiles, test/dynamo/test_logging.py::LoggingTests::test_schedule, test/dynamo/test_logging.py::LoggingTests::test_trace_call, test/dynamo/test_logging.py::LoggingTests::test_trace_call_graph_break, test/dynamo/test_logging.py::LoggingTests::test_trace_call_inline_call, test/dynamo/test_logging.py::LoggingTests::test_trace_source_cond, test/dynamo/test_logging.py::LoggingTests::test_trace_source_funcname, test/dynamo/test_logging.py::LoggingTests::test_trace_source_if_stmt, test/dynamo/test_logging.py::LoggingTests::test_trace_source_nested, test/dynamo/test_logging.py::LoggingTests::test_trace_source_simple 2024-08-20T22:21:25.7171950Z 2024-08-20T22:21:28.3136476Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:21:28.3711112Z Running dynamo/test_exc 1/1 ... [2024-08-20 22:21:28.370697] 2024-08-20T22:21:28.3711868Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:21:28.3714529Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_exc.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:21:28.371075] 2024-08-20T22:21:31.3910398Z 2024-08-20T22:21:31.3912120Z dynamo/test_exc 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_exc_1.1_94f9c357d423bd7d_.log 2024-08-20T22:21:31.3918625Z Running 9 items in this shard: test/dynamo/test_exc.py::ExcTests::test_backend_suppress_line, test/dynamo/test_exc.py::ExcTests::test_graph_break_log, test/dynamo/test_exc.py::ExcTests::test_internal_error_no_suppress, test/dynamo/test_exc.py::ExcTests::test_internal_error_suppress_errors, test/dynamo/test_exc.py::ExcTests::test_not_implemented_error, test/dynamo/test_exc.py::ExcTests::test_trigger_bisect_on_error, test/dynamo/test_exc.py::ExcTests::test_trigger_on_error, test/dynamo/test_exc.py::ExcTests::test_unsupported_error, test/dynamo/test_exc.py::ExcTests::test_unsupported_real_stack 2024-08-20T22:21:31.3922015Z 2024-08-20T22:21:33.8761418Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:21:33.9332835Z Running dynamo/test_global 1/1 ... [2024-08-20 22:21:33.932814] 2024-08-20T22:21:33.9334120Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:21:33.9337737Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_global.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:21:33.933173] 2024-08-20T22:21:41.5117518Z 2024-08-20T22:21:41.5119594Z dynamo/test_global 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_global_1.1_529fc0b03402724c_.log 2024-08-20T22:21:41.5125242Z Running 12 items in this shard: test/dynamo/test_global.py::TestGlobals::test_store_global_1, test/dynamo/test_global.py::TestGlobals::test_store_global_2, test/dynamo/test_global.py::TestGlobals::test_store_global_cross_file, test/dynamo/test_global.py::TestGlobals::test_store_global_crossfile_inline, test/dynamo/test_global.py::TestGlobals::test_store_global_dict, test/dynamo/test_global.py::TestGlobals::test_store_global_dict_2, test/dynamo/test_global.py::TestGlobals::test_store_global_inline_1, test/dynamo/test_global.py::TestGlobals::test_store_global_inline_2, test/dynamo/test_global.py::TestGlobals::test_store_global_list, test/dynamo/test_global.py::TestGlobals::test_store_global_list_2, test/dynamo/test_global.py::TestGlobals::test_store_global_new, test/dynamo/test_global.py::TestGlobals::test_store_global_object 2024-08-20T22:21:41.5129660Z 2024-08-20T22:21:44.1228330Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:21:44.1807858Z Running dynamo/test_unspec 1/1 ... [2024-08-20 22:21:44.180346] 2024-08-20T22:21:44.1808659Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:21:44.1811193Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_unspec.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:21:44.180761] 2024-08-20T22:22:03.3295938Z 2024-08-20T22:22:03.3298158Z dynamo/test_unspec 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_unspec_1.1_1c8b113096e203cb_.log 2024-08-20T22:22:03.3329599Z Running 43 items in this shard: test/dynamo/test_unspec.py::UnspecTests::test_argmin_coerces_symint_to_intlist_spec, test/dynamo/test_unspec.py::UnspecTests::test_bool_tensor_ctor, test/dynamo/test_unspec.py::UnspecTests::test_builtin_functions_on_cuda, test/dynamo/test_unspec.py::UnspecTests::test_builtin_getitem, test/dynamo/test_unspec.py::UnspecTests::test_builtin_max_min, test/dynamo/test_unspec.py::UnspecTests::test_compiled_random_calls_are_random, test/dynamo/test_unspec.py::UnspecTests::test_conv1d_symint_padding, test/dynamo/test_unspec.py::UnspecTests::test_data_dependent_evaluate_expr_graph_break, test/dynamo/test_unspec.py::UnspecTests::test_defaults, test/dynamo/test_unspec.py::UnspecTests::test_exponential, test/dynamo/test_unspec.py::UnspecTests::test_feed_random_values_into_graph_only, test/dynamo/test_unspec.py::UnspecTests::test_isinstance_symint, test/dynamo/test_unspec.py::UnspecTests::test_item_max, test/dynamo/test_unspec.py::UnspecTests::test_mark_01_dynamic, test/dynamo/test_unspec.py::UnspecTests::test_mark_static_inside, test/dynamo/test_unspec.py::UnspecTests::test_mark_unbacked, test/dynamo/test_unspec.py::UnspecTests::test_mark_unbacked_channels_last, test/dynamo/test_unspec.py::UnspecTests::test_mark_unbacked_hint_consistency, test/dynamo/test_unspec.py::UnspecTests::test_multiple_consecutive_random_calls_before_graph, test/dynamo/test_unspec.py::UnspecTests::test_no_recompilations, test/dynamo/test_unspec.py::UnspecTests::test_no_recompiles, test/dynamo/test_unspec.py::UnspecTests::test_no_recompiles_prod_backward, test/dynamo/test_unspec.py::UnspecTests::test_numpy_correctness, test/dynamo/test_unspec.py::UnspecTests::test_propagate_dynamic_dim, test/dynamo/test_unspec.py::UnspecTests::test_prune_torch_check, test/dynamo/test_unspec.py::UnspecTests::test_random_call_with_while_loop, test/dynamo/test_unspec.py::UnspecTests::test_random_object, test/dynamo/test_unspec.py::UnspecTests::test_random_object_methods, test/dynamo/test_unspec.py::UnspecTests::test_random_object_overriden_methods, test/dynamo/test_unspec.py::UnspecTests::test_random_values_with_graph_break, test/dynamo/test_unspec.py::UnspecTests::test_rshift_dynamic, test/dynamo/test_unspec.py::UnspecTests::test_shape_graph_break, test/dynamo/test_unspec.py::UnspecTests::test_specializing_numpy_float_in_control_flow, test/dynamo/test_unspec.py::UnspecTests::test_split_aot_autograd, test/dynamo/test_unspec.py::UnspecTests::test_sum_dimlist_spec, test/dynamo/test_unspec.py::UnspecTests::test_sym_int_conversion, test/dynamo/test_unspec.py::UnspecTests::test_symbol_guard_limit_before_specialize, test/dynamo/test_unspec.py::UnspecTests::test_symfloat_to_tensor, test/dynamo/test_unspec.py::UnspecTests::test_to_tensor, test/dynamo/test_unspec.py::UnspecTests::test_unspec_float_input, test/dynamo/test_unspec.py::UnspecTests::test_unspec_float_output, test/dynamo/test_unspec.py::UnspecTests::test_unspec_float_precision, test/dynamo/test_unspec.py::UnspecTests::test_use_and_specialize 2024-08-20T22:22:03.3359041Z 2024-08-20T22:22:05.8944697Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:22:05.9526554Z Running inductor/test_cudagraph_trees 1/1 ... [2024-08-20 22:22:05.952116] 2024-08-20T22:22:05.9527748Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:22:05.9529965Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_cudagraph_trees.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:22:05.952478] 2024-08-20T22:22:09.4757549Z 2024-08-20T22:22:09.4759761Z inductor/test_cudagraph_trees 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cudagraph_trees_1.1_9b3faf06e9e29d87_.log 2024-08-20T22:22:09.4761330Z 2024-08-20T22:22:12.0143060Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:22:12.0718538Z Running dynamo/test_ctx_manager 1/1 ... [2024-08-20 22:22:12.071354] 2024-08-20T22:22:12.0719854Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:22:12.0722379Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_ctx_manager.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:22:12.071774] 2024-08-20T22:22:16.5440369Z 2024-08-20T22:22:16.5442432Z dynamo/test_ctx_manager 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_ctx_manager_1.1_8985e2c8ebdb0657_.log 2024-08-20T22:22:16.5467559Z Running 52 items in this shard: test/dynamo/test_ctx_manager.py::CtxManagerTests::test_autocast, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_autocast_arguments_binding, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_autocast_cpu, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_autocast_cpu_graph_break, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_autocast_cpu_graph_break_2, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_autocast_cpu_graph_break_inner_fn, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_autocast_decorator, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_autocast_device, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_autocast_float64, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_autocast_graph_break_method, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_autocast_sdpa, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_autograd_profiler, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_autograd_profiler_enabled, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_context_wrapping_grad_mode_decorator, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_context_wrapping_grad_mode_nested_function_decorator, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_context_wrapping_set_grad_enabled_nested_function, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_cuda_amp_autocast, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_cuda_device, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_cuda_event_across_graph_break, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_cuda_event_created_outside_of_graph, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_cuda_event_method, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_cuda_event_method_create_stream_outside_of_compile, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_cuda_event_reconstruct, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_cuda_stream_across_graph_break, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_cuda_stream_compared_with_constant, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_cuda_stream_compared_with_stream, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_cuda_stream_context_manager1, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_cuda_stream_context_manager2, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_cuda_stream_method, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_disable_saved_tensors_hooks, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_disable_saved_tensors_hooks_graph_break, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_disable_saved_tensors_hooks_prev_disabled, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_disable_saved_tensors_hooks_prev_disabled_nested, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_generic_context_manager, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_generic_context_manager_with_graph_break, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_generic_ctx_manager_with_graph_break, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_grad_mode_guard, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_graph_break_inlining_autocast, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_graph_break_inlining_grad, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_inactive_context_graph_break_local, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_inactive_context_graph_break_local_nullctx, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_inactive_context_graph_break_local_nullctx2, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_inactive_context_graph_break_stack, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_inactive_context_graph_break_stack2, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_is_autocast_cpu_enabled, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_nested_generic_context_manager, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_nested_generic_context_manager_with_graph_break, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_nested_grad_mode_graph_break, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_no_grad, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_return_context_manager, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_return_context_manager_with_graph_break, test/dynamo/test_ctx_manager.py::CtxManagerTests::test_torch_profiler 2024-08-20T22:22:16.5491086Z 2024-08-20T22:22:19.0685136Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:22:19.1270343Z Running dynamo/test_subgraphs 1/1 ... [2024-08-20 22:22:19.126583] 2024-08-20T22:22:19.1271306Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:22:19.1274096Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_subgraphs.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:22:19.126986] 2024-08-20T22:22:24.1992736Z 2024-08-20T22:22:24.1995417Z dynamo/test_subgraphs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_subgraphs_1.1_35b97e4dc7659ef8_.log 2024-08-20T22:22:24.2012335Z Running 44 items in this shard: test/dynamo/test_subgraphs.py::SubGraphTests::test_capi_call1, test/dynamo/test_subgraphs.py::SubGraphTests::test_capi_call2, test/dynamo/test_subgraphs.py::SubGraphTests::test_capi_call3, test/dynamo/test_subgraphs.py::SubGraphTests::test_control_flow1, test/dynamo/test_subgraphs.py::SubGraphTests::test_control_flow2, test/dynamo/test_subgraphs.py::SubGraphTests::test_control_flow3, test/dynamo/test_subgraphs.py::SubGraphTests::test_control_flow4, test/dynamo/test_subgraphs.py::SubGraphTests::test_control_flow5, test/dynamo/test_subgraphs.py::SubGraphTests::test_dynamic_duck_size, test/dynamo/test_subgraphs.py::SubGraphTests::test_dynamic_getitem, test/dynamo/test_subgraphs.py::SubGraphTests::test_dynamic_kwarg, test/dynamo/test_subgraphs.py::SubGraphTests::test_dynamic_order_dependence, test/dynamo/test_subgraphs.py::SubGraphTests::test_dynamic_zero_inference, test/dynamo/test_subgraphs.py::SubGraphTests::test_enumerate_not_break_graph, test/dynamo/test_subgraphs.py::SubGraphTests::test_extended_args, test/dynamo/test_subgraphs.py::SubGraphTests::test_graph_break_on_item, test/dynamo/test_subgraphs.py::SubGraphTests::test_indirect_unsupported1, test/dynamo/test_subgraphs.py::SubGraphTests::test_indirect_unsupported2, test/dynamo/test_subgraphs.py::SubGraphTests::test_indirect_unsupported3, test/dynamo/test_subgraphs.py::SubGraphTests::test_multigraph, test/dynamo/test_subgraphs.py::SubGraphTests::test_no_graph_break_on_item, test/dynamo/test_subgraphs.py::SubGraphTests::test_pop_after_resume, test/dynamo/test_subgraphs.py::SubGraphTests::test_restore_range, test/dynamo/test_subgraphs.py::SubGraphTests::test_restore_range_iter, test/dynamo/test_subgraphs.py::SubGraphTests::test_restore_state, test/dynamo/test_subgraphs.py::SubGraphTests::test_resume1, test/dynamo/test_subgraphs.py::SubGraphTests::test_resume2, test/dynamo/test_subgraphs.py::SubGraphTests::test_resume3, test/dynamo/test_subgraphs.py::SubGraphTests::test_resume4, test/dynamo/test_subgraphs.py::SubGraphTests::test_resume5, test/dynamo/test_subgraphs.py::SubGraphTests::test_resume_freevars, test/dynamo/test_subgraphs.py::SubGraphTests::test_resume_paths_join, test/dynamo/test_subgraphs.py::SubGraphTests::test_resume_tuple_iterator, test/dynamo/test_subgraphs.py::SubGraphTests::test_resume_with_no_grad1, test/dynamo/test_subgraphs.py::SubGraphTests::test_resume_with_no_grad2, test/dynamo/test_subgraphs.py::SubGraphTests::test_resume_with_no_grad3, test/dynamo/test_subgraphs.py::SubGraphTests::test_stack_state1, test/dynamo/test_subgraphs.py::SubGraphTests::test_stack_state2, test/dynamo/test_subgraphs.py::SubGraphTests::test_start1, test/dynamo/test_subgraphs.py::SubGraphTests::test_start2, test/dynamo/test_subgraphs.py::SubGraphTests::test_start3, test/dynamo/test_subgraphs.py::SubGraphTests::test_start4, test/dynamo/test_subgraphs.py::SubGraphTests::test_tuple_iterator_mutate, test/dynamo/test_subgraphs.py::SubGraphTests::test_tuple_iterator_return 2024-08-20T22:22:24.2028438Z 2024-08-20T22:22:26.7564594Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:22:26.8138491Z Running dynamo/test_autograd_function 1/1 ... [2024-08-20 22:22:26.813391] 2024-08-20T22:22:26.8139597Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:22:26.8142809Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_autograd_function.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:22:26.813773] 2024-08-20T22:22:32.7379083Z 2024-08-20T22:22:32.7381284Z dynamo/test_autograd_function 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_autograd_function_1.1_bef85538f64f3dde_.log 2024-08-20T22:22:32.7408552Z Running 30 items in this shard: test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_allow_in_graph, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_amp_custom_fwd_bwd, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_autograd_function_equivalence, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_autograd_function_has_graph_break, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_backward_returns_none_for_tensor_input, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_classmethod, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_default_values, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_enum_arg, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_function_context_mark_and_save, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_function_context_save_and_mark, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_function_with_bound_free_variable, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_linear_setup_context, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_materialize_grad, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_multi_output, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_multiple_different_non_tensor_inputs, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_needs_input_grad, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_once_differentiable, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_print_in_bwd, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_repeated_save_for_backward_calls, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_save_for_bwd, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_smoke_from_test_autograd, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_smuggle_symint_issue_111031, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_smuggle_tensor_and_complex_structures, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_stride_in_bwd, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_tensor_list_as_input, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_tensor_subclass_intermediary_input, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_triton_kernel_basic, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_triton_kernel_multiple_out, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_tuple_arg, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_user_defined_object_as_input 2024-08-20T22:22:32.7446488Z 2024-08-20T22:22:35.2823821Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:22:35.3400569Z Running dynamo/test_activation_checkpointing 1/1 ... [2024-08-20 22:22:35.339582] 2024-08-20T22:22:35.3401719Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:22:35.3404381Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_activation_checkpointing.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:22:35.339972] 2024-08-20T22:22:39.7116925Z 2024-08-20T22:22:39.7119427Z 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_722cd7ece98e23ce_.log 2024-08-20T22:22:39.7157626Z Running 29 items in this shard: test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTests::test_autocast_flash_attention, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTests::test_compile_selective_checkpoint_custom_rule, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTests::test_compile_selective_checkpoint_inplace_op, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTests::test_compile_selective_checkpoint_invalid_context, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTests::test_compile_selective_checkpoint_must_not_recompute_gemm, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTests::test_compile_selective_checkpoint_must_recompute, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTests::test_compile_selective_checkpoint_outplace_op, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTests::test_compile_selective_checkpoint_parametrization, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTests::test_compile_selective_checkpoint_partial_ctx_fn, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTests::test_compile_selective_checkpoint_random_op, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTests::test_compile_selective_checkpoint_tensor_subclass, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTests::test_distributed_utils_checkpoint_wrapper, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTests::test_dynamo_does_not_trace_getattr_as_top_frame, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTests::test_error_msg, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTests::test_fallback, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTests::test_kwargs, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTests::test_list_inputs, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTests::test_pattern_matcher, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTests::test_symints_location, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTests::test_tags_decomps, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTests::test_tags_dropout, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTests::test_tags_function, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTests::test_tags_function_via_global_checkpoint, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTests::test_tags_function_with_kwargs, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTests::test_tags_module, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTests::test_tags_multiple_checkpoints, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTests::test_tags_rand, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTests::test_tags_recomputed_rand, test/dynamo/test_activation_checkpointing.py::ActivationCheckpointingViaTagsTests::test_tags_sequential_layers 2024-08-20T22:22:39.7193845Z 2024-08-20T22:22:42.3385831Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:22:42.3981771Z Running inductor/test_inductor_freezing 1/1 ... [2024-08-20 22:22:42.397669] 2024-08-20T22:22:42.3983015Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:22:42.3986505Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_inductor_freezing.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:22:42.398065] 2024-08-20T22:23:41.3660910Z 2024-08-20T22:23:41.3663344Z inductor/test_inductor_freezing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_inductor_freezing_1.1_8288affbc5c20670_.log 2024-08-20T22:23:41.3685628Z Running 22 items in this shard: test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_aliased_param_return_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_autocast_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_conv_bn_with_multi_bn_share_conv_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_conv_functional_bn_with_multi_bn_share_conv_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_conv_layout_convert_with_view_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_conv_multiple_uses_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_conv_weight_layout_convert_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_conv_with_as_strided_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_cpp_wrapper_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_dont_change_dtype_folding_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_error_on_eager_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_folded_conv_bn_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_folded_conv_bn_with_module_sharing_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_folded_conv_functional_bn_with_module_sharing_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_mm_concat_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_mutation_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_param_deallocated_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_redundant_clone_for_layout_convert_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_rng_op_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_symint_not_folded_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_unequal_bias_horizontal_addmm_fusion_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_unfolded_bn_cpu 2024-08-20T22:23:41.3697757Z 2024-08-20T22:23:43.9911701Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:23:44.0486041Z Running inductor/test_mkldnn_pattern_matcher 1/2 ... [2024-08-20 22:23:44.048118] 2024-08-20T22:23:44.0487254Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:23:44.0489948Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_mkldnn_pattern_matcher.py', '-m', 'not serial', '--shard-id=1', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:23:44.048492] 2024-08-20T22:25:15.9106577Z 2024-08-20T22:25:15.9108992Z inductor/test_torchinductor_opinfo 35/39 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_35.39_d0fd0fff42616aa4_.log 2024-08-20T22:25:15.9164260Z Running 89 items in this shard: test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_acosh_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_aminmax_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_arange_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_arange_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_argmax_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_argmin_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_argsort_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_argwhere_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_as_strided_copy_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_asinh_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atanh_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atleast_1d_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bfloat16_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bmm_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bool_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bool_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cartesian_prod_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cdouble_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_char_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_clone_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_column_stack_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_deg2rad_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_div_floor_rounding_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_empty_permuted_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_empty_strided_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_empty_strided_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_erfinv_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_exp_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_expand_copy_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_fftn_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_hfftn_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ifftn_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_floor_divide_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fmax_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_hsplit_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_hstack_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_add_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_put_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isclose_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isnan_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_ldexp_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_ldexp_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_eigh_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_inv_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linspace_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log2_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_argmax_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_normalize_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_matrix_exp_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_max_reduction_with_dim_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_maximum_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_min_reduction_with_dim_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nanquantile_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_new_zeros_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_avg_pool2d_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_cosine_similarity_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_margin_ranking_loss_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_margin_ranking_loss_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pairwise_distance_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pdist_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_soft_margin_loss_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_triplet_margin_loss_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_norm_fro_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_polygamma_polygamma_n_4_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_randint_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_remainder_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_repeat_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_repeat_interleave_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_rot90_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scalar_tensor_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_signal_windows_hann_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sinc_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sinh_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_slice_scatter_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_bessel_j1_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_chebyshev_polynomial_w_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_legendre_polynomial_p_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_shifted_chebyshev_polynomial_u_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_square_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sum_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sum_to_size_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_take_along_dim_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tensor_split_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_transpose_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_true_divide_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unsafe_split_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_var_mean_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_vdot_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_zero__cpu_bool 2024-08-20T22:25:15.9216733Z 2024-08-20T22:25:18.7175196Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:25:18.7759767Z Running inductor/test_cuda_repro 1/1 ... [2024-08-20 22:25:18.775452] 2024-08-20T22:25:18.7760914Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:25:18.7764334Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_cuda_repro.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:25:18.775868] 2024-08-20T22:25:23.4902266Z 2024-08-20T22:25:23.4904653Z inductor/test_cuda_repro 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cuda_repro_1.1_2137d05d0f79b89f_.log 2024-08-20T22:25:23.4906496Z 2024-08-20T22:25:24.7486061Z 2024-08-20T22:25:24.7494539Z inductor/test_torchinductor_codegen_dynamic_shapes 3/3 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_codegen_dynamic_shapes_3.3_c3f85b8f3bdcfe27_.log 2024-08-20T22:25:24.7875242Z Running 210 items in this shard: test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_AllenaiLongformerBase_repro_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test__unsafe_masked_index_put_accumulate_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_abs_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_adaptive_avg_pool1d_argmax_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_adaptive_avg_pool2d_low_prec_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_adaptive_max_pool2d2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_add_complex4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_add_const_float_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_add_const_int_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_adding_tensor_offsets_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_any_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_aoti_eager_support_str_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_arange6_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_argmax_argmin1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_argmax_argmin2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_argmax_min_int32_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_as_strided_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_as_strided_scatter_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_avg_pool2d1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_avg_pool2d2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_avg_pool2d6_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_avg_pool3d_backward3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_avg_pool3d_backward4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bitwise_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bmm1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bmm2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bool_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bucketize_default_kwargs_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bucketize_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_buffer_batch_norm_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_buffer_use_after_remove_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_builtins_round_float_ndigits_neg_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_builtins_round_int_ndigits_pos_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cat_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cat_extern_kernel_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cat_of_loops_and_extern_kernel_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cat_unbacked_2d_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cat_unbacked_legacy_empty_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cauchy_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_clamp_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_clone_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_complex_fallback_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_concat_add_inplace_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_consecutive_split_cumsum_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_constant_pad_1d_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_conv2d_channels_last_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_conv3d_channels_last_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_conv_inference_heuristics_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_convolution2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_convolution3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_convolution4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cumprod_zero_dim_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cumsum_inf_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cumsum_zero_dim_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_custom_op_3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_custom_op_fixed_layout_channels_last_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_custom_scan_op_compiled_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_custom_scan_op_multi_input_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dense_mask_index_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dist_bf16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_div4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_div_prim_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_div_zero_dim_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_erfc_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_exp_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_expand_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fallback_mutable_op_basic_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fallback_mutable_op_with_return_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fft_real_input_real_output_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_flip_cat_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_float32_to_int32_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_forced_buffer_realize_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fractional_max_pool2d2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_functionalize_rng_wrappers_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fusing_write_into_disjoint_read_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_gather1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_gather2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_gather_scatter_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_glu_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_grid_sampler_2d_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_hardsigmoid_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_hardtanh_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_propagation_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_propagation_floordiv_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_propagation_nested_indirect_indexing_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_put1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_put3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_put4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_put_as_masked_fill_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_put_fallback2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_put_index_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_select_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_tensor_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_indirect_load_broadcast_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_inf_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_inner_fn_str_and_stride_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_inplace_activations_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_inplace_mixed_dtype_ops_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_inplace_resize_as_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_input_mutation2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_input_mutation4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_isinf_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_large_broadcast_reduction_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_large_grid_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_large_strided_reduction_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_large_tensor_reduction_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_leaky_relu_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_lerp_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_like_rands2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_linear1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_log_fp64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_logcumsumexp_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_long_tensor_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_masked_fill_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_max_pool2d4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_max_pool2d8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_max_pool2d_with_indices_backward4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_max_pool2d_with_indices_backward6_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_mean_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_min_max_reduction_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_multi_device_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_multilayer_prime_size_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_multilayer_var_lowp_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_neg_max_uint8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_new_ones_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_nll_loss_backward_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_one_hot_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pattern_matcher_multi_user_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pixel_shuffle_channels_last_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_bessel_j1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_bessel_y0_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_bessel_y1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_chebyshev_polynomial_v_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_erfc_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_erfcx_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_erfinv_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_gammaincc_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_i0e_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_laguerre_polynomial_l_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_legendre_polynomial_p_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_log1p_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_modified_bessel_i1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_modified_bessel_k1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_scaled_modified_bessel_k0_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_scaled_modified_bessel_k1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_pointwise_shifted_chebyshev_polynomial_t_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_polar_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_reduction5_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_reduction_config_limit_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_reflection_pad2d_backward_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_relu_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_remainder_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_remove_no_ops_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_remove_noop_clone_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_repeat_as_strided_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_repeat_interleave_2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_repeat_interleave_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_resize_as_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_roll_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_round_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_scalar_cpu_tensor_arg_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_scatter_add1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_scatter_reduce2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sdpa_prefer_nd_tiling_False_use_block_ptr_False_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sdpa_prefer_nd_tiling_True_use_block_ptr_False_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_setitem_with_int_parameter_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sgn_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_shape_prop_torch_ones_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_signbit_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_simplify_loops_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_slice2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_slice3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_slice4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_slice_mutation1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_slice_scatter3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_slice_scatter_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_slice_scatter_reinplace_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_split_cumprod_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_split_cumsum_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_split_with_list_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_split_with_sizes_with_unbacked_symints_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_split_with_unbacked_symints_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_squeeze1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_squeeze2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_squeeze_varargs_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_std_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sum4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sum5_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_sum_keepdims_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_tensor2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_tensor3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_tensor_index_slice_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_tmp_not_defined_issue1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_tmp_not_defined_issue3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_to_device_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_transpose_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_transposed_propagates_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_unfold_zero_dimension_tensor_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_upsample_bicubic2d_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_upsample_nearest2d_backward_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_upsample_nearest2d_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_vdd_clamp_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_view_as_complex_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_view_on_aliased_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_xblock_divides_xnumel_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_zeros_dynamic_shapes_cpu 2024-08-20T22:25:24.8023760Z 2024-08-20T22:25:26.4573736Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:25:26.5160235Z Running inductor/test_kernel_benchmark 1/1 ... [2024-08-20 22:25:26.515526] 2024-08-20T22:25:26.5161335Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:25:26.5163981Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_kernel_benchmark.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:25:26.515924] 2024-08-20T22:25:27.6490297Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:25:27.7077753Z Running inductor/test_triton_heuristics 1/1 ... [2024-08-20 22:25:27.707253] 2024-08-20T22:25:27.7078864Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:25:27.7082195Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_triton_heuristics.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:25:27.707660] 2024-08-20T22:25:29.3590215Z 2024-08-20T22:25:29.3592469Z inductor/test_kernel_benchmark 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_kernel_benchmark_1.1_0a9dfe0563bbcba8_.log 2024-08-20T22:25:29.3594362Z 2024-08-20T22:25:30.6138597Z 2024-08-20T22:25:30.6140675Z inductor/test_triton_heuristics 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_triton_heuristics_1.1_33c89379771b7886_.log 2024-08-20T22:25:30.6141804Z 2024-08-20T22:25:32.8371609Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:25:32.8960142Z Running inductor/test_cudacodecache 1/1 ... [2024-08-20 22:25:32.895489] 2024-08-20T22:25:32.8961869Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:25:32.8967368Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_cudacodecache.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:25:32.895944] 2024-08-20T22:25:34.1731374Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:25:35.7447431Z 2024-08-20T22:25:35.7450348Z inductor/test_cudacodecache 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cudacodecache_1.1_da9f10e7913f0207_.log 2024-08-20T22:25:35.7537782Z 2024-08-20T22:25:38.5631316Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:27:17.8578389Z 2024-08-20T22:27:17.8580501Z inductor/test_mkldnn_pattern_matcher 1/2 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_mkldnn_pattern_matcher_1.2_57a01e0595df6700_.log 2024-08-20T22:27:17.8603924Z Running 40 items in this shard: test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_conv2d_binary, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_conv2d_binary_fusion_failed, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_conv3d_unary_cpu, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_dynamic_qlinear_input_dim_exceeds_2, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_dynamic_qlinear_qat_cpu, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_hardtanh_pattern_fallback, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_leaky_relu_pattern_fallback, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_linear_unary, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qat_qconv2d_relu, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qat_qconv2d_relu6, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qcat, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qconv2d_add_2, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qconv2d_add_3, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qconv2d_add_cpu, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qconv2d_add_relu_cpu, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qconv2d_add_relu_int8_mixed_bf16, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qconv2d_cpu, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qconv2d_hardtanh_int8_mixed_bf16_cpu, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qconv2d_int8_mixed_bf16, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qconv2d_relu6_cpu, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qconv2d_relu_cpu, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qconv2d_relu_int8_mixed_bf16, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qconv2d_silu_int8_mixed_bf16_cpu, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qflatten, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qlinear_add_cpu, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qlinear_add_int8_mixed_bf16, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qlinear_add_relu_cpu, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qlinear_cpu, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qlinear_dequant_promotion_int8_mixed_bf16, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qlinear_dequant_promotion_int8_mixed_bf16_input_dim_exceeds_2, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qlinear_input_dim_exceeds_2, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qlinear_input_dim_exceeds_2_and_not_contiguous, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qlinear_int8_mixed_bf16_input_dim_exceeds_2, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qlinear_mul_cpu, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qmaxpool2d, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_reproduce_121253_issue, test/inductor/test_mkldnn_pattern_matcher.py::TestDynamicPatternMatcher::test_conv3d_binary_dynamic_shapes, test/inductor/test_mkldnn_pattern_matcher.py::TestDynamicPatternMatcher::test_conv3d_unary_dynamic_shapes, test/inductor/test_mkldnn_pattern_matcher.py::TestDynamicPatternMatcher::test_q_attention_block, test/inductor/test_mkldnn_pattern_matcher.py::TestDynamicPatternMatcher::test_qconv2d_maxpool2d_linear_dynamic_cpu 2024-08-20T22:27:17.8624811Z 2024-08-20T22:27:18.4374818Z Running test batch 'tests to run' cost 2944.38 seconds 2024-08-20T22:27:19.2422115Z 2024-08-20T22:27:19.2422756Z real 49m9.209s 2024-08-20T22:27:19.2423445Z user 137m48.331s 2024-08-20T22:27:19.2425866Z sys 14m6.669s 2024-08-20T22:27:19.2426527Z + assert_git_not_dirty 2024-08-20T22:27:19.2427348Z + [[ linux-focal-py3.12-clang10 != *rocm* ]] 2024-08-20T22:27:19.2430713Z + [[ linux-focal-py3.12-clang10 != *xla* ]] 2024-08-20T22:27:19.2436999Z ++ git status --porcelain 2024-08-20T22:27:19.2437739Z ++ grep -v '?? third_party' 2024-08-20T22:27:42.7368231Z ++ true 2024-08-20T22:27:42.7368675Z + git_status= 2024-08-20T22:27:42.7370682Z + [[ -n '' ]] 2024-08-20T22:27:42.7371059Z + test_aten 2024-08-20T22:27:42.7371586Z + echo 'Running ATen tests with pytorch lib' 2024-08-20T22:27:42.7372331Z Running ATen tests with pytorch lib 2024-08-20T22:27:42.7373246Z + [[ -n '' ]] 2024-08-20T22:27:42.7374603Z + echo 'Running test with the build folder' 2024-08-20T22:27:42.7375336Z Running test with the build folder 2024-08-20T22:27:42.7375967Z + TEST_BASE_DIR=build/bin 2024-08-20T22:27:42.7376971Z + ln -sf /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libc10.so build/bin 2024-08-20T22:27:42.7411704Z + ln -sf '/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libcaffe2*' build/bin 2024-08-20T22:27:42.7421366Z + ln -sf '/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libmkldnn*' build/bin 2024-08-20T22:27:42.7430711Z + ln -sf '/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libnccl*' build/bin 2024-08-20T22:27:42.7442941Z + 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 2024-08-20T22:27:42.7449509Z + ls build/bin 2024-08-20T22:27:42.7501243Z BackoffTest cpu_rng_test 2024-08-20T22:27:42.7502076Z CMakeFiles dispatch_key_set_test 2024-08-20T22:27:42.7502949Z CTestTestfile.cmake dlconvertor_test 2024-08-20T22:27:42.7503712Z CppSignature_test example_allreduce 2024-08-20T22:27:42.7504175Z Dict_test extension_backend_test 2024-08-20T22:27:42.7504927Z Dimname_test half_test 2024-08-20T22:27:42.7505331Z FileStoreTest inline_container_test 2024-08-20T22:27:42.7505779Z HashStoreTest ivalue_test 2024-08-20T22:27:42.7506231Z IListRef_test kernel_function_legacy_test 2024-08-20T22:27:42.7506754Z KernelFunction_test kernel_function_test 2024-08-20T22:27:42.7507228Z List_test kernel_lambda_legacy_test 2024-08-20T22:27:42.7507672Z Makefile kernel_lambda_test 2024-08-20T22:27:42.7508123Z MaybeOwned_test kernel_stackbased_test 2024-08-20T22:27:42.7508585Z NamedTensor_test lazy_tensor_test 2024-08-20T22:27:42.7509062Z ProcessGroupGlooTest legacy_vmap_test 2024-08-20T22:27:42.7509523Z StorageUtils_test libc10.so 2024-08-20T22:27:42.7510091Z TCPStoreTest 'libcaffe2*' 2024-08-20T22:27:42.7510558Z aot_model_compiler_test 'libmkldnn*' 2024-08-20T22:27:42.7511045Z apply_utils_test 'libnccl*' 2024-08-20T22:27:42.7511419Z atest libtorch.so 2024-08-20T22:27:42.7511819Z backend_fallback_test libtorch_cpu.so 2024-08-20T22:27:42.7512275Z basic libtorch_global_deps.so 2024-08-20T22:27:42.7512708Z broadcast_test libtorch_python.so 2024-08-20T22:27:42.7513176Z c10_Bitset_test libtorchbind_test.so 2024-08-20T22:27:42.7513807Z c10_CompileTimeFunctionPointer_test make_boxed_from_unboxed_functor_test 2024-08-20T22:27:42.7514455Z c10_ConstexprCrc_test math_kernel_test 2024-08-20T22:27:42.7515127Z c10_DeadlockDetection_test memory_format_test 2024-08-20T22:27:42.7515675Z c10_DeviceGuard_test memory_overlapping_test 2024-08-20T22:27:42.7516175Z c10_Device_test mobile_memory_cleanup 2024-08-20T22:27:42.7516750Z c10_DispatchKeySet_test native_test 2024-08-20T22:27:42.7517204Z c10_Half_test op_allowlist_test 2024-08-20T22:27:42.7517747Z c10_InlineDeviceGuard_test op_registration_test 2024-08-20T22:27:42.7518306Z c10_InlineStreamGuard_test operator_name_test 2024-08-20T22:27:42.7518811Z c10_LeftRight_test operators_test 2024-08-20T22:27:42.7519336Z c10_Metaprogramming_test packedtensoraccessor_test 2024-08-20T22:27:42.7519869Z c10_Scalar_test parallel_benchmark 2024-08-20T22:27:42.7520316Z c10_SizesAndStrides_test pow_test 2024-08-20T22:27:42.7520730Z c10_StreamGuard_test protoc 2024-08-20T22:27:42.7521212Z c10_SymInt_test protoc-3.13.0.0 2024-08-20T22:27:42.7521715Z c10_Synchronized_test quantized_test 2024-08-20T22:27:42.7522221Z c10_ThreadLocal_test reduce_ops_test 2024-08-20T22:27:42.7522697Z c10_TypeIndex_test reportMemoryUsage_test 2024-08-20T22:27:42.7523196Z c10_TypeList_test scalar_tensor_test 2024-08-20T22:27:42.7523648Z c10_TypeTraits_test scalar_test 2024-08-20T22:27:42.7524095Z c10_accumulate_test static_runtime_bench 2024-08-20T22:27:42.7524586Z c10_bfloat16_test static_runtime_test 2024-08-20T22:27:42.7525069Z c10_bit_cast_test stride_properties_test 2024-08-20T22:27:42.7525572Z c10_complex_math_test tensor_iterator_test 2024-08-20T22:27:42.7526037Z c10_complex_test test_api 2024-08-20T22:27:42.7526523Z c10_cow_test test_cpp_rpc 2024-08-20T22:27:42.7526961Z c10_exception_test test_dist_autograd 2024-08-20T22:27:42.7527456Z c10_flags_test test_edge_op_registration 2024-08-20T22:27:42.7527918Z c10_generic_math_test test_jit 2024-08-20T22:27:42.7528347Z c10_intrusive_ptr_benchmark test_lazy 2024-08-20T22:27:42.7528819Z c10_intrusive_ptr_test test_mobile_nnc 2024-08-20T22:27:42.7529267Z c10_irange_test test_parallel 2024-08-20T22:27:42.7529659Z c10_lazy_test test_tensorexpr 2024-08-20T22:27:42.7530083Z c10_logging_test thread_init_test 2024-08-20T22:27:42.7530535Z c10_optional_test torch_shm_manager 2024-08-20T22:27:42.7531054Z c10_ordered_preserving_dict_test tutorial_tensorexpr 2024-08-20T22:27:42.7531612Z c10_registry_test type_ptr_test 2024-08-20T22:27:42.7532154Z c10_small_vector_test type_test 2024-08-20T22:27:42.7532585Z c10_ssize_test undefined_tensor_test 2024-08-20T22:27:42.7533086Z c10_string_util_test vec_test_all_types_AVX2 2024-08-20T22:27:42.7533626Z c10_string_view_test vec_test_all_types_AVX512 2024-08-20T22:27:42.7534159Z c10_tempfile_test vec_test_all_types_DEFAULT 2024-08-20T22:27:42.7534679Z c10_typeid_test verify_api_visibility 2024-08-20T22:27:42.7535142Z cmake_install.cmake weakref_test 2024-08-20T22:27:42.7535565Z cpu_allocator_test wrapdim_test 2024-08-20T22:27:42.7536007Z cpu_generator_test xla_tensor_test 2024-08-20T22:27:42.7536441Z cpu_profiling_allocator_test 2024-08-20T22:27:42.7536803Z + aten/tools/run_tests.sh build/bin 2024-08-20T22:27:42.7561014Z + set -e 2024-08-20T22:27:42.7563388Z ++ dirname aten/tools/run_tests.sh 2024-08-20T22:27:42.7572596Z + VALGRIND_SUP=/var/lib/jenkins/workspace/aten/tools/valgrind.sup 2024-08-20T22:27:42.7573237Z + export CPP_TESTS_DIR=build/bin 2024-08-20T22:27:42.7573631Z + CPP_TESTS_DIR=build/bin 2024-08-20T22:27:42.7573985Z + VALGRIND=ON 2024-08-20T22:27:42.7576691Z + 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 2024-08-20T22:27:42.8672830Z /var/lib/jenkins/workspace/test/run_test.py:21: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html 2024-08-20T22:27:42.8674021Z import pkg_resources 2024-08-20T22:27:44.9188361Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:27:45.8981981Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2024-08-20T22:27:45.9145189Z Found test times from artifacts 2024-08-20T22:27:45.9706082Z Found test times from artifacts 2024-08-20T22:27:45.9722544Z Running 25% of tests based on TD 2024-08-20T22:27:45.9727883Z Running parallel tests on 3 processes 2024-08-20T22:27:45.9728639Z Name: tests to run (est. time: 0.0min) 2024-08-20T22:27:45.9729530Z Serial tests (0): 2024-08-20T22:27:45.9729991Z Parallel tests (5): 2024-08-20T22:27:45.9730376Z cpp/Dict_test 1/1 2024-08-20T22:27:45.9730689Z cpp/Dimname_test 1/1 2024-08-20T22:27:45.9731026Z cpp/NamedTensor_test 1/1 2024-08-20T22:27:45.9731378Z cpp/apply_utils_test 1/1 2024-08-20T22:27:45.9731716Z cpp/atest 1/1 2024-08-20T22:27:45.9732042Z Name: excluded (est. time: 0.0min) 2024-08-20T22:27:45.9732406Z Serial tests (0): 2024-08-20T22:27:45.9732707Z Parallel tests (14): 2024-08-20T22:27:45.9733024Z cpp/basic 1/1 2024-08-20T22:27:45.9733306Z cpp/broadcast_test 1/1 2024-08-20T22:27:45.9733649Z cpp/cpu_generator_test 1/1 2024-08-20T22:27:45.9734015Z cpp/dlconvertor_test 1/1 2024-08-20T22:27:45.9734364Z cpp/extension_backend_test 1/1 2024-08-20T22:27:45.9734747Z cpp/lazy_tensor_test 1/1 2024-08-20T22:27:45.9735097Z cpp/legacy_vmap_test 1/1 2024-08-20T22:27:45.9735429Z cpp/native_test 1/1 2024-08-20T22:27:45.9735764Z cpp/operators_test 1/1 2024-08-20T22:27:45.9736104Z cpp/scalar_tensor_test 1/1 2024-08-20T22:27:45.9736442Z cpp/scalar_test 1/1 2024-08-20T22:27:45.9736779Z cpp/tensor_iterator_test 1/1 2024-08-20T22:27:45.9737160Z cpp/undefined_tensor_test 1/1 2024-08-20T22:27:45.9737528Z cpp/wrapdim_test 1/1 2024-08-20T22:27:45.9793362Z Running cpp/Dict_test 1/1 ... [2024-08-20 22:27:45.978893] 2024-08-20T22:27:45.9794284Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:27:45.9811339Z 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-5f47a001b9992334.xml', '-x', '--reruns=2'] ... [2024-08-20 22:27:45.980565] 2024-08-20T22:27:48.4633023Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:27:48.4842337Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:27:48.6355899Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:27:48.8049152Z 2024-08-20T22:27:48.8050817Z cpp/Dict_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dict_test_1.1_1eb15489e53440a9_.log 2024-08-20T22:27:48.8051688Z 2024-08-20T22:27:48.8052023Z Running cpp/Dimname_test 1/1 ... [2024-08-20 22:27:48.804892] 2024-08-20T22:27:48.8052549Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:27:48.8055825Z 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-15adb00d10b6a955.xml', '-x', '--reruns=2'] ... [2024-08-20 22:27:48.805256] 2024-08-20T22:27:50.4731002Z 2024-08-20T22:27:50.4732539Z cpp/Dimname_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dimname_test_1.1_156d04d78a9391b6_.log 2024-08-20T22:27:50.4733489Z 2024-08-20T22:27:50.4733900Z Running cpp/NamedTensor_test 1/1 ... [2024-08-20 22:27:50.472878] 2024-08-20T22:27:50.4734454Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:27:50.4736556Z 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-4a307ce78c6480af.xml', '-x', '--reruns=2'] ... [2024-08-20 22:27:50.473247] 2024-08-20T22:27:52.1408772Z 2024-08-20T22:27:52.1410440Z cpp/NamedTensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.NamedTensor_test_1.1_34f72551579724ec_.log 2024-08-20T22:27:52.1411468Z 2024-08-20T22:27:52.1411913Z Running cpp/apply_utils_test 1/1 ... [2024-08-20 22:27:52.140735] 2024-08-20T22:27:52.1412661Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:27:52.1414879Z 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-c97080deae32d5cd.xml', '-x', '--reruns=2'] ... [2024-08-20 22:27:52.141081] 2024-08-20T22:27:53.8085176Z 2024-08-20T22:27:53.8086915Z 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_5bf06649346e3c2b_.log 2024-08-20T22:27:53.8087855Z 2024-08-20T22:27:53.8088141Z Running cpp/atest 1/1 ... [2024-08-20 22:27:53.808389] 2024-08-20T22:27:53.8088681Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:27:53.8091297Z 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-7b40527789454488.xml', '-x', '--reruns=2'] ... [2024-08-20 22:27:53.808782] 2024-08-20T22:27:55.4763347Z 2024-08-20T22:27:55.4765011Z cpp/atest 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.atest_1.1_3a46d11cf4c6a0c1_.log 2024-08-20T22:27:55.4765845Z 2024-08-20T22:27:55.4770298Z Running cpp/Dict_test 1/1 ... [2024-08-20 22:27:55.476768] 2024-08-20T22:27:55.4771081Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:27:55.4772155Z Running cpp/Dimname_test 1/1 ... [2024-08-20 22:27:55.477001] 2024-08-20T22:27:55.4772818Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:27:55.4773511Z Running cpp/NamedTensor_test 1/1 ... [2024-08-20 22:27:55.477049] 2024-08-20T22:27:55.4774335Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:27:55.4776838Z 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-93c965f91cf386aa.xml', '-x', '--reruns=2'] ... [2024-08-20 22:27:55.477372] 2024-08-20T22:27:55.4780199Z 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-ca7cae1e5faf668c.xml', '-x', '--reruns=2'] ... [2024-08-20 22:27:55.477645] 2024-08-20T22:27:55.4785229Z 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-a8fa9e0b350752bb.xml', '-x', '--reruns=2'] ... [2024-08-20 22:27:55.477665] 2024-08-20T22:27:59.4002432Z 2024-08-20T22:27:59.4004665Z cpp/Dimname_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dimname_test_1.1_f1ad7c8666779825_.log 2024-08-20T22:27:59.4008280Z 2024-08-20T22:28:00.4523647Z 2024-08-20T22:28:00.4525993Z cpp/NamedTensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.NamedTensor_test_1.1_cd53b7b3e8cd0a50_.log 2024-08-20T22:28:00.4527749Z 2024-08-20T22:28:02.6351278Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:28:02.6947761Z Running cpp/apply_utils_test 1/1 ... [2024-08-20 22:28:02.693822] 2024-08-20T22:28:02.6948815Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:28:02.6951322Z 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-4e57bdb325744909.xml', '-x', '--reruns=2'] ... [2024-08-20 22:28:02.694432] 2024-08-20T22:28:03.6918931Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:28:03.7947434Z Running cpp/atest 1/1 ... [2024-08-20 22:28:03.794034] 2024-08-20T22:28:03.7948391Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:28:03.7955189Z 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-3e0bce4b2cc7376c.xml', '-x', '--reruns=2'] ... [2024-08-20 22:28:03.794863] 2024-08-20T22:28:06.7547972Z 2024-08-20T22:28:06.7552866Z 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_bf874814947eeae7_.log 2024-08-20T22:28:06.7554800Z 2024-08-20T22:28:07.1555878Z 2024-08-20T22:28:07.1557891Z cpp/Dict_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dict_test_1.1_a8a81afa6244f645_.log 2024-08-20T22:28:07.1559362Z 2024-08-20T22:28:09.3912802Z 2024-08-20T22:28:09.3918736Z cpp/atest 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.atest_1.1_be3801d80d5d2194_.log 2024-08-20T22:28:09.3920201Z 2024-08-20T22:28:10.0372323Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:28:10.2318422Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:28:10.8538217Z Running test batch 'tests to run' cost 24.88 seconds 2024-08-20T22:28:11.4037858Z + run_if_exists tensor_interop_test 2024-08-20T22:28:11.4038694Z + local test_name=tensor_interop_test 2024-08-20T22:28:11.4039794Z + [[ -x build/bin/tensor_interop_test ]] 2024-08-20T22:28:11.4040654Z + echo 'Warning: tensor_interop_test does not exist.' 2024-08-20T22:28:11.4041456Z Warning: tensor_interop_test does not exist. 2024-08-20T22:28:11.4042533Z + run_if_exists cudnn_test 2024-08-20T22:28:11.4043061Z + local test_name=cudnn_test 2024-08-20T22:28:11.4043829Z + [[ -x build/bin/cudnn_test ]] 2024-08-20T22:28:11.4044886Z + echo 'Warning: cudnn_test does not exist.' 2024-08-20T22:28:11.4045974Z Warning: cudnn_test does not exist. 2024-08-20T22:28:11.4047245Z + run_if_exists cuda_generator_test 2024-08-20T22:28:11.4048194Z + local test_name=cuda_generator_test 2024-08-20T22:28:11.4049160Z + [[ -x build/bin/cuda_generator_test ]] 2024-08-20T22:28:11.4050134Z + echo 'Warning: cuda_generator_test does not exist.' 2024-08-20T22:28:11.4050805Z Warning: cuda_generator_test does not exist. 2024-08-20T22:28:11.4051231Z + run_if_exists apply_test 2024-08-20T22:28:11.4051577Z + local test_name=apply_test 2024-08-20T22:28:11.4051983Z + [[ -x build/bin/apply_test ]] 2024-08-20T22:28:11.4052433Z + echo 'Warning: apply_test does not exist.' 2024-08-20T22:28:11.4052894Z Warning: apply_test does not exist. 2024-08-20T22:28:11.4053285Z + run_if_exists stream_test 2024-08-20T22:28:11.4053624Z + local test_name=stream_test 2024-08-20T22:28:11.4054024Z + [[ -x build/bin/stream_test ]] 2024-08-20T22:28:11.4054489Z + echo 'Warning: stream_test does not exist.' 2024-08-20T22:28:11.4054926Z Warning: stream_test does not exist. 2024-08-20T22:28:11.4055324Z + run_if_exists cuda_half_test 2024-08-20T22:28:11.4055685Z + local test_name=cuda_half_test 2024-08-20T22:28:11.4056091Z + [[ -x build/bin/cuda_half_test ]] 2024-08-20T22:28:11.4056584Z + echo 'Warning: cuda_half_test does not exist.' 2024-08-20T22:28:11.4057053Z Warning: cuda_half_test does not exist. 2024-08-20T22:28:11.4057467Z + run_if_exists cuda_vectorized_test 2024-08-20T22:28:11.4057880Z + local test_name=cuda_vectorized_test 2024-08-20T22:28:11.4058495Z + [[ -x build/bin/cuda_vectorized_test ]] 2024-08-20T22:28:11.4059040Z + echo 'Warning: cuda_vectorized_test does not exist.' 2024-08-20T22:28:11.4059630Z Warning: cuda_vectorized_test does not exist. 2024-08-20T22:28:11.4060092Z + run_if_exists cuda_distributions_test 2024-08-20T22:28:11.4060534Z + local test_name=cuda_distributions_test 2024-08-20T22:28:11.4061027Z + [[ -x build/bin/cuda_distributions_test ]] 2024-08-20T22:28:11.4061614Z + echo 'Warning: cuda_distributions_test does not exist.' 2024-08-20T22:28:11.4062195Z Warning: cuda_distributions_test does not exist. 2024-08-20T22:28:11.4062647Z + run_if_exists cuda_optional_test 2024-08-20T22:28:11.4063038Z + local test_name=cuda_optional_test 2024-08-20T22:28:11.4063546Z + [[ -x build/bin/cuda_optional_test ]] 2024-08-20T22:28:11.4064073Z + echo 'Warning: cuda_optional_test does not exist.' 2024-08-20T22:28:11.4064578Z Warning: cuda_optional_test does not exist. 2024-08-20T22:28:11.4065031Z + run_if_exists cuda_tensor_interop_test 2024-08-20T22:28:11.4065460Z + local test_name=cuda_tensor_interop_test 2024-08-20T22:28:11.4065966Z + [[ -x build/bin/cuda_tensor_interop_test ]] 2024-08-20T22:28:11.4066560Z + echo 'Warning: cuda_tensor_interop_test does not exist.' 2024-08-20T22:28:11.4067105Z Warning: cuda_tensor_interop_test does not exist. 2024-08-20T22:28:11.4067578Z + run_if_exists cuda_complex_test 2024-08-20T22:28:11.4067964Z + local test_name=cuda_complex_test 2024-08-20T22:28:11.4068396Z + [[ -x build/bin/cuda_complex_test ]] 2024-08-20T22:28:11.4068911Z + echo 'Warning: cuda_complex_test does not exist.' 2024-08-20T22:28:11.4069409Z Warning: cuda_complex_test does not exist. 2024-08-20T22:28:11.4069837Z + run_if_exists cuda_complex_math_test 2024-08-20T22:28:11.4070257Z + local test_name=cuda_complex_math_test 2024-08-20T22:28:11.4070739Z + [[ -x build/bin/cuda_complex_math_test ]] 2024-08-20T22:28:11.4071306Z + echo 'Warning: cuda_complex_math_test does not exist.' 2024-08-20T22:28:11.4071831Z Warning: cuda_complex_math_test does not exist. 2024-08-20T22:28:11.4072282Z + run_if_exists cuda_cub_test 2024-08-20T22:28:11.4072638Z + local test_name=cuda_cub_test 2024-08-20T22:28:11.4073044Z + [[ -x build/bin/cuda_cub_test ]] 2024-08-20T22:28:11.4073521Z + echo 'Warning: cuda_cub_test does not exist.' 2024-08-20T22:28:11.4074039Z Warning: cuda_cub_test does not exist. 2024-08-20T22:28:11.4074443Z + run_if_exists cuda_atomic_ops_test 2024-08-20T22:28:11.4074853Z + local test_name=cuda_atomic_ops_test 2024-08-20T22:28:11.4075319Z + [[ -x build/bin/cuda_atomic_ops_test ]] 2024-08-20T22:28:11.4075850Z + echo 'Warning: cuda_atomic_ops_test does not exist.' 2024-08-20T22:28:11.4076371Z Warning: cuda_atomic_ops_test does not exist. 2024-08-20T22:28:11.4076832Z + '[' ON == ON ']' 2024-08-20T22:28:11.4077841Z + valgrind --suppressions=/var/lib/jenkins/workspace/aten/tools/valgrind.sup --error-exitcode=1 build/bin/basic '--gtest_filter=-*CUDA' 2024-08-20T22:28:11.4355790Z ==36571== Memcheck, a memory error detector 2024-08-20T22:28:11.4357186Z ==36571== Copyright (C) 2002-2022, and GNU GPL'd, by Julian Seward et al. 2024-08-20T22:28:11.4358576Z ==36571== Using Valgrind-3.20.0 and LibVEX; rerun with -h for copyright info 2024-08-20T22:28:11.4359800Z ==36571== Command: build/bin/basic --gtest_filter=-*CUDA 2024-08-20T22:28:11.4360319Z ==36571== 2024-08-20T22:28:40.4912458Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc 2024-08-20T22:28:40.5136189Z Note: Google Test filter = -*CUDA 2024-08-20T22:28:40.5186949Z [==========] Running 4 tests from 1 test suite. 2024-08-20T22:28:40.5214438Z [----------] Global test environment set-up. 2024-08-20T22:28:40.5254668Z [----------] 4 tests from BasicTest 2024-08-20T22:28:40.5275542Z [ RUN ] BasicTest.BasicTestCPU 2024-08-20T22:28:41.9722228Z 383 ms 2024-08-20T22:28:42.0601816Z 52 ms 2024-08-20T22:28:42.1338985Z 65 ms 2024-08-20T22:28:42.8581382Z [ OK ] BasicTest.BasicTestCPU (2328 ms) 2024-08-20T22:28:42.8590766Z [ RUN ] BasicTest.BasicTestHalfCPU 2024-08-20T22:28:42.9967800Z 86 ms 2024-08-20T22:28:43.0463332Z 44 ms 2024-08-20T22:28:43.1128429Z 64 ms 2024-08-20T22:28:43.1690673Z [ OK ] BasicTest.BasicTestHalfCPU (306 ms) 2024-08-20T22:28:43.1691220Z [ RUN ] BasicTest.FactoryMethodsTest 2024-08-20T22:28:43.2103292Z [ OK ] BasicTest.FactoryMethodsTest (40 ms) 2024-08-20T22:28:43.2103867Z [ RUN ] BasicTest.BasicStdTestCPU 2024-08-20T22:28:43.3777500Z Simple example: called once 2024-08-20T22:28:43.3859580Z throw: call_once will retry 2024-08-20T22:28:43.4280272Z throw: call_once will retry 2024-08-20T22:28:43.4288481Z Didn't throw, call_once will not attempt again 2024-08-20T22:28:43.4309269Z [ OK ] BasicTest.BasicStdTestCPU (220 ms) 2024-08-20T22:28:43.4333075Z [----------] 4 tests from BasicTest (2904 ms total) 2024-08-20T22:28:43.4333738Z 2024-08-20T22:28:43.4344526Z [----------] Global test environment tear-down 2024-08-20T22:28:43.4377505Z [==========] 4 tests from 1 test suite ran. (2925 ms total) 2024-08-20T22:28:43.4389170Z [ PASSED ] 4 tests. 2024-08-20T22:28:45.2679868Z ==36571== 2024-08-20T22:28:45.2684179Z ==36571== HEAP SUMMARY: 2024-08-20T22:28:45.2684984Z ==36571== in use at exit: 239,952 bytes in 3,997 blocks 2024-08-20T22:28:45.2686167Z ==36571== total heap usage: 734,532 allocs, 730,535 frees, 213,503,022 bytes allocated 2024-08-20T22:28:45.2687339Z ==36571== 2024-08-20T22:28:45.3060833Z ==36571== LEAK SUMMARY: 2024-08-20T22:28:45.3061644Z ==36571== definitely lost: 0 bytes in 0 blocks 2024-08-20T22:28:45.3062609Z ==36571== indirectly lost: 0 bytes in 0 blocks 2024-08-20T22:28:45.3063568Z ==36571== possibly lost: 0 bytes in 0 blocks 2024-08-20T22:28:45.3064582Z ==36571== still reachable: 239,952 bytes in 3,997 blocks 2024-08-20T22:28:45.3065630Z ==36571== suppressed: 0 bytes in 0 blocks 2024-08-20T22:28:45.3067130Z ==36571== Rerun with --leak-check=full to see details of leaked memory 2024-08-20T22:28:45.3068181Z ==36571== 2024-08-20T22:28:45.3069088Z ==36571== For lists of detected and suppressed errors, rerun with: -s 2024-08-20T22:28:45.3070507Z ==36571== ERROR SUMMARY: 0 errors from 0 contexts (suppressed: 0 from 0) 2024-08-20T22:28:45.3593120Z + [[ -x build/bin/tensor_interop_test ]] 2024-08-20T22:28:45.3593894Z + [[ -n '' ]] 2024-08-20T22:28:45.3594446Z + assert_git_not_dirty 2024-08-20T22:28:45.3594889Z + [[ linux-focal-py3.12-clang10 != *rocm* ]] 2024-08-20T22:28:45.3595417Z + [[ linux-focal-py3.12-clang10 != *xla* ]] 2024-08-20T22:28:45.3601862Z ++ git status --porcelain 2024-08-20T22:28:45.3602393Z ++ grep -v '?? third_party' 2024-08-20T22:28:45.5524154Z ++ true 2024-08-20T22:28:45.5525354Z + git_status= 2024-08-20T22:28:45.5526047Z + [[ -n '' ]] 2024-08-20T22:28:45.5526531Z + test_libtorch 1 2024-08-20T22:28:45.5526919Z + local SHARD=1 2024-08-20T22:28:45.5527210Z + [[ default != \s\l\o\w ]] 2024-08-20T22:28:45.5527624Z + echo 'Testing libtorch' 2024-08-20T22:28:45.5527959Z Testing libtorch 2024-08-20T22:28:45.5529162Z + ln -sf /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libbackend_with_compiler.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/bin 2024-08-20T22:28:45.5539454Z + ln -sf /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libjitbackend_test.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/bin 2024-08-20T22:28:45.5550046Z + ln -sf /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libcaffe2_nvrtc.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/bin 2024-08-20T22:28:45.5560405Z + ln -sf /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libc10.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/bin 2024-08-20T22:28:45.5570659Z + ln -sf /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libshm.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/bin 2024-08-20T22:28:45.5583305Z + 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 /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/bin 2024-08-20T22:28:45.5591664Z + ln -sf '/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libnvfuser*' /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/bin 2024-08-20T22:28:45.5601091Z + export CPP_TESTS_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/bin 2024-08-20T22:28:45.5602147Z + CPP_TESTS_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/bin 2024-08-20T22:28:45.5603481Z + [[ -z 1 ]] 2024-08-20T22:28:45.5603757Z + [[ 1 == \1 ]] 2024-08-20T22:28:45.5604043Z + test_libtorch_api 2024-08-20T22:28:45.5604464Z + MNIST_DIR=/var/lib/jenkins/workspace/test/cpp/api/mnist 2024-08-20T22:28:45.5605331Z + python tools/download_mnist.py --quiet -d /var/lib/jenkins/workspace/test/cpp/api/mnist 2024-08-20T22:28:45.6111724Z Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz ... 2024-08-20T22:28:45.7145601Z Failed to download (trying next): 2024-08-20T22:28:45.7146256Z HTTP Error 403: Forbidden 2024-08-20T22:28:45.7147361Z Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz ... 2024-08-20T22:28:46.0578314Z Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz ... 2024-08-20T22:28:46.1241009Z Failed to download (trying next): 2024-08-20T22:28:46.1241507Z HTTP Error 403: Forbidden 2024-08-20T22:28:46.1242433Z Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz ... 2024-08-20T22:28:46.1444404Z Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz ... 2024-08-20T22:28:46.2129734Z Failed to download (trying next): 2024-08-20T22:28:46.2130244Z HTTP Error 403: Forbidden 2024-08-20T22:28:46.2131200Z Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz ... 2024-08-20T22:28:46.3151028Z Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz ... 2024-08-20T22:28:46.3831303Z Failed to download (trying next): 2024-08-20T22:28:46.3831947Z HTTP Error 403: Forbidden 2024-08-20T22:28:46.3833283Z Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz ... 2024-08-20T22:28:46.4180874Z + [[ linux-focal-py3.12-clang10 == *asan* ]] 2024-08-20T22:28:46.4181534Z + [[ linux-focal-py3.12-clang10 == *slow-gradcheck* ]] 2024-08-20T22:28:46.4182024Z + OMP_NUM_THREADS=2 2024-08-20T22:28:46.4182515Z + TORCH_CPP_TEST_MNIST_PATH=/var/lib/jenkins/workspace/test/cpp/api/mnist 2024-08-20T22:28:46.4183415Z + python test/run_test.py --cpp --verbose -i cpp/test_api -k 'not IMethodTest' 2024-08-20T22:28:46.5275385Z /var/lib/jenkins/workspace/test/run_test.py:21: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html 2024-08-20T22:28:46.5276569Z import pkg_resources 2024-08-20T22:28:48.5319903Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:28:49.4948689Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2024-08-20T22:28:49.5058358Z Found test times from artifacts 2024-08-20T22:28:49.5495380Z Found test times from artifacts 2024-08-20T22:28:49.5510994Z Running 25% of tests based on TD 2024-08-20T22:28:49.5514673Z Running parallel tests on 3 processes 2024-08-20T22:28:49.5515556Z Name: tests to run (est. time: 0.0min) 2024-08-20T22:28:49.5516048Z Serial tests (0): 2024-08-20T22:28:49.5516423Z Parallel tests (1): 2024-08-20T22:28:49.5516742Z cpp/test_api 1/1 2024-08-20T22:28:49.5517096Z Name: excluded (est. time: 0.0min) 2024-08-20T22:28:49.5517496Z Serial tests (0): 2024-08-20T22:28:49.5517780Z Parallel tests (0): 2024-08-20T22:28:49.5575413Z Running cpp/test_api 1/1 ... [2024-08-20 22:28:49.557099] 2024-08-20T22:28:49.5579825Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:28:49.5583954Z Executing ['pytest', '/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/bin/test_api', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-6e070e82fc2cb1c1.xml', '-k', 'not IMethodTest', '-x', '--reruns=2'] ... [2024-08-20 22:28:49.557745] 2024-08-20T22:28:51.9257702Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:28:51.9369389Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:28:51.9909567Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:28:52.5864987Z 2024-08-20T22:28:52.5866508Z cpp/test_api 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.test_api_1.1_c0aafbd863595428_.log 2024-08-20T22:28:52.5867441Z 2024-08-20T22:28:52.5870723Z Running cpp/test_api 1/1 ... [2024-08-20 22:28:52.586852] 2024-08-20T22:28:52.5871269Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:28:52.5876980Z Executing ['pytest', '/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/bin/test_api', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-da4907517138a01a.xml', '-k', 'not IMethodTest', '-x', '--reruns=2'] ... [2024-08-20 22:28:52.587358] 2024-08-20T22:31:24.6027755Z 2024-08-20T22:31:24.6029198Z cpp/test_api 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.test_api_1.1_8df526c29c1356a1_.log 2024-08-20T22:31:24.6038786Z 2024-08-20T22:31:25.1810356Z Running test batch 'tests to run' cost 155.63 seconds 2024-08-20T22:31:25.7157145Z + python test/run_test.py --cpp --verbose -i cpp/test_tensorexpr 2024-08-20T22:31:25.8257397Z /var/lib/jenkins/workspace/test/run_test.py:21: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html 2024-08-20T22:31:25.8258594Z import pkg_resources 2024-08-20T22:31:27.8615838Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:31:28.8289690Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2024-08-20T22:31:28.8400148Z Found test times from artifacts 2024-08-20T22:31:28.8841247Z Found test times from artifacts 2024-08-20T22:31:28.8857961Z Running 25% of tests based on TD 2024-08-20T22:31:28.8862052Z Running parallel tests on 3 processes 2024-08-20T22:31:28.8862589Z Name: tests to run (est. time: 0.0min) 2024-08-20T22:31:28.8864603Z Serial tests (0): 2024-08-20T22:31:28.8865422Z Parallel tests (1): 2024-08-20T22:31:28.8866075Z cpp/test_tensorexpr 1/1 2024-08-20T22:31:28.8866494Z Name: excluded (est. time: 0.0min) 2024-08-20T22:31:28.8867172Z Serial tests (0): 2024-08-20T22:31:28.8868071Z Parallel tests (0): 2024-08-20T22:31:28.8933387Z Running cpp/test_tensorexpr 1/1 ... [2024-08-20 22:31:28.891409] 2024-08-20T22:31:28.8934575Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:31:28.8940146Z Executing ['pytest', '/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/bin/test_tensorexpr', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-7bb7eb0d9ce8f329.xml', '-x', '--reruns=2'] ... [2024-08-20 22:31:28.892071] 2024-08-20T22:31:31.1631712Z 2024-08-20T22:31:31.1634334Z cpp/test_tensorexpr 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.test_tensorexpr_1.1_9addc9170b00d22c_.log 2024-08-20T22:31:31.1635416Z 2024-08-20T22:31:31.4802908Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:31:31.5344474Z Running cpp/test_tensorexpr 1/1 ... [2024-08-20 22:31:31.533980] 2024-08-20T22:31:31.5345142Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:31:31.5349023Z Executing ['pytest', '/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/bin/test_tensorexpr', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-0b0debd96b315ab1.xml', '-x', '--reruns=2'] ... [2024-08-20 22:31:31.534477] 2024-08-20T22:31:31.5365212Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:31:31.5441140Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:33:30.3387986Z 2024-08-20T22:33:30.3389652Z cpp/test_tensorexpr 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.test_tensorexpr_1.1_eff9b9bd4522ef06_.log 2024-08-20T22:33:30.3402750Z 2024-08-20T22:33:30.9193549Z Running test batch 'tests to run' cost 122.03 seconds 2024-08-20T22:33:31.4677676Z + [[ linux-focal-py3.12-clang10 != *android* ]] 2024-08-20T22:33:31.4679529Z + [[ linux-focal-py3.12-clang10 != *cuda* ]] 2024-08-20T22:33:31.4680120Z + [[ linux-focal-py3.12-clang10 != *asan* ]] 2024-08-20T22:33:31.4680589Z + export CPP_TESTS_DIR=build/bin 2024-08-20T22:33:31.4680965Z + CPP_TESTS_DIR=build/bin 2024-08-20T22:33:31.4681526Z + python test/run_test.py --cpp --verbose -i cpp/static_runtime_test 2024-08-20T22:33:31.5773880Z /var/lib/jenkins/workspace/test/run_test.py:21: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html 2024-08-20T22:33:31.5775080Z import pkg_resources 2024-08-20T22:33:33.6303746Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:33:34.6090498Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2024-08-20T22:33:34.6200484Z Found test times from artifacts 2024-08-20T22:33:34.6639733Z Found test times from artifacts 2024-08-20T22:33:34.6655481Z Running 25% of tests based on TD 2024-08-20T22:33:34.6658954Z Running parallel tests on 3 processes 2024-08-20T22:33:34.6659933Z Name: tests to run (est. time: 0.0min) 2024-08-20T22:33:34.6660376Z Serial tests (0): 2024-08-20T22:33:34.6660863Z Parallel tests (1): 2024-08-20T22:33:34.6662242Z cpp/static_runtime_test 1/1 2024-08-20T22:33:34.6662975Z Name: excluded (est. time: 0.0min) 2024-08-20T22:33:34.6663345Z Serial tests (0): 2024-08-20T22:33:34.6663650Z Parallel tests (0): 2024-08-20T22:33:34.6716317Z Running cpp/static_runtime_test 1/1 ... [2024-08-20 22:33:34.671202] 2024-08-20T22:33:34.6717405Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:33:34.6724707Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/static_runtime_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-8c9ea708e09a9893.xml', '-x', '--reruns=2'] ... [2024-08-20 22:33:34.671851] 2024-08-20T22:33:36.9414962Z 2024-08-20T22:33:36.9417276Z cpp/static_runtime_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.static_runtime_test_1.1_556f6aad1c484725_.log 2024-08-20T22:33:36.9419097Z 2024-08-20T22:33:37.1844444Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:33:37.2441246Z Running cpp/static_runtime_test 1/1 ... [2024-08-20 22:33:37.243639] 2024-08-20T22:33:37.2442361Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:33:37.2446504Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/static_runtime_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-dff33ea90d546bc1.xml', '-x', '--reruns=2'] ... [2024-08-20 22:33:37.244157] 2024-08-20T22:33:37.4275537Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:33:37.4498220Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:46:06.6421874Z 2024-08-20T22:46:06.6423726Z cpp/static_runtime_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.static_runtime_test_1.1_f4623d44e12f72fd_.log 2024-08-20T22:46:06.6427418Z 2024-08-20T22:46:07.2160429Z Running test batch 'tests to run' cost 752.55 seconds 2024-08-20T22:46:07.7619808Z + [[ -z 1 ]] 2024-08-20T22:46:07.7620215Z + [[ 1 == \2 ]] 2024-08-20T22:46:07.7620669Z + assert_git_not_dirty 2024-08-20T22:46:07.7623118Z + [[ linux-focal-py3.12-clang10 != *rocm* ]] 2024-08-20T22:46:07.7623779Z + [[ linux-focal-py3.12-clang10 != *xla* ]] 2024-08-20T22:46:07.7626029Z ++ git status --porcelain 2024-08-20T22:46:07.7626796Z ++ grep -v '?? third_party' 2024-08-20T22:46:07.9512726Z ++ true 2024-08-20T22:46:07.9513378Z + git_status= 2024-08-20T22:46:07.9514202Z + [[ -n '' ]] 2024-08-20T22:46:07.9514767Z + [[ linux-focal-py3.12-clang10 == *xpu* ]] 2024-08-20T22:46:07.9515340Z + cleanup_workspace 2024-08-20T22:46:07.9516510Z + echo 'sudo may print the following warning message that can be ignored. The chown command will still run.' 2024-08-20T22:46:07.9517649Z sudo may print the following warning message that can be ignored. The chown command will still run. 2024-08-20T22:46:07.9518588Z + echo ' sudo: setrlimit(RLIMIT_STACK): Operation not permitted' 2024-08-20T22:46:07.9519221Z sudo: setrlimit(RLIMIT_STACK): Operation not permitted 2024-08-20T22:46:07.9520043Z + echo 'For more details refer to https://github.com/sudo-project/sudo/issues/42' 2024-08-20T22:46:07.9520932Z For more details refer to https://github.com/sudo-project/sudo/issues/42 2024-08-20T22:46:07.9521623Z + sudo chown -R 1000 /var/lib/jenkins/workspace 2024-08-20T22:46:11.1461852Z ##[group]Run cat test/**/*_toprint.log || true 2024-08-20T22:46:11.1462391Z cat test/**/*_toprint.log || true 2024-08-20T22:46:11.1726014Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T22:46:11.1726540Z env: 2024-08-20T22:46:11.1726917Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:46:11.1727696Z DOCKER_CONTAINER_ID: 2340f3deffd39d0eed0996837220f02e294a105661d3e11a7b09c171901f5c27 2024-08-20T22:46:11.1728347Z ##[endgroup] 2024-08-20T22:46:11.1807584Z cat: 'test/**/*_toprint.log': No such file or directory 2024-08-20T22:46:11.1840412Z ##[group]Run kill "$MONITOR_SCRIPT_PID" 2024-08-20T22:46:11.1840865Z kill "$MONITOR_SCRIPT_PID" 2024-08-20T22:46:11.1847460Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T22:46:11.1847958Z env: 2024-08-20T22:46:11.1848213Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:46:11.1848806Z DOCKER_CONTAINER_ID: 2340f3deffd39d0eed0996837220f02e294a105661d3e11a7b09c171901f5c27 2024-08-20T22:46:11.1849481Z MONITOR_SCRIPT_PID: 363198 2024-08-20T22:46:11.1849818Z ##[endgroup] 2024-08-20T22:46:11.2184312Z Prepare all required actions 2024-08-20T22:46:11.2184844Z Getting action download info 2024-08-20T22:46:11.3544472Z Download action repository 'actions/upload-artifact@v3' (SHA:a8a3f3ad30e3422c9c7b888a15615d19a852ae32) 2024-08-20T22:46:11.5100596Z ##[group]Run ./.github/actions/upload-test-artifacts 2024-08-20T22:46:11.5101054Z with: 2024-08-20T22:46:11.5101470Z file-suffix: test-default-1-4-amz2023.linux.2xlarge_29025267541 2024-08-20T22:46:11.5102026Z s3-bucket: gha-artifacts 2024-08-20T22:46:11.5102329Z env: 2024-08-20T22:46:11.5102578Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:46:11.5103171Z DOCKER_CONTAINER_ID: 2340f3deffd39d0eed0996837220f02e294a105661d3e11a7b09c171901f5c27 2024-08-20T22:46:11.5103806Z ##[endgroup] 2024-08-20T22:46:11.5131261Z ##[group]Run # Remove any previous test jsons if they exist 2024-08-20T22:46:11.5131973Z # Remove any previous test jsons if they exist 2024-08-20T22:46:11.5132473Z rm -f test-jsons-*.zip 2024-08-20T22:46:11.5133021Z zip -r "test-jsons-${FILE_SUFFIX}.zip" test -i '*.json' 2024-08-20T22:46:11.5139621Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T22:46:11.5140109Z env: 2024-08-20T22:46:11.5140351Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:46:11.5141046Z DOCKER_CONTAINER_ID: 2340f3deffd39d0eed0996837220f02e294a105661d3e11a7b09c171901f5c27 2024-08-20T22:46:11.5141906Z FILE_SUFFIX: test-default-1-4-amz2023.linux.2xlarge_29025267541 2024-08-20T22:46:11.5142431Z ##[endgroup] 2024-08-20T22:46:11.5460549Z adding: test/allowlist_for_publicAPI.json (deflated 79%) 2024-08-20T22:46:11.5488071Z adding: test/benchmark_utils/callgrind_artifacts.json (deflated 92%) 2024-08-20T22:46:11.5488764Z adding: test/minioptest_failures_dict.json (deflated 70%) 2024-08-20T22:46:11.5494665Z adding: test/profiler/profiler_utils_mock_events.json (deflated 87%) 2024-08-20T22:46:11.5500040Z adding: test/slow_tests.json (deflated 82%) 2024-08-20T22:46:11.5509692Z adding: test/test-reports/td_exclusions-03eab898dedbc96bc46a.json (deflated 82%) 2024-08-20T22:46:11.5510689Z adding: test/test-reports/td_exclusions-318a795e5965ecd59241.json (deflated 73%) 2024-08-20T22:46:11.5511668Z adding: test/test-reports/td_exclusions-f08a286e8b57b4948e57.json (deflated 18%) 2024-08-20T22:46:11.5512646Z adding: test/test-reports/td_exclusions-7a60bbb1e98077bc4c3f.json (deflated 16%) 2024-08-20T22:46:11.5513602Z adding: test/test-reports/td_exclusions-9b3d6956fa92778b563c.json (deflated 14%) 2024-08-20T22:46:11.5522062Z adding: test/.pytorch-disabled-tests.json (deflated 88%) 2024-08-20T22:46:11.5553526Z ##[group]Run # Remove any previous test reports if they exist 2024-08-20T22:46:11.5554175Z # Remove any previous test reports if they exist 2024-08-20T22:46:11.5554702Z rm -f test-reports-*.zip 2024-08-20T22:46:11.5555285Z zip -r "test-reports-${FILE_SUFFIX}.zip" test -i '*.xml' -i '*.csv' 2024-08-20T22:46:11.5561263Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T22:46:11.5561759Z env: 2024-08-20T22:46:11.5562017Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:46:11.5562618Z DOCKER_CONTAINER_ID: 2340f3deffd39d0eed0996837220f02e294a105661d3e11a7b09c171901f5c27 2024-08-20T22:46:11.5563436Z FILE_SUFFIX: test-default-1-4-amz2023.linux.2xlarge_29025267541 2024-08-20T22:46:11.5564069Z ##[endgroup] 2024-08-20T22:46:11.5791285Z adding: test/test-reports/python-pytest/inductor.test_distributed_patterns/inductor.test_distributed_patterns-0ed5f3a9a7501a5c.xml (deflated 86%) 2024-08-20T22:46:11.5856838Z adding: test/test-reports/python-pytest/test_utils/test_utils-58cee363dce3f8e5.xml (deflated 96%) 2024-08-20T22:46:11.5862407Z adding: test/test-reports/python-pytest/test_utils/test_utils-201b7b6a5e6e09f4.xml (deflated 38%) 2024-08-20T22:46:11.5882806Z adding: test/test-reports/python-pytest/test_utils/test_utils-0a537fe028a4479e.xml (deflated 96%) 2024-08-20T22:46:11.5935447Z adding: test/test-reports/python-pytest/test_nn/test_nn-9a50d153d1374f8f.xml (deflated 97%) 2024-08-20T22:46:11.5936880Z adding: test/test-reports/python-pytest/inductor.test_torchinductor/inductor.test_torchinductor-ce8ec743f28eb2e7.xml (deflated 28%) 2024-08-20T22:46:11.5941733Z adding: test/test-reports/python-pytest/inductor.test_torchinductor/inductor.test_torchinductor-a3c0eb645f5bf858.xml (deflated 91%) 2024-08-20T22:46:11.5944329Z adding: test/test-reports/python-pytest/inductor.test_torchinductor_opinfo/inductor.test_torchinductor_opinfo-2e44cfbd7e78793e.xml (deflated 28%) 2024-08-20T22:46:11.5946472Z adding: test/test-reports/python-pytest/inductor.test_torchinductor_opinfo/inductor.test_torchinductor_opinfo-e7e5140b82c144ee.xml (deflated 29%) 2024-08-20T22:46:11.5948988Z adding: test/test-reports/python-pytest/inductor.test_torchinductor_opinfo/inductor.test_torchinductor_opinfo-e6519cb8a65b3747.xml (deflated 28%) 2024-08-20T22:46:11.5951157Z adding: test/test-reports/python-pytest/inductor.test_torchinductor_opinfo/inductor.test_torchinductor_opinfo-7ca6543ce073808b.xml (deflated 28%) 2024-08-20T22:46:11.5953502Z adding: test/test-reports/python-pytest/inductor.test_torchinductor_opinfo/inductor.test_torchinductor_opinfo-e2adb85c1ac4d8ff.xml (deflated 28%) 2024-08-20T22:46:11.5955968Z adding: test/test-reports/python-pytest/inductor.test_torchinductor_opinfo/inductor.test_torchinductor_opinfo-28c914932796a718.xml (deflated 28%) 2024-08-20T22:46:11.5958137Z adding: test/test-reports/python-pytest/inductor.test_torchinductor_opinfo/inductor.test_torchinductor_opinfo-08736285f9782635.xml (deflated 28%) 2024-08-20T22:46:11.5960282Z adding: test/test-reports/python-pytest/inductor.test_torchinductor_opinfo/inductor.test_torchinductor_opinfo-ce73f923d68379b8.xml (deflated 28%) 2024-08-20T22:46:11.5962709Z adding: test/test-reports/python-pytest/inductor.test_torchinductor_opinfo/inductor.test_torchinductor_opinfo-b1afb231bac53e21.xml (deflated 29%) 2024-08-20T22:46:11.5964488Z adding: test/test-reports/python-pytest/inductor.test_torchinductor_opinfo/inductor.test_torchinductor_opinfo-a36cdce3684f657e.xml (deflated 91%) 2024-08-20T22:46:11.5966234Z adding: test/test-reports/python-pytest/inductor.test_torchinductor_opinfo/inductor.test_torchinductor_opinfo-e13a509cf126bf77.xml (deflated 90%) 2024-08-20T22:46:11.5968378Z adding: test/test-reports/python-pytest/inductor.test_torchinductor_opinfo/inductor.test_torchinductor_opinfo-ee7d86c741221a2d.xml (deflated 91%) 2024-08-20T22:46:11.5970282Z adding: test/test-reports/python-pytest/inductor.test_torchinductor_opinfo/inductor.test_torchinductor_opinfo-357ea6d2faa78d53.xml (deflated 90%) 2024-08-20T22:46:11.5972017Z adding: test/test-reports/python-pytest/inductor.test_torchinductor_opinfo/inductor.test_torchinductor_opinfo-e5eb911867bece19.xml (deflated 90%) 2024-08-20T22:46:11.5973879Z adding: test/test-reports/python-pytest/inductor.test_torchinductor_opinfo/inductor.test_torchinductor_opinfo-4ffef931614da9f7.xml (deflated 91%) 2024-08-20T22:46:11.5976467Z adding: test/test-reports/python-pytest/inductor.test_torchinductor_opinfo/inductor.test_torchinductor_opinfo-e1bb6567a4b5f1c2.xml (deflated 92%) 2024-08-20T22:46:11.5978712Z adding: test/test-reports/python-pytest/inductor.test_torchinductor_opinfo/inductor.test_torchinductor_opinfo-446f86123bdff85a.xml (deflated 90%) 2024-08-20T22:46:11.5980634Z adding: test/test-reports/python-pytest/inductor.test_torchinductor_opinfo/inductor.test_torchinductor_opinfo-9f66a286a3d52cad.xml (deflated 90%) 2024-08-20T22:46:11.5982570Z adding: test/test-reports/python-pytest/inductor.test_torchinductor_codegen_dynamic_shapes/inductor.test_torchinductor_codegen_dynamic_shapes-86ba81b7b14e6f35.xml (deflated 28%) 2024-08-20T22:46:11.5984665Z adding: test/test-reports/python-pytest/inductor.test_torchinductor_codegen_dynamic_shapes/inductor.test_torchinductor_codegen_dynamic_shapes-a063b2dc733dd414.xml (deflated 28%) 2024-08-20T22:46:11.5986944Z adding: test/test-reports/python-pytest/inductor.test_torchinductor_codegen_dynamic_shapes/inductor.test_torchinductor_codegen_dynamic_shapes-2ffafd215f0c0167.xml (deflated 93%) 2024-08-20T22:46:11.5989029Z adding: test/test-reports/python-pytest/inductor.test_torchinductor_codegen_dynamic_shapes/inductor.test_torchinductor_codegen_dynamic_shapes-68175f5c521183f3.xml (deflated 93%) 2024-08-20T22:46:11.5990779Z adding: test/test-reports/python-pytest/inductor.test_mmdecomp/inductor.test_mmdecomp-273fa141378482d1.xml (deflated 28%) 2024-08-20T22:46:11.5992259Z adding: test/test-reports/python-pytest/inductor.test_mmdecomp/inductor.test_mmdecomp-dbfaecf49e0500c9.xml (deflated 93%) 2024-08-20T22:46:11.5993715Z adding: test/test-reports/python-pytest/dynamo.test_interop/dynamo.test_interop-15b2606e9689036c.xml (deflated 28%) 2024-08-20T22:46:11.5995107Z adding: test/test-reports/python-pytest/dynamo.test_interop/dynamo.test_interop-60889fde176a0e31.xml (deflated 71%) 2024-08-20T22:46:11.5996511Z adding: test/test-reports/python-pytest/dynamo.test_logging/dynamo.test_logging-797faaa7e74cdee5.xml (deflated 28%) 2024-08-20T22:46:11.6025129Z adding: test/test-reports/python-pytest/dynamo.test_logging/dynamo.test_logging-42e3ae3a8329dee5.xml (deflated 94%) 2024-08-20T22:46:11.6026592Z adding: test/test-reports/python-pytest/dynamo.test_exc/dynamo.test_exc-a1e9104dbbb78686.xml (deflated 28%) 2024-08-20T22:46:11.6039147Z adding: test/test-reports/python-pytest/dynamo.test_exc/dynamo.test_exc-c3de1f7c276d8082.xml (deflated 95%) 2024-08-20T22:46:11.6040876Z adding: test/test-reports/python-pytest/dynamo.test_global/dynamo.test_global-d7d6e912156477dd.xml (deflated 28%) 2024-08-20T22:46:11.6042308Z adding: test/test-reports/python-pytest/dynamo.test_global/dynamo.test_global-96aca663679417ee.xml (deflated 86%) 2024-08-20T22:46:11.6044208Z adding: test/test-reports/python-pytest/dynamo.test_unspec/dynamo.test_unspec-a1803db5d086b949.xml (deflated 29%) 2024-08-20T22:46:11.6046109Z adding: test/test-reports/python-pytest/dynamo.test_unspec/dynamo.test_unspec-e5ff20c19dc312ce.xml (deflated 84%) 2024-08-20T22:46:11.6047896Z adding: test/test-reports/python-pytest/dynamo.test_ctx_manager/dynamo.test_ctx_manager-7456f13a247c1b2a.xml (deflated 28%) 2024-08-20T22:46:11.6049861Z adding: test/test-reports/python-pytest/dynamo.test_ctx_manager/dynamo.test_ctx_manager-4d62720fa18ee375.xml (deflated 89%) 2024-08-20T22:46:11.6051783Z adding: test/test-reports/python-pytest/dynamo.test_subgraphs/dynamo.test_subgraphs-5bb36f60d987f53d.xml (deflated 28%) 2024-08-20T22:46:11.6053903Z adding: test/test-reports/python-pytest/dynamo.test_subgraphs/dynamo.test_subgraphs-d20d54cd98ef4231.xml (deflated 96%) 2024-08-20T22:46:11.6055900Z adding: test/test-reports/python-pytest/dynamo.test_autograd_function/dynamo.test_autograd_function-43231928667b55b0.xml (deflated 28%) 2024-08-20T22:46:11.6057547Z adding: test/test-reports/python-pytest/dynamo.test_autograd_function/dynamo.test_autograd_function-ef0e2e069933a151.xml (deflated 85%) 2024-08-20T22:46:11.6059461Z adding: test/test-reports/python-pytest/dynamo.test_activation_checkpointing/dynamo.test_activation_checkpointing-309bc039469fd8a4.xml (deflated 28%) 2024-08-20T22:46:11.6062763Z adding: test/test-reports/python-pytest/dynamo.test_activation_checkpointing/dynamo.test_activation_checkpointing-3fcea84929ea0657.xml (deflated 89%) 2024-08-20T22:46:11.6065382Z adding: test/test-reports/python-pytest/inductor.test_inductor_freezing/inductor.test_inductor_freezing-119597718709f3da.xml (deflated 28%) 2024-08-20T22:46:11.6067053Z adding: test/test-reports/python-pytest/inductor.test_inductor_freezing/inductor.test_inductor_freezing-478fadd6d6efaf15.xml (deflated 88%) 2024-08-20T22:46:11.6068785Z adding: test/test-reports/python-pytest/inductor.test_mkldnn_pattern_matcher/inductor.test_mkldnn_pattern_matcher-c17c8a6fee83e729.xml (deflated 28%) 2024-08-20T22:46:11.6070570Z adding: test/test-reports/python-pytest/inductor.test_mkldnn_pattern_matcher/inductor.test_mkldnn_pattern_matcher-583c1d0ba6ab1558.xml (deflated 95%) 2024-08-20T22:46:11.6072229Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-5f47a001b9992334.xml (deflated 30%) 2024-08-20T22:46:11.6073480Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-15adb00d10b6a955.xml (deflated 29%) 2024-08-20T22:46:11.6074715Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-4a307ce78c6480af.xml (deflated 29%) 2024-08-20T22:46:11.6075938Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-c97080deae32d5cd.xml (deflated 30%) 2024-08-20T22:46:11.6077161Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-7b40527789454488.xml (deflated 29%) 2024-08-20T22:46:11.6078849Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-a8fa9e0b350752bb.xml (deflated 58%) 2024-08-20T22:46:11.6080092Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-ca7cae1e5faf668c.xml (deflated 73%) 2024-08-20T22:46:11.6081318Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-4e57bdb325744909.xml (deflated 67%) 2024-08-20T22:46:11.6082561Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-93c965f91cf386aa.xml (deflated 83%) 2024-08-20T22:46:11.6083909Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-3e0bce4b2cc7376c.xml (deflated 79%) 2024-08-20T22:46:11.6085132Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-6e070e82fc2cb1c1.xml (deflated 29%) 2024-08-20T22:46:11.6086365Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-da4907517138a01a.xml (deflated 88%) 2024-08-20T22:46:11.6087718Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-7bb7eb0d9ce8f329.xml (deflated 29%) 2024-08-20T22:46:11.6090124Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-0b0debd96b315ab1.xml (deflated 89%) 2024-08-20T22:46:11.6091354Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-8c9ea708e09a9893.xml (deflated 29%) 2024-08-20T22:46:11.6095933Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-dff33ea90d546bc1.xml (deflated 90%) 2024-08-20T22:46:11.6127107Z ##[group]Run # Remove any previous usage logs if they exist 2024-08-20T22:46:11.6127748Z # Remove any previous usage logs if they exist 2024-08-20T22:46:11.6128239Z rm -f logs-*.zip 2024-08-20T22:46:11.6128910Z # this workflow is also run in bazel build test, but we dont generate usage reports for it 2024-08-20T22:46:11.6129690Z # so check to see if the file exists first 2024-08-20T22:46:11.6130184Z if [ -f 'usage_log.txt' ]; then 2024-08-20T22:46:11.6130703Z  zip "logs-${FILE_SUFFIX}.zip" 'usage_log.txt' 2024-08-20T22:46:11.6131179Z fi 2024-08-20T22:46:11.6131507Z if ls test/**/*.log 1> /dev/null 2>&1; then 2024-08-20T22:46:11.6132068Z  zip -r "logs-${FILE_SUFFIX}.zip" test -i '*.log' 2024-08-20T22:46:11.6132547Z fi 2024-08-20T22:46:11.6138376Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T22:46:11.6138876Z env: 2024-08-20T22:46:11.6139126Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:46:11.6139715Z DOCKER_CONTAINER_ID: 2340f3deffd39d0eed0996837220f02e294a105661d3e11a7b09c171901f5c27 2024-08-20T22:46:11.6140543Z FILE_SUFFIX: test-default-1-4-amz2023.linux.2xlarge_29025267541 2024-08-20T22:46:11.6141135Z ##[endgroup] 2024-08-20T22:46:11.6224021Z adding: usage_log.txt (deflated 92%) 2024-08-20T22:46:11.6477004Z adding: test/test-reports/inductor.test_max_autotune_1.1_065053d3f8299dea_.log (stored 0%) 2024-08-20T22:46:11.6484469Z adding: test/test-reports/inductor.test_distributed_patterns_1.1_30c8a3c1e0c95688_.log (deflated 82%) 2024-08-20T22:46:11.6643515Z adding: test/test-reports/test_utils_1.1_6bb4898f3c88eb75_.log (deflated 95%) 2024-08-20T22:46:11.6698690Z adding: test/test-reports/test_nn_1.1_0f17624421021c3b_.log (deflated 95%) 2024-08-20T22:46:11.6699805Z adding: test/test-reports/inductor.test_torchinductor_3.4_981815e81d615304_.log (deflated 51%) 2024-08-20T22:46:11.6701375Z adding: test/test-reports/inductor.test_torchinductor_opinfo_9.39_48e4427ed6613f27_.log (deflated 52%) 2024-08-20T22:46:11.6702744Z adding: test/test-reports/inductor.test_torchinductor_opinfo_10.39_ebad1739ac29331c_.log (deflated 52%) 2024-08-20T22:46:11.6704111Z adding: test/test-reports/inductor.test_torchinductor_opinfo_11.39_66539e414fa4e6e6_.log (deflated 51%) 2024-08-20T22:46:11.6705452Z adding: test/test-reports/inductor.test_torchinductor_opinfo_21.39_c47656872eb0923e_.log (deflated 51%) 2024-08-20T22:46:11.6706838Z adding: test/test-reports/inductor.test_torchinductor_opinfo_22.39_ac65fa055e1601a7_.log (deflated 52%) 2024-08-20T22:46:11.6708146Z adding: test/test-reports/inductor.test_torchinductor_opinfo_23.39_2c404fda42b4c5be_.log (deflated 52%) 2024-08-20T22:46:11.6709639Z adding: test/test-reports/inductor.test_torchinductor_opinfo_33.39_b7516d18cca7b60f_.log (deflated 51%) 2024-08-20T22:46:11.6710893Z adding: test/test-reports/inductor.test_torchinductor_opinfo_34.39_c73a538749dad314_.log (deflated 51%) 2024-08-20T22:46:11.6712138Z adding: test/test-reports/inductor.test_torchinductor_opinfo_35.39_e52f852de0821d98_.log (deflated 52%) 2024-08-20T22:46:11.6713545Z adding: test/test-reports/inductor.test_torchinductor_codegen_dynamic_shapes_2.3_5dfa75bf8653ed6d_.log (deflated 53%) 2024-08-20T22:46:11.6715008Z adding: test/test-reports/inductor.test_torchinductor_codegen_dynamic_shapes_3.3_d74033d66f78ea2a_.log (deflated 53%) 2024-08-20T22:46:11.6716214Z adding: test/test-reports/inductor.test_mmdecomp_1.1_33b439996fc8e461_.log (deflated 50%) 2024-08-20T22:46:11.6717239Z adding: test/test-reports/dynamo.test_interop_1.1_53a51a845bf8522e_.log (deflated 50%) 2024-08-20T22:46:11.6718310Z adding: test/test-reports/dynamo.test_logging_1.1_8c0485e03a553c47_.log (deflated 50%) 2024-08-20T22:46:11.6719306Z adding: test/test-reports/dynamo.test_exc_1.1_7d0b16acef5bd912_.log (deflated 49%) 2024-08-20T22:46:11.6720310Z adding: test/test-reports/dynamo.test_global_1.1_7ca2269c39d76dc5_.log (deflated 50%) 2024-08-20T22:46:11.6721360Z adding: test/test-reports/dynamo.test_unspec_1.1_ba9bb451c648dab2_.log (deflated 50%) 2024-08-20T22:46:11.6722739Z adding: test/test-reports/inductor.test_cudagraph_trees_1.1_4281314fe2a5e2cf_.log (stored 0%) 2024-08-20T22:46:11.6724004Z adding: test/test-reports/dynamo.test_ctx_manager_1.1_3fcfa8f593b5fac7_.log (deflated 50%) 2024-08-20T22:46:11.6725458Z adding: test/test-reports/dynamo.test_subgraphs_1.1_d9a22598ae6118d8_.log (deflated 50%) 2024-08-20T22:46:11.6726551Z adding: test/test-reports/dynamo.test_autograd_function_1.1_dd2786a46876a16a_.log (deflated 51%) 2024-08-20T22:46:11.6728258Z adding: test/test-reports/dynamo.test_activation_checkpointing_1.1_69901fcf9b3ac032_.log (deflated 52%) 2024-08-20T22:46:11.6729460Z adding: test/test-reports/inductor.test_inductor_freezing_1.1_714f08d6949c7e60_.log (deflated 51%) 2024-08-20T22:46:11.6731057Z adding: test/test-reports/inductor.test_mkldnn_pattern_matcher_1.2_82352819c36cedc5_.log (deflated 52%) 2024-08-20T22:46:11.6732222Z adding: test/test-reports/inductor.test_cuda_repro_1.1_dc31c456540ba31e_.log (stored 0%) 2024-08-20T22:46:11.6733715Z adding: test/test-reports/inductor.test_kernel_benchmark_1.1_701eb82d554bdf2a_.log (stored 0%) 2024-08-20T22:46:11.6735033Z adding: test/test-reports/inductor.test_triton_heuristics_1.1_577632b2e95e1526_.log (stored 0%) 2024-08-20T22:46:11.6736134Z adding: test/test-reports/inductor.test_cudacodecache_1.1_dbdda3d01aaf443d_.log (stored 0%) 2024-08-20T22:46:11.6737267Z adding: test/test-reports/inductor.test_torchinductor_3.4_6687661e4569e7c8_.log (deflated 87%) 2024-08-20T22:46:11.6738450Z adding: test/test-reports/inductor.test_torchinductor_opinfo_10.39_52013efaee73e542_.log (deflated 90%) 2024-08-20T22:46:11.6739689Z adding: test/test-reports/inductor.test_torchinductor_opinfo_9.39_ffc05fcc1e48f661_.log (deflated 89%) 2024-08-20T22:46:11.6740996Z adding: test/test-reports/inductor.test_torchinductor_opinfo_11.39_6293ab6bae3481ea_.log (deflated 90%) 2024-08-20T22:46:11.6742236Z adding: test/test-reports/inductor.test_torchinductor_opinfo_22.39_d1a40eb3e369f1af_.log (deflated 89%) 2024-08-20T22:46:11.6743476Z adding: test/test-reports/inductor.test_torchinductor_opinfo_21.39_dc1319bc6cb18637_.log (deflated 89%) 2024-08-20T22:46:11.6744683Z adding: test/test-reports/inductor.test_torchinductor_opinfo_23.39_4a8366a92531ada5_.log (deflated 89%) 2024-08-20T22:46:11.6745905Z adding: test/test-reports/inductor.test_torchinductor_opinfo_33.39_bf3cba02aefd04be_.log (deflated 90%) 2024-08-20T22:46:11.6748316Z adding: test/test-reports/inductor.test_torchinductor_opinfo_34.39_fb0a849ff42e78b8_.log (deflated 89%) 2024-08-20T22:46:11.6749644Z adding: test/test-reports/inductor.test_torchinductor_codegen_dynamic_shapes_2.3_9a84b9d9021c4143_.log (deflated 92%) 2024-08-20T22:46:11.6750855Z adding: test/test-reports/inductor.test_mmdecomp_1.1_2f96aaf16045af96_.log (deflated 85%) 2024-08-20T22:46:11.6751922Z adding: test/test-reports/dynamo.test_interop_1.1_bbc12aa6a52e730f_.log (deflated 60%) 2024-08-20T22:46:11.6752949Z adding: test/test-reports/dynamo.test_logging_1.1_d0fbfde556f44c46_.log (deflated 87%) 2024-08-20T22:46:11.6754328Z adding: test/test-reports/dynamo.test_exc_1.1_94f9c357d423bd7d_.log (deflated 70%) 2024-08-20T22:46:11.6755310Z adding: test/test-reports/dynamo.test_global_1.1_529fc0b03402724c_.log (deflated 75%) 2024-08-20T22:46:11.6756318Z adding: test/test-reports/dynamo.test_unspec_1.1_1c8b113096e203cb_.log (deflated 80%) 2024-08-20T22:46:11.6757381Z adding: test/test-reports/inductor.test_cudagraph_trees_1.1_9b3faf06e9e29d87_.log (stored 0%) 2024-08-20T22:46:11.6758450Z adding: test/test-reports/dynamo.test_ctx_manager_1.1_8985e2c8ebdb0657_.log (deflated 86%) 2024-08-20T22:46:11.6759506Z adding: test/test-reports/dynamo.test_subgraphs_1.1_35b97e4dc7659ef8_.log (deflated 83%) 2024-08-20T22:46:11.6760604Z adding: test/test-reports/dynamo.test_autograd_function_1.1_bef85538f64f3dde_.log (deflated 82%) 2024-08-20T22:46:11.6761807Z adding: test/test-reports/dynamo.test_activation_checkpointing_1.1_722cd7ece98e23ce_.log (deflated 86%) 2024-08-20T22:46:11.6762981Z adding: test/test-reports/inductor.test_inductor_freezing_1.1_8288affbc5c20670_.log (deflated 81%) 2024-08-20T22:46:11.6764186Z adding: test/test-reports/inductor.test_torchinductor_opinfo_35.39_d0fd0fff42616aa4_.log (deflated 89%) 2024-08-20T22:46:11.6765307Z adding: test/test-reports/inductor.test_cuda_repro_1.1_2137d05d0f79b89f_.log (stored 0%) 2024-08-20T22:46:11.6770717Z adding: test/test-reports/inductor.test_torchinductor_codegen_dynamic_shapes_3.3_c3f85b8f3bdcfe27_.log (deflated 91%) 2024-08-20T22:46:11.6771962Z adding: test/test-reports/inductor.test_kernel_benchmark_1.1_0a9dfe0563bbcba8_.log (stored 0%) 2024-08-20T22:46:11.6773094Z adding: test/test-reports/inductor.test_triton_heuristics_1.1_33c89379771b7886_.log (stored 0%) 2024-08-20T22:46:11.6774211Z adding: test/test-reports/inductor.test_cudacodecache_1.1_da9f10e7913f0207_.log (stored 0%) 2024-08-20T22:46:11.6775352Z adding: test/test-reports/inductor.test_mkldnn_pattern_matcher_1.2_57a01e0595df6700_.log (deflated 90%) 2024-08-20T22:46:11.6776432Z adding: test/test-reports/cpp.Dict_test_1.1_1eb15489e53440a9_.log (deflated 49%) 2024-08-20T22:46:11.6777531Z adding: test/test-reports/cpp.Dimname_test_1.1_156d04d78a9391b6_.log (deflated 49%) 2024-08-20T22:46:11.6778542Z adding: test/test-reports/cpp.NamedTensor_test_1.1_34f72551579724ec_.log (deflated 49%) 2024-08-20T22:46:11.6779563Z adding: test/test-reports/cpp.apply_utils_test_1.1_5bf06649346e3c2b_.log (deflated 49%) 2024-08-20T22:46:11.6780536Z adding: test/test-reports/cpp.atest_1.1_3a46d11cf4c6a0c1_.log (deflated 48%) 2024-08-20T22:46:11.6781479Z adding: test/test-reports/cpp.Dimname_test_1.1_f1ad7c8666779825_.log (deflated 60%) 2024-08-20T22:46:11.6782463Z adding: test/test-reports/cpp.NamedTensor_test_1.1_cd53b7b3e8cd0a50_.log (deflated 72%) 2024-08-20T22:46:11.6783657Z adding: test/test-reports/cpp.apply_utils_test_1.1_bf874814947eeae7_.log (deflated 66%) 2024-08-20T22:46:11.6784655Z adding: test/test-reports/cpp.Dict_test_1.1_a8a81afa6244f645_.log (deflated 84%) 2024-08-20T22:46:11.6785586Z adding: test/test-reports/cpp.atest_1.1_be3801d80d5d2194_.log (deflated 74%) 2024-08-20T22:46:11.6786498Z adding: test/test-reports/cpp.test_api_1.1_c0aafbd863595428_.log (deflated 48%) 2024-08-20T22:46:11.6810393Z adding: test/test-reports/cpp.test_api_1.1_8df526c29c1356a1_.log (deflated 93%) 2024-08-20T22:46:11.6811382Z adding: test/test-reports/cpp.test_tensorexpr_1.1_9addc9170b00d22c_.log (deflated 48%) 2024-08-20T22:46:11.6834402Z adding: test/test-reports/cpp.test_tensorexpr_1.1_eff9b9bd4522ef06_.log (deflated 94%) 2024-08-20T22:46:11.6835459Z adding: test/test-reports/cpp.static_runtime_test_1.1_556f6aad1c484725_.log (deflated 49%) 2024-08-20T22:46:11.6845413Z adding: test/test-reports/cpp.static_runtime_test_1.1_f4623d44e12f72fd_.log (deflated 90%) 2024-08-20T22:46:11.6879071Z ##[group]Run # Remove any previous debugging artifacts if they exist 2024-08-20T22:46:11.6879804Z # Remove any previous debugging artifacts if they exist 2024-08-20T22:46:11.6880466Z rm -f debug-*.zip 2024-08-20T22:46:11.6880854Z if [ -d 'test/debug' ]; then 2024-08-20T22:46:11.6881370Z  zip -r "debug-${FILE_SUFFIX}.zip" test/debug 2024-08-20T22:46:11.6881839Z fi 2024-08-20T22:46:11.6888286Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T22:46:11.6888774Z env: 2024-08-20T22:46:11.6889036Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:46:11.6889650Z DOCKER_CONTAINER_ID: 2340f3deffd39d0eed0996837220f02e294a105661d3e11a7b09c171901f5c27 2024-08-20T22:46:11.6890478Z FILE_SUFFIX: test-default-1-4-amz2023.linux.2xlarge_29025267541 2024-08-20T22:46:11.6891010Z ##[endgroup] 2024-08-20T22:46:11.7005257Z ##[group]Run seemethere/upload-artifact-s3@v5 2024-08-20T22:46:11.7005695Z with: 2024-08-20T22:46:11.7005942Z s3-bucket: gha-artifacts 2024-08-20T22:46:11.7006374Z s3-prefix: pytorch/pytorch/10479309237/1/artifact 2024-08-20T22:46:11.7006953Z retention-days: 14 2024-08-20T22:46:11.7007259Z if-no-files-found: warn 2024-08-20T22:46:11.7007594Z path: test-jsons-*.zip 2024-08-20T22:46:11.7007911Z name: artifact 2024-08-20T22:46:11.7008181Z region: us-east-1 2024-08-20T22:46:11.7008453Z env: 2024-08-20T22:46:11.7008699Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:46:11.7009283Z DOCKER_CONTAINER_ID: 2340f3deffd39d0eed0996837220f02e294a105661d3e11a7b09c171901f5c27 2024-08-20T22:46:11.7009931Z ##[endgroup] 2024-08-20T22:46:12.1589424Z NOTE: s3-prefix specified, ignoring name parameter 2024-08-20T22:46:12.1590406Z With the provided path, there will be 1 file uploaded 2024-08-20T22:46:12.1591069Z Uploading to s3 prefix: pytorch/pytorch/10479309237/1/artifact 2024-08-20T22:46:12.2624392Z Starting upload of test-jsons-test-default-1-4-amz2023.linux.2xlarge_29025267541.zip 2024-08-20T22:46:12.3440475Z Finished upload of test-jsons-test-default-1-4-amz2023.linux.2xlarge_29025267541.zip 2024-08-20T22:46:12.3645052Z ##[group]Run seemethere/upload-artifact-s3@v5 2024-08-20T22:46:12.3645489Z with: 2024-08-20T22:46:12.3645751Z s3-bucket: gha-artifacts 2024-08-20T22:46:12.3646170Z s3-prefix: pytorch/pytorch/10479309237/1/artifact 2024-08-20T22:46:12.3647086Z retention-days: 14 2024-08-20T22:46:12.3647577Z if-no-files-found: error 2024-08-20T22:46:12.3648112Z path: test-reports-*.zip 2024-08-20T22:46:12.3648485Z name: artifact 2024-08-20T22:46:12.3648766Z region: us-east-1 2024-08-20T22:46:12.3649050Z env: 2024-08-20T22:46:12.3649285Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:46:12.3649881Z DOCKER_CONTAINER_ID: 2340f3deffd39d0eed0996837220f02e294a105661d3e11a7b09c171901f5c27 2024-08-20T22:46:12.3650533Z ##[endgroup] 2024-08-20T22:46:12.7276125Z NOTE: s3-prefix specified, ignoring name parameter 2024-08-20T22:46:12.7276817Z With the provided path, there will be 1 file uploaded 2024-08-20T22:46:12.7277432Z Uploading to s3 prefix: pytorch/pytorch/10479309237/1/artifact 2024-08-20T22:46:12.7319245Z Starting upload of test-reports-test-default-1-4-amz2023.linux.2xlarge_29025267541.zip 2024-08-20T22:46:12.9110661Z Finished upload of test-reports-test-default-1-4-amz2023.linux.2xlarge_29025267541.zip 2024-08-20T22:46:12.9310239Z ##[group]Run seemethere/upload-artifact-s3@v5 2024-08-20T22:46:12.9310800Z with: 2024-08-20T22:46:12.9311066Z s3-bucket: gha-artifacts 2024-08-20T22:46:12.9311492Z s3-prefix: pytorch/pytorch/10479309237/1/artifact 2024-08-20T22:46:12.9311941Z retention-days: 14 2024-08-20T22:46:12.9312265Z if-no-files-found: ignore 2024-08-20T22:46:12.9312605Z path: logs-*.zip 2024-08-20T22:46:12.9312879Z name: artifact 2024-08-20T22:46:12.9313164Z region: us-east-1 2024-08-20T22:46:12.9313447Z env: 2024-08-20T22:46:12.9313690Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:46:12.9314292Z DOCKER_CONTAINER_ID: 2340f3deffd39d0eed0996837220f02e294a105661d3e11a7b09c171901f5c27 2024-08-20T22:46:12.9314947Z ##[endgroup] 2024-08-20T22:46:13.2906020Z NOTE: s3-prefix specified, ignoring name parameter 2024-08-20T22:46:13.2906949Z With the provided path, there will be 1 file uploaded 2024-08-20T22:46:13.2908410Z Uploading to s3 prefix: pytorch/pytorch/10479309237/1/artifact 2024-08-20T22:46:13.2950255Z Starting upload of logs-test-default-1-4-amz2023.linux.2xlarge_29025267541.zip 2024-08-20T22:46:13.4672782Z Finished upload of logs-test-default-1-4-amz2023.linux.2xlarge_29025267541.zip 2024-08-20T22:46:13.4880345Z ##[group]Run seemethere/upload-artifact-s3@v5 2024-08-20T22:46:13.4880771Z with: 2024-08-20T22:46:13.4881039Z s3-bucket: gha-artifacts 2024-08-20T22:46:13.4881459Z s3-prefix: pytorch/pytorch/10479309237/1/artifact 2024-08-20T22:46:13.4881909Z retention-days: 14 2024-08-20T22:46:13.4882224Z if-no-files-found: ignore 2024-08-20T22:46:13.4882562Z path: debug-*.zip 2024-08-20T22:46:13.4882838Z name: artifact 2024-08-20T22:46:13.4883118Z region: us-east-1 2024-08-20T22:46:13.4883393Z env: 2024-08-20T22:46:13.4883626Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:46:13.4884237Z DOCKER_CONTAINER_ID: 2340f3deffd39d0eed0996837220f02e294a105661d3e11a7b09c171901f5c27 2024-08-20T22:46:13.4884889Z ##[endgroup] 2024-08-20T22:46:13.8436130Z No files were found with the provided path: debug-*.zip. No artifacts will be uploaded. 2024-08-20T22:46:13.8631271Z ##[group]Run # shellcheck disable=SC2156 2024-08-20T22:46:13.8631754Z # shellcheck disable=SC2156 2024-08-20T22:46:13.8632564Z find . -iname "core.[1-9]*" -exec docker exec "${DOCKER_CONTAINER_ID}" sh -c "gdb python {} -ex 'bt' -ex 'q'" \; 2024-08-20T22:46:13.8638822Z shell: /usr/bin/bash -e {0} 2024-08-20T22:46:13.8639170Z env: 2024-08-20T22:46:13.8639434Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:46:13.8640021Z DOCKER_CONTAINER_ID: 2340f3deffd39d0eed0996837220f02e294a105661d3e11a7b09c171901f5c27 2024-08-20T22:46:13.8640679Z ##[endgroup] 2024-08-20T22:46:14.0894077Z ##[group]Run pytorch/test-infra/.github/actions/teardown-linux@main 2024-08-20T22:46:14.0894627Z with: 2024-08-20T22:46:14.0894863Z env: 2024-08-20T22:46:14.0895114Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:46:14.0895697Z DOCKER_CONTAINER_ID: 2340f3deffd39d0eed0996837220f02e294a105661d3e11a7b09c171901f5c27 2024-08-20T22:46:14.0896354Z ##[endgroup] 2024-08-20T22:46:14.0918322Z ##[group]Run set -eou pipefail 2024-08-20T22:46:14.0918816Z set -eou pipefail 2024-08-20T22:46:14.0919155Z  2024-08-20T22:46:14.0919662Z echo "Holding runner for 2 hours until all ssh sessions have logged out" 2024-08-20T22:46:14.0920298Z for _ in $(seq 1440); do 2024-08-20T22:46:14.0920760Z  # Break if no ssh session exists anymore 2024-08-20T22:46:14.0921245Z  if [ "$(who)" = "" ]; then 2024-08-20T22:46:14.0921634Z  break 2024-08-20T22:46:14.0921916Z  fi 2024-08-20T22:46:14.0922244Z  echo "." 2024-08-20T22:46:14.0922543Z  sleep 5 2024-08-20T22:46:14.0922839Z done 2024-08-20T22:46:14.0929024Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T22:46:14.0929513Z env: 2024-08-20T22:46:14.0929773Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:46:14.0930392Z DOCKER_CONTAINER_ID: 2340f3deffd39d0eed0996837220f02e294a105661d3e11a7b09c171901f5c27 2024-08-20T22:46:14.0931046Z ##[endgroup] 2024-08-20T22:46:14.0956590Z Holding runner for 2 hours until all ssh sessions have logged out 2024-08-20T22:46:14.1374178Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2024-08-20T22:46:14.1374962Z # ignore expansion of "docker ps -q" since it could be empty 2024-08-20T22:46:14.1375548Z # shellcheck disable=SC2046 2024-08-20T22:46:14.1375995Z docker stop $(docker ps -q) || true 2024-08-20T22:46:14.1376449Z # Prune all of the docker images 2024-08-20T22:46:14.1376884Z docker system prune -af 2024-08-20T22:46:14.1382742Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T22:46:14.1383221Z env: 2024-08-20T22:46:14.1383488Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:46:14.1384091Z DOCKER_CONTAINER_ID: 2340f3deffd39d0eed0996837220f02e294a105661d3e11a7b09c171901f5c27 2024-08-20T22:46:14.1384854Z ##[endgroup] 2024-08-20T22:46:14.7349036Z 2340f3deffd3 2024-08-20T22:46:16.5283770Z Deleted Containers: 2024-08-20T22:46:16.5284454Z 2340f3deffd39d0eed0996837220f02e294a105661d3e11a7b09c171901f5c27 2024-08-20T22:46:16.5284920Z 2024-08-20T22:46:23.9537126Z Deleted Images: 2024-08-20T22:46:23.9538989Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T22:46:23.9540945Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10@sha256:53bea9665c81f7bdf502e7eb907e80d01ec46372a842842380b5284cbc52773e 2024-08-20T22:46:23.9542461Z deleted: sha256:2aaa7058a7e1d063370f2bda46a0fffde19193dd83023b51f0f1d1c55b4a88e4 2024-08-20T22:46:23.9543355Z deleted: sha256:c4327edcdd2d1c61d95766db714d2ecc276d9c30a9a36dc9760825081e3a6b75 2024-08-20T22:46:23.9544246Z deleted: sha256:0b0762b8ce05ed267d7ee72100ddcb97a7d6a9edb90e541deef48458743148e3 2024-08-20T22:46:23.9545109Z deleted: sha256:7b21342863823d38d262243de8b4b2ff876b9dead47105e0b91fee5cd2aa6664 2024-08-20T22:46:23.9545976Z deleted: sha256:c93e3118ca613926ad893a7a4882c4ea65a0cd1c005e87cdac6a0dafdc068673 2024-08-20T22:46:23.9547087Z deleted: sha256:eeb36fe6aefeed0dd6323f9282ce313f853b82ac94a4fc92307a0f97e53f8d03 2024-08-20T22:46:23.9547988Z deleted: sha256:e0d362451e85b9a1f6fe0b338cb306fa0bda1ef3bb3a0287021478dd2e53b55d 2024-08-20T22:46:23.9548858Z deleted: sha256:de15823680bea59a9a9fef9db33d9365617ed91ae7a21ba631ee00f6ec41ee62 2024-08-20T22:46:23.9549744Z deleted: sha256:f1ee653bc15555c650b76fe1ddd12e411fd1c78a89263c1e258ccaa24eb342bd 2024-08-20T22:46:23.9550909Z deleted: sha256:62e0582215a97eadcc92bce49df7a6f3a14e15d9c7022d45d57f0dbaf54c943e 2024-08-20T22:46:23.9551778Z deleted: sha256:b6f418f55606e2d9702380e416c9ef0979ea9bdfe3ea9d22abdab147b00a6f97 2024-08-20T22:46:23.9552646Z deleted: sha256:e7634347199ce784bfedebe8e19469b06da1e9779bd022b1f0ae04d61df78b12 2024-08-20T22:46:23.9553524Z deleted: sha256:38dc55b5ddc2c012fcde6976392d8e3c19ebd904c57c842952cebe0cec690971 2024-08-20T22:46:23.9554391Z deleted: sha256:c6d6f1cc46c81ac2e7e919cd541787c64d315349e7052162f4d66fa77da23054 2024-08-20T22:46:23.9555377Z deleted: sha256:d4ac4ce29bb4a23bcac697c79ef7c0cb33a1acdcbd1aa5d7b48204aabfd788b0 2024-08-20T22:46:23.9556306Z deleted: sha256:f865a119b2376c83d32f176e80b916cad2c99eb5c99458c2a3fa7f86e9e4eca9 2024-08-20T22:46:23.9557570Z deleted: sha256:8cf6eee4c7b2dc442434e49002245d96b726593760539cb2dbdb59e104ab2183 2024-08-20T22:46:23.9558935Z deleted: sha256:50cf04b85cce2ca9fa895d1d4e605aaf0b4eb3967bff399122a38359276eeefc 2024-08-20T22:46:23.9560303Z deleted: sha256:f3a3c870e3f52638f6ac8c8cdb082d71346c08a6908b079298a4ce71ccc4f8e0 2024-08-20T22:46:23.9561771Z deleted: sha256:debb6d37bb23e463d78a30ddf9a16f31c2fc04c77730f8516817f921049b4bda 2024-08-20T22:46:23.9563200Z deleted: sha256:8b6ac666cbc84b6658afdb4dc4a6e79a632fc39a85c26415a6118ff5c6e0db43 2024-08-20T22:46:23.9564610Z deleted: sha256:ee4cbc346209967d909332dd7f3bf779f9aa33fa1ce7bbe3b5fc16ee0ad4bdf6 2024-08-20T22:46:23.9565949Z deleted: sha256:76548955820014e1d84f7c01afb464eda4c5de9d28f3692d48a35ecf9be362ab 2024-08-20T22:46:23.9567453Z deleted: sha256:0ca7ff47c44230d7264b2ad2f7ddd909f6eac72597bcbb246b1c0d86a3abfa0e 2024-08-20T22:46:23.9568862Z deleted: sha256:1d2bc48c4c0a2308abcae066ed4261a7dd7f8fee27aee8b5607f85601cecda52 2024-08-20T22:46:23.9570364Z deleted: sha256:55f58417672c84b0058991b388082139d6c6b092b490af065eefc6515b32a984 2024-08-20T22:46:23.9571808Z deleted: sha256:12abc759ff3c4b1bd8631a7c3ddf4bd3a91489aea3242446096f17a659a33338 2024-08-20T22:46:23.9573168Z deleted: sha256:b2c3f01077f1569553349df3739565868739a7bdb05a6a54a54525f279f32400 2024-08-20T22:46:23.9574123Z deleted: sha256:b820700bcf0961d51071347993f4691234b569494857e7e0b16c2ce60f110953 2024-08-20T22:46:23.9574967Z deleted: sha256:bf0e904cd117e9da13cf649d4c548494852e71eba590f849929a91367723d18e 2024-08-20T22:46:23.9575808Z deleted: sha256:d030d9ef2385c3c5e9d80850566041a6013db5d33fa420d0317d8d07d4b6a872 2024-08-20T22:46:23.9577002Z deleted: sha256:2adc34628fc9de0ab6c7f993699ef94b7e9f32cf3279f58906c05afc65ba197b 2024-08-20T22:46:23.9577880Z deleted: sha256:06d15c551f74049cee65927ec38d8b592afdf01b967ffb9acd450f6307773957 2024-08-20T22:46:23.9578740Z deleted: sha256:efec11f21c1b9247ac7960491976e7417e7679bddeb4dc3d0e485bbd07a40d2d 2024-08-20T22:46:23.9579600Z deleted: sha256:72e62c6157de51e24a9e4876af7d22dc224440748462a2c9fd1b4ab8d415b4b9 2024-08-20T22:46:23.9580463Z deleted: sha256:a353aec707be888144438dec5b09db32bc2bca04ed9214fd2067337b7129f904 2024-08-20T22:46:23.9581324Z deleted: sha256:874116f3fe37248cb7182fbe7fe6f6ca32f42233a0b348270530c9f1807e4e44 2024-08-20T22:46:23.9582165Z deleted: sha256:106c5e8591535e1ea0ff5d799264da47979770a20f2c40d3817ac8b18c98484c 2024-08-20T22:46:23.9583033Z deleted: sha256:5b306f32dec3703abdbbebf4532df7bdae645634b08aa7918171a90f7d435fcf 2024-08-20T22:46:23.9583907Z deleted: sha256:0b2e32ec8ac8331e42468271a3f163c3b984d35c4ee45545bff85ae635230425 2024-08-20T22:46:23.9584750Z deleted: sha256:2900b5c4949b9ae9b167b1dd9c65e1b08a65b7c378b1365ec7a37a04f4fe0cca 2024-08-20T22:46:23.9585607Z deleted: sha256:9b3772be786148cf925ad52c902648d50294502ccfc3df6af3d6ca90c08ff498 2024-08-20T22:46:23.9586481Z deleted: sha256:77eea026e216b1f37d9c6d76d3fdf5007fb0144f9d4e9d8b2b21983ebd6f2d66 2024-08-20T22:46:23.9587313Z deleted: sha256:3411e06788418fa7e954ef69829867ff08c064d5650390092144caa74dfbd35b 2024-08-20T22:46:23.9588162Z deleted: sha256:2bd0a8a697700d8bdeb8a7b2e87aa1e50bc9b4fd6275388568712c256fe89584 2024-08-20T22:46:23.9589026Z deleted: sha256:5639359e57b0d3a1ece08b4a38355e0d4a0f269619cc478802759ff0e98f0ff7 2024-08-20T22:46:23.9589897Z deleted: sha256:b09437bbc1bd867fea309cc4644fa916a6fed1402e17f3eb34e17bf8e18d7009 2024-08-20T22:46:23.9590886Z deleted: sha256:df7d1579d03b7a0edd932199a9aa851474000fb44796e10de779c0d66a083ae4 2024-08-20T22:46:23.9591768Z deleted: sha256:e4d92eab101be2bd5a07ba6c0dfb44079c7e3f70ded3964a1b6703848f974739 2024-08-20T22:46:23.9592665Z deleted: sha256:e01a9a8feeeadf0a0ac92544e4179962b4e30df04e25637bc650e0ac752122c0 2024-08-20T22:46:23.9593526Z deleted: sha256:4649cc1ebf67b08ad0d95082a9782b52f066b69a6cf38714c93c8e532bd6f421 2024-08-20T22:46:23.9594436Z deleted: sha256:ff31c2fb329ef8c677a33677bc5822f5666c0bb277f6f7f10289e00660ae7d26 2024-08-20T22:46:23.9595313Z deleted: sha256:4e6dde2bff0a3412b9ac97cf3996185af39cd852ecadafcd51fc7f2de2e60644 2024-08-20T22:46:23.9596188Z deleted: sha256:1408cd2bc15a24fe2f0133f057f08bc41c7891b23571560cbfcb92f41737bf8f 2024-08-20T22:46:23.9597072Z deleted: sha256:e3845fc42ecda662fc976333fdb8f2b04b20e095e8fe472fbce7fdde9dd56f9f 2024-08-20T22:46:23.9597935Z deleted: sha256:971dc82f5b52499398206b72592ac545507803ca4c834e6b57dd17a05a318af6 2024-08-20T22:46:23.9598796Z deleted: sha256:2ba90b110e446d308cab9fa506f6583c651a15fc139cd31bd48673e7b8186b68 2024-08-20T22:46:23.9599645Z deleted: sha256:4e0a4010ae49187e72acfef1ec7a619b4c88a5316cb822150789d8049b523a42 2024-08-20T22:46:23.9600520Z deleted: sha256:80ae73c748b1ea8328b7939acd5f3f9338a8ddc80f79c95fdb3b747727456c33 2024-08-20T22:46:23.9601398Z deleted: sha256:ba756c0b5b0f49651e5716b6d4acae21ccb2e620799359f6b585cc7265442648 2024-08-20T22:46:23.9602272Z deleted: sha256:5fc12b6b10dce46d82c0444dca8fafbc6537f91c0a0a709814a5ec75ff4213dd 2024-08-20T22:46:23.9603162Z deleted: sha256:b1cceb9f66e1b375c20250503e2cc77102a45061f8e27bd8d26603ec577e3324 2024-08-20T22:46:23.9604040Z deleted: sha256:9ef68344f78c82f3e4a1dce1c20fcaec98f93b2b5bb5f595f69f15b036df80c4 2024-08-20T22:46:23.9604909Z deleted: sha256:92e3ae41630763cdffdc4ec45e0816cdf105fd0522f4554a7b8c994d65f87698 2024-08-20T22:46:23.9605783Z deleted: sha256:0cc23ead7af6378314421b6c5da1557f21d480b5cebdc56cc05aa782329a9ed4 2024-08-20T22:46:23.9606685Z deleted: sha256:ac9f4d03b374faa8fed9b8606e8c6c15a589c599b6ada6faf45b03a6944f0dc5 2024-08-20T22:46:23.9607743Z deleted: sha256:6df91f14a01b0131911db47613f40adbc1ee52b21e7f9eff4a166096e967b79f 2024-08-20T22:46:23.9608587Z deleted: sha256:661937797c97143f9855791f19df28ecdb55b81d42567ca3bef845ff7eee2672 2024-08-20T22:46:23.9609442Z deleted: sha256:e22f46c28caf62b7a2460b6a50b7158885afc0b2df537a665c230060e61786f0 2024-08-20T22:46:23.9610378Z deleted: sha256:3f21a66da1a9e3347cf4c3eab81866121691ba4e5c062a3fd21e7e3ea0f3932d 2024-08-20T22:46:23.9611234Z deleted: sha256:8976f2964795315cb560b369e83ab75cbc76fba56571a06cc8b836bec324fc9b 2024-08-20T22:46:23.9612093Z deleted: sha256:3741000c648bb5527edf9483c0a70b12ac3bb8f846cd3fe6d99eee8dcb9ad000 2024-08-20T22:46:23.9612985Z deleted: sha256:6a88ac146bcb1ff342a7b14cdcbe5c8d22a044705c4488d3d2ccc7021c1f8988 2024-08-20T22:46:23.9613840Z deleted: sha256:1966f91f3c9772471c354e781cb9cf68063d6be539e42509f00b7417baffe09d 2024-08-20T22:46:23.9614686Z deleted: sha256:31c1981f30729cd87955af1e86a9d0ca45763ac850c941691d2fb930174beb88 2024-08-20T22:46:23.9615546Z deleted: sha256:065fa69f7e39454c49479268684c0ec107e032daebedaf2fa1670ea3fc5485bb 2024-08-20T22:46:23.9616428Z deleted: sha256:fd51a6d8905bf2ad062bf66480dcdd8479fc11e5c398d9e3f6b9ee803d1097b1 2024-08-20T22:46:23.9617304Z deleted: sha256:9b0bb65bc5356eb00c526f431e4aec67a8763ff56e02b4bc0ab5580da4436fdc 2024-08-20T22:46:23.9618188Z deleted: sha256:5db5cc8b8a28593d06b949375372eac3be6384f06612d6f947d52c5d769ddd8c 2024-08-20T22:46:23.9619068Z deleted: sha256:2ddb8a250e9dd4921b2c90570756cb2d1c4cf5938501bfa8f229b1364ea98d43 2024-08-20T22:46:23.9619930Z deleted: sha256:7348c4dfb0a36bcd0916a13ed0ea0d8c216050a67c007a4cf5524ca0225171e5 2024-08-20T22:46:23.9620826Z deleted: sha256:5dafc7b501f1d888200eb86b4f01d1ae6c7b82d4a06b47b07e20f9b08be6fbb4 2024-08-20T22:46:23.9621706Z deleted: sha256:479a0268f88636fc336017efeae90d7cb9737b67372b8624a78eefdef85da57f 2024-08-20T22:46:23.9622562Z deleted: sha256:a6870ec51add96bf2af430ac53172e60386bb0067af7673684dcfeaeaa18f0e6 2024-08-20T22:46:23.9623561Z deleted: sha256:e3b4c2110bbe029ce3695df4cebcd55b8b10167bffc37347263a27504af949e7 2024-08-20T22:46:23.9624435Z deleted: sha256:5575454e31142b0e45b2c2290cde2805e297f47f2ac715d1761a15252418bcd2 2024-08-20T22:46:23.9625292Z deleted: sha256:a136d5e02789e5b7355638b08961dddbe47bba7c60588992df1e9a0117e273c4 2024-08-20T22:46:23.9626159Z deleted: sha256:dc15e8f0ceb13e55e3832faa7211b1cd51c629ebe82682f6c9f5775bcab3ece9 2024-08-20T22:46:23.9627079Z deleted: sha256:a941d5a736d59b59b13bfe1369d7e6f211e519f2e07b4e545347697619702d66 2024-08-20T22:46:23.9627929Z deleted: sha256:d52495f3415768450531f4a9d565b41e6b32e2d7bb406bbf43d27a4d6d359be5 2024-08-20T22:46:23.9628770Z deleted: sha256:7ede778ed1133f24fa049860f33b10e50b549d8b190a67421c6275bcfb2d7ff7 2024-08-20T22:46:23.9629644Z deleted: sha256:da86860f998359be1de01ea91e55c8f634a59eaa1d2760c0c6b2b4f64c33ed61 2024-08-20T22:46:23.9630530Z deleted: sha256:3ec3ded77c0ce89e931f92aed086b2a2c774a6fbd51617853decc8afa4e1087a 2024-08-20T22:46:23.9631068Z 2024-08-20T22:46:23.9631247Z Total reclaimed space: 9.999GB 2024-08-20T22:46:23.9724470Z Post job cleanup. 2024-08-20T22:46:23.9790372Z Post job cleanup. 2024-08-20T22:46:24.0711651Z [command]/usr/bin/git version 2024-08-20T22:46:24.0792266Z git version 2.40.1 2024-08-20T22:46:24.0831854Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/55eca392-d89f-47de-ac61-2bdada9b5b12' before making global git config changes 2024-08-20T22:46:24.0833177Z Adding repository directory to the temporary git global config as a safe directory 2024-08-20T22:46:24.0837482Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-08-20T22:46:24.0878164Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2024-08-20T22:46:24.0912584Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'core\.sshCommand' && git config --local --unset-all 'core.sshCommand' || :" 2024-08-20T22:46:24.1234523Z Entering 'android/libs/fbjni' 2024-08-20T22:46:24.1288160Z Entering 'third_party/FP16' 2024-08-20T22:46:24.1339163Z Entering 'third_party/FXdiv' 2024-08-20T22:46:24.1391750Z Entering 'third_party/NNPACK' 2024-08-20T22:46:24.1444199Z Entering 'third_party/VulkanMemoryAllocator' 2024-08-20T22:46:24.1496289Z Entering 'third_party/XNNPACK' 2024-08-20T22:46:24.1564888Z Entering 'third_party/benchmark' 2024-08-20T22:46:24.1619674Z Entering 'third_party/cpp-httplib' 2024-08-20T22:46:24.1672983Z Entering 'third_party/cpuinfo' 2024-08-20T22:46:24.1726770Z Entering 'third_party/cudnn_frontend' 2024-08-20T22:46:24.1782157Z Entering 'third_party/cutlass' 2024-08-20T22:46:24.1845166Z Entering 'third_party/eigen' 2024-08-20T22:46:24.1903277Z Entering 'third_party/fbgemm' 2024-08-20T22:46:24.1959412Z Entering 'third_party/fbgemm/third_party/asmjit' 2024-08-20T22:46:24.2010882Z Entering 'third_party/fbgemm/third_party/cpuinfo' 2024-08-20T22:46:24.2063529Z Entering 'third_party/fbgemm/third_party/cutlass' 2024-08-20T22:46:24.2123188Z Entering 'third_party/fbgemm/third_party/googletest' 2024-08-20T22:46:24.2175528Z Entering 'third_party/fbgemm/third_party/hipify_torch' 2024-08-20T22:46:24.2230587Z Entering 'third_party/flatbuffers' 2024-08-20T22:46:24.2287663Z Entering 'third_party/fmt' 2024-08-20T22:46:24.2341673Z Entering 'third_party/gemmlowp/gemmlowp' 2024-08-20T22:46:24.2399972Z Entering 'third_party/gloo' 2024-08-20T22:46:24.2454621Z Entering 'third_party/googletest' 2024-08-20T22:46:24.2508012Z Entering 'third_party/ideep' 2024-08-20T22:46:24.2560015Z Entering 'third_party/ideep/mkl-dnn' 2024-08-20T22:46:24.2619922Z Entering 'third_party/ittapi' 2024-08-20T22:46:24.2675465Z Entering 'third_party/kineto' 2024-08-20T22:46:24.2728627Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2024-08-20T22:46:24.2781724Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2024-08-20T22:46:24.2835821Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2024-08-20T22:46:24.2888534Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2024-08-20T22:46:24.2941017Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-08-20T22:46:24.2992297Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2024-08-20T22:46:24.3048915Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2024-08-20T22:46:24.3102758Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2024-08-20T22:46:24.3156971Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2024-08-20T22:46:24.3212404Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2024-08-20T22:46:24.3267055Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2024-08-20T22:46:24.3317932Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2024-08-20T22:46:24.3371547Z Entering 'third_party/mimalloc' 2024-08-20T22:46:24.3425982Z Entering 'third_party/nccl/nccl' 2024-08-20T22:46:24.3479364Z Entering 'third_party/nlohmann' 2024-08-20T22:46:24.3535318Z Entering 'third_party/onnx' 2024-08-20T22:46:24.3604009Z Entering 'third_party/onnx/third_party/benchmark' 2024-08-20T22:46:24.3658217Z Entering 'third_party/onnx/third_party/pybind11' 2024-08-20T22:46:24.3713904Z Entering 'third_party/opentelemetry-cpp' 2024-08-20T22:46:24.3769420Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2024-08-20T22:46:24.3820454Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2024-08-20T22:46:24.3872936Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2024-08-20T22:46:24.3923172Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2024-08-20T22:46:24.3979480Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2024-08-20T22:46:24.4030171Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-08-20T22:46:24.4081890Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-08-20T22:46:24.4132733Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2024-08-20T22:46:24.4186880Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2024-08-20T22:46:24.4239438Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-08-20T22:46:24.4310851Z Entering 'third_party/pocketfft' 2024-08-20T22:46:24.4363364Z Entering 'third_party/protobuf' 2024-08-20T22:46:24.4419249Z Entering 'third_party/protobuf/third_party/benchmark' 2024-08-20T22:46:24.4470948Z Entering 'third_party/protobuf/third_party/googletest' 2024-08-20T22:46:24.4524594Z Entering 'third_party/psimd' 2024-08-20T22:46:24.4580085Z Entering 'third_party/pthreadpool' 2024-08-20T22:46:24.4634589Z Entering 'third_party/pybind11' 2024-08-20T22:46:24.4689376Z Entering 'third_party/python-peachpy' 2024-08-20T22:46:24.4742318Z Entering 'third_party/sleef' 2024-08-20T22:46:24.4795818Z Entering 'third_party/tensorpipe' 2024-08-20T22:46:24.4850462Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-08-20T22:46:24.4904529Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-08-20T22:46:24.4957918Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-08-20T22:46:24.5011236Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-08-20T22:46:24.5062318Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-08-20T22:46:24.5135159Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2024-08-20T22:46:24.5164305Z http.https://github.com/.extraheader 2024-08-20T22:46:24.5173711Z [command]/usr/bin/git config --local --unset-all http.https://github.com/.extraheader 2024-08-20T22:46:24.5209846Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'http\.https\:\/\/github\.com\/\.extraheader' && git config --local --unset-all 'http.https://github.com/.extraheader' || :" 2024-08-20T22:46:24.5497615Z Entering 'android/libs/fbjni' 2024-08-20T22:46:24.5533596Z http.https://github.com/.extraheader 2024-08-20T22:46:24.5571003Z Entering 'third_party/FP16' 2024-08-20T22:46:24.5606543Z http.https://github.com/.extraheader 2024-08-20T22:46:24.5640083Z Entering 'third_party/FXdiv' 2024-08-20T22:46:24.5675488Z http.https://github.com/.extraheader 2024-08-20T22:46:24.5708562Z Entering 'third_party/NNPACK' 2024-08-20T22:46:24.5742322Z http.https://github.com/.extraheader 2024-08-20T22:46:24.5775377Z Entering 'third_party/VulkanMemoryAllocator' 2024-08-20T22:46:24.5809563Z http.https://github.com/.extraheader 2024-08-20T22:46:24.5842773Z Entering 'third_party/XNNPACK' 2024-08-20T22:46:24.5877074Z http.https://github.com/.extraheader 2024-08-20T22:46:24.5927294Z Entering 'third_party/benchmark' 2024-08-20T22:46:24.5963294Z http.https://github.com/.extraheader 2024-08-20T22:46:24.5995342Z Entering 'third_party/cpp-httplib' 2024-08-20T22:46:24.6029959Z http.https://github.com/.extraheader 2024-08-20T22:46:24.6063443Z Entering 'third_party/cpuinfo' 2024-08-20T22:46:24.6099492Z http.https://github.com/.extraheader 2024-08-20T22:46:24.6132625Z Entering 'third_party/cudnn_frontend' 2024-08-20T22:46:24.6169353Z http.https://github.com/.extraheader 2024-08-20T22:46:24.6202363Z Entering 'third_party/cutlass' 2024-08-20T22:46:24.6236993Z http.https://github.com/.extraheader 2024-08-20T22:46:24.6278781Z Entering 'third_party/eigen' 2024-08-20T22:46:24.6313347Z http.https://github.com/.extraheader 2024-08-20T22:46:24.6348460Z Entering 'third_party/fbgemm' 2024-08-20T22:46:24.6383537Z http.https://github.com/.extraheader 2024-08-20T22:46:24.6417820Z Entering 'third_party/fbgemm/third_party/asmjit' 2024-08-20T22:46:24.6454475Z http.https://github.com/.extraheader 2024-08-20T22:46:24.6488607Z Entering 'third_party/fbgemm/third_party/cpuinfo' 2024-08-20T22:46:24.6524442Z http.https://github.com/.extraheader 2024-08-20T22:46:24.6559464Z Entering 'third_party/fbgemm/third_party/cutlass' 2024-08-20T22:46:24.6595286Z http.https://github.com/.extraheader 2024-08-20T22:46:24.6637231Z Entering 'third_party/fbgemm/third_party/googletest' 2024-08-20T22:46:24.6673196Z http.https://github.com/.extraheader 2024-08-20T22:46:24.6708086Z Entering 'third_party/fbgemm/third_party/hipify_torch' 2024-08-20T22:46:24.6742747Z http.https://github.com/.extraheader 2024-08-20T22:46:24.6780048Z Entering 'third_party/flatbuffers' 2024-08-20T22:46:24.6816350Z http.https://github.com/.extraheader 2024-08-20T22:46:24.6853295Z Entering 'third_party/fmt' 2024-08-20T22:46:24.6889087Z http.https://github.com/.extraheader 2024-08-20T22:46:24.6923051Z Entering 'third_party/gemmlowp/gemmlowp' 2024-08-20T22:46:24.6961273Z http.https://github.com/.extraheader 2024-08-20T22:46:24.6995585Z Entering 'third_party/gloo' 2024-08-20T22:46:24.7031441Z http.https://github.com/.extraheader 2024-08-20T22:46:24.7066418Z Entering 'third_party/googletest' 2024-08-20T22:46:24.7102216Z http.https://github.com/.extraheader 2024-08-20T22:46:24.7137874Z Entering 'third_party/ideep' 2024-08-20T22:46:24.7174718Z http.https://github.com/.extraheader 2024-08-20T22:46:24.7209595Z Entering 'third_party/ideep/mkl-dnn' 2024-08-20T22:46:24.7245180Z http.https://github.com/.extraheader 2024-08-20T22:46:24.7287541Z Entering 'third_party/ittapi' 2024-08-20T22:46:24.7323859Z http.https://github.com/.extraheader 2024-08-20T22:46:24.7360393Z Entering 'third_party/kineto' 2024-08-20T22:46:24.7398518Z http.https://github.com/.extraheader 2024-08-20T22:46:24.7434655Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2024-08-20T22:46:24.7472074Z http.https://github.com/.extraheader 2024-08-20T22:46:24.7507694Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2024-08-20T22:46:24.7543485Z http.https://github.com/.extraheader 2024-08-20T22:46:24.7580285Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2024-08-20T22:46:24.7616565Z http.https://github.com/.extraheader 2024-08-20T22:46:24.7652390Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2024-08-20T22:46:24.7687045Z http.https://github.com/.extraheader 2024-08-20T22:46:24.7722113Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-08-20T22:46:24.7756996Z http.https://github.com/.extraheader 2024-08-20T22:46:24.7790170Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2024-08-20T22:46:24.7825232Z http.https://github.com/.extraheader 2024-08-20T22:46:24.7862315Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2024-08-20T22:46:24.7896128Z http.https://github.com/.extraheader 2024-08-20T22:46:24.7930787Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2024-08-20T22:46:24.7965683Z http.https://github.com/.extraheader 2024-08-20T22:46:24.8000321Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2024-08-20T22:46:24.8036228Z http.https://github.com/.extraheader 2024-08-20T22:46:24.8072753Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2024-08-20T22:46:24.8107457Z http.https://github.com/.extraheader 2024-08-20T22:46:24.8142638Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2024-08-20T22:46:24.8176779Z http.https://github.com/.extraheader 2024-08-20T22:46:24.8209857Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2024-08-20T22:46:24.8243705Z http.https://github.com/.extraheader 2024-08-20T22:46:24.8279515Z Entering 'third_party/mimalloc' 2024-08-20T22:46:24.8315835Z http.https://github.com/.extraheader 2024-08-20T22:46:24.8351634Z Entering 'third_party/nccl/nccl' 2024-08-20T22:46:24.8386922Z http.https://github.com/.extraheader 2024-08-20T22:46:24.8421055Z Entering 'third_party/nlohmann' 2024-08-20T22:46:24.8456415Z http.https://github.com/.extraheader 2024-08-20T22:46:24.8491190Z Entering 'third_party/onnx' 2024-08-20T22:46:24.8525324Z http.https://github.com/.extraheader 2024-08-20T22:46:24.8574927Z Entering 'third_party/onnx/third_party/benchmark' 2024-08-20T22:46:24.8609089Z http.https://github.com/.extraheader 2024-08-20T22:46:24.8642957Z Entering 'third_party/onnx/third_party/pybind11' 2024-08-20T22:46:24.8679467Z http.https://github.com/.extraheader 2024-08-20T22:46:24.8714595Z Entering 'third_party/opentelemetry-cpp' 2024-08-20T22:46:24.8751747Z http.https://github.com/.extraheader 2024-08-20T22:46:24.8787504Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2024-08-20T22:46:24.8821315Z http.https://github.com/.extraheader 2024-08-20T22:46:24.8853777Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2024-08-20T22:46:24.8888574Z http.https://github.com/.extraheader 2024-08-20T22:46:24.8921961Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2024-08-20T22:46:24.8955910Z http.https://github.com/.extraheader 2024-08-20T22:46:24.8988729Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2024-08-20T22:46:24.9022260Z http.https://github.com/.extraheader 2024-08-20T22:46:24.9058774Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2024-08-20T22:46:24.9091784Z http.https://github.com/.extraheader 2024-08-20T22:46:24.9124906Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-08-20T22:46:24.9158514Z http.https://github.com/.extraheader 2024-08-20T22:46:24.9190949Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-08-20T22:46:24.9224659Z http.https://github.com/.extraheader 2024-08-20T22:46:24.9257913Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2024-08-20T22:46:24.9292402Z http.https://github.com/.extraheader 2024-08-20T22:46:24.9327233Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2024-08-20T22:46:24.9361739Z http.https://github.com/.extraheader 2024-08-20T22:46:24.9396416Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-08-20T22:46:24.9431302Z http.https://github.com/.extraheader 2024-08-20T22:46:24.9488746Z Entering 'third_party/pocketfft' 2024-08-20T22:46:24.9525246Z http.https://github.com/.extraheader 2024-08-20T22:46:24.9560421Z Entering 'third_party/protobuf' 2024-08-20T22:46:24.9596349Z http.https://github.com/.extraheader 2024-08-20T22:46:24.9633459Z Entering 'third_party/protobuf/third_party/benchmark' 2024-08-20T22:46:24.9669714Z http.https://github.com/.extraheader 2024-08-20T22:46:24.9703989Z Entering 'third_party/protobuf/third_party/googletest' 2024-08-20T22:46:24.9739769Z http.https://github.com/.extraheader 2024-08-20T22:46:24.9777780Z Entering 'third_party/psimd' 2024-08-20T22:46:24.9815305Z http.https://github.com/.extraheader 2024-08-20T22:46:24.9850959Z Entering 'third_party/pthreadpool' 2024-08-20T22:46:24.9887848Z http.https://github.com/.extraheader 2024-08-20T22:46:24.9922962Z Entering 'third_party/pybind11' 2024-08-20T22:46:24.9959829Z http.https://github.com/.extraheader 2024-08-20T22:46:24.9994117Z Entering 'third_party/python-peachpy' 2024-08-20T22:46:25.0031557Z http.https://github.com/.extraheader 2024-08-20T22:46:25.0064102Z Entering 'third_party/sleef' 2024-08-20T22:46:25.0099808Z http.https://github.com/.extraheader 2024-08-20T22:46:25.0135796Z Entering 'third_party/tensorpipe' 2024-08-20T22:46:25.0172033Z http.https://github.com/.extraheader 2024-08-20T22:46:25.0207361Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-08-20T22:46:25.0244725Z http.https://github.com/.extraheader 2024-08-20T22:46:25.0280312Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-08-20T22:46:25.0317541Z http.https://github.com/.extraheader 2024-08-20T22:46:25.0351589Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-08-20T22:46:25.0387119Z http.https://github.com/.extraheader 2024-08-20T22:46:25.0421181Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-08-20T22:46:25.0455951Z http.https://github.com/.extraheader 2024-08-20T22:46:25.0488833Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-08-20T22:46:25.0523919Z http.https://github.com/.extraheader 2024-08-20T22:46:25.0650470Z A job completed hook has been configured by the self-hosted runner administrator 2024-08-20T22:46:25.0675534Z ##[group]Run '/home/ec2-user/runner-scripts/after_job.sh' 2024-08-20T22:46:25.0680987Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T22:46:25.0681494Z ##[endgroup] 2024-08-20T22:46:33.8351725Z Cleaning up orphan processes